DIFFERENTIATION OF LYME DISEASE AND SOUTHERN TICK-ASSOCIATED RASH ILLNESS
The present disclosure provides a biosignature that distinguishes Lyme disease, including early Lyme disease, from STARI. The present disclosure also provides methods for detecting Lyme disease and STARI, as well as methods for treating subjects diagnosed with Lyme disease or STARI.
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This application is a continuation application of U.S. application Ser. No. 16/703,401, filed Dec. 4, 2019, which application is a continuation of International Application No. PCT/US2018/036688, with an international filing date of Jun. 8, 2018, which PCT application claims the benefit of U.S. provisional application No. 62/516,824, filed Jun. 8, 2017. The contents of the above-mentioned applications are hereby incorporated by reference in their entirety.
GOVERNMENTAL RIGHTSThis invention was made with government support under AI100228 and AI099094, each awarded by National Institutes of Health. The government has certain rights in the invention.
REFERENCE TO SEQUENCE LISTINGThe instant application contains a Sequence Listing which has been submitted electronically in ASCII format and is hereby incorporated by reference in its entirety. Said ASCII copy, created on Apr. 27, 2021, is named “CSURF_065620-642266_ST25.txt”, and is 2.58 KB in size.
FIELD OF THE INVENTIONThe present disclosure relates to human disease detection tools and methods, and in particular pertains to tools and methods for detecting Lyme disease and southern tick-associated rash illness (STARI), and for distinguishing Lyme disease from STARI.
BACKGROUND OF THE INVENTIONLyme disease is a multisystem bacterial infection that in the United States is primarily caused by infection with Borrelia burgdorferi sensu stricto. Over 300,000 cases of Lyme disease are estimated to occur annually in the United States, with over 3.4 million laboratory diagnostic tests performed each year. Symptoms associated with this infection include fever, chills, headache, fatigue, muscle and joint aches, and swollen lymph nodes; however, the most prominent clinical manifestation in the early stage is the presence of one or more erythema migrans (EM) skin lesions. This annular, expanding erythematous skin lesion occurs at the site of the tick bite in 70 to 80% of infected individuals and is typically 5 cm or more in diameter. Although an EM lesion is a hallmark for Lyme disease, other types of skin lesions can be confused with EM, including the rash of southern tick-associated rash illness (STARI).
A strict geographic segregation of Lyme disease and STARI does not exist, as there are regions where STARI and Lyme disease are co-prevalent. Clinically, the skin lesions of STARI and early Lyme disease are indistinguishable, and no laboratory tool or method exists for the diagnosis of STARI or differentiation of STARI from Lyme disease. The only biomarkers evaluated for differential diagnosis of early Lyme disease and STARI have been serum antibodies to B. burgdorferi. However, these tests have poor sensitivity for early stages of Lyme disease, and thus a lack of B. burgdorferi antibodies cannot be used as a reliable differential marker for STARI. Thus, there is a need for diagnostic methods to differentiate between Lyme disease and STARI, and that facilitate proper treatment, patient management and disease surveillance.
SUMMARY OF THE INVENTIONIn one aspect, the present disclosure encompasses a method for analyzing a blood sample from a subject, the method comprising: (a) deproteinizing the blood sample to produce a metabolite extract; (b) performing liquid chromatography coupled to mass spectrometry on a sample of the metabolite extract; and (c) providing abundance values for each molecular feature in Table A, Table B, Table C, or Table D.
In another aspect, the present disclosure encompasses a method for classifying a subject as having Lyme disease or STARI, the method comprising: (a) deproteinizing a blood sample from a subject to produce a metabolite extract, wherein the subject has at least one symptom that is associated with Lyme disease or STARI; (b) performing liquid chromatography coupled to mass spectrometry on a sample of the metabolite extract; (c) providing abundance values for each molecular feature in Table A, Table B, Table C, or Table D; and (d) inputting the abundance values from step (c) into a classification model trained with samples of metabolite extracts derived from suitable controls, wherein the classification model produces a disease score and the disease score distinguishes subjects with Lyme disease or STARI.
In another aspect, the present disclosure encompasses a method for treating a subject with Lyme disease, the method comprising: (a) obtaining a disease score from a test; (b) diagnosing the subject with Lyme disease based on the disease score; and (c) administering a treatment to the subject with Lyme disease, wherein the test comprises measuring the amount of each molecular feature in Table A, Table B, Table C, or Table D; providing abundance values for each molecular feature measured; and inputting the abundance values into a classification model trained with samples derived from suitable controls, wherein the classification model produces a disease score and the disease score distinguishes subjects with Lyme disease from subjects with STARI, and optionally from healthy subjects. In certain examples, the test comprises (i) deproteinizing a blood sample from a subject to produce a metabolite extract; (ii) performing liquid chromatography coupled to mass spectrometry on a sample of the metabolite extract; (iii) providing abundance values for each molecular feature in Table A, Table B, Table C, or Table D; and (iv) inputting the abundance values from step (iii) into a classification model trained with samples of metabolite extracts derived from suitable controls, wherein the classification model produces a disease score and the disease score distinguishes subjects with Lyme disease. In further examples, the subject has at least one symptom of Lyme disease. In still further examples, the Lyme disease is early Lyme disease and optionally the symptom is an EM rash.
In another aspect, the present disclosure encompasses a method for treating a subject with STARI, the method comprising: (a) obtaining a disease score from a test; (b) diagnosing the subject with STARI based on the disease score; and (c) administering a treatment to the subject with STARI, wherein the test comprises measuring the amount of each molecular feature in Table A, Table B, Table C, or Table D; providing abundance values for each molecular feature measured; and inputting the abundance values into a classification model trained with samples derived from suitable controls, wherein the classification model produces a disease score and the disease score distinguishes subjects with STARI. In certain examples, the test comprises (i) deproteinizing a blood sample from a subject to produce a metabolite extract; (ii) performing liquid chromatography coupled to mass spectrometry on a sample of the metabolite extract; (iii) providing abundance values for each molecular feature in Table A, Table B, Table C, or Table D; and (iv) inputting the abundance values from step (iii) into a classification model trained with samples of metabolite extracts derived from suitable controls, wherein the classification model produces a disease score and the disease score distinguishes subjects with STARI from subjects with Lyme disease, including early Lyme disease, and optionally from healthy subjects. In further examples, the subject has at least one symptom of STARI. In still further examples, the symptom is an EM or an EM-like rash.
Other aspects and iterations of the invention are described below.
The disclosure contains at least one photograph executed in color. Copies of this patent application publication with color photographs will be provided by the Office upon request and payment of the necessary fee.
Lyme disease is an illness caused by a Borrelia species (e.g., Borrelia burgdorferi, Borrelia garinii, Borellia afzelii, etc.) and is transmitted to humans through the bite of infected blacklegged ticks (Ixodes species). Lyme disease can go through several stages and may cause different symptoms, depending on how long a subject has been infected and where in the body the infection has spread. The stages of Lyme disease include Stage 1, Stage 2, and Stage 3. Stage 1 Lyme disease may also be referred to as “early localized Lyme disease” or “early Lyme disease” and usually develops about 1 day to about 4 weeks after infection. Non-limiting examples of symptoms of Stage 1 Lyme disease include erythema migrans and flu-like symptoms, such as lack of energy, headache, stiff neck, fever, chills, muscle pain, joint pain, and swollen lymph nodes. Stage 1 Lyme disease may result in one or more than one symptom. In some cases, Stage 1 Lyme disease does not result in any symptoms. Stage 2 Lyme disease may also be referred to as “early disseminated infection” and usually develops about 1 month to about 4 months after infection. Non-limiting examples of symptoms of Stage 2 Lyme disease include an erythema migrans (or additional erythema migrans rash sites), pain, weakness, numbness in the arms and/or legs, Bell's palsy (facial drooping), headaches, fainting, poor memory, reduced ability to concentrate, conjunctivitis, episodes of pain, redness and swelling in one or more large joints, rapid heartbeats (palpitations), and serious heart problems. Stage 3 Lyme disease may also be referred to as “late persistent Lyme disease” and usually develops months to years after infection. Non-limiting examples of symptoms of Stage 3 Lyme disease include arthritis, numbness and tingling in the hands, numbness and tingling in the feet, numbness and tingling in the back, tiredness, Bell's palsy (facial drooping), problems with memory, mood, sleep speaking, and heart problems (pericarditis). A subject diagnosed with Lyme disease, or suspected of having Lyme disease, may be identified on the basis of one or more symptoms, geographic location, and possibility of tick bite. Currently, several routine diagnostic tests are known for diagnosing Lyme disease. Typically these tests detect and/or quantify antibodies to one or more Borellia antigens, and are performed using common immunoassay methods such as enzyme-linked immunoassays (EIA or ELISA), immunofluorescence assays, or Western immunoblots. Generally, these tests are most reliable only a few weeks after an infection. Positive PCR and/or positive culture may also be used. (See, e.g., Moore et al., “Current Guidelines, Common Clinical Pitfalls, and Future Directions for Laboratory Diagnosis of Lyme Disease, United States,” Emerg Infect Dis. 2016, Vol. 22, No. 7). In one example, diagnostic testing may comprise a commercially-available C6 EIA. The C6 Lyme EIA measures antibody reactivity to a synthetic peptide corresponding to the sixth invariable region of VlsE, a highly conserved surface protein of the causative Borrelia burgdorferi bacterium. Alternatively, or in addition, diagnostic testing may comprise using IgM and/or IgG immunoblots following a positive or equivocal first-tier assay. As used herein, a subject that is negative for antibodies to Lyme disease causing Borrelia species need only be negative by one method of testing.
