LUNG CANCER METHYLATION MARKERS

The present invention discloses a method of diagnosing lung cancer by using methylation specific markers from a set, having diagnostic power for lung cancer diagnosis and distinguishing lung cancer types in diverse samples; as well as methods to identify sets of prognostic and diagnostic value.

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Description

The present invention relates to cancer diagnostic methods and means therefor.

Neoplasms and cancer are abnormal growths of cells. Cancer cells rapidly reproduce despite restriction of space, nutrients shared by other cells, or signals sent from the body to stop re-production. Cancer cells are often shaped differently from healthy cells, do not function properly, and can spread into many areas of the body. Abnormal growths of tissue, called tumors, are clusters of cells that are capable of growing and di-viding uncontrollably. Tumors can be benign (noncancerous) or malignant (cancerous). Benign tumors tend to grow slowly and do not spread. Malignant tumors can grow rapidly, invade and destroy nearby normal tissues, and spread throughout the body. Malignant cancers can be both locally invasive and metastatic. Locally invasive cancers can invade the tissues surrounding it by sending out “fingers” of cancerous cells into the normal tissue. Metastatic cancers can send cells into other tissues in the body, which may be distant from the original tumor. Cancers are classified according to the kind of fluid or tissue from which they originate, or according to the location in the body where they first developed. All of these parameters can effectively have an influence on the cancer characteristics, development and progression and subsequently also cancer treatment. Therefore, reliable methods to classify a cancer state or cancer type, taking diverse parameters into consideration is desired. Since cancer is predominantly a genetic disease, trying to classify cancers by genetic parameters is one extensively studied route.

Extensive efforts have been undertaken to discover genes relevant for diagnosis, prognosis and management of (cancerous)disease. Mainly RNA-expression studies have been used for screening to identify genetic biomarkers. Over recent years it has been shown that changes in the DNA-methylation pattern of genes could be used as biomarkers for cancer diagnostics. In concordance with the general strategy identifying RNA-expression based biomarkers, the most convenient and prospering approach would start to identify marker candidates by genome-wide screening of methylation changes.

The most versatile genome-wide approaches up to now are using microarray hybridization based techniques. Although studies have been undertaken at the genomic level (and also the single-gene level) for elucidating methylation changes in diseased versus normal tissue, a comprehensive test obtaining a good success rate for identifying biomarkers is yet not available.

Developing biomarkers for disease (especially cancer)-screening, -diagnosis, and -treatment was improved over the last decade by major advances of different technologies which have made it easier to discover potential biomarkers through high-throughput screens. Comparing the so called “OMICs”-approaches like Genomics, Proteomics, Metabolomics, and derivates from those, Genomics is best developed and most widely used for biomarker identification. Because of the dynamic nature of RNA expression and the ease of nucleic acid extraction and the detailed knowledge of the human genome, many studies have used RNA expression profiling for elucidation of class differences for distinguishing the “good” from the “bad” situation like diseased vs. healthy, or clinical differences between groups of diseased patients. Over the years especially microarray-based expression profiling has become a standard tool for research and some approaches are currently under clinical validation for diagnostics. The plasticity over a broad dynamic range of RNA expression levels is an advantage using RNA and also a prerequisite of successful discrimination of classes, the low stability of RNA itself is often seen as a drawback. Because stability of DNA is tremendously higher than stability of RNA, DNA based markers are more promising markers and expected to give robust assays for diagnostics. Many of clinical markers in oncology are more or less DNA based and are well established, e.g. cytogenetic analyses for diagnosis and classification of different tumor-species. However, most of these markers are not accessible using the cheap and efficient molecular-genetic PCR routine tests. This might be due to 1) the structural complexity of changes, 2) the inter-individual differences of these changes at the DNA-sequence level, and 3) the relatively low “quantitative” fold-changes of those “chromosomal” DNA changes. In comparison, RNA-expression changes range over some orders of magnitudes and these changes can be easily measured using genome-wide expression microarrays. These expression arrays are covering the entire translated transcriptome by 20000-45000 probes. Elucidation of DNA changes via microarray techniques re-quires in general more probes depending on the requested resolution. Even order(s) of magnitude more probes are required than for standard expression profiling to cover the entire 3×109 by human genome. For obtaining best resolution when screening biomarkers at the structural genomic DNA level, today genomic tiling arrays and SNP-arrays are available. Although costs of these techniques analysing DNA have decreased over recent years, for biomarker screening many samples have to be tested, and thus these tests are cost intensive.

Another option for obtaining stable DNA-based biomarkers re-lies on elucidation of the changes in the DNA methylation pattern of (malignant; neoplastic) disease. In the vertebrate genome methylation affects exclusively the cytosine residues of CpG dinucleotides, which are clustered in CpG islands. CpG islands are often found associated with gene-promoter sequences, present in the 5′-untranslated gene regions and are per default unmethylated. In a very simplified view, an unmethylated CpG island in the associated gene-promoter enables active transcription, but if methylated gene transcription is blocked. The DNA methylation pattern is tissue- and clone-specific and almost as stable as the DNA itself. It is also known that DNA-methylation is an early event in tumorigenesis which would be of interest for early and initial diagnosis of disease. In principle screening for biomarkers suitable to answering clinical questions including DNA-methylation based approaches would be most successful when starting with a genome-wide approach.

Shames D et al. (PLOS Medicine 3(12) (2006): 2244-2262) identified multiple genes that are methylated with high penetrance in primary lung, breast, colon and prostate cancers.

Sato N et al. (Cancer Res 63(13) (2003): 3735-3742) identified potential targets with aberrant methylation in pancreatic cancer. These genes were tested using a treatment with a de-methylating agent (5-aza-2′-deoxycytidine and/or the histone deacetylase inhibitor trichostatin A) after which certain genes were increased transcribed.

Bibikova M et al. (Genome Res 16(3) (2006): 383-393) analysed lung cancer biopsy samples to identify methylated cpu sites to distinguish lung adenocarcinomas from normal lung tissues.

Yan P S et al. (Clin Cancer Res 6(4) (2000): 1432-1438) analysed CpG island hypermethylation in primary breast tumor.

Cheng Y et al. (Genome Res 16(2) (2006): 282-289) discussed DNA methylation in CpG islands associated with transcriptional silencing of tumor suppressor genes.

Ongenaert M et al. (Nucleic Acids Res 36 (2008) Database issue D842-D846) provided an overview over the methylation database “PubMeth”.

Microarray for human genome-wide hybridization testings are known, e.g. the Affymetrix Human Genome U133A Array (NCB1 Database, Acc. No. GLP96).

A substantial number of differentially methylated genes has been discovered over years rather by chance than by rationality. Albeit some of these methylation changes have the potential being useful markers for differentiation of specifically defined diagnostic questions, these would lack the power for successful delineation of various diagnostic constellations. Thus, the rational approach would start at the genomic-screen for distinguishing the “subtypes” and diagnostically, prognostically and even therapeutically challenging constellations. These rational expectations are the base of starting genomic (and also other—omics) screenings but do not warrant to obtain the maker panel for all clinical relevant constellations which should be distinguished. This is neither unreliable when thinking about a universal approach (e.g. transcriptomics) suitable to distinguish for instance all subtypes in all different malignancies by focusing on a single class of target-molecules (e.g. RNA). Rather all omics-approaches together would be necessary and could help to improve diagnostics and finally patient management.

Lung cancer is the third most common malignant neoplasm in the EU following breast and colon cancers. Lung cancer presents the second worst 5-year survival figures following pancreas. Thus, although it accounts for 14% of all cancer diagnoses, lung cancer is responsible for 22% of cancer deaths, indicating the poor prognosis of this tumour type and the comparative lack of progress in treatment. Therapy is hampered by the tendency for lung cancer to be diagnosed at a late stage, hence the need to develop markers for early detection. Approximately 80% of lung cancer cases are of the non-small cell type (NSCLC), with squamous cell carcinoma and adenocarcinoma being the most frequent subtypes. A goal of the present invention is to provide an alternative and more cost-efficient route to identify suitable markers for lung cancer diagnostics.

Therefore, in a first aspect, the present invention provides a set of nucleic acid primers or hybridization probes being specific for a potentially methylated region of marker genes being suitable to diagnose or predict lung cancer or a lung cancer type, preferably being selected from adenocarcinoma or squamous cell carcinoma, the marker genes comprising WT1, SALL3, TERT, ACTB, CPEB4. Preferably the set further comprises any one of the markers ABCB1, ACTB, AIM1L, APC, AREG, BMP2K, BOLL, C5AR1, C5orf4, CADM1, CDH13, CDX1, CLIC4, COL21A1, CPEB4, CXADR, DLX2, DNAJA4, DPH1, DRD2, EFS, ERBB2, ERCC1, ESR2, F2R, FAM43A, GABRA2, GAD1, GBP2, GDNF, GNA15, GNAS, HECW2, HIC1, HIST1H2AG, HLA-G, HOXA1, HOXA10, HSD17B4, HSPA2, IRAK2, ITGA4, JUB, KCNJ15, KCNQ1, KIF5B, KL, KRT14, KRT17, LAMC2, MAGEB2, MBD2, MSH4, MT1G, MT3, MTHFR, NEUROD1, NHLH2, NKX2-1, ONECUT2, PENK, PITX2, PLAGL1, PTTG1, PYCARD, RASSF1, S100A8, SALL3, SERPINB5, SERPINE1, SERPINI1, SFRP2, SLC25A31, SMAD3, SPARC, SPHK1, SRGN, TERT, THRB, TJP2, TMEFF2, TNFRSF10C, TNFRSF25, TP53, ZDHHC11, ZNF256, ZNF711, F2R, HOXA10, KL, SALL3, SPARC, TNFRSF25, WT1.

In a further aspect, the present invention provides a method of determining a subset of diagnostic markers for potentially methylated genes from the genes of gene marker IDs 1-359 of table 1, suitable for the diagnosis or prognosis of lung cancer or lung cancer type, comprising

    • a) obtaining data of the methylation status of at least 50 random genes selected from the 359 genes of gene marker IDs 1-359 in at least 1 sample, preferably 2, 3, 4 or at least 5 samples, of a confirmed lung cancer or lung cancer type state and at least one sample of a lung cancer or lung cancer type negative state,
    • b) correlating the results of the obtained methylation status with the lung cancer or lung cancer type,
    • c) optionally repeating the obtaining a) and correlating b) steps for a different combination of at least 50 random genes selected from the 359 genes of gene marker IDs 1-359 and
    • d) selecting as many marker genes which in a classification analysis have a p-value of less than 0.1 in a random-variance t-test, or selecting as many marker genes which in a classification analysis together have a correct lung cancer or lung cancer type prediction of at least 70% in a cross-validation test,
      wherein the selected markers form the subset of diagnostic markers.

The present invention provides a master set of 359 genetic markers which has been surprisingly found to be highly relevant for aberrant methylation in the diagnosis or prognosis of lung cancer. It is possible to determine a multitude of marker subsets from this master set which can be used to diagnose and differentiate between various lung cancer or tumor types, e.g. adenocarcinoma and squamous cell carcinoma.

The inventive 359 marker genes of table 1 (given in example 1 below) are: NHLH2, MTHFR, PRDM2, MLLT11, S100A9 (control), S100A9, S100A8 (control), S100A8, S100A2, LMNA, DUSP23, LAMC2, PTGS2, MARK1, DUSP10, PARP1, PSEN2, CLIC4, RUNX3, AIM1L, SFN, RPA2, TP73, TP73 (p73), POU3F1, MUTYH, UQCRH, FAF1, TACSTD2, TN-FR5F25, DIRAS3, MSH4, GBP2, GBP2, LRRC8C, F3, NANOS1, MGMT, EBF3, DCLRE1C, KIF5B, ZNF22, PGBD3, SRGN, GATA3, PTEN, MMS19, SFRP5, PGR, ATM, DRD2, CADM1, TEAD1, OPCML, CALCA, CTSD, MYOD1, IGF2, BDNF, CDKN1C, WT1, HRAS, DDB1, GSTP1, CCND1, EPS8L2, PI-WIL4, CHST11, UNG, CCDC62, CDK2AP1, CHFR, GRIN2B, CCND2, VDR, B4GALNT3, NTF3, CYP27B1, GPR92, ERCC5, GJB2, BRCA2, KL, CCNA1, SMAD9, C13orf15, DGKH, DNAJC15, RB1, RCBTB2, PARP2, APEX1, JUB, JUB (control NM 198086), EFS, BAZ1A, NKX2-1, ESR2, HSPA2, PSEN1, PGF, MLH3, TSHR, THBS1, MYO5C, SMAD6, SMAD3, NOX5, DNAJA4, CRABP1, BCL2A1 (ID NO: 111), BCL2A1 (ID NO: 112), BNC1, ARRDC4, SOCS1, ERCC4, NTHL1, PYCARD, AXIN1, CYLD, MT3, MT1A, MT1G, CDH1, CDH13, DPH1, HIC1, NEUROD2 (control), NEUROD2, ERBB2, KRT19, KRT14, KRT17, JUP, BRCA1, COL1A1, CACNA1G, PRKAR1A, SPHK1, SOX15, TP53 (TP53_CGI23_1 kb), TP53 (TP53_both_CGIs_1 kb), TP53 (TP53_CGI36_1 kb), TP53, NPTX1, SMAD2, DCC, MBD2, ONECUT2, BCL2, SERPINB5, SERPINB2 (control), SERPINB2, TYMS, LAMA1, SALL3, LDLR, STK11, PRDX2, RAD23A, GNA15, ZNF573, SPINT2, XRCC1, ERCC2, ERCC1, C5AR1 (NM_001736), C5AR1, POLD1, ZNF350, ZNF256, C3, XAB2, ZNF559, FHL2, IL1B, IL1B (control), PAX8, DDX18, GAD1, DLX2, ITGA4, NEUROD1, STAT1, TMEFF2, HECW2, BOLL, CASP8, SERPINE2, NCL, CYP1B1, TACSTD1, MSH2, MSH6, MXD1, JAG1, FOXA2, THBD, CTCFL, CTSZ, GATA5, CXADR, APP, TTC3, KCNJ15, RIPK4, TFF1, SEZ6L, TIMP3, BIK, VHL, IRAK2, PPARG, MBD4, RBP1, XPC, ATR, LXN, RARRES1, SERPINI1, CLDN1, FAM43A, IQCG, THRB, RARB, TGFBR2, MLH1, DLEC1, CTNNB1, ZNF502, SLC6A20, GPX1, RASSF1, FHIT, OGG1, PITX2, SLC25A31, FBXW7, SFRP2, CHRNA9, GABRA2, MSX1, IGFBP7, EREG, AREG, ANXA3, BMP2K, APC, HSD17B4 (ID No 249), HSD17B4 (ID No 250), LOX, TERT, NEUROG1, NR3C1, ADRB2, CDX1, SPARC, C5orf4, PTTG1, DUSP1, CPEB4, SCGB3A1, GDNF, ERCC8, F2R, F2RL1, VCAN, ZDHHC11, RHOBTB3, PLAGL1, SASH1, ULBP2, ESR1, RNASET2, DLL1, HIST1H2AG, HLA-G, MSH5, CDKN1A, TDRD6, COL21A1, DSP, SERPINE1 (ID No 283), SERPINE1 (ID No 284), FBXL13, NRCAM, TWIST1, HOXA1, HOXA10, SFRP4, IGFBP3, RPA3, ABCB1, TFPI2, COL1A2, ARPC1B, PILRB, GATA4, MAL2, DLC1, EPPK1, LZTS1, TNFRSF10B, TNFRSF10C, TNFRSF10D, TNFRSF10A, WRN, SFRP1, SNAI2, RDHE2, PENK, RDH10, TGFBR1, ZNF462, KLF4, CDKN2A, CDKN2B, AQP3, TPM2, TJP2 (ID NO 320), TJP2 (ID No 321), PSAT1, DAPK1, SYK, XPA, ARMCX2, RHOXF1, FHL1, MAGEB2, TIMP1, AR, ZNF711, CD24, ABL1, ACTB, APC, CDH1 (Ecad 1), CDH1 (Ecad2), FMR1, GNAS, H19, HIC1, IGF2, KCNQ1, GNAS, CDKN2A (P14), CDKN2B (P15), CDKN2A (P16_VL), PITXA, PITXB, PITXC, PITXD, RB1, SFRP2, SNRPN, XIST, IRF4, UNC13B, GSTP1. Table 1 lists some marker genes in the double such as for different loci and control sequences. It should be understood that any methylation specific region which is readily known to the skilled man in the art from prior publications or available databases (e.g. PubMeth at www.pubmeth.org) can be used according to the present invention. Of course, double listed genes only need to be represented once in an inventive marker set (or set of probes or primers therefor) but preferably a second marker, such as a control region is included (IDs given in the list above relate to the gene ID (or gene loci ID) given in table 1 of the example section).

One advantage making DNA methylation an attractive target for biomarker development, is the fact that cell free methylated DNA can be detected in body-fluids like serum, sputum, and urine from patients with cancerous neoplastic conditions and disease. For the purpose of biomarker screening, clinical samples have to be available. For obtaining a sufficient number of samples with clinical and “outcome” or survival data, the first step would be using archived (tissue) samples. Preferably these materials should fulfill the requirements to obtain intact RNA and DNA, but most archives of clinical samples are storing formalin fixed paraffin embedded (FFPE) tissue blocks. This has been the clinic-pathological routine done over decades, but that fixed samples are if at all only suitable for extraction of low quality of RNA. It has now been found that according to the present invention any such samples (as any comprising tumor DNA) can be used for the method of generating an inventive subset, including fixed samples. The samples can be of lung tissue or any body fluid, e.g. sputum, bronchial lavage, or serum derived from peripheral blood or blood cells. Blood or blood derived samples preferably have reduced, e.g. <95%, or no leukocyte content but comprise DNA of the cancerous cells or tumor. Preferably the inventive markers are of human genes. Preferably the samples are human samples.

The present invention provides a multiplexed methylation testing method which 1) outperforms the “classification” success when compared to genomewide screenings via RNA-expression profiling, 2) enables identification of biomarkers for a wide variety of diseases, without the need to prescreen candidate markers on a genomewide scale, and 3) is suitable for minimal invasive testing and 4) is easily scalable.

In contrast to the rational strategy for elucidation of biomarkers for differentiation of disease, the invention presents a targeted multiplexed DNA-methylation test which outperforms genome-scaled approaches (including RNA expression profiling) for disease diagnosis, classification, and prognosis.

The inventive set of 359 markers enables selection of a subset of markers from this 359 set which is highly characteristic of lung cancer and a given lung cancer type. Further indicators differentiating between cancer types or generally neoplastic conditions are e.g. benign (non (or limited) proliferative) or malignant, metastatic or non-metastatic tumors or nodules. It is sometimes possible to differentiate the sample type from which the methylated DNA is isolated, e.g. urine, blood, tissue samples.

The present invention is suitable to differentiate diseases, in particular neoplastic conditions, or tumor types. Diseases and neoplastic conditions should be understood in general including benign and malignant conditions. According to the present invention benign nodules (being at least the potential onset of malignancy) are included in the definition of a disease. After the development of a malignancy the condition is a preferred disease to be diagnosed by the markers screened for or used according to the present invention. The present invention is suitable to distinguish benign and malignant tumors (both being considered a disease according to the present invention). In particular the invention can provide markers (and their diagnostic or prognostic use) distinguishing between a normal healthy state together with a benign state on one hand and malignant states on the other hand. A diagnosis of lung cancer may include identifying the difference to a normal healthy state, e.g. the absence of any neoplastic nodules or cancerous cells. The present invention can also be used for prognosis of lung cancer, in particular a prediction of the progression of lung cancer or lung cancer type. A particularly preferred use of the invention is to perform a diagnosis or prognosis of metastasizing lung cancer (distinguished from non-metastasizing conditions).

In the context of the present invention “prognosis”, “prediction” or “predicting” should not be understood in an absolute sense, as in a certainty that an individual will develop lung cancer or lung cancer type (including cancer progression), but as an increased risk to develop cancer or the lung cancer type or of cancer progression. “Prognosis” is also used in the context of predicting disease progression, in particular to predict therapeutic results of a certain therapy of the disease, in particular neoplastic conditions, or lung cancer types. The prognosis of a therapy can e.g. be used to predict a chance of success (i.e. curing a disease) or chance of reducing the severity of the disease to a certain level. As a general inventive concept, markers screened for this purpose are preferably derived from sample data of patients treated according to the therapy to be predicted. The inventive marker sets may also be used to monitor a patient for the emergence of therapeutic results or positive disease progressions.

Some of the inventive, rationally selected markers have been found methylated in some instances. DNA methylation analyses in principle rely either on bisulfite deamination-based methylation detection or on using methylation sensitive restriction enzymes. Preferably the restriction enzyme-based strategy is used for elucidation of DNA-methylation changes. Further methods to determine methylated DNA are e.g. given in EP 1 369 493 A1 or U.S. Pat. No. 6,605,432. Combining restriction digestion and multiplex PCR amplification with a targeted microarray-hybridization is a particular advantageous strategy to perform the inventive methylation test using the inventive marker sets (or subsets). A microarray-hybridization step can be used for reading out the PCR results. For the analysis of the hybridization data statistical approaches for class comparisons and class prediction can be used. Such statistical methods are known from analysis of RNA-expression derived microarray data.

If only limiting amounts of DNA were available for analyses an amplification protocol can be used enabling selective amplification of the methylated DNA fraction prior methylation testing. Subjecting these amplicons to the methylation test, it was possible to successfully distinguish DNA from sensitive cases from normal healthy controls. In addition it was possible to distinguish lung-cancer patients from healthy normal controls using DNA from serum by the inventive methylation test upon preamplification. Both examples clearly illustrate that the inventive multiplexed methylation testing can be successfully applied when only limiting amounts of DNA are available. Thus, this principle might be the preferred method for minimal invasive diagnostic testing.

In most situations several genes are necessary for classification. Although the 359 marker set test is not a genome-wide test and might be used as it is for diagnostic testing, running a subset of markers—comprising the classifier which enables best classification—would be easier for routine applications. The test is easily scalable. Thus, to test only the subset of markers, comprising the classifier, the selected subset of primers/probes could be applied directly to set up of the lower multiplexed test (or single PCR-test). Serum DNA can be used to classify or distinguish healthy patients from individuals with lung-tumors. Only the specific primers comprising the gene-classifier obtained from the methylation test may be set up together in multiplexed PCR reactions.

In summary the inventive methylation test is a suitable tool for differentiation and classification of neoplastic disease. This assay can be used for diagnostic purposes and for defining biomarkers for clinical relevant issues to improve diagnosis of disease, and to classify patients at risk for disease progression, thereby improving disease treatment and patient management.

The first step of the inventive method of generating a subset, step a) of obtaining data of the methylation status, preferably comprises determining data of the methylation status, preferably by methylation specific PCR analysis, methylation specific digestion analysis. Methylation specific digestion analysis can include either or both of hybridization of suitable probes for detection to non-digested fragments or PCR amplification and detection of non-digested fragments.

The inventive selection can be made by any (known) classification method to obtain a set of markers with the given diagnostic (or also prognostic) value to categorize a lung cancer or lung cancer type. Such methods include class comparisons wherein a specific p-value is selected, e.g. a p-value below 0.1, preferably below 0.08, more preferred below 0.06, in particular preferred below 0.05, below 0.04, below 0.02, most preferred below 0.01.

Preferably the correlated results for each gene b) are rated by their correct correlation to lung cancer or lung cancer type positive state, preferably by p-value test or t-value test or F-test. Rated (best first, i.e. low p- or t-value) markers are the subsequently selected and added to the subset until a certain diagnostic value is reached, e.g. the herein mentioned at least 70% (or more) correct classification of lung cancer or lung cancer type.

Class comparison procedures include identification of genes that were differentially methylated among the two classes using a random-variance t-test. The random-variance t-test is an improvement over the standard separate t-test as it permits sharing information among genes about within-class variation without assuming that all genes have the same variance (Wright G. W. and Simon R, Bioinformatics 19:2448-2455, 2003). Genes were considered statistically significant if their p value was less than a certain value, e.g. 0.1 or 0.01. A stringent significance threshold can be used to limit the number of false positive findings. A global test can also be performed to determine whether the expression profiles differed between the classes by permuting the labels of which arrays corresponded to which classes. For each permutation, the p-values can be re-computed and the number of genes significant at the e.g. 0.01 level can be noted. The proportion of the permutations that give at least as many significant genes as with the actual data is then the significance level of the global test. If there are more than 2 classes, then the “F-test” instead of the “t-test” should be used.

Class Prediction includes the step of specifying a significance level to be used for determining the genes that will be included in the subset. Genes that are differentially methylated between the classes at a univariate parametric significance level less than the specified threshold are included in the set. It doesn't matter whether the specified significance level is small enough to exclude enough false discoveries. In some problems better prediction can be achieved by being more liberal about the gene sets used as features. The sets may be more bio-logically interpretable and clinically applicable, however, if fewer genes are included. Similar to cross-validation, gene selection is repeated for each training set created in the cross-validation process. That is for the purpose of providing an unbiased estimate of prediction error. The final model and gene set for use with future data is the one resulting from application of the gene selection and classifier fitting to the full dataset.

Models for utilizing gene methylation profile to predict the class of future samples can also be used. These models may be based on the Compound Covariate Predictor (Radmacher et al. Journal of Computational Biology 9:505-511, 2002), Diagonal Linear Discriminant Analysis (Dudoit et al. Journal of the American Statistical Association 97:77-87, 2002), Nearest Neighbor Classification (also Dudoit et al.), and Support Vector Machines with linear kernel (Ramaswamy et al. PNAS USA 98:15149-54, 2001). The models incorporated genes that were differentially methylated among genes at a given significance level (e.g. 0.01, 0.05 or 0.1) as assessed by the random variance t-test (Wright G. W. and Simon R. Bioinformatics 19:2448-2455, 2003). The prediction error of each model using cross validation, preferably leave-one-out cross-validation (Simon et al. Journal of the National Cancer Institute 95:14-18, 2003), is preferably estimated. For each leave-one-out cross-validation training set, the entire model building process was repeated, including the gene selection process. It may also be evaluated whether the cross-validated error rate estimate for a model was significantly less than one would expect from random prediction. The class labels can be randomly permuted and the entire leave-one-out cross-validation process is then repeated. The significance level is the proportion of the random permutations that gave a cross-validated error rate no greater than the cross-validated error rate obtained with the real methylation data. About 1000 random permutations may be usually used.

Another classification method is the greedy-pairs method described by Bo and Jonassen (Genome Biology 3(4):research0017.1-0017.11, 2002). The greedy-pairs approach starts with ranking all genes based on their individual t-scores on the training set. The procedure selects the best ranked gene gi and finds the one other gene gi that together with provides the best discrimination using as a measure the distance between centroids of the two classes with regard to the two genes when projected to the diagonal linear discriminant axis. These two selected genes are then removed from the gene set and the procedure is repeated on the remaining set until the specified number of genes have been selected. This method attempts to select pairs of genes that work well together to discriminate the classes.

Furthermore, a binary tree classifier for utilizing gene methylation profile can be used to predict the class of future samples. The first node of the tree incorporated a binary classifier that distinguished two subsets of the total set of classes. The individual binary classifiers were based on the “Support Vector Machines” incorporating genes that were differentially expressed among genes at the significance level (e.g. 0.01, 0.05 or 0.1) as assessed by the random variance t-test (Wright G. W. and Simon R. Bioinformatics 19:2448-2455, 2003). Classifiers for all possible binary partitions are evaluated and the partition selected was that for which the cross-validated prediction error was minimum. The process is then repeated successively for the two subsets of classes determined by the previous binary split. The prediction error of the binary tree classifier can be estimated by cross-validating the entire tree building process. This overall cross-validation included re-selection of the optimal partitions at each node and re-selection of the genes used for each cross-validated training set as described by Simon et al. (Simon et al. Journal of the National Cancer Institute 95:14-18, 2003). 10-fold cross validation in which one-tenth of the samples is withheld can be utilized, a binary tree developed on the remaining 9/10 of the samples, and then class membership is predicted for the 10% of the samples withheld. This is repeated 10 times, each time withholding a different 10% of the samples. The samples are randomly partitioned into 10 test sets (Simon R and Lam A. BRB-ArrayTools User Guide, version 3.2. Biometric Research Branch, National Cancer Institute).

Preferably the correlated results for each gene b) are rated by their correct correlation to lung cancer or lung cancer type positive state, preferably by p-value test. It is also possible to include a step in that the genes are selected d) in order of their rating.

Independent from the method that is finally used to produce a subset with certain diagnostic or predictive value, the subset selection preferably results in a subset with at least 60%, preferably at least 65%, at least 70%, at least 75%, at least 80% or even at least 85%, at least 90%, at least 92%, at least 95%, in particular preferred 100% correct classification of test samples of lung cancer or lung cancer type. Such levels can be reached by repeating c) steps a) and b) of the inventive method, if necessary.

To prevent increase of the number of the members of the subset, only marker genes with at least a significance value of at most 0.1, preferably at most 0.8, even more preferred at most 0.6, at most 0.5, at most 0.4, at most 0.2, or more preferred at most 0.01 are selected.

In particular preferred embodiments the at least 50 genes of step a) are at least 70, preferably at least 90, at least 100, at least 120, at least 140, at least 160, at least 180, at least 190, at least 200, at least 220, at least 240, at least 260, at least 280, at least 300, at least 320, at least 340, at least 350 or all, genes.

Since the subset should be small it is preferred that not more than 60, or not more than 40, preferably not more than 30, in particular preferred not more than 20, marker genes are selected in step d) for the subset.

In a further aspect the present invention provides a method of identifying lung cancer or lung cancer type in a sample comprising DNA from a patient, comprising providing a diagnostic subset of markers identified according to the method depicted above, determining the methylation status of the genes of the subset in the sample and comparing the methylation status with the status of a confirmed lung cancer or lung cancer type positive and/or negative state, thereby identifying lung cancer or lung cancer type in the sample.

The methylation status can be determined by any method known in the art including methylation dependent bisulfite deamination (and consequently the identification of mC—methylated C—changes by any known methods, including PCR and hybridization techniques). Preferably, the methylation status is determined by methylation specific PCR analysis, methylation specific digestion analysis and either or both of hybridisation analysis to non-digested or digested fragments or PCR amplification analysis of non-digested fragments. The methylation status can also be determined by any probes suitable for determining the methylation status including DNA, RNA, PNA, LNA probes which optionally may further include methylation specific moieties.

As further explained below the methylation status can be particularly determined by using hybridisation probes or amplification primer (preferably PCR primers) specific for methylated regions of the inventive marker genes. Discrimination between methylated and non-methylated genes, including the determination of the methylation amount or ratio, can be performed by using e.g. either one of these tools.

The determination using only specific primers aims at specifically amplifying methylated (or in the alternative non-methylated) DNA. This can be facilitated by using (methylation dependent) bisulfite deamination, methylation specific enzymes or by using methylation specific nucleases to digest methylated (or alternatively non-methylated) regions—and consequently only the non-methylated (or alternatively methylated) DNA is obtained. By using a genome chip (or simply a gene chip including hybridization probes for all genes of interest such as all 359 marker genes), all amplification or non-digested products are detected. I.e. discrimination between methylated and non-methylated states as well as gene selection (the inventive set or subset) is before the step of detection on a chip.

Alternatively it is possible to use universal primers and amplify a multitude of potentially methylated genetic regions (including the genetic markers of the invention) which are, as described either methylation specific amplified or digested, and then use a set of hybridisation probes for the characteristic markers on e.g. a chip for detection. I.e. gene selection is performed on the chip.

Either set, a set of probes or a set of primers, can be used to obtain the relevant methylation data of the genes of the present invention. Of course, both sets can be used.

The method according to the present invention may be performed by any method suitable for the detection of methylation of the marker genes. In order to provide a robust and optionally re-useable test format, the determination of the gene methylation is preferably performed with a DNA-chip, real-time PCR, or a combination thereof. The DNA chip can be a commercially available general gene chip (also comprising a number of spots for the detection of genes not related to the present method) or a chip specifically designed for the method according to the present invention (which predominantly comprises marker gene detection spots).

Preferably the methylated DNA of the sample is detected by a multiplexed hybridization reaction. In further embodiments a methylated DNA is preamplified prior to hybridization, preferably also prior to methylation specific amplification, or digestion. Preferably, also the amplification reaction is multiplexed (e.g. multiplex PCR).

The inventive methods (for the screening of subsets or for diagnosis or prognosis of lung cancer or lung cancer type) are particularly suitable to detect low amounts of methylated DNA of the inventive marker genes. Preferably the DNA amount in the sample is below 500 ng, below 400 ng, below 300 ng, below 200 ng, below 100 ng, below 50 ng or even below 25 ng. The inventive method is particularly suitable to detect low concentrations of methylated DNA of the inventive marker genes. Preferably the DNA amount in the sample is below 500 ng, below 400 ng, below 300 ng, below 200 ng, below 100 ng, below 50 ng or even below 25 ng, per ml sample.

In another aspect the present invention provides a subset comprising or consisting of nucleic acid primers or hybridization probes being specific for a potentially methylated region of at least marker genes selected from a set of nucleic acid primers or hybridization probes being specific for a potentially methylated region of marker genes being suitable to diagnose or predict lung cancer or a lung cancer type, preferably being selected from adenocarcinoma or squamous cell carcinoma, the marker genes comprising WT1, SALL3, TERT, ACTB, CPEB4 or any other subset selected from one of the following groups

    • a) WT1, DLX2, SALL3, TERI, PITX2, HOXA10, F2R, CPEB4, NHLH2, SMAD3, ACTB, HOXA1, BOLL, APC, MT1G, PENK, SPARC, DNAJA4, RASSF1, HLA-G, ERCC1, ONECUT2, APC, ABCB1, ZNF573, KCNJ15, ZDHHC11, SFRP2, GDNF, PTTG1, SERPINI1, TNFRSF10C
    • b) WT1, PITX2, SALL3, F2R, DLX2, TERI, HOXA10, MSH4, NHLH2, GNA15, PENK, RASSF1, BOLL, HOXA1, ONECUT2, ABCB1, SPARC, MT1G, HSPA2, SFRP2, PYCARD, GAD1, C5orf4, C5AR1, GDNF, ZDHHC11, SERPINE1, NKX2-1, PITX2, C5AR1, ZNF256, FAM43A, SFRP2, MT3, SERPINE1, CLIC4, TNFRSF10C, GABRA2, MTHFR, ESR2, NEUROG1, PITX2, PLAGL1, TMEFF2, PTTG1, CADM1, S100A8, EFS, JUB, ITGA4, MAGEB2, ERBB2, SRGN, GNAS, TJP2, KCNJ15, SLC25A31, ZNF573, TNFRSF25, APC, KCNQ1, LAMC2, SPHK1, DNAJA4, APC, MBD2, ERCC1, HLA-G, CXADR, TP53, ACTB, KL, SMAD3, HIST1H2AG, CPEB4
    • c) WT1, DLX2, SALL3, TERT, TNFRSF25, ACTB, SMAD3, CPEB4
    • d) WT1, DLX2, SALL3, TERT, PITX2, TNFRSF25, KL, ACTB, SMAD3, CPEB4
    • e) WT1, PITX2, SALL3, DLX2, TERT, HOXA10, RASSF1, SPARC, IRAK2, ZNF711, DNAJA4, HLA-G, CXADR, TP53, ACTB, CPEB4
    • f) WT1, PITX2, SALL3, F2R, TERT, HOXA10, RASSF1, SPARC, IRAK2, ZNF711, DRD2, DNAJA4, CXADR, TP53, ACTB, CPEB4
    • g) WT1, ACTB, DLX2, PITX2, SALL3, HOXA10, TERT, CPEB4, HLA-G, SPARC, RASSF1, DNAJA4, CXADR, TP53, IRAK2, ZNF711
    • h) F2R, ZNF256, CDH13, SERPINB5, KRT14, DLX2, AREG, THRB, HSD17B4, SPARC, HECW2, COL21A1
    • i) KL, HIST1H2AG, TJP2, SRGN, CDX1, TNFRSF25, APC, HIC1, APC, GNA15, ACTB, WT1, KRT17, AIM1L, DPH1, PITX2, PITX2, KIF5B, BMP2K, GBP2, NHLH2, GDNF, BOLL
    • j) WT1, DLX2, SALL3, TERT, PITX2, HOXA10, F2R, CPEB4, NHLH2, SMAD3, ACTB, HOXA1, BOLL, APC, MT1G, PENK, SPARC, DNAJA4, RASSF1, HLA-G, ERCC1, ONECUT2, APC, ABCB1, ZNF573, KCNJ15, ZDHHC11, SFRP2, GDNF, PTTG1, SERPINI1, TNFRSF10C
    • k) HOXA10, NEUROD1
    • l) WT1, PITX2, SALL3, F2R, TERT, HOXA10, RASSF1, SPARC, IRAK2, ZNF711, DRD2, DNAJA4, CXADR, TP53, ACTB, CPEB4, DLX2, TN-FR5F25, KL, SMAD3
    • m) TNFRSF25, SALL3, RASSF1, TERT, SPARC, F2R, HOXA10, ZNF711, PITX2
    • n) SALL3, PITX2, SPARC, F2R, TERT, RASSF1, HOXA10, CXADR, KL
    • o) SALL3, SPARC, PITX2, F2R, TERT, RASSF1, HOXA10, KL
    • p) SALL3, PITX2, SPARC, F2R, HOXA10, DRD2, ACTB, DNAJA4, CXADR, KL
    • q) SALL3, SPARC, PITX2, F2R, TERT, RASSF1, HOXA10, TNFRSF25, DNAJA4, TP53, CXADR, KL
    • r) SPARC, SALL3, F2R, PITX2, RASSF1, HOXA10, TERT, KL, TNFRSF25
    • s) SALL3, SPARC, PITX2, F2R, TERT, RASSF1, HOXA10, KL, TN-FR5F25, CXADR
    • t) HOXA10, RASSF1, F2R
    • or

a set of at least 50%, preferably at least 60%, at least 70%, at least 80%, at least 90%, 100% of the markers of anyone of the above a) to t). The present inventive set also includes sets with at least 50% of the above markers for each set since it is also possible to substitute parts of these subsets being specific for—in the case of binary conditions/differentiations—e.g. good or bad prognosis or distinguish between lung cancer or lung cancer types, wherein one part of the subset points into one direction for a certain lung cancer type or cancer/differentiation. It is possible to further complement the 50% part of the set by additional markers specific for diagnosing lung cancer or determining the other part of the good or bad differentiation or differentiation between two lung cancer types. Methods to determine such complementing markers follow the general methods as outlined herein.

Each of these marker subsets is particularly suitable to diagnose lung cancer or lung cancer type or distinguish between certain cancers, samples or cancer types in a methylation specific assay of these genes.

The inventive primers or probes may be of any nucleic acid, including RNA, DNA, PNA (peptide nucleic acids), LNA (locked nucleic acids). The probes might further comprise methylation specific moieties.

The present invention provides a (master) set of 360 marker genes, further also specific gene locations by the PCR products of these genes wherein significant methylation can be detected, as well as subsets therefrom with a certain diagnostic value to detect or diagnose lung cancer or distinguish lung cancer type(s). Preferably the set is optimized for a lung cancer or a lung cancer type. Lung cancer types include, without being limited thereto, adenocarcinoma and squamous cell carcinoma. Further indicators differentiating between disease(s), including the diagnosis of any type of lung cancer or lung tumor, or between tumor type(s) are e.g. benign (non (or limited) proliferative) or malignant, metastatic or non-metastatic. The set can also be optimized for a specific sample type in which the methylated DNA is tested. Such samples include blood, urine, saliva, hair, skin, tissues, in particular tissues of the cancer origin mentioned above, in particular lung tissue such as potentially affected or potentially cancerous lung tissue, or serum, sputum, bronchial lavage. The sample my be obtained from a patient to be diagnosed. In preferred embodiments the test sample to be used in the method of identifying a subset is from the same type as a sample to be used in the diagnosis.

In practice, probes specific for potentially aberrant methylated regions are provided, which can then be used for the diagnostic method.

It is also possible to provide primers suitable for a specific amplification, like PCR, of these regions in order to perform a diagnostic test on the methylation state.

Such probes or primers are provided in the context of a set corresponding to the inventive marker genes or marker gene loci as given in table 1.

Such a set of primers or probes may have all 359 inventive markers present and can then be used for a multitude of different cancer detection methods. Of course, not all markers would have to be used to diagnose a lung cancer or lung cancer type. It is also possible to use certain subsets (or combinations thereof) with a limited number of marker probes or primers for diagnosis of certain categories of lung cancer.

Therefore, the present invention provides sets of primers or probes comprising primers or probes for any single marker subset or any combination of marker subsets disclosed herein. In the following sets of marker genes should be understood to include sets of primer pairs and probes therefor, which can e.g. be provided in a kit.

Set a, WT1, DLX2, SALL3, TERT, PITX2, HOXA10, F2R, CPEB4, NHLH2, SMAD3, ACTB, HOXA1, BOLL, APC, MT1G, PENK, SPARC, DNAJA4, RASSF1, HLA-G, ERCC1, ONECUT2, APC, ABCB1, ZNF573, KCNJ15, ZDHHC11, SFRP2, GDNF, PTTG1, SERPINI1, TNFRSF10C and sets with at least 50%, preferably at least 60%, at least 70%, at least 80% or at least 90% of these markers are in particular suitable to detect lung cancer and to distinguish between normal lung tissue (non-cancerous) from lung tumor tissue.

Set b, WIT1, PITX2, SALL3, F2R, DLX2, TERT, HOXA10, MSH4, NHLH2, GNA15, PENK, RASSF1, BOLL, HOXA1, ONECUT2, ABCB1, SPARC, MT1G, HSPA2, SFRP2, PYCARD, GAD1, C5orf4, C5AR1, GDNF, ZDHHC11, SERPINE1, NKX2-1, PITX2, C5AR1, ZNF256, FAM43A, SFRP2, MT3, SERPINE1, CLIC4, TNFRSF10C, GABRA2, MTHFR, ESR2, NEUROG1, PITX2, PLAGL1, TMEFF2, PTTG1, CADM1, S100A8, EFS, JUB, ITGA4, MAGEB2, ERBB2, SRGN, GNAS, TJP2, KCNJ15, SLC25A31, ZNF573, TNFRSF25, APC, KCNQ1, LAMC2, SPHK1, DNAJA4, APC, MBD2, ERCC1, HLA-G, CXADR, TP53, ACTB, KL, SMAD3, HIST1H2AG, CPEB4 and sets with at least 50%, preferably at least 60%, at least 70%, at least 80% or at least 90% of these markers are also suitable to detect lung cancer and to distinguish between normal lung tissue and lung tumor tissue. The distinction or diagnosis can be made by using any sample as described above, including serum, sputum, bronchial lavage.

Set c, WT1, DLX2, SALL3, TERT, TNFRSF25, ACTB, SMAD3, CPEB4 and sets with at least 50%, preferably at least 60%, at least 70%, at least 80% or at least 90% of these markers are suitable to detect lung cancer and to distinguish between normal lung tissue (non-cancerous) from lung tumor tissue. The distinction or diagnosis can be made by using any sample as described above, including serum, sputum, bronchial lavage.

Set d, WT1, DLX2, SALL3, TERT, PITX2, TNFRSF25, KL, ACTB, SMAD3, CPEB4 and sets with at least 50%, preferably at least 60%, at least 70%, at least 80% or at least 90% of these markers are in particular suitable to detect lung cancer and to distinguish between normal lung tissue (non-cancerous) from lung tumor tissue. The distinction or diagnosis can be made by using any sample as described above, including serum, sputum, bronchial lavage.

Set e, WT1, PITX2, SALL3, DLX2, TERT, HOXA10, RASSF1, SPARC, IRAK2, ZNF711, DNAJA4, HLA-G, CXADR, TP53, ACTB, CPEB4 and sets with at least 50%, preferably at least 60%, at least 70%, at least 80% or at least 90% of these markers are also suitable to detect lung cancer and to distinguish between normal lung tissue (non-cancerous) from lung tumor tissue. The distinction or diagnosis can be made by using any sample as described above, including serum, sputum, bronchial lavage.

Set f, WT1, PITX2, SALL3, F2R, TERT, HOXA10, RASSF1, SPARC, IRAK2, ZNF711, DRD2, DNAJA4, CXADR, TP53, ACTB, CPEB4 and sets with at least 50%, preferably at least 60%, at least 70%, at least 80% or at least 90% of these markers can be used to detect lung cancer and to distinguish between normal lung tissue (non-cancerous) from lung tumor tissue. The distinction or diagnosis can be made by using any sample as described above, including serum, sputum, bronchial lavage.

Set g, WT1, ACTB, DLX2, PITX2, SALL3, HOXA10, TERT, CPEB4, HLA-G, SPARC, RASSF1, DNAJA4, CXADR, TP53, IRAK2, ZNF711 and sets with at least 50%, preferably at least 60%, at least 70%, at least 80% or at least 90% of these markers can be used to diagnose lung carcinoma, in particular using blood samples, e.g. to distinguish blood from healthy persons from tumor samples, including tumor tissue sample or blood from tumor patients. The distinction or diagnosis can be made by using any sample as described above, including serum, sputum, bronchial lavage.

Set h, F2R, ZNF256, CDH13, SERPINB5, KRT14, DLX2, AREG, THRB, HSD17B4, SPARC, HECW2, COL21A1 and sets with at least 50%, preferably at least 60%, at least 70%, at least 80% or at least 90% of these markers can be used to diagnose lung cancer and distinguish the grade of differentiation of poor, moderate and well predictions. The distinction or diagnosis can be made by using any sample as described above, including serum, sputum, bronchial lavage.

Set i, KL, HIST1H2AG, TJP2, SRGN, CDX1, TNFRSF25, APC, HIC1, APC, GNA15, ACTB, WT1, KRT17, AIM1L, DPH1, PITX2, PITX2, KIF5B, BMP2K, GBP2, NHLH2, GDNF, BOLL and sets with at least 50%, preferably at least 60%, at least 70%, at least 80% or at least 90% of these markers can be used to diagnose lung cancer and distinguish between malign states (in particular adenocarcinoma and squamous cell carcinoma) together with lung tissue against healthy blood or serum samples. The distinction or diagnosis can be made by using any sample as described above, including serum, sputum, bronchial lavage.

Set j, WT1, DLX2, SALL3, TERT, PITX2, HOXA10, F2R, CPEB4, NHLH2, SMAD3, ACTB, HOXA1, BOLL, APC, MT1G, PENK, SPARC, DNAJA4, RASSF1, HLA-G, ERCC1, ONECUT2, APC, ABCB1, ZNF573, KCNJ15, ZDHHC11, SFRP2, GDNF, PTTG1, SERPINI1, TNFRSF10C and sets with at least 50%, preferably at least 60%, at least 70%, at least 80% or at least 90% of these markers can be used to diagnose, lung cancer and distinguish between malign states selected from adenocarcinoma and squamous cell carcinoma from healthy lung tissue. The distinction or diagnosis can be made by using any sample as described above, including serum, sputum, bronchial lavage.

Set k, HOXA10, NEUROD1 and/or either HOXA10 or NEUR001 can be used to diagnose lung cancer and further to distinguish between adenocarcinoma from squamous cell carcinoma.

Set l, WT1, PITX2, SALL3, F2R, TERT, HOXA10, RASSF1, SPARC, IRAK2, ZNF711, DRD2, DNAJA4, CXADR, TP53, ACTB, CPEB4, DLX2, TNFRSF25, KL, SMAD3 and sets with at least 50%, preferably at least 60%, at least 70%, at least 80% or at least 90% of these markers can be used to diagnose lung cancer and distinguish between cancerous lung tissue from healthy lung tissue.

Set m, TNFRSF25, SALL3, RASSF1, TERT, SPARC, F2R, HOXA10, ZNF711, PITX2 and sets with at least 50%, preferably at least 60%, at least 70%, at least 80% or at least 90% of these markers can be used to diagnose lung cancer and distinguish between cancerous lung tissue from healthy lung tissue. The distinction or diagnosis can be made by using any sample as described above, including serum, sputum, bronchial lavage.

Set n, SALL3, PITX2, SPARC, F2R, TERT, RASSF1, HOXA10, CXADR, KL and sets with at least 50%, preferably at least 60%, at least 70%, at least 80% or at least 90% of these markers can be used to diagnose lung cancer and distinguish between cancerous lung tissue from healthy lung tissue. The distinction or diagnosis can be made by using any sample as described above, including serum, sputum, bronchial lavage.

Set o, SALL3, SPARC, PITX2, F2R, TERT, RASSF1, HOXA10, KL and sets with at least 50%, preferably at least 60%, at least 70%, at least 80% or at least 90% of these markers can be used to diagnose lung cancer and distinguish between cancerous lung tissue from healthy lung tissue. The distinction or diagnosis can be made by using any sample as described above, including serum, sputum, bronchial lavage.

Set p, SALL3, PITX2, SPARC, F2R, HOXA10, DRD2, ACTB, DNAJA4, CXADR, KL and sets with at least 50%, preferably at least 60%, at least 70%, at least 80% or at least 90% of these markers can be used to diagnose lung cancer and to distinguish between normal lung tissue (non-cancerous) from lung tumor tissue.

Set q, SALL3, SPARC, PITX2, F2R, TERT, RASSF1, HOXA10, TNFRSF25, DNAJA4, TP53, CXADR, KL and sets with at least 50%, preferably at least 60%, at least 70%, at least 80% or at least 90% of these markers can be used to diagnose lung cancer and to distinguish between normal lung tissue (non-cancerous) from lung tumor tissue. The distinction or diagnosis can be made by using any sample as described above, including serum, sputum, bronchial lavage.

Set r, SPARC, SALL3, F2R, PITX2, RASSF1, HOXA10, TERT, KL, TNFRSF25 and sets with at least 50%, preferably at least 60%, at least 70%, at least 80% or at least 90% of these markers can be used to diagnose lung cancer, distinguish between adenocarcinoma, healthy lung tissue and squamous cell carcinoma. The distinction or diagnosis can be made by using any sample as described above, including serum, sputum, bronchial lavage.

Set s, SALL3, SPARC, PITX2, F2R, TERT, RASSF1, HOXA10, KL, TNFRSF25, CXADR and 50%, preferably at least 60%, at least 70%, at least 80% or at least 90% of these markers can be used to diagnose lung cancer, distinguish adenocarcinoma and squamous cell carcinoma from healthy (benign) lung tissue. The distinction or diagnosis can be made by using any sample as described above, including serum, sputum, bronchial lavage.

Set t, HOXA10, RASSF1, F2R and sets with at least 50%, preferably at least 60%, at least 70%, at least 80% or at least 90% of these markers can be used to diagnose lung cancer, distinguish between adenocarcinoma and squamous cell carcinoma. The distinction or diagnosis can be made by using any sample as described above, including serum, sputum, bronchial lavage.

Also provided are combinations of the above mentioned subsets a) to t), in particular sets comprising markers of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 or more of these subsets, preferably for the lung cancer type or preferably complete sets a) to t). One preferred set comprises gene markers WT1, SALL3, TERT, ACTB and CPEB4. These markers are common in a set for the diagnosis of lung cancer and suitable to distinguish normal from lung cancer samples. This set preferably is supplemented by the marker genes DLX2, TNFRSF25 or SMAD3. Furthermore, the inventive set may comprise any one of the markers ABCB1, ACTB, AIM1L, APC, AREG, BMP2K, BOLL, C5AR1, C5orf4, CADM1, CDH13, CDX1, CLIC4, COL21A1, CPEB4, CXADR, DLX2, DNAJA4, DPH1, DRD2, EFS, ERBB2, ERCC1, ESR2, F2R, FAM43A, GABRA2, GAD1, GBP2, GDNF, GNA15, GNAS, HECW2, HIC1, HIST1H2AG, HLA-G, HOXA1, HOXA10, HSD17B4, HSPA2, IRAK2, ITGA4, JUB, KCNJ15, KCNQ1, KIF5B, KL, KRT14, KRT17, LAMC2, MAGEB2, MBD2, MSH4, MT1G, MT3, MTHFR, NEUROD1, NHLH2, NKX2-1, ONECUT2, PENK, PITX2, PLAGL1, PTTG1, PYCARD, RASSF1, S100A8, SALL3, SERPINB5, SERPINE1, SERPINI1, SFRP2, SLC25A31, SMAD3, SPARC, SPHK1, SRGN, TERT, THRB, TJP2, TMEFF2, TNFRSF10C, TNFRSF25, TP53, ZDHHC11, ZNF256, ZNF711, F2R, HOXA10, KL, SALL3, SPARC, TNFRSF25, WT1 or any combination thereof, in particular preferred are markers ACTB, APC, CPEB4, CXADR, DLX2, DNAJA4, F2R, HOXA10, KL, PITX2, RASSF1, SALL3, SPARC, TERT, (either TNFRSF10C or TNFRSF25 or both), WT1 or any combination thereof, even more preferred are markers HOXA10, PITX2, RASSF1, SALL3, SPARC, TERT or any combination thereof, in a marker set according to the present invention, in particular as additional markers for any one of sets a) to t), especially the marker set of markers WT1, SALL3, TERT, ACTB and CPEB4.

According to a preferred embodiment of the present invention, the methylation of at least two genes, preferably of at least three genes, especially of at least four genes, is determined. Specifically if the present invention is provided as an array test system, at least ten, especially at least fifteen genes, are preferred. In preferred test set-ups (for example in microarrays (“gene-chips”)) preferably at least 20, even more preferred at least 30, especially at least 40 genes, are provided as test markers. As mentioned above, these markers or the means to test the markers can be provided in a set of probes or a set of primers, preferably both.

In a further embodiment the set comprises up to 100000, up to 90000, up to 80000, up to 70000, up to 60000 or 50000 probes or primer pairs (set of two primers for one amplification product), preferably up to 40000, up to 35000, up to 30000, up to 25000, up to 20000, up to 15000, up to 10000, up to 7500, up to 5000, up to 3000, up to 2000, up to 1000, up to 750, up to 500, up to 400, up to 300, or even more preferred up to 200 probes or primers of any kind, particular in the case of immobilized probes on a solid surface such as a chip.

In certain embodiments the primer pairs and probes are specific for a methylated upstream region of the open reading frame of the marker genes.

Preferably the probes or primers are specific for a methylation in the genetic regions defined by SEQ ID NOs 1081 to 1440, including the adjacent up to 500 base pairs, preferably up to 300, up to 200, up to 100, up to 50 or up to 10 adjacent, corresponding to gene marker IDs 1 to 359 of table 1, respectively. I.e. probes or primers of the inventive set (including the full 359 set, as well as subsets and combinations thereof) are specific for the regions and gene loci identified in table 1, last column with reference to the sequence listing, SEQ ID NOs: 1081 to 1440. As can be seen these SEQ IDs correspond to a certain gene, the latter being a member of the inventive sets, in particular of the subsets a) to t), e.g.

Examples of specific probes or primers are given in table 1 with reference to the sequence listing, SEQ ID NOs 1 to 1080, which form especially preferred embodiments of the invention.

Preferably the set of the present invention comprises probes or primers for at least one gene or gene product of the list according to table 1, wherein at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, especially preferred at least 100%, of the total probes or primers are probes or primers for genes of the list according to table 1. Preferably the set, in particular in the case of a set of hybridization probes, is provided immobilized on a solid surface, preferably a chip or in form of a microarray. Since—according to current technology—detection means for genes on a chip allow easier and more robust array design, gene chips using DNA molecules (for detection of methylated DNA in the sample) is a preferred embodiment of the present invention. Such gene chips also allow detection of a large number of nucleic acids.

Preferably the set is provided on a solid surface, in particular a chip, whereon the primers or probes can be immobilized. Solid surfaces or chips may be of any material suitable for the immobilization of biomolecules such as the moieties, including glass, modified glass (aldehyde modified) or metal chips.

The primers or probes can also be provided as such, including lyophilized forms or being in solution, preferably with suitable buffers. The probes and primers can of course be provided in a suitable container, e.g. a tube or micro tube.

The present invention also relates to a method of identifying lung cancer or lung cancer type in a sample comprising DNA from a subject or patient, comprising obtaining a set of nucleic acid primers (or primer pairs) or hybridization probes as defined above (comprising each specific subset or combinations thereof), determining the methylation status of the genes in the sample for which the members of the set are specific for and comparing the methylation status of the genes with the status of a confirmed lung cancer or lung cancer type positive and/or negative state, thereby identifying the lung cancer or lung cancer type in the sample. In general the inventive method has been described above and all preferred embodiments of such methods also apply to the method using the set provided herein.

The inventive marker set, including certain disclosed subsets and subsets, which can be identified with the methods disclosed herein, are suitable to diagnose lung cancer and distinguish between different lung cancer forms, in particular for diagnostic or prognostic uses. Preferably the markers used (e.g. by utilizing primers or probes of the inventive set) for the inventive diagnostic or prognostic method may be used in smaller amounts than e.g. in the set (or kit) or chip as such, which may be designed for more than one fine tuned diagnosis or prognosis. The markers used for the diagnostic or prognostic method may be up to 100000, up to 90000, up to 80000, up to 70000, up to 60000 or 50000, preferably up to 40000, up to 35000, up to 30000, up to 25000, up to 20,000, up to 15000, up to 10000, up to 7500, up to 5000, up to 3000, up to 2000, up to 1000, up to 750, up to 500, up to 400, up to 300, up to 200, up to 100, up to 80, or even more preferred up to 60. The inventive set of marker primers or probes can be employed in chip (immobilised) based assays, products or methods, or in PCR based kits or methods. Both, PCR and hybridisation (e.g. on a chip) can be used to detect methylated genes.

The inventive marker set, including certain disclosed subsets, which can be identified with the methods disclosed herein, are suitable to distinguish between lung cancer from normal tissue, in particular for diagnostic or prognostic uses.

The inventive marker set, including certain disclosed subsets, which can be identified with the methods disclosed herein, are suitable to distinguish between adenocarcinoma from squamous cell carcinoma, in particular for diagnostic or prognostic uses.

The present invention is further illustrated by the following examples, without being restricted thereto.

FIGURES

FIG. 1: Cross-Validation ROC curve from the Bayesian Compound Covariate Predictor.

 EXAMPLES Example 1 Gene List

TABLE 1 360 master set (with the 359 marker genes and one control) and sequence annotation hybrid- primer primer isation 1 2 PCR probe (lp) (rp) product alt. (SEQ (SEQ (SEQ (SEQ gene Gene Gene ID ID ID ID ID Symbol Symbol NO:) NO:) NO:) NO:) 1 NHLH2 NHLH2 1 361 721 1081 2 MTHFR MTHFR 2 362 722 1082 3 PRDM2 RIZ1 3 363 723 1083 (PRDM2) 4 MLLT11 MLLT11 4 364 724 1084 5 S100A9 control_ 5 365 725 1085 S100A9 6 S100A9 S100A9 6 366 726 1086 7 S100A8 S100A8 7 367 727 1087 8 S100A8 control_ 8 368 728 1088 S100A8 9 S100A2 S100A2 9 369 729 1089 10 LMNA LMNA 10 370 730 1090 11 DUSP23 DUSP23 11 371 731 1091 12 LAMC2 LAMC2 12 372 732 1092 13 PTGS2 PTGS2 13 373 733 1093 14 MARK1 MARK1 14 374 734 1094 15 DUSP10 DUSP10 15 375 735 1095 16 PARP1 PARP1 16 376 736 1096 17 PSEN2 PSEN2 17 377 737 1097 18 CLIC4 CLIC4 18 378 738 1098 19 RUNX3 RUNX3 19 379 739 1099 20 AIM1L NM_ 20 380 740 1100 017977 21 SFN SFN 21 381 741 1101 22 RPA2 RPA2 22 382 742 1102 23 TP73 TP73 23 383 743 1103 24 TP73 p73 24 384 744 1104 25 POU3F1 01.10.06 25 385 745 1105 26 MUTYH MUTYH 26 386 746 1106 27 UQCRH UQCRH 27 387 747 1107 28 FAF1 FAF1 28 388 748 1108 29 TACSTD2 TACSTD2 29 389 749 1109 30 TNFRSF25 TNFRSF25 30 390 750 1110 31 DIRAS3 DIRAS3 31 391 751 1111 32 MSH4 MSH4 32 392 752 1112 33 GBP2 Control 33 393 753 1113 34 GBP2 GBP2 34 394 754 1114 35 LRRC8C LRRC8C 35 395 755 1115 36 F3 F3 36 396 756 1116 37 NANOS1 NM_ 37 397 757 1117 001009553 38 MGMT MGMT 38 398 758 1118 39 EBF3 EBF3 39 399 759 1119 40 DCLRE1C DCLRE1C 40 400 760 1120 41 KIF5B KIF5B 41 401 761 1121 42 ZNF22 ZNF22 42 402 762 1122 43 PGBD3 ERCC6 43 403 763 1123 44 SRGN Control 44 404 764 1124 45 GATA3 GATA3 45 405 765 1125 46 PTEN PTEN 46 406 766 1126 47 MMS19 MMS19L 47 407 767 1127 48 SFRP5 SFRP5 48 408 768 1128 49 PGR PGR 49 409 769 1129 50 ATM ATM 50 410 770 1130 51 DRD2 DRD2 51 411 771 1131 52 CADM1 IGSF4 52 412 772 1132 53 TEAD1 Control 53 413 773 1133 54 OPCML OPCML 54 414 774 1134 55 CALCA CALCA 55 415 775 1135 56 CTSD CTSD 56 416 776 1136 57 MYOD1 MYOD1 57 417 777 1137 58 IGF2 IGF2 58 418 778 1138 59 BDNF BDNF 59 419 779 1139 60 CDKN1C CDKN1C 60 420 780 1140 61 WT1 WT1 61 421 781 1141 62 HRAS HRAS1 62 422 782 1142 63 DDB1 DDB1 63 423 783 1143 64 GSTP1 GSTP1 64 424 784 1144 65 CCND1 CCND1 65 425 785 1145 66 EPS8L2 EPS8L2 66 426 786 1146 67 PIWIL4 PIWIL4 67 427 787 1147 68 CHST11 CHST11 68 428 788 1148 69 UNG UNG 69 429 789 1149 70 CCDC62 CCDC62 70 430 790 1150 71 CDK2AP1 CDK2AP1 71 431 791 1151 72 CHFR CHFR 72 432 792 1152 73 GRIN2B GRIN2B 73 433 793 1153 74 CCND2 CCND2 74 434 794 1154 75 VDR VDR 75 435 795 1155 76 B4GALNT3 control 76 436 796 1156 (wrong chr of HRAS1) 77 NTF3 NTF3 77 437 797 1157 78 CYP27B1 CYP27B1 78 438 798 1158 79 GPR92 GPR92 79 439 799 1159 80 ERCC5 ERCC5 80 440 800 1160 81 GJB2 GJB2 81 441 801 1161 82 BRCA2 BRCA2 82 442 802 1162 83 KL KL 83 443 803 1163 84 CCNA1 CCNA1 84 444 804 1164 85 SMAD9 SMAD9 85 445 805 1165 86 C13orf15 RGC32 86 446 806 1166 87 DGKH DGKH 87 447 807 1167 88 DNAJC15 DNAJC15 88 448 808 1168 89 RB1 RB1 89 449 809 1169 90 RCBTB2 RCBTB2 90 450 810 1170 91 PARP2 PARP2 91 451 811 1171 92 APEX1 APEX1 92 452 812 1172 93 JUB JUB 93 453 813 1173 94 JUB control_ 94 454 814 1174 NM_19808 95 EFS EFS 95 455 815 1175 96 BAZ1A BAZ1A 96 456 816 1176 97 NKX2-1 TITF1 97 457 817 1177 98 ESR2 ESR2 98 458 818 1178 99 HSPA2 HSPA2 99 459 819 1179 100 PSEN1 PSEN1 100 460 820 1180 101 PGF PGF 101 461 821 1181 102 MLH3 MLH3 102 462 822 1182 103 TSHR TSHR 103 463 823 1183 104 THBS1 THBS1 104 464 824 1184 105 MYO5C MYO5C 105 465 825 1185 106 SMAD6 SMAD6 106 466 826 1186 107 SMAD3 SMAD3 107 467 827 1187 108 NOX5 SPESP1 108 468 828 1188 109 DNAJA4 DNAJA4 109 469 829 1189 110 CRABP1 CRABP1 110 470 830 1190 111 BCL2A1 BCL2A1 111 471 831 1191 112 BCL2A1 BCL2A1 112 472 832 1192 113 BNC1 BNC1 113 473 833 1193 114 ARRDC4 ARRDC4 114 474 834 1194 115 SOCS1 SOCS1 115 475 835 1195 116 ERCC4 ERCC4 116 476 836 1196 117 NTHL1 NTHL1 117 477 837 1197 118 PYCARD PYCARD 118 478 838 1198 119 AXIN1 AXIN1 119 479 839 1199 120 CYLD NM_015247 120 480 840 1200 121 MT3 MT3 121 481 841 1201 122 MT1A MT1A 122 482 842 1202 123 MT1G MT1G 123 483 843 1203 124 CDH1 CDH1 124 484 844 1204 125 CDH13 CDH13 125 485 845 1205 126 DPH1 DPH1 126 486 846 1206 127 HIC1 HIC1 127 487 847 1207 128 NEUROD2 control_ 128 488 848 1208 NEUROD2 129 NEUROD2 NEUROD2 129 489 849 1209 130 ERBB2 ERBB2 130 490 850 1210 131 KRT19 KRT19 131 491 851 1211 132 KRT14 KRT14 132 492 852 1212 133 KRT17 KRT17 133 493 853 1213 134 JUP JUP 134 494 854 1214 135 BRCA1 BRCA1 135 495 855 1215 136 COL1A1 COL1A1 136 496 856 1216 137 CACNA1G CACNA1G 137 497 857 1217 138 PRKAR1A PRKAR1A 138 498 858 1218 139 SPHK1 SPHK1 139 499 859 1219 140 SOX15 SOX15 140 500 860 1220 141 TP53 TP53_ 141 501 861 1221 CGI23_1kb 142 TP53 TP53_ 142 502 862 1222 bothCGIs_ 1kb 143 TP53 TP53_ 143 503 863 1223 CGI36_1kb 144 TP53 TP53 144 504 864 1224 145 NPTX1 NPTX1 145 505 865 1225 146 SMAD2 SMAD2 146 506 866 1226 147 DCC DCC 147 507 867 1227 148 MBD2 MBD2 148 508 868 1228 149 ONECUT2 ONECUT2 149 509 869 1229 150 BCL2 BCL2 150 510 870 1230 151 SERPINB5 SERPINB5 151 511 871 1231 152 SERPINB2 Control 152 512 872 1232 153 SERPINB2 SERPINB2 153 513 873 1233 154 TYMS TYMS 154 514 874 1234 155 LAMA1 LAMA1 155 515 875 1235 156 SALL3 SALL3 156 516 876 1236 157 LDLR LDLR 157 517 877 1237 158 STK11 STK11 158 518 878 1238 159 PRDX2 PRDX2 159 519 879 1239 160 RAD23A RAD23A 160 520 880 1240 161 GNA15 GNA15 161 521 881 1241 162 ZNF573 ZNF573 162 522 882 1242 163 SPINT2 SPINT2 163 523 883 1243 164 XRCC1 XRCC1 164 524 884 1244 165 ERCC2 ERCC2 165 525 885 1245 166 ERCC1 ERCC1 166 526 886 1246 167 C5AR1 NM_001736 167 527 887 1247 168 C5AR1 C5AR1 168 528 888 1248 169 POLD1 POLD1 169 529 889 1249 170 ZNF350 ZNF350 170 530 890 1250 171 ZNF256 ZNF256 171 531 891 1251 172 C3 C3 172 532 892 1252 173 XAB2 XAB2 173 533 893 1253 174 ZNF559 ZNF559 174 534 894 1254 175 FHL2 FHL2 175 535 895 1255 176 IL1B IL1B 176 536 896 1256 177 IL1B control_IL1B 177 537 897 1257 178 PAX8 PAX8 178 538 898 1258 179 DDX18 DDX18 179 539 899 1259 180 GAD1 GAD1 180 540 900 1260 181 DLX2 DLX2 181 541 901 1261 182 ITGA4 ITGA4 182 542 902 1262 183 NEUROD1 NEUROD1 183 543 903 1263 184 STAT1 STAT1 184 544 904 1264 185 TMEFF2 TMEFF2 185 545 905 1265 186 HECW2 HECW2 186 546 906 1266 187 BOLL BOLL 187 547 907 1267 188 CASP8 CASP8 188 548 908 1268 189 SERPINE2 SERPINE2 189 549 909 1269 190 NCL NCL 190 550 910 1270 191 CYP1B1 CYP1B1 191 551 911 1271 192 TACSTD1 TACSTD1 192 552 912 1272 193 MSH2 MSH2 193 553 913 1273 194 MSH6 MSH6 194 554 914 1274 195 MXD1 MXD1 195 555 915 1275 196 JAG1 JAG1 196 556 916 1276 197 FOXA2 FOXA2 197 557 917 1277 198 THBD THBD 198 558 918 1278 199 CTCFL BORIS 199 559 919 1279 200 CTSZ CTSZ 200 560 920 1280 201 GATA5 GATA5 201 561 921 1281 202 CXADR CXADR 202 562 922 1282 203 APP APP 203 563 923 1283 204 TTC3 TTC3 204 564 924 1284 205 KCNJ15 Control 205 565 925 1285 206 RIPK4 RIPK4 206 566 926 1286 207 TFF1 TFF1 207 567 927 1287 208 SEZ6L SEZ6L 208 568 928 1288 209 TIMP3 TIMP3 209 569 929 1289 210 BIK BIK 210 570 930 1290 211 VHL VHL 211 571 931 1291 212 IRAK2 IRAK2 212 572 932 1292 213 PPARG PPARG 213 573 933 1293 214 MBD4 MBD4 214 574 934 1294 215 RBP1 RBP1 215 575 935 1295 216 XPC XPC 216 576 936 1296 217 ATR ATR 217 577 937 1297 218 LXN LXN 218 578 938 1298 219 RARRES1 RARRES1 219 579 939 1299 220 SERPINI1 SERPINI1 220 580 940 1300 221 CLDN1 CLDN1 221 581 941 1301 222 FAM43A FAM43A 222 582 942 1302 223 IQCG IQCG 223 583 943 1303 224 THRB THRB 224 584 944 1304 225 RARB RARB 225 585 945 1305 226 TGFBR2 TGFBR2 226 586 946 1306 227 MLH1 MLH1 227 587 947 1307 228 DLEC1 DLEC1 228 588 948 1308 229 CTNNB1 CTNNB1 229 589 949 1309 230 ZNF502 ZNF502 230 590 950 1310 231 SLC6A20 SLC6A20 231 591 951 1311 232 GPX1 GPX1 232 592 952 1312 233 RASSF1 RASSF1A 233 593 953 1313 234 FHIT FHIT 234 594 954 1314 235 OGG1 OGG1 235 595 955 1315 236 PITX2 PITX2 236 596 956 1316 237 SLC25A31 SLC25A31 237 597 957 1317 238 FBXW7 FBXW7 238 598 958 1318 239 SFRP2 SFRP2 239 599 959 1319 240 CHRNA9 CHRNA9 240 600 960 1320 241 GABRA2 GABRA2 241 601 961 1321 242 MSX1 MSX1 242 602 962 1322 243 IGFBP7 IGFBP7 243 603 963 1323 244 EREG EREG 244 604 964 1324 245 AREG AREG 245 605 965 1325 246 ANXA3 ANXA3 246 606 966 1326 247 BMP2K BMP2K 247 607 967 1327 248 APC APC 248 608 968 1328 249 HSD17B4 HSD17B4 249 609 969 1329 250 HSD17B4 HSD17B4 250 610 970 1330 251 LOX LOX 251 611 971 1331 252 TERT TERT 252 612 972 1332 253 NEUROG1 NEUROG1 253 613 973 1333 254 NR3C1 NR3C1 254 614 974 1334 255 ADRB2 ADRB2 255 615 975 1335 256 CDX1 CDX1 256 616 976 1336 257 SPARC SPARC 257 617 977 1337 258 C5orf4 Control 258 618 978 1338 259 PTTG1 PTTG1 259 619 979 1339 260 DUSP1 DUSP1 260 620 980 1340 261 CPEB4 CPEB4 261 621 981 1341 262 SCGB3A1 SCGB3A1 262 622 982 1342 263 GDNF GDNF 263 623 983 1343 264 ERCC8 ERCC8 264 624 984 1344 265 F2R F2R 265 625 985 1345 266 F2RL1 F2RL1 266 626 986 1346 267 VCAN CSPG2 267 627 987 1347 268 ZDHHC11 ZDHHC11 268 628 988 1348 269 RHOBTB3 RHOBTB3 269 629 989 1349 270 PLAGL1 PLAGL1 270 630 990 1350 271 SASH1 SASH1 271 631 991 1351 272 ULBP2 ULBP2 272 632 992 1352 273 ESR1 ESR1 273 633 993 1353 274 RNASET2 RNASET2 274 634 994 1354 275 DLL1 DLL1 275 635 995 1355 276 HIST1H2AG HIST1H2AG 276 636 996 1356 277 HLA-G HLA-G 277 637 997 1357 278 MSH5 MSH5 278 638 998 1358 279 CDKN1A CDKN1A 279 639 999 1359 280 TDRD6 TDRD6 280 640 1000 1360 281 COL21A1 COL21A1 281 641 1001 1361 282 DSP DSP 282 642 1002 1362 283 SERPINE1 SERPINE1 283 643 1003 1363 284 SERPINE1 SERPINE1 284 644 1004 1364 285 FBXL13 FBXL13 285 645 1005 1365 286 NRCAM NRCAM 286 646 1006 1366 287 TWIST1 TWIST1 287 647 1007 1367 288 HOXA1 HOXA1 288 648 1008 1368 289 HOXA10 HOXA10 289 649 1009 1369 290 SFRP4 SFRP4 290 650 1010 1370 291 IGFBP3 IGFBP3 291 651 1011 1371 292 RPA3 RPA3 292 652 1012 1372 293 ABCB1 ABCB1 293 653 1013 1373 294 TFPI2 TFPI2 294 654 1014 1374 295 COL1A2 COL1A2 295 655 1015 1375 296 ARPC1B ARPC1B 296 656 1016 1376 297 PILRB PILRB 297 657 1017 1377 298 GATA4 GATA4 298 658 1018 1378 299 MAL2 NM_052886 299 659 1019 1379 300 DLC1 DLC1 300 660 1020 1380 301 EPPK1 NM_031308 301 661 1021 1381 302 LZTS1 LZTS1 302 662 1022 1382 303 TNFRSF10B TNFRSF10B 303 663 1023 1383 304 TNFRSF10C TNFRSF10C 304 664 1024 1384 305 TNFRSF10D TNFRSF10D 305 665 1025 1385 306 TNFRSF10A TNFRSF10A 306 666 1026 1386 307 WRN WRN 307 667 1027 1387 308 SFRP1 SFRP1 308 668 1028 1388 309 SNAI2 SNAI2 309 669 1029 1389 310 RDHE2 RDHE2 310 670 1030 1390 311 PENK PENK 311 671 1031 1391 312 RDH10 RDH10 312 672 1032 1392 313 TGFBR1 TGFBR1 313 673 1033 1393 314 ZNF462 ZNF462 314 674 1034 1394 315 KLF4 KLF4 315 675 1035 1395 316 CDKN2A p14_ 316 676 1036 1396 CDKN2A 317 CDKN2B CDKN2B 317 677 1037 1397 318 AQP3 AQP3 318 678 1038 1398 319 TPM2 TPM2 319 679 1039 1399 320 TJP2 TJP2 320 680 1040 1400 321 TJP2 TJP2 321 681 1041 1401 322 PSAT1 PSAT1 322 682 1042 1402 323 DAPK1 DAPK1 323 683 1043 1403 324 SYK SYK 324 684 1044 1404 325 XPA XPA 325 685 1045 1405 326 ARMCX2 ARMCX2 326 686 1046 1406 327 RHOXF1 OTEX 327 687 1047 1407 328 FHL1 FHL1 328 688 1048 1408 329 MAGEB2 MAGEB2 329 689 1049 1409 330 TIMP1 TIMP1 330 690 1050 1410 331 AR AR_humara 331 691 1051 1411 332 ZNF711 ZNF6 332 692 1052 1412 333 CD24 CD24 333 693 1053 1413 334 ABL1 ABL 334 694 1054 1414 335 ACTB Aktin_VL 335 695 1055 1415 336 APC APC 336 696 1056 1416 337 CDH1 Ecad1 337 697 1057 1417 338 CDH1 Ecad2 338 698 1058 1418 339 FMR1 FX 339 699 1059 1419 340 GNAS GNASexAB 340 700 1060 1420 341 H19 H19 341 701 1061 1421 342 HIL1 Igf2 342 702 1062 1422 343 IGF2 Igf2 343 703 1063 1423 344 KCNQ1 LIT1 344 704 1064 1424 345 GNAS NESP55 345 705 1065 1425 346 CDKN2A P14 346 706 1066 1426 347 CDKN2B P15 347 707 1067 1427 348 CDKN2A P16_VL 348 708 1068 1428 349 PITX2 PitxA 349 709 1069 1429 350 PITX2 PitxB 350 710 1070 1430 351 PITX2 PitxC 351 711 1071 1431 352 PITX2 PitxD 352 712 1072 1432 353 RB1 Rb 353 713 1073 1433 354 SFRP2 SFRP2_VL 354 714 1074 1434 355 SNRPN SNRPN 355 715 1075 1435 356 XIST XIST 356 716 1076 1436 357 IRF4 chr6_ 357 717 1077 1437 control 358 UNC13B chr9_ 358 718 1078 1438 control 359 GSTP1 GSTP1 360 720 1080 1440 360 Lamda lambda_ 359 719 1079 1439 (control) PCR

Example 2 Samples

Samples from solid tumors were derived from initial surgical resection of primary tumors. Tumor tissue sections were derived from histopathology and histopathological data as well clinical data were monitored over the time of clinical management of the patients and/or collected from patient reports in the study center. Anonymised data and DNA were provided.

Example 3 Principle of the Assay and Design

The invention assay is a multiplexed assay for DNA methylation testing of up to (or even more than) 360 methylation candidate markers, enabling convenient methylation analyses for tumor-marker definition. In its best mode the test is a combined multiplex-PCR and microarray hybridization technique for multiplexed methylation testing. The inventive marker genes, PCR primer sequences, hybridization probe sequences and expected PCR products are given in table 1, above.

Targeting hypermethylated DNA regions in the inventive marker genes in several neoplasias, methylation analysis is performed via methylation dependent restriction enzyme (MSRE) digestion of 500 ng of starting DNA. A combination of several MSREs warrants complete digestion of unmethylated DNA. All targeted DNA regions have been selected in that way that sequences containing multiple MSRE sites are flanked by methylation independent restriction enzyme sites. This strategy enables pre-amplification of the methylated DNA fraction before methylation analyses. Thus, the design and pre-amplification would enable methylation testing on serum, urine, stool etc. when DNA is limiting.

When testing DNA without pre-amplification upon digestion of 500 ng the methylated DNA fraction is amplified within 16 multiplex PCRs and detected via microarray hybridization. Within these 16 multiplex-PCR reactions 360 different human DNA products can be amplified. From these about 20 amplicons serve as digestion & amplification controls and are either derived from known differentially methylated human DNA regions, or from several regions without any sites of MSREs used in this system. The primer set (every reverse primer is biotinylated) used is targeting 347 different sites located in the 5′UTR of 323 gene regions.

After PCR amplicons are pooled and positives are detected using strepavidin-Cy3 via microarray hybridization. Although the melting temperature of CpG rich DNA is very high, primer and probe-design as well as hybridization conditions have been optimized, thus this assay enables unequivocal multiplexed methylation testing of human DNA samples. The assay has been designed such that 24 samples can be run in parallel using 384well PCR plates.

Handling of many DNA samples in several plates in parallel can be easily performed enabling completion of analyses within 1-2 days.

The entire procedure provides the user to setup a specific PCR test and subsequent gel-based or hybridization-based testing of selected markers using single primer-pairs or primer-subsets as provided herein or identified by the inventive method from the 360 marker set.

Example 4 MSRE Digestion of DNA

MSRE digestion of DNA (about 500 ng) was performed at 37° C. over night in a volume of 30 μl in 1× Tango-restriction enzyme digestion buffer (MBI Fermentas) using 8 units of each MSREs AciI (New England Biolabs), Hin 6 I and Hpa II (both from MBI Fermentas). Digestions were stopped by heat inactivation (10 min, 75° C.) and subjected to PCR amplification.

Example 5 PCR Amplification

An aliquot of 20 μl MSRE digested DNA (or in case of preamplification of methylated DNA—see below—about 500 ng were added in a volume of 20 μl) was added to 280 μl of PCR-Premix (without primers). Premix consisted of all reagents obtaining a final concentration of 1× HotStarTaq Buffer (Qiagen); 160 μM dNT-Ps, 5% DMSO and 0.6 U Hot Firepol Taq (Solis Biodyne) per 20 μl reaction. Alternatively an equal amount of HotStarTaq (Qiagen) could be used. Eighteen (18) μl of the Pre-Mix including digested DNA were aliquoted in 16 0.2 ml PCR tubes and to each PCR tube 2 μl of each primer-premix 1-16 (containing 0.83pmol/μl of each primer) were added. PCR reactions were amplified using a thermal cycling profile of 15 min/95° C. and 40 cycles of each 40 sec/95° C., 40 sec/65° C., 1 min20 sec/72° C. and a final elongation of 7 min/72° C., then reactions were cooled. After amplification the 16 different multiplex-PCR amplicons from each DNA sample were pooled. Successful amplification was controlled using 10 μl of the pooled 16 different PCR reactions per sample. Positive amplification obtained a smear in the range of 100-300 bp on EtBr stained agarose gels; negative amplification controls must not show a smear in this range.

Example 6 Microarray Hybridization and Detection

Microarrays with the probes of the 360 marker set are blocked for 30 min in 3M Urea containing 0.1% SDS, at room temperature submerged in a stirred choplin char. After blocking slides are washed in 0.1×SSC/0.2% SDS for 5 min, dipped into water and dried by centrifugation.

The PCR-amplicon-pool of each sample is mixed with an equal amount of 2× hybridization buffer (7×SSC, 0.6% SDS, 50% formamide), desaturated for 5 min at 95° C. and held at 70° C. until loading an aliquot of 100 μl onto an array covered by a gasket slide (Agilent). Arrays are hybridized under maximum speed of rotation in an Agilent-hybridization oven for 16 h at 52° C. After removal of gasket-slides microarray-slides are washed at room temperature in wash-solution I (1×SSC, 0.2% SDS) for 5 min and wash solution II (0.1×SSC, 0.2% SDS) for 5 min, and a final wash by dipping the slides 3 times into wash solution III (0.1×SSC), the slides are dried by centrifugation.

For detection of hybridized biotinylated PCR amplicons, streptavidin-Cy3-conjugate (Caltag Laboratories) is diluted 1:400 in PBST-MP (1×PBS, 0.1% Tween 20; 1% skimmed dry milk powder [Sucofin; Germany]), pipetted onto microarrays covered with a coverslip and incubated 30 min at room temperature in the dark. Then coverslips are washed off from the slides using PBST (1×PBS, 0.1% Tween 20) and then slides are washed in fresh PEST for 5 min, rinsed with water and dried by centrifugation.

Example 7 DNA Preamplification for Methylation Profiling (Optional)

In many situations DNA amount is limited. Although the inventive methylation test is performing well with low amounts of DNA (see above), especially minimal invasive testing using cell free DNA from serum, stool, urine, and other body fluids is of diagnostic relevance.

Samples can be preamplified prior methylation testing as follows: DNA was digested with restriction enzyme FspI (and/or Csp6I, and/or MseI, and/or Tsp5091; or their isoschizomeres) and after (heat) inactivation of the restriction enzyme the fragments were circularized using T4 DNA ligase. Ligation-products were digested using a mixture of methylation sensitive restriction enzymes. Upon enzyme-inactivation the entire mixture was amplified using rolling circle amplification (RCA) by phi29-phage polymerase. The RCA-amplicons were then directly subjected to the multiplex-PCRs of the inventive methylation test without further need of digestion of the DNA prior amplification.

Alternatively the preamplified DNA which is enriched for methylated DNA regions can be directly subjected to fluorescent-labelling and the labeled products can be hybridized onto the microarrays using the same conditions as described above for hybridization of PCR products. Then the streptavidin-Cy3 detection step has to be omitted and slides should be scanned directly upon stringency washes and drying the slides. Based on the experimental design for microarray analyses, either single labeled or dual-labeled hybridizations might be generated. From our experiences we successfully used the single label-design for class comparisons. Although the preamplification protocol enables analyses of spurious amounts of DNA, it is also suited for performing genomic methylation screens.

To elucidate methylation biomarkers for prediction of meta-stasis risk on a genomewide level we subjected 500 ng of DNA derived from primary tumor samples to amplification of the methylated DNA using the procedure outlined above. RCA-amplicons derived from metastasized and non-metastasized samples were labelled using the CGH Labeling Kit (Enzo, Farmingdale, N.Y.) and labelled products hybridized onto human 244 k CpG island arrays (Agilent, Waldbronn, Germany). All manipulations were according the instructions of the manufacturers.

Example 8 Data Analysis

Hybridizations performed on a chip with probes for the inventive 360 marker genes were scanned using a GenePix 4000A scanner (Molecular Devices, Ismaning, Germany) with a PMT set-ting to 700V/cm (equal for both wavelengths). Raw image data were extracted using GenePix 6.0 software (Molecular Devices, Ismaning, Germany).

Microarray data analyses were performed using BRB-ArrayTools developed by Dr. Richard Simon and BRB-ArrayTools Development Team. The software package BRB Array Tools (version 3.6; in the www at linus.nci.nih.gov/BRB-ArrayTools.html) was used according recommendations of authors and settings used for analyses are delineated in the results if appropriate. For every hybridization, background intensities were subtracted from foreground intensities for each spot. Global normalization was used to median center the log-ratios on each array in order to adjust for differences in spot/label intensities.

P-values (p) used for feature selection for classification and prediction were based on the univariate significance levels (alpha). P-values (p) and mis-classification rate during cross validation (MCR) were given along the result data.

Example 9 Lung Cancer Test

DNA methylation analysis of 96 DNA samples derived from both normal and lung-tumour tissue of 48 patient samples and 8 DNA samples isolated from peripheral blood (PB) of healthy individuals were analysed for methylation deviations in the inventive set of 359 genes.

From this analysis DNA-methylation-biomarkers suitable for distinction of tumour and normal lung DNA as well as DNA-methylation-profiles from blood DNA of healthy controls were deduced. Diagnostic and prognostic markers subsets are suitable for diagnostic testing and presymptomatic screening for early detection of lung cancer were determined, in DNA derived from lung tissue, but also in DNA extracts from patients other than lung, like sputum, serum or plasma.

DNA Methylation testing results and data analyses of chip results as well as qPCR validation of a subset of markers derived from chip-based testing are provided.

DNA Samples analysed were from blood of 8 healthy individuals (PB), 19 tumours (AdenoCa, adenocarcinoma) and 19 normal lung tissue (N) of adenocarcinoma patients and 29 tumours (SqCCL, squamous cell carcinoma) and 29 normal lung tissue (N) of squamous cell carcinoma patients.

For DNA methylation testing 600 ng of DNA were digested and data derived from DNA-microarray hybridizations analysed using the BRB array tools statistical software package. Class comparison, and class prediction analysis were performed with respect to sample groups as listed above or for delineation of biomarkers for tumour samples both AdenoCa and SqCCL were treated as one tumour sample group (TU).

The design of the test enables methylation testing on DNA directly derived from the biological source. The test is also suitable for using a DNA preamplification upon MSRE digestion (as outlined above). Thus using the methylation specific preamplification of minute amounts of DNA samples, biomarker testing is feasible on small samples and limited amounts of DNA. Thus multiplexed PCR and methylation testing is easily performed on preamplified DNA obtained from these DNA samples. This strategy would improve also testing of serum, urine, stool, synovial fluid, sputum and other body fluids using the conceptual design of the methylation test.

The possibility of preamplification enables also differential methylation hybridization of the preamplified DNA itself. This option is warranted by the design of the test and the probes. Thus using the probes of the methylation test (or the array) for hybridization of labelled DNA after enrichment of either the methylated as well as the unmethylated DNA fractions of any DNA sample, can be used for methylation testing omitting the multiplex PCR.

In addition the biomarkers described herein could be applied for methylation testing using alternative approaches, e.g. methylation sensitive PCR and strategies which are sodium-bisulfite DNA deamination based and not based on MSRE digestion of DNA. These sets of methylation markers are suitable markers for disease-monitoring, -progression, -prediction, therapy-decision and -response.

Example 10 Biomarkers from Microarray-Testing of Patient Samples Example 10a CLASS COMPARISON: TU Vs. Normal: p<0.005, Unpaired Samples; 2 Fold Change

These list of methylation markers were found significant (p<0.005) between TU and N using “unpaired” statistical testing of DNA methylation of 48 tumour samples versus 48 healthy lung tissue samples. Significant markers with 2 fold difference of signal intensities of both classes with p<0.005 are listed.

TABLE 2 Sorted by p-value of the univariate test. Class 1: N; Class 2: T. The 32 genes are significant at the nominal 0.005 level of the univariate test with the fold change 2 Per- Geom Geom muta- mean of mean of Parametric tion p- intensities intensities Fold- Gene p-value FDR value in class 1 in class 2 change symbol 1 <1e−07 <1e−07 <1e−07 1411.8016 13554.578246 0.1041568 WT1 2 <1e−07 <1e−07 <1e−07 85.5069224 1125.7940428 0.0759525 DLX2 3 <1e−07 <1e−07   1e−07 852.3850013 7392.282404 0.1153074 SALL3 4   1e−07 <1e−07   1e−07 235.4745892 592.5077157 0.3974203 TERT 5 <1e−07 <1e−07 <1e−07 274.9097126 833.6648468 0.3297605 PITX2 6 <1e−07 <1e−07 <1e−07 80.5286413 265.3042755 0.3035331 HOXA10 7 <1e−07 <1e−07 <1e−07 112.6645619 855.6410585 0.1316727 F2R 8   1e−07 4.5e−06  <1e−07 2002.2452679 266.6906343 7.507745 CPED4 9   4e−07 1.46e−05     1e−07 718.311462 4609.4380991 0.1558349 NHLH2 10   4e−07 1.46e−05   <1e−07 10347.8184959 3603.9811381 2.8712188 SMAD3 11   5e−07 1.65e−05   <1e−07 2993.3054637 1117.4218527 2.6787604 ACTB 12 2.8e−06 8.49e−05     1e−07 296.6448711 3941.769913 0.0752568 HOXA1 13 3.6e−06 0.0001008 <1e−07 2792.0699393 17199.6551909 0.1623329 BOLL 14 5.9e−06 0.0001342 <1e−07 8664.2840567 2178.4607085 3.9772506 APC 15 1.21e−05  0.0002591 <1e−07 96.7848387 472.6945117 0.2047513 MT1G 16 1.36e−05  0.000275   1e−07 653.0579403 2188.6201533 0.298388 PENK 17 1.97e−05  0.0003774 <1e−07 1710.9865406 4044.9737351 0.4229908 SPARC 18 3.16e−05  0.0005751 <1e−07 1639.128227 811.4430136 2.0200164 DNAJA4 19 3.85e−05  0.0006673 <1e−07 114.7065029 292.8694482 0.3916643 RASSF1 20 4.28e−05  0.0007081 <1e−07 564.6571983 189.2105463 2.9842797 HLA-G 21 4.98e−05  0.0007881   1e−04 1339.8175413 446.1370253 3.0031525 ERCC1 22   6e−05 0.00091   1e−04 395.6248705 1158.1502714 0.3416006 ONECUT2 23 6.58e−05  0.000958 <1e−07 2517.3232246 1024.0897145 2.4581081 APC 24 8.45e−05  0.0011392 <1e−07 232.2537844 701.7843246 0.3309475 ABCB1 25 0.0002382 0.0029898   1e−04 3027.5067641 1165.5391698 2.5975161 ZNF573 26 0.0003469 0.003946 <1e−07 360.9888133 148.6109072 2.4290869 KCNJ15 27 0.0003582 0.0039511   3e−04 1818.1186026 4147.2970277 0.4383864 ZDHHC11 28 0.0012332 0.01192 0.0013 238.5488592 512.9101159 0.465089 SFRP2 29 0.0019349 0.0176076 0.0015 310.5591882 1215.8855725 0.2554181 GDNF 30 0.002818 0.0227945 0.0022 4930.1368809 2261.9370298 2.1796084 PTTG1 31 0.0038228 0.0267596 0.0045 2402.9850212 974.5347994 2.4657765 SERPIN-I1 32 0.0039256 0.0269326 0.0031 208.6539745 417.3186041 0.4999872 TN-FRSF10C

Example 10b CLASS Prediction: TU Vs Normal: p<0.005, Unpaired Samples; 2Fold Change

Class prediction using different statistical methods for elucidating marker panels enabling best correct classification of TU and N (p<0.005).

Performance of Classifiers During Cross-Validation.

Diagonal Mean Compound Linear 3- Support Number of Covariate Discriminant 1- Nearest Nearest Vector genes in Predictor Analysis Nearest Neighbors Centroid Machines classifier Correct? Correct? Neighbor Correct? Correct? Correct? Mean percent 100 100 98 98 98 98 of correct classification:

TABLE 3 Composition of classifier: Sorted by t -value Geometric mean Parametric % CV of intensities Gene p-value t-value support (class N/class T) symbol 1  <1e−07 −10.859 100 0.1041568 WT1 2  <1e−07 −7.903 100 0.3297605 PITX2 3  <1e−07 −7.314 100 0.1153074 SALL3 4  <1e−07 −7.063 100 0.1316727 F2R 5  <1e−07 −7.028 100 0.0759525 DLX2 6  <1e−07 −6.592 100 0.3974203 TERT 7  <1e−07 −6.539 100 0.3035331 HOXA10 8  <1e−07 −6.495 100 0.7772068 MSH4 9  <1e−07 −6.357 100 0.1558349 NHLH2 10   4e−07 −5.915 100 0.5405671 GNA15 11   4e−07 −5.908 100 0.298388 PENK 12  4.2e−06 −5.206 100 0.3916643 RASSF1 13   5e−06 −5.155 100 0.1623329 BOLL 14 1.05e−05 −4.935 100 0.0752568 HOXA1 15  3.1e−05 −4.61 100 0.3416006 ONECUT2 16 4.26e−05 −4.514 100 0.3309475 ABCB1 17 4.59e−05 −4.491 100 0.4229908 SPARC 18 4.96e−05 −4.467 100 0.2047513 MT1G 19 8.53e−05 −4.301 100 0.6381881 HSPA2 20 0.0002478 −3.966 100 0.465089 SFRP2 21 0.0002786 −3.929 100 0.7532617 PYCARD 22 0.0003286 −3.876 100 0.6491186 GAD1 23 0.0004296 −3.789 100 0.8137828 C5orf4 24 0.0004695 −3.76 100 0.7676414 C5AR1 25 0.0004699 −3.76 100 0.2554181 GDNF 26 0.0006369 −3.66 100 0.4383864 ZDHHC11 27 0.0008023 −3.584 100 0.8171479 SERPINE1 28 0.0009028 −3.544 100 0.6392075 NKX2-1 29 0.0009179 −3.539 100 0.5993327 PITX2 30 0.0010255 −3.501 100 0.7691876 C5AR1 31 0.0011267 −3.47 100 0.5118859 ZNF256 32 0.0014869 −3.375 100 0.5593175 FAM43A 33 0.0015714 −3.356 100 0.6862518 SFRP2 34 0.0019233 −3.287 100 0.3698669 MT3 35 0.0019731 −3.278 100 0.7715219 SERPINE1 36 0.0019838 −3.276 100 0.8088555 CLIC4 37 0.0023911 −3.21 100 0.4999872 TNFRSF10C 38 0.0027742 −3.158 92 0.8776257 GABRA2 39 0.0028024 −3.154 92 0.7069999 MTHFR 40 0.0030868 −3.12 81 0.6837301 ESR2 41 0.0033263 −3.093 79 0.6327604 NEUROG1 42 0.0036825 −3.057 67 0.6444277 PITX2 43 0.0044243 −2.99 44 0.732542 PLAGL1 44 0.004896  −2.953 40 0.4992372 TMEFF2 45 0.0037996 3.046 65 2.1796084 PTTG1 46 0.0034628 3.079 73 1.1394289 CADM1 47 0.0024932 3.196 100 1.0870547 S100A8 48 0.0024284 3.205 100 1.3497772 EFS 49 0.0020087 3.271 100 1.2801593 JUB 50 0.0017007 3.329 100 1.1823596 ITGA4 51 0.0015061 3.371 100 1.5959594 MAGEB2 52 0.0013429 3.41 100 1.294098 ERBB2 53 0.0011103 3.475 100 1.3485708 SRGN 54 0.0007894 3.589 100 1.3193821 GNAS 55 0.0007437 3.609 100 1.9621539 TJP2 56 0.000457  3.769 100 2.4290869 KCNJ15 57 0.0004291 3.789 100 1.3004513 SLC25A31 58 0.0001587 4.107 100 2.5975161 ZNF573 59 0.0001331 4.163 100 1.4996674 TNFRSF25 60 9.26e−05 4.276 100 2.4581081 APC 61 4.88e−05 4.472 100 1.9612086 KCNQ1 62 3.62e−05 4.564 100 1.4971047 LAMC2 63 1.82e−05 4.77 100 1.5467277 SPHK1 64 1.68e−05 4.794 100 2.0200164 DNAJA4 65 1.45e−05 4.838 100 3.9772506 APC 66   9e−06 4.979 100 1.388284 MBD2 67  8.6e−06 4.994 100 3.0031525 ERCC1 68  4.5e−06 5.182 100 2.9842797 HLA-G 69  4.2e−06 5.202 100 1.7516486 CXADR 70  1.4e−06 5.521 100 1.9112579 TP53 71  1.1e−06 5.605 100 2.6787604 ACTB 72   9e−07 5.647 100 1.9365988 KL 73   6e−07 5.755 100 2.8712188 SMAD3 74   2e−07 6.05 100 1.4368727 HIST1H2AG 75   2e−07 6.115 100 7.507745 CPEB4

Example 10c 4 Greedy Pairs>>92% Correct Using SVM (Support Vector Machine)

Using “4 pairs of methylation markers” derived from greedy pairs class prediction with supportive vector machines enables 92% correct classification of TU and N.

Performance of Classifiers During Cross-Validation.

Diagonal Compound Linear Support Covariate Discriminant 3-Nearest Nearest Vector Predictor Analysis 1-Nearest Neighbors Centroid Machines Correct? Correct? Neighbor Correct? Correct? Correct? Mean percent 90 90 90 89 91 92 of correct classification:

Performance of the Support Vector Machine Classifier:

Class Sensitivity Specificity PPV NPV N 0.917 0.917 0.917 0.917 T 0.917 0.917 0.917 0.917

TABLE 4 Composition of classifier: Sorted by t-value (Sorted by gene pairs) Class 1: N; Class 2: T. Parametric Geom mean Geom mean p- t- % CV of intensities of intensities Fold- Gene value value support in class 1 in class 2 change symbol 1 <1e−07 −9.452 100 1411.8016 13554.578246 0.1041568 WT1 2 <1e−07 −7.222 100 85.5069224 1125.7940428 0.0759525 DLX2 3 <1e−07 −6.648 99 852.3850013 7392.282404 0.1153074 SALL3 4 <1e−07 −6.48 70 235.4745892 592.5077157 0.3974203 TERT 5 0.0017994 3.213 27 437.7037557 291.867223 1.4996674 TNFRSF25 6   5e−07 5.391 100 2993.3054637 1117.4218527 2.6787604 ACTB 7   4e−07 5.474 76 10347.818495 3603.9811381 2.8712188 SMAD3 8 <1e−07 5.832 98 2002.2452679 266.6906343 7.507745 CPEB4

Example 10d (BRB v3.8) 5 Greedy Pairs

Using “5 pairs of methylation markers” derived from greedy pairs class prediction with supportive vector machines enables 95% correct classification of TU and N.

Performance of Classifiers During Cross-Validation:

Mean Diagonal Bayesian Number Compound Linear 3- Support Compound of genes Covariate Discriminant 1- Nearest Nearest Vector Covariate in Predictor Analysis Nearest Neighbors Centroid Machines Predictor classifier Correct? Correct? Neighbor Correct? Correct? Correct? Correct? Mean percent 92 94 90 94 92 95 95 of correct classification: Note: NA denotes the sample is unclassified. These samples are excluded in the compuation of the mean percent of correct classification

Performance of the Support Vector Machine Classifier:

Class Sensitivity Specificity PPV NPV N 0.958 0.938 0.939 0.957 T 0.938 0.958 0.957 0.939

TABLE 5 Composition by classifier: Sorted by t-value (Sorted by gene pairs) Class 1: N; Class 2: T. Geom mean Geom mean Parametric % CV of intensities of intensities Fold- Gene p-value t-value support in class 1 in class 2 change symbol 1   <1e−07 −9.531 100 1378.5556347 13613.2679786 0.1012656 WT1 2   <1e−07 −7.419 100 78.691453 1122.0211285 0.0701337 DLX2 3   <1e−07 −6.702 100 832.1044249 7415.7421008 0.1122078 SALL3 4   <1e−07 −6.625 100 223.339058 595.0731922 0.03753136 TERT 5   <1e−07 −6.586 100 267.2568518 837.2745062 0.3191986 PITX2 6 0.0029082 3.057 35 427.3964613 286.9546694 1.4894215 TNFRSF25 7   1.26e−05 4.612 70 7297.8279144 3875.9637585 1.8828421 KL 8     9e−07 5.255 99 2922.8174216 1122.2601272 2.6044028 ACTB 9     9e−07 5.266 98 10104.1419624 3617.8969167 2.792822 SMAD3 10     2e−07 5.603 100 1911.6531674 265.654275 7.1960188 CPEB4

Example 10e Recursive Feature Elimination Method

Using “16 methylation markers” derived from the Recursive Feature Elimination method for class prediction with Diagonal Linear Discriminant Analysis enables 100% correct classification of TU and N.