Southern tick-associated rash illness (STARI) is an illness associated with a bite from the lone star tick, Amblyomma americanum. The causative agent of STARI is unknown. The rash of STARI is a red, expanding “bull's-eye” lesion that develops around the site of a lone star tick bite. The rash of STARI may be referred to as an EM rash or an EM-like rash. The rash usually appears within 7 days of tick bite and expands to a diameter of 8 centimeters (3 inches) or more. Non-limiting examples of additional symptoms associated with STARI include discomfort and/or itching at the bite site, muscle pain, joint pain, fatigue, fever, chills, and headache. A subject diagnosed with STARI, or suspected of having STARI, may be identified on the basis of one or more symptom, geographic location, and possibility of tick bite.
Complicating the clinical differentiation between Lyme disease, and in particular early Lyme disease, and STARI are shared symptoms (for example, an EM or EM-like rash), co-prevalence of STARI and Lyme disease in certain geographic regions, and poor sensitivity of common diagnostic methods for early stages Lyme disease. The present disclosure provides a biosignature that identifies Lyme disease and southern tick-associated rash illness (STARI), and distinguishes one from the other. Various aspects of the biosignature and its use are described in detail below.
I. DefinitionsSo that the present disclosure may be more readily understood, certain terms are first defined. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which examples of the disclosure pertain. Many methods and materials similar, modified, or equivalent to those described herein can be used in the practice of the examples of the present disclosure without undue experimentation, the preferred materials and methods are described herein. In describing and claiming the examples of the present disclosure, the following terminology will be used in accordance with the definitions set out below.
The term “about,” as used herein, refers to variation of in the numerical quantity that can occur, for example, through typical measuring techniques and equipment, with respect to any quantifiable variable, including, but not limited to, mass, volume, time, distance, wave length, frequency, voltage, current, and electromagnetic field. Further, given solid and liquid handling procedures used in the real world, there is certain inadvertent error and variation that is likely through differences in the manufacture, source, or purity of the ingredients used to make the compositions or carry out the methods and the like. The term “about” also encompasses these variations, which can be up to ±5-10%, but can also be ±9%, 8%, 7%, 6%, 5%, 4%, 3%, 2%, 1%, etc. Whether or not modified by the term “about,” the claims include equivalents to the quantities.
As used herein, the term “accuracy” refers to the ability of a test (e.g., a diagnostic test, a classification model, etc.) to correctly differentiate one type of subject (e.g., a subject with Lyme disease) from one or more different types of subjects (e.g., a subject with STARI, a healthy subject, etc.). Accuracy is equal to (true positive result)+(true negative result)/(true positive result)+(true negative result)+(false positive result)+(false negative result).
The term “biosignature” refers to a plurality of molecular features forming a distinctive pattern which is indicative of a disease or condition of an animal, preferably a human.
The term “molecular feature” refers to a small molecule metabolite in a blood sample that has a mass less than 3000 Da. The term “abundance value” refers to an amount of a molecular feature in a blood sample. The abundance value for a molecular feature may be identified via any suitable method known in the art. Molecular features are defined herein by a positive ion m/z charge ratio ±a suitable tolerance to account for instrument variability (e.g., ±15 ppm) and optionally one or more additional characteristic such as retention time or a chemical structure based on accurate mass; and abundance values for each molecular feature are obtained from a measurement of the area under the peak for the monoisotopic mass of each molecular feature determined by mass spectrometry. Given that the present disclosure identifies the molecular features (i.e., small molecule metabolites) forming each biosignature, alternative methods for measuring the amount of the metabolites may be used without departing from the scope of the invention.
As used herein, the term “ROC” means “receiver operating characteristic”. A ROC analysis may be used to evaluate the diagnostic performance, or predictive ability, of a test or a method of analysis. A ROC graph is a plot of sensitivity and specificity of a test at various thresholds or cut-off values. Each point on a ROC curve represents the sensitivity and its respective specificity. A threshold value can be selected based on an ROC curve, wherein the threshold value is a point where sensitivity and specificity both have acceptable values. The threshold value can be used in applying the test for diagnostic purposes. It will be understood that if only specificity is optimized, then the test will be less likely to generate a false positive (diagnosis of the disease in more subjects who do not have the disease) at the cost of an increased likelihood that some cases of disease will not be identified (e.g., false negatives). If only sensitivity is optimized, the test will be more likely to identify most or all of the subjects with the disease, but will also diagnose the disease in more subjects who do not have the disease (e.g., false positives). A user is able to modify the parameters, and therefore select an ROC threshold value suitable for a given clinical situation, in ways that will be readily understood by those skilled in the art.
Another useful feature of the ROC curve is an area under the curve (AUC) value, which quantifies the overall ability of the test to discriminate between different sample properties, for example, to discriminate between subjects with Lyme disease and those STARI; to discriminate between subjects with STARI and healthy subjects; subjects or to discriminate between subjects with Lyme disease, STARI, and healthy subjects. A test that is no better at identifying true positives than random chance will generate a ROC curve with an AUC of 0.5. A test having perfect specificity and sensitivity (i.e., generating no false positives and no false negatives) will have an AUC of 1.00. In reality, most tests will have an AUC somewhere between these two values.
As used herein, the term “sensitivity” refers to the percentage of truly positive observations which is classified as such by a test, and indicates the proportion of subjects correctly identified as having a given condition. In other words, sensitivity is equal to (true positive result)/[(true positive result)+(false negative result)].
As used herein, the term “specificity” refers to the percentage of truly negative observations which is classified as such by a test, and indicates the proportion of subjects correctly identified as not having a given condition. Specificity is equal to (true negative result)/[(true negative result)+(false positive result).
As used herein, the term “subject” refers to a mammal, preferably a human. The mammals include, but are not limited to, humans, primates, livestock, rodents, and pets. A subject may be waiting for medical care or treatment, may be under medical care or treatment, or may have received medical care or treatment.
As used interchangeably herein, the terms “control group,” “normal group,” “control subject,” or “healthy subject” refer to a subject, or a group of subjects, not previously diagnosed with the disease in question and/or treated for the disease in question for atherapeutically effective amount of time (e.g., 12 months or more).
As used herein, the term “blood sample” refers to a biological sample derived from blood, preferably peripheral (or circulating) blood. The blood sample can be whole blood, plasma or serum.
The terms “treat,” “treating,” or “treatment” as used herein, refer to the provision of medical care by a trained and licensed health professional to a subject in need thereof. The medical care may be a diagnostic test, a therapeutic treatment, and/or a prophylactic or preventative measure. The object of therapeutic and prophylactic treatment is to prevent or slow down (lessen) an undesired physiological change or disease/disorder. Beneficial or desired clinical results of therapeutic or prophylactic treatments include, but are not limited to, alleviation of symptoms, diminishment of extent of disease, stabilized (i.e., not worsening) state of disease, a delay or slowing of disease progression, amelioration or palliation of the disease state, and remission (whether partial or total), whether detectable or undetectable. “Treatment” can also mean prolonging survival as compared to expected survival if not receiving treatment. Alternatively, the medical care may be a recommendation for no intervention. For example, no medical intervention may be needed for diseases that are self-limiting. Those in need of treatment include those already with the disease, condition, or disorder as well as those prone to have the disease, condition or disorder or those in which the disease, condition or disorder is to be prevented.
II. BiosignaturesIn an aspect, the present disclosure provides a biosignature that provides an accuracy of detecting Lyme disease equal to or greater than about 80%. In another aspect, the present disclosure provides a biosignature that provides an accuracy of detecting STARI disease equal to or greater than about 80%.
A method for identifying a Lyme disease biosignature and/or a STARI biosignature is detailed in the examples. Generally speaking, the method comprises: a) obtaining test blood samples and control blood samples; b) analyzing the test blood samples and control blood samples by mass spectrometry to obtain abundance values for a plurality of molecular features in the test blood samples and the control blood samples; and c) applying a statistical modeling technique to select for a plurality of molecular features that distinguish test blood samples from control blood samples with an accuracy equal to or greater than about 80%. Test blood samples are from subjects with Lyme disease and/or STARI, either of which is confirmed using known diagnostic methods as described above; and control bloods samples are from subjects confirmed to be free of Lyme, STARI, or both using known diagnostic methods for each. The molecular features that distinguish test blood samples from control blood samples comprise the biosignature for that disease.
A blood sample may be a whole blood sample, a plasma sample, or a serum sample. Any of a variety of methods generally known in the art for collecting a blood sample may be utilized. Generally speaking, the sample collection method preferably maintains the integrity of the sample such that abundance values for each molecular feature can be accurately measured. A blood sample may be used “as is”, or a blood sample may be processed to remove undesirable constituents. In preferred examples, a blood sample is processed using standard techniques to remove high-molecular weight species, and thereby obtain an extract comprising small molecule metabolites. This is referred to herein as “deproteinization” or a “deproteinization step.” For example, a solvent or solvent mixture (e.g., methanol or the like) may be added to a blood sample to precipitate these high-molecular weight species followed by a centrifugation step to separate the precipitate and the metabolite-containing supernatant. In another example, proteases may be the added to a blood sample. In another example, size exclusion chromatography may be used.