Performance of Classifiers During Cross-Validation.

Mean Diagonal Number Compound Linear 3- Support of genes Covariate Discriminant 1- Nearest Nearest Vector in Predictor Analysis Nearest Neighbors Centroid Machines classifier Correct? Correct? Neighbor Correct? Correct? Correct? Mean percent 98 100 96 96 94 96 of correct classification:

TABLE 6 Composition of classifier: Sorted by t-value Geometric mean Parametric % CV of intensities Gene p-value t-value support (class N/class T) symbol 1 <1e−07 −10.859 100 0.1041568 WT1 2 <1e−07 −7.903 100 0.3297605 PITX2 3 <1e−07 −7.314 98 0.1153074 SALL3 4 <1e−07 −7.028 81 0.0759525 DLX2 5 <1e−07 −6.592 98 0.3974203 TERT 6 <1e−07 −6.539 98 0.3035331 HOXA10 7 4.2e−06  −5.206 98 0.3916643 RASSF1 8 4.59e−05  −4.491 94 0.4229908 SPARC 9 0.0329896 −2.197 88 0.5237754 IRAK2 10 0.0496307 −2.015 98 0.6640548 ZNF711 11 1.68e−05  4.794 79 2.0200164 DNAJA4 12 4.5e−06  5.182 79 2.9842797 HLA-G 13 4.2e−06  5.202 79 1.7516486 CXADR 14 1.4e−06  5.521 75 1.9112579 TP53 15 1.1e−06  5.605 100 2.6787604 ACTB 16  2e−07 6.115 100 7.507745 CPEB4

Example 10f (BRB v3.8) Recursive Feature Elimination Method

Due to some differences in data importing/normalisation repeated collation of data for statistics (using BRB v. 3.8) a genelist with minor differences (compared to example 12e) has been calculated form data, and is as given below:

Performance of Classifiers During Cross-Validation.

Mean Diagonal Number Compound Linear 3- Support of genes Covariate Discriminant 1- Nearest Nearest Vector in Predictor Analysis Nearest Neighbors Centroid Machines classifier Correct? Correct? Neighbor Correct? Correct? Correct? Mean percent 96 100 96 96 96 96 of correct classification:

TABLE 7 Composition of classifier: Sorted by t-value Geometric mean Parametric % CV of intensities Gene p-value t-value support (class N/class TU) symbol 1 <1e−07 −10.777 100 0.1012656 WT1 2 <1e−07 −8.046 88 0.3191986 PITX2 3 <1e−07 −7.336 98 0.1122078 SALL3 4 <1e−07 −7.232 85 0.1264427 F2R 5 <1e−07 −6.712 100 0.3753136 TERT 6 <1e−07 −6.524 98 0.2930706 HOXA10 7 1.6e−06  −5.49 98 0.3695951 RASSF1 8 3.87e−05  −4.543 83 0.4112493 SPARC 9 0.0313421 −2.219 88 0.5143877 IRAK2 10 0.0366617 −2.151 98 0.6452171 ZNF711 11 0.3333009 0.978 58 1.1102014 DRD2 12 4.91e−05  4.471 77 1.9749991 DNAJA4 13 2.25e−05  4.707 75 1.7030259 CXADR 14 7.4e−06  5.036 88 1.8582045 TP53 15 2.1e−06  5.402 100 2.6044028 ACTB 16  5e−07 5.815 100 7.1960188 CPEB4

Example 10g Recursive Geneset for “PB-N-TU” Distinction Using CLASS Prediction

To distinguish PB, N, and TU is of interest when minimal invasive testing for lung cancer has to be performed using serum- or plasma from peripheral blood. The markers distinguishing PB, N and TU will be best suited therefore. Using “16 methylation markers” derived from the Recursive Feature Elimination method for class prediction with Diagonal Linear Discriminant Analysis enables 91% correct classification.

Performance of Classifiers During Cross-Validation:

Diagonal Linear 3-Nearest Nearest Discriminant 1-Nearest Neighbors Centroid Analysis Correct? Neighbor Correct? Correct? Mean percent 91 89 87 88 of correct classification:

Performance of the Diagonal Linear Discriminant Analysis Classifier:

Class Sensitivity Specificity PPV NPV N 0.875 0.946 0.933 0.898 PB 1 0.948 0.615 1 T 0.938 0.982 0.978 0.948

Performance of the 1-Nearest Neighbor Classifier:

Class Sensitivity Specificity PPV NPV N 0.979 0.821 0.825 0.979 PB 0.75 0.99 0.857 0.979 T 0.833 1 1 0.875

Performance of the 3-Nearest Neighbors Classifier:

Class Sensitivity Specificity PPV NPV N 1 0.75 0.774 1 PB 0.125 1 1 0.932 T 0.854 1 1 0.889

Performance of the Nearest Centroid Classifier:

Class Sensitivity Specificity PPV NPV N 0.812 0.929 0.907 0.852 PB 1 0.917 0.5 1 T 0.917 0.982 0.978 0.932

TABLE 8 Composition by classifier: Sorted by p-value Class 1: N; Class 2: PB; Class 3: T. Geom mean Geom mean Geom mean Parametric % CV of intensities of intensities of intensities Gene p-value t-value support in class 1 in class 2 in class 3 symbol 1   <1e−07 65.961 100 1411.8016 335.9542052 13554.578246 WT1 2   <1e−07 34.742 100 2993.3054637 240.5599546 1117.4218527 ACTB 3   <1e−07 30.862 100 85.5069224 70.3843498 1125.7940428 DLX2 4   <1e−07 30.03 100 274.9097126 128.8159291 833.6648468 PITX2 5   <1e−07 28.153 100 852.3850013 349.2428569 7392.282404 SALL3 6   <1e−07 23.333 100 80.5286413 62.0661721 265.3042755 HOXA10 7   <1e−07 21.159 100 235.4745892 296.8149796 592.5077157 TERT 8     2e−07 17.8 100 2002.2452679 1697.5965438 266.6906343 CPEB4 9    4.3e−06 13.991 100 564.6571983 1254.1750649 189.2105463 HLA-G 10   1.54e−05 12.388 100 1710.9865406 1310.5286603 4044.9737351 SPARC 11    1.9e−05 12.132 100 114.7065029 81.1382549 292.8694482 RASSF1 12   6.55e−05 10.614 100 1639.128227 1576.0887022 811.4430136 DNAJA4 13 0.0008203 7.63 100 1484.6917542 1429.9219493 847.5968076 CXADR 14 0.0008501 7.589 100 11761.052468 9062.1655722 6153.5665863 TP53 15 0.041843 3.276 100 105.5844903 94.1143599 201.5835284 IRAK2 16 0.3946752 0.938 100 483.3048928 567.8776158 727.8087385 ZNF711

Example 10h Class Prediction “Differentiation”→Poor-Moderate-Well

Distinguishing the grade of differentiation of the tumours could be also achieved by DNA methylation marker testing. Although the correct classification is only about 60% in this example, the lung tumour groups “AdenoCa” and “SqCCL” can be split and used separately for determining the grade of tumour-differentiation for better performance.

Performance of Classifiers During Cross-Validation.

Diagonal Linearn 3-Nearest Nearest Discriminant 1-Nearest Neighbors Centroid Analysis Correct? Neighbor Correct? Correct? Mean percent 50 52 57 62 of correct classification:

TABLE 9 Composition by classifier: Sorted by p-value Class 1: moderate; Class 2: poor; Class 3: well. Geom mean Geom mean Geom mean Parametric % CV of intensities of intensities of intensities Gene p-value t-value support in class 1 in class 2 in class 3 symbol 1 0.0002337 10.127 100 2426.5840626 190.6171197 840.042225 F2R 2 0.002636 6.796 100 409.0809522 178.099004 3103.6338503 ZNF256 3 0.0034931 6.432 100 67.1145733 81.4305823 63.5786575 CDH13 4 0.0044626 6.118 100 30915.9294466 15055.465308 6829.1471271 SERPINB5 5 0.0082321 5.35 100 289.011498 400.2767665 163.1721958 KRT14 6 0.0092929 5.2 100 2890.2702155 418.2345934 211.3575002 DLX2 7 0.0111512 4.977 100 68.3488191 83.3593382 60.6607364 AREG 8 0.0286999 3.846 98 62.1904027 62.94364 74.3029102 THRB 9 0.0326517 3.696 92 64.7904336 80.1596633 60.6607364 HSD17B4 10 0.0414877 3.418 62 5631.0373836 2622.6315852 3310.1373187 SPARC 11 0.0449927 3.325 79 894.5655128 1191.0908574 510.2671098 HECW2 12 0.0480858 3.249 40 441.1103703 1018.9640546 852.4793505 COL21A1

Example 10i BinTreePred “Differentiation” AdenoCa, SqCCL, N PB

Using Binary Tree prediction (applicable for elucidation of markers for more than 2 classes) provides several sets of predictors which enable classification of PB, AdenoCa, SqCCL, N. These marker sets could be used alternatively for classification.

Optimal Binary Tree: Cross-Validation Error Rates for a Fixed Tree Structure Shown Below

Mis-classifi- Node Group 1 Classes Group 2 Classes cation rate (%) 1 AdenoCa, N, SqCCL PB 0.0 2 AdenoCa, SqCCL N 9.4 3 AdenoCa SqCCL 31.2

Results of Classification, Node 1:

TABLE 10 Composition of classifier (23 genes): Sorted by p-value Geom mean of Geom mean of Parametric % CV intensities in group intensities in group p-value t-value support 1 2 Gene symbol 1   <1e−07 11.494 100 5370.6044342 241.377309 KL 2   <1e−07 13.624 100 15595.1182874 226.4099812 HIST1H2AG 3   <1e−07 14.042 100 15562.4306923 62.0661607 TJP2 4   <1e−07 20.793 100 36238.4478078 169.7749739 SRGN 5   <1e−07 8.845 92 2847.6405879 176.5970582 CDX1 6   <1e−07 7.452 100 357.4232278 64.4047416 TNFRSF25 7   <1e−07 6.909 97 4344.5133099 90.5259025 APC 8   <1e−07 6.607 100 38027.3831138 10046.5061814 HIC1 9   <1e−07 6.428 100 1605.6039019 115.3436683 APC 10     2e−07 5.611 100 439.58106 107.9138518 GNA15 11     2e−07 5.53 100 1828.8750958 240.5597144 ACTB 12   2.47e−05 4.42 100 4374.5147937 335.954606 WT1 13   3.53e−05 −4.327 100 693.9070151 2419.282873 KRT17 14   4.73e−05 −4.251 100 3086.6035554 8432.6551975 AIM1L 15   5.58e−05 −4.207 100 11780.3636838 25260.4242674 DPH1 16 0.0001755 3.895 96 2120.616338 688.5899191 PITX2 17 0.0005056 3.593 100 478.7300449 128.8159563 PITX2 18 0.0012022 −3.332 100 167.4354555 461.2140013 KIF5B 19 0.0015431 −3.254 100 865.090709 2041.1567322 BMP2K 20 0.0020491 −3.164 100 10857.4258468 26743.6730071 GBP2 21 0.0023603 3.119 100 1819.6185255 218.3422479 NHLH2 22 0.0040506 2.941 96 614.495327 62.0661607 GDNF 23 0.0043281 2.918 98 6929.8366248 784.5416613 BOLL

Results of Classification, Node 2:

TABLE 11 Composition of classifier (32 genes): Sorted by p-value Geom mean of Geom mean of Parametric % CV intensities in group intensities in group p-value t-value support 1 2 Gene symbol 1   <1e−07 9.452 92 13554.5792299 1411.801824 WT1 2   <1e−07 7.222 92 1125.7939487 85.5069135 DLX2 3   <1e−07 6.648 69 7392.2771156 852.3852836 SALL3 4   <1e−07 6.48 92 592.5077475 235.4746794 TERT 5   <1e−07 6.445 92 833.6646395 274.909652 PITX2 6   <1e−07 6.123 92 265.3043233 80.5286481 HOXA10 7   <1e−07 6.019 92 855.6411657 112.6645794 F2R 8   <1e−07 −5.832 92 266.6907851 2002.2457379 CPEB4 9     4e−07 5.482 92 4609.4395265 718.3111003 NHLH2 10     4e−07 −5.474 92 3603.9808376 10347.8149677 SMAD3 11     5e−07 −5.391 92 1117.4212918 2993.3062317 ACTB 12    2.8e−06 4.984 92 3941.7717994 296.6448908 HOXA1 13    3.6e−06 4.922 92 17199.6559171 2792.0695552 BOLL 14    5.9e−06 −4.802 92 2178.4609569 8664.280092 APC 15   1.21e−05 4.622 92 472.6943985 96.784825 MT1G 16   1.36e−05 4.593 69 2188.6204084 653.0580827 PENK 17   1.97e−05 4.497 92 4044.9730493 1710.9865557 SPARC 18   3.16e−05 −4.373 92 811.4434055 1639.128128 DNAJA4 19   3.85e−05 4.321 92 292.869462 114.7064501 RASSF1 20   4.28e−05 −4.293 92 189.210499 564.6573579 HLA-G 21   4.98e−05 −4.253 92 446.1371701 1339.8173509 ERCC1 22     6e−05 4.203 92 1158.1503785 395.6249449 ONECUT2 23   6.58e−05 −4.178 92 1024.089614 2517.3225611 APC 24   8.45e−05 4.11 92 701.7840426 232.2538242 ABCB1 25 0.0002382 −3.821 92 1165.5392514 3027.5052576 ZNF573 26 0.0003469 −3.713 92 148.6108699 360.9887854 KCNJ15 27 0.0003582 3.704 92 4147.2987214 1818.1188972 ZDHHC11 28 0.0012332 3.332 46 512.9098469 238.5488699 SFRP2 29 0.0019349 3.19 92 1215.8855046 310.5592635 GDNF 30 0.002818 −3.068 92 2261.9371454 4930.1357863 PTTG1 31 0.0038228 −2.966 92 974.5345902 2402.9849125 SERPINI1 32 0.0039256 2.957 90 417.3184202 208.6541481 TNFRSF10C

Results of Classification, Node 3:

TABLE 12 Composition of classifier (2 genes): Sorted by p-value Geom mean Geom mean of of Parametric t- % CV intensities intensities Gene p-value value support in group 1 in group 2 symbol 1 0.000302 3.91 40 584.5327307 158.116767 HOXA10 2 0.0038089 3.048 46 180.3474561 67.115885 NEUROD1

Example 11 qPCR Validation of Biomarkers

Quantitative PCR with primers for markers elucidated by microarray analysis were run on MSRE-digested DNAs from the same sample groups as analyzed on microarrays. Marker sets for SYBRGreen qPCR were from Example 10f and Example 10d.

TABLE 13 Markers used for SYBRGreen-qPCR: Gene Unique id symbol Ahy_61_chr11:32411664-32412266 +_401-464 WT1 349_hy_35-PitxA_chr4:111777754-111778067 PITX2 Ahy_156_chr18:74841510-74841935 +_336-389 SALL3 Ahy_265_chr5:76046889-76047178 +_134-197 F2R Ahy_252_chr5:1348529-1348893 +_138-187 TERT Ahy_289_chr7:27180142-27180796 +_181-238 HOXA10 Ahy_233_chr3:50352877-50353278 +_108-157 RASSF1 Ahy_257_chr5:151046476-151047183 +_57-106 SPARC Ahy_212_chr3:10181572-10181986 +_249-298 IRAK2 Ahy_332_chrX:84385510-84385717 +_42-106 ZNF711 Ahy_51_chr11:112851438-112851650 +_57-107 DRD2 Ahy_109_chr15:76343347-76343876 +_373-428 DNAJA4 Ahy_202_chr21:17806218-17806561 +_104-167 CXADR Ahy_143_chr17:7532353-7532949 +_415-476 TP53 335_hy_4-Aktin_VL_chr7:5538506-5538805 ACTB Ahy_261_chr5:173247753-173248208 +_350-404 CPEB4 Ahy_181_chr2:172672873-172673656 +_177-227 DLX2 Ahy_30_chr1:6448693-6448938 +_57-107 TNFRSF25 Ahy_83_chr13:32489371-32489688 +_181-245 KL Ahy_107_chr15:65146236-65146654 +_305-366 SMAD3

Negative amplification (no Cp-value generated upon 45 cycles of PCR amplification with SYBR green) were set to Cp=45; all qPCR-Cp-values were subtracted from 45.01 to obtain transformed data directly comparable to microarray data,—thus the higher the value the more product was generated (resembles a lower Cp-value. Statistical testing of the transformed data was performed in the same manner as the microarray data using BRB-AT software.

Class comparison and different strategies/methods for class prediction using the qPCR enables correct classification of different sample groups. Although qPCR conditions were not optimized but run under our standard conditions, successful classification of groups with markers deduced from microarray-analysis confirms reliability of methylation markers.

TABLE 14 9 markers from Table 13 showed significant class difference fold changes mean of log mean of log Gene intensities intensities Unique id symbol for N for T FoldDiff Ahy_30_chr1:6448693-6448938 +_57-107 TNFRSF25 7.40354 8.5125 0.46 Ahy_156_chr18:74841510-74841935 +_336-389 SALL3 1.59063 7.04229 0.02 Ahy_233_chr3:50352877-50353278 +_108-157 RASSF1 5.80167 7.95708 0.22 Ahy_252_chr5:1348529-1348893 +_138-187 TERT 0.01 1.1725 0.45 Ahy_257_chr5:151046476-151047183 +_57-106 SPARC 11.76 14.10521 0.20 Ahy_265_chr5:76046889-76047178 +_134-197 F2R 0.70917 4.87917 0.06 Ahy_289_chr7:27180142-27180796 +_181-238 HOXA10 1.67708 3.88125 0.22 Ahy_332_chrX:84385510-84385717 +_42-106 ZNF711 4.635 6.48875 0.28 349_hy_35-PitxA_chr4:111777754-111778067 PITX2 5.48854 8.61813 0.11

Example 11a CLASS Prediction: TU Vs Normal: p<0.01>>SVM 100%, Paired Samples Performance of Classifiers During Cross-Validation Mean Percentage of Correction Classification:

Diagonal Compound Linear 3- Support Covariate Discriminant 1- Nearest Nearest Vector Predictor Analysis Nearest Neighbors Centroid Machines Correct? Correct? Neighbor Correct? Correct? Correct? Mean percent of 96 98 94 94 94 100 correct classification: n = 48

TABLE 15 Composition of classifier: Sorted by t -value Geometric mean Parametric % CV of intensities Gene p-value t-value support (class N/class T) symbol 1 1e−07 −6.184 100 0.0228499 SALL3 2 2e−07 −6.162 100 0.1142619 PITX2 3 4e−07 −5.879 100 0.1967986 SPARC 4 3.5e−06  −5.254 100 0.0555527 F2R 5 8.08e−05   −4.318 100 0.4467377 TERT 6 0.0009183 −3.538 100 0.2244683 RASSF1 7 0.0011335 −3.468 100 0.21701 HOXA10 8 0.0045818 2.978 100 1.7787126 CXADR 9 0.0012761 3.427 100 3.3134481 KL

Example 11b CLASS Prediction: TU vs Normal: p<0.01 Performance of the Support Vector Machine Classifier:

Class Sensitivity Specificity PPV NPV N 0.917 0.875 0.88 0.913 T 0.875 0.917 0.913 0.88

Performance of the Bayesian Compound Covariate Classifier:

Class Sensitivity Specificity PPV NPV N 0.792 0.604 0.667 0.744 T 0.604 0.792 0.744 0.667

TABLE 16 Composition of classifier: Sorted by t-value Class 1: N; Class 2: T. Geom mean Geom mean Parametric % CV of intensities of intensities Fold- Gene p-value t-value support in class 1 in class 2 change Unique id symbol 1   <1e−07 −6.713 100 3.011798 131.8077746 0.0228499 Ahy_156_chr18:74841510- SALL3 74841935 +_336-389 2   <1e−07 −6.491 100 3468.2688243 17623.4446406 0.1967986 Ahy_257_chr5:151046476- SPARC 151047183 +_57-106 3   <1e−07 −6.208 100 44.8968301 392.9290497 0.1142619 349_hy_35-PitxA_chr4: PITX2 111777754-111778067 4     1e−06 −5.248 100 1.6348595 29.429 0.0555527 Ahy_265_chr5:76046889- F2R 76047178 +_134-197 5   3.91e−05 −4.318 100 1.0069555 2.2540195 0.4467377 Ahy_252_chr5:1348529- TERT 1348893 +_138-187 6 0.0003748 −3.691 100 55.7796365 248.4967761 0.2244683 Ahy_233_chr3:50352877- RASSF1 50353278 +_108-157 7 0.0009309 −3.419 100 3.1978081 14.7357642 0.21701 Ahy_289_chr7:27180142- HOXA10 27180796 +_181-238 8 0.0009772 3.404 100 3114.5146028 939.9618007 3.3134481 Ahy_83_chr13:32489371- KL 32489688 +_181-245

TABLE 16b Prediction rule from the linear predictors Table. Compound Diagonal Linear Support Gene Covariate Discriminant Vector Weights Genes Predictor Analysis Machines 1 Ahy_83_chr13:32489371-32489688 +_181-245 3.4041 0.2794 1.2796 2 Ahy_156_chr18:74841510-74841935 +_336-389 −6.7126 −0.3444 −0.2136 3 Ahy_233_chr3:50352877-50353278 +_108-157 −3.6907 −0.2633 0.0512 4 Ahy_252_chr5:1348529-1348893 +_138-187 −4.3175 −0.6681 −1.1674 5 Ahy_257_chr5:151046476-151047183 +_57-106 −6.4911 −0.7486 −0.7093 6 Ahy_265_chr5:76046889-76047178 +_134-197 −5.2477 −0.2752 −0.0135 7 Ahy_289_chr7:27180142-27180796 +_181-238 −3.419 −0.221 −0.3187 8 349_hy_35-PitxA_chr4:111777754-111778067 −6.2083 −0.5132 −0.353

The prediction rule is defined by the inner sum of the weights (wi) and expression (xi) of significant genes. The expression is the log ratios for dual-channel data and log intensities for single-channel data.

A sample is classified to the class N if the sum is greater than the threshold; that is, Σiwi xi>threshold.

The threshold for the Compound Covariate predictor is −172.255
The threshold for the Diagonal Linear Discriminant predictor is −15.376
The threshold for the Support Vector Machine predictor is 0.838

Example 11c Recursive Feature Extraction (n=10) Prediction: TU Vs Normal 98% Correct, Paired Samples

TABLE 17 Composition of classifiers: Sorted by t-value Geometric mean Parametric % CV of intensities Gene p-value t-value support (class N/class T) symbol 1 1e−07 −6.184 100 0.0228499 SALL3 2 2e−07 −6.162 100 0.1142619 PITX2 3 4e−07 −5.879 100 0.1967986 SPARC 4 3.5e−06  −5.254 100 0.0555527 F2R 5 0.0011335 −3.468 100 0.21701 HOXA10 6 0.0188086 −2.434 92 0.5671786 DRD2 7 0.3539709 0.936 94 1.2886257 ACTB 8 0.1083921 1.637 100 1.8305684 DNAJA4 9 0.0045818 2.978 98 1.7787126 CXADR 10 0.0012761 3.427 100 3.3134481 KL

Example 11d Greedy Pairs (6) Prediction: TU Vs Normal: 88% SVM, UNpaired Samples Performance of the Support Vector Machine Classifier:

Class Sensitivity Specificity PPV NPV N 0.896 0.854 0.86 0.891 T 0.854 0.896 0.891 0.86

Performance of the Bayesian Compound Covariate Classifier:

Class Sensitivity Specificity PPV NPV N 0.812 0.604 0.672 0.763 T 0.604 0.812 0.763 0.672

TABLE 18 Composition of classifier: Sorted by t-value (Sorted by gene pairs) Class 1: N; Class 2: T. Geom mean Geom mean Parametric % CV of intensities of intensities Fold- Gene p-value t-value support in class 1 in class 2 change symbol 1   <1e−07 −6.713 100 3.011798 131.8077746 0.0228499 SALL3 2   <1e−07 −6.491 100 3468.2688243 17623.4446406 0.1967986 SPARC 3   <1e−07 −6.208 100 44.8968301 392.9290497 0.1142619 PITX2 4     1e−06 −5.248 100 1.6348595 29.429 0.0555527 F2R 5   3.91e−05 −4.318 100 1.0069555 2.2540195 0.4467377 TERT 6 −0.0003748 −3.691 100 55.7796365 248.4967761 0.2244683 RASSF1 7 0.0009309 −3.419 100 3.1978081 14.7357642 0.21701 HOXA10 8 0.0137274 −2.512 100 169.3121483 365.1891236 0.4636287 TNFRSF25 9 0.1465343 1.464 98 4255.1669082 2324.5057894 1.8305684 DNAJA4 10 0.1463194 1.465 50 326.8534389 203.1873409 1.6086309 TP53 11 0.0176345 2.416 100 2588.5288498 1455.2822633 1.7787126 CXADR 12 0.0009772 3.404 100 3114.5146028 939.9618007 3.3134481 KL

Cross-Validation ROC curve from the Bayesian Compound Covariate Predictor. The area under the curve is 0.944 (FIG. 1).

Example 11e CLASS Prediction: Histology: p<0.05 Using all qPCRs for Class Prediction Analysis of Tumor-Subtype Versus Normal Lung Tissue

TABLE 19 Composition of classifier: Sorted by p-value Class 1: AdenoCa; Class 2: N; Class 3: SqCCL. Geom mean Geom mean Geom mean Parametric % CV of intensities of intensities of intensities Gene p-value t-value support in class 1 in class 2 in class 3 symbol 1   <1e−07 23.305 100 11832.9848147 3468.2688243 22878.8045137 SPARC 2   <1e−07 22.546 100 98.6115161 3.011798 159.4048479 SALL3 3     1e−07 19.146 100 7.6044403 1.6348595 71.4209691 F2R 4     1e−07 19.124 100 359.9316118 44.8968301 416.1715345 PITX2 5   2.81e−05 11.753 100 90.8736104 55.7796365 480.3462809 RASSF1 6   3.15e−05 11.611 100 48.8581148 3.1978081 6.7191365 HOXA10 7 0.0001543 9.66 100 1.9602703 1.0069555 2.4699516 TERT 8 0.0042218 5.802 100 1047.8074626 3114.5146028 875.3966524 KL 9 0.0233243 3.914 100 263.7738716 169.3121483 451.9439364 TNFRSF25

Performance of Classifiers During Cross-Validation

Mean Percent of Correct Classification, n=96:

Diagonal Linear 3-Nearest Nearest Discriminant 1-Nearest Neighbors Centroid Analysis Correct? Neighbor Correct? Correct? Mean percent 72 74 74 72 of correct classification:

Example 11f Bintree Prediction: Histology—p<0.05 UNpaired Samples “Compound Covariate Classifier” Optimal Binary Tree: Cross-Validation Error Rates for a Fixed Tree Structure Shown Below

Group 1 Group 2 Mis-classification rate Node Classes Classes (%) 1 AdenoCa, N 14.6 SqCCL 2 AdenoCa SqCCL 31.2

Results of Classification, Node 1:

TABLE 20 Composition of classifiers (10 genes): Sorted by p-value Geom mean of Geom mean of Parametric % CV intensities in group intensities in group p-value t-value support 1 2 Gene symbol 1   <1e−07 6.713 100 131.8077753 3.011798 SALL3 2   <1e−07 6.491 100 17623.4448347 3468.2687994 SPARC 3   <1e−07 6.208 100 392.9290438 44.8968296 PITX2 4     1e−06 5.248 100 29.4290011 1.6348595 F2R 5   3.91e−05 4.317 100 2.2540195 1.0069556 TERT 6 0.0003748 3.691 100 248.4967776 55.779638 RASSF1 7 0.0009309 3.419 100 14.7357644 3.197808 HOXA10 8 0.0009772 −3.404 100 939.9618108 3114.5147006 KL 9 0.0137274 2.511 100 365.1891266 169.3121466 TNFRSF25 10 0.0176345 −2.416 100 1455.2823102 2588.528822 CXADR

Results of Classification, Node 2:

TABLE 21 Composition of classifier (3 genes): Sorted by p-value Geom mean Geom mean Parametric % CV of intensities of intensities Gene p-value t-value support in group 1 in group 2 symbol 1 0.0058346 2.892 50 48.8581156 6.7191366 HOXA10 2 0.0253305 −2.312 50 90.8736092 480.3462899 RASSF1 3 0.0330755 −2.197 49 7.6044405 71.4209719 F2R

SEQUENCE LISTING SEQ ID NO: DNA-SEQUENCE 1 CGGCCGGTCAGGAATCCCCATCCTGGAGCGCAGGCGGAGAGCCAGTGGCT 2 CCAAAAAAGGTGACACTGCCCCCTCCCAGTGGCTCCATGCTCCTCAGCTATGGCTGTCCGGGCC 3 CGCCCCGCCCCCGCCAACAACCGCCGCTCTGATTGGCCCGGCGCTTGTCTCTT 4 AGCGGCCTCAGCCTGCGCACCCCAGGAGCGTGGATGACTACGGCCACCCC 5 GCAGCCGAGAGGGTCAGGCCCCCATAGGTCCTCAGCCTGCTTCAACCTCAAAGGGGATGGGGG 6 TCCTGGCAGCATTACCACACTGCTCACCTGTGAAGCAATCTTCCGGAGACAGGGCCAAAGGGCCA 7 CTGACAAGAGACATGCAGGGCTGAGAGGCAGCTCCTTTTTATAGCGGTTAGGCTTGGCCAGCTGC 8 TGGCATCCACTTGCTTGATCCAGCCAGATTCCCACTCCCATGCCCTCTCCACTATTGCGATTGC 9 CTGCTTCGTGCCCTCTGGTGGCTAAGGCGTGTCATTGCAGTGCCGGCCTCCTGTCATCCTCC 10 CCGGCGCACTCCGACTCCGAGCAGTCTCTGTCCTTCGACCCGAGCCCCGC 11 TAGGTGGTGAGTTACTTGGCTCGGAGCGGGCGAGGGGACGCGTGGGCGGAGCG 12 AACCACCTGATCAAGGAAAAGGAAGGCACAGCGGAGCGCAGAGTGAGAACCACCAACCGAGGCGC 13 CGGGGGTAGGCTTTGCTGTCTGAGGGCGTCTGGCTGTGGAGCTGAAGGAGGCGCTGCTGAG 14 GCCCCGCATCCCTAATGAGGGAATGAATGGAGAGGCCCCCTCGGCTGGCGCCC 15 CGGGGCCACGCGCTAAGGGCCCGAACTTGGCAGCTGACCGTCCCGGACAG 16 CCACCGAACACGCCGCACCGGCCACCGCCGTTCCCTGATAGATTGCTGATGC 17 GAACTGGGTCGTGGAAGGATCGCGGGGAGCGGCCCTCAGGCCTTCGGCCTCACT 18 CCAGCACTTTGGGAGGCCGAGGCGGGCGGATCACGAGGTCAGGAGATCGAGACCATCCT 19 GGCGGCTGGTGCTTGGGTCTACGGGAATACGCATAACAGCGGCCGTCAGGGCGCC 20 TCAGATTCCTCAGGGCCGCAGAGGTGTGGAGCTGGTTGGGCCGGTTCTTCACCCTCCTCCC 21 CTGGCCGAGGTGGCCACCGGTGACGACAAGAAGCGCATCATTGACTCAGCCCGG 22 CTAACCTTCCTCGCCGCCTTCCTGCGGGTGACCCCCAAACGCCCCAGCTCCGC 23 CCGACTTGGACGCGGCCAGCTGGAGAGGCGGAGCGCCGGGAGGAGACCTT 24 ACAGAGTCGGCACCGGCGTCCCCAGCTCTGCCGAAGATCGCGGTCGGGTC 25 GGGGATGGAGAACTCTCCTCGCTTCGTCCTCTCTCCCGGGGAATCCCTAACCCCGCACTGCG 26 GTGGCTCGGGTCCACCCGGGCTGCGAGCCGGCAGCACAGGCCAATAGGCAATTAG 27 CTCACCCCGCGACTTACCCCACACCCCGCTCTCCAGAACCCCCATATGGGCGCTCACC 28 ACACACCACTGCAGCGTTCAAACGCTGGGAAGAAGACTCCCTTGTGGCACCGGAAACCCACGAGG 29 CCGCCACGAACTTGGGGTGCAGCCGATAGCGCTCGCGGAAGAGCCGCCTC 30 CTCCATAGCCCTCCGACGGGCGCCCAGGGGCTTCCCGGCTCCGTGCTCTCT 31 TGGACACCCCAAGAGCTCACTCCTCCGCGGTTTTATATTCCGACTTGCGCACAGGAGCGGGGTGC 32 CCGCCCGTTTCAGCGGCGCAGCTTCTGTAGTTGGGCTACTGGAGGGGTCGCTCAGAAACCTCA 33 AACCCAGGCTTGTCAGCCTAAGAACACGGGATCTCTTCACTGTGGTTCATGTGTAGAGTG- GAGTTTCCA 34 CAGTCCCCTGCCGTGCGCTCGCATTCCTCAGCCCTTGGGTGGTCCATGGGA 35 CAGGTGGGCGTCTCAGGGGTGGGAGTGGCCGCGTCGTGAAGCGGAGAGAGGA 36 CTGCAAGGGATGACTCACCCCAGTGATTCAACCGCGCCACCGAGCGCGGAGCTG 37 TTGTATGGATTTCGCCCAGGGGAAAGCGCTCCAACGCGCGGTGCAAACGGAAGCCACTG 38 GAGGACCAGGGCCGGCGTGCCGGCGTCCAGCGAGGATGCGCAGACTGCCT 39 ACGCACCGCGGCTCCTCGCGTCCAGCCGCGGCCAAGGAAGTTACTACTCGCCCAAAT 40 CGCTGCCTCGCCATTGGGCGGCCGAACGCAGCCACGTCCAATCAGAGGAGT 41 GAGGTTCTGGGGACCGGGAGAGTGGCCACCTTCTTCCTCCTCGCGAAGAGCAGGCCGGG 42 AGTGGGATTGGGGCACTTGGGGCGCTCGGGGCCTGCGTCGGATACTCGGGTC 43 TCAAGCCGCCTCAGGTGAGCGCTCCTTGGCGCTACTTCCGGTCTCAGGTGAGGCCGC 44 TTGTGACGTGTGTTCTGGGCAGGGTTTGAGGTTTTGGAACATTTTCTAAAAGGGACAGAGAGCAC- CCTGC 45 CGGGGGGAGAAGTCCTGGAGCGGGTTTGGGTTGCAGTTTCCTTGTGCCGGGGATCCTGTCC 46 GAGGATTATTCGTCTTCTCCCCATTCCGCTGCCGCCGCTGCCAGGCCTCTGGCTGCTGAGG 47 CCCTCTCTCCCCTGGCCCGCAAAGTTTTGGCGGAGCCATCGCTGGGGCTGAGC 48 CCAGGGGGAACTTGTGGCAGTGCAGCATCTCAGGCCAGGGGAAGCCGTAGGCCTCCATGA 49 CGCCACCCAGAGCCCGAGGTTTGCCCTTCAGAAGCGGACCCGCAGACTCCTCGGACT 50 CGCCGAAATGAAACCCGCCTCCGTTCGCCTTCGGAACTGTCGTCACTTCCGTCCTCAGACTTGGA 51 TCCCTTGTTTTGAGGCGGGAACGCAACCCTCGACCGCCCACTGCGCTCCCA 52 GGCAGCCGGGAAATCCCGTGTCCCCACTCGTGGCAGAGGACGCTGTGGGG 53 CCCCCACAGTTTTCATGTGATCAGGAATTCAGCATAGGCTATAAGACGGAGTGCTCCATGTCAA 54 GGGGTTGTCATGGCAGCAGCTCCATCCCTGACCGCCACTTTCTCCCGGTGCCG 55 AAGTTCCGCCAGTGCACAGCAACCAATGGGCGGAGGGGTCCTTTGCCCCTGGGTTGC 56 AGTTGGGCCGGATCAGCTGACCCGCGTGTTTGCACCCGGACCGGTCACGTG 57 GGGCCGCTGCCTACTGTGGGCCTGCAAGGCGTGCAAGCGCAAGACCACCA 58 ACCTCCCTGCTGCGTGTCGCAAACCGAACAGCGGGCGTTGGCCCTCCTGC 59 GGGACCCGGAGCTCCAGGCTGCGCCTTGCGCCCGGGTCAGACATTATTTAGCTCTTCGGTTGAGC 60 GGCCGTGCGGGGCTCACCGGAGATCAGAGGCCCGGACAGCTTCTTGATCGCC 61 CCACTGCCTGCGGTAGAACCTGGTCCCGCATAGCTTGGACTCGGATAAGTCAAGTTCTCTTCCA 62 GGGCCGCAGGCCCCTGAGGAGCGATGACGGAATATAAGCTGGTGGTGGTGGGC 63 GCAGGACCCGGATGAGAGCGCACGCTTCGGGGTCTCCGGGAAGTCGCGGC 64 AAGAGGGAAAGGCTTCCCCGGCCAGCTGCGCGGCGACTCCGGGGACTCCAG 65 AGGGATGGCTTTTGGGCTCTGCCCCTCGCTGCTCCCGGCGTTTGGCGCCC 66 GCCCGCTCTCGGGTGACTCCGCAACCTGTCGCTCAGGTTCCTCCTCTCCCGGCC 67 TGCTGGACATCCACCGCCTCCAGGCAGTTTCGCCGTCACACCGTCGCCATCTGTAGC 68 GGCCGCGAAGCGACTCCGATCCTCCCTCTGAGCCTTGCTCAGCTCTGCCCCGC 69 CGCGCGTTCGCTGCCTCCTCAGCTCCAGGATGATCGGCCAGAAGACGCTCTACTCCTTTTTCTCC 70 CGGGGGCGGAGGAAACACCTATGAACCCTCCGGCAGCCTTCCTTGCCGGGCG 71 AGGGCCAGCCCTTGGGGGCTCCCAGATGGGGCGTCCACGTGACCCACTGC 72 GTGAAAGGTCGGCGAAAGAGGAGTAAAGACGGCGAGACGCGTCCACGCAGGGGGAGTCTGTGCG 73 GCGCTGAGGTGCAGCGCACGGGGCTTCACCTGCAACGTGTCGATTGGACG 74 GAGGCCTCATGCCTCCGGGGAAAGGAAGGGGTGGTGGTGTTTGCGCAGGGGGAGC 75 CGAAGTGGAAACCGGAGTTGCGTCATTGCTCCCACCCGATATCACCTTGGCAGCGACCGCG 76 ATGGGGTGCTCATCTTCCTGGAGCTGAGGAGCTGGGACGGGCATGGGGTGCTCATCCTCCTG 77 TTCCAGCCGGTGATTGCAATGGACACCGAACTGCTGCGACAACAGAGACGCTACAACTCACCGCG 78 CAGAGAAGACTCACGCAGTGAGCAGTCCGCAAGCCCGCTGGCGGCAGCGGC 79 GACACACCCACCTCAGCAGATCTCAGCCCATCCCTCCCAGCTCAGTGCACTCACCCAACCCCAC 80 CGGAGTGCTGCAAGCGCAGAAAATATACGTCATGTGCGGAGGCGGAGCTTCCGCCCTGCG 81 GGCCCAAGGACGTGTGTTGGTCCAGCCCCCCGGTTCCCCGAGACCCACGC 82 ACCTCTGGAGCGGGTTAGTGGTGGTGGTAGTGGGTTGGGACGAGCGCGTCTTCCGCAGTCCCA 83 CCCTTGGAAGGCGTGGAATTAGGAGAGAAATCCCTTAGTGGGCACACGAGTGAGTGCCCCTTGGA 84 CCGGCCGCCTCCCAGGCTGGAATCCCTCGACACTTGGTCCTTCCCGCCCC 85 TGCGTGGGTCGCCTCGCGTCTCTCTCTCCCACCCCACCTCTGAGATTTCTTGCCAGCACC 86 GACTTCGCGTCGCCCTTCCACGAGCGCCACTTCCACTACGAGGAGCACCTGGAGCGCAT 87 GAGGCTGCGAGCCTGGGCTCCCAGGGAGTTCGACTGGCAGAGGCGGGTGCAG 88 CCATTCTCCTGCCTCAGCCTCCCAAGTAGCTGGGACTACAGGCGCCTGCCACCACTCCCGGC 89 CAGGGGACGTTGAAATTATTTTTGTAACGGGAGTCGGGAGAGGACGGGGCGTGCCCCGACGTGCG 90 ACCCTGGAACGACGCCAAACGCGACCCCTACCAGAGGACTCGCGCATGCGCAGC 91 GTTCCCAAAGGGTTTCTGCAGTTTCACGGAGCTTTTCACATTCCACTCGG 92 GAAAGACACCGCGGAACTCCCGCGAGCGGAGACCCGCCAAGGCCCCTCCAG 93 CCCTCTCCGCCCCAAACAGCTCCCCACTCCCCCAGCCTGCCCCCACCCTC 94 ATTGGGGCTACACTCACCACAAGAGCAGCAAACAAAGCACTGGGTGTGGTAGAGGCTGTCCAGGG 95 CCCAGCGGGGCCCTTAGCAGAGCCTCTCCAATCCTCGGCGCCTCCCCTACACAGGGTTCG 96 GCGCCCAAGGCCCTGCTTCTTCCCCCTTCCTCTTCCCCTTGCCCAGCCGCGACTTC 97 CCCAGCCGAGCAGGGGGAAGCATCCCCAGCTCCCGCACCCAAGTCCCTGG 98 GCCGCCACCTGTTGAGGAAAGCGAGCGCACCTCCTGCAGCTCAGGCTCCGGG 99 CGGGAGCGGATTGGGTCTGGGAGTTCCCAGAGGCGGCTATAAGAACCGGGAACTGGGCGCG 100 GGCGGGGAAGCGTATGTGCGTGATGGGGAGTCCGGGCAAGCCAGGAAGGCACC 101 GGAGCCCGCAGTGCGTGCGAGGGGCTCTCGGCAGGTCCAGACGCCTCGCC 102 CGCATCCGGCTCCGAAAGCTGCGCGCAGCCATCATCAGGGCCCTTCTGGTGTT 103 GCCGCTGCCAGTCGACTCAACCACCGGAGTGGCCCCTGCAGTTGGATAGCAACGAGAATCCTCC 104 GGCAGGAAAGGGCCCGAAGGCAGCGAAGGCGAACGCGGCGCACCAACCTG 105 ACAGGGTCTTCCCACCCACAGGGCACCCAGGCGCAGCGGAGCCAGGAGGG 106 ACCAGCCGCACAACTTTTGAAGGCTCGCCGGCCCATGTGGGGTCTTTCTGGCGGC 107 CAGCCGGGCAGATAACAAAACACACCCCAAAGTGGGCCTCGCATCGGCCCTCGCATTCCTGT 108 GGCCTCGACGCCGAGGGGTGTCCCTCTCCTCTCCTGGTCAGGGAACGCAGCAACTGA 109 GGGCGGCAGTCAGAGCTGGAGCTCCGGGGAATCAGACGGGCAGCCAAAGGAGCAGA 110 CGGAAGTGCCCCGGTCCTGGAGGGGGTGGAAGTTGGGGAGCCCAGGCAGGA 111 CCGAGAGGGAAGAAAAAAATACCCTCTTTGGGCCAGGCACGGTGGCTCACCCCTGTAATCCCAGC 112 TCCCAGCACTTTGGGAGGCTGAGGCGAGCGGATCACGAGATCAGAAGATCGAGACCATCCTGGC 113 CCCCGGGACCGGATAACGCCCTAAATCAGCGCAGCTGAGGCGAGGCCGTGGCC 114 CTCGCGACCCCGGCTCCGGGCCTCTGCCGACCTCAGGGGCAGGAAAGAGTC 115 CCCGAGGCTCGCCCGACTCCTGGCTGCCCTGGACTCCCCTCCCTCCTCCCT 116 CTCCAGCTGCACTGCCACCCAGCCTGCCTGGTGCTGGTGCTCAACACGCAGC 117 CCGGCCTTTCCGCCAGAGGGCGGCACAGAACTACAACTCCCAGCAAGCTCCCAAGGCG 118 GGGAAGGAGCCTCAGCTCCGCTCCAGGTCCTCCACCAGGTAGGACTGGGACTCCCTTAGGGCCTG 119 GGGAGTGTCCTCCTCCGGGACAGCCGGACTCCCGCCGACTTCTGGGCGGC 120 GGGGAGCGTGCGGGGTCGCCACCATCGGGACCCCCAGAGGAGAGAGGACTTG 121 GACAGATGCAGTGCGTGCGCCGGAGCCCAAGCGCACAAACGGAAAGAGCGGG 122 TCCTTTGCGTCCGGCCCTCTTTCCCCTGACCATAAAAGCAGCCGCTGGCTGCTGGGCC 123 TGCGGCTTCTCTCACCCTGCCAGGCCTTCCCAGCTTCCCTGAGGTTGCCTGCTACACCCG 124 GCCCCAGCCCTGCGCCCCTTCCTCTCCCGTCGTCACCGCTTCCCTTCTTCCA 125 CCCGCACCCCTATTGTCCAGCCAGCTGGAGCTCCGGCCAGATCCCGGGCTG 126 GCAGAGTTCGTGCAGGGAGTTCGCACATAGGAGAGCACCGGTCCGGGAGTGCCAGGCTCG 127 CGGCCGGTGTGTGTCCCCGCAGGAGAGTGTGCTGGGCAGACGATGCTGGAC 128 TTTTTGGGACAACCATGGAGGGGTCCTCCGTCTCGGCCTCTTCGCATATCCCCCTCCGTGATCC 129 CGGCGGGTCAGATCTCGCTCCCTTTCGGACAACTTACCTCGGAGAGGAGTCAAGGGGAGAGGGGA 130 CCCGGACGAGCTCTCCTATCCCGAAGTTGTGGACAGTCGAGACGCTCAGGGCAGCCGGGC 131 CGGCCGGTGGAGGGGGGAAGGGAGGAATGGTGTCAGGGGCGGATATCTGAGCCCTGAG 132 CACCAAAGCCACCACCCAAGCCAGCACCAAGGCCACCACCATATCCTCCCCCAAAGCCACTACCA 133 CCGCCAGGCCCGCTGGGTGGAATGTGGTCATGTTTCAGACTGCCGATGGCTTCCA 134 CCTGTCCGGATCCCTCCCCGCCTTGCTCAGATCTCTGGTTCGCGGAGCTCCGAGGC 135 GCGCAGGGGCCCAGTTATCTGAGAAACCCCACAGCCTGTCCCCCGTCCAGGAAGTCTCAGCGAG 136 TCCTGCCCCAGTAAGCGTTGGACCGGGAGACGCAGTGCTCAGCATCGGTCAGCAGGG 137 GCGCCGAGGAGTCGGGACAGCCCCGGAGCTTCATGCGGCTCAACGACCTG 138 GGCCCCAGCGGAGACTCGGCAGGGCTCAGGTTTCCTGGACCGGATGACTGACCTGAGC 139 CGCCGGCTGCGAAGTTGAGCGAAAAGTTTGAGGCCGGAGGGAGCGAGGCCGG 140 GGAGCCGCTTGGCCTCCTCCACGAAGGGCCGCTTCTCGTCCTCGTCCAGCAGC 141 AAATGTGGAGCCAAACAATAACAGGGCTGCCGGGCCTCTCAGATTGCGACGGTCCTCCTCGGCC 142 CCTCTCAGATTGCGACGGTCCTCCTCGGCCTGGCGGGCAAACCCCTGGTTTAGCACTTCTCA 143 TCTCCCCACGCTTCCCCGATGAATAAAAATGCGGACTCTGAACTGATGCCACCGCCTCCCGA 144 GCCCAATCGGAAGGTGGACCGAAATCCCGCGACAGCAAGAGGCCCGTAGCGACCCG 145 CGTGGGGGGCTGTTTCCCGTCTGTCCAGCCGCGCCCACTTCTCAGGCCCAAAG 146 GGGGCCCTCGTGTTGCTGAACGAGGGCGGGTTCGCGATGTAAATAAGCCCAGAGGTGGGGTC 147 CCTGGGTCCCCTCGGCTCTCGGAAGAAAAACCAACAGCATCTCCAGCTCTCGCGCGGAATTGTC 148 CATAAGATGCCCTCCTGCGGGCCCTCACCTTTTGACACTGCCTCCCACCGCACTGGGGTCAA 149 ATCCCGCTGCACCACGCCATGAGCATGTCCTGCGACTCGTCTCCGCCTGGC 150 GCGCGGTGAAGGGCGTCAGGTGCAGCTGGCTGGACATCTCGGCGAAGTCG 151 CATTTCTTTCAATTGTGGACAAGCTGCCAAGAGGCTTGAGTAGGAGAGGAGTGCCGCCGAGGCGG 152 AAGTTCACTGAGGGTTGTAAGAGTCAGAATGGACTCCATGGAAGTTATGGGGTGTGAATCAAACCT- CACA 153 CAGCACTTTGGGAGGCCGAGGTGGGCGGATTGCCTGAGGTCAGGAGTTTGAGACCAGCCTGG 154 GGGCAACACACACAGCAGCGACAGCCGGGAGGTAAGCCGCGTCCCAGCGG 155 CTGAGGGGAGGAGAAACTGGGCTGCGGGGGTCCGGGAGGGTGGATTCCGAGAAACTATGTGCCC 156 GTGTCCCAGCGCGTTGACGCAGCCTGTGATCCCTCGCGAGGCGAGGAGAAGGTC 157 AACCCCGACCTCAGGTGATCTGCCCAAAAGTGCTGGGATTACAGGCGTCAGCCACCGCGCC 158 AGGACGAAGTTGACCCTGACCGGGCCGTCTCCCAGTTCTGAGGCCCGGGTCCCACTGGAACT 159 GGAGACGCGTTGCCTTCGGCCGGGACCACTGCACCTGCCCGCGTGGGTAAT 160 CACAAAGGCCAAGGAGGGAGTGCGCAGGTCACGTGCGCCGGTGGTCAGCG 161 CTGACCTGGCGCTGCTGCCCCTGGTGCCTGACGGAGGATGAGAAGGCCGCC 162 AAAAGTGGCTCGGAACCCCAAATCCCGGTTAGATTGCAGGCACCGCCGGACGCTGGCTCCC 163 GTTCTGTTGGGGGCGAGGCCCGCGCAAGCCCCGCCTCTTCCCCGGCACCAG 164 GCGTCGACACTGCGCAAGCCCAGTCGCGCCTCTCCAGAGCGGGAAGAGCG 165 TGTCTGAGTATTGATCGAACCCAGGAGTTCGAGATCAGCTTGAGCAAGATAGCGAGAACCCCCGC 166 GAAAGACTGCAGAGGGATCGAGGCGGCCCACTGCCAGCACGGCCAGCGTGG 167 TTAGAGTCCCCTGGGTGTGTGCCCCGCAGAGGGAGCTCTGGCCTCAGTGCCCAGTGTGC 168 TTAGAGTCCCCTGGGTGTGTGCCCCGCAGAGGGAGCTCTGGCCTCAGTGCCCAGTGTGC 169 GGGGACGAGCAGGAAAAGGCCGGGGTGGGGGTGGAATTCCTCGGCGGGCAG 170 GGGAGCCTGAGGCAGGAGAATCGCTTGAATCCGGGAGGCGGAGGTTGCAGTAAGCCGAGATCGC 171 CTTTCGGAGGCCTCATTGGCTGAAGGTCGCCGTCGCCCAACGCAGGCCATTCTGGGT 172 CCTCCTGGGGTCAAGTGATCATCCTGGCTCAACCACCCAAGTAGCCGGGACTACGGGTGGCCGC 173 CCAATGCCCCAACGCAGGCCACCCCCGGCTCCTCTGTGGACTCACGAAGACAAGGTC 174 CTCTGAGAGCCACAGTCAGGTCTGTCCTCAGGGGTCGAGGCGGCTGCGCTGGGGCCT 175 GGACAGCCCGCTCGGGAGTCGGGCCTGGAAGCAGGCGGACAGCGTCACCT 176 GCCAGGATGGTCTCGATCTCCTGACCTTGTGATCTGCCCGCCTCGGCCTCCCAAAGTGTTGGG 177 TGCCCAGGGGAGCCCTCCATTTGTAGAATGAATGAGAGTCCAGGTTATGAACAGTGCCTGGAGTG 178 GACCGGTTTTATCCCGCTGAGGCCCTGGGAGATGGGTCTGGCGAGGCTCGTAGGCCGC 179 GCGGAACCTCAAATTGCGGCAGCGGAACCTAAAGTTTCAGGGTGAGATGCGTTGACTCGCGGTGG 180 GCTCAGTCCCTCCGGTGTGCAGGACCCCGGAAGTCCTCCCCGCACAGCTCTCGCT 181 CGGGCAGGCGGGACCGGGAGGTCAATAACTGCAGCGTCCGAGCTGAGCCCA 182 CGCGGTGGGCCGACTTCCCCTCCTCTTCCCTCTCTCCTTCCTTTAGCCCGCTGGCGCC 183 TCCCCGGCATGCGCCATATGGTCTTCCCGGTCCAGCCAAGAGCCTGGAACCACG 184 CTCCGCGCTCAGCCAATTAGACGCGGCTGTTCCGTGGGCGCCACCGCCTC 185 GCGAGAGGGTCGTCCGCTGAGAAGCTGCGCCGGAGACGCGGGAAGCTGCTG 186 GACCCGCCTGCGTCGCCACCCTCTCGCCGCTCCCTGCCGCCACCTTCCTC 187 GAGGGGTCCGGGACGAAGCCACCCGCGCGGTAGGGGGCGACTTAGCGGTTTCA 188 CCCCGAACAAAAAATTCAAATGGGAAAGAGAGGCAGATGGCAGAGAACAGGGGAGGGGCTGGGCA 189 GCGGCGAGGAGGGTCACAGCCGGAAAGAGGCAGCGGTGGCGCCTGCAGAC 190 GGCGGTCTCCGGTTCGCCAATGTGGCTGGGTCCGTAGGCTTGGGCAGCCT 191 CCTCCCCTTTGCGTGCGGAGCTGGGCTTTGCGTGCGCCGCTTCTGGAAAGTCG 192 AGCCTACTCACTCCCCCAACTCCCGGGCGGTGACTCATCAACGAGCACCAGCGGCCAGA 193 CAGGAGGTGAGGAGGTTTCGACATGGCGGTGCAGCCGAAGGAGACGCTGCAGTTGGAGAGCG 194 AGATTTCCCGCCAGCAGGAGCCGCGCGGTAGATGCGGTGCTTTTAGGAGCTCCGTCCGACA 195 CGGGCGTGGTGGTGGGCACCTGTAATCCCAGCTACTCAGAAGGTTGAGGCAGGAGAATCGCTTGA 196 TCCCAAATCCGAGTCTGCGGAGCCTGGGAGGGCTCCCAGCTTCCTATCCAAACCGCGCC 197 CCCTGGTCGAGCCCCCTTTCCTCCCGGGTCCACAGCGAGTCCCCTGAGGAAGGAGGG 198 CAGGGACCCGCGAGTCCCTGGACACGCACTGGCCAACGCCAGACCCCATC 199 CAAGCAGCCCTCGGCCAGACCAAGCACACTCCCTCGGAGGCCTGGCAGGG 200 GAGAAGGAGCGACCCCCAAAACGAAGCGGCTGGATCTGACCTTCCAAGGCCTGTTGGCGACGC 201 TTCTTCCCCGCAGGGTCAGCGCTGGGGCTCCGGCCGTAGAGCCACGTGACC 202 ATTCATTTCTGTTATGGAACTGTCGCGGCACTACAAAGTCTCTATGTAGTTATAAATAAACGTT 203 ACCGAGTGCGCTGCTGTGCGAGTGGGATCCGCCGCGTCCTTGCTCTGCCC 204 GTGTGGTGAGTGTGGGTGTGTGCGCGTCTCCTCGCGTCCCTCGCTGAGGTGCCT 205 GCCTGGGCTGCCAGACGTCGCCATCATTGTTCCATGCAGATCATGCCCATCCTGTGCAGAAG 206 GCGGGTCCGAGGCGCAAGGCGAGCTGGAGACCCCGAAAACCAGGGCCACTC 207 TCTCCATGGTGGCCATTGCCTCCTCTCTGCTCCAAAGGCGACCCCGAGTCAGGGATGAGAGGC 208 CGCGGGACTCCGCGGGATCTCGCTGTTCCTCGCTCTGCTCCTGGGGAGCC 209 CGCCCCCTTTTTGGAGGGCCGATGAGGTAATGCGGCTCTGCCATTGGTCTGAGGGGGC 210 GTTCTGTTGCCAATGCCATTCAGACCCCAGTCCGGGATTCCGCGCTCGGGGTGCG 211 TTTCCGCGAGCGCGTTCCATCCTCTACCGAGCGCGCGCGAAGACTACGGAGGTCGA 212 ACCCGGGTTCAGCGGGTCCCGATCCGAGGGCGTGCGAGCTGAGCCTCCTG 213 GAGAGTGGACGCGGGAAAGCCGGTGGCTCCCGCCGTGGGCCCTACTGTGC 214 GGCTACAGCCGCCATTTCCACGCTCCACCAATCAAATCCATTCTCGAGGAAGACGCACCGCCCC 215 AGCGCGCACAAAGCCTGCGGGAGGATCCATTGTAGCGGTCGCTCCTCCCCGCTTAGCG 216 ATCGGGCGAAGCTCGCGGGAAACCGCTCTGGGTGCGCAGGACAAAGACGCG 217 CGACGGAGCCGTGTGGAGGCCAAAACTCCTCCCGGAAGCCGCTACTGGCCCCG 218 CGCCCCACTACTGCCTGCAGCGGGCTTCCTTACTCCGCCTGCTGGTTCCTACTGGAGGAGAGGCC 219 GCACTCGTAGCGCGCTGGGCGAGCCGGACCGGAAGTTGAAGAAGTGAAGCGCCG 220 TGAAGGGAGGGCTTGGTGTGGGGACTTGCACTGGGCAGAGGGGCAGCTTCCCTGAGAGCAGCTA 221 CGGGAGCGCCCGGTTGGGGAACGCGCGGCTGGCGGCGTGGGGACCACCCG 222 CAGCACCGGAGAGGGCGCACTGCAAAGGCGGGCAGCAGACCGTGGAGAGC 223 GGCGCAGAGGCGTCACGCACTCCATGGTAACGACGCTCGGCCCGAAGATGGC 224 GCCGCGTCTGCGAACCGGTGACCTGGTTTCCCCTCCAGCCCTCACGGCTG 225 CGAGCTGTTTGAGGACTGGGATGCCGAGAACGCGAGCGATCCGAGCAGGGTTTGTCTGGGC 226 GCAGCGCTGAGTTGAAGTTGAGTGAGTCACTCGCGCGCACGGAGCGACGACACCCC 227 CGCGCGCTCGCCGTCCGCCACATACCGCTCGTAGTATTCGTGCTCAGCCTCGTAGTGGC 228 CGGAAGGGGTGAGGCCGGAAGCCGAAGTGCCGCAGGGAGTTAGCGGCGTCTCG 229 GGGGGCGTCGGGCTTGGGACAGGGGAGGATACCAGGGCCACCTTCCCCAACCC 230 CGGGCTGGAGGGTTATCTGGGAAGTCAGCCCCGGCCTCGGTCCTCTCCACGTTGCTGC 231 GGAACGAGGTGTCCTGGGAACACTCCCGGGTCTGTAACTTCGGACAAATCACGCTCGCTTTCCCG 232 AAACGAGAGAGTAGCCAGACTCTCCGCGCATGGAGCCGACGGCACCCACCAGCACACCG 233 TACTCACGCGCGCACTGCAGGCCTTTGCGCACGACGCCCCAGATGAAGTC 234 TGACCGGACAGAGCAGAGCGGGGACTGCAATTCCCAGAAGACCCCACGGTAGGGGCGG 235 AGACAATCCCGGAGGGGGAAAGGCGAGCAGCTGGCAGAGAGCCCAGTGCCGGCC 236 GGCCGAAGAGTCGGGAGCCGGAGCCGGGAGAGCGAAAGGAGAGGGGACCTGGC 237 CCAGGCTCCGCTCGTAGAAGTGCGCAGGCGTCACCGCGCATCCAGGAGCCAC 238 CTCTGATGACGCTCCAAGGGAAGAGGAAGTGGGGATCGGCGAGCGGGTGGGTGCGC 239 TGAAGGGTAATCCGAGGAGGGCTGGTCACTACTTTCTGGGTCTGGTTTTGCGTTGAGAATGCCCC 240 CGGTCCTGCATGCAATGCAAGCCTGAGCTCTCCCGCCATAAGGCTGCAGCGGTGTGG 241 CCTGGAGGAGGAGGAGTCAGGCCGGGTAGGAGGGCTAAGGAGGTTCCCGGGAAGGCAGGGCCC 242 GCTGCTGACATGACTTCTTTGCCACTCGGTGTCAAAGTGGAGGACTCCGCCTTCGGCAAGCCGGC 243 GAGCGGCGCAGGGTTGGAGAGGGAAGCGCTCGTGCCCACCTTGCTCGCAG 244 CCGATGACCGCGGGGAGGAGGATGGAGATGCTCTGTGCCGGCAGGGTCCC 245 GCCGCCCTACAGACGTTCGCACACCTGGGTGCCAGCGCCCCAGAGGTCCC 246 GGGCCGCAATCAGGTGGAGTCGAGAGGCCGGAGGAGGGGCAGGAGGAAGGGGTG 247 CGGCGGGACCATGAAGAAGTTCTCTCGGATGCCCAAGTCGGAGGGCGGCAGCGG 248 GTGGGCGCACGTGACCGACATGTGGCTGTATTGGTGCAGCCCGCCAGGGTGT 249 GAAAGAGCCGGAAACACCTGGTCTCTCAAGCAGGTACAGCCCGCTTCTCCCCAGCACCCCGGTG 250 GCAGCCGCAGCTGAGGTCACCCCGCTGAGGTGGTGGGGAGGGGAATGGTT 251 GGGCGGCCAGCGGTGACTCCAGATGAGCCGGCCGTCCGCGTTCGCGCCGC 252 GGGCACCACGAATGCCGGACGTGAAGGGGAGGACGGAGGCGCGTAGACGC 253 GAGGCCGCCATCGCCCCTCCCCCAACCCGGAGTGTGCCCGTAATTACCGCC 254 CGCGGGGAACGATGCAACCTGTTGGTGACGCTTGGCAACTGCAGGGGCGC 255 CTTGAGACCTCAAGCCGCGCAGGCGCCCAGGGCAGGCAGGTAGCGGCCAC 256 CACACCGTCCTCGCCCGGAGCGCAGAGGCCGACGCCCTACGAGTGGATGC 257 CCCTTGCACACGAGCTGACGGCGTGAACGGGGGTGTCGGGGTTGGTGCAA 258 GCAAAGTGATACCTGGCCGTCCCACCCTCTGGTCCCAGAAGGAGCTCTCGCTGGAGCCAGGCA 259 GGTTGGGGGACTGCCCGGGGCTTAGATGGCTCCGAGCCCGTTTGAGCGTGGTCTCG 260 CGTTGAAAGCGAAGAAGGAGCGGCAGTCCAGCAGCAGGCATTGCGCCGCTCGCTC 261 GAGTCCTCAACAACGACAGCGGGGACTGCGGGACCAGGGTAAAGCGGCGACGGCG 262 GCTCCTGAGAAAGCCCTGCCCGCTCCGCTCACGGCCGTGCCCTGGCCAACTT 263 GATGCTGCTGCCGGAGCTGAGGTCTTGCCTGGAGATCCGAACGAGACACCACGTCAACCGG 264 TGGTGGCAGGAGAGCGATGAGACGGGAAAGTGTGGGGCAAAGCTTACAGTCATTGGTCCAGA 265 CCACTCGCAGTCTGCGTGTGGGGGAAACGAGTGCCCGGCGTATGAAACGCCTAACTTCGCGAAA 266 CAGGCGGCTCCCGCAGTCTAAGGGACCTGGCGCGAGTCCGGGAAGCGGAGG 267 CTGCACGCGGTGCGAAGGGGCCAGCAGGGAAGGAGCAGAGGATGGGGGGT 268 CGGGGCCACAGGACCCTGGGGCTTGAGTCACACAAGAATGTCTCTGGGAGACCCGAGAGACTCA 269 CTTAGAGGAGGAGGAGCAGCGGCAGCGGCAGCAGGAGGCGACAGCTGCCAGCCG 270 CTCATACCAGATAGGCGCGAACGCCTCTGGCAGCGGCGTCCAGGGGGTCCGGC 271 GGGTGCTGGCACATCCGAGGCGTTCTCCCGACTCTGGACCGACGTGATGGGTATCCTGG 272 CATGATAAGCCAGGGACCTCGCGGCGCAGGCGGAGGGAGGGAGAGCGTCGC 273 CCCCCCACTCAACAGCGTGTCTCCGAGCCCGCTGATGCTACTGCACCCGCCG 274 TCCCACCTGCTGCCCGAGGAAGACTTCCGGGAGAAACGCTGTCTCCGAGCCCCCG 275 CCAGGTGAAGCCGAAGGGGAAGCGGATGGGGTTGCTGAACGCGGAGTCGGCG 276 CAGTGGCCCTGCGCGACGTTCGGCGCTACCAGAACTCCGAGCTGCTGATCAGCAAGC 277 AAGGATTACCTCGCCCTGAACGAGGACCTGCGCTCCTGGACCGCAGCGGACACTGCGG 278 GCAGGCTCGTGGCGGTCGGTCAGCGGGGCGTTCTCCCACCTGTAGCGACTCAGGTTACTGAAAA 279 GAGGGAAGTGCCCTCCTGCAGCACGCGAGGTTCCGGGACCGGCTGGCCTG 280 GAAGCGCGACCTCGGGCGGTTGGAGGGGCTACCGGGTCTTACCAGTCCGTGGCG 281 CCCAACCCGAGCAAGACCTGCGCTGAAACGGATTGGCTGCCCTCCGCCCG 282 AGCCGCTCTCCCGATTGCCCGCCGACATGAGCTGCAACGGAGGCTCCCACC 283 ACCACACGGCCAAGGGCACCTGACCCTGTCAAAACCCCAAATCCAGCTGGGCGCG 284 CCGAGGCAGCCGGATCACGAAGTCAGGAGTTCGAGACCAGCCTGACCAACATGGTGAAACCCCGT 285 CCGGCGTCTCCGCGTGGGGCGCACCGTCCGACCCCCCCCTCCCGGTGTGC 286 GGCGCAGATGGCGCTCGCTGCGAGATGGATGCTCCAGGGCGGGTAATCACTCCTG 287 CCAGGCCTCCTGGAAACGGTGCCGGTGCTGCAGAGCCCGCGAGGTGTCTG 288 GGCGAGAGGTGAGAAGGGAAGAGGGCTCCCGGCTCTCTCGGGGCGGGAATCAGTGGGC 289 GCCTGCCTCGCCTCTGCCCGAGCTGATGAGCGAGTCGACCAAAAAAGAGTTCGCGGCG 290 CATTGCGGGACCCTATTTATCCCGACACCTCCCCTGACGTGGGCTCGGAACGCTCCCTTGGCAG 291 CGAAGGCCGGAGCCACAGCGCTCGGTGTAGATGCCGCACGGCTGGCCCTC 292 GGGCTGGATGAGTCCGGAAGTGGAGATTGGCTGCTTAGTGACGCGCGGCGTCCCGG 293 CGCCAGTGCGATTCTCCCTCCCGGTTCCAGTCGCCGCGGACGATGCTTCCTC 294 CGTCCGAGAAAGCGCCTGGCGGGAGGAGGTGCGCGGCTTTCTGCTCCAGG 295 TCCGGCTGCGCCACGCTATCGAGTCTTCCCTCCCTCCTTCTCTGCCCCCTCCGCTCC 296 CAGCCTCAGTTTCCCCATTGGTAAAGCATTGACGGTGGTTGCGGACGGCTTCTGCGGACAGAGCC 297 CCTGAGACAGGCCGAACCCAACTCTTCACAGGGCCGAATTCTTTGCCCGCAGCCCAGCACC 298 CAGAGGGGGGTGCCGGGGTCGCGGACTGCCACCAGGTTGAGGAAAGGAGGGG 299 CGACATCCTGCGGACCTACTCGGGCGCCTTCGTCTGCCTGGAGATTGTAAGTGGGGCCGC 300 ACCGCCTCCTCCCCGCTGTCTGGGTCGCAGGCCTTAGCGACGGGCTGTTCTCCG 301 CTCGGGACTCCAGGGCTGTCCCTCCCGCAGGCTGTCCTTCCACCTCCACCCCA 302 CGGCCGCTCCTCGTAGGCCAGGCTGGAGGCAAGCTCCTTCTCCTCAAAGCTGCGCTGC 303 CATCTCTTCCCCCGACTCCGACGACTGGTGCGTCTTGCCCGGACATGCCCGG 304 CCCAAGACCCTAAAGTTCGTCGTCGTCATCGTCGCGGTCCTGCTGCCAGTGAGTCCCGGCC 305 CCCACTCTTCCCCTGACTCCGACGGCGGGTTCGTCCTGCCCAGACATGCCCG 306 GTCCCCCTCTCTCTCTGCCCCCTCCCGGTGCCAGGCGCGCTTTTCCCCAGG 307 CAGCCTGCTGAGGGGAAGAGGGGGTCTCCGCTCTTCCTCAGTGCACTCTCTGACTGAAGCCCGGC 308 ACTGACTCCGGAGGCTGCAGGGCTGGAGTGCGCGGGGCTCCTACGGCCGAG 309 GGCCAGGCTCGGGCAGGGGCCGTGCTCAGGTGCGGCAGACGGACGGGCCG 310 CCGGGCTTCTGGGACGCTCAGCCGTGCGCTACCCGGTGCAGCTGCTTTCTCACC 311 TTTAGGTAGACGTGGAGGCGACTCAGATCGCCTCGCGGTTCCCGGGATGGCGCGGTCG 312 TGACCAGGACCGCAGGCAAGCACCGCGGCGACGGTTCCAGCCAGGAAAATGAG 313 GGGCCGGACCCGGCCTCTGGCTCGCTCCTGCTCTTTCTCAAACATGGCGCG 314 GCCGCGCTCCTCGCACCGCCTTCTCCGCAGGTCTTTATTCATCATCTCATCTCCCTCTTCCCC 315 GAGCTGCGAACTGGTCGGCGGCGCAAGGCGCGGACTCCGGTGAGTTGTGT 316 GCCCGCGTTCCTCTCCCTCCCGCCTACCGCCACTTTCCCGCCCTGTGTGC 317 ACGCGTCGCGGAGTCCTCACTGCCCCGCCTCGCTCTGGCAGAGTGGGGAG 318 GCGAGCAGCGGCCTCCAGCGCTGGTGGCTCCCTTTATAGGAGCGCTGGAGACACGGG 319 GGGGAAGGCGGAGGGCGAGGGGAAGAGTCACTGAGCTGCGGGGCATAGGGGGTCC 320 CTCTGCTCGCGTGCTGCTCTGAAGTTGTTCCCCGATGCGCCGTAGGAAGCTGGGATTCTCCCA 321 AGGGAGGTCGTTTTCTTCAGCTCCCCAGGTGGTCTGTGCTGGGTGTGCTGACGGTCCTTTTGGGA 322 GCCCCTGGCCCTGACTGCTGGTGCGAGGCAGTGCACGACTCAGCTGGCCG 323 GGCCGGGTAACGGAGAGGGAGTCGCCAGGAATGTGGCTCTGGGGACTGCCTCGCTCG 324 GGCCGGGACTTTCTGGTAAGGAGAGGAGGTTACGGGGAACGACGCGCTGCTTTCATGCCC 325 CAGTCTGGGGACCGGGGAGGCGGGGAGAGGGAAGGGGAAAGCGCGGACGC 326 GTCTATCAAAAGTCTTTTCGTTTCCCCCTCCCCCTTTCCCCACCGCCCACCAAAATGAGCCGCG 327 ATGCCGCCATCGCGGTTCATGCCGTTCTCGTGGTTCACACCGCCCTCAGGG 328 TCCCGGTCTTCGGATCCGAGCCGGTCCTCGGGAAAGAGCCTGCCACCGCGT 329 TGAGAGGCTCCGGTAAAGCCGTCCGGCAATGTTCCACCTGGAAAGTTCCAGGGCAGGGGAAGGG 330 CCCAGGGAGAGGGAGAGGAGGCGGGTGGGAGAGGAGGAGGGTGTATCTCCTTTCGTCGGCCCG 331 CCCGTCTTCTCTCCCGCAGCTGCCTCAGTCGGCTACTCTCAGCCAACCCC 332 GACCCCCCTTTGGCCCCCTACCCTGCAGCAAGGGTAGCGTGACGTAATGCAACCTCAGCATGTCA 333 CCCCGAAGCCCTTGCTTTGTTCTGTGAGCGCCTCGTGTCAGCCAGGCGCAGTGAGCTCAC 334 CGCGCGGCCTTCCCCCTGCGAGGATCGCCATTGGCCCGGGTTGGCTTTGGAAAGCGG 335 CCACCCAGTTCAACGTTCCACGAACCCCCAGAACCAGCCCTCATCAACAGGCAGCAAGAAGGGCC 336 AAGCAGCTGTGTAATCCGCTGGATGCGGACCAGGGCGCTCCCCATTCCCGTCGGGAG 337 CCACGCACCCCCTCTCAGTGGCGTCGGAACTGCAAAGCACCTGTGAGCTTGCGGAAGTCAGT 338 CCACGCACCCCCTCTCAGTGGCGTCGGAACTGCAAAGCACCTGTGAGCTTGCGGAAGTCAGT 339 CCCTCCACCGGAAGTGAAACCGAAACGGAGCTGAGCGCCTGACTGAGGCCGAACCCCC 340 TTGTCCCTTTTTCGTTTGCTCATCCTTTTTGGCGCTAACTCTTAGGCAGCCAGCCCAGCAGCCCG 341 TTCTCAGGCCTATGCCGGAGCCTCGAGGGCTGGAGAGCGGGAAGACAGGCAGTGCTCGG 342 CAGCGTTTCCTGTGGCCTCTGGGACCTCTTGGCCAGGGACAAGGACCCGTGACTTCCTTGCTTGC 343 AGGCAGGCCCGCAAGCCGTGTGAGCCGTCGCAGCCGTGGCATCGTTGAGGAGTGCTGTTT 344 GACTCTGGGTATGTTCTCGAAAGTTGTTACAACCCCAACCCAGGGTTGACCTCAAACACAGGAGG 345 CTCTGGCTCTCCTGCTCCATCGCGCTCCTCCGCGCCCTTGCCACCTCCAACGCCCGT 346 CGGGAGCGCGGCTGTTCCTGGTAGGGCCGTGTCAGGTGACGGATGTAGCTAGGGGGCG 347 CCCCAAGCCGCAGAAGGACGACGGGAGGGTAATGAAGCTGAGCCCAGGTCTCCTAGGAAGGAGA 348 GGGCTCTTCCGCCAGCACCGGAGGAAGAAAGAGGAGGGGCTGGCTGGTCACCAGAGGGTG 349 TTCTCTTCCATCCCATCCTCCCTTCTGGTCCTCCTTTCCACAGTGGGAGTCCGTGCTCCTGCTCC 350 CCGCCTCTGTGCCTCCGCCAACCCGACAACGCTTGCTCCCACCCCGATCCCCGCACC 351 CCGCGCCACGTGAGGGCGGCAAGAGGGCACTGGCCCTGCGGCGAGGCCCCAGCGAGG 352 CACTGCTGATAGGTGCAGGCAGGACAGTCCCTCCACCGCGGCTCGGGGCGTCCTGATT 353 CGGGAGCCTCGCGGACGTGACGCCGCGGGCGGAAGTGACGTTTTCCCGCGGTTGGAC 354 TGTCCTCCCGGTGTCCCGCTTCTCCGCGCCCCAGCCGCCGGCTGCCAGCTTTTCGGG 355 GGTGTCGCGACAGGTCCTATTGCGGGTGTCTGCGGTGGGAAGGGCGGTGGTGACTGG 356 ACATATGACAACGCCTGCCATATTGTCCCTGCGGCAAAACCCAACACGAAAAGCACACAGCA 357 GGAAACCCTCACCCAGGAGATACACAGGAGCACTGGCTTTGGCAGCAGCTCACAATGAGAAAGA 358 TTACCATTGGCTTAGGGAAAGGAGCTTACTGGGAACTGGGAGCTAGGTGGCCTGAGGAGACTGGG 359 AAGAACAGGCACGCGTGCTGGCAGAAACCCCCGGTATGACCGTGAAAACGGCCCGCC 360 ccggggactccagggcgcccctctgcggccgacgcccggggtgcagcggccgccggggctggggccg- gcgggagtccgcgggaccctccagaagagcggccggcgccgtgactca 361 TCACGGGGGCGGGGAGACGC 362 GCACAGGGTGGGGCAGGGAGCA 363 accgggccttccgcgcccct 364 TCCCACCTCCCCCAACATTCCAGTTCCT 365 TCACAGAGCCAGGCAAGCATGGGTGA 366 ggagcagcaggctcgctcgggga 367 gcccaaagtgcggggccaaccc 368 CGGAAAGAGGAAGGCATTTGCTGGGCAAT 369 CCAGCGGCCCCGCGGGATTT 370 ccgacagcgcccggcccaga 371 TGGGCCAATCCCCGCGGCTG 372 GGGCGGCTGCGGGGAGCGAT 373 CGCCAGGACCGCGCACAGCA 374 GCGGGCAAGAGAGCGCGggag 375 AGCGCGCAGCCAGGGGCGAC 376 CGTGCGCTCACCCAGCCGCAG 377 TGAGGGCCCGGGGTGGGGCT 378 ATATGCgcccggcgcggtgg 379 CCGCAGGGGAAGGCCGGGGA 380 TCCTGAGGCGGGGCCGTCCG 381 GGAGGCCGGGGACGCCGAGA 382 GCCGCCGGCTCCCCCGTATG 383 GCAGGAGCGACGCGCGCCAA 384 cgggggaaacgcaggcgtcgg 385 ccccccaccctggacccgcag 386 CGCCCGGCTTTCCGGCGCAC 387 ccgctgggccgccccTTGCT 388 CGCTTCTCCATAGCTCGCCACACACACAC 389 TCCGCGCACGCGCAAGTCCA 390 CGTCTCAACTCACCGCCGCCACCG 391 GACAAATGCGCTGCTCGGAGAGACTGCC 392 TGCGCCTGCGCAGTGCAGCTTAGTG 393 gaagtcaagggctttcaacctcccctgcc 394 tggatcccgcacaggggctgca 395 GCCGCCTGTGGTTTTCCGCGCAT 396 Gcgcgctctcccgcgcctct 397 TTCCGGCCCAGCCCCAACCC 398 TCCGGGTCAGGCGCACAGGGC 399 GGGGGCGGTGCCTGCGCCATA 400 GGCGCGGGCCCTCAGGTTCTCC 401 gcgtccgcggcTCCTCAGCG 402 GGGAGGCGCCCAGCGAGCCA 403 GCGCGCAGGGGGCCTTATACAAAGTCG 404 CCCCCACCCCCTTTCTTTCTGGGTTTTG 405 CGCGCGTTCCCTCCCGTCCG 406 gccggcggAGGCAGCCGTTC 407 TGCCTGGTGCCCCGAGCGAGC 408 CGGCGGCGGCGCTACCTGGA 409 GTGGTGGCCAGCGGGGAGCG 410 GGCGGCACTGAACTCGCGGCAA 411 CCTCGGCGATCCCCGGCCTGA 412 ACGCAGGGAGCGCGCGGAGG 413 TGAAATACTCCCCCACAGTTTTCATGTG 414 TCCGGGCGCACGGGGAGCTG 415 ggcggcggcgTCCAGCCAGA 416 AGGGTCGCCGAGGCCGTGCG 417 CCGCGCCTGATGCACGTGGG 418 gccgggagcgggcggaggaa 419 AGGGGCGCACCGGGCTGGCT 420 TGCCACGGGAGGAGGCGGGAA 421 cgggcatcggcgcgggatga 422 acaccgccggcgcccaccac 423 CCCCCAACAGCGCGCAGCGA 424 GCCCCGCTGGGGACCTGGGA 425 TCCCGGGGGACCCACTCGAGGC 426 GCCCGCGGAGGGGCACACCA 427 GGCCCACGTGCTCGCGCCAA 428 CGGCGGAGCGGCGAGGAGGA 429 GCCTCGCCGGTTCCCGGGTG 430 gcaggcgcgccgATGGCGTT 431 CCTCCCGGCTTCTGCATCGAGGGC 432 GCGGTCCGCGAGTGGGAGCG 433 AGCAGCGCCGCCTCCCACCC 434 CCGACCGTGCTGGCGGCGAC 435 TCCCGGGCTCCGCTCGCCAA 436 GCATGGGGTGCTCATCTTCCCGGAGC 437 CCCGAGAGCCGGAGCGGGGA 438 GCCGCTGCAGGGCGTCTGGG 439 gcgctgccccaagctggcttcc 440 TCAGGATGCCAGCGTGACGGAAGCAA 441 GGGCGGTGCCATCGCGTCCA 442 GGTGGGTCGCCGCCGGGAGA 443 AGGCGGAGGGCCACGCAGGG 444 GGTCCGGGGGCGCCGCTGAT 445 GCGGCCTGCGGCTCGGTTCC 446 CGGGAACCGTGGCGGCCCCT 447 gcggggaaggcggggaaggc 448 gcctcccggtttcaggcc 449 CAGCCCGCGCACCGACCAGC 450 CCCCCAGCCACACCAGACGTGGG 451 tgggcttcctgccccatggttccct 452 TCCGCGCTGGGCCGCAGCTTT 453 gcatggcccggtggcctgca 454 TGGGCAGGGGAGGGGAGTGCTTGA 455 TCCCCGGCGCCTTCCTCCTCC 456 TCCACCGCGCTTCCCGGCTATGC 457 CCCGCATCTGACCGCAGGACCCC 458 TGCGGACACGTGCTTTTCCCGCAT 459 GGAGCTGGAAGAGTTTGTGAGGGCGGTCC 460 CGGCCGCCAACGACGCCAGA 461 AGCGCCCGGTCAGCCCGCAG 462 TCCCGCCAGGCCCAGCCCCT 463 CCGATTCTTCCCAGCAGATGGCCCCAA 464 ACGCACACCGCCCCCAAGCG 465 TAGGCCCCGAGGCCGGAGCG 466 GGGGTTCGCGCGAGCGCTTTG 467 GCCAGTCTCCCGCCCCCTGAGCA 468 TGAGGAGGCAGCGGACCGGGGA 469 GCCGGCTCCACGGACCCACG 470 GCCGCCACCGCCACCATGCC 471 TTGAGTAAGGATGATACCGAGAGGGAAGA 472 tgggccaggcacggtggctca 473 CCCGGCGAAGTGGGCGGCTC 474 GGCGGCCTTACCCTGCCGCGAG 475 ggtggggccggcgAGGGTCA 476 TCGGCGCGGACCGGCTCCTCTA 477 GGCCCATGCGGCCCCGTCAC 478 TGGGATTGCCAGGGGCTGACCG 479 CGCCGGAGCACGCGGCTACTCA 480 CCCTCGGCGCCGGCCCGTTA 481 GCACAGCGGCGGCGAGTGGG 482 TCACCTCGGGCGGGGCGGAC 483 GAGACGGGGCCGGGCGCAGA 484 CGCATTCGGGCCGCAAGCTCC 485 GGCCCGAAAGGGCCGGAGCG 486 ACGGCGGCCGGGTGACCGAC 487 TCCACCGGCGGCCGCTCACC 488 GCGGTCAGGGACCCCCTTCCCC 489 CGGCCGAAGCTGCCGCCCCT 490 GGCGGCCTTGTGCCGCTGGG 491 TCGCGGGAGGAGCGGCGAGG 492 TGCCCACCAGAAGCccatcaccacc 493 TGGGCCATGTGCCCCACCCC 494 CCCGCCAGCCCAGGGCGAGA 495 gccccctgtccctttcccgggact 496 GGTGGGGGTCCGCACCCAGCAAT 497 ggggcccccgggTTGCGTGA 498 TGCCTGCACAGACGACAGCACCCC 499 AGGCCGCGCCGGGCTCAGGT 500 CGGGGTAGTCGCGCAGGTGTCGG 501 tgcaggcggagaatagcagcctccctc 502 ccggaaatgctgctgcaagaggca 503 gcgtcggatccctgagaacttcgaagcca 504 CCCGGCTCCGCGGGTTCCGT 505 GCGTCGCCGGGGCTGGACGTT 506 GGGGCCTGCCGCCTCGTCCA 507 CGCACACCGCTGGCGGACACC 508 CGCAAACCATCTTCCCCGACGCCTT 509 GGGCCCTCCGCCGCCTCCAA 510 CCACCACCGTGGCAAAGCGTCCC 511 TCACAGCCCCTTCCTGCCCGAACA 512 TGCTTGATGCTCACCACTGTTCTTGCTGC 513 ggccaggcccggtggctcaca 514 TGCGGGACGGGTGGCGGGAA 515 gGCTTGGCCCCGCCACCCAG 516 GGCGGGGAAGGCGACCGCAG 517 ggcgcccaaccaccacgcc 518 GAAAAGCCCCGGCCGGCCTCC 519 CCGCAGGTGCGGGGGAGCGT 520 CCCCGCCCACAGCGCGGAGTT 521 AGCAGGGGCCCGGGGGCGAT 522 CCATGACCGCGGTGGCTTGTGGG 523 GGCAGGTGCTCAGCGGGCAGACG 524 GGGTGCGCCCTGCGCTGGCT 525 GAATTTGGTCCTCCTGCGCCTGCCA 526 TGGCTTCCGCGGCGCCAATC 527 GGCCAGGAGAGGGGCCGAGCCT 528 cgagcgccggccccccttct 529 CGGTTGCGAGGGCACCCTTTGGC 530 tacccggacgcggtggcg 531 GCGCCGCCGAGCCTCAGCCA 532 tgcagcctcaacctcctgggg 533 CCTTGCCGACCCAGCCTCGATCCC 534 GGCGGCGTTCGGTGGTGTCCC 535 CCCGGACTCCCCCGCGCAGA 536 cggccccctgcaagttccgc 537 TGCCCAGGGGAGCCCTCCA 538 GCCGGCTGCAGGCCCTCACTGGT 539 TGTCACACCTGCCGATGAAACTCCTGCG 540 CCCCTGCGCACCCCTACCAGGCA 541 TCCTGGGGGAGCGCGGTGGG 542 AGTGGGGCCGGGCGAGTGCG 543 GCGTCCAGGCTGTGCGctcccc 544 GGCGCGGCGGTGCAGCCTCT 545 gaggcggcggcggtggcagt 546 CGCGCGACCCGCCGATTGTG 547 CCGCGGACGCCGCTCTGCAC 548 tgaacccgggaggcggaggttgc 549 TCTCGGCGGCGCGGGGAGTC 550 aggcggccacgggaggggga 551 GGACCCGAGCGGGGCGGAGA 552 AAGCACCTggggcggggcggag 553 GCCGCTCGGGGGACGTGGGA 554 CACCGCCAGCGTGCCAGCCC 555 TATTCTTggccgggtgcggt 556 CCGCTTCCCGCGAGCGAGCC 557 CAGCCGGCGCTCCGCACCTG 558 GCGGAGCGCGCTTGGCCTCA 559 ggcctcgagcccacccagacttggc 560 TGCCGCGCCGTAAGGGCCACC 561 ACGGCGGTGGCGGTGGGTCG 562 AACCTGCCCAGTTACTGCCCCACTCCG 563 TCCAGCGCCCGAGCCGTCCA 564 GCTGCTGCTGCCCGCGTCCG 565 CACTGCTTAGGCCACACGATCCCCCAA 566 GGCCGGACGCGCCTCCCAAG 567 TCGGCCAGGGTGCCGAGGGC 568 tccgcccgcccCACAGCCAG 569 CGCGCCCCAGCCCACCCACT 570 ccgtgctgggcgcaggggaa 571 TGCGCACGCGCACAGCCTCC 572 CGGTGAGTGCGGCCCGGGGA 573 TGGCCGAGAGGGAGCCCCACACC 574 CCCAGCGCCGCAACGCCCAG 575 GCCACAAGCGGGCGGGACGG 576 TCCTCTGGACAACGGGGAGCGGGAA 577 CGCGGGTTCCCGGCGTCTCC 578 GCGCCGCCCGTCCTGCTTGC 579 ACGCGCGGCCCTCCTGCACC 580 GGGCGGGGCAAGCCCTCACCTG 581 GGGAGCGCCCCCTGGCGGTT 582 GCGAATGGTTCGCGCCGGCCT 583 TTTCCGCCGGCTGGGCCCTC 584 TCTCCGGGTcccccgcgtgc 585 GCAGCCCGGGTAGGGTTCACCGAAA 586 GGGCGGAGAGAGGTCCTGCCCAGC 587 CCCTCACCCCAGCCGCGACCCTT 588 GCGATGACGGGATCCGAGAGAAAGGCA 589 TCCGCAGGCCGCGGGAAAGG 590 GGCCCCAGTCCACCTCTGGGAGCG 591 GCTTGGCCGCCCCCGGGATG 592 CCCTCCATGCGCAATCCCAAGGGC 593 gcggcgactgcgctgcccct 594 TGGGCTTGCCTCCCCGCCCCT 595 GGCGGCCCAAGGAGGGCGAA 596 gctgcgcggcTGGCGATCCA 597 TCACCGCCTCCGGACCCCTCCC 598 CCCTTCCAGCCACCCCGCCCTG 599 GCGGGACACCGGGAGGACAGCG 600 CCCTGGGTTCCCGGCTTCTCAGCCA 601 TGGCGGTGATGGGCggaggagg 602 CCAGCCCGCCCGGAGCCCAT 603 TGCCCGCGGGGGAATCGCAG 604 TGCCGCGAGCCCGTCTGCTCC 605 TGCGGCCCCCTCCCGGCTGA 606 GCAGCAGGGCGCGGCTTCCC 607 GCCGCAGCACGCTCGGACGG 608 TGCGGAGTGCGGGTCGGGAAGC 609 GGCGCGGGGGCAGGTGAGCA 610 ggcgcgggggcaggtgagcat 611 CAGTGACGGGCGGTGGGCCTG 612 CGGCGACCCTTTGGCCGCTGG 613 CCGCGGCAGCCCGGGTGAA 614 GGGCGAGCGAGCGGGACCGA 615 TGGGGCAGTGCCGGTGTGCTG 616 TCGCTGGCATTCGGGCCCCCT 617 GGAGCCGTGATGGAGCCGGGAGG 618 TGCCAGGGTGTCTTGGCTCTGGCCT 619 CCGGCTCCGGCGGGGAAGGA 620 GGCCAGGGTGCCGTCGCGCTT 621 TCGGCTCGGTCCTGAGGAGAAGGACTCA 622 GCGCGGGGAACCTGCGGCTG 623 GCCGCCGCTGCTTTGGGTGGG 624 CACCTGAGCCCGCGGGGGAAcc 625 GAACGCCGGCCTCACCGGCA 626 CCCGTGGTCCCAGCGCTCCTGCT 627 GTGCGACCCGGCGCCCAAGC 628 TGGCTCTGCGCTGCCTTTGGTGGC 629 cgcgcgggcggcTCCTTTGT 630 TGGCCCGTTGGCGAGGTTAGAGCG 631 gacccggcatccgggcaggc 632 GCCCGGACTGTAATCACGTCCACTGGGA 633 CCGCCGCCAACGCGCAGGTC 634 CGCTGCCAGCTGCCGCTCCG 635 AGCGCCCACCTGCGCCTCGC 636 GCGGGCCAGGGCGGCATGAA 637 GGCTGCGACCTGGGGTCCGACG 638 GGTTAGGAGGGCGGGGCGCGTG 639 CAGCGCACCAACGCAGGCGAGG 640 TCGGCTGGCCCCGCCCACTC 641 CGGGGTTGCCGTCGCAGCCA 642 TCCGCACTCCCGCCCGGTTCC 643 ggaccccctgggcagcaccctg 644 cgaggcagccggatcacg 645 GGCGCGTGCGGGCGTTGTCC 646 CCAGGATGCGGCAGCGCCCAC 647 cgATGCGGCCCGCGGAGGAG 648 CGTTCTGCGCGCGCCCGACTC 649 CCCCGCCGTGGGCGTAGTAAccg 650 AACCCGCCCGGGCAGCTCCA 651 GCAGCGGTCGCGCCTCGTCG 652 CGCAATCGCGCTGTCTCTGAAAGGGG 653 GGAGCGCCCGCCGTTGATGCC 654 CCATGGCCCGCTGCGCCCTC 655 TGGGGGCGGGGTGCAGGGGT 656 CCGACCCTGCGCCCGGCAGT 657 CGGCTTCAAGTCCACGGCCCTGTGATG 658 ACCCCACCTGCCCGCGCTGC 659 ggcgcgcggagacgcagcag 660 CGTGAGCCGGCGCTCCTGATGC 661 CTGCCGCGGGGGTGCCAAGG 662 CCTGCTGCGCGCGCTGGCTC 663 CCTGGCGGCCCAGGTCGCTCCT 664 GAGCGCCCCGGCCGCCTGAT 665 CGCCGCACGGGACAGCCAGG 666 GCCCGGACATGCCCCGCCAC 667 cgggggccgccgcctgactt 668 CCAGTGGCGGCCCTCGGCCT 669 CGCCCGGCGCGGATAACGGTC 670 TGCTCCGGGTGGGGAGGGAGGC 671 TGCCTGGGCGCAGAACGGGGTC 672 GGGTCCTAATCCCCAGGCTGCGCTGA 673 TCCGCGTCCCCGGCTGCTCC 674 GGGCAGGGCTGACGTTGGGAGCG 675 GCCGTGGGCGCAGGGGCTGT 676 cctgcgcacgcgggaagggc 677 CGCGGACGCAGCCGAGCTCAA 678 CGACCCATGGCGGGGCAGGC 679 tccgctccccgcccctggct 680 tgtgccgcgcggttgggagg 681 TCACTCACGCTCTCAGCCCGGGGA 682 CGGCAAGCGGGCTTCGGGAAGAA 683 CCCCGCGGGCCGGGTGAGAA 684 CGGCGGCGGCTGGAGAGCGA 685 CGGGCCCCGGGACTCGGCTT 686 GACGGAATGTGGGGTGCGGGCCT 687 TGCGGCTGCTGCCGAGGCTCC 688 ACCGCTGCGCGAGGGAgggg 689 GGGGGTGCGGCGTCTGGTCAGC 690 GGCCGGGGGAAATGCGGCCT 691 tgcctggtaggactgacggctgcctttg 692 AGCGCGGGCGCCTCGATCTCC 693 TCCCGGCTGGTCGGCGCTCCT 694 CCGGGGCTGGGACGGCGCTT 695 GGGCGGGGTGGGGCTGGAGC 696 GTGCGGTTGGGCGGGGCCCT 697 GGCGGTGCCTCCGGGGCTCA 698 GGCGGTGCCTCCGGGGCTCA 699 CGGGAGCCCGCCCCCGAGAG 700 TCCTGCCATCCGCGCCTTTGCA 701 AGGCACAGGGGCAGCTCCGGCAC 702 CGACCCCTCCGACCGTGCTTCCG 703 CCCGCAGGGTGGCTGCGTCC 704 GCGTCTGCCGGCCCCTCCCC 705 TAGGCCGCCGGGCAGCCACC 706 GGGGAGCGGGGACGCGAGCA 707 GCCGGCTGGCTCCCCACTCTGC 708 TCGCTCACGGCGTCCCCTTGCC 709 TCCCCGCTGCCCTGGCGCTC 710 GGCCAGAGGCAGGCCCGCAGC 711 TGCCCGGGTCATCGGACGGGAG 712 CCCAGTGCGCACGGCGAGGC 713 AGCGTCCCAGCCCGCGCACC 714 TGCTCCCCCGGGTCGGAGCC 715 CGCTCGCATTGGGGCGCGTC 716 TGCGGCAAGCCCGCCATGATG 