Analysis using mass spectrometry, preferably high resolution mass spectrometry, yields abundance measures for a plurality of molecular features. The abundance value for each molecular feature may be obtained from a measurement of the area under the peak for the monoisotopic mass of each molecular feature. Identification and extraction of molecular features involves finding and quantifying all the known and unknown compounds/metabolites down to the lowest abundance, and extracting all relevant spectral and chromatographic information. Algorithms are available to identify and extract molecular features. Such algorithms include for example the Molecular Feature Extractor (MFE) by Agilent. MFE locates ions that are covariant (rise and fall together in abundance) but the analysis is not exclusively based on chromatographic peak information. The algorithm uses the accuracy of the mass measurements to group related ions-related by charge-state envelope, isotopic distribution, and/or the presence of adducts and dimers. It assigns multiple species (ions) that are related to the same neutral molecule (for example, ions representing multiple charge states or adducts of the same neutral molecule) to a single compound that is referred to as a feature. Using this approach, the MFE algorithm can locate multiple compounds within a single chromatographic peak. Specific parameters for MFE may include a minimum ion count of 600, an absolute height of 2,000 ion counts, ion species H+ and Na+, charge state maximum 1, and compound ion count threshold of 2 or more ions. Once the molecular feature has been identified and extracted, the area under the peak for the monoisotopic mass is used to determine the abundance value for the molecular feature. The monoisotopic mass is the sum of the masses of the atoms in a molecule using the unbound, ground-state, rest mass of the principal (most abundant) isotope for each element instead of the isotopic average mass. Monoisotopic mass is typically expressed in unified atomic mass units (u), also called daltons (Da).
A molecular feature is identified as a potential molecular feature for utilization in a biosignature of the present disclosure if it is present in at least 50% of either the test blood samples or the control blood samples. For example, the molecular feature may be present in 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% or 100% of either the test blood samples or the control blood samples. Additionally, a molecular feature is identified as a potential molecular feature for utilization in a biosignature of the present disclosure if it is significantly different in abundance between the test blood samples and the control blood samples. Specifically, a molecular feature is identified as being significantly different if the difference in abundance value of the molecular feature in the test blood samples versus the abundance value of the molecular feature in the blood biological samples has a p-value is less than 0.1, preferably less than 0.05, less than 0.01, less than 0.005, or less than 0.001.
To increase the stringency of the biosignature, replicates of the test blood samples and control blood samples may be analyzed. For example, the test blood samples and control blood samples may be analyzed in duplicate. Alternatively, the test biological samples and control biological samples may be analyzed in triplicate. Additionally, the test blood samples and control blood samples may be analyzed four, five or six times. The replicate analysis is used to down-select the plurality of molecular features. The down-selection results in a biosignature with increased stringency.
Once a plurality of potential molecular features has been generated, a statistical modeling technique may be applied to select for the molecular features that provide an accuracy of disease detection that is clinically meaningful. Several statistical models are available to select the molecular features that comprise a biosignature of the present disclosure. Non-limiting examples of statistical modeling techniques include LDA, classification tree (CT) analysis, random forests, and LASSO (least absolute shrinkage and selection operator) logistic regression analysis. Various methods are known in the art for determining an optimal cut-off that maximizes sensitivity and/or specificity to serve as a threshold for discriminating samples obtained from subjects with Lyme disease or STARI. For the biosignatures in Table A, Table C, and Table D, the cut-off is determined by a data point of the highest specificity at the highest sensitivity on the ROC curve. However, the cut-off can be set as required by situational circumstances. For example, in certain clinical situations it may be desirable to minimize false-positive rates. These clinical situations may include, but are not limited to, the use of an experimental treatment (e.g., in a clinical trial) or the use of a treatment associated with serious adverse events and/or a higher than average number of side effects. Alternatively, it may be desirable to minimize false-negative rates in other clinical situations. Non-limiting examples may include treatment with a non-pharmacological intervention, the use of a treatment with a good risk-benefit profile, or treatment with an additional diagnostic agent. For the biosignatures in Table A, molecular feature stability across many samples and different LC-MS analyses was used as the cut-off.
In one example, the present disclosure provides a biosignature comprising each molecular feature in Table A, wherein the molecular features in the table are defined at least by their m/z ratio ±15 ppm, in some examples ±10 ppm, in some examples ±5 ppm (depending upon instrument variability). A biosignature comprising each molecular feature in Table A provides a 9800 probability of accurately detecting a sample from a subject with Lyme disease, including early Lyme disease, and an 89% probability of accurately detecting a sample from a subject with STARI, when discriminating between a classification of Lyme disease and STARI. A skilled artisan will appreciate that in certain examples one or more molecular feature may be eliminated from the model without a clinically meaningful, negative impact on the model.
In one example, the present disclosure provides a biosignature comprising each molecular feature in Table B, wherein the molecular features in the table are defined at least by their m/z ratio ±15 ppm, in some examples ±10 ppm, in some examples ±5 ppm (depending upon instrument variability). The biosignature comprises each molecular feature in Table B that maintains an absolute abundance fold change of 2 or greater between Lyme disease and STARI, and maintains an abundance coefficient of variation of 0.2 or less between STARI blood samples, and maintains an abundance coefficient of variation of 0.2 or less between Lyme disease blood samples. A skilled artisan will appreciate that in certain examples one or more molecular features may be eliminated from the model without a clinically meaningful, negative impact on the model.
In one example, the present disclosure provides a biosignature comprising each molecular feature in Table C, wherein the molecular features in the table are defined at least by their m/z ratio ±15 ppm, in some examples ±10 ppm, in some examples ±5 ppm (depending upon instrument variability). The biosignature comprising each molecular feature in Table C provides an 85% probability of accurately detecting a sample from a subject with Lyme disease, including early Lyme disease, an 92% probability of accurately detecting a sample from a subject with STARI, and a 93% probability of accurately detecting a sample from a healthy subject, when discriminating between a status (classification) of Lyme disease, STARI, and healthy. A skilled artisan will appreciate that in certain examples one or more molecular features may be eliminated from the model without a clinically meaningful, negative impact on the model.
In one example, the present disclosure provides a biosignature comprising each molecular feature in Table D, wherein the molecular features in the table are defined at least by their m/z ratio ±15 ppm, in some examples ±10 ppm, in some examples ±5 ppm (depending upon instrument variability). The biosignature comprising each molecular feature in Table D provides an 97% probability of accurately detecting a sample from a subject with Lyme disease, including early Lyme disease, and an 89% probability of accurately detecting a sample from a subject with STARI, when discriminating between a classification of Lyme disease and STARI. The biosignature comprising each molecular feature in Table D provides an 85% probability of accurately detecting a sample from a subject with Lyme disease, including early Lyme disease, a 92% probability of accurately detecting a sample from a subject with STARI, and a 93% probability of accurately a sample from a healthy subject, when discriminating between a classification of Lyme disease, STARI, and healthy. A skilled artisan will appreciate that one or more molecular features may be eliminated from the model without a clinically meaningful, negative impact on the model.
In another aspect, the present disclosure provides a method for analyzing a blood sample from a subject. The method comprises performing liquid chromatography coupled to mass spectrometry on a blood sample, and providing abundance values for each molecular feature in Table A, Table B, Table C, or Table D. Preferably, the method further comprises deproteinizing a blood sample from a subject to produce a metabolite extract and then performing liquid chromatography coupled to mass spectrometry on a sample of the metabolite extract. The method may comprise providing abundance values for each molecular feature in Table A or Table C. The method may comprise providing abundance values for each molecular feature in Table B or Table D. The method may comprise providing abundance values for each molecular feature in Table A, Table B, or Table D. The method may comprise providing abundance values for each molecular feature in Table C or Table D.
A subject may be a human or a non-human mammal including, but not limited to, a livestock animal, a companion animal, a lab animal, or a zoological animal. A subject may be a rodent, e.g., a mouse, a rat, a guinea pig, etc. A subject may also be a livestock animal. Non-limiting examples of suitable livestock animals may include pigs, cows, horses, goats, sheep, llamas and alpacas. Alternatively, a subject may be a companion animal. Non-limiting examples of companion animals may include pets such as dogs, cats, rabbits, and birds. A subject may be a zoological animal. As used herein, a “zoological animal” refers to an animal that may be found in a zoo. Such animals may include non-human primates, large cats, wolves, and bears. In preferred examples, a subject is human.
Methods of the present disclosure for analyzing a blood sample may be used to monitor the progression or resolution of Lyme disease or STARI. A skilled artisan will also appreciate that infection with Borrelia species that cause Lyme disease, or with the causative agent(s) of STARI, likely commences prior to diagnosis or the onset of symptoms associated with the disease. For at least these reasons, a suitable blood sample may be from a subject that may or may not have a symptom associated with Lyme disease or STARI. Non-limiting examples of symptoms associated with Lyme disease and STARI are described above. A subject may have at least one symptom associated with Lyme disease, at least one symptom associated with STARI, or at least one symptom associated with Lyme disease and STARI. As a non-limiting example, a subject can have an erythema migrans (EM) rash or an EM-like rash. Alternatively, a subject may not have a symptom of Lyme disease or STARI but may be at risk of having Lyme disease or STARI. Non-limiting examples of risk factors for Lyme disease or STARI include living in or visiting a region endemic for Lyme disease or STARI, spending time in wooded or grassy areas, camping, fishing, gardening, hiking, hunting and/or picnicking in a region endemic for Lyme disease or STARI, and not removing tick(s) promptly or properly. In each of the above examples, suitable subjects, whether or not they have a symptom associated with Lyme disease or STARI at the time a blood sample is obtained, may or may not have received (or be receiving) treatment for Lyme disease, STARI, or another disease with symptoms similar to Lyme disease or STARI.
A blood sample may be a whole blood sample, a plasma sample, or a serum sample. Any of a variety of methods generally known in the art for collecting a blood sample may be utilized. Generally speaking, the sample collection method preferably maintains the integrity of the sample such that abundance values for each molecular feature in Table A, Table B, Table C, or Table D can be accurately measured according to the disclosure. A blood sample may be used “as is”, or a blood sample may be processed to remove undesirable constituents. In preferred examples, a blood sample is processed using standard techniques to remove high-molecular weight species, and thereby obtain an extract comprising small molecule metabolites. This is referred to herein as “deproteinization” or a “deproteinization step.” For example, a solvent or solvent mixture (e.g., methanol or the like) may be added to a blood sample to precipitate these high-molecular weight species followed by a centrifugation step to separate the precipitate and the metabolite-containing supernatant. In another example, proteases may be the added to a blood sample. In another example, size exclusion chromatography may be used.
A single blood sample may be obtained from a subject. Alternatively, the molecular features may be detected in blood samples obtained over time from a subject. As such, more than one blood sample may be collected from a subject over time. For instance, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 or more blood samples may be collected from a subject over time. For example, 2, 3, 4, 5, or 6 blood samples are collected from a subject over time. Alternatively, 6, 7, 8, 9, or 10 blood samples are collected from a subject over time. Further, 10, 11, 12, 13, or 14 blood samples are collected from a subject over time. Still further, 14, 15, 16 or more blood samples are collected from a subject over time. The blood samples collected from the subject over time may be used to monitor Lyme disease or STARI in a subject. Alternatively, the blood samples collected from the subject over time may be used to monitor response to treatment in a subject.
When more than one sample is collected from a subject over time, blood samples may be collected 0.5, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 or more days apart. For example, blood samples may be collected 0.5, 1, 2, 3, or 4 days apart. Alternatively, blood samples may be collected 4, 5, 6, or 7 days apart. Further, blood samples may be collected 7, 8, 9, or 10 days apart. Still further, blood samples may be collected 10, 11, 12 or more days apart.
Once a sample is obtained, it is processed in vitro to measure abundance values for each molecular feature in Table A, Table B, Table C, or Table D. All suitable methods for measuring the abundance value for each of the molecular features known to one of skill in the art are contemplated within the scope of the invention. For example, mass spectrometry may be used to measure abundance values for each molecular feature in Table A, Table B, Table C, or Table D. The abundance values may be determined through direct infusion into the mass spectrometer. Alternatively, techniques coupling a chromatographic step with a mass spectrometry step may be used. The chromatographic step may be liquid chromatography. In certain examples, the abundance value for each of the molecular features may be determined utilizing liquid chromatography followed by mass spectrometry (LC-MS). In some examples, the liquid chromatography is high performance liquid chromatography (HPLC). Non-limiting examples of HPLC include partition chromatography, normal phase chromatography, displacement chromatography, reversed phase chromatography, size exclusion chromatography, ion exchange chromatography, bioaffinity chromatography, aqueous normal phase chromatography or ultrafast liquid chromatography. As used herein “mass spectrometry” describes methods of ionization coupled with mass selectors. Non-limiting examples of methods of ionization include matrix-assisted laser desorption/ionization (MALDI), electrospray ionization (ESI), and atmospheric pressure chemical ionization (ACPI). Non-limiting examples of mass selectors include quadropole, time of flight (TOF), and ion trap. Further, the mass selectors may be used in combination such as quadropole-TOF or triple quadropole.
In one example, an aliquot of a serum metabolite extract may be applied to a Poroshell 120, EC-C8, 2.1×100 mm, 2.7 μm LC Column (Agilent Technologies, Palo Alto, Calif.), and metabolites may be eluted with a nonlinear gradient of acetonitrile in formic acid (e.g., 0.1%) at a flow rate of 250 μl/min with an Agilent 1200 series LC system. The eluent may be introduced directly into an Agilent 6520 quadrapole time of flight mass (Q-TOF) spectrometer and MS may be performed as previously described (27, 50). LC-MS and LC-MS/MS data may be collected under the following parameters: gas temperature, 310° C.; drying gas at 10 liters per min; nebulizer at 45 lb per in2; capillary voltage, 4,000 V; fragmentation energy, 120 V; skimmer, 65 V; and octapole RF setting, 750 V. The positive-ion MS data for the mass range of 75 to 1,700 Da may be acquired at a rate of 2 scans per sec in both centroid and profile modes in 4-GHz high-resolution mode. Positive-ion reference masses may be used to ensure mass accuracy. To monitor instrument performance, quality control samples having a metabolite extract of healthy control serum may be analyzed in duplicate at the beginning of each analysis day and every 20 samples during the analysis day. In view of the specifics disclosed in this example, a skilled artisan will be able to optimize conditions as needed when using alternative equipment or approaches.
IV. Methods for Classifying a Subject as Having Lyme Disease or STARIIn another aspect, the present disclosure provides a method for classifying a subject as having Lyme disease or STARI. The method comprises analyzing a blood sample from a subject as described in Section III to provide abundance values for each molecular feature in Table A, Table B, Table C, or Table D; and comparing the abundance values to a reference set of abundance values. The statistical significance of any difference between the abundance values measured in the subject's blood sample as compared to the abundance values from the reference set is then determined. If the difference is statistically significant then a subject may be classified as having Lyme disease or STARI; if the difference is not statistically significant then a subject may be classified as not having Lyme disease or STARI. For instance, when using p-values, the abundance value of a molecular feature in a test blood sample is identified as being significantly different from the abundance value of the molecular feature in the reference set when the p-value is less than 0.1, preferably less than 0.05, less than 0.01, less than 0.005, or less than 0.001. Abundance values for the molecular features from the reference set may be determined before, after, or at the same time, as the abundance values for the molecular features from the subject's blood sample. Alternatively, abundance values for the molecular features from a reference set stored in a database may be used.
Any suitable reference set known in the art may be used; alternatively a new reference set may be generated. A suitable reference set comprises the abundance values for each of the molecular feature in Table A, Table B, Table C, or Table D in blood sample(s) obtained from control subjects known to be positive for Lyme disease, known to be positive for STARI, known to be negative for Lyme disease, known to be negative for STARI, known to be negative for Lyme disease and STARI, healthy subjects, or any combination thereof. Further, control subjects known to be negative for Lyme disease and/or STARI may also be known to be suffering from a disease with overlapping symptoms, may exhibit serologic cross-reactivity with Lyme disease, and/or may be suffering for another spirochetal infection. A subject suffering from a disease with overlapping symptoms may have one or more of the symptoms of Lyme disease described above. Non-limiting examples of diseases with overlapping symptoms include tick-bite hypersensitivity reactions, certain cutaneous fungal infections and bacterial cellulitis with non-Lyme EM-like lesions, syphilis, fibromyalgia, lupus, mixed connective tissue disorders (MCTD), chronic fatigue syndrome (CFS), rheumatoid arthritis, depression, mononucleosis, multiple sclerosis, sarcoidosis, endocarditis, colitis, Crohn's disease, early ALS, early Alzheimers disease, encephalitis, Fifth's disease, gastroesophageal reflux disease, infectious arthritis, interstitial cystis, irritable bowel syndrome, juvenile arthritis, Menieres syndrome, osteoarthritis, prostatitis, psoriatic arthritis, psychiatric disorders (bipolar, depression, etc.), Raynaud's syndrome, reactive arthritis, scleroderma, Sjogren's syndrome, sleep disorders, and thyroid disease. Specifically, a disease with overlapping symptoms is selected from the group consisting of syphilis and fibromyalgia. Further, the disclosure provides a method of correctly distinguishing a subject with early Lyme disease from a subject exhibiting serologic cross-reactivity with Lyme disease. A 2-tier serology-based assay is frequently used to diagnose Lyme disease. However, such an assay suffers from poor sensitivity in subjects with early Lyme disease. Non-limiting examples of diseases that exhibit serologic cross-reactivity with Lyme disease include infectious mononucleosis, syphilis, periodontal disease caused by Treponema denticola, granulocytic anaplasmosis, Epstein-Barr virus infection, malaria, Helicobacter pylori infections, bacterial endocarditis, rheumatoid arthritis, multiple sclerosis, infections caused by other spirochetes, and lupus. Specifically, a disease with serologic cross-reactivity is selected from the group consisting of infectious mononucleosis and syphilis. Non-limiting examples of other spirochetal infections include syphilis, severe periodontitis, leptospirosis, relapsing fever, rate-bite fever, bejel, yaws, pinta, and intestinal spirochaetosis. Specifically, another spirochetal infection is selected from the group consisting of syphilis and severe periodontitis.
In one example, a method for classifying a subject as having Lyme disease comprises: (a) deproteinizing a blood sample from a subject to produce a metabolite extract; (b) performing liquid chromatography coupled to mass spectrometry on a sample of the metabolite extract; (c) providing abundance values for each molecular feature in Table A, Table B, Table C, or Table D; and (d) inputting the abundance values from step (c) into a classification model trained with samples of metabolite extracts derived from suitable controls, wherein the classification model produces a disease score and the disease score distinguishes subjects with Lyme disease. In one example, the subject has at least one symptom associated with Lyme disease. In a specific example, the subject has an erythema migrans rash or an EM-like rash. In another example, the subject does not have a symptom of Lyme disease but is at risk of having Lyme disease. In each of the above examples, the subject may or may not have received (or be receiving) treatment for Lyme disease, STARI, or another disease with symptoms similar to Lyme disease or STARI.
In another example, a method for classifying a subject as having STARI comprises: (a) deproteinizing a blood sample from a subject to produce a metabolite extract; (b) performing liquid chromatography coupled to mass spectrometry on a sample of the metabolite extract; (c) providing abundance values for each molecular feature in Table A, Table B, Table C, or Table D; and (d) inputting the abundance values from step (c) into a classification model trained with samples of metabolite extracts derived from suitable controls, wherein the classification model produces a disease score and the disease score distinguishes subjects with STARI. In one example, the subject has at least one symptom associated with STARI. In a specific example, the subject has an erythema migrans rash or an EM-like rash. In another example, the subject does not have a symptom of STARI but is at risk of having STARI. In each of the above examples, the subject may or may not have received (or be receiving) treatment for Lyme disease, STARI, or another disease with symptoms similar to Lyme disease or STARI.