717 TCTTGAGCCTCAGGAGTGAAAAGGCCCCTTG 718 GGACCATGAGTGTTTCCATGCTTGGCATCAGA 719 tcagccactgcttcgcaggctgacg 720 cggccagctgcgcggcgact 721 TCGGAGAAGCGCGAGGGGTCCA 722 GCCGGGTGGGGGCTGCCTTG 723 tcctcgcccggcgcgattgg 724 GGCCGTGCAGTTGGTCCCCTGGC 725 GCGAGCCTGCTGCTCCTCTGGCACC 726 gccagagctgtgcaggctcggcattt 727 tgcccagcaaatgccttcctctttccg 728 TGGCCTGACCACCAATGCAGGGGA 729 TCCACCTGGGCTTCTGGGCAGGGA 730 agctggcctgcgccccgctg 731 AGCCGCGGCAGCGCCAGTCC 732 GGGGCCGGGCCGCTCAGTCTCT 733 GCAGTGAGCGTCAGGAGCACGTCCAGG 734 cccgATCCCCCGGCGCGAAT 735 GGCGTGACCGTGGCGCGGAA 736 AGCGGCCCGCAGAGCTCCACCC 737 GGCAGGCGGGCGCAGGGAAG 738 tctgccccgggttcacgccat 739 CGGGCGGGCCCTGGCGAGTA 740 GCAAGCCCGCCACCCCAGGGAC 741 GGCCCAGGCGGATGGGGTTGG 742 TCCGAGAGGCGTGTGGTAGCGGGAGA 743 AGGCGGCCGCGGGCGTTAGC 744 aaggcagcgcgggccaccga 745 ggcatcctgcccgccgcctg 746 TGGGGCGGGGTCTCGCCGTC 747 TCGGGCTCGCGCACCTCCCC 748 CCAGGTGCGCGCTTCGCTCCC 749 ACCTGCGCCACCGCCCCACC 750 GCCGAGCAGAGGGGGCACCTGG 751 TCGCGCCGCTCTGCGTTGGG 752 CCGCCGGGGCAGAAGGCGAG 753 TCcactggacaggggtgggagcctctg 754 gcccaccggcgctgcgctct 755 GCGGTGCCAGCCCCGCTGTG 756 GACCCGCCTGCGTCCTCCAGGG 757 CCCATCACAGCCGCCCAACCAGC 758 GAGCggggcggagccgagga 759 TGCAATTGTGCAGTGGCTGCGTTTGTTTC 760 CCCGACCGGATGCTCCTTGACTTTGCC 761 GCGAGCGCGCGCACCGATTG 762 CACTCCGCCGGCCGCTCCTCA 763 TCGGGGGTCCCGGCCGAATG 764 GCTCTCCCAGCTGCACGCCAACTTCTTG 765 GGAGGAGCCTGGCGCTGGCGAGT 766 TGGCTCTGGACCGCAGCCGGGTA 767 ACGGCGGCGTCCCGGGTCAA 768 TGGCCAAGCGCTGCCACTCGGA 769 CGCAGGCCGCTGCGGTGGAG 770 GCGCCTGCGCCATGTCCACCA 771 TGGTGCCTCCCGCAACCCTTGGC 772 GCCCGGCTCCAGGCGGGGAA 773 GCAATGCTGGCTGACCTGGACC 774 CGCCCGCCCGTCGGGATGAG 775 TGCCCCCACCATCCCCCACCA 776 GGCGCGAGCGGCGGGAACTG 777 GGCGCCGCTCGCGCATGGT 778 CCCGCTCTGCCCCGTCGCAC 779 GTAGCGCGGGCGAGCgggga 780 AGCGCCGAGCAGGGCGCGAA 781 ggcggcggccacgcaggttc 782 ccctcccgcacgctgggttgc 783 TCACGGCCGCATCCGCCACA 784 CGGCGCCGGCCGCTCTTCTG 785 CCGGCAGAGAATgggagcgggagg 786 TCGGCCGGGGCGCCAGGTCT 787 TGGGGCTGCGGGCGATGCCT 788 GGCTGCGGGGACCGGGGTGT 789 CGGCCCAAGCCGCGCCTCAC 790 ccgcgcccggAACCGCTGCT 791 TCGGCCGGGAGCGTGGGAGC 792 TGCAGACATTGGCGCGTTCCTCCA 793 GGACCCACGCGCCGAGCCCAT 794 GGAGGGGGCGAGTGAGGGATTAGGTCCG 795 TCCCCTCACGCCGATGCCACG 796 CCATGCCCGCCCCAGCTCCTCA 797 CCGCCGTGATGTTCTGTTCGCCACC 798 CGTGGCTGCCCCTGCACTCGTCG 799 tctggccagtccgtgaaggcctctga 800 CCGGGGTGCAAGGGCCACGC 801 cgccgcgcTTCCTCCCGACG 802 AGCGACCCGGGGCGTGAGGC 803 TGCGGAAACCTATCACCGCTTCCTTTCCA 804 ggcagggcggggcagggttg 805 GGGTCTCCAGACTGATGGGCCGGTGA 806 CGCTGAAGCCGCTGCTGTCGCTGA 807 TCCCACGCTCCCGCCGAGCC 808 AAATATgccggacgcggtgg 809 CGCCTTTCCGCGGCGGGAGC 810 CCCAGCCCAGGCCGCAGGCA 811 ccccgcaggggacctcataacccaa 812 GAGTTGGCTCGGCGTCCCTGGCA 813 tccctccgcctggtgggtcccc 814 TGACCCCTGGCACATCAGGAAAGGGC 815 TGCCCCGCAAGAACGGCCCAG 816 GGCCTCGGAGTGCGACGCGAGC 817 GCGCCAACCCAGACCCGCGCTT 818 TGCAAGCGCGGAGGCTGCGA 819 AGCCGGGCCACGGGCAGACA 820 CCCGGGCGGCCACAAAGGGC 821 CCCCATCCCAGGTGACCGCCCTG 822 TGACTCTGGGGGAAGCACGCGACG 823 GGTGCGGCCGAAGCCGTCGC 824 TGCCCCTCGGGCCCTCGCTG 825 GGCCACGGGGACCGGGGACA 826 GGGCGCCGCAGGGCGACAAC 827 GCAGCGCGCTTTGGGAAGGAAGGC 828 GGGTTCCACCCGCGCCCACG 829 TCGCGGCCCAGACCCCCGAC 830 CGAGACCCGGTGCGCCTGGGAG 831 aggtgcccgccaccatgc 832 cgcccaggctggagtgcagtggc 833 GCCGGCGAGGTCTCCGCGGTCT 834 CGCAGGGCCACCGGCTCGGA 835 GCCCCGGAGCATGCGCGAGA 836 CCCCTGGGGACCCCTGCCATCCTT 837 TTAccccgcgccgcgccacc 838 GCGGGCCGAGCCCACCAACC 839 GCGCGGTGGCCGCTTGGAGG 840 CCCGCCAGCGGCctgtgcct 841 CGCGCATGCCAAGCCCGCTG 842 GGCGCAGGAGCAGTTGGGGTCCA 843 TGGGGTAGGCGGAACGCCAAGGG 844 CCCGCTTCACGCCCCCACCG 845 GCAGCCCGGGTGGGCAAGGC 846 TGCAGTTGCCCTTGCCCTGCGAC 847 TGGCCGGGCGCCTCCATCGT 848 GCCTGCGATGGGCTCGGTGGG 849 CCGCGGTTCGCATGGCGCTC 850 TGGGCCATCTCGAGCCGCTGCC 851 TGGGGGAGTGCGGGTCGGAGC 852 CTGCCGCGCCCCCAGCACCT 853 GGCTGCTGGCGGGGCCGTCT 854 GGGCGCGGCGACTTGGGGGT 855 aaactgcgactgcgcggcgtgag 856 TGCTGGGGCCGTGGGGGTGC 857 TCCGCGCTGCCCGGGTCCTT 858 GTGGCGGCCCCCGCGGATCT 859 GGGGAGGCGCCACCGCCGTT 860 GGAGCGGGAGGGCGCTGGGA 861 tgaaggctgtcagtcgtggaagtgagaagtgc 862 ggagaaaatccaattgaaggctgtcagtcgtgg 863 ggggacaaccggggcggatccc 864 CCCGGGAGGAGAGGCGAACAGCG 865 AGTGCGCGGGTGCCGGGTGG 866 TGGCATCCCCTACCCGGGCCCTA 867 GAGGCTGGTTCCTTGTCGTCGGTTGGG 868 GCGGGGTCAGGCCGGGGTCA 869 GGCAGCGGCTGGAGCGGTGTCA 870 GCCCGGGCACACGCCCCATC 871 gcaccgccacgcccactgcc 872 TGTCATGCTTCTTTCTCCCCACTGACTCA 873 gcccaggctggggtgcaatggc 874 CGCCTCGGGGGCCACGGCAT 875 CGTGGGTCCTGGCCCGGGGA 876 TCCCCGGGCGGCCATTAGGCA 877 GGCGGGGGTGGGAGTGATCCC 878 CGTCAGTCCCGGCTGCGAGTCCA 879 CCGGGGTCCGCGCCATGCTG 880 CATGGCGGGGCCCGAGCGAC 881 CCGCCTCCTTGCCCCGACACCC 882 TCGGACACGCCTTCGCCTCAGCC 883 CGAGCTGGGCGCAGGCGCAA 884 GCGGGGTTGTGTGTGGCGGAGG 885 accgcgcccggccTGCAAAG 886 GCGGGGCCAGAGAGGCCGGAA 887 GCCCCAAGGGAAGATGCAGGGAGGAA 888 gccccaagggaagatgcagggaggaa 889 GCCCGCACGTGCACCACCCA 890 GGGTGACGAAGTGGTGTCTTTACCGAgga 891 CCGCCGTGCGCCTGTGGGAA 892 ggctgctgcgggaggatcac 893 TGGGCATCCAGAAAAATGGTGGTGATGGC 894 gccgcgccgggccCTATGAG 895 CCGCCATGCGGGCAGGGACC 896 TGTTACAggctggacacggtggctc 897 cggaacttgcagggggccga 898 TGCAAAATCCTCCCCTTCCCGCACCC 899 GCGCTGGAGCCACGCGACGA 900 GGGGTCCGCTCCCGCGTTCG 901 CGCCCCGGGCTGAGAGCTGGGT 902 GGCCCTTCGGGGGCCGGGTT 903 TGGCCACAAAGGGGCCGGAATGG 904 ACCCCAGCGCGTGGGCGGAG 905 GGGCTGCGGGGCGCCTTGAC 906 GCACCGCGGCTGGAGCGGAC 907 AGGCGATCCCAAGGCTGTTGGAGGC 908 tccacccgccttggcctccca 909 cggcgggaaggcggggcaag 910 ggagccgcggcgtgagtgcg 911 GGCCGGCACCCCACGCCAAG 912 GCGGGGCGGAGCGCACACCT 913 GCGGCCAGCAGCGCGTCCTC 914 CCGACAGCCGGCAAGGCCCAA 915 ttgtttttgtttgtttgttttgaaagggag 916 CCCCGGTTTCCCCGCGCCTC 917 GGCTGGACGCGCCCTCCGACA 918 TCCCACGCGCCCGCCCCTAC 919 cggccacgccttccgcggtg 920 GGCTCCGCTGGGGCGCAGGT 921 GCCGCCCCGTGTCGTGCGTC 922 GGCGTCAGTTGGAGTGTGGGGTCGG 923 CCGAGCGGGGTGGGCCGGAT 924 CATCGCGCGGGACCCAACCCA 925 CAGTGGGTGGATCTCACCTGCCTTCGG 926 GAGGCCGCGGGGCTCCGACA 927 GAGCCTGCCCTATAAAATCCGGGGCTCG 928 TCCCGGCGGGTGGTGCCTGA 929 TCTGAGCGCCCGCCGCCTGC 930 GGCTGCCGGCGCGGGACCTA 931 TCCGGGGCATTCCCTCCGCGAT 932 TGGCGGCGGCCCCTGCTCGT 933 cggcgCGCGACTGGGAGGGA 934 GGCGCCAGCGCAACCAGAGCG 935 CGAAGGTGGCGCGGCCTGGA 936 CCCAGCGGGCTTCGCGGGAG 937 CCCGCTTGCCCCGCCCCCTA 938 CCCACACCTCCACCTGCTGGTGCCT 939 ATGCAGCCCCGCCGGCAACG 940 CCGGATGCCCGGTGTGCCTGG 941 GCGAGCAGGGACGCAGCTCTGGTG 942 CGCGCTCGGCCCGCTCAGTG 943 TGGTGCCGGCAGGGAGGGGA 944 GGGCGGTGGCGATGGCTGGC 945 GGCTGTTGGTCTTTTTCCCAGCCCCGAA 946 CCGGGCCGGCAGCGCAGATGT 947 CGGAGGGCGATGGGGCCCTG 948 GGGGCCGGGCTGCGAAGCTG 949 TGCCTGGGCACCCCACGGACG 950 GCCCTACGTCCGGGCAGCACGC 951 CTGTGCGCGTCCCCGCCGTG 952 TGCAGCGGCGCCTCGGACCC 953 ccgctgggcgcgctgggaag 954 GGCGCATGCTCTGCGCGTATTGGC 955 GGGTGGGCGGGCCGTTCTGAGG 956 GGGCTGCCGGGTTGGCGCAG 957 GGCGCGTGCGGAAAAGCTGCG 958 TCCAGGCCGCCCTCGGGTCA 959 GGGGAGGGGGCGCAGCCAGA 960 GGCAGCGTGGTCTTCCACTTCCCCCT 961 GGGATCGAGGGATCGAGGCAGGGGA 962 CGGCCATGAGCGCCTCCACGC 963 CCCGGTGTGCGGCAGCGACG 964 TTGGGGCGGCCGGAAGCCAG 965 CGCAGCGGCGGCGTCTCGGT 966 CCGCGACCTCCCCAAGCCACCC 967 GGCGGCCGACCGCGAACACC 968 CCCCATTTCCGAGTCCGGCAGCA 969 CCCAGCCTGGCCTCTCCTCTCAGGCA 970 cggctctttcctcctcaagagatgcggtg 971 CGCCGCCGTCCCTGGTGCAG 972 TGGGGACCCCTCGCCGCCTG 973 GCGCCCAGCCCGCCCCAAGA 974 caggggacgcgggcgtgcag 975 CCGGGCGGGGCCCAACTGCT 976 CCCGAGCAGGGCCGGAGCAGA 977 CCCCTCCACATTCCCGCGGTCCT 978 TCCTTTGTGGCCTGGGCAGGATGCAG 979 GCAGCGCGCGGTTTGGGGCT 980 GAGGCCTGCGGGCGCTGCTG 981 TCACGGTTGCTGGGCCGTCGC 982 CGGGGTGGGCCTCGCGGAGA 983 GCCTGCGCTCCTGGCGCCCT 984 CGCCTTCGGAGAGCAGAGTCAACACGGA 985 TGCCCCTAAATGAGAAAGGGCCCTTGAG 986 GCCACGCCCCGGGACCGGAA 987 TCCCGCCCAGGGGCCTCCCA 988 ccccgcgcccggccAAAGAA 989 GGACCGCCGCACAGCCCCAA 990 GGGCAGCGGTGGCCGTGCAT 991 TTCCTGCGCCGCCCCCTCCC 992 GGCGTCTCCCTGTCCCCGCCTG 993 GCCGGCCTCGCGCACCGTGT 994 CCCGGGACGTGCGCGCTTGG 995 TGTCCCCCGAGCCGCCCTGC 996 TCGCTCTCGTGCAGCGGCGTCA 997 CCCGCGCGCTGCAGCATCTCC 998 CCCCAGCTGCCGCCATCGCA 999 GCCCGGGCCCGCCTCAAGGA 1000 TGCCGGCGAGGCCTTTTCTCGG 1001 GGCGGGTGGGGAGCGCGAAC 1002 CCCGCCGCCGCTGGTCACCT 1003 ccggctgcctcggcctccca 1004 ggtgtgcaccaccacgcc 1005 GGCGCGTCCCGGCGGCTTCT 1006 AGTCCCTGCGCCCCGCCCTG 1007 TGCCCCCAAACTTTCCGCCTGCAC 1008 CTTGCGGCCACCCGGCGAGC 1009 TCGCGCGGAAACTCTGGCTCGG 1010 GCTGCGGCCCAGAGGGGGTGA 1011 CGGCGGGCTTGGGTCCCGTG 1012 TCCCCCGCCGCACCAGCACC 1013 GCGCGGTGCGGGGACCTGCT 1014 GCCGGACGCTCGCCCCGCAT 1015 GAGTGCTCTGCAGCCCCGACATGGG 1016 CCGCGCAGACGTCGGAGCCCAA 1017 TGGCCGAGGCGCGTGGCGAG 1018 GGCCGCGCTGCCCCAGGGAT 1019 CCGGGGGCGGACGCAGAGGA 1020 GGGGGCGGAGCCTGGGAATGGG 1021 GGGCGGGCCCTGTGGGTGGA 1022 CCGCTCCCCCATCTCCACGGACG 1023 GACCCAGGGAGGCGCGGGGA 1024 TGCCCGGCCGCAGGTGACCA 1025 GCGCCGGGAGTGGGCAGGGA 1026 ACCCAGGCCGGCGCGGGAAG 1027 ttcccgccgcccggtcctca 1028 CGCGCCGGTGACGGACGTGG 1029 AACCCTCCCAGCCAAAACGGGCTCA 1030 CGGGCGAGGCCGCCCTTTGG 1031 GGCCGCGGACGCCCAGGAAA 1032 CCGTTTGGAACGTGGCCCAAGAGGC 1033 CCCGCCTCCGCTCCCCGCTT 1034 ggtggcggcggcagaggagga 1035 CGCGGGGAGCAGAGGCGGTG 1036 gggcgcccgcgctgagggt 1037 GGGCCTGGCCTCCCGGCGAT 1038 CACCCGGCGTCCGCACCAGC 1039 CGGCGCTGGTTTGGCGGCCT 1040 ccaggagccccggaggccacg 1041 GCGATCTCCTGCCCAGGTGTGTGCTC 1042 ACTGCCCGGGCTCGCCGCAC 1043 TGCGGCAACGGTGGCACCCC 1044 GGAGCGAAGCTGGCGGAACCCACC 1045 GGCGGCCGACGGGGCTTTGC 1046 GGCCGCGGGTGCCTCGGTCT 1047 GCGCTCCAGCCATGGCGCGTT 1048 GCCGGACGGGCGTGGGGAGA 1049 TCCCCCGCGACTGCCCCTCC 1050 GGGTGGCAGCGGGTGCGGAA 1051 gctcgcccgctcgcagccaa 1052 CGAGGTTCCGCAGCCCGAGCCA 1053 GCGCGGGGGACCGAAACCGTG 1054 GCCGAGCCCGGCCCAAAGCC 1055 TGCCAACGTTCACCCGGCTGGC 1056 GACAGTGCGAGGGAAAACCACCTTCCCC 1057 GGGTCGGGCCGGGCTGGAGC 1058 GGGTCGGGCCGGGCTGGAGC 1059 GCGGGGCCGAGGGGCTGAGC 1060 GCCCGGCCACCTCGGGGAGC 1061 ACTGTCTGCCAAGCCAGCCCCAGGG 1062 GGATGGTGGCGCCGGGCTGC 1063 TCCAGGAGGGCCAGGTCACAGCTGC 1064 CGGCTGGCTCGCTTGGCTGGC 1065 TCCGGCGCTGTTGGGCAGCC 1066 CCTGCGCACGCGGGAAGGGC 1067 TCTTCCCTTCTTTCCCACGCTGCTCCG 1068 CAGCGCCCCCGCCTCCAGCA 1069 GCTGCGCGGCTGGCGATCCA 1070 GCCGACGACCGGAGGGCCCACT 1071 TGCCCAGGCTGGCCCCTCGG 1072 CGCGGCCCTCCCCAGCCCTC 1073 CCCCGCCCGGCAACTGAGCG 1074 AAGAGCCCGCGCGCCGAGCC 1075 TGCCCACTGCGGTTACCCCGCAT 1076 GCATGGTGGTGGACATGTGCGGTCA 1077 CATAGAAGAGGAAGGCAAAGGCTGTGACAGGCA 1078 TCATCCTAGACTTGCAGTCAAGATGCCTGCCC 1079 agccagcggtgccggtgccc 1080 gccccgctccgccccagtgc 1081 CACGGGGGCGGGGAGACGCGGGGTGCACTTCTCGCCCCGAGGGCCTCCGGCGAAGCAACCCGGCAGC- CGCGGCGCCCGAGGGCCTGGCGCTGGTCTGGGGCTGCGCCGGGGGCGCCTGGCTCTGGGGTGCGGCCGGTCAG- GAATCCCCATCCTGGAGCGCAGGCGGAGAGCCAGTGGCTGGGGGCGGGAAGGCTTCTTGGACCCCTCGCGCTTC TCCGA 1082 CACAGGGTGGGGCAGGGAGCATCAGGGGGCAGGCAGCCACACCCCCGACACATCAAGACACCTGAGT- GGCAGGTTCAAGCCGGAGGCGCTGTATTTCCACACAGGAAGAAGGCCAAAAAAGGTGACACTGC- CCCCTCCCAGTGGCTCCATGCTCCTCAGCTATGGCTGTCCGGGCCGCCTCACTCAAAGCCTTGCCCTCCGCTGC TGCCAGGCTCCTTGCATGCAAGGCAGCCCCCACCCGGC 1083 accgggccttccgcgcccctcgccccacgccgcgggtgcggtcctccctccagcagagggttccgg- gcgccggcgcggcccgcacggggccgggagcccttcctgccggccgggtgcgcgcggcgccgccgacagct- gtttgccatcggcgccgctcccgcccgcgtcccggtgcgcgccccgcccccgccaacaaccgccgctctgattg gcccggcgcttgtctcttctctccccgcagccaatcgcgccggg 1084 CCCACCTCCCCCAACATTCCAGTTCCTTCTTTTCCTTCTACTCTTCAGCGGCCTCAGCCTGCGCAC- CCCAGGAGCGTGGATGACTACGGCCACCCCGGGCGCGCACCCCTTTCCCACCACCCCAGCATCTCTGCAGC- CCAGGACACCCGCCTCCCCCACACCCCGCATCCGGTGTGTCTCCGCCTGGCCCGGCCGGCGCGGCAGGCGGGCC AGGGGACCAACTGCACGGCC 1085 CACAGAGCCAGGCAAGCATGGGTGAGAGCTCAGACCATCCTTGTTGGACTAAAAGGAAGGG- GCAGACTGCCCATGGGGGGCAGCCGAGAGGGTCAGGCCCCCATAGGTCCTCAGCCTGCTTCAACCTCAAAGGG- GATGGGGGGCTGAGTGGTGCCAGAGGAGCAGCAGGCTCGC 1086 ggagcagcaggctcgctcggggagagtagggccttaggatagaagggaaatgaactaaacaac- cagcttcctcccaaaccagtttcaggccagggctgggaatttcacaaaaaagcagaaggcgctctgtgaa- catttcctgccccgccccagcccccttcctggcagcattaccacactgctcacctgtgaagcaatcttccggag acagggccaaagggccaagtgccccagtcaggagctgcctataaatgc 1087 gcccaaagtgcggggccaacccagacagtcccacttaccaggtcttctgaaagacagctgacaa- gagacatgcagggctgagaggcagctcctttttatagcggttaggcttggccagctgcccacagcttcaggc- catcagagacagcttctccctgccagagttgctacagtctctggtttctcaaccaggtgaatgtggcaatcact gtgcagaatgaaaattttgggtggggaggtaggagaagcggaaag 1088 GGAAAGAGGAAGGCATTTGCTGGGCAATAGTGCCCAGAAGGAAAAAGCAGGTAGGGGG- GCTCTTTTTCTGGGCTGCTGGCATCCACTTGCTTGATCCAGCCAGATTCCCACTCCCATGCCCTCTCCACTAT- TGCGATTGCTAATCCCCTGCATTGGTGGTCAGGCCA 1089 CAGCGGCCCCGCGGGATTTTGCCCAGCTGCTTCGTGCCCTCTGGTGGCTAAGGCGTGTCATTGCAGT- GCCGGCCTCCTGTCATCCTCCCTTTCTTGTCCGCCAGACCCTCTGGCGCCCTGCTTACGACTCAAACAG- GAGACAGTGCTGATTCATTTCCAAGCGGCCTTCCTACACCCACACCTGCTTCACATAGATGAGGTTTCCCGGAC AGTCCCTGCCCAGAAGCCCAGGTGGA 1090 ccgacagcgcccggcccagatccccacgcctgccaggagcaagccgagagccagccggccg- gcgcactccgactccgagcagtctctgtccttcgacccgagccccgcgccctttccgggacccctgc- cccgcgggcagcgctgccaacctgccggccatggagaccccgtcccagcggcgcgccacccgcagcggggcgca ggccagct 1091 GGGCCAATCCCCGCGGCTGGGCAGAGCGACCCGAGGGCGGCGCCCTGCAGACCACGTGGCCCGGGAG- GCGCCGAGGCCAGGTAGGTGGTGAGTTACTTGGCTCGGAGCGGGCGAGGGGACGCGTGGGCGGAGCGGGGCTG- GCCAGCCTCGGCCCCCATGACCCGCTGTCCTGTGCCCTTTCCCAGCGATGGGCGTGCAGCCCCCCAACTTCTCC TGGGTGCTTCCGGGCCGGCTGGCGGGACTGGCGCTGCCGCG 1092 GGCGGCTGCGGGGAGCGATTTTCCAGCCCGGTTTGTGCTCTGTGTGTTTGTCTGCCTCTGGAGGGCT- GGGTCCTCCTTATTCACAGGTGAGTCACACCCTGAAACACAGGCTCTCTTCCTGTCAGGACTGAGTCAG- GTAGAAGAGTCGATAAAACCACCTGATCAAGGAAAAGGAAGGCACAGCGGAGCGCAGAGTGAGAACCACCAACC GAGGCGCCGGGCAGCGACCCCTGCAGCGGAGACAGAGACTGAGCG 1093 GCCAGGACCGCGCACAGCAGCAGGGCGCGGGCGAGCATCGCAGCGGCGGGCAGGGCGCGGCGCGGGG- GTAGGCTTTGCTGTCTGAGGGCGTCTGGCTGTGGAGCTGAAGGAGGCGCTGCTGAGGAGTTCCTGGACGT- GCTCCTGACGCTCACTGC 1094 CGGGCAAGAGAGCGCGggaggaggaggaggagaaaaaggaggaggaggaggaggaggaggCGGC- CCCGCATCCCTAATGAGGGAATGAATGGAGAGGCCCCCTCGGCTggcgcccgcccacccggcggcggccgc- cAAGTGCCTCTGGGCGCTGCGTGCCGCGCCCGCTGCTCCGCGCGCAGCCGGCTCGGGCCGCTCCTCCTGACTGA GGCGCGGCGGCGGCGGTGGCTGTGACCGCGCGGACCGAGCCGAGAC 1095 GCGCGCAGCCAGGGGCGACGCTTCCGCTCCGAGCCGCGGCCCGGGGCCACGCGCTAAGGGC- CCGAACTTGGCAGCTGACCGTCCCGGACAGGGAGGCCCTTCAGCCTCGACGCGGCCTGCGTCCTCCGGAGGGC- CCTGCTCCGCCCGGGAAGCGTCCGCCTCCCGCCCGCCCGCCCGCAGATGTCGCTGCCCCTCTGGCTGTCCCGGC CTGACCGCCGCGCGCCGCCCTGCTGCTCACCTACTTCCGCGCCACGG 1096 GTGCGCTCACCCAGCCGCAGGCGCCTGAGCGGCCAGAGCCGCCACCGAACACGCCGCACCGGCCAC- CGCCGTTCCCTGATAGATTGCTGATGCCTGGCCGCGGGAACGCCCACGGAACCCGCGTCCAcggggcggggc- cggcggcgcgcgcgccccctgccggccggggggcggAGTTTCCCGGGCGCCTGCCGGGTGGAGCTCTGCGGGCC GCT 1097 GAGGGCCCGGGGTGGGGCTGCGCCCTGAGGGCCCTGCCCTGCCCTCCGCACGCCTCTGGCCACG- GTCCCTTCCCCGGCTGTGGGTCTGCGGCCCCTGCGTGCGCAGCGCTCCTGGCCTCTGCGGCCAGCGCGGGG- GCGGAGAGAGGAGAGTGCCCGGCAGGCGGCGGCTGGGCCGGCCCGGAACTGGGTCGTGGAAGGATCGCGGGGAG CGGCCCTCAGGCCTTCGGCCTCACTGCGTCCCCACTTCCCTGCGCC 1098 TATGCgcccggcgcggtggctcacgcctgtaatcccagcactttgggaggccgaggcgggcggat- cacgaggtcaggagatcgagaccatcctgactaacacggtgaaaccccgtctc- tactaaaaatacaaaaattagccgggcgcggtggcgggtgcctgtagtcccagctacttgggaggctgaggcag gagaatggcgtgaacccggggcaga 1099 CGCAGGGGAAGGCCGGGGAGGGAGGTGTGAAGCGGCGGCTGGTGCTTGGGTCTACGG- GAATACGCATAACAGCGGCCGTCAGGGCGCCGGGCAGGCGGAGACGGCGCGGCTTcccccgggggcggccg- gcgcgggcgccTCCTCGGCCGCCGCTGCCGCGAGAAGCGGGAAAGCAGAAgcggcggggcccgggcctcagggc gcagggggcggcgcccggccACTACTCGCCAGGGCCCGCCCG 1100 CCTGAGGCGGGGCCGTCCGGCACCCTGTGATGGGGCGTGGCCCCTGGGGAGGCTCCCACCAGCCCT- CAGATTCCTCAGGGCCGCAGAGGTGTGGAGCTGGTTGGGCCGGTTCTTCACCCTCCTCCCCTGGTGCTTGCCT- GTGCCCCAGCAGGGTGACAGTGATGTAGTAGCGGGTCCTCCTGGAAGAGGGACGCGTGTGTAGGGTCTGGGCAG GCTCTGGCAAGGCAGTCCCTGGGGTGGCGGGCTTGC 1101 GAGGCCGGGGACGCCGAGAGCCGGGTCTTCTACCTGAAGATGAAGGGTGACTACTACCGCTACCTG- GCCGAGGTGGCCACCGGTGACGACAAGAAGCGCATCATTGACTCAGCCCGGTCAGCCTACCAGGAGGCCATG- GACATCAGCAAGAAGGAGATGCCGCCCACCAACCCCATCCGCCTGGGCC 1102 CCGCCGGCTCCCCCGTATGAGGAGCTGCCATAGCTTTCGAATCCACCTGTTTTGAACAACAG- GATTAGTGCCTGTGCCACGTCCCACGCCTCCGAGAAACCCGCAGGCTCCCGGAGGCTTCGC- CCCTTCAAACACTGCCCGAGTCTCCCTAACCTTCCTCGCCGCCTTCCTGCGGGTGACCCCCAAACGCCCCAGCT CCGCTCCCGCCCTTCCTCTCCCGCTACCACACGCCTCTCGGA 1103 CAGGAGCGACGCGCGCCAAAAGGCGGCGGGAAGGAGGCGGGGCAGAGCGCGCCCGGGACCCCGACT- TGGACGCGGCCAGCTGGAGAGGCGGAGCGCCGGGAGGAGACCTTGGCCCCGCCGCGACTCGGTGGCCCGCGCT- GCCTTCCCGCGCGCCGGGCTAAAAAGGCGCTAACGCCCGCGGCCGCCT 1104 cgggggaaacgcaggcgtcgggcacagagtcggcaccggcgtccccagctctgccgaagatcgcg- gtcgggtctggcccgcgggaggggccctggcgccggacctgcttcggccctgcgtgggcggcctcgccgg- gctctgcaggagcgacgcgcgccaaaaggcggcgggaaggaggcggggcagagcgcgcccgggaccccgacttg gacgcggccagctggagaggcggagcgccgggaggagaccttggcc 1105 ccccccaccctggacccgcaggctcaggagtccacgcggggagaggggatg- gagaactctcctcgcttcgtcctctctcccggggaatccctaaccccgcactgcgttacctgtcgctttggg- gaggccgctgccgggatccggccccgaacagcccgggggggcaggggcgggggtcgtcgaggggatgggggcag agagcaggcggcgggcaggatgcc 1106 GCCCGGCTTTCCGGCGCACTCCAGGGGGCGTGGCTCGGGTCCACCCGGGCTGCGAGCCG- GCAGCACAGGCCAATAGGCAATTAGCGCGCGCCAGGCTGCCTTCCCCGCGCCGGACCCGGGACGTCTGAACG- GAAGTTCGACCCATCGGCGACCCGACGGCGAGACCCCGCCCCA 1107 cgctgggccgccccTTGCTCTTAGCCAGAGGTAGCCCCTCACCCCGCGACTTACCCCACAC- CCCGCTCTCCAGAACCCCCATATGGGCGCTCACCGCCCGCCCGCACAGCTCGAACAGGGCGGGGGGAGCGT- TGGGGCCCGAGGCCGAGCTCTTCGCTGGCGCCGCCTCCCGGGACGTGGCCTCCATGGTCGTTGCCGCCGCTACC TCACAGAACCAGCAACTCCGGGCGCGCCAGGCCTCGGGCGCCGCCATCT 1108 GCTTCTCCATAGCTCGCCACACACACACACACACGCCACGCACCGTATAAAAGCCTAAATGACACAC- CACTGCAGCGTTCAAACGCTGGGAAGAAGACTCCCTTGTGGCACCGGAAACCCACGAGGTTGGAAGTGG- GAGGGGAAGAGGGCCAGATACTTCACCTGAAAATCCGCCAGGATCATCTCCCGGTCCATGTTGGACGCCATGGC GGCCGCCGAGTTCCGCGGCTCCGGGAGCGAAGCGCGCACCTGG 1109 CCGCGCACGCGCAAGTCCAGGCCGCCGCGGCCCTGGAATAGAGACTCGCCCTTGAT- GTCCCTCTCGAAGTAGTAGGCGGCATCGCCGATATCCACGTCACCGGCGGCCTTCTGAGACGTGTTCTGC- CGCAGCTCGATCTGGATGGTGGGCTGCTCGTAGTGCACGGCCGCCACGAACTTGGGGTGCAGCCGATAGCGCTC GCGGAAGAGCCGCCTCAGCTCGGCGTCCAGGTCTGAGTGGTTGAAGGCGCCGGCG 1110 GTCTCAACTCACCGCCGCCACCGCCGCGCAGCCCCGCGGCCGCTGCTCCATAGCCCTCCGACGG- GCGCCCAGGGGCTTCCCGGCTCCGTGCTCTCTGCCCGTCGTGGTTCCGCCTTCAgccccgcgcccgcagggc- ccgccccgcgccgtcgagaagggcccgcctggcgggcggggggaggcggggccgcccgAGCCCAACCGAGTCCG ACCAGGTGCCCCCTCTGCTCGGC 1111 ACAAATGCGCTGCTCGGAGAGACTGCCGCGGCAACCAACTGGACACCCCAAGAGCT- CACTCCTCCGCGGTTTTATATTCCGACTTGCGCACAGGAGCGGGGTGCGGGGGCGCAGGGAGTGTGG- GTAACAGGCATAGATTCCGCTTGCGCAATACGTGGTAAGAAACCAGCTGTGAGGGGCTGGCCCAACGCAGAGCG GCGCGA 1112 GCGCCTGCGCAGTGCAGCTTAGTGCGTCGGCGCGCAGTTCTCCCGCCCGTTTCAGCG- GCGCAGCTTCTGTAGTTGGGCTACTGGAGGGGTCGCTCAGAAACCTCATACTTCTCGGGTCAGGGAAG- GTTTGGGAGGATGCTGAGGCCTGAGATCTCATCAACCTCGCCTTCTGCCCCGGCGG 1113 aagtcaagggctttcaacctcccctgccccattcatacagtggaaggtctaacccaggcttgt- cagcctaagaacacgggatctcttcactgtggttcatgtgtagagtggagtttccatgctgaga- gagacaagcaaagaagaccagaggctcccacccctgtccagtgGA 1114 tggatcccgcacaggggctgcaggtggagctacctgccagtcccctgccgtgcgctcgcattcct- cagcccttgggtggtccatgggactgggcgccatggagcagggggtggtgcttgtcggggaggctggggc- cgcacaggagcccatggagtgggtgggaggctcaggcatggcgggctgcaggtccggagccctgccctgcggga acgcagctaaggctcggtgagaaatagagcgcagcgccggtgggc 1115 CCGCCTGTGGTTTTCCGCGCATTGTGAGGGATGAGGGGTGGAGGTGGTATTAGACGCAGC- CGAATCCTCCCTCAGAGTCCGCCAGGTGGGCGTCTCAGGGGTGGGAGTGGCCGCGTCGTGAAGCGGAGAGAG- GATTTCTCTCCTGGTCCTGGAGAAGGCCCCCGGCGGCCGGCGGCATCCCTCGCTGGCGAGTCCCGGGAGCGAGG TGGTCTCTGCAGGGGAGGAAGTTCCCGGGCGGCGCGGCCTGCGTCACAG 1116 cgcgctctcccgcgcctctgcccgcccccggcgcccgcccccgccgctcctcccgactccccgc- ccccggcccGGGTCACTTGCCGTCGCGGTGGGCGGCCCCCGGCGAGTCCACACCCCTGCCCCGCCTCCTCCCG- GTAGGAAACTCCGGGACCCTGCAAGGGATGACTCACCCCAGTGATTCAACCGCGCCACCGAGCGCGGAGCTGCC CTGGAGGACGCAGGCGGGTC 1117 TCCGGCCCAGCCCCAACCCCGACCTAAGTAACCGGCTATCGGCCACCCATTGGCTGAAGTCCCT- GAGCACCTGTTGGGAGGAAGGCTGCTGCGTGCAGCCGGAAAGTCCTGCGTCCCTCCGCTCTTACCGCGGCAG- GAACCACAGCCTCCCCGAACCTCAGGGTTTGTATGGATTTCGCCCAGGGGAAAGCGCTCCAACGCGCGGTGCAA ACGGAAGCCACTGGCTGGTTGGGCGGCTGTGATGGG 1118 CCGGGTCAGGCGCACAGGGCAGCGGCGCTGCCGGAGGACCAGGGCCGGCGTGCCGGCGTCCAGCGAG- GATGCGCAGACTGCCTCAGGCCCGGCGCCGCCGCACAGGGCATGCGCCGACCCGGTCGGGCGGGAACAc- cccgcccctcccgggctccgccccagctccgcccccgcgcgccccggccccgcccccgcgcgctctcttgcttt tctcaggtcctcggctccgccccGCTC 1119 GGGGCGGTGCCTGCGCCATATATGGGAgcggccgcccctcgccgcgcccctcgccgccgccgccgc- cgcgctcgccgactgactgcctgacggcgccgcgagccggcccgagccccgcgagccccgcgagccccgccgc- cgccgagcgccaccgagcgccgccgccgccccccgccacgcaccgcggcTCCTCGCGTCCAGCCGCGGCCAAGG AAGTTACTACTCGCCCAAATAAATCTTGAAAAGAAACAAACG 1120 GCGCGGGCCCTCAGGTTCTCCCTATCGAAGCGGTCTATGGAGATAGTTGGATACTCGGCCATCTGC- CCCTCGAAAGAACTCATAGCGCCGCCGATCCCAGAGTCCGGGACCCCAAAACCGCAGCTGAAGCCAAGGC- CAGCCCTGACCGCGCCGCCACTTCCGGGAAGCCGCGCGCTGCCTCGCCATTGGGCGGCCGAACGCAGCCACGTC CAATCAGAGGAGTCCGGAGACCGGGGGCAAAGTCAAGGAGCATCC 1121 cgtccgcggcTCCTCAGCGTCCCCCTTTACGGTCTGGGCGGACTGCGGGGGCTGGGGAGGTTCTGGG- GACCGGGAGAGTGGCCACCTTCTTCCTCCTCGCGAAGAGCAGGCCGGGCCTACCCGTCCGCCCGCTCTGC- CGTCCGCTGGCCGGCCGACTGCTGCCCGATCACTCCTGAGGCCGCCGTTGGGCGACAGGGCGGTGCGGGAG- GAGGACTGCGCAGGCGCAGTGGGCCAGGCGGCCCGGCGACCAATCGG 1122 GGAGGCGCCCAGCGAGCCAGAGTGGTGGCTGGTCCCGCGCGGTGAGTGGGATTGGGGCACTTGGG- GCGCTCGGGGCCTGCGTCGGATACTCGGGTCCGCTCGGGAGCGCGCTGGCCGCAACGAGGGCGGCGCGGGC- CCGGGCGATGGCGTGGCTTGCGTCTCCCGCCTccgggcagggcctggccgccgggcgggggcgggagggccacg cgggcccagggtggggccgcggcctgcgcggcgggcgggccgggt 1123 CGCGCAGGGGGCCTTATACAAAGTCGGAGAAGTAGCTGGGTCGCTGGCCGGCCAGGGACTCAAGC- CGCCTCAGGTGAGCGCTCCTTGGCGCTACTTCCGGTCTCAGGTGAGGCCGCCGGAAGCGGGCACTTGGC- CCTAAGACCCGCTACAGTGCGTCCTCGCTGACAGGCTCAATCACCACGGCGAGGCCAAggcgcggggccgcggc ccgcccgAGAAGCCTGAGCTGGGCCCCGACACCCCCTGCCCGACATT 1124 CCCCACCCCCTTTCTTTCTGGGTTTTGATGTGGATGTCTTTCTATTTGTTCAGGAAATTGTGACGT- GTGTTCTGGGCAGGGTTTGAGGTTTTGGAACATTTTCTAAAAGGGACAGAGAGCACCCTGCTA- CATTTCCTAATCAAGAAGTTGGCGTGCAGCTGGGAGAGC 1125 GCGCGTTCCCTCCCGTCCGCCCCCAAgccccgcgggcctcgcccaccctgcccgccgcccctccgc- cggcggccgcccTCTGCGGCGCCCCTTTCCGGTCAGTGGAGGGGCGGGAGGAGGGGCGGGGGTGCGCGGG- GCGGGGGGAGAAGTCCTGGAGCGGGTTTGGGTTGCAGTTTCCTTGTGCCGGGGATCCTGTCCCCTACTCGCCAG CGCCAGGCTCCTCC 1126 ccggcggAGGCAGCCGTTCGGAGGATTATTCGTCTTCTCCCCATTCCGCTGCCGCCGCTGCCAG- GCCTCTGGCTGCTGAGGAGAAGCAGGCCCAGTCGCTGCAACCATCCAGCAGCCGCCGCAGCAGCCATTACCCG- GCTGCGGTCCAGAGCCA 1127 GCCTGGTGCCCCGAGCGAGCCGGGAGTAGCTGCGGCGGTGCCCGCCCCCTCTCTCCGC- CCCTCCAGCGGAGCTGGTCTCCGGCCGGGCACCGTCGCGGGCCCCCCTGGCCCGGCCACCTGGGACCGTGCT- GGGGAGTCTGCCACTTCCCTCTCTCCCCTGGCCCGCAAAGTTTTGGCGGAGCCATCGCTGGGGCTGAGCGCGCC CCCGGGGGGAGATCGGGGAGCGCCCGATGCCGGGCGGCCGGAGCCATTGAC 1128 GGCGGCGGCGCTACCTGGAGGCGCGGTGGCGGGCAGGTGCCCGAACTGCACGGCGATGCAGAG- GTCGTTGTCCAGGGGGAACTTGTGGCAGTGCAGCATCTCAGGCCAGGGGAAGCCGTAGGCCTCCATGAGCG- GCGCGCAGCCGGCGCGCACGGCCTCGCACAGCGAGCGGCACGGGTAGATGGGCCGGTCGAGACAGACGGGCGCA AAGAGCGAGCACAGGAAGACCTGCGTATCCGAGTGGCAGCGCTTGGC 1129 TGGTGGCCAGCGGGGAGCGCCCGGGCGCCATCGGCGCGTCCTGCTCCACCAGGGCGACCCTGG- GCGCTGAGAAGCGGGAATCTTCCTTGGGGACCAGGGCGACGCCTCCTGCTGCCGCCCCCGGCGGGACAGC- CGCGGCTCCTCCTCCAGCCGCCGCGCCACCCAGAGCCCGAGGTTTGCCCTTCAGAAGCGGACCCGCAGACTCCT CGGACTCAGAGCCATCCTCCTCCTCAACCTCCACCGCAGCGGCCTGCG 1130 GCGGCACTGAACTCGCGGCAATTTGTCCCGCCTCTTTCGCTTCACGGCAGCCAATCGCTTCCGCCA- GAGAAAGAAAGGCGCCGAAATGAAACCCGCCTCCGTTCGCCTTCGGAACTGTCGTCACTTCCGTCCTCAGACT- TGGAGGGGCGGGGATGAGGAGGGCGGGGAGGACGACGAGGGCGAAGAGGGTGGGTGAGAGCCCCGGAGCCCGAG CCGAAGGGCGAGCCGCAAACGCTAAGTCGCTGGCCATTGGTG 1131 CTCGGCGATCCCCGGCCTGAACGGGTAGGAGGGGTTGGGGGATTCCGCCATCCCTTGTTTTGAG- GCGGGAACGCAACCCTCGACCGCCCACTGCGCTCCCACCCACACCCAGAGTAATAAGCTGTGATTGCAGGCT- GGGTCCTCACCGTCTGCTCGCCAGTCTTCTCCTTTGAGGACTCAGAAGCCAAGGGTTGCGGGAGGCACCA 1132 CGCAGGGAGCGCGCGGAGGCCCGCAGGGTGCCCGCCTGGCCGCAGAGGCCGCGACGCCCCCTCCGC- CACCCTCGGGCCGCCGAAAGAACGGGCAGCCGGGAAATCCCGTGTCCCCACTCGTGGCAGAGGACGCTGTGGG- GCGGGCGGGCTGCGGGCTCCCGGCGCCTTCCCGCAGAGGCGGCGACAgcggccgccccccccgcggggccgggc cggggAACTTTCCCCGCCTGGAGCCGGGC 1133 GAAATACTCCCCCACAGTTTTCATGTGATCAGGAATTCAGCATAGGCTATAAGACGGAGTGCTCCAT- GTCAATAGAGAATATTTCCACAGGTGTGCTAGGCACTTGTGGTAGATGTTGCAGGGAAGTCAGGACTGGG- GACAGCTTGGTCCCTACTTCAAGGTTACAGTCTAGGAGCTGAGAGTGGCAAAGTGACCTGATTCTACAGGGTAA AAGCCCCAGAGATAAATGACATAGGTCCAGGTCAGCCAGCATTG 1134 CCGGGCGCACGGGGAGCTGGGCGGACGGCGGCCCCCGCCTCCTCCGGGGACGCGGCAC- GAGACGCGGGGACGCGCGGACGCCACGCTCAGCGGCCGCCCCCGGCCTCCGCGCCGCCTTCCTCCCGG- GAGCAGCCCCGACGCGCGCGGGCCCGGACCGCCGGGGTTGTCATGGCAGCAGCTCCATCCCTGACCGCCACTTT CTCCCGGTGCCGCCTCGGAGCGAGCGGGCTGGCGGGCGGCGCGGACTGCGCGCTC 1135 gcggcggcgTCCAGCCAGAGCCCTGTGGAAGCGGCGGCGACACTTGGGCTGGGCAGTGTCTCTGAT- GCCTCCCAGCGCCAGCGACTGCTCTTATTCCCGCCGCTGTGGGTCGGGAAAGTTCCGCCAGTGCACAGCAAC- CAATGGGCGGAGGGGTCCTTTGCCCCTGGGTTGCGTCACCCTCATGCTTCCAGAACCTGGAGGATCCAGCAGGA CCGTCCCACTTGTATTTGCATTGAGGTCATTGATGGAAATGGT 1136 GGGTCGCCGAGGCCGTGCGCTTATAGCCGGGATGACGCCGCAGTTGGGCCGGATCAGCTGAC- CCGCGTGTTTGCACCCGGACCGGTCACGTgggcgcggccggcgtgcgcggggcggggcggagcggggcctg- gcctgggcggggcAACCTCGGCGCACGCGCACAgcgcccgggcggggggcggggTGGTGGTGCGCCTGCCGCGC CTACAGTTCCCGCCGCTCGCGCC 1137 CGCGCCTGATGCACGTGGGCGCGCTCCTGAAACCCGAAGAGCACTCGCACTTCCCCGCGGCGGT- GCACCCGGCCCCGGGCGCACGTGAGGACGAGCATGTGCGCGCGCCCAGCGGGCACCACCAGGCGGGCCGCT- GCCTACTGTGGGCCTGCAAGGCGTGCAAGCGCAAGACCACCAACGCCGACCGCCGCAAGGCCGCCACCATGCGC GAGCGGCGCC 1138 ccgggagcgggcggaggaagggccgggcgtccggcgcaagcccgcgccgccccagcccoggccccg- gcccggcccgcACACGCCGCTTACCTGGAAGCCGGCGACGCTGCCGCCCACCTCCCTGCTGCGTGTCGCAAAC- CGAACAGCGGGCGTTGGCCCTCCTGCCGGACACTCCTCTGCCAGCGCCGCTCTGGCCGAGTCGCGGGGGCCGAA TGTGCGACGGGGCAGAGCGGG 1139 GGGGCGCACCGGGCTGGCTCCTCTGTCCGGCCCGGGAGCCCGAGGCGCTACGGGGTGCGCGG- GACAGCGAgcgggcgggtgcgcccgggcgcggcggcggcAGCGTCGGGGACCCGGAGCTCCAGGCTGCGCCT- TGCGCCCGGGTCAGACATTATTTAGCTCTTCGGTTGAGCTTCGATTGGTCAAACGGCGCCGccccccccccccc gccccccgccccccgctccccGCTCGCCCGCGCTAC 1140 GCCACGGGAGGAGGCGGGAACCCAGCGAGGCCCCCGAgggctggggggaccggccggccg- gacaaagcggggccgggccgggccggggcggggccgtgcggggcTCACCGGAGATCAGAGGCCCG- GACAGCTTCTTGATCGCCGCGCCGTTGGCGCTGGCGGCCGCGGTGCCGGCCGCGGGACGTCCCGAAATCCCCGA GTGCAGCTGGTCAGCGAGAGGCTCCTGGCCGCGCTGCCCCTGGTTCGCGCCCTGCT 1141 cgggcatcggcgcgggatgagaaaccaacctgatacttatcgtgtgccgagttccctcct- tgtatcctgactaagcacagcgaataaccctgtccttgttctaaccccaggtcttgaagaaatact- gtcccagctgagccccgcgtttacaagatgaagaggcgccccagatgcgctgaaagaaaggccaaagctcgtgc ctccttccactgcctgcggtagaacctggtcccgcatagcttggactcggataag 1142 acaccgccggcgcccaccaccaccagcttatattccgtcatcgctcctcaggggcctgcggcccggg- gtcctcctacagggtctcctgccccacctgccaaggagggccctgctcagccaggcccaggcccagccccag- gccccacagggcagctgctggcagggccatctgaagggcaaacccacagcggtccctgggccccaacgccaggc agcaaggactgcagcgtgcctacctgtgcagctgcaacccag 1143 CCCCAACAGCGCGCAGCGAACTCCACTGCCGCTGCCTCCGCCCCAGAGACACGTTGCAGGCCA- GAGCGGCCGGGGCGCGGGGCATCACGGGACGGCCTCACCTGGCCTCTTGGAGGACTCCCGAAGCCCGAGGC- CGCCAACCGAAGGAGGCCCCGCCCCCGGAGGCACCGCCTCGCCTCTTTCCGCCAGCGCCCGCAGGACCCGGATG AGAGCGCACGCTTCGGGGTCTCCGGGAAGTCGCGGCGCCTTCGGATG 1144 CCCCGCTGGGGACCTGGGAAAGAGGGAAAGGCTTCCCCGGCCAGCTGCGCGGCGACTCCGGG- GACTCCAGGGCGCCCCTCTGCGGCCGACGCCCGGGGTGCAGCGGCCGCCGGGGCTGGGGCCGGCGG- GAGTCCGCGGGACCCTCCAGAAGAGCGGCCGGCGCCG 1145 CCCGGGGGACCCACTCGAGGCGGACGGGGCCCCCTGCACCCCTCTTCCCTGGCGGGGAGAAAGGCT- GCAGCGGGGCGATTTGCATTTCTATGAAAACCGGACTACAGGGGCAACTCCGCCGCAGGGCAGGCGCG- GCGCCTCAGGGATGGCTTTTGGGCTCTGCCCCTCGCTGCTCCCGGCGTTTGGcgcccgcgccccctccccctgc gcccgcccccgcccccctcccgctcccATTCTCTGCCGG 1146 CCCGCGGAGGGGCACACCAggcgggtgttggggaggacgcagagggctggggctggagcccag- gcggggcagggggcggggcggagctgggtccgaggccggCGGGGGCGCCTCCATCCCACGC- CCTCCTCCCCCGCGCGCCCGCCCGCTCTCGGGTGACTCCGCAACCTGTCGCTCAGGTTCCTCCTCTcccggccc cgccccggcccggccccgccgAGCGTCCCACCCGCCCGCGGGAGACCTGGCGCCCCG 1147 GCCCACGTGCTCGCGCCAACCCCTACGCCCCAGCGCGCCTTCTCCACCCACGCACGGGCCTCG- GACGCATTTCCAGCCCCGGCGTTGGTTGTGGATGCTGGACATCCACCGCCTCCAGGCAGTTTCGCCGTCACAC- CGTCGCCATCTGTAGCCAAAGCAAAACATATCCTAACTGAGACTTTGCAGCTCTTGTGGCCACTCTGGGCTCAC CGGGAACATGAGTGGAAGAGCCCGAGTGAAGGCCAGAGGCATCGC 1148 GGCGGAGCGGCGAGGAGGAGGAGCAGGAGCGCGCAGCCAGCGGGTCCACGCATCT- CAGCACTTCCAGACCAACTCCGGCACCTTCCACACCCCTGCCCGGGCTGGGGGCTCCGAGAGCGGC- CGCGAAGCGACTCCGATCCTCCCTCTGAGCCTTGCTCAGCTCTGCCCCGCGCCTCCCGGGCTCCGGTCCGCGCG GCGGGGTCCCTGCTCCTGCGCCCCGGGCGCGCTTCCCGGACACCCCGGTCCCCGCAGCC 1149 CCTCGCCGGTTCCCGGGTGGCGCGCGTTCGCTGCCTCCTCAGCTCCAGGATGATCGGC- CAGAAGACGCTCTACTCCTTTTTCTCCCCCAGCCCCGCCAGGAAGCGACACGCCCCCAGCCCCGAGCCGGC- CGTCCAGGGGACCGGCGTGGCTGGGGTGCCTGAGGAAAGCGGAGATGCGGCGGTGAGGCGCGGCTTGGGCCG 1150 caggcgcgccgATGGCGTTTCTGAGGTGACGCCGCCCACACCGGGCTTCTCCGGGGGCGGAG- GAAACACCTATGAACCCTCCGGCAGCCTTCCTTGCCGGGCGCCAGGTAAGCAGCGGTTccgggcgcgg 1151 CTCCCGGCTTCTGCATCGAGGGCCTTCCAGGGCCAGCCCTTGGGGGCTCCCAGATGGG- GCGTCCACGTGACCCACTGCCCCCACGCCCGCGCGCGGGCCCCAGCAGCCCCAGAGCTGCGC- CAACTTCGTTCACTCCGCGCTCACCTTACGGGGGTCCCCGCGTGACCGCATGGGGTAGCCCCTGCTCCCACGCT CCCGGCCGA 1152 CGGTCCGCGAGTGGGAGCGGCTGCTTGTGGGCAGGGTGGACGCGGGGCCACGTCTTGGCCG- GCGTTTTGCGGGGTCTTCCTGTTCTGAACGCGCGTAACTTTTGCCTCAGTATCTCACTTCTTGGAATCCGGCG- GCGTTCACGTGTGTGCTCCAGAGAAGGGCGCCAGAGGGTATTCCCTGAAAGTGAAAGGTCGGCGAAAGAGGAGT AAAGACGGCGAGACGCGTCCACGCAGGGGGAGTCTGTGCGGTTTGGA 1153 GCAGCGCCGCCTCCCACCCCGGGCTTGTGCTGAATGGGTTCTGATTGTGCACGGGGTGCACACTGG- GCATTTCTTGGAAGGGGCACACTGacgcgcgcacacacgcccccgacgcgcacgcgccccgcgcgcact- cacactcacccccgcgcacactcacccccgcgcacactcacgcTGCCGCCGCGCTGAGGTGCAGCGCACGGGGC TTCACCTGCAACGTGTCGATTGGACGGATGGGCTCGGCGCGTGGGT 1154 CGACCGTGCTGGCGGCGACTTCACCGCAGTCGGCTCCCAGGGAGAAAGCCTGGCGAGTGAG- GCGCGAAACCGGAGGGGTCGGCGAGGATGCGGGCGAAGGACCGAGCGTGGAGGCCTCATGCCTCCGGGGAAAG- GAAGGGGTGGTGGTGTTTGCGCAGGGGGAGCGAGGGGGAGCCGGACCTAATCCCTCACTCGCCCCCTCC 1155 CCCGGGCTCCGCTCGCCAACCTGTTACTGCTGCAGAACGCCAGGAAGCTCAGCCTG- ATCCCACAGATTAGGGTAAAATATCCCGGGGGGCCGAAGTGGAAACCGGAGTTGCGTCATTGCTCCCAC- CCGATATCACCTTGGCAGCGACCGCGGCTGACCACGTTCCCGGCCTGTCGCGAATCTCACCCAAGGGAGCTGAG TCTCAGCTTCCCTGGTCCCTGGTCCCGAGTTCCGCCTTCCCCCCCCGCCCCGTGGC 1156 CATGGGGTGCTCATCTTCCCGGAGCTGAGGAGCTGGGGCGGGCATGGGGTGCTCATCTTCCTG- GAGCTGAGGAGCTGGGACGGGCATGGGGTGCTCATCCTCCTGGAGCTGAGGATCTGGGGCGGGTGTGGGAT- GCTCATCCTCCTGGAGCTGAGGAGCTGGGGCGGGCATGGGGTGCTCATCTTCCCGGAGCTGAGGAGCTGGGGCG GGCATGGGGTGCTCATCTTCCCAGAGCTGAGGAGCTGGGGCGGGCAT 1157 CCGAGAGCCGGAGCGGGGAGGGCCCGCCAAGTCAGCATTCCAGCCGGTGATTGCAATGGACAC- CGAACTGCTGCGACAACAGAGACGCTACAACTCACCGCGGGTCCTGCTGAGCGACAGCACCCCCTTGGAGC- CCCCGCCCTTGTATCTCATGGAGGATTACGTGGGCAGCCCCGTGGTGGCGAACAGAACATCACGGCGG 1158 CCGCTGCAGGGCGTCTGGGCTTCTGGGGGCAGAGAAGACTCACGCAGTGAGCAGTCCGCAAGC- CCGCTGGCGGCAGCGGCGGTGCTCCGTCCAGGGCGAGAAGCTGCAGCGCTCGGGCCGGGGTCCCTCCT- GTCGCAGCAGCTCCTCGACGAGTGCAGGGGCAGCCACG 1159 gcgctgccccaagctggcttccgctgcctgctctgggctgggctgggctgggctgggctggtag- gacctgctcccagggcgggaggggacacacccacctcagcagatctcagcccatccctcccagctcagt- gcactcacccaaccccacacgggccaaggagagagtgaagaggaagcattgccctcagaggccttcacggactg gccaga 1160 CAGGATGCCAGCGTGACGGAAGCAAGTAACCACCAAGGCATCACCACTGGCGCTAAACTTCT- CACTTCCGGAGTGCTGCAAGCGCAGAAAATATACGTCATGTGCGGAGGCGGAGCTTCCGCCCT- GCGCGTCGTATTAGACGGAAACCGAGCGGGCCCATTTTTCATGGGTTTGCGGACCCACCAGCGAAGGCGGGAGG TGTCGCAGGGACATCTTCTGGCTGTTTCCGTCGCCTGCGTGGCCCTTGCACCCCGG 1161 GGCGGTGCCATCGCGTCCACTTCCCCGGCCGCCCCATTCCAGCTCCGGAGCTCGGCCGCAGAAACGC- CCGCTCCAGAAggcggcccccgccccccggcccAAGGACGTGTGTTGGTCCAGCCCCCCGGTTCCCCGAGAC- CCACGCGGCCGGGCAACCGCTCTGGGTCTCGCGGTCCCTCCCCGCGCCAGGTTCCTGGCCGGGCAGTCCGGGGC CGGCGGGCTCACCTGCGTCGGGAGGAAgcgcggcg 1162 GTGGGTCGCCGCCGGGAGAAGCGTGAGGGGACAGATTTGTGACCGGCGCGGTTTTTGTCAGCT- TACTCCGGCCAAAAAAGAACTGCACCTCTGGAGCGGGTTAGTGGTGGTGGTAGTGGGTTGGGAC- GAGCGCGTCTTCCGCAGTCCCAGTCCAGCGTGGCGGGGGAGCGCCTCACGCCCCGGGTCGCT 1163 GGCGGAGGGCCACGCAGGGGAGACAGAGGGCCTCCACAGGGGCCAGGGGGAAGTGTGGGAACT- GAGTCTCCCCCAGACGAGGCTTCACTTGGACACGTGTATGTGGTCACCGGGGGAAACTGAGCAGTTCT- GACTTCCCTTGGAAGGCGTGGAATTAGGAGAGAAATCCCTTAGTGGGCACACGAGTGAGTGCCCCTTGGAGTCC ATCTGTGGAAAGGAAGCGGTGATAGGTTTCCGCA 1164 GTCCGGGGGCGCCGCTGATTGGCCGATTCAACAGACGCGGGTGGGCAGCTCAGCCGCATCGCTAAGC- CCGGCCGCCTCCCAGGCTGGAATCCCTCGACACTTGGTCCTTcccgccccgcccttccgtgccctgc- ccttccctgcccttccccgccctgccccgcccggcccggcccggccctgcccaaccctgccccgccctgcc 1165 CGGCCTGCGGCTCGGTTCCCGCCTCTTCCCCACCCCCAGCCCCGCGCTGCCCTCTCGGTCCCCCT- GCGCGACCCCAGGCTCGGCCCCTGCCCGGCCTGCCGGGGTGGCCCGGGGGTGGGGTGGGAGCCCTTTGTCT- GCGTGGGTCGCCTCGCGTCTCTCTCTCCCACCCCACCTCTGAGATTTCTTGCCAGCACCTGGAGCCCGAAACCA GAAGAGTTGTCAGCCCAACAAGAATATAGGATCACCGGCCCATCA 1166 GGGAACCGTGGCGGCCCCTCCTGGCCCTGGGAGGTGGTCCCGCTGCCCCCCTGACTTCCGTGCACT- GAGCCCCTGGCCCTGCCCGCAGCCCCGGCCCTGGACTCGGCGGCCGCGGAGGACCTGTCGGACGCGCTGTGC- GAGTTTGACGCGGTGCTGGCCGACTTCGCGTCGCCCTTCCACGAGCGCCACTTCCACTACGAGGAGCACCTGGA GCGCATGAAGCGGCGCAGCAGCGCCAGTGTCAGCGACAGCAGC 1167 cggggaaggcggggaaggcggggaaggcggggaaggcggggaaggcggggaaggcggggatggT- GAGACggtgaggcggggcggggcctggggcgcgggcggggcggggaggggtggggcggggcCCGGGGGCGCTG- GACCGCGGTGCTGCGGGACGGATTCCCGGCGGCTGCGCGGGAGGCTGCGAGCCTGGGCTCCCAGGGAGTTCGAC TGGCAGAGGCGGGTGCAGGGAACCCGCGGCTCGGCGGGAGCGTG 1168 cctcccggtttcaggccattctcctgcctcagcctcccaagtagctgggactacaggcgcctgccac- cactcccggctaattttttgtatttttagtagagacgggggtttcaccgtgttagccaggatggtctcgatct- gcttacctcgtgatccgcccgcctcggcctcccaaagtgctgggattacaggcgtgagccaccgcgtccggcAT ATTT 1169 AGCCCGCGCACCGACCAGCGCCCCAGTTCCCCACAGACGCCGGCGGGCCCGGGAGCCTCGCGGACGT- GACGCCGCGGGCGGAAGTGACGTTTTCCCGCGGTTGGACGCGGCGCTCAGTTGCCGGGCGGGGGAGG- GCGCGTCCGGTTTTTCTCAGGGGACGTTGAAATTATTTTTGTAACGGGAGTCGGGAGAGGACGGGGCGTGCCCC GACGTGCGCGCGCGTCGTCCTCCCCGGCGCTCCTCCACAGCTCGCTG 1170 CCCCAGCCACACCAGACGTGGGAGCTTAGGATGAGAGCGGCCTCCGAGCAGATGATCACCCTG- GAACGACGCCAAACGCGACCCCTACCAGAGGACTCGCGCATGCGCAGCGCAGCCTGGGCCGGCGGCCTGG- GCAGGATGTAGTCGCGAGCAGCGCACCGGGCCCACGCCAGCGGAATTGCGCATGCGCAGGGCCGCCTCTGCCTG CGGCCTGGGCTGGG 1171 