In another example, a method for classifying a subject as having Lyme disease or STARI comprises: (a) deproteinizing a blood sample from a subject to produce a metabolite extract; (b) performing liquid chromatography coupled to mass spectrometry on a sample of the metabolite extract; (c) providing abundance values for each molecular feature in Table A, Table B, Table C, or Table D; and (d) inputting the abundance values from step (c) into a classification model trained with samples of metabolite extracts derived from suitable controls, wherein the classification model produces a disease score and the disease score distinguishes subjects with Lyme disease from subjects STARI, and optionally further distinguishes healthy subjects. In one example, the subject has at least one symptom associated with Lyme disease and/or at least one symptom associated with STARI. In a specific example, the subject has an erythema migrans rash or an EM-like rash. In another example, the subject does not have a symptom of Lyme disease or STARI but is at risk of having Lyme disease or STARI. In each of the above examples, the subject may or may not have received (or be receiving) treatment for Lyme disease, STARI, or another disease with symptoms similar to Lyme disease or STARI.
In each of the above examples, the classification model has been trained with samples derived from suitable controls. Any suitable classification system known in the art may be used, provided the model produced therefrom has an accuracy of at least 80% for detecting a sample from a subject with Lyme disease, including early Lyme disease, and/or an accuracy of at least 80% for detecting a sample from a subject with STARI. For example, a classification model may have an accuracy of about 80%, about 85%, about 90%, about 95%, or greater for detecting a sample from a subject with Lyme disease, including early Lyme disease, and/or an accuracy of about 80%, about 85%, about 90%, about 95%, or greater for detecting a sample from a subject with STARI. Non-limiting examples of suitable classification models include LASSO, RF, ridge regression, elastic net, linear discriminant analysis, logistic regression, support vector machines, CT, and kernel estimation. In various examples, the model has a sensitivity from about 0.8 to about 1, and/or a specificity from about 0.8 to about 1. In certain examples, area under the ROC curve may be used to evaluate the suitability of a model, and an AUC ROC value of about 0.8 or greater indicates the model has a suitable accuracy.
The classification model produces a disease score and the disease score distinguishes: (i) samples from subjects with Lyme disease from samples from subjects with STARI, or (ii) distinguishes samples from subjects with Lyme disease from samples from control subjects, or (iii) distinguishes samples from subjects with STARI from samples from control subjects, or (iv) distinguishes samples from subjects with Lyme disease, samples from subjects with STARI and samples from control subjects from one another. As a non-limiting example, LASSO scores for a subject's sample may be calculated by multiplying the respective regression coefficients resulting from LASSO analysis by the transformed abundance of each MF in the biosignature and summing for each sample. In a further example, the sample score may be transformed into probabilities for each sample being classified to each sample group. As another non-limiting example, the transformed abundances of all MFs are used to classify the sample into one of the sample groups in each classification tree developed in an RF model, where the levels of chosen MFs are used sequentially to classify the samples, and the final classification is determined by majority votes among all such classification trees in the RF model. Scores from alternative classification models may be calculated as is known in the art.
In one example, abundance values are provided for each molecular feature in Table A, Table B, or Table D; the suitable controls comprise a blood sample known to be positive for Lyme disease and a blood sample known to be positive for STARI; and the classification model has an accuracy of at least 80%, at least 85%, at least 90%, or at least 95% for detecting a sample from a subject with Lyme disease and an accuracy of at least 80% or at least 85% for detecting a sample from a subject with STARI. Alternatively, abundance values are provided for each molecular feature in Table A, Table B, or Table D; the suitable controls include a blood sample known to be positive for Lyme disease, a blood sample known to be positive for STARI, and a blood sample known to be negative for both Lyme disease and STARI; and the classification model has an accuracy of at least 80%, at least 85%, or at least 90%, even more preferably at least 95% for detecting a sample from a subject with Lyme disease and an accuracy of at least 80% or at least 85% for detecting a sample from a subject with STARI. In still another alternative, abundance values are provided for each molecular feature in Table C or Table D; the suitable controls include a blood sample known to be positive for Lyme disease, a blood sample known to be positive for STARI, and a blood sample known to be negative for both Lyme disease and STARI; and the classification model has an accuracy of at least 80%, preferably at least 85% for detecting a sample from a subject with Lyme disease; an accuracy of at least 80%, at least 85%, or at least 90% for detecting a sample from a subject with STARI; and an accuracy of at least 80%, at least 85%, at least 90%, or at least 95% for detecting a sample from a healthy subject.
V. Methods for Treating a Subject as Having Lyme Disease or STARIAnother aspect of the disclosure is a method for treating a subject based on the subject's classification as having Lyme disease or STARI as described in Section IV. Treatment may be with a non-pharmacological treatment, a pharmacological treatment, or an additional diagnostic test.
In one example, the method comprises (a) obtaining a disease score from a test; (b) diagnosing the subject with Lyme disease based on the disease score; and (c) administering a treatment to the subject with Lyme disease, wherein the test comprises measuring the amount of each molecular feature in Table A, Table B, Table C, or Table D; providing abundance values for each molecular feature measured; and inputting the abundance values into a classification model trained with samples derived from suitable controls, wherein the classification model produces a disease score and the disease score distinguishes subjects with Lyme disease from subjects with STARI, and optionally from healthy subjects. In some examples, the test is a method of Section IV. In further examples, the test comprises (i) deproteinizing a blood sample from a subject to produce a metabolite extract; (ii) performing liquid chromatography coupled to mass spectrometry on a sample of the metabolite extract; (iii) providing abundance values for each molecular feature in Table A, Table B, Table C or Table D; and (iv) inputting the abundance values from step (iii) into a classification model trained with samples of metabolite extracts derived from suitable controls, wherein the classification model produces a disease score and the disease score distinguishes subjects with Lyme disease. Suitable controls are described above. In one example, abundance values are provided for each molecular feature in Table A, Table B, or Table D; the suitable controls comprise a blood sample known to be positive for Lyme disease and a blood sample known to be positive for STARI; and the classification model has an accuracy of at least 80%, at least 85%, at least 90%, or at least 95% for detecting a sample from a subject with Lyme disease and an accuracy of at least 80% or at least 85% for detecting a sample from a subject with STARI. Alternatively, abundance values are provided for each molecular feature in Table A, Table B, or Table D; the suitable controls include a blood sample known to be positive for Lyme disease, a blood sample known to be positive for STARI, and a blood sample known to be negative for both Lyme disease and STARI; and the classification model has an accuracy of at least 80%, at least 85%, or at least 90%, even more preferably at least 95% for detecting a sample from a subject with Lyme disease and an accuracy of at least 80% or at least 85% for detecting a sample from a subject with STARI. In still another alternative, abundance values are provided for each molecular feature in Table C or Table D; the suitable controls include a blood sample known to be positive for Lyme disease, a blood sample known to be positive for STARI, and a blood sample known to be negative for both Lyme disease and STARI; and the classification model has an accuracy of at least 80%, preferably at least 85% for detecting a sample from a subject with Lyme disease; an accuracy of at least 80%, at least 85%, or at least 90% for detecting a sample from a subject with STARI; and an accuracy of at least 80%, at least 85%, at least 90%, or at least 95% for detecting a sample from a healthy subject.
Treatment may comprise one or more standard treatments for Lyme disease. Non-limiting examples of standard pharmacological treatments for Lyme disease include an antibiotic, an antibacterial agent, a vaccine, an immune modulator, an anti-inflammatory agent, or a combination thereof. Suitable antibiotics include, but are not limited to, amoxicillin, doxycycline, cefuroxime axetil, amoxicillin-clavulanic acid, macrolides, ceftriaxone, cefotaxmine, and penicillin G. Antibiotics may be administered orally or parenterally. Alternatively, treatment may comprise one or more experimental pharmacological treatment (e.g., treatment in a clinical trial). In each of the above examples, treatment may be for the acute or disseminated stage of the disease, or may be a prophylactic treatment. For example, following successful resolution of a primary Borrrelia infection, the subject may be treated with a vaccine to prevent future infections. In still other examples, treatment may comprise further diagnostic testing. For example, if a subject has early Lyme disease but was negative for Lyme disease by current diagnostic testing (e.g., first-tier testing performed using the C6 EIA and second tier testing using IgM and/or IgG immunoblots following a positive or equivocal first-tier assay), additional testing may be ordered after an amount of time has elapsed (e.g., 3, 5, 7, 10, 14 days or more) to confirm the initial diagnosis.
In another example, the method comprises (a) obtaining a disease score from a test; (b) diagnosing the subject with STARI based on the disease score; and (c) administering a treatment to the subject with STARI, wherein the test comprises measuring the amount of each molecular feature in Table A, Table B, Table C, or Table D; providing abundance values for each molecular feature measured; and inputting the abundance values into a classification model trained with samples derived from suitable controls, wherein the classification model produces a disease score and the disease score distinguishes subjects with STARI from subjects with Lyme disease, including early Lyme disease, and optionally from healthy subjects. In some examples, the test is a method of Section IV. In further examples, the test comprises (i) deproteinizing a blood sample from a subject to produce a metabolite extract; (ii) performing liquid chromatography coupled to mass spectrometry on a sample of the metabolite extract; (iii) providing abundance values for each molecular feature in Table A, Table B, Table C or Table D; and (iv) inputting the abundance values from step (iii) into a classification model trained with samples of metabolite extracts derived from suitable controls, wherein the classification model produces a disease score and the disease score distinguishes subjects with STARI. Suitable controls are described above. In one example, abundance values are provided for each molecular feature in Table A, Table B, or Table D; the suitable controls comprise a blood sample known to be positive for Lyme disease and a blood sample known to be positive for STARI; and the classification model has an accuracy of at least 80%, at least 85%, at least 90%, or at least 95% for detecting a sample from a subject with Lyme disease and an accuracy of at least 80% or at least 85% for detecting a sample from a subject with STARI. Alternatively, abundance values are provided for each molecular feature in Table A, Table B, or Table D; the suitable controls include a blood sample known to be positive for Lyme disease, a blood sample known to be positive for STARI, and a blood sample known to be negative for both Lyme disease and STARI; and the classification model has an accuracy of at least 80%, at least 85%, or at least 90%, even more preferably at least 95% for detecting a sample from a subject with Lyme disease and an accuracy of at least 80% or at least 85% for detecting a sample from a subject with STARI. In still another alternative, abundance values are provided for each molecular feature in Table C or Table D; the suitable controls include a blood sample known to be positive for Lyme disease, a blood sample known to be positive for STARI, and a blood sample known to be negative for both Lyme disease and STARI; and the classification model has an accuracy of at least 80%, preferably at least 85% for detecting a sample from a subject with Lyme disease; an accuracy of at least 80%, at least 85%, or at least 90% for detecting a sample from a subject with STARI; and an accuracy of at least 80%, at least 85%, at least 90%, or at least 95% for detecting a sample from a healthy subject.
Treatment may comprise one or more standard treatments for STARI. There are no therapeutic agents specifically approved for STARI, in part because the causative agent is not known. Nonetheless, non-limiting examples of standard pharmacological treatments for STARI include an antibiotic, an antibacterial agent, a vaccine, an immune modulator, an anti-inflammatory agent, or a combination thereof. Suitable antibiotics include, but are not limited to, amoxicillin, doxycycline, cefuroxime axetil, amoxicillin-clavulanic acid, macrolides, ceftriaxone, cefotaxmine, and penicillin G. Antibiotics may be administered orally or parenterally. Alternatively, treatment may comprise one or more experimental pharmacological treatment (e.g., treatment in a clinical trial). In each of the above examples, treatment may be for acute disease, or may be a prophylactic treatment. For example, following successful treatment of STARI (as defined the by the current clinical standard of the time), the subject may be treated with a vaccine to prevent future infections. In still other another example, treatment may comprise further diagnostic testing. For example, if a subject is diagnosed with STARI, additional testing may be ordered after an amount of time has elapsed (e.g., 3, 5, 7, 10, 14 days or more) to confirm the initial diagnosis. In yet another example, treatment may consist of supportive care only, e.g., non-pharmacological treatments or over-the-counter pharmaceutical agents to alleviate symptoms, such as fever, aches, etc.
In certain examples, obtaining a result from a test of Section IV comprises analyzing a blood sample obtained from the subject as described in Section III and/or classifying the subject as described in Section IV. In certain examples, obtaining a result from a test of Section IV comprises requesting (e.g., placing a medical order or prescription) from a third party a test that analyzes a blood sample obtained from the subject as described in Section III and classifies the subject as described in Section IV, or requesting from a third party a test that analyzes a blood sample obtained from the subject as described in Section III, and then performing the classification as described in Section IV.
In each of the above examples, the method may further comprise obtaining a second result (for a sample obtained from the subject after treatment has begun) from the same test of Section IV as before treatment and adjusting treatment based on the test result.
Accordingly, yet another aspect of the disclosure is a method for monitoring the effectiveness of a therapeutic agent intended to treat a subject with Lyme disease or STARI. The method comprises obtaining a result from a test of Section IV, administering a therapeutic agent to the subject, obtaining a result from the same test of Section IV as before treatment, wherein the treatment is effective if the disease score classifies the subject as more healthy than before. A first sample obtained before treatment began may be used as a baseline. Alternatively, the first sample may be obtained after treatment has begun. Samples may be collected from a subject over time, including 0.5, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 or more days apart. For example, blood samples may be collected 0.5, 1, 2, 3, or 4 days apart. Alternatively, blood samples may be collected 4, 5, 6, or 7 days apart. Further, blood samples may be collected 7, 8, 9, or 10 days apart. Still further, blood samples may be collected 10, 11, 12 or more days apart.
The following examples are included to demonstrate preferred examples of the invention. It should be appreciated by those of skill in the art that the techniques disclosed in the examples that follow represent techniques discovered by the inventors to function well in the practice of the invention. Those of skill in the art should, however, in light of the present disclosure, appreciate that changes may be made in the specific examples that are disclosed and still obtain a like or similar result without departing from the spirit and scope of the invention. Therefore, all matter set forth or shown in the examples and accompanying drawings is to be interpreted as illustrative and not in a limiting sense.
EXAMPLESThe following examples illustrate various iterations of the invention.
Example 1Lyme disease is a multisystem bacterial infection that in the United States is primarily caused by infection with Borrelia burgdorferi sensu stricto. Over 300,000 cases of Lyme disease are estimated to occur annually in the United States, with over 3.4 million laboratory diagnostic tests performed each year (1, 2). Symptoms associated with this infection include fever, chills, headache, fatigue, muscle and joint aches, and swollen lymph nodes; however, the most prominent clinical manifestation in the early stage is the presence of one or more erythema migrans (EM) skin lesions (3). This annular, expanding erythematous skin lesion occurs at the site of the tick bite in 70 to 80% of infected individuals and is typically 5 cm or more in diameter (4, 5). Although an EM lesion is a hallmark for Lyme disease, other types of skin lesions can be confused with EM (3, 5, 6). These include rashes caused by tick-bite hypersensitivity reactions, certain cutaneous fungal infections, bacterial cellulitis and the rash of southern tick-associated rash illness (STARI) (7, 8).
STARI is associated with a bite from the lone star tick (Amblyomma americanum) and, in addition to the development of an EM-like skin lesion, individuals with STARI can present with mild systemic symptoms (including muscle and joint pains, fatigue, fever, chills, and headache) that are similar to those occurring in patients with Lyme disease (7, 9, 10). These characteristics of STARI have led some to postulate that the etiology of this illness is a Borrelia species, including B. burgdorferi (10, 11) or B. lonestari (12-15); however, multiple studies have refuted that STARI is caused by B. burgdorferi (7, 16-19) and additional cases associating B. lonestari with STARI have not emerged (20, 21). Additionally, STARI patients have been screened serologically for reactivity to rickettsial agents, but no evidence was obtained to demonstrate that rickettsia causes this illness (10, 22). Thus, at present no infectious etiology is known for STARI.
STARI cases occur over the geographic region where the lone star tick is present. This includes a region that currently expands from central Texas and Oklahoma upward into the Midwestern states and eastward, including the southern states and along the Atlantic coast into Maine (23). Unlike STARI, Lyme disease is transmitted to humans through the bite of the blacklegged tick (Ixodes scapularis) that is present in the northeastern, mid-Atlantic, and north-central United States, and the western blacklegged tick (I. pacificus), which is present on the Pacific Coast (24). The geographic distribution of human Lyme disease and the vectors for this disease is expanding (24-26), and there is a similar expansion of areas inhabited by the lone star tick (23). Importantly, a strict geographic segregation of Lyme disease and STARI does not exist, as there are regions where STARI and Lyme disease are co-prevalent (25). Thus, there is a growing need for diagnostic methods to differentiate between Lyme disease and STARI, and that facilitate proper treatment, patient management and disease surveillance.
Clinically, the skin lesions of STARI and early Lyme disease are indistinguishable, and no laboratory tool or method exists for the diagnosis of STARI or differentiation of STARI from Lyme disease. The only biomarkers evaluated for differential diagnosis of early Lyme disease and STARI have been serum antibodies to B. burgdorferi (10, 16). However, these tests have poor sensitivity for early stages of Lyme disease, and thus a lack of B. burgdorferi antibodies cannot be used as a reliable differential marker for STARI.
The experiments herein describe the development of a metabolomics-driven approach to identify biomarkers that discriminate early Lyme disease from STARI, and provide evidence that these two diseases are biochemically distinct. A retrospective cohort of well-characterized sera from patients with early Lyme disease and STARI was evaluated to identify a differentiating metabolic biosignature. Using statistical modeling, this metabolic biosignature accurately classified test samples that included healthy controls. Additionally, the metabolic biosignature revealed that N-acyl ethanolamine (NAE) and primary fatty acid amide (PFAM) metabolism differed significantly between these two diseases.
Clinical samples: A total of 220 well-characterized retrospective serum samples from three different repositories were used to develop and test a metabolic biosignature that accurately classifies early Lyme disease and STARI (Table 2). All samples from Lyme disease patients were culture confirmed and/or PCR positive for B. burgdorferi. The median age for early Lyme disease patients was 45 years and 74% were males. STARI patients had an overall median age of 45 years and 55% were males.
To establish a Lyme disease diagnostic baseline, the recommended two-tiered serology testing for Lyme disease was performed on all samples. First-tier testing was performed using the C6 EIA and was positive for 66% of Lyme disease samples. When STARI and healthy controls were tested by the C6 EIA, two STARI samples (2%) and five healthy controls (9%) tested positive or equivocal. Two-tiered testing using IgM and IgG immunoblots as the second-tier test following a positive or equivocal first-tier assay resulted in a sensitivity of 44% for early Lyme disease samples (duration of illness was not considered for IgM immunoblot testing). The sensitivity of two-tiered testing for early Lyme disease samples included in the Discovery/Training-Sets and the Test-Sets was 38% and 53%, respectively. All STARI and healthy control samples were negative by two-tiered testing (Table 2).