tgggcttcctgccccatggttccctctgttcccaaagggtttctgcagtttcacggagcttttca- cattccactcggtttttttttttttgagactcgctctgtcgcccaggctggaatgcagtggcgcgatctcg- gctcactgcaagctccgcctcccgggttcacgccattctgcttcagcctcccaagtagctgggattataggcgc ccgccaccacgcccggctaatggctaattttttgtattttttttt 1172 CCGCGCTGGGCCGCAGCTTTCCGGAGCGCAGAGGAAGCTGGCCAGCCTGCAGATAGCACTGG- GAAAGACACCGCGGAACTCCCGCGAGCGGAGACCCGCCAAGGCCCCTCCAGGGACCTGTCTTCCTAACTGC- CAGGGACGCCGAGCCAACTC 1173 gcatggcccggtggcctgcactccagtgaggtggctgaactctgaccagccaagagaaaac- ccccctctccgccccaaacagctccccactcccccagcctgcccccaccctccccacattccagtctttcact- gtcgccccaggcaacttggctgcccaagaccaagccccaccaagaagctggagggccaggcaagtccaggatgg gcaagcagggaagcacgagagggagaaacagaggtgaggaaggaagg 1174 GGGCAGGGGAGGGGAGTGCTTGAGTATTGGGGCTACACTCACCACAAGAGCAGCAAACAAAGCACT- GGGTGTGGTAGAGGCTGTCCAGGGCCTGGCAGGCATTGCTCTGCCCATAGATGCCTTTGTTGCACT- TGATACAGGTGCCTGAGAAGAGAAAAGTGTCACACTCTACTCCCCCAGGTCAAAACCAGG- GATTCCCAAGCTTTCCTGACTGCCCTTTCCTGATGTGCCAGGGGTCA 1175 CCCCGGCGCCTTCCTCCTCCGGACTCCGCTGCATGCCTCGCTTGCGGTGGTCCGATCG- GCTTTCTCCGGGAGCTTTCCTCTCCCCGCCACGCCCCCGTCTCCCCGGCCGTCCCCGCGCCTCTCG- GCCTCCCTTTCATTAGCCCCACATCTGTCTTTCCCATGGGAGGGAGCGCGCGCCTTCCGCCCAGCGGGGCCCTT AGCAGAGCCTCTCCAATCCTCGGCGCCTCCCCTACACAGGGTTCGCTGGGCCGTTCT 1176 CCACCGCGCTTCCCGGCTATGCGAAAGTGAAAACGAGGGGCGCCCAAGGCCCT- GCTTCTTCCCCCTTCCTCTTCCCCTTGCCCAGCCGCGACTTCTTCCTCACTGATCTCCCGGGGGCG- GAGACGCTGAGTTCCCCGGAGACGAGTTAGTCACCAAGAAGAGGCGGTGACAGAGAGCGCGGCTCGCGTCGCAC TCCGAGGCC 1177 CCGCATCTGACCGCAGGACCCCAGCGCTACCAAGTGCCTGTTCTTGGACCCCCAGCCGAGCAGGGG- GAAGCATCCCCAGCTCCCGCACCCAAGTCCCTGGCGCCGCTGCCGGGCCGCCCTCCCTGATGC- CCAGCGCGCAGCCTGCCGGCGCCGCGCCTTCTGGACGGCTCTCGCCGCACCTCCTGAGCTCAGCCCGCGGCCCC GCAGTGGGGCGGCCTCACTTACTGGCGGGGAAGCGCGGGTCTGGGTTGGCGC 1178 GCGGACACGTGCTTTTCCCGCATTAGGGGGGGTCTcccggcgcgcgccccgccgccACCTGTTGAG- GAAAGCGAGCGCACCTCCTGCAGCTCAGGCTCCGGGCGCCAGCCCTGCCCCGCAGCCCCAGAGC- CCGTCGCAGCTCGGGTGGTCCCTCCCCGGCCCAGCGCTCGCCGCCTGCTCTTCGCCCTGCAAGTTTCAAGAGGC AGTTATTTCTCGCAGCCTCCGCGCTTGCA 1179 GAGCTGGAAGAGTTTGTGAGGGCGGTCCCGGGAGCGGATTGGGTCTGGGAGTTCCCAGAGGCG- GCTATAAGAACCGGGAACTGGGCGCGGGGAGCTGAGTTGCTGGTAGTGCCCGTGGTGCTTGGTTCGAGGTGGC- CGTTAGTTGACTCCGCGGAGTTCATCTCCCTGGTTTTCCCGTCCTAACGTCGCTCGCCTTTCAGTCAGGATGTC TGCCCGTGGCCCGGCT 1180 GGCCGCCAACGACGCCAGAGCCGGAAATGACGACAACGGTGAGGGTTCTCGGGCGGGGCCTGG- GACAGGCAGCTCCGGGGTCCGCGGTTTCACATCGGAAACAAAACAGCGGCTGGTCTGGAAGGAACCTGAGC- TACGAGCCGCGGCGGCAGCGGGGCGGCGGGGAAGCGTATGTGCGTGATGGGGAGTCCGGGCAAGCCAGGAAGGC ACCGCGGACATGGGCGGCCGCGGGCAGGGCCCGGCCCTTTGTGGCCG 1181 GCGCCCGGTCAGCCCGCAGCGCCCGGCCAGCCCGCAGCGCCGGAGCCCGCAGTGCGTGCGAGGG- GCTCTCGGCAGGTCCAGACGCCTCGCCGAGCCCAGCCCGCAGCTccccgggccgcgccgcgcccgcccACAGG- GCCCACAGCCCTGCTTCGGCTCTCAGGGCGGTCACCTGGGATGGGG 1182 CCCGCCAGGCCCAGCCCCTCCCTGGCCAGCCCCGTCCTTGTCCCCAAACTgggcccgcccggccgc- caggccgccgggcctccggggcccTCGCGCATCCGGCTCCGAAAGCTGCGCGCAGCCATCATCAGGGC- CCTTCTGGTGTTAGAAGAGACCCCGGCATCATCTTTTCGTCGCGTGCTTCCCCCAGAGTCA 1183 CGATTCTTCCCAGCAGATGGCCCCAAAGTTCAGTTCCTGAATTGCCTCGCGGAGCCGCGGGCT- GCAACGTGAGGCGGCCGCTGCCAGTCGACTCAACCACCGGAGTGGCCCCTGCAGTTGGATAGCAAC- GAGAATCCTCCAGGGGTGCAGGGCGACGGCTTCGGCCGCACC 1184 CGCACACCGCCCCCAAGCGGCCGGCCGAGGGAGCGCCGCGGCAGCGGGAGAGGCGTCTCTGTGGGC- CCCCTGGCAGCCGCGGCAGGAAAGGGCCCGAAGGCAGCGAAGGCGAACGCGGCGCACCAACCTGCCGGC- CCCGCCGACGCCGCGCTCACCTCCCTCCGGGGCGGGCGTGGGGCCAGCTCAGGACAGGCGCTCGGGGGACGCGT GTCCTCACCCCACGGGGACGGTGGAGGAGAGTCAGCGAGGGCCCGA 1185 AGGCCCCGAGGCCGGAGCGGCGGAGGGGGCGGCCCCTCCCACAGGGTCTTCCCACCCACAGGGCAC- CCAGGCGCAGCGGAGCCAGGAGGGGGCTTACCCGCGGGCAGGGACGGAGCACGCCGGGGCCCTGGAGGG- GCGACGCTCGCTCGTGTCCCCGGTCCCCGTGGCC 1186 GGGTTCGCGCGAGCGCTTTGTGCTCATGGACCAGCCGCACAACTTTTGAAGGCTCGCCGGCCCATGT- GGGGTCTTTCTGGCGGCGCGCCGCCTGCAGCCCCCCTAAAGCGCGGGGGCTGGAGTTGTTGAGCAGCCCCGC- CGCTGTGGTCCATGTAGCCGCTGGCCGCGCGCGGACTGCGGCTCGGCGTGCGCGTGTTCCCGGCCGTCCCGCCT CGGCGAGCTCCCTCATGTTGTCGCCCTGCGGCGCCC 1187 CCAGTCTCCCGCCCCCTGAGCATGCACGCACTTTGGTTGCAGTGCAATGCTCTGACTTCCAAATGG- GAGAGACAAGTGGCGGAAAATAGGGTCTTCTCCCACCTCCCACCCCCCCATCCCGACTCTTTTGC- CCTTCTTTTGGTCCAAGAGATTTTGAAACCGTGCAGAACGAGGGAGAGGGGCAGGCTGCAGCCGGGCAGATAAC AAAACACACCCCAAAGTGGGCCTCGCATCGGCCCTCGCATTCCTGTAGAG 1188 GAGGAGGCAGCGGACCGGGGACACCCTGGGGGAACTTCCCGAGCTCCGCGACCTCGAAGCCTGGC- CCTTCCTTCTCCCTGGTCCTACATGCCTCCCTCCCCCACTGTCCGGGGTCCTGGCCTCGACGCCGAGGGGT- GTCCCTCTCCTCTCCTGGTCAGGGAACGCAGCAACTGAGGCGGCGCGGCCCAGATGAGACGGGAAGCGCCTGCG GGCCGTGGGCGCGGGTGGAACCC 1189 CCGGCTCCACGGACCCACGGAAGGGCAAGGGGGCGGCCTCGGGGCGGCGGGACAGTTGTCGGAGG- GCGCCCTCCAGGCCCAAGCCGCCTTCTCCGGCCCCCGCCATGGCCCGGGGCGGCAGTCAGAGCTG- GAGCTCCGGGGAATCAGACGGGCAGCCAAAGGAGCAGACGCCCGAGAAGCCCAGGTGAGCGGCTGGGCCGCGCC GGACGGGCGTCGGGGGTCTGGGCCGCGA 1190 CCGCCACCGCCACCATGCCCAACTTCGCCGGCACCTGGAAGATGCGCAGCAGCGAGAATTTCGAC- GAGCTGCTCAAGGCACTGGGTAAGCTGGTGCAGAGGGCGCGCCCCGACGGGGAGATGCGGCCCGGAGGTGC- CCTGGTCCCGGAAGTGCCCCGGTCCTGGAGGGGGTGGAAGTTGGGGAGCCCAGGCAGGAGGGAGTCCCCGGGGC AATAGATCGCCTTGTCTCCCAGGCGCACCGGGTCTCG 1191 TGAGTAAGGATGATACCGAGAGGGAAGAAAAAAATACCCTCTTTGggccaggcacggtggctcac- ccctgtaatcccagcactttgggaggctgaggcgagcggatcacgagatcagaagatcgagaccatcctg- gctaacacagtgaaaccccatctctaccaaaaatacaaaaaattagccaggcatggtggcgggcacct 1192 tgggccaggcacggtggctcacccctgtaatcccagcactttgggaggctgaggcgagcggatcac- gagatcagaagatcgagaccatcctggctaacacagtgaaaccccatctctaccaaaaatacaaaaaattagc- caggcatggtggcgggcacctgtagtcccagctacttgggaggctgaggcaggagaatcctttgaacccaggag gcggagcttgcagtgagctgagattgtgccactgcactccag 1193 CCGGCGAAGTGGGCGGCTCCCCAAGCGCCCAGGCTGCGCAGCACGATggccgcccccgccgcgcac- cgcgtgtgcccgcacgcccgccccctgcgccccggggacgcctctccgcccctccccctgcccctccgcccac- cgcgcggtcgccccacgccgcgggcgctgcttcgccgcccgggaggccgcctcccgccccgggACCGGATAACG CCCTAAATCAGCGCAGCTGAGGCGAGGCCGTGGCCCCCGCAG 1194 GCGGCCTTACCCTGCCGCGAGCGCCTGTGACAGCGGCGCCGCTGTGCTCGCGACCCCGGCTCCGG- GCCTCTGCCGACCTCAGGGGCAGGAAAGAGTCGCCCGGCGGGATGGGCGGGGAGGCTGGGTGCGCGGCGGC- CGTGGGTGCCGAGGGCCGCGTGAAGAGCCTGGGTCTGGTGTTCGAGGACGAGCGCAAGGGCTGCTATTCCAGCG GCGAGACAGTGGCCGGGCACGTGCTGCTGGAGGCGTCCGAGCCGG 1195 gtggggccggcgAGGGTCAGGGGCATCGCGGCCGCGACCCCATTCTGCAGCCCCCGAGGCTCGC- CCGACTCCTGGCTGCCCTGGACTCCCCTCCCTCCTCCCTCCCGCCTCCTCGCCCAGGGCCCGGCTCACCTg- gcggcggggcgcgggacgccgcgggcgggacggcggggggctccggggcgctccggggcggcTCTCGCGCATGC TCCGGGGC 1196 CGGCGCGGACCGGCTCCTCTACCACTTTCTCCAGCTGCACTGCCACCCAGCCTGCCTGGTGCTGGT- GCTCAACACGCAGCCGGCCGAGGAGGTGCGGCCGCGCTGGCGCGGGAGTGAGGGGACTCCGAGAGTGTTGAGG- GCCTCCTGAGCGGATGCGAGGCCTCTGACAGGGATGGAGGGGCTCTGAGGGGGATTCAGGCCCCTGACACTACG CGATGACACAGAGAAGGATGGCAGGGGTCCCCAGGGG 1197 GCCCATGCGGCCCCGTCACGTGATGCAAGGATCGCCGGCCTTTCCGCCAGAGGGCGGCACAGAAC- TACAACTCCCAGCAAGCTCCCAAGGCGGCCCTCCGCGCAATGCCGCTACCGGAAGTGCGGGTCGCGCTTCCG- GCGGCGTCCCGGGGCCAGGGGGGTGCGCCTTTCTCCGCGTcggggcggcccggagcgcggtggcgcggcgcggg gTAA 1198 GGGATTGCCAGGGGCTGACCGGAGTGTTGCTGGGAAGGAGCCTCAGCTCCGCTCCAGGTCCTCCAC- CAGGTAGGACTGGGACTCCCTTAGGGCCTGGAGGAGCAAGTCCTTGCAGGTCCAGTTCCAGGCTGGTGT- GAAACTGAAGAGCTTCCGCATCTTGCTTGGGTTGGTGGGCTCGGCCCGC 1199 GCCGGAGCACGCGGCTACTCAGGCCGAACCCCGACCCGGACCCGGCACGCGGCCTCGGCGAGGGCGG- GCGGGAGTGTCCTCCTCCGGGACAGCCGGACTCCCGCCGACTTCTGGGCGGCGGGGAGGGCTCCAGGCCCG- GCTCTCCCGGGCCCCCGCACGCGATGCGCGGCCCCTGCAGCTGCTCCGTGCCCCGAGACGCGCCCGAGGCCTCG GACCTCCAAGCGGCCACCGCGC 1200 CCTCGGCGCCGGCCCGTTAGTTgcccgggcccgagccggccgggcccgcgggTTGCCGAGCCCGCT- GACGTCAGCCCGGGTTTCCCCCCCCCACCGGGGCTTCCCCATCCCCCGAGGCTTCCCGGGAGGGCTGC- GAGTCCGGGGAGCGTGCGGGGTCGCCACCATCGGGACCCCCAGAGGAGAGAGGACTTGGGGCGGGAGCCGCGCG GGACGCTGTCCCCCTCCCGCCCCCCAccccatttacagattgggaga 1201 CACAGCGGCGGCGAGTGGGTCGTGCACGCGGATGCGGGGTGGGAGTGGGGGCGCACGCGCGGGCGT- GGGCGAGCGGGCCCCGGCAGTGCACACACACGGCAGGGGCGGGCGACAGATGCAGTGCGTGCGCCGGAGC- CCAAGCGCACAAACGGAAAGAGCGGGCGCGGTGCGCAGGGGCGGGCGCCCAGCGGGCTTGGCATGCGCG 1202 CACCTCGGGCGGGGCGGACTCGGCTGGGCGGACTCAGCGGGGCGGGCGCAGGCGCAGGGCGG- GTCCTTTGCGTCCGGCCCTCTTTCCCCTGACCATAAAAGCAGCCGCTGGCTGCTGGGCCCTAC- CAAGCCTTCCACGTGCGCCTTATAGCCTCTCAACTTCTTGCTTGGGATCTCCAACCTCACCGCGGCTCGAAATG GACCCCAACTGCTCCTGCGCC 1203 AGACGGGGCCGGGCGCAGACGCCCCGCCCCGCCCTTGCACCCAGCCCGCTGAGTCCGCACCGC- CCGCGGTCCCGGCCTGGGCTGTGCGCAGGAGATGGGCCAAGTGCAAGGTCCCTTGAGCGCAGCTGG- GCGCACACCGCAGGACGGCCCCTTTCGCACCGGCTCGCGAGGGAGGCGCTGTGCCCCCCGTGTGCGGCTTCTCT CACCCTGCCAGGCCTTCCCAGCTTCCCTGAGGTTGCCTGCTACACCCGCCCC 1204 GCATTCGGGCCGCAAGCTCCGCGCCCCAGCCCTGCGCCCCTTCCTCTCCCGTCGTCAC- CGCTTCCCTTCTTCCAAGAAAGTTCGGGTCCTGAGGAGCGGAGCGGCCTGGAAGCCTCGCGCGCTCCGGAC- CCCCCAGTGATGGGAGTGGGGGGTGGGTGGTGAGGGGCGAGCGCGGCTTTCCTGCCCCCTCCAGCGCAGACCGA GGCGGGGGCGTCTGGCCGCGGAGTCCGCGGGGTGGGCTCGCGCGGGCGGTGG 1205 GCCCGAAAGGGCCGGAGCGTGTCCCCCGCCAGGGCGCAGGCCCCAGCCCCCCGCACCCCTAT- TGTCCAGCCAGCTGGAGCTCCGGCCAGATCCCGGGCTGCCGCCTCTGCTGCCTTCCCTGAGCGGGAGCG- GAGCGCAGAGAAAAGTTCAAGCCTTGCCCACCCGGGCTGC 1206 CGGCGGCCGGGTGACCGACCACTGCTTACCAGGAGGGGAGACTGGCAGGGGGGGCTCAAGGAA- CATCTGGTGGGTGTCCCCTTCACAAGACTCGGCCTGCAGAGTTCGTGCAGGGAGTTCGCACATAGGAGAGCAC- CGGTCCGGGAGTGCCAGGCTCGTGCCCGGCCGGGGAGAGGAGTGGGAGACTAAGTCGCAGGGCAAGGGCAACT- GCA 1207 CCACCGGCGGCCGCTCACCTCCTGCTCCTTCTCCTGGTCCGGGCGGGCCGGCCTGG- GCTCCCACTCCAGAGGGCAGCCGGTCCTTCGCCGGTGCCCAGGCCGCAGGGCTGATGCCCCCGCTCAGCT- GAGGGAAGGGGAAGTGGAGGGGAGAAGTGCCGGGCTGGGGCCAGGCGGCCAGGGCGCCGCACGGCTCTCACCCG GCCGGTGTGTGTCCCCGCAGGAGAGTGTGCTGGGCAGACGATGCTGGACACGATG 1208 CGGTCAGGGACCCCCTTCCCCCTTCAAGCTGACTCCCTCCCACAAGGCTCTTCAGATCTCGT- TGTATTTTGGGATTGATGGGGGAAAAATCCAAATTTGTTTGTTTGCTTCCCTTTTTTCGGTGGTGGGGAAAG- GTGGCAGGCTTTTTGGGACAACCATGGAGGGGTCCTCCGTCTCGGCCTCTTCGCATATCCCCCTCCGTGATCCT GCCTTCCCCCCCCACCGAGCCCATCGCAGGC 1209 GGCCGAAGCTGCCGCCCCTCCTCCCAACCGGCGGGTCAGATCTCGCTCCCTTTCGGACAACT- TACCTCggagaggagtcaaggggagaggggaggggagggggggagggggcaagagagagaggggggagaa- gaggGATCTTCTCGCTTATTTCATTGTTCCCCCATCTTCAGGGAGCGGGGGCAGCGGCTCCTCAAGGCGGCGGG CGCCGGCGTCTTCAGAGCGCCATGCGAACCGCGG 1210 GCGGCCTTGTGCCGCTGGGGGCTCCTCCTCGCCCTCTTGCCCCCCGGAGCCGCGAGCACCCAAGGT- GGGTCTGGTGTGGGGAGGGGACGGAGCAGCGGCGGGACCCTGCCCTGTGGATGCCCCGCCGAGGTCCCGCGGC- CGGCGGGGCCAGAGGGGCCCGGACGAGCTCTCCTATCCCGAAGTTGTGGACAGTCGAGACGCTCAGGGCAGCCG GGCCCTGGGGCCCTCGGGCGGGAGGGGGCAGTTACACGGCAG 1211 CGCGGGAGGAGCGGCGAGGCCCTCACCTGGCGCCTTTTATGCCCGCGGCCGGTGGAGGGGGGAAGG- GAGGAATGGTGTCAGGGGCGGATATCTGAGCCCTGAGGAATTTGCAGGCTCCTGAGAGCAAATATGG- GCTCTCTCCCCATTGGTCAATTCCCTCCCCTCCCAGAGACCAGAGGCCCCTGCCCTCCAGAGGTGCCCCGCCCC GGTCCGCGCAGAAGCTCCGACCCGCACTCCCCCA 1212 GCCCACCAGAAGCccatcaccaccagcaaagccaccaccaaagccaccacccaagccagcaccaag- gccaccaccatatcctcccccaaagccactaccaAAGCTGCTGCTGCTGCTGCTGAAGCCACCGCCATAGC- CGCCCCCCAGCCCGCAGGCTCCCCCAGAGGAGAAGCGGGAGGATGAGACAGACAGGCCGCCCCCGTAGGTGCTG GGGGCGCGGCAG 1213 GGGCCATGTGCCCCACCCCACAGCCCCACCCTGCCCTGCCCACCACCCCAAGCCCGGCCCTGG- GTCCCAGGGTCCCGCCAGGCCCGCTGGGTGGAATGTGGTCATGTTTCAGACTGCCGATG- GCTTCCACTTCCCAGACAGGCCCAGACGGCCCCGCCAGCAGCC 1214 CCGCCAGCCCAGGGCGAGAGTCAGGGACGCGGCGTCGGGCGAGCTGCGCGGGCCCCGGGGGAG- GCGCGACCCCGGAGGCACCTGTCCGGATCCCTCCCCGCCTTGCTCAGATCTCTGGTTCGCGGAGCTCCGAG- GCGCGCTCGGCCCGAACCGCGCGACCCCCAAGTCGCCGCGCCC 1215 gccccctgtccctttcccgggactctactacctttacccagagcagagggtgaaggcctcct- gagcgcaggggcccagttatctgagaaaccccacagcctgtcccccgtccaggaagtctcagcgagctcacgc- cgcgcagtcgcagttt 1216 GTGGGGGTCCGCACCCAGCAATAACCCGGGTCTTCCCGCTCCGGCTCCTGCCCCAGTAAGCGTTG- GACCGGGAGACGCAGTGCTCAGCATCGGTCAGCAGGGGGCGCAAGGACCCCGCCCCGCCGAGTCCGCGC- CAAAGTTTCTCATCCTCCACCCGCCCACGCTCCGCACCCCCTCCGCGGCTGCCCAGCACCCCCACGGCCCCAGC A 1217 gggcccccgggTTGCGTGAGGACACCTCCTCTGAGGGGCGCCGCTTGCCCCTCTCCGGATCGC- CCGGGGCCCCGGCTGGCCAGAGGATGGACGAGGAGGAGGATGGAGCGGGCGCCGAGGAGTCGGGACAGCCCCG- GAGCTTCATGCGGCTCAACGACCTGTcgggggccgggggccggccggggccggggTCAGCAGAAAAGGAC- CCGGGCAGCGCGGA 1218 GCCTGCACAGACGACAGCACCCCCGGCGGGGGAGAGCGGCCCCAGCGGAGACTCGGCAGGGCTCAG- GTTTCCTGGACCGGATGACTGACCTGAgcccggggcccgggcggcgctggccgggcACAGGATGCGCGGCCCG- GAGAGCGCATCCCGGCCATCCGCCCGCGCTCGGCCCCGCAGCGCAGCTGCTGCAGATCCGCGGGGGCCGCCAC 1219 GGCCGCGCCGGGCTCAGGTTCCACCCCCGGGAGCGCGGGGCGGAGCCAGGCCGGCGCCGAGGCT- CAGTGCCCTCCCCGCTCCGCGGCGCCGGCTGCGAAGTTGAGCGAAAAGTTTGAGGCCGGAGGGAGCGAGGC- CGGGGAGTCCGCTCCAGCGGGGCGCTCCAGTCCCTCAGACGTGGGCTGAGCTTGGGACGAGCTGCGTTCCGCCC CAGGCCACTGTAGGGAACGGCGGTGGCGCCTCCCC 1220 GGGGTAGTCGCGCAGGTGTCGGGCGCGGAGCCGCTTGGCCTCCTCCACGAAGGGC- CGCTTCTCGTCCTCGTCCAGCAGCTTCCACTGCGCGCCCAGGCGCTTGGAGATCTCGGAGTTGTGCATCT- TGGGGTTCTGCTGCGCCATCTGGCGGCGCTGAGCGGAGCTCCACACCATGAACGCGTTCATCGGCCGCTTCACC TTCTCCAGGGGCAGCGTCCCGGGGGCCGCGGGGCTCCCAGCGCCCTCCCGCTCC 1221 tgcaggcggagaatagcagcctccctctgccaagtaagaggaaccggcctaaagga- cattttctctctctctcctcccctctcatcgggtgaatagtgagctgctccggcaaaaagaaaccggaaat- gctgctgcaagaggcagaaatgtaaatgtggagccaaacaataacagggctgccgggcctctcagattgcgacg gtcctcctcggcctggcgggcaaacccctggtttagcacttctcacttccacga 1222 ccggaaatgctgctgcaagaggcagaaatgtaaatgtggagccaaacaataacagggctgccgg- gcctctcagattgcgacggtcctcctcggcctggcgggcaaacccctggtttagcacttct- cacttccacgactgacagccttcaattggattttctcc 1223 gcgtcggatccctgagaacttcgaagccatcctggctgaggctaatctccgctgtgcttcctct- gcagtatgaagactttggagactcaaccgttagctccggactgctgtccttcagaccaggacccagctccagc- ccatccttctccccacgcttccccgatgaataaaaatgcggactctgaactgatgccaccgcctcccgaaaggg gggatccgccccggttgtcccc 1224 CCGGCTCCGCGGGTTCCGTGGGTCGCCCGCGAAATCTGATCCGGGATGCGGCGGCCCAATCGGAAG- GTGGACCGAAATCCCGCGACAGCAAGAGGCCCGTAGCGACCCGCGGTGCTAAGGAACACAGT- GCTTTCAAAAGAATTGGCGTCCGCTGTTCGCCTCTCCTCCCGGG 1225 CGTCGCCGGGGCTGGACGTTCGCAGCGGCGCTTCGGAAGGGGGCCCCGCGGGAGCAGCCGC- CCGCGTCTCCAGCAGCTTCCCCTTGCCAGGCGCCGCGCGCGCCCGGTATCCCCGGGTGTCCACCTGTGCGT- GGGGGGCTGTTTCCCGTCTGTCCAGCCGCGCCCACTTCTCAGGCCCAAAGGCCAGCAGGAAGGGTCCCGGAGGT GGCTGGGGGCGTCCACCTGAGAAGCTCCGCTCTCGCTCAGACACCCCAC 1226 GGGCCTGCCGCCTCGTCCACCGTCCGTCGTGAGGCCGGCAGCGGACACGTGCTCATCCCACGGGGAG- GCCCCGCGCAGCGCGGAGGACGCGCCTGAGAGAGAAAAGGGGTTCGGGAGAAGCCCGAGGACCCGGCCCGT- GACTGGGCGCGCCCTATGCAAATGAGCGGGCGGGGCCCTCGTGTTGCTGAACGAGGGCGGGTTCGCGATGTAAA TAAGCCCAGAGGTGGGGTCTTTGGAGAGCACTTAGGGCCCGGG 1227 GCACACCGCTGGCGGACACCCCAGTAACAAGTGAGAGCGCTCCAC- CCCGCAGTCCCCCCCGCCTCTCCTCCCTGGGTCCCCTCGGCTCTCGGAAGAAAAAC- CAACAGCATCTCCAGCTCTCGCGCGGAATTGTCTCTTCAACTTTACCCAACCGACGACAAGGAACCAGCCTC 1228 GCAAACCATCTTCCCCGACGCCTTCCACATAAGATGCCCTCCTGCGGGCCCTCACCTTTTGACACT- GCCTCCCACCGCACTGGGGTCAACTCTCACCCAAGGGTTCCGCCACCTTCCACCACCAAACCAGCCTGTCCCT- GCCACATGCCCCCCGGGCCCCAGCGCTCATCCTCTGCCCAGGCCCGCTCTTGACCCCTGACCCCGGCCTGAC- CCCGC 1229 GGCCCTCCGCCGCCTCCAACCGCGCACCAGGAGCTGGGCAcggcggcagcggcggcagcggcg- gcgTCGCGCTCGGCCATGGTCACCAGCATGGCCTCGATCCTGGACGGCGGCGACTACCGGC- CCGAGCTCTCCATCCCGCTGCACCACGCCATGAGCATGTCCTGCGACTCGTCTCCGCCTGGCATGGGCATGAGC AACACCTACACCACGCTGACACCGCTCCAGCCGCTGCC 1230 CACCACCGTGGCAAAGCGTCCCCGCGCGGTGAAGGGCGTCAGGTGCAGCTGGCTGGACATCTCG- GCGAAGTCGCGGCGGTAGCGGCGGGAGAAGTCGTCGCCGGCCTGGCGGAGGGTCAGGTGGACCACAGGTG- GCACCGGGCTGAGCGCAggccccgcggcggcgccgggggcagccggggTCTGCAGCGGCGAGGTCCTGGCGACC GGGTCCCGGGATGCGGCTGGATGGGGCGTGTGCCCGGGC 1231 CACAGCCCCTTCCTGCCCGAACATGTTGGAGGCCTTTTGGAAGCTGT- GCAGACAACAGTAACTTCAGCCTGAATCATTTCTTTCAATTGTGGACAAGCTGCCAAGAGGCTTGAGTAGGA- GAGGAGTGCCGCcgaggcggggcggggcggggcgtggagctgggctggcagtgggcgtggcggtgc 1232 GCTTGATGCTCACCACTGTTCTTGCTGCTCAAGGGAAACCAAGTATATATTTGTGGATAG- ATCCTAACTCAGATGATACTGTCAGAATATATAAGATTCCTATACCACATCCTGAACTCTGAAAGT- TGCAGTTCTACGTAGAAGTTCACTGAGGGTTGTAAGAGTCAGAATGGACTCCATGGAAGTTATGGGGTGTGAAT CAAACCTCACAGGTGAGTCAGTGGGGAGAAAGAAGCATGACA 1233 ggccaggcccggtggctcacacctgtaatcccagcactttgggaggccgaggtgggcggattgcct- gaggtcaggagtttgagaccagcctggccaacatggtgaaaccccgtctctactaaaaataccaaaaattagc- cagtcgtagtggtgggcacctgtaatcccagctattcaggaggctgaggcaggaggatcacttgaacccaagag gcgggagttgcagtgagcagagatcacgccattgcaccccag 1234 GCGGGACGGGTGGCGGGAAGGAGGGAGGCGCGGCTGGGGAGAGCGCTCGGGAGCTGCCGGGCGCT- GCGGaccccgtttagtcctaacctcaatcctgcgagggaggggacgcatcgtcctcctcgccttacagacgc- cgaaacggagggtcccattagggacgtgactggcgcgggcaacacacacagcagcgacagccgggaGGTAAGCC GCGTCCCAGCGGCTCCGCGGCCGGGCTCGCAGTCGCCCCAGTGA 1235 GCTTGGCCCCGCCACCCAGACCCCTCCCCCGGGGGCGCCCAGCTTGGCCTCTGGGTCCCG- GCGCACGCGGACCCCAAGTCGGGGAGGCCGGGCTGACCGCGGCCGCCTCCCCGGCTCCGGGTAGGAGGTGG- GCAGAGAAGGTGGGCTGAGGGGAGGAGAAACTGGGCTGCGGGGGTCCGGGAGGGTGGATTCCGAGAAACTATGT GCCCAGCTGACCCTGCCCGCCCCGCCGCGGCCCTGCAGTCCCCGGGCCAG 1236 GCGGGGAAGGCGACCGCAGCCCACCTACCGCTGGACGCGGGTTGGGGACCCCGCCGCCCGGC- CAGCTTTGTTcgggggcccgcggcccctcccgggcccccgcACCGCCTCGGGTGACCCGCGGT- GTCCCAGCGCGTTGACGCAGCCTGTGATCCCTCGCGAGGCGAGGAGAAGGTCGGGGGCTTGGCTCTGCCTAATG GCCGCCCGGGGA 1237 gcgcccaaccaccacgcccgcctaatttttgtatttttagtagagacgggttttcaccattttggc- caggctggtctcgaaccccgacctcaggtgatctgcccaaaagtgctgggattacaggcgtcagccaccgcgc- ccggccGGGACCCTCTCTTCTAACTCGGAGCTGGGTGTGGGGACCTCCAGTCCTAAAACAAGGGATCACTCCCA CCCCCGCC 1238 AAAAGCCCCGGCCGGCCTCCCCAGGGTCCCCGAGGACGAAGTTGACCCTGACCGGGC- CGTCTCCCAGTTCTGAGGCCCGGGTCCCACTGGAACTCGCGTCTGAGCCGCCGTCCCGGACCCCCGGTGC- CCGCCGGTCCGCAGACCCTGCACCGGGCTTGGACTCGCAGCCGGGACTGACG 1239 CGCAGGTGCGGGGGAGCGTGCGGCCGGGTCCATGCGCCTGCGGGCGGCGGGGGGAGACGCGT- TGCCTTCGGCCGGGACCACTGCACCTGCCCGCGTGGGTAATGCGCCCGC- CGCAGACTCCGCGCACGACTCCGCCTGGGAGCGCGTTGGGGGCCGTTGGAGTCCAGCATGGCGCGGACCCCGG 1240 CCCGCCCACAGCGCGGAGTTTAGTCTGCGCGTGCCTCGCTCGAGAACGCGCTCGTGCGCATGC- CCACAAAGGCCAAGGAGGGAGTGCGCAGGTCACGTGCGCCGGTGGTCAGCGCGCGCATTGCCTGCCCCG- GAAGTGGTcggcgcgcggcgcggcgcgccTGGGCGCTAAGATGGCGGCGGCGTGAGTTGCATGTTGTGTGAGGA TCCCGGGGCCGCCGCGTCGCTCGGGCCCCGCCATG 1241 GCAGGGGCCCGGGGGCGATGCCACCCGGTGCCGACTGAGGCCACCGCACCATGGCCCGCTCGCT- GACCTGGCGCTGCTGCCCCTGGTGCCTGACGGAGGATGAGAAGGCCGCCGCCCGGGTGGACCAGGAGAT- CAACAGGATCCTCTTGGAGCAGAAGAAGCAGGACCGCGGGGAGCTGAAGCTGCTGCTTTTGGGTGAGTCCAGGG TCGGTGGGCGGTGGGTGGTGGGCAGTGGGCGGTGGCCAGCCGGCAGGG 1242 CATGACCGCGGTGGCTTGTGGGAAAAGTGGCTCGGAACCCCAAATCCCGGTTAGATTGCAGGCAC- CGCCGGACGCTGGCTCCCGGAGGTTTTAGTTTTCCCTCTACCAGGAGTGTGAAGACACAGAGACTTAT- TGCGCTGGCGAAGATGGCTGAGGCGAAGGCGTGTCCGA 1243 GCAGGTGCTCAGCGGGCAGACGCCCCGCCCCGCCCCGCCAGGTTCTGTTGGGGGCGAGGC- CCGCGCAAGCCCCGCCTCTTCCCCGGCACCAGGGGCGGGCCCAGGTGCGCCCAGGGCCGGGGAGCGGC- CGCGCAGGTGCCTGCCCTTTGCGCCTGCGCCCAGCTCG 1244 GGTGCGCCCTGCGCTGGCTAAAGTGCGCAAGCGCGCGAGGCTCGGGCCTTTCAAAccccggcgcgc- cggcgccggcgTCGACACTGCGCAAGCCCAGTCGCGCCTCTCCAGAGCGGGAAGAGCGCTGCGTTCCT- TAGCAACGAGCGTTTCCTCCAGCCCCGCCTCCCTCCGCCACACACAACCCCGC 1245 AATTTGGTCCTCCTGCGCCTGCCAAGATTGTCTgagtattgatcgaacccaggagttcgagat- cagcttgagcaagatagcgagaacccccgcccctccacctcgtctcaaaaaaaaaaaaaaaTCGTCT- CAGTAGCGAATAGTCTAACGGAGAATGACAGGGAAATTGGTGATCCTTTCTGGGCCCAAGAGTTAGAAATGGCT TTGCAggccgggcgcggt 1246 GGCTTCCGCGGCGCCAATCTCCACCCGCAGTCTCCGCCTCCCGCACCTGTGGTCCGGGCCTCACG- GTTTCAGCGCCGCGAGGCCTCACCTGCTGGTCTTGGAGCCTCAAGGGAAAGACTGCAGAGGGATCGAGGCGGC- CCACTGCCAGCACGGCCAGCGTGGCCCAGGGCTCGCAGCACTTCCGGCCTCTCTGGCCCCGC 1247 GCCAGGAGAGGGGCCGAGCCTGCACAGGAGCTTCCTCGGTTTTCCGAGCGCCGGCCCCCCTTCTCT- GCCTGGGAGGAGGTGGTTAGAGTCCCCTGGGTGTGTGCCCCGCAGAGGGAGCTCTGGCCTCAGTGCCCAGTGT- GCAGACCAATGAGAGCCCCAGAGAGAAAGACGGTCATTTCCTCCCTGCATCTTCCCTTGGGGC 1248 cgagcgccggccccccttctctgcctgggaggaggtggttagagtcccctgggtgtgtgccccgca- gagggagctctggcctcagtgcccagtgtgcagaccaatgagagccccagagagaaagacggt- catttcctccctgcatcttcccttggggc 1249 GGTTGCGAGGGCACCCTTTGGCCCGGGGGCGCGCAGGAGAGGGCAGGGGCCAGGGGTTTCCTGGGC- GAGGGCGCGGGGACGAGCAGGAAAAGGCCGGGGTGGGGGTGGAATTCCTCGGCGGGCAGGGGGCGCATGCGC- CGGGCACCGTGGGGCGGGACGTGGCCCGGGAGGAGCTGGGGGGACTGGGTGGTGCACGTGCGGGC 1250 acccggacgcggtggcgcgcgcctgtaatcccagctactcgggagcctgaggcaggagaatcgct- tgaatccgggaggcggaggttgcagtaagccgagatcgcgccactgcaccccagcctgggcgaca- gagcaagactccTCGGTAAAGACACCACTTCGTCACCC 1251 CGCCGCCGAGCCTCAGCCACGCCTCTGTGCAGCGGGGAAGACTCCTCTCGCGCCTTCTCAGTCAGT- CACGGATGATGCTGACCCAGCGCTCCGGGGCTTTCTACCAAGTAATCAGTCCAGACAAATGCCAAAACGAC- CGCCACAAGGAGGACAACGGAAGTCCCGCCGCGACCGCGCGTGCGCTTACGGAAACACCACCTTTCGGAGGCCT CATTGGCTGAAGGTCGCCGTCGCCCAACGCAGGCCATTCTGGGT 1252 gcagcctcaacctcctggggtcaagtgatcatcctggctcaaccacccaagtagccgggactacgg- gtggccgccaccatgcccggataatttttttatttttgtggagatgggggtcccacgatgttgc- ccagtccagtcttgaactcctgggctcaagtgatcctcccgcagcagcc 1253 CTTGCCGACCCAGCCTCGATCCCCTGCGGCGTCCAGGTCCCAATGCCCCAACGCAGGCCACCCCCG- GCTCCTCTGTGGACTCACGAAGACAAGGTCCGGCCGCTCGGGCCGCGAGAGTCGCGCCATCACCAC- CATTTTTCTGGATGCCCA 1254 GCGGCGTTCGGTGGTGTCCCGGTGCAGCCACGCGAGAGTAGAAGGGTGGAAAGGGGAGGTGCCCAGT- GAAATGGAGCCTGTCCCGTGCACTTTCGGGCATTTCGAGCATCTTGTGGGCTCTCCCAAGTCGCGGC- CCCTCCTCTGAGAGCCACAGTCAGGTCTGTCCTCAGGGGTCGAGGCGGCTGCGCTGGGGCCTCGGCCCGGGAGG AGGCGGGGGGCACGGCCTTTCCATTTTCCCTGCTCCCCTCTGCAGAA 1255 CCGGACTCCCCCGCGCAGACCACCGTGCCAGGACAGCCCGCTCGGGAGTCGGGCCTGGAAGCAGGCG- GACAGCGTCACCTCCCCGCAGCCGCCGGCTGGGACCCGCGGCCAGCCTTTACCCAGGCTCGCCCGGTCCCTGC- CCGCATGGCGG 1256 ggccccctgcaagttccgcctcccgggttcacaccattctcctgcctcagcctccccagcagctgg- gactacaggcacctgccgccacgcccggctaattttttgtatttttagtagagacagggtttcaccatgt- tagccaggatggtctcgatctcctgaccttgtgatctgcccgcctcggcctcccaaagtgttgggattacaggc gtgagccaccgtgtccagccTGTAACA 1257 GCCCAGGGGAGCCCTCCATTTGTAGAATGAATGAGAGTCCAGGTTATGAACAGTGCCTGGAGTGTAG- GAACACCCTCCTTTGCCTCTTTGACAGGTCTGCATCATAACACtttttttttttttttgagacagagtct- cactctgtcgcccaggctggagtgcagtggcacgatctcggccccctgcaagttccg 1258 CCGGCTGCAGGCCCTCACTGGTTGGGTCCGCCCGCGAGGGTGCCCTGGGCCCGGT- GTCTCTCCTCCTTCTGAAGTTTGTTCCCATCCACCCGGCATCACCGACCGGTTTTATCCCGCTGAGGCCCTGG- GAGATGGGTCTGGCGAGGCTCGTAGGCCGCGGATTGGCTGGCTGGGTGCAGGGGGGTGCGGGAAGGGGAGGATT TTGCA 1259 GTCACACCTGCCGATGAAACTCCTGCGTAAGAAGATCGAGAAGCGGAACCTCAAATTGCGGCAGCG- GAACCTAAAGTTTCAGGGTGAGATGCGTTGACTCGCGGTGGCTCAGAAGACCCACGCGCGAGCCCTG- GCGCGTTCGGGCGGCCGGGGGCCCAGCTGCTCTGTGTGACGGAGGCAGCTTCCCCTGCAGCGTGTGTGATTGGG GAGAGTGAAAAGGCAGCTTCCACTCGGGACCCGCGCTGCTGCCCACTC 1260 CCCTGCGCACCCCTACCAGGCAGGCTCGCTGCCTTTCCTCCCTCTTGTCTCTCCAGAGCCG- GATCTTCAAGGGGAGCCTCCGTGCCCCCGGCTGCTCAGTCCCTCCGGTGTGCAGGACCCCG- GAAGTCCTCCCCGCACAGCTCTCGCTTCTCTTTGCAGCCTGTTTCTGCGCCGGACCAGTCGAGGACTCTGGACA GTAGAGGCCCCGGGACGACCGAGCTGATGGCGTCTTCGACCCCATCTTCGTCCGCAACC 1261 CCTGGGGGAGCGCGGTGGGGGTAAGATAAGGGATGGGGGCTCCGAGGGCTGGGAACTGCAGGAAG- GAAAGAAGCGGCGGGGCCGCCCGGGTCAAGGGGCCACGTGGGGGAGGGCGGGCAGGCGGGACCGGGAGGT- CAATAACTGCAGCGTCCGAGCTGAGCCCAGGGGAGCGGGCGAGGAGAAAGAAGCCTCAGAGCGCCCGGGAAGCC TCGCGCGCCTGGGAGGCTTCCATCTCCCGGGACCCAGCTCTCAGCC 1262 GTGGGGCCGGGCGAGTGCGCGGCATCCCAGGCCGGCCCGAACGCTCCGCCCGCGGTGGGC- CGACTTCCCCTCCTCTTCCCTCTCTCCTTCCTTTAGCCCGCTGGCGCCGGACACGCTGCGCCTCATCTCT- TGGGGCGTTCTTCCCCGTTGGCCAACCGTCGCATCCCGTGCAACTTTGGGGTAGTGGCCGTTTAGTGTTGAATG TTCCCCACCGAGAGCGCATGGCTTGGGAAGCGAGGCGCGAACCCGGCCCCC 1263 CGTCCAGGCTGTGCGctccccgttctcccctcctccccacttctccccacgcct- tgctcgtctcccgccctcctccgacaaccgctcccctcaccctccacccctacccccgc- ccctcctccttcctccccGGCATGCGCCATATGGTCTTCCCGGTCCAGCCAAGAGCCTGGAACCACGTGACCTG CCCATTTGTATGCCGCGGAGCGCTCCATTCCGGCCCCTTTGTGGCCA 1264 GCGCGGCGGTGCAGCCTCTCCCGAGCGCGCTGGGTCGCCTCTGCTCGGTCTGGGGTCTGCCAG- GCGCGATCCCCCCGGTGCAGCCGAGCCCCTCCGCAGACTCTGCGCAGGAAAGCGAAACTACCCGGCAG- GAGAAAAGGCAGCGCTGGCGCCCGGCCCCCTTCCGCCCCCACCAATCACCGGGCGGCTCCGCGCTCAGCCAATT AGACGCGGCTGTTCCGTGGGCGCCACCGCCTCCCTCTGCGGGCCGCTGCT 1265 aggcggcggcggtggcagtggcacccggcggggaagcagcagcCAAACCCGCGCATGATCTCGA- GAGTTTCAGCAACATCCAGGGACTGGGCTCAGCCCCGGAGCGAGAGGGTCGTCCGCTGAGAAGCTGCGCCG- GAGACGCGGGAAGCTGCTGCCATAAGGAGG GAGCTCTGGGAAGCCGGAGGACAGGAGGAGACGGGAGTCCAGGG GCAGACGAGTGGAGCCCGAGGAGGCAGGGTGGAGGGAGAGTCAAGG 1266 GCGCGACCCGCCGATTGTGTCGAGTCAGCAGCGGCAGCGGGGACGCGCGAAGCCATGGCTCCCGC- CCGCGCTCGGGAGGGCGCCGGGGGTCCTGCGCCTCCGGGAGGTTTGTGGCCGAgcgcggcgcggccccgagcg- gccccgcagcgcccggctccccgccgcTCGCTCTCCAGGCGCCGACCCGCCTGCGTCGCCACCCTCTCGCCGCT CCCTGCCGCCACCTTCCTCCCGCCCGGGTGCCGGGCGTCCGCT 1267 CGCGGACGCCGCTCTGCACCTGTTGCCGCCGTCACTCATCCCGCCAGGCGGGCGGGGCCGCGCGGGT- GGCTTGGTCAGGACCTGCCATTCAGCCCAGTCGGGCTCCGGTGCTCGCCCCGGACGGCGCCCCAAGCGG- GTCCCGGCCCCGCTGAGCACCTCCAGCAGTGGCACAGCCTCTGGAGGGGTCCGGGACGAAGCCACCCGCGCGGT AGGGGGCGACTTAGCGGTTTCAGCCTCCAACAGCCTTGGGATCGC 1268 tgaacccgggaggcggaggttgctgtgagccgagatggcaccattgcactccagcctgggcaacaa- gagcgaaactccgtcccccgaacaaaaaattcaaatgggaaagagaggcagatggcagagaacaggggaggg- gctgggcaccgtggctcatgcctgtaatcccagcactttgggaggccaaggcgggtgga 1269 CTCGGCGGCGCGGGGAGTCGGAGGACGCAGCCAAGCGGCGGCGGCGAGGAGGGTCACAGCCGGAAA- GAGGCAGCGGTGGCGCCTGCAGACGCCGCGCAGCCCGGGCAGCCCCACAGCGCAAGCTGGCTGCCGCGGCG- GCGGGGGCTTTATCGGCGGCGCCGCGCGGGCccccgccccttcctgccgcccccgcccccggcccgccttgccc cgccttcccgccg 1270 aggcggccacgggagggggaggggctggcaacggcgccgtgggggcggggctcgctttgtgcaag- gtccgcgctgattgggccgtgggcgcgcgggtcccggcctgcgtcgtgggactggcgtttttggcgccggct- gtgaggggagcgcgggggtggtggaatcgggcggtctccggttcgccaatgtggctgggtccgtaggcttgggc agccttggagttcctcagagaccccgcgctcggtcccggcacgc 1271 GACCCGAGCGGGGCGGAGAGTGGCAGGAGGAGGCGAATCTCCGCGCTCCGGCGAACTTTATCGGGT- TGAAGTTTCTGCTGTCGCCTCCCCTTTGCGTGCGGAGCTGGGCTTTGCGTGCGCCGCTTCTGGAAAGTCG- GCTCCAGTCATATCCCTGGGCGCTGCCTGCGGCCGCTCCTCCCGCGCTTCTCACGGCACCTGACACGCGGAGGC GGCGGCCGAGGGTGGGGTGCCGGCCACCACCACCCTTGGCGTGGG 1272 AGCACCTggggcggggcggagcggggcgcgcgggcccACACCTGTGGAGAGGGCCGCGCCCCAACT- GCAGCGCCGGGGCTGGGGGAGGGGAGCCTACTCACTCCCCCAACTCCCGGGCGGTGACTCATCAACGAGCAC- CAGCGGCCAGAGGTGAGCAGTCCCGGGAAGGGGCCGAGAGGCGGGGCCGCCAGGTCGGGCAGGTGT- GCGCTCCGCCCCGC 1273 CCGCTCGGGGGACGTGGGAGGGGAGGCGGGAAACAGCTTAGTGGGTGTGGGGTCGCGC- ATTTTCTTCAACCAGGAGGTGAGGAGGTTTCGACATGGCGGTGCAGCCGAAGGAGACGCTGCAGTTGGA- GAGCGCGGCCGAGGTCGGCTTCGTGCGCTTCTTTCAGGGCATGCCGGAGAAGCCGACCACCACAGTGCGCCTTT TCGACCGGGGCGACTTCTATACGGCGCACGGCGAGGACGCGCTGCTGGCCGC 1274 ACCGCCAGCGTGCCAGCCCCGCCCCTACCCACCAGTGTGCCAGCCCCGCCCTTCCCCACGTcgc- cgcgcgcccgggggcggggcctggcgcgcaccgcccgcgcACGGCGAGGCGCCTGTTGATTGGCCACTGGGGC- CCGGGTTCCTCCGGCGGAGCGCGCCTCCCCCCAGATTTCCCGCCAGCAGGAGCCGCGCGGTAGATGCGGTGCTT TTAGGAGCTCCGTCCGACAGAACGGTTGGGCCTTGCCGGCTGTC 1275 ATTCTTggccgggtgcggtggctcacgcctgtaatcccagcactttgggaggctgaggtgggtggat- cacctgaggtcaagagttcgagaccagcctggccaacatggtgaaaccccgtctc- tactaaaaatacaaaaattagccgggcgtggtggtgggcacctgtaatcccagctactcagaaggttgaggcag gagaatcgcttgaacccgggagaaggaggttgcagtgagccgagatcgcgccattgcac 1276 CGCTTCCCGCGAGCGAGCCGCCCAGAGCGCTCTGCTGGCGGCAGAGGCGGCGGCGAGGCTG- GCGCGCTTGCCGCCGTCTGCTCGCCCCGCGGAGGCGACCTGGGCAGACGCTGCTGG- GAACTTTGAAAAACTTTCCTGGAGCCAGGCTTGCCGCAGATTCGAGGGGAAGCCTCGGCCGCGTCCCACCCCCT CCCAAATCCGAGTCTGCGGAGCCTGGGAGGGCTCCCAGCTTCCTATCCAAACCGCGCCGGGGCA 1277 AGCCGGCGCTCCGCACCTGCCCCTCAGCGCCTGCCGTCCGCCCCACCGCCGCGGCGC- CCCGCACTCCTGGGCGGGCCAGGGGAGCGGGCTGGGCGGGCGATCGGGCACGCGGGATCCCTGGTCGAGC- CCCCTTTCCTCCCGGGTCCACAGCGAGTCCCCTGAGGAAGGAGGGACCTGGGAGGAAACCACCCTCTGGGGCGG CTCCGGCCTCCAGCCCCCGCCCCGTCTCATCGCGCCGGGCGCCCGGTGCGCCTG 1278 CGGAGCGCGCTTGGCCTCACAGGACAGTGGGTGTGGCTGGGGTGACGGGGCAGGGTGGGGAAGACTG- GCCTAACACCAGCGCCCTCTGCCCCATGGCTGGCCAGGGACCCGCGAGTCCCTGGACACGCACTGGCCAACGC- CAGACCCCATCTCATCGGGTGGGGAAGTCGCGGGGACACTGTCAGGGCGCCGAAGTCCGGACCCGGCTCAGAGG CGGTGGCAGGTGAATTGCTGCGGCGCCGGG TAGG GGCGGGC 1279 ggcctcgagcccacccagacttggccaagcagccctcggccagaccaagcacactccctcggag- gcctggcagggcccctgctttaccctgccccccacgccccgccccgacccgaccctcccaggcagcccct- cagcgtctgccgcccgcccttgggcctttccggccagcccctccctccgcccacgcccagaacagcccatgctc ttggaggagagcaggtgggcttgaccgggactggcccctcaccgcgg 1280 GCCGCGCCGTAAGGGCCACCCCCAGAGGCCGAGGAGGTGGGGCTGGCCTGGCTTTCTGGCCAGGT- GGGGCTTGTCCAACCCCACAAACATCAGGGCTCACCCTGGATGTGGAAGAGAAGGAGCGAC- CCCCAAAACGAAGCGGCTGGATCTGACCTTCCAAGGCCTGTTGGCGACGCAGGGCCCCCAGGAGGCAGAGCGCG CGCCTGGCCCGGGCGATGGGCCTCCCGTCCCCCCAGGGCTGCCTCCCCGCCGGTG 1281 CGGCGGTGGCGGTGGGTCGGCGACCGGCGGGCCGAAGACTGGAAGCCCGGGCCGCTGAG- GCTCCGCAgccccctccgcgccgccccggcccgcccccgccgcgccgccccttccctccccgcgcccgc- cccTTCTTCCCCGCAGGGTCAGCGCTGGGGCTCCGGCCGTAGAGCCACGTGACCCTGGCAGGCCCTGCTCGCGG GGCTTGGCGACAAGGACGCACGACACGGGGCGGC 1282 ACCTGCCCAGTTACTGCCCCACTCCGCGGAATAAGCTCTTACCCAC- CGCTCCTCTTCTTCAATTCATTTCTGTTATGGAACTGTCGCGGCACTACAAAGTCTCTATGTAGT- TATAAATAAACGTTATCTGGAAGAGCAGCCGACAACAACTTTCAAGATCTCCAATTCCCCGAC- CCCACACTCCAACTGACGCC 1283 CCAGCGCCCGAGCCGTCCAGGCGGCCAGCAGGAGCAGTGCCAAACCGGGCAGCATCGCGACCCT- GCGCGGGGCACCGAGTGCGCTGCTGTGCGAGTGGGATCCGCCGCGTCCTTGCTCTGCCCGCGCCGCCACCGC- CGCCGTCTCCCGGGGCCCCCGCGCACGCTCCTCCGCGTGCTCTCGCCTACCGCTGCCGAGGAAACTGACGGAGC CCGAGCGCGGCGGCGGGGCTCAGAGCCAGGCGAGTCAGCTGATCC 1284 CTGCTGCTGCCCGCGTCCGAGGCTCGCGGGCGGCGGGCCCGGGTGAGTGCACACCCGGCGCGCTGC- CGGGCTCCCGGATGTGTCACCTTGTCCCGCTGCAGCCGAGATGCCGGGGGAGCGGGGCCTTCCACAC- CCCCTCCGTGGGTGTGTGGTGAGTGTGGGTGTGTGCGCGTCTCCTCGCGTCCCTCGCTGAGGTGCCTACTGTGT CTGCATGGGTTGGGTCCCGCGCGATG 1285 ACTGCTTAGGCCACACGATCCCCCAAGCCTGGGCTGCCAGACGTCGCCATCATTGTTCCATGCAGAT- CATGCCCATCCTGTGCAGAAGGTCACTATAGGAACACATGGCACAGGGAAGAAAACGCCCATAGAAATTCA- CATGGTGCTTGTCTAAACCGAAGGCAGGTGAGATCCACCCACTG 1286 GCCGGACGCGCCTCCCAAGGGCGCGGGTCCGAGGCGCAAGGCGAGCTGGAGACCCCGAAAACCAGG- GCCACTCGGGGAGTGTCAGGAAGCACGACTGGGCGCCTTAGGACGTCCGGGCAGACGCGGCCCCCGAGGAGC- CCCAGAGGAGCCCCAGAGGAGCCGCCTGACCCGGCCCCGACGTGCGCGATCGAGCCCGGGCTCGCCAAAGCCCC CGCGCCCCTCCGGCCCGGACAGGCCGAGTGGACATTGTCGGAG 1287 CGGCCAGGGTGCCGAGGGCCAGCATGGACACCAGGACCAGGGCGCAGATCACCTTGTTCTCCATGGT- GGCCATTGCCTCCTCTCTGCTCCAAAGGCGACCCCGAGTCAGGGATGAGAGGCCGCCCGAGCCCCG- GATTTTATAGGGCAGGCTC 1288 ccgcccgcccCACAGCCAGCGGCTCCGCGCCCCCTGCAGCCACGATGCCCGCGGCCCGGCCGCCCGC- CGCGGGACTCCGCGGGATCTCGCTGTTCCTCGCTCTGCTCCTGGGGAGCCCGGCGGCAGCGCTGGAGCGAG- GTAAGCGCCCCGAGGGGCGGGGCGGGCAGGGGGCAAAGTTGCCGGGAGAGCGGGGCAGCCAGGGGTCGGGGCTG ACCAGGGCGACTCAGGCACCACCCGCCGGGA 1289 GCGCCCCAGCCCACCCACTCGCGTGCCCACGGCGGCATTATTCCCTATAAGGATCTGAACG- ATCCGGGGGCGGCCCCGCCCCGTTACCCCTTGCCCCCGGCCCCGCCCCCTTTTTGGAGGGCCGATGAGGTAAT- GCGGCTCTGCCATTGGTCTGAGGGGGCGGGCCCCAACAGCCCGAGGCGGGGTCCCCGGGGGCCCAGCGCTATAT CACTCGGCCGCCCAGGCAGCGGCGCAGAGCGGGCAGCAGGCAGGCGG 1290 cgtgctgggcgcaggggaaacagcgacgcacgggacaaaACAAGCTTGCAGAACAGCAGGGGGCAGA- GAGGCTGTAAACAAGCCAACGGGCTGCACTTGTAGCGGTTCTGTTGCCAATGCCATTCAGACCCCAGTCCGG- GATTCCGCGCTCGGGGTGCGAGAGGCCGCTCCcggggaggggcgggacccgggcggggcgggaggggcggggcg CCCGGGCCTATTAGGTCCCGCGCCGGCAGCC 1291 GCGCACGCGCACAGCCTCCGGCCGGCTATTTCCGCGAGCGCGTTCCATCCTCTAC- CGAGCGCGCGCGAAGACTACGGAGGTCGACTCGGGAGCGCGCACGCAGCTCCGCCCCGCGTCCGACCCGCG- GATCCCGCGGCGTCCGGCCCGGGTGGTCTGGATCGCGGAGGGAATGCCCCGGA 1292 GGTGAGTGCGGCCCGGGGAGGGGAGGGGACCAGGGCGACCGGAGCCCCCAGCGATCCCGCCTG- GAGCGGCCGCCAAGCTCCCTCGGGCACCCGGGTTCAGCGGGTCCCGATCCGAGGGCGTGCGAGCT- GAGCCTCCTGGACCGGGTCCGCCGCGGACCTCGGCCTGTCACCTGAAGGTGCCGCGTGGTCTCTGAGGACGTCT GTCGACGAGCAGGGGCCGCCGCCA 1293 GGCCGAGAGGGAGCCCCACACCTCGGTCTCCCCAGACCGGCCCTGGCCGGGG- GCATCCCCCTAAACTTCGGATCCCTCCTCGGAAATGGGACCCTCTCTGGGCCGCCTCCCAGCGGTGGTGGC- GAGGAGCAAACGACACCAGGTAGCCTGCCGCGGGGCAGAGAGTGGACGCGGGAAAGCCGGTGGCTCCCGCCGTG GGCCCTACTGTgcgcgggcggcggccgagcccgggccgcTCCCTCCCAGTCGCGcgcc 1294 CCAGCGCCGCAACGCCCAGGGTGTGGGGCGGAGTAAGATGTGAAACCTCTTCAGCTCACGGCACCGG- GCTGCAACCGAGGTCTGAATGTTGCGAAAGCGCCCCAGACGCCGCCGCTGCTTTCCGGCCGCCCCCTCGGC- TACAGCCGCCATTTCCACGCTCCACCAATCAAATCCATTCTCGAGGAAGACGCACCGCCCCCACACGCCCCGAC CAATCGCTCGCGCTCTGGTTGCGCTGGCGCC 1295 CCACAAGCGGGCGGGACGGCTGGAGACTGCCGGGACAGCGGCTGCCGGTGCTACGCGGGTGGTGG- GCGGCCCGGAAATGAGCGCCCTCCGGGGACAGGGGGCTCTGCGGGGCGGCGACAGCTG- GATTCCCAGCGCGCACAAAGCCTGCGGGAGGATCCATTGTAGCGGTCGCTCCTCCCCGCTTAGCGAGGGCGGGC GCAGGGGCGGGGGATGTCGAAGGGTCAGGTTTGTCCAGGCCGCGCCACCTTCG 1296 CCTCTGGACAACGGGGAGCGGGAAAAAAGCTACGCAGGAGCTTGGATCGGGCGAAGCTCGCGG- GAAACCGCTCTGGGTGCGCAGGACAAAGACGCGGGGACAGCGGGGAGGGCCGGCCGCAGCCTGCCGGGCTGC- CCCCACGGCGCGGAACGCGCGCAGCAACCTCCACCAGGCCTCCGCGTCTGGACTCCCGCCCTGCCTCTGGGCCT CCTCCGCCCACCGGCGGCGTCTCCCGCGAAGCCCGCTGGG 1297 GCGGGTTCCCGGCGTCTCCAAAGCTACCGCTGCCGGAAGAGCGCGGCGCCCGACGGAGCCGTGTG- GAGGCCAAAACTCCTCCCGGAAGCCGCTACTGGCCCCGCTTGCCAGGCCCAGCGTCTTTTCTGCATAGGAC- CCGGGGGAAGCCGGGAAGCCGTTAGGGGGCGGGGCAAGCGGG 1298 CGCCGCCCGTCCTGCTTGCTGCTGGGTCCGGTTGCCGAGGCGGAAAAGTCGCAAGCTCCTTCAGT- CAGTCTTCTTCCTCAGCTCCTTCCGACTCCGGAAGCTGCTGTTTGGGCCCAGGCTCCCTGCATCCGAGAGC- CCTGGGCTGACTGCTTCTGAGGCCCCGCCCCACTACTGCCTGCAGCGGGCTTCCTTACTCCGCCTGCTGGTTCC TACTGGAGGAGAGGCCAGCATGCTTGTCAGGCACCAGCAGGTGGA 1299 CGCGCGGCCCTCCTGCACCTCGGCCAGCACTCGTAGCGCGCTGGGCGAGCCGGACCGGAAGT- TGAAGAAGTGAAGCGCCGCGCGCGCCGCCTGCTGCAGGAGCCTGCGCGGGACCCCAGCATCCTGAGGCTGC- CCAGGGTCGTCGGGGTCCCCGGACCCCGCGGGCGCCGCCACCGGGGCGAGCAACAGCAGCAGCGCGAGCAGCGG GGCGGTGGGGCGCGGGCCCCTGGGCCCGGACCAGGGAGCAGGCAGCCG 1300 GGCGGGGCAAGCCCTCACCTGCGCCAATCAGGGTGCGGAGTAGGCCCCGCAGGCGCCTCACCCAT- TGAGGGGGCGGGCTGACAGAGCAGAGGAAGGAAGGGGGTGAGGGGCCTGTGGTGGGGATCCTGGGGCTGTCGG- GCTGAGTATGCCGTGTGGGTGGAGAGGAAGCCTCGGGGAAATCGCCCAGGTGAAGGGAGGGCTTGGTGTGGGGA CTTGCACTGGGCAGAGGGGCAGCTTCCCTGAGAGCAGCTAAGC 1301 GGAGCGCCCCCTGGCGGTTTCAGGGCGGCTCACCGAGAGGGCGCCGGGAGCGCCCGGTTGGG- GAACGCGCGGCTGGCGGCGTGGGGACCACCCGGCAGGACCAGGCACCAGAGCTGCGTCCCTGCTCGC 1302 CGAATGGTTCGCGCCGGCCTATATTTACCCGAGATCTTCCTCCCGGACGGCAAGGATGTGAGGCAG- GCGAGCCGGACGCCGCTCGCAGCACCGGAGAGGGCGCACTGCAAAGGCGGGCAGCAGACCGTGGAGAGCCCGG- GAGCGGAGCTGGACACCGCCTCGGAGGGAAGAAATGAGGTAGCGGCGGTTCCCGGACCCGGCCATGCCCGTCCC CTGTTCTCGGAGCCCAGCGCCGTCTCGGCCAGGCCAGCCCGG 1303 TTCCGCCGGCTGGGCCCTCCGTCTACCCCCAGCGGCGAggggcggggccggcgcgggcgcAGAG- GCGTCACGCACTCCATGGTAACGACGCTCGGCCCGAAGATGGCGGCCGAATGGGGCGGAGGAGTGGGT- TACTCGGGCTCAGGCCCGGGCCGGAGCCGGTGGCGCTGGAGCGGGTCTGTGTGGGTCCGAAGCGTTTTACTCCT GTTGGGCGGGCTCCGGGCCAGCGCCACATCTACTCCCGTCTCCTTGGGC 1304 CTCCGGGTcccccgcgtgcccggcccgccccggcccgcTTCCCGGGCGCTGTCTTACTCCGGGC- CCGGGGCGCCTGCTCCGCGCCGCGTCTGCGAACCGGTGACCTGGTTTCCCCTCCAGCCCTCACGGCT- GTCCGACTTGCGCGGCGGTGGCGGCGGCGGCCAAGAGCAGGCAAACCCGGCTCCGCCAGGGGCGCAGCGAGGAA