Development of a metabolic biosignature for early Lyme disease and STARI differentiation: Metabolic profiling by liquid chromatography-mass spectrometry (LC-MS) of a retrospective cohort of well-characterized sera from patients with early Lyme disease (n=40) and STARI (n=36) (Table 2 and
In silico analysis of metabolic pathways: Presumptive chemical identification was applied to the 261 MFs. This yielded predicted chemical formulae for 149 MFs, and 122 MFs were assigned a putative chemical structure based on interrogation of each MF's monoisotopic mass (+ or −15 ppm) against the Metlin database and the Human Metabolome Database (HMDB) (Table 3). An in silico interrogation of potentially altered metabolic pathways was performed using the presumptive identifications for the 122 MFs and MetaboAnalyst (28). Four differentiating pathways were predicted to have the greatest impact, with the most significant being glycerophospholipid metabolism and sphingolipid metabolism (
Elucidation of altered NAE metabolism: The prediction of altered metabolic pathways was based on the presumptive structural identification of the early Lyme disease versus STARI differentiating MFs. Thus, to further define the metabolic differences between these two patient groups, structural confirmation of selected MFs was undertaken. Two MFs that displayed relatively large abundance differences (m/z 300.2892, RT 19.66; and m/z 328.3204, RT 20.72) were putatively identified as sphingosine-C18 or 3-ketosphinganine, and sphingosine-C20 or N,N-dimethyl sphingosine, respectively. However, both of these MFs had alternative predicted structures of palmitoyl ethanolamide and stearoyl ethanolamide, respectively. The interrogation of authentic standards against these two serum MFs revealed RTs and MS/MS spectra that identified the m/z 300.2892 and m/z 328.3204 products as palmitoyl ethanolamide (
The large number of differentiating MFs associated with NAE metabolism suggested that this is a major biological difference between STARI and early Lyme disease (
The 261 MF biosignature list revealed metabolic dissimilarity between Lyme disease and STARI: To test whether early Lyme disease and STARI represent distinct metabolic states that would be reflected in the comparison of MF abundances in these two disease states to those of healthy controls, the abundance fold-change for each structurally confirmed MF in early Lyme disease and STARI sera as compared to healthy controls was determined. This revealed that the majority of these MFs maintained fold change differences with respect to healthy controls that allowed for segregation of early Lyme disease and STARI patient samples (
Diagnostic classification of early Lyme disease vs STARI: Classification models were used to determine whether the 261 MF biosignature could be applied to discriminate early Lyme disease from STARI (Table 1 and
Diagnostic classification of early Lyme disease vs STARI vs healthy controls: Separate three-way classification models using LASSO and RF were developed by including LC-MS data collected for healthy controls in the Training-Set samples (
Of the 38 MFs selected by LASSO for the two-way classification model, 33 were included in the 82 MFs of the LASSO three-way classification model (Table 3). The 82 MFs of the LASSO three-way classification included seven of the 14 structurally confirmed metabolites: 3-ketosphingosine, glycocholic acid and pentadecanoyl ethanolamide, as well as the four included in the LASSO two-way classification model (Table 3).
Biosignature was not influenced by geographic variability: Since retrospective samples collected by multiple laboratories were used in these studies, analyses were performed to assess whether a geographic bias was introduced. To address this, three healthy control groups and three STARI groups (all early Lyme disease samples came from one geographic region) were evaluated by linear discriminant analysis using the 82 MFs of the LASSO three-way classification model (
Discussion: The inability to detect B. burgdorferi by PCR or culture and the lack of a serological response to B. burgdorferi antigens in STARI patients is widely accepted as evidence that the etiologies of STARI and Lyme disease differ (7, 16). This is further supported by the different tick species associated with these two diseases (8, 25). Nevertheless, the strong overlap in clinical symptoms, including the development of an EM-like skin lesion, creates confusion and controversy for the clinical differentiation of STARI and Lyme disease (30). The data reported here demonstrated marked differences between the metabolic profiles of early Lyme disease and STARI patients, and thus provide compelling positive data to support the concept that these two illnesses are distinct entities. Interestingly, metabolic pathway analyses and the structural identification of several MFs with significant abundance differences between early Lyme disease and STARI identified multiple NAEs. These endogenous lipid mediators are derived from phosphatidylcholine and phospahtidylethanolamine via the endocannabinoid system (
This current work expands demonstrates the ability to distinguish early Lyme disease from an illness with nearly identical symptoms or what would be considered a Lyme disease-like illness (37). The existing diagnostic algorithm for Lyme disease is a two-tiered serologic approach that utilizes an EIA or IFA as a first-tier test followed by IgM and IgG immunoblotting as the second-tier test (38). For early Lyme disease, the sensitivity of this diagnostic is 29-40% and the specificity is 95-100% (39). The current antibody-based approaches do not distinguish between active and previous infections, an important limitation. In the current study all of the STARI samples were negative by two-tiered testing, and only 2% were positive by the first-tier EIA. Early Lyme disease samples were 44% positive (38% positivity for the early Lyme disease samples used in the Discovery and Training Sets and 53% positivity for early Lyme disease samples used in the Test Sets) by two-tiered testing. In contrast, when classification modeling was applied to the 261 MFs of the early Lyme disease-STARI biosignature, diagnostic accuracy for early Lyme disease was dramatically increased (85 to 98% accuracy depending on the model) as compared to serology. Classification by RF or LASSO was overall highly accurate for early Lyme disease and STARI, in particular when using the two-way classification models. Interestingly, when healthy controls were introduced and used to develop a three-way classification model there was a slight increase in the accuracy for STARI and decrease in the accuracy for early Lyme disease, but healthy controls were classified with a 93-95% accuracy. This was surprising as healthy controls were not used to create the initial 261 MF biosignature, and furthers supported that STARI and early Lyme disease are metabolically distinct from healthy controls, but in different ways.
To date the development of a diagnostic tool for STARI or for differentiation of early Lyme disease and STARI has received little attention. As the geographic distribution of Lyme disease continues to expand (25, 26), so will the geographic range where there is overlap of Lyme disease and STARI. Thus, a diagnostic tool that accurately differentiates these two diseases could have a major impact on patient management. Lyme disease is treated with antibiotics, and although there is no defined infectious etiology for STARI, this illness is also commonly treated in a similar manner (7, 20, 40). Establishment of a robust diagnostic tool would not only facilitate antibiotic stewardship, it would also allow for proper studies to assess the true impact of therapies for STARI. Lyme disease is also a reportable disease and in order to maintain accurate disease surveillance in low incidence areas, it is essential that diseases such as STARI be excluded (30). Additionally, vaccines are currently being developed for Lyme disease (41-44) and as these are tested, it will be important to identify STARI patients in order to properly assess vaccine efficacy.
To apply the discoveries of this work towards the development of an assay that can be used for the clinical differentiation of early Lyme disease and STARI, it should first be determined whether an emphasis should be placed on the diagnosis of Lyme disease or STARI. As there is no defined etiology of STARI, and Lyme disease is not necessarily self-limiting without antibiotics and can have subsequent complications if untreated, we envision that the final assay would focus on being highly sensitive for early Lyme disease and be primarily applied in regions where Lyme disease and STARI overlap. Although existing laboratory tests for Lyme disease emphasize specificity, this strategy needs to be reconsidered for a differential diagnostic test of STARI and early Lyme disease, since any illness presenting with an EM in a region with a known incidence of Lyme disease would likely be treated with antibiotics (7, 20, 40). As with all diagnostic tests, use of a metabolic biosignature for differentiation of early Lyme disease and STARI would need to be performed in conjunction with clinical evaluation of the patient, and consideration of their medical history and epidemiologic risk for these two diseases.
The approach outlined in this study applies semi-quantitative mass spectrometry and the use of biochemical signatures for the classification of patients. Clinical application of such an approach would likely occur in a specialized clinical diagnostic laboratory. However, it should be noted that the second-tier immunoblot assays for the serological diagnosis of Lyme disease are already performed in specialized laboratories (1, 45, 46). Mass spectrometry assays are currently used in clinical laboratories for the analyses of small molecule metabolites. The majority of these tests are under Clinical Laboratory Improvement Amendments (CLIA) guidelines, but an FDA cleared mass spectrometry-based test for inborn metabolic errors is in use (47). The most accurate quantification of metabolites by mass spectrometry is achieved by Multiple Reaction Monitoring (MRM) assays (48). Such assays are developed with the knowledge of a MF's chemical structure. To this end, the chemical structure of 14 MFs have been identified. The chemical structure of the remainder of the MFs can be identified by the methods described herein. It should be noted that the NAEs and PFAMs that were revealed via our pathway analyses are amenable to MRM assays (49). These metabolites are now being investigated for their ability to accurately classify STARI and early Lyme disease.
The data reported here were generated from the analysis of retrospectively collected serum samples from various repositories that have been archived for different lengths of time. To reduce the impact of the potential variability associated with these samples, stringent criteria were applied to the data analysis. In addition to the requirement of a significant fold change, those MFs selected for the final early Lyme disease-STARI biosignature were required to be present in at least 80% of samples within a sample group and maintain the median fold-change difference in at least 50% of samples within a group. While the STARI and healthy control sera were collected by multiple laboratories and from multiple geographic locations, the early Lyme disease sera were obtained from a single laboratory. This is a potential limitation of the study. However, linear discriminate analysis was applied to assess the variability within the healthy control and STARI samples collected by different laboratories. This analysis demonstrated little to no variability among the STARI or healthy control samples indicating that the criteria used for MF selection effectively reduced non-biological variability. As noted, data were collected by non-absolute semi-quantitative mass spectrometry. Nevertheless, this is a common practice applied in the development of differentiating biosignatures for infectious diseases (27, 50-53), and the workflow ensured that the most robust MFs were selected and used for classification modeling.
Without knowledge of a known etiologic agent, it is recognized that STARI simply encompasses a clinical syndrome. The STARI samples used in this current work included those collected in studies used to define this illness (9), as well as samples collected outside those original studies. Additional samples collected prospectively will be useful to assess the applicability of our current metabolic biosignature in a real world scenario. Future sample collection will also target patient populations with non-Lyme EM-like lesions, including tick-bite hypersensitivity reactions, certain cutaneous fungal infections and bacterial cellulitis. Additionally, other factors such as confections with other vector-borne pathogens will need to be addressed with prospective studies. In the Southeastern United States, there is evidence for enzootic transmission of B. burgdorferi; however, it is debatable whether Lyme disease occurs in this region (11, 30, 54, 55). The current study was not designed to provide evidence for or against the presence of Lyme disease in the southern United States. Nevertheless, metabolic profiling offers a novel approach that is orthogonal to the methods currently employed to address this issue.
Example 2STARI is an illness that has received little attention over the years, but is a confounding factor in diagnosing early Lyme disease in areas where both illnesses overlap and contributes to the debate surrounding the presence of Lyme disease in the southern United States. No diagnostic tool exists for STARI or for differentiating early Lyme disease from STARI. Based on documented differences between early Lyme disease and STARI (9, 16, 56), we metabolically profiled serum to develop a biochemical biosignature that when applied could accurately classify early Lyme disease and STARI patients (See Example 1). This example describes the design of the study described in Example 1.
An unbiased-metabolomics study was designed to directly compare the metabolic host responses between these two illnesses, and subsequently evaluate how this metabolic biosignature distinguishes these two illnesses. The use of unbiased metabolomics for biosignature discovery does not lend itself to power calculations to determine sample size. Thus, sample sizes were selected based on our previous studies (27, 50, 51). To obtain a sufficient number of well-characterized STARI sera, retrospectively collected samples from two separate studies were used. Specifically, the first set of STARI serum samples (n=33) was obtained from the CDC repository. These samples were collected through a prospective study performed between 2007 and 2009 (57). Patients were enrolled through CDC outreach efforts (n=17) or by contract with the University of North Carolina at Chapel Hill (n=16). The states where patients were recruited included NC, 18; VA, 4; TN, 3; KY, 2; GA, 2; IA, 2; AL, 1; and NE, 1. All samples were collected pre-treatment with the exception of one patient who was treated with doxycycline 1-2 days before the serum sample was obtained. The second set of STARI samples (n=22) was obtained from the New York Medical College serum repository (20). These samples were collected between 2001 and 2004 from patients living in Missouri.
Sufficient numbers of well-characterized early Lyme disease serum samples were acquired from New York, an area of high incidence for Lyme disease and low incidence of STARI (9). Specifically, all early Lyme disease samples (n=70) were culture and/or PCR positive for B. burgdorferi and were collected pre-treatment. To ensure appropriate representation of both non-disseminated and disseminated forms of early EM Lyme disease, samples from patients with a single EM that were skin culture and/or PCR positive for B. burgdorferi and blood culture negative (n=35), and patients with multiple EMs or a single EM that were blood culture positive (n=35) were used. Early Lyme disease samples were collected between 1992 and 2007, and 1 to 33 days post-onset of symptoms. To understand the relationship of our findings to a healthy control population serum samples from healthy donors were also included in the study. These were procured from repositories at New York Medical College, the CDC and the University of Central Florida. A detailed description of inclusion and exclusion criteria for each patient and donor population is provided in Table 2. All participating institutions obtained institutional review board (IRB) approval for this study. IRB review and approval for this study ensured that the retrospective samples used had been collected under informed consent.
All samples were analyzed in duplicate and were randomized prior to processing for LC-MS analyses. Healthy control sera were used as quality control samples for each LC-MS experiment. The serum samples and respective LC-MS data files of each patient group and healthy controls were randomly separated into a Discovery-Set/Training-Sets 1 and 2, and Test-Sets 1 and 2. Specifically, 40 of the 70 early Lyme disease and 36 of the 55 STARI samples were randomly selected as the Discovery-Set samples. This sample set was used for molecular feature selection. To train the classification models, two training-sets were used. The first, Training-Set 1, was identical to the Discovery-Set (i.e. contained the same early Lyme disease and STARI samples) and the second, Training-Set 2, had the same samples as Training-Set 1 with the addition of 38 of the 58 healthy control samples. Lastly, Test-Sets 1 and 2 were created. Test-Set 1 was comprised of 30 early Lyme disease and 19 STARI samples that were not included in the Discovery/Training samples sets. Test-Set 2 had the same samples as those used in Test-Set 1 with the addition of 20 healthy control samples that were not included in the Training-Set 2 samples. Test-Sets 1 and 2 were exclusively used for blinded testing of the classification models.
Randomization into Discovery/Training-Sets or Test-Sets was done in a manner that ensured bias was not introduced based on the repository from which STARI samples were obtained or on whether the early Lyme disease samples were from a non-disseminated or disseminated case. Biosignature development was performed by screening MFs based on stringent criteria outlined in
This example describes methods used for Lyme disease serologic testing of all serum samples used in the examples above. Standard two-tiered testing was performed on all samples (38). The C6 B. burgdorferi (Lyme) ELISA (Immunetics, Boston, Mass.) was used as a first-tier test, and any positive or equivocal samples were reflexed to Marblot IgM and IgG immunoblots (MarDx Diagnostics, Inc., Carlsbad, Calif.) as the second-tier test. Serologic assays were performed according to the manufacturer's instructions, and the data were interpreted according to established CDC guidelines (38). Duration of illness, however, was not considered for test interpretation.
Example 4This example describes liquid chromatography-mass spectrometry (LC-MS) methods used in the examples above. Serum samples were randomized prior to extraction of small molecule metabolites and LC-MS analyses. Small molecule metabolites were extracted from sera as previously reported (27). An aliquot (10 μl) of the serum metabolite extract was applied to a Poroshell 120, EC-C8, 2.1×100 mm, 2.7 μm LC Column (Agilent Technologies, Palo Alto, Calif.). The metabolites were eluted with a 2-98% nonlinear gradient of acetonitrile in 0.1% formic acid at a flow rate of 250 μl/min with an Agilent 1200 series LC system. The eluent was introduced directly into an Agilent 6520 quadrapole time of flight mass (Q-TOF) spectrometer and MS was performed as previously described (27, 50). LC-MS and LC-MS/MS data were collected under the following parameters: gas temperature, 310° C.; drying gas at 10 liters per min; nebulizer at 45 lb per in2; capillary voltage, 4,000 V; fragmentation energy, 120 V; skimmer, 65 V; and octapole RF setting, 750 V. The positive-ion MS data for the mass range of 75 to 1,700 Da were acquired at a rate of 2 scans per sec. Data were collected in both centroid and profile modes in 4-GHz high-resolution mode. Positive-ion reference masses of 121.050873 m/z and 922.009798 m/z were introduced to ensure mass accuracy. To monitor instrument performance, quality control samples having a metabolite extract of healthy control serum (BioreclamationIVT, Westbury, N.Y.) was analyzed in duplicate at the beginning of each analysis day and every 20 samples during the analysis day.
Example 5This example describes the methods used for biosignature development as described in the examples above. LC-MS data from an initial Discovery-Set of samples comprised of randomly selected early Lyme disease (n=40) and randomly selected STARI patients (n=36) that were exclusively used for molecular feature selection and classification model training were processed with the Molecular Feature Extractor algorithm tool of the Agilent MassHunter Qualitative Analysis software version B.05.00 (Agilent Technologies, Santa Clara, Calif.). The MFs were aligned between data files with a 0.25 min retention time window and 15 ppm mass tolerance. Comparative analyses of differentiating MFs between patient groups were performed using the workflow presented in
Abundance data from the Targeted-Discovery-Set data files were normalized using a two-step method. First, abundances (area under the peak for the monoisotopic mass) of each Discovery MF were normalized by the median intensity of the stable MFs detected in each individual sample (58). Stable MFs were those identified in the original extraction of LC-MS data files with the Agilent MassHunter Qualitative Analysis software and present in at least 50% of all sample data files. Secondly, median fold changes of stable MFs between the initial quality control sample (applied at the beginning of the LC-MS analysis) and each of the subsequent quality control samples (applied every 20 clinical samples throughout the LC-MS analysis) were calculated. The median fold change calculated for the quality control sample that directly followed each series of 20 clinical samples was multiplied against the normalized Discovery-MF abundances in the clinical samples of that series. This second normalization step was performed to correct for instrument variability. To apply stringency to the development of a final early Lyme disease-STARI biosignature, MFs were filtered based on consistency in the duplicate LC-MS data sets by requiring the same directional abundance change between the patient groups. Specifically, MFs with at least a ≥2-fold abundance difference and a 1.5-fold abundance difference between the medians of the two groups (early Lyme disease and STARI) for LC-MS analysis-1 and LC-MS analysis-2, respectively, were selected. Further criteria applied to ensure that the most robust MFs were being selected included: removing MFs with >20% missing values in both groups, and selecting only MFs where at least 50% of the samples within a patient group produced a fold change of ≥2 in comparison to the mean of the other patient group. This selection process resulted in the MFs included in the early Lyme disease-STARI biosignature.
Example 6This example describes the methods used for prediction and verification of MF chemical structure. Confirmation of the chemical structures of selected MFs was performed by LC-MS-MS to provide level-1 or level-2 identifications (59). Commercial standards palmitoyl ethanolamide, stearoyl ethanolamide, eicosanoyl ethanolamide, glycerophospho-N-palmitoyl ethanolamine, pentadecanoyl ethanolamide, and erucamide were obtained from Cayman Chemical (Ann Arbor, Mich., USA). Commercial standards piperine and nonanedioic acid were obtained from Sigma Aldrich (Saint Louis, Mo., USA). Commercial standards methyl oleate, stearamide, palmitamide, CMPF, and glycocholic acid were obtained from Santa Cruz Biotechnology, Inc. (Santa Cruz, Calif., USA). The LC conditions used were the same as those used for the LC-MS analyses of serum metabolites. MS/MS spectra of the targeted MFs and commercial standards were obtained with an Agilent 6520 Q-TOF mass spectrometer. Electrospray ionization was performed in the positive ion mode as described for MS analyses, except the mass spectrometer was operated in the 2 GHz extended dynamic range mode. The positive ion MS/MS data (50 to 1,700 Da) were acquired