ATGGCCTCCTGGCGCACACCCCGCCGCCGCCGCCAGCCATCGCCACCGCC 1305 CAGCCCGGGTAGGGTTCACCGAAAGTTCACTCGCATATATTAGGCAATTCAATCTTTCATTCTGTGT- GACAGAAGTAGTAGGAAGTGAGCTGTTCAGAGGCAGGAGGGTCTATTCTTTGCCAAAGGGGGGAC- CAGAATTCCCCCATGCGAGCTGTTTGAGGACTGGGATGCCGAGAACGCGAGCGATCCGAGCAGGGTTTGTCTGG GCACCGTCGGGGTAGGATCCGGAACGCATTCGGAAGGCTTTTTGCAAGC 1306 GGCGGAGAGAGGTCCTGCCCAGCTGTTGGCGAGGAGTTTCCTGTTTCCCCCGCAGCGCTGAGT- TGAAGTTGAGTGAGTCACTCGCGCGCACGGAGCGACGACACCCCCGCGCGTGCACCCGCTCGGGACAGGAGC- CGGACTCCTGTGCAGCTTCCCTCGGCCGCCGGGGGCCTCCCCGCGCCTCGCCGGCCTCCAGGCCCCCTCCTGGC TGGCGAGCGGGCGCCACATCTGGCCCGCACATCTGCGCTGCCGGCC 1307 CCTCACCCCAGCCGCGACCCTTCAAGGCCAAGAGGCGGCAGAGCCCGAGGCCTGCAC- GAGCAGCTCTCTCTTCAGGAGTGAAGGAGGCCACGGGCAAGTCGCCCTGACGCAGACGCTCCACCAGGGC- CGCGCGCTCGCCGTCCGCCACATACCGCTCGTAGTATTCGTGCTCAGCCTCGTAGTGGCGCCTGACGTCGCGTT CGCGGGTAGCTACGATGAGGCGGCGACAGACCAGGCACAGGGCCCCATCGCCCT 1308 CGATGACGGGATCCGAGAGAAAGGCAAGGCGGAAGGGGTGAGGCCGGAAGCCGAAGTGCCGCAGG- GAGTTAGCGGCGTCTCGGTTGCCATGGAGACCAGGAGCTCCAAAACGCGGAGGTCTTTAGCGTCCCGGAC- CAACGAGTGCCAGGGGACAATGTGGGCGCCAACTTCGCCACCAGCCGGGTCCAGCAGCCCCAGCCAGCCCACCT GGAAGTCCTCCTTGTATTCCTCCCTCGCCTACTCTGAGGCCTTCCA 1309 CCGCAGGCCGCGGGAAAGGCGCGCCGAGTCCTGCAGCTGCTCTCCCGGTTCGGGAAACGCGCGGG- GCGGGGGCGTCGGGCTTGGGACAGGGGAGGATACCAGGGCCACCTTCCCCAACCCAGGCCGCGGGGGCCCG- GCCTCCCCGATGCAGACCACAGCGCCCTCACGGGCTGCCCTCAGGCCGCGCAGCGGGCAGCCGCCAGCCGTCAC CCCGGGGAGCGTCCGTGGGGTGCCCAGGCA 1310 GCCCCAGTCCACCTCTGGGAGCGCCTGCGCCGCTCCGCGGAGAGTCCGTGGATCTCACAGTGAGC- GAGTTGGGACCCAGGGAGGGGAAAAGAGAGGACCCCGGCGAGCCATTGCTGGGGCGGCGGGCTGGAGGGT- TATCTGGGAAGTCAGCCCCGGCCTCGGTCCTCTCCACGTTGCTGCCTACGCGTGCTGCCCGGACGTAGGGC 1311 CTTGGCCGCCCCCGGGATGGGGCGAGGGGTTCCCGAGGGCTtgggagggcggcttgggaga- gagctccggctccggaacgaggtgtcctgggaacactcccgggtctgtaacttcggacaaat- cacgctcgctttcccggcctcagtgtgccgttctgtaacttgggtctaaCCCCGGCTCGCACACACGGCGGGGA CGCGCACAG 1312 CCTCCATGCGCAATCCCAAGGGCGGAGAGGAATTTCAGCAGCTACGAGCAACAGAAAGGAAACGAGA- GAGTAGCCAGACTCTCCGCGCATGGAGCCGACGGCACCCACCAGCACACCGCCGGCGCCCCCAGCCACTACT- GCACGTCCGCccccgccccgccccgctccgcccGGCGCACCTGATGCCCAAACTGGTTGCACGGGAAGCCGAGC ACCACCAGGCCCCGGGGTCCGAGGCGCCGCTGCA 1313 gcggcgactgcgctgccccttggctgccccttccgctctcgtaggcgcgcggggccactact- cacgcgcgcactgcaggcctttgcgcacgacgccccagatgaagtcgccacagaggtcgcaccacgtgtgcgt- ggcgggccccgcgggctggaagcggtggccacggccagggaccagctgccgtgtggggttgcacgcggtgcccc gcgcgatgcgcagcgcgttggcacgctccagccgggtgcggccctt 1314 GGGCTTGCCTCCCCGCCCCTACCTTCCAGGATGTTGACAGCTGGGAATGAAAGGCAGAGGGAGG- GAGCGCGGGGCCGGAGCGCCGCCTGGGAGTGTGCCCACTGGGTGGCCGCCTGAGGGACCCGGGAACAGAGG- GCAAAAAGTCCTGTGACCGGACAGAGCAGAGCGGGGACTGCAATTCCCAGAAGACCCCACGGTAGGGGCGGGAC CCAAGATGGCCGCTTGTCTGGGGACAGGAGCGGAGGCCAATACGCG 1315 GCGGCCCAAGGAGGGCGAACGCCTAAGACTGCAAAGGCTCGGGGGAGAACGGCTCTCGGAGAACGG- GCTGGGGAAGGACGTGGCTCTGAAGACGGACAGCCCTGAGGAACCGCGGGGCGCCCAGATGGAACTCGT- TAGCGCCCCGAGTGCAGACAATCCCGGAGGGGGAAAGGCGAGCAGCTGGCAGAGAGCCCAGTGCCGGCCAACCG CGCGAGCGCCTCAGAACGGCCCGCCCACCC 1316 ctgcgcggcTGGCGATCCAGGAGCGAGCACAGCGCCCGGGCGAGCGCCGGGGGGAGCGAGCAGGG- GCGACGAGAAACGAGGCAGGGGAGGGAAGCAGATGCCAGCGGGCCGAAGAGTCGGGAGCCGGAGCCGGGA- GAGCGAAAGGAGAGGGGACCTGGCGGGGCACTTAGGAGCCAACCGAGGAGCAGGAGCACGGACTCCCACTGTGG AAAGGAGGACCAGAAGGGAGGATGGGATGGAAGAGAAGAAAAAGCA 1317 CACCGCCTCCGGACCCCTCCCTCATCAGAAAGCCCAGGCTCCGCTCGTAGAAGTGCGCAGGCGTCAC- CGCGCATCCAGGAGCCACGTGTCAGGAGTCACGTGTCAGGTGTCACGTGTCAGGCGTCACGTGGCTGGAGGC- CGTTGGAGCGCCTGCGCAGCTTTTCCGCACGCGCC 1318 CCTTCCAGCCACCCCGCCCTGGGCGCCTCTGGCGCGCTCTGATGACGCTCCAAGGGAAGAGGAAGT- GGGGATCGGCGAGCGGGTGGGTGCGCCTCGGGCCGCGGGACTCGCAGCCGCCACCGCCGCTGCCGCCTCTACG- GCCGCGTCAGAACTGAAGAGAGGAAGGGGAGGAGCCGAGTCGAGCCTAAGCTGCCGCCCGATCTTACCCCTGAC CCGAGGGCGGCCTGGA 1319 CGGGACACCGGGAGGACAGCGCGGGCGAGGCGCTGCAAGCCCGCGCGCAGCTCCGGGGGGCTCCGAC- CCGGGGGAGCAGAATGAGCCGTTGCTGGGGCACAGCCAGAGTTTTCTTGGCCTTTTTTATGCAAATCTGGAGG- GTGGGGGGAGCAAGGGAGGAGCCAATGAAGGGTAATCCGAGGAGGGCTGGTCACTACTTTCTGGGTCTGGTTTT GCGTTGAGAATGCCCCTCACGCGCTTGCTGGAAGGGAATTC 1320 CCTGGGTTCCCGGCTTCTCAGCCACTGGAGCTGCCAGTCTCAAATTACCGGAGGGGAGGGAGGGCAG- GCCTGGATCTCAGGATCTCGGTCCTGCATGCAATGCAAGCCTGAGCTCTCCCGCCATAAGGCTGCAGCGGTGT- GGGCTCCTTGTGCCCAGATCCTTTGTATTCATAGGGGGAAGTGGAAGACCACGCTGCC 1321 GGCGGTGATGGGCggaggaggaggaagaggaggaggaggaagaggaggagggggaAAACGATGACAG- GAGCTGGGGCCGGGGGGGGAAATTGGGGGGACGCGGGCGGAGGCGCGGTGCGCGCCGGCGGTGGCGGGCAC- GAGCCCCGCGCCTGGAGGAGGAGGAGTCAGGCCGGGTAGGAGGGCTAAGGAGGTTCCCGGGAAGGCAGGGcccc ccctcccccccctcccccccccccACACACACACACTCCCCTG 1322 CAGCCCGCCCGGAGCCCATGCCCGGCGGCTGGCCAGTGCTGCGGCAGAAGGGGGGGCCCGGCTCTGC- ATGGCCCCGGCTGCTGACATGACTTCTTTGCCACTCGGTGTCAAAGTGGAGGACTCCGCCTTCGGCAAgccg- gcggggggaggcgcgggccaggcccccagcgccgccgcggccACGGCAGCCGCCATGGGCGCGGACGAGGAGGG GGCCAAGCCCAAAGTGTCCCCTTCGCTCCTGCCCTTCAGCGT 1323 GCCCGCGGGGGAATCGCAGTGAGCAGCGCGGGGCGAGGCCGCCGCGGACGCCCCGTCGGATGTGC- CCTTCGCTGGGCCGAGCGGCGCAGGGTTGGAGAGGGAAGCGCTCGTGCCCACCTTGCTCGCAGGTGCCCT- TGCTGACCTGGGTGATGGCCTTCTCCCCGCGGCTCTCGGCCCTCTGGCTGGCGGCGCGCAGCTGGCAGCCGCTC GGGTAGGTGGTGCCGTCGCTGCCGCACACCGGG 1324 GCCGCGAGCCCGTCTGCTCCCGCCCTGCCCGTGCACTCTCCGCAGCCGCCCTCCGCCAAGC- CCCAGCGCCCGCTCCCATCGCCGATGACCGCGGGGAGGAGGATGGAGATGCTCTGTGCCGGCAGGGTCCCT- GCGCTGCTGCTCTGCCTGGGTAAGTTCTCCCCCTCTGGCTTCCGGCCGCCCCAA 1325 GCGGCCCCCTCCCGGCTGAGCCTATAAAGCGGCAGGTGCGCGCCGCCCTACAGACGTTCGCACACCT- GGGTGCCAGCGCCCCAGAGGTCCCGGGACAGCCCGAggcgccgcgcccgccgccccgAGCTCCCCAAGCCTTC- GAGAGCGGCGCACACTCCCGGTCTCCACTCGCTCTTCCAACACCCGCTCGTTTTGGCGGCAGCTCGTGTCCCAG AGACCGAGTTGCCCCAGAGACCGAGACGCCGCCGCTGCG 1326 CAGCAGGGCGCGGCTTCCCTTTCCCGGGGCCTGGGGCCGCAATCAGGTGGAGTCGAGAGGCCGGAG- GAGGGGCAGGAGGAAGGGGTGCGGTCGCGATCCGGACCCGGAGCCAGCGCGGAGCACCTGCGCCCGCGGCT- GACACCTTCGCTCGCAGTTTGTTCGCAGTTTACTCGCACACCAGTTTCCCCCACCGCGCTTTGGGTAAGTTCAG CCTCCCGGCGCGTCCCCGCGAGCCTCGCCCACAGCCGCCTGCTG 1327 CCGCAGCACGCTCGGACGGGCCAGGGGCGGCGACCCCTCGCGGACGCCCGGCTGCGCGCCGGGC- CGGGGACTTGCCCTTGCACGCTCCCTGCGCCCTCCAGCTCGCCGGCGGGACCATGAAGAAGTTCTCTCGGAT- GCCCAAGTCGGAGggcggcagcggcggcggagcggcgggtggcggggctggcggggccggggccggggccggct gcggctccggcggcTCGTCCGTGGGGGTCCGGGTGTTCGCGGTCG 1328 GCGGAGTGCGGGTCGGGAAGCGGAGAGAGAAGCAGCTGTGTAATCCGCTGGATGCGGACCAGG- GCGCTCCCCATTCCCGTCGGGAGCCCGCCGATTGGCTGGGTGTGGGCGCACGTGACCGACATGTGGCTGTAT- TGGTGCAGCCCGCCAGGGTGTCACTGGAGACAGAATGGAGGTGCTGCCGGACTCGGAAATGGGG 1329 GCGCGGGGGCAGGTGAGCATGCGAAGGTTGGAGGCCGCGCCCCTTGCTGAGGCGCAGCTGGCT- GCTCTTTTCGGGCCGGCATACGCGCGCAGCCGCAGCTGAGGTCACCCCGCTGAGGTGGTGGGGAGGGGAATG- GTTATTCTTGAGGCACCGCATCTCTTGAGGAGGAAAGAGCCGGAAACACCTGGTCTCTCAAGCAGGTACAGCCC GCTTCTCCCCAGCACCCCGGTGTGGGCTTCCCAAGGTCCTGCCTGA 1330 ggcgcgggggcaggtgagcatgcgaaggttggaggccgcgccccttgctgaggcgcagctggct- gctcttttcgggccggcatacgcgcgcagccgcagctgaggtcaccccgctgaggtggtggggaggggaatg- gttattcttgaggcaccgcatctcttgaggaggaaagagccg 1331 AGTGACGGGCGGTGGGCCTGGGGCGGCCAGCGGTGACTCCAGATGAGCCGGCCGTCCGCGTTCGCGC- CGCGGCGGTGCGGTTGTCGCGGATCAGCAGGATCGGAGTGCGGGGCTGCTGGGCGGAGGCGTTGGCTGCAC- CAGGGACGGCGGCG 1332 GGCGACCCTTTGGCCGCTGGCCTGATCCGGAGACCCAGGGCTGCCTCCAGGTCCGGACGCGGG- GCGTCGGGCTCCGGGCACCACGAATGCCGGACGTGAAGGGGAGGACGGAGGCGCGTAGACGCGGCTGGG- GACGAACCCGAGGACGCATTGCTCCCTGGACGGGCACGCGGGACCTCCCGGAGTGCCTCCCTGCAACACTTCCC CGCGACTTGGGCTCCTTGACACAGGCCCGTCATTTCTCTTTGCAGGTTC 1333 CGCGGCAGCCCGGGTGAATGGAGCGAGGCGGCAGGTCATCCCCGTGCAGCGCCCGG- GTATTTGCATAATTTATGCTCGCGGGAGGCCGCCATCGCCCCTCCCCCAACCCGGAGTGTGCCCGTAATTAC- CGCCGGCCAATCGGCGGCGTCGCGCGGCCCCGGGAGTCGGCTCGGGCTAAGCTGGCCAGGGCGTCTCCAGGCAG TGAAACAGAGGCGGGGTCGGCGGGCGATTAGCGGCCGAGGCACGCTCCTCTTG 1334 GGCGAGCGAGCGGGACCGAGCGGGGAGCGGGTGGAGGCGGCGCCACG- GCGCGCACACACTCGCACACACGCGCTCCCACTCCAcccccggccgctccccgcccgaggggccgcgcggcg- gccgcggggAACGATGCAACCTGTTGGTGACGCTTGGCAACTGCAggggcgcccgcggtccctgcccccacgcc ctccgcgcgggccccgccaccccggccccgacggcgcctgcacgcccgcgtcccctg 1335 GGGGCAGTGCCGGTGTGCTGCCCTCTGCCTTGAGACCTCAAGCCGCGCAGGCGCCCAGGGCAGGCAG- GTAGCGGCCACAGAAGAGCCAAAAGCTCCCGGGTTGGCTGGTAAGGACACCACCTCCAGCTTTAGCCCTCT- GGGGCCAGCCAGGGTAGCCGGGAAGCAGTGGTGGCCCGCCCTCCAGGGAGCAGTTGGGCCCCGCCCGG 1336 CGCTGGCATTCGGGCCCCCTCCAGACTTTAGCCCGGTgccggcgccccctgggcccggcccgg- gcctcctggcgcagcccctcgggggcccgggcACACCGTCCTCGCCCGGAGCGCAGAGGCCGACGCCCTAC- GAGTGGATGCGGCGCAGCGTGGCGGCCGGAGGCGGCGGTGGCAGCGGTAAGGACCCTTCCCTCGCCCTGCGCCT CTGGACCTGCAGGTGCTCGGGCGCGGCCCAGGCCGCCCCCTGTCTGA 1337 GAGCCGTGATGGAGCCGGGAGGAGAGGCGCATCCTCAGCAGAGCTTCCCTCCCTTGCACACGAGCT- GACGGCGTGAACGGGGGTGTCGGGGTTGGTGCAACTATAGAAGGGAAAGGCTGGGCGGGGGTCACACATACCT- CAGTGGCAGGCAGGCAGGCGGCAGGCAGAGCGCGCTCTCCGGGCAGTCTGAAGGACCGCGGGAATGTGGAGGGG 1338 GCCAGGGTGTCTTGGCTCTGGCCTGAGTCGGGTATGTGAAAGCCTTTTGGGGCAGGAAGGG- GCAAAGTGATACCTGGCCGTCCCACCCTCTGGTCCCAGAAGGAGCTCTCGCTGGAGCCAG- GCAGCCTCCAGTCCCCCTCCTTTCAGCCTTGTCATTCTCTGCATCCTGCCCAGGCCACAAAGGA 1339 CGGCTCCGGCGGGGAAGGAGGCgggctgcggctgcggctggggctgaagctggggctggggttgggg- GACTGCCCGGGGCTTAGATGGCTCCGAGCCCGTTTGAGCGTGGTCTCGGACTGCTAACTGGACCAACG- GCAACTGTCTGATGAGTGCCAGCCCCAAACCGCGCGCTGC 1340 GCCAGGGTGCCGTCGCGCTTGGCGCCGTCCAGGGCGGCGCTGCGCTCGTCCAGCAACACCACGGCGT- GGTAGGCGCCGGCCAGCAGGCGGCCGCGGAGCTCGGCGTTGGGCACGATGTGCTCCAGGCCCATGGCGCCCT- TGGCCCGGCGCCGCACGATGGTGCTGAAGCGCACGTTGACAGAGCCGGCGATGTGGCCGGCGTTGAAAGCGAAG AAGGAGCGGCAGTCCAGCAGCAGGCATTGCGCCGCTCGCTCC 1341 CGGCTCGGTCCTGAGGAGAAGGACTCAGCCGCGGCTGCGGGACCCGGGCACCGGGAggcggtggcg- gcggcggcggcggcagcagcggcgacagcagaggaggaagaggaggaagaaggaaagaaaaagaagaaCCAG- GAGGAGTCCTCAACAACGACAGCGGGGACTGCGGGACCAGGGTAAAGCGGCGACGGCGGCGACGGCCCAGCAAC CGTGA 1342 CGCGGGGAACCTGCGGCTGCCCGGGCAAGGCCACGAGGCTTCTTATACCCGGTCCTCGC- CCCTCCAGCGCCGGCCTCGCCCGCGCTCCTGAGAAAGCCCTGCCCGCTCCGCTCACGGCCGTGCCCTGGC- CAACTTCCTGCTGCGGCCGGCGGGCCCTGGGAAGCCCGTGCCCCCTTCCCTGCCCGGGCCTCGAGGACTTCCTC TTGGCAGGCGCTGGGGCCCTCTGAGAGCAGGCAGGCCCGGCCTTTGTCTCCG 1343 CCGCCGCTGCTTTGGGTGGGGGGCTGACAGGGCTGCGCGCGTCGCGCTCTTGGCTGGGGCTGCGCGG- GCCCGGGGCGCTGCGGGCGGCTCAGCGGCAGCTGCCGCGCTCTGCGCCTCCTCTGGGCGCACTGCCTGG- GAGCACGAGACTGGTTTGTCTGATGCTGCTGCCGGAGCTGAGGTCTTGCCTGGAGATCCGAACGAGACACCACG TCAACCGGCGCGGGGAGTCCCGTGAAGACATGAG GGCGCCAGGAG 1344 ACCTGAGCCCGCGGGGGAAccccccccccaccccoggggaaccccccccacccccgccgc- cccccgccTGCAAGTTGTTACCAGTAAATAAAAGGGATCCTATTTTAGCAAGCCACACAGCATTAGAGG- GCAAATAATAGTTTGGTGGCAGGAGAGCGATGAGACGGGAAAGTGTGGGGCAAAGCTTACAGTCATTGGTCCAG ATTCTAACTGGCCTGTTAGCCAAAAAGTAAGGTTTTCTTTACCTCCGTGTTG 1345 AACGCCGGCCTCACCGGCAGACGCGCGCCCTCCTCCCAGATGCGCAGGTGACCCCGGCGGGCG- GCGCGGGAAAGGGAAGAGCTCCGCGAGGCCGCGCGGGGGGGAAGCGGGAGAAGC- CGCTCTTCCTATTCCACTCGCAGTCTGCGTGTGGGGGAAACGAGTGCCCGGCGTATGAAACGCCTAACTTCGCG AAATAAAGAGAGACGTATAAAAGTTCAAGAATTCTGTCCAGACTCAAGGGCCCTTTCTCATTTA 1346 CCGTGGTCCCAGCGCTCCTGCTATTTGCATTCCAAAGCAGACACCTCATGCGCTCAACCCCGC- CCGCAGGCGGCTCCCGCAGTCTAAGGGACCTGGCGCGAGTCCGGGAAGCGGAGGGCGCAGCTGCGCAGG- GAAGGGGGCCGGGGGCGGGACCAGGGCGCGCGTTCCGGTCCCGGGGCGTGGC 1347 TGCGACCCGGCGCCCAAGCAGCCTGGGACCTTGCGCGGACCTGACCCCTTCAGACCGCAGGCAGTCT- GGGAGGAGGTCCGGCCGGGGGAGGTGCAGGATCCCCGCCGTGTCTCTTTGACGACTTGGGGACTGTCACG- GTTCTCTCCCGGCGCCCCTGGGTTCTTTTGTCCTGCACGCGGTGCGAAGGGGCCAGCAGGGAAGGAGCAGAGGA TGGGGGGTGGGGTTGTTGGAGCCCCGCGGAGGTCTGGGAGGCCC 1348 GGCTCTGCGCTGCCTTTGGTGGCTCCTCCCTGGTCCTCTAAATGTGACACCAGGCGGATGCGGGGC- CACAGGACCCTGGGGCTTGAGTCACACAAGAATGTCTCTGGGAGACCCGAGAGACTCACAGTTATGAAACAG- GACCATGGTTCTTTggccgggcgcgggg 1349 gcgcgggcggcTCCTTTGTGTCCAGCCGCCGCCACCGGAGCTCCCGGGGCCTCCGCGGG- GAGCGCGTCCCCCGCATCCGCCCGACCCCCGGGGCTGGCACGTGCTGCGCCCGGTCCGCTGAGGGGGCGGAG- GCCCCGATCTCCCCGACCCCCCTTCTCTGCTTAGAGGAGGAGGAGCAGCGGCAGCGGCAGCAGGAGGCGACAGC TGCCAGCCGAGGAGGCGCGGCGGAGAGGGGACTGCGGTCAGCTGCGTCCA 1350 GGCCCGTTGGCGAGGTTAGAGCGCCAGGTTGTAAGAATCGGGTCTGTGGACCTCATACCAGATAG- GCGCGAACGCCTCTGGCAGCGGCGTCCAGGGGGTCCGGCGGCACTCGCGGTGGGGCTGCCTGGGTTGCGGGT- GACGATCTGCGGGGTCCCGCACCCGGCCCCGCGGAGCCCGGACCCGCACGTAGGCGGCGCGGCAAAGGCACACC CTCCTCGCGGCCGCGAACCCAGCGCCGTCCTCGCAGCGCGGCAA 1351 acccggcatccgggcaggctgcgcgcgggtgcggggcgagggcgccgcggggACTGGGACGCACGGC- CCGCGCGCGGGACACGGCCATGGAGGACGCGGGAGCAGCTGGCCCGGGGCCGGAGCCTGAGCCCGAGC- CCGAGCCGGAGCCCGAGCCCGCGCCGGAGCCGGAACCGGAGCCCAAGCCGGGTGCTGGCACATCCGAGGCGTTC TCCCGACTCTGGACCGACGTGATGGGTATCCTGGTAAGTTACCTGG 1352 CCCGGACTGTAATCACGTCCACTGGGAACTGGCGCAGTAGTGGAGGGGACGCGATCAGGCCCGTG- GCTGCGCCCAGAGCATGATAAGCCAGGGACCTCGCGGCGCAGGCGGAGGGAGGGAGAGCGTCGCGGACCCAG- GCGGGGACAGGGAGACGCC 1353 CGCCGCCAACGCGCAGGTCTACGGTCAGACCGGCCTCCCCTACGGCCCCGGGTCTGAGGCTGCG- GCGTTCGGCTCCAACGGCCTGGGGGGTTTCCCCCCACTCAACAGCGTGTCTCCGAGCCCGCTGATGCTACT- GCACCCGCCGCCGCAGCTGTCGCCTTTCCTGCAGCCCCACGGCCAGCAGGTGCCCTACTACCTGGAGAACGAGC CCAGCGGCTACACGGTGCGCGAGGCCGGC 1354 GCTGCCAGCTGCCGCTCCGGCTCCCACTTCCCACCTGCTGCCCGAGGAAGACTTCCGG- GAGAAACGCTGTCTCCGAGCCCCCGCGCCGCCGCGCTCCCTCCGCTGCAGCAGCGGCCACCGGGTGCGCCCG- GAGCCCTGGGACGGCCTAAACCAGTATCTCGCGGGCCCCGCGCCGGGCTCCGGGAATGGCCGCAGCAGCCCTGG CGACCCGGGCCCCTCGGAGCTCCCCTTCAGGATCGTGCACCAAGCGCGCAC 1355 GCGCCCACCTGCGCCTCGCGGGGTCCCCGAGGTCCCGCCACCGAGCGCCCAAGGCGG- GATCCCAGCGCGTCCTGCAGCCCGCCCAGCTTCAGGGCCGGCCCGGCGCGCGCAGGTGCGGCACTCACCGGC- CAGGTGAAGCCGAAGGGGAAGCGGATGGGGTTGCTGAACGCGGAGTCGGCGCCCCCGCCGTCGGGCAGACTGAA GGAGTCGACGCCCAGCACGGGGGTGACGGCGCTGCCGTAGGTGCAGGGCGGC 1356 CGGGCCAGGGCGGCATGAAGAAGTCCCGCCGCTACGTGCCCGGCACAGTGGCCCTGCGCGACGTTCG- GCGCTACCAGAACTCCGAGCTGCTGATCAGCAAGCTGCCGCTCCTGCGAGAGCTCGGCGGTGACGCCGCT- GCACGAGAGCGA 1357 GCTGCGACCTGGGGTCCGACGGACGCCTCCTCCGCGGGTATGAACAGTATGCCTACGATGGCAAG- GATTACCTCGCCCTGAACGAGGACCTGCGCTCCTGGACCGCAGCGGACACTGCGGCTCAG- ATCTCCAAGCGCAAGTGTGAGGCGGCCAATGTGGCTGAACAAAGGAGAGCCTACCTGGAGGGCACGTGCGTG- GAGTGGCTCCACAGATACCTGGAGAACGGGAAGGAGATGCTGCAGCGCGCGGG 1358 GTTAGGAGGGCGGGGCGCGTGCGCGCGCACCTCGCTCACGCGCCGGCGCGCTCCTTTTGCAG- GCTCGTGGCGGTCGGTCAGCGGGGCGTTCTCCCACCTGTAGCGACTCAGGTTACTGAAAAGGCGG- GAAAACGCTGCGATGGCGGCAGCTGGGG 1359 AGCGCACCAACGCAGGCGAGGGACTGGGGGAGGAGGGAAGTGCCCTCCTGCAGCACGCGAG- GTTCCGGGACCGGCTGGCCTGCTGGAACTCGGCCAGGCTCAGCTGGCTCGGCGCTGGGCAGCCAGGAGCCTGG- GCCCCGGGGAGGGCGGTCCCGGGCGGCGCGGTGGGCCGAGCGCGGGTCCCGCCTCCTTGAGGCGGGCCCGGGC 1360 CGGCTGGCCCCGCCCACTCTCCGCGGCCGGAAGTGGCGGCGCCGAGTGAGGTAAATGCGTGCCCG- GAAGCGCGACCTCGGGCGGTTGGAGGGGCTACCGGGTCTTACCAGTCCGTGGCGGGAGTCCCGGAGGAC- CCTCGACGGGGGAGTTGCCGAGAAAAGGCCTCGCCGGCA 1361 GGGGTTGCCGTCGCAGCCAGCTGAGTGTTGCGCCAGGGGGACAGGTATGTTCCAGGCAGTGGCAAGC- CCAACCCGAGCAAGACCTGCGCTGAAACGGATTGGCTGCCCTCCGCCCGGAGTCCGTTCTCCCTGCAGCGGC- CAGTGCAGAGCTCAGAGGCTCAGAAACTCGCTCTCAGCCCCCTGGAGGCGGAGCCCGGGAGATAAGGTTCGCGC TCCCCACCCGCC 1362 CCGCACTCCCGCCCGGTTCCCCGGCCGTCCGCCTATCCTTGGCCCCCTCCGCTTTCTCCGCGCCGGC- CCGCCTCGCTTATGCCTCGGCGCTGAGCCGCTCTCCCGATTGCCCGCCGACATGAGCTGCAACGGAG- GCTCCCACCCGCGGATCAACACTCTGGGCCGCATGATCCGCGCCGAGTCTGGCCCGGACCTGCGCTACGAGGTG ACCAGCGGCGGCGGG 1363 ggaccccctgggcagcaccctggccacccttccatccacaacatccagaccacacggccaagg- gcacctgaccctgtcaaaaccccaaatccagctgggcgcggtggctcatgcctgtaatcccagcatttgggag- gccgaggcagccgg 1364 gaggcagccggatcacgaagtcaggagttcgagaccagcctgaccaacatggtgaaaccccgtctc- tactaaaatacaaaaattagccgggcgtggtggtgcacacc 1365 GCGCGTGCGGGCGTTGTCCCGGCAACCAGGGGGCGGGGCTGGGCGTGGCACCGCCCCGCGCTCCGCT- GCCAGGGGCGGGAGGGAGGAATGGTTGCTTCACGCCCCGGGGGAAGAGACGGGAAGCTCGGCTCTGGGT- TGCGGGCCCCGGCGTCTCCGCGTGGGGCGCACCGTCCGACCCCCCCCTCCCGGTGTGCAGCGCCCCGCACCGCC CCGCCTCGCCTGGGAGAAGCCGCCGGGACGCGCC 1366 CAGGATGCGGCAGCGCCCACCCGCGCGGCGTGGAGGGGGCCGGGGGCGGCGCTCGGCGCAGATG- GCGCTCGCTGCGAGATGGATGCTCCAGGGCGGGTAATCACTCCTGGCTCAACACAGCATCCCGGGCGGAGCG- GATGCCAGATCCCACCGCTAAGAGCCTGGGCTGGGAAAGCAATCTTTCCAGGCAGCCCCCAGCCCGGTGCGCCG GCCCCGACAAGTCCCAGCCCTCGGAGGCAGGGCGGGGCGCAGGGA 1367 gATGCGGCCCGCGGAGGAGAGAGCAGGAGGACGGACGGGAGGGACCTCCGCGGGGAGG- GCGCGCgggggaggcggggagggaggcgggagggggaggggACGGTGTGGATGGCCCCGAG- GTCCAAAAAGAAAGCGCCCAACGGCTGGACGCACACCCCGCCAGGCCTCCTGGAAACGGTGCCGGTGCTGCAGA GCCCGCGAGGTGTCTGGGAGTTGGGCGAGAGCTGCAGACTTGGAGGCTCTTATACCTCCGTG 1368 GTTCTGCGCGCGCCCGACTCCGCTGCCCGCCCCGCCAGGCCTCCGGGAGGTGGGGGCTGGGAG- GCGTCCCCCGCTCCCGCCCCCTCCCCACCGTTCAATGAAAGATGAACTGGCGAGAGGTGAGAAGGGAAGAGG- GCTCCCGGCTCTCTCGGGGCGGGAATCAGTGGGCCAGAGCTCGCCGGGTGGCCGCAAG 1369 CCCGCCGTGGGCGTAGTAAccgccaccgccgccgccccccgcgccaccaccaccgccgccT- GCCTCGCCTCTGCCCGAGCTGATGAGCGAGTCGACCAAAAAAGAGTTCGCGGCGGGGCTCTCCGAGCATGA- CATTGTTGTGGGATAATTTGGCGAAGGGAGCAGATAGCCCTTTCTGGCTGACATTTCTTGTGCAAAACATGCT- GAATACGATTAGCAATCCCCCCGCACCGCGGCGGGCGCCCGCAGCCAATC 1370 ACCCGCCCGGGCAGCTCCAGTCCCGGACTCCGCAGCTCGGAGCGCAGCCAGCCACGGCCATTGCGG- GACCCTATTTATCCCGACACCTCCCCTGACGTGGGCTCGGAACGCTCCCTTGGCAGCTGCAGCCGCGGCGCGG- GCTCCCCCTCGGCCGCCCCACCCCCAGGCCCGTCGGTGCAGAAGCGGTGACATCACCCCCTCTGGGCCGCAGC 1371 CAGCGGTCGCGCCTCGTCGGGCGACGGCTGGCAGCGAAGGCCGGAGCCACAGCGCTCGGTGTAGAT- GCCGCACGGCTGGCCCTCGCTCAGTGCGCACGTCAGGCAGCAGCCGCAGCCCGGCTCGCGCAC- CAGCTCCGCGCACACGGCGGGCGGAGGCGCGCACTGGGCCAGTGCACGCGCGTCGCACGGCTCGCAGCGCACCA CGGGACCCAAGCCCGCCG 1372 GCAATCGCGCTGTCTCTGAAAGGGGTGGAGAAGGGGCTGGATGAGTCCGGAAGTGGAGATTGGCT- GCTTAGTGACGCGCGGCGTCCCGGAAGTTGACAGATACAGGGCGAGAGGCAGTGGAGGCGGGACTTG- GATAGGGGCGGAACCTGAGACTACCTTTCTGCGATCACAGGATTCCCGGCGGTGACTTGACCCCGGAAGTGGGG TGTGAAGCTCCGGTGCTGGTGCGGCGGGGGA 1373 GAGCGCCCGCCGTTGATGCCCCAGCTGCTCTGGCCGCGATGGGCACTGCAGGGGCTTTCCTGT- GCGCGGGGTCTCCAGCATCTCCACGAAGGCAGAGTTGGGGGTCTGGCAGCGCGTTCTGGACTTTGCCCGCCGC- CAGTGCGATTCTCCCTCCCGGTTCCAGTCGCCGCGGACGATGCTTCCTCCCACCCACCGCCCGCGGGCTCAGAG AGCAGGTCCCCGCACCGCGC 1374 CATGGCCCGCTGCGCCCTCTCCGCCGGTTGGGGAGAGAAGCTCCTGGAGCGGCCAGATACCTGTTG- GCTCCTGAGCAGCATCGCCCAGTGCAGCCTCCGTCAGGAAAAGCAGCAGAATCGACAGCCCCAGGGGGC- GAGCGGGGTCCATGGTGCAGGGGGTCGGGCGGCCCGCTGGGCAAGGCGTCCGAGAAAGCGCCTGGCGGGAGGAG GTGCGCGGCTTTCTGCTCCAGGCGGCCCGGGTGCCCGCTTTATGCG 1375 GGGGGCGGGGTGCAGGGGTGGAGGGGCGGGGAGGCGGGCTCCGGCTGCGCCACGCTATC- GAGTCTTCCCTCCCTCCTTCTCTGCCCCCTCCGCTCCCGCTGGAGCCCTCCACCCTACAAGTGGCCTACAGG- GCACAGGTGAGGCGGGACTGGACAGCTCCTGCTTTGATCGCCGGAGATCTGCAAATTCTGCCCATGTCGGGGCT GCAGAGCACTC 1376 CGACCCTGCGCCCGGCAGTCCCCGGGGGCCGTGCGCCCGGCCCAGGCTCGGAGGTCCAGCCCAGCG- GCGGCTCAGGCTGCGCGCCTGGCTCCCAGCCTCAGTTTCCCCATTGGTAAAGCATTGACGGTGGTTGCGGACG- GCTTCTGCGGACAGAGCCTTGGGCTCCGACGTCTGCGCGG 1377 GGCTTCAAGTCCACGGCCCTGTGATGGGATGTGGGCAGGGCCTGAGACAGGCCGAAC- CCAACTCTTCACAGGGCCGAATTCTTTGCCCGCAGCCCAGCACCCCGAAGGAGCTTGCCTCGGCTTCAAG- GCGCACCTAATGGGCACCGGATCGCTGGGGCGCTGAGGATGCCGCTCCGGGGCCTCCACGAGGCGGCCTCGCCA CGCGCCTCGGCCA 1378 CCCCACCTGCCCGCGCTGCTTCTACCTGAAACTGGCCAAGGGCCCGAGCCCGGACCGGAGCCGT- GACTTCCCTCCGCCGGCCACGGGGCTGCCCGGATCCGCCGGGTTATGTCGCTTGGCTTTGGGCTCAGGGGT- CACCGTGGGCAGAGGGGGGTGCCGGGGTCGCGGACTGCCACCAGGTTGAGGAAAGGAGGGGCCTTTTGGCTGGG GAAAGAGCGTGGTGGGGGACCCGCGGCCGATGGAATCCCTGGGGCA 1379 gcgcgcggagacgcagcagcggcagcggcagcATGTCGGCCGGCGGAGCGTCAGTCCCGCCGC- CCCCGAACCCCGCCGTGTCCTTCCCGCCGCCCCGGGGTCACCCTGCCCGCCGGCCCCGACATCCTGCG- GACCTACTCGGGCGCCTTCGTCTGCCTGGAGATTGTAAGTGGGGCCGCCGGAGCGAGGGTCGCGCGGGGAGCGA GGACAGGCGGCGGCATCCTTGTCCCCCGGGCTGTCTTCCTCTGCGTCCGC 1380 GTGAGCCGGCGCTCCTGATGCGGAGAGGTGCGGCCATGTCCTGGCTGGGAGCGAAGCGC- CCTCGCTCGGGCAGTCGGAGCGAACTGTCTCCCGCGCGCTCCGCCAGCCGGGCCCTCCCGCTGGGCCCAC- CCCCCGAGGGGCGGGGCCAGAGCGGGCGGCACCGCCTCCTCCCCGCTGTCTGGGTCGCAGGCCTTAGCGACGGG CTGTTCTCCGGCCCCGCCCCATTCCCAGGCTCCGCCCCC 1381 TGCCGCGGGGGTGCCAAGGGAAGTGCCAGCTCAGAGGGACCATGTGGGCGCAGGCACCCAGGCG- GCGCCGGGAGGCCTCTCGGGACTCCAGGGCTGTCCCTCCCGCAGGCTGTCCTTCCACCTCCACCCCAGGC- CAACGCCCTCCCGCCAGCCCAGGGTCCTGTGTCCTCGAGTCCTTCCTGGGCACCCTGGTCCCATCCTTAGCCCT GCCCGAGGGGCCCAGCCCTGCTCCAAAAGGGCTGTGGCTCCACCCAC 1382 CTGCTGCGCGCGCTGGCTCTTCTGCGAGGCCTGCTTGAGCTTGTTGCCGCCTTTGGGCTCCGGGC- CCTCCAGCTCGTCCCTGCAGCGCCGCGGCCGCTCCTCGTAGGCCAGGCTGGAGGCAAGCTCCTTCTCCT- CAAAGCTGCGCTGCAGCTTCTGGAGGGCGCCCTCCCTCTCCAACAGCTTCTGCTCCAGCTCCTGGATGCTGCAC TCGTCCGTGGAGATGGGGGAGCGG 1383 CTGGCGGCCCAGGTCGCTCCTGCCCAACCCGGGGACCCATCTCTTCCCCCGACTCCGACGACTGGT- GCGTCTTGCCCGGACATGCCCGGCCGCAGGCGACCCGGGCCACGCACCCCCGCCGTGTCCCCCTCTCTCCCT- GCCCTCTCCAGGCGCCAGGCACGCTCTTCCCCAGCCAGGGACCGCGGCGGGGACTCACCAACAGCAGGACCGCG GCGACAACGAGCACAAGGGTCTTGGGGACCCGGGGCCCAGGCC 1384 AGCGCCCCGGCCGCCTGATGGCCGAGGCAGGGTGCGACCCAGGACCCAGGACGGCGTCGGGAAC- CATACCATGGCCCGGATCCCCAAGACCCTAAAGTTCGTCGTCGTCATCGTCGCGGTCCTGCTGCCAGT- GAGTCCCGGCCGCGGTCCCTGGCTGGGGAAGAGCGCACCTGGCGCCGGGAGGGGGCAGGGAGACGGGGACACGG CAGGGATGCCTGGCCCTGGTCACCTGCGGCCGGGCA 1385 GCCGCACGGGACAGCCAGGGGGAGCGCGCGCTCTGCTCCCTCGCGGCCCGGTCGCTCCTGCCCAGC- CCGGGCACCCCACTCTTCCCCTGACTCCGACGGCGGGTTCGTCCTGCCCAGACATGCCCGGCCGCAGGCGAC- CCGGGCCAAGCATCCCCACCGTGTCCCCCTCTCTCCCTGCCCACTCCCGGCGC 1386 CCCGGACATGCCCCGCCACAAGTGACCCGGGCCAGGCACCCCCGCCGCGTCCCCCTCTCTCTCTGC- CCCCTCCCGGTGCCAGGCGCGCTTTTCCCCAGGCAGGACCGCGGTGGGGACTCACCTGCAGCAGGAC- CCCGACGACGACAAACTTGAAGGTCTTGTGGACCCGGAGCCGAGGGCTGGCTTCCCGCGCCGGCCTGGGT 1387 cgggggccgccgcctgacttcggacaccggccccgcacccgccaggaggggagggaaggggag- gcggggagagcgacggcggggggcgggcggtggaccccgcctcccccggcacagcctgctgaggggaa- gagggggtctccgctcttcctcagtgcactctctgactgaagcccggcgcgtggggtgcagcgggagtgcgagg ggactggacaggtgggaagatgggaatgaggaccgggcggcgggaa 1388 CAGTGGCGGCCCTCGGCCTGCGGTCGGAGGCGGCGCGGGCGGGGAGGCGGCGCTGCGGGCTGGGT- GCGCCCCGGCTCCCGGAGGTGCGGCGAGCAGGAAggcgcggggcggcgggcgcgcggcACTGACTCCGGAG- GCTGCAGGGCTGGAGTGCGCGGGGCTCCTACGGCCGAGCCCTCGGAGCCGCCCCGCGCAGCCAATCAGCTCCCG GCGGGGCGAGCCGCACTCGTTACCACGTCCGTCACCGGCGCG 1389 GCCCGGCGCGGATAACGGTCCGGCGGGAGGACACGGCGGTCCCTACAGCATCGCGGCGGGCCAG- GCTCGGGCAGGGGCCGTGCTCAGGTGCGGCAGACGGACGGGCCGGCGCCTCTGAAGTCACCCG- GCTCCTTTACGAACTGAGCCCGTTTTGGCTGGGAGGGTT 1390 GCTCCGGGTGGGGAGGGAGGCTGGCAGCTCACCCCCGGGGGCGAGGGGTCTGCGTTAGCCGTAGC- CACGGGAGCCCGGGCTTCTGGGACGCTCAGCCGTGCGCTACCCGGTGCAGCTGCTTTCTCACCAGCTCGCGG- GTGGGTCCTGCCGCGGCTCGGCGACCCGCGCCCCCTTGCGAGCGACCCAGCGTGAAACCAGCCCAAAGGGCG- GCCTCGCCCG 1391 GCCTGGGCGCAGAACGGGGTCCCTCGGCAGGACCCTCGCCGCGACAGCCTCAGCAGGGGATCGTC- GAGCAAAAGCCCGCAGGAATGCTCCTTTCTGGGGCCCCGCCCTCCCGGCCGACAGCTTTTAGGTAGACGTG- GAGGCGACTCAGATCGCCTCGCGGTTCCCGGGATGGCGCGGTCGCCCCCAACGCGAGGCTGCCTGGGGCACCCG GCTCTTTTCCTGGGCGTCCGCGGCC 1392 GGTCCTAATCCCCAGGCTGCGCTGACAGGATTAGGCTCCGTTCCTCCCCATAATGTTCCCAGGAC- GAGCCTCATGGGGACGAACTACAAATCCCAGCATGCACCAGTCTTCGCCCGCCCGGCGGGAGGGCAACGGCT- GACCAGGACCGCAGGCAAGCACCGCGGCGACGGTTCCAGCCAGGAAAATGAGAGCCTCTTGGGCCACGTTCCAA ACGG 1393 CCGCGTCCCCGGCTGCTCCTCCTCGTGCTggcggcggcggcggcggcggcggcggcgCT- GCTCCCGGGGGCGACGGGTGAgcggcggcgcggcgggcgggcgactgcggggcgcgcgggccggacccg- gccTCTGGCTCGCTCCTGCTCTTTCTCAAACATggcgcggggccgggggcgcaggtggcggcgccggggcccgg gccgggctctcgtggcgccgcgcggctcggcggctgccgggcgAACCGCAAGC 1394 GGCAGGGCTGACGTTGGGAGCGCTATGAGCTGCCGGGCAGGGTCCTCACCGGGGGCTTCCTCTGCGG- GCCAGGGCTGCCGGGCGCCACCGGGACGCGAGCGCGCACGCCTCGGCCCGGCGGCCGCGCTCCTCGCAC- CGCCTTCTCCGCAGGTCTTTATTCATCATCTCATctccctcttccccttctccttctcctttgcctccttctcc tttgcctccttctcctcctcttcctccccctcctccaccaccacc 1395 CCGTGGGCGCAGGGGCTGTGGCCGGGGCGGTGGGCGGGCGGTGCCGCCAGGTGAGACTGGCTGCCGT- GGCGCGGAGCTGCGAACTGGTCGGCGGCGCAAGGCGCGGACTCCGGTGAGTTGTGTGGAGCGCGCGCGGCCAT- GGGCGCGGGCCACGGGCGGGTGGGAGGGTGGGGGGCCAGAGGGGCGGGGGAGGGTCACTCGGCGGCTCCCGGTG CCGCCGCCGCCCGCCACCGCCTCTGCTCCCCGCG 1396 cctgcgcacgcgggaagggctgccggaggcgcccgtagggaggcgcgcgcgcgggcggctcagggc- ccgcgttcctctccctcccgcctaccgccactttcccgccctgtgtgcgcccccacccccaccac- catcttcccaccctcagcgcgggcgccc 1397 GCGGACGCAGCCGAGCTCAAAGCCGCTCTGGCCGCAGGGTGCGGACGCGTCGCGGAGTCCTCACTGC- CCCGCCTCGCTCTGGCAGAGTGGGGAGCCAGCCGGCAAAGAATTCCGTTTTCAGCTGGGCCAAGGGGCCG- GCGTCTCCCCACCCCCTTAGGCTCCGCCCCCTGTCCGCTGTGATCGCCGGGAGGCCAGGCCC 1398 GACCCATGGCGGGGCAGGCGGCGGCGCTGTCGGGCGGGCAGGGGTGGCGGGAGGCGGTGGCGCAGC- GAGCAGCGGCCTCCAGCGCTGGTGGCTCCCTTTATAGGAGCGCTGGAGACACGGGCCCCGCCCGCCCTGCAGC- CCCGCCCTGCAGTCCCGGAGCGCCGAGGAGTGCGCGCCCCCTCGCCCCCGCCCCACCTCGGCTGGGAGGCTGGT GCGGACGCCGGGTG 1399 ccgctccccgcccctggctccgcctggc- cccactcccctccgcgcgccttccctcttctcccccgctccccGCGGACGCTCCTCTCTTTCCCAGTGGGC- CAACTTTATGCTGAAATTTCTTTTCTGCCCTTTTTTGGGATGTTTCCCCATTGGGAGGCGGAGCCGGGCTGCGG CGGGGAAGGCGGAGGGCGAGGGGAAGAGTCACTGAGCTGCGGGGCATAGGGGGTCCGGGGCGAGGT- GCCTTCTCCCACCCAG 1400 tgtgccgcgcggttgggaggagggtcgtgagcgtgagcgtgggagcgctgggggctctgctcgcgt- gctgctctgaagttgttccccgatgcgccgtaggaagctgggattctcccatccggacgtgggacgcaggg- gaggggtaggtttcaccgtccgggctgatgactcgtggcctccggggctcctgg 1401 CACTCACGCTCTCAGCCCGGGGAATCCCAGCGGGGAGGAGGGAGGGAG- GTCGTTTTCTTCAGCTCCCCAGGTGGTCTGTGCTGGGTGTGCTGACGGTCCTTTTGGGAAAACAG- GTCCACCTTTGCCAGCGTAATTCAGAAAGAGATGTAATTTTCTGAGAGCACACACCTGGGCAGGAGATCGC 1402 GGCAAGCGGGCTTCGGGAAGAATGCAGTTGGTGAGGAAGCTCGGCGAGGCGTGCCCGTGCAGCTGC- CCCTGGCCCTGACTGCTGGTGCGAGGCAGTGCACGACTCAGCTGGCCGGGGCCTGCTGTCCCGCCGGTGC- CACGCACCTGCAGACGCCCGGGCTGTGCCATCTCCTGGGCCGGTCCGGGGGCTGGGGCGGGGCGAAAAAGAAAA AGCTCTGATCTCTGCCTTCGCCTCGCGCAGCTGTGCGGCGAGCCC 1403 CCCGCGGGCCGGGTGAGAACAGGTGGCGCCGGCCCGACCAGGCGCTTTGTGTCGGGGCGCGAG- GATCTGGAGCGAACTGCTGCGCCTCGGTGGGCCGCTCCCTTCCCTCCCTTGCTCCCCCGGGCGGCCGCACGC- CGGGTCGGCCGGGTAACGGAGAGGGAGTCGCCAGGAATGTGGCTCTGGGGACTGCCTCGCTCGGGGAAGGGGAG AGGGTGGCCACGGTGTTAGGAGAGGCGCGGGAGCCGAGAGGTGGCG 1404 GGCGGCGGCTGGAGAGCGAGGAGGAGCGGGTGGCCCCGCGCTGCGCCCGCCCTCGCCTCACCTG- GCGCAGGTAGGTGTGGCCGCGTCCCCTACCCGGCCGGGACTTTCTGGTAAGGAGAGGAGGTTACGGG- GAACGACGCGCTGCTTTCATGCCCTTTCTTGTTCTACCTTCATCGGCCGAGGTAAAAGTGCTGAAACCATGTGA ATAAAATACAGGTGGGTTCCGCCAGCTTCGCTCC 1405 GGGCCCCGGGACTCGGCTTGCACGAGCCAGTCTGGGGACCGGGGAGGCGGGGAGAGGGAAGGG- GAAAGCGCGGACGCGGCCCAAACCTCCAGTAGCCGCAGCCGCCGTCGCCGAGTAGGGCCGGGCAGCCAGCCGG- GCCTGGCGCAGCATCAGTGCCCGCTGCCGCTTCCGCTCGATACTCGCCCGCACCGAGGCAGGCAGCTCCGCGGG TTGCTCTAAAGCCGCCGCCTCCGGCAAAGCCCCGTCGGCCGCC 1406 ACGGAATGTGGGGTGCGGGCCTGAATATTATAAACAAAACCAAAAAACACTGGCTGGAAAG- GAAGTAAGCGGATTCTTCGTAAAGTCTATCAAAAGTCTTTTCGTTTCCCCCTCCCCCTTTCCCCACCGCCCAC- CAAAATGAGCCGCGTTTGAGCACCTCAGGTCTGGAAAGCCGGCCAGGAGTGGGGGAGACCGAGGCACCCGCGGC C 1407 GCGGCTGCTGCCGAGGCTCCTGGTTTCCACCGCCGCCCTCGGGGATCATGCCGCCATCGCGGTTCAT- GCCGTTCTCGTGGTTCACACCGCCCTCAGGGTTCATATTACCCATGAGGCCTGGAGCTCCTTGGCCAACATG- GCCTTCTGCGCTTGATGCTGCCCCCAGCTGAGGTGTGGGGCTTATTTTTACCTGGTATACACTCAGGCAGTAGA ACACGGTGTCGTGGACGAGCGAACGCGCCATGGCTGGAGCGC 1408 CCGCTGCGCGAGGGAgggggcccgaggcgcccccggcccgcccTCCTCCCGGTCTTCGGATCCGAGC- CGGTCCTCGGGAAAGAGCCTGCCACCGCGTCCCCGCAGCCACCCTCTCCGCGTGCCCGGCCCTCTCCAGTG- GCGGGGGCACGTGGGCGCGCGGGGTGCGTGGCAAGCCGCCCCTCTCCCCACGCCCGTCCGGC 1409 GGGGTGCGGCGTCTGGTCAGCCAGGGGTGAATTCTCAGGACTGGTCGGCAGTCAAGGTGAGGACCCT- GAGTGTAAACTGAAGAGACCACCCCCACCTGTAACAAAGAGGGCCCCACTAAGTCCCGCTTCTGCATTTG- GTCCTGAGAGGCTCCGGTAAAGCCGTCCGGCAATGTTCCACCTGGAAAGTTCCAGGGCAGGGGAAGGGTGGGGG GAGGGGCAGTCGCGGGGGA 1410 GCCGGGGGAAATGCGGCCTCTAAGCTCTCCGCTGAGGCGGCTTGGAAGGAATAGTGACTGACGTg- gaggtgggggaggtggctggcccgggcgaggcccagggagagggagaggaggcgggtgggagaggaggagggT- GTATCTCCTTTCGTCGGCCCGCCCCTTGGCTTCTGCACTGATGGTGGGTGGATGAGTAATGCATCCAGGAAGCC TGGAGGCCTGTGGTTTCCGCACCCGCTGCCACCC 1411 tgcctggtaggactgacggctgcctttgtcctcctcctctccaccccgcctccccccaccct- gccttccccccctcccccgtcttctctcccgcagctgcctcagtcggctactctcagccaacccccctcac- cacccttctccccacccgcccccccgcccccgtcggcccagcgctgccagcccgagtttgcagagag- gtaactccctttggctgcgagcgggcgagc 1412 GCGCGGGCGCCTCGATCTCCCGCGCGCGCGCGTGCGCGAGACCCCCCTTTGGCCCCCTACCCT- GCAGCAAGGGTAGCGTGACGTAATGCAACCTCAGCATGTCAGCAGCAATATAAAGGAGAATGAGGCG- GCGCGCCTCCCAGACGCAGAGTAGATTGTGATTGGCTCGGGCTGCGGAACCTCG 1413 CCCGGCTGGTCGGCGCTCCTCGCAGGCGGTGTCCCGGTCCGGAGCGATCTGCGCGCTCGGCCCCGCG- GCCGCGCCCTCCCCGAAGCCCTTGCTTTGTTCTGTGAGCGCCTCGTGTCAGCCAGGCGCAGTGAGCT- CACGGGGGCGTCCCGGGTCCGCATCCTCCCAGGAGCTGGGGAGCCGCTCGCTGGGCGCGGACCCGCTGCCTGAC GCTGCAAACTACACGGTTTCGGTCCCCCGCGC 1414 CCGGGGCTGGGACGGCGCTTccaggcggagaaagacctccgcgggccgcgcgcggccttccccctgc- gaggatcgccattggcccgggttggctttggaaagcggcggtGGCTTTGGGCCGGGCTCGGC 1415 GGGCGGGGTGGGGCTGGAGCtcctgtctcttggccagctgaatggaggcccagtggcaacacag- gtcctgcctggggatcaggtctgctctgcaccccaccttgctgcctggagccgcccacctgacaacctct- catccctgctctgcagatccggtcccatccccactgcccaccccacccccccagcactccacccagttcaacgt tccacgaacccccagaaccagccctcatcaacaggcagcaagaaggg 1416 GTGCGGTTGGGCGGGGCCCTgtgccccactgcggagtgcgggtcgggaagcggagagagaagcagct- gtgtaatccgctggatgcggaccagggcgctccccattcccgtcgggagcccgccgattggctgggtgtgg- gcgcacgtgaccgacatgtggctgtattggtgcagcccgccagggtgtcactggagacagaatggaggtgctgc cggactcggaaatggggtaggtgctggagccaccatggccagg 1417 GGCGGTGCCTCCGGGGCTCAcctggctgcagccacgcaccccctctcagtggcgtcggaact- gcaaagcacctgtgagcttgcggaagtcagttcagactccagcccGCTCCAGCCCGGCCCGACCC 1418 GGCGGTGCCTCCGGGGCTCAcctggctgcagccacgcaccccctctcagtggcgtcggaact- gcaaagcacctgtgagcttgcggaagtcagttcagactccagcccGCTCCAGCCCGGCCCGACCC 1419 CGGGAGCCCGCCCCCGAGAGgtgggctgcgggcgctcgaggcccagccgccgccgccgccgccgc- cgccgccgcctccgccgccgccgccgccgccgccgccgccgcgctgccgcacgccccctggcagcg- gcgcctccgtcaccgccgccgcccgcgctcgccgtcggcccgccgcccgctcagaggcggccctccaccggaag tgaaaccgaaacggagctgagcgcctgactgaggccgaacccccggcccg 1420 TCCTGCCATCCGCGCCTTTGCActtttctttttgagttgacatttcttggtgctttttg- gtttctcgctgttgttgggtgctttttggtttgttcttgtccctttttcgtttgctcatcctttttg- gcgctaactcttaggcagccagcccagcagcccgaagcccgggcagccgcgctccgcggccccggggcagcgcg gcgggaaccgcagccaagccccccgacacggggcgcacgggggccgggcagcccg 1421 AGGCACAGGGGCAGCTCCGGCACggctttctcaggcctatgccggagcctcgagggctggagagcgg- gaagacaggcagtgctcggggagttgcagcaggacgtcaccaggagggcgaagcggccacgggaggggggc- cccgggacattgcgcagcaaggaggctgcaggggctcggcctgcgggcgccggtcccacgaggcactgcggccc agggtctggtgcggagagggcccacagtggacttggtgacgct 1422 CGACCCCTCCGACCGTGCTTCCGgtgagggtcctgggcccctttcccactctctagagaca- gagaaatagggcttcgggcgcccagcgtttcctgtggcctctgggacctcttggccagggacaaggacccgt- gacttccttgcttgctgtgtggcccgggagcagctcagacgctggctccttctgtccctctgcccgtggacatt agctcaagtcactgatcagtcacaggggtggcctgtcaggtcaggcgg 1423 CCCGCAGGGTGGCTGCGTCCttccagggcctggcctgagggcaggggtg- gtttgctcccccttcagcctccgggggctggggtcagtgcggtgctaacacggctctctctgtgctgtgg- gacttccaggcaggcccgcaagccgtgtgagccgtcgcagccgtggcatcgttgaggagtgct- gtttccGCAGCTGTGACCTGGCCCTCCTGGA 1424 GCGTCTGCCGGCCCCTCCCCttgtccgtcccctccgcgccgctggcgcgcgccttctgaatgc- caagcattgccataaactccggggacaaaagcctgggtcacaaaagccccctctagaagttcacaccctgag- gcttccctggcaaggctgggggccgtttggcccttccatgtggactgcaaaaacagtgttggaatgcaggactc tgggtatgttctcgaaagttgttacaaccccaacccagggttgacc 1425 TAGGCCGCCGGGCAGCCACCgcgctcctctggctctcctgctccatcgcgctcctccgcgcccttgc- cacctccaacgcccgtgcccagcagcgcgcGGCTGCCCAACAGCGCCGGA 1426 GGGGAGCGGGGACGCGAGCAgcaccagaatccgcgggagcgcggctgttcctggtagggccgtgt- caggtgacggatgtagctagggggcgagctgcctggagttgcgttccaggcgtccggcccctgggccgtcac- cgcggggcgcccgcgctgagggtgggaagatggtggtgggggtgggggcgcacacagggcgggaaagtggcggt aggcgggagggagaggaacgcgggccctgagccgcccgcgcgcg 1427 GCCGGCTGGCTCCCCACTCTGCcagagcgaggcggggcagtgaggactccgcgacgcgtccgcac- cctgcggccagagcggctttgagctcggctgcgtccgcgctaggcgctttttcccagaagcaatccag- gcgcgcccgctggttcttgagcgccaggaaaagcccggagctaacgaccggccgctcggccactgcacggggcc ccaagccgcagaaggacgacgggagggtaatgaagctgagcccaggtc 1428 TCGCTCACGGCGTCCCCTTGCCtggaaagataccgcggtccctccagaggatttgagggacagg- gtcggagggggctcttccgccagcaccggaggaagaaagaggaggggctggctggtcaccagagggtggggcg- gaccgcgtgcgctcggcggctgcggagagggggagagcaggcagcgggcggcggggagcagcatggagccggcg gcggggagcagcatggagccttcggctgactggctggccacggc 1429 TCCCCGCTGCCCTGGCGCTCcccctttgatttattagggctgccgggttggcgcagat- tgctttttcttctcttccatcccatcctcccttctggtcctcctttccacagtgggagtccgtgctcct- gctcctcggttggctcctaagtgccccgccaggtcccctctcctttcgctctcccggctccggctcccgactct tcggcccgctggcatctgcttccctcccctgcctcgtttctcgtcgcccctgct 1430 GGCCAGAGGCAGGCCCGCAGCtccctgccccgcctctgtgcctccgccaacccgacaacgct- tgctcccaccccgatccccgcacccgcgcgaAGTGGGCCCTCCGGTCGTCGGC 1431 TGCCCGGGTCATCGGACGGGAGgccgcgccacgtgagggcggcaagagggcactggccctgcggc- gaggccccagcgaggggcgcttccCCGAGGGGCCAGCCTGGGCA 1432 CCCAGTGCGCACGGCGAGGCagtagcccggccccgcactgctgataggtgcaggcag- gacagtccctccaccgcggctcggggcgtcctgattggtgcggagccacgtcagtcgcacccggagaagg- gtctgggaggaggcggaggcggaGAGGGCTGGGGAGGGCCGCG 1433 AGCGTCCCAGCCCGCGCACCgaccagcgccccagttccccacagacgccggcgggcccgg- gagcctcgcggacgtgacgccgcgggcggaagtgacgttttcccgcggttggacgcggCGCTCAGTTGCCGG- GCGGGG 1434 TGCTCCCCCGGGTCGGAGCCccccggagctgcgcgcgggcttgcagcgcctcgcccgcgct- gtcctcccggtgtcccgcttctccgcgccccagccgccggctgccagcttttcggggccccgagtcgcac- ccagcgaagagagcgggcccgggacaagctcgaactccggccgcctcgcccttccccggctccgctccctctgc cccctcggggtcgcgcgcccacgatgctgcagggccctggctcgctgctg 1435 CGCTCGCATTGGGGCGCGTCccccatccgcccccaactgtggtgtcgcgacaggtcctattgcgggt- gtctgcggtgggaagggcggtggtgactgggagcATGCGGGGTAACCGCAGTGGGCA 1436 TGCGGCAAGCCCGCCATGATGtccacgtgacaaaagccatgatatacatatgacaacgcctgccata- ttgtccctgcggcaaaacccaacacgaaaagcacacagcaaagacaaagaggcccgccatgttttacactgcg- gcaagaccttcagccgccatcttttcctgtgTGACCGCACATGTCCACCACCATGC 1437 TCTTGAGCCTCAGGAGTGAAAAGGCCCCTTGggaaaccctcacccaggagatacacaggagcactg- gctttggcagcagctcacaatgagaaagaTGCCTGTCACAGCCTTTGCCTTCCTCTTCTATG 1438 GGACCATGAGTGTTTCCATGCTTGGCATCAGAcatgtcttctacccctattcagtctgtcatccact- ggtcaagaatcccaaacattctaaaactgtgtccacatctcttctgggtaactcttatgattggagg- gcttcctgaggtgtgaagtctatcacagatccagtgactaacttctagcttcatcttattctcacttaggggag aagagttgaggcccaagcaaacctcttcttaccattggcttagggaa 1439 tcagccactgcttcgcaggctgacgttactgacgtggtgccagcgacggagggcgagaacgc- cagcgcggcgcagccggacgtgaacgcgcagatcaccgcagcggttgcggcagaaaacagccgcattatggg- gatcctcaactgtgaggaggctcacggacgcgaagaacaggcacgcgtgctggcagaaacccccggtatgaccg tgaaaacggcccgccgcattctggccgcagcaccacagagtgcacag 1440 cggccagctgcgcggcgactccggggactccagggcgcccctctgcggccgacgcccggggt- gcagcggccgccggggctggggccggcgggagtccgcgggaccctccagaagagcggccggcgccgtgact- cagcactggggcggagcggggc

Claims

1.-15. (canceled)

16. A nucleic acid primer or hybridization probe set specific for at least one potentially methylated region of at least one marker gene suitable to diagnose or predict lung cancer or a lung cancer type.

17. The set of claim 16, wherein the at least one the marker gene is further defined as WT1, SALL3, TERT, ACTB, or CPEB4.

18. The set of claim 16, wherein the lung cancer is adenocarcinoma or squamous cell carcinoma.

19. The set of claim 16, further comprising a nucleic acid primer or hybridization probe specific for at least one additional marker gene defined as ABCB1, ACTB, AIM1L, APC, AREG, BMP2K, BOLL, C5AR1, C5orf4, CADM1, CDH13, CDX1, CLIC4, COL21A1, CPEB4, CXADR, DLX2, DNAJA4, DPH1, DRD2, EFS, ERBB2, ERCC1, ESR2, F2R, FAM43A, GABRA2, GAD1, GBP2, GDNF, GNA15, GNAS, HECW2, HIC1, HIST1H2AG, HLAG, HOXA1, HOXA10, HSD17B4, HSPA2, IRAK2, ITGA4, JUB, KCNJ15, KCNQ1, KIF5B, KL, KRT14, KRT17, LAMC2, MAGEB2, MBD2, MSH4, MT1G, MT3, MTHFR, NEUROD1, NHLH2, NKX2-1, ONECUT2, PENK, PITX2, PLAGL1, PTTG1, PYCARD, RASSF1, S100A8, SALL3, SERPINB5, SERPINE1, SERPINI1, SFRP2, SLC25A31, SMAD3, SPARC, SPHK1, SRGN, TERT, THRB, TJP2, TMEFF2, TNFRSF10C, TNFRSF25, TP53, ZDHHCI1, ZNF256, ZNF711, F2R, HOXA10, KL, SALL3, SPARC, TNFRSF25, or WT1.

20. The set of claim 16, further defined as a nucleic acid primer or hybridization probe set comprising nucleic acid primers or hybridization probes being specific for potentially methylated regions of at least 50% of the marker genes in at least one of the following combinations:

WT1, DLX2, SALL3, TERT, PITX2, HOXA10, F2R, CPEB4, NHLH2, SMAD3, ACTB, HOXA1, BOLL, APC, MT1G, PENK, SPARC, DNAJA4, RASSF1, HLA-G, ERCC1, ONECUT2, APC, ABCB1, ZNF573, KCNJ15, ZDHHC11, SFRP2, GDNF, PTTG1, SERPINI1, and TNFRSF10C;
WT1, PITX2, SALL3, F2R, DLX2, TERT, HOXA10, MSH4, NHLH2, GNA15, PENK, RASSF1, BOLL, HOXA1, ONECUT2, ABCB1, SPARC, MT1G, HSPA2, SFRP2, PYCARD, GAD1, C5orf4, C5AR1, GNDF, ZDHHC11, SERPINE1, NKX2-1, PITX2, C5AR1, GDNF, ZDHHC11, SERPINE1, NKX2-1, PITX2, C5AR1, ZNF256, FAM43A, SFRP2, MT3, SERPINE1M, CLIC4, TNFRSF10C, GABRA2, MTHFR, ESR2, NEUROG1, PITX2, PLAGL1, TMEFF2, PTTG1, CADM1, S100A8, EFS, JUB, ITGA4, MAGEB2, ERBB2, SRGN, GNAS, TJP2, KCNJ15, SLC25A31, ZNF573, TNFRSF25, APC, KCNQ1, LAMC2, SPHK1 DNAJA4, APC, MBD2, ERCC1 HLA-G, CXADR, TP53, ACTB, KL, SMAD3, HIST1H2AG, and CPEB4;
WT1 DLX2, SALL3, TERT, TNFRSF25, ACTB, SMAD3, and CPEB4;
WT1, DLX2, SALL3, TERT, PITX2, TNFRSF25, KL, ACTB, SMAD3, and CPEB4;
WT1, PITX2, SALL3, DLX2, TERT, HOXA10, RASSF1, SPARC, IRAK2, ZNF711, DNAJA4, HLA-G, CXADR, TP53, ACTB, and CPEB4;
WT1, PITX2, SALL3, F2R, TERT, HOXA10, RASSF1, SPARC, IRAK2, ZNF711, DRD2, DNAJA4, CXADR, TP53, ACTB, and CPEB4;
WT1, ACTB, DLX2, PITX2, SALL3, HOXA10, TERT, CPEB4, HLA-G, SPARC, RASSF1, DNAJA4, CXADR, TP53, IRAK2, and ZNF711;
F2R, ZNF256, CDH13, SERPINB5, KRT14, DLX2, AREG, THRB, HSD17B4, SPARC, HECW2, and COL21A1;
KL, HIST1H2AG, TJP2, SRGN, CDX1, TNFRSF25, APC, HIC1, APC, GNA15, ACTB, WT1, KRT17, AIM1L, DPH1, PITX2, PITX2, KIF5B, BMP2K, GBP2, NHLH2, GDNF, and BOLL;
WT1, DLX2, SALL3, TERT, PITX2, HOXA10, F2R, CPEB4, NHLH2, SMAD3, ACTB, HOXA1, BOLL, APC, MT1G, PENK, SPARC, DNAJA4, RASSF1, HLA-G, ERCC1, ONECUT2, APC, ABCB1, ZNF573, KCNJ15, ZDHHC11, SFRP2, GDNF, PTTG1, SERPINI1, and TNFRSF10C;
HOXA10 and NEUROD1;
WT1, PITX2, SALL3, F2R, TERT, HOXA10, RASSF1, SPARC, IRAK2, ZNF711, DRD2, DNAJA4, CXADR, TP53, ACTB, CPEB4, DLX2, TNFRSF25, KL, and SMAD3;
TNFRSF25, SALL3, RASSF1, TERT, SPARC, F2R, HOXA10, ZNF711, and PITX2
SALL3, PITX2, SPARC, F2R, TERT, RASSF1, HOXA10, CXADR, and KL
SALL3, SPARC, PITX2, F2R, TERT, RASSF1, HOXA10, and KL;
SALL3, PITX2, SPARC, F2R, HOXA10, DRD2, ACTB, DNAJA4, CXADR, KL;
SALL3, SPARC, PITX2, F2R, TERT, RASSF1, HOXA10, TNFRSF25, DNAJA4, TP53, CXADR, and KL;
SPARC, SALL3, F2R, PITX2, RASSF1, HOXA10, TERT, KL, and TNFRSF25;
SALL3, SPARC, PITX2, F2R, TERT, RASSF1, HOXA10, KL, TNFRSF25, CXADR; and
HOXA10, RASSF1, and F2R.

21. The set of claim 16, further defined as comprising not more than 100000 probes or primer pairs.

22. The set of claim 16, further defined as comprising not more than 100000 probes or primer pairs.

23. The set of claim 22, further defined as comprising immobilized probes on a solid surface.

24. The set of claim 22, wherein the primer pairs and probes are specific for a methylated upstream region of an open reading frame of the marker genes.

25. The set of claim 22, wherein the probes or primers are specific for methylation in the genetic regions defined by any of SEQ ID NOs 1081 to 1440 including the adjacent up to 500 base pairs corresponding to any of gene marker IDs 1 to 359.

26. The set of claim 25, wherein the probes or primers are of SEQ ID NOs 1 to 1080.

27. A method of identifying or predicting a lung cancer or a lung cancer type in a patient, comprising:

obtaining a set of nucleic acid primers or hybridization probes of claim 16;
using the set to determine the methylation status of genes for which the members of the set are specific in a sample of DNA from the patient; and
comparing the methylation status of the genes with the status of a confirmed lung cancer type positive and/or negative state, thereby identifying lung cancer or lung cancer type, if any, in the patient.

28. The method of claim 27, wherein the methylation status is determined by methylation specific PCR analysis, methylation specific digestion analysis and either or both of hybridization analysis to non-digested or digested fragments or PCR amplification analysis of non-digested or digested fragments.

29. A method of determining a subset of diagnostic markers for potentially methylated genes from the genes of gene marker IDs 1-359 of Table 1, suitable for the diagnosis or prognosis of lung cancer or lung cancer type, comprising: wherein the selected markers form the subset of diagnostic markers.

a) obtaining data of the methylation status of at least 50 random genes selected from the 359 genes of gene marker IDs 1-359 in at least 1 sample of a confirmed lung cancer or lung cancer type state and at least one sample of a lung cancer or lung cancer type negative state;
b) correlating the results of the obtained methylation status with the lung cancer or lung cancer type;
c) optionally repeating the obtaining a) and correlating b) steps for a different combination of at least 50 random genes selected from the 359 genes of gene marker IDs 1-359; and
d) selecting as many marker genes which in a classification analysis have a p-value of less than 0.1 in a random-variance t-test, or selecting as many marker genes which in a classification analysis together have a correct lung cancer or lung cancer type prediction of at least 70% in a cross-validation test;

30. The method of claim 29, wherein a) is further defined as comprising obtaining data of the methylation status of at least 50 random genes selected from the 359 genes of gene marker IDs 1-359 in at least 5 samples of a confirmed lung cancer or lung cancer type state.

31. The method of claim 29, wherein the correlated results for each gene b) are rated by their correct correlation to the disease or tumor type positive state, preferably by p-value test, and selected in step d) in order of the rating.

32. The method of claim 29, wherein not more than 40 marker genes are selected in step d) for the subset.

33. The method of claim 29, wherein the step a) of obtaining data of the methylation status comprises determining data of the methylation status by methylation specific PCR analysis, methylation specific digestion analysis, or hybridization analysis to non-digested or digested fragments, or PCR amplification analysis of non-digested or digested fragments.

34. A method of identifying or predicting a lung cancer or a lung cancer type in a patient, comprising:

providing a set of a diagnostic subset of markers identified by a method of claim 29;
using the set to determine methylation status of genes for which the members of the set are specific in a sample comprising DNA from the patient; and
comparing the methylation status of the genes with the status of a confirmed lung cancer type positive and/or negative state, thereby identifying lung cancer or lung cancer type, if any, in the patient.

35. The method of claim 34, wherein the methylation status is determined by methylation specific PCR analysis, methylation specific digestion analysis and either or both of hybridization analysis to non-digested or digested fragments or PCR amplification analysis of non-digested or digested fragments.

Patent History
Publication number: 20160281175
Type: Application
Filed: Apr 12, 2016
Publication Date: Sep 29, 2016
Inventors: Andreas WEINHÄUSEL (Neckenmarkt), Rudolf PICHLER (Wampersdorf), Christa Nöhammer (Vienna)
Application Number: 15/096,848
Classifications
International Classification: C12Q 1/68 (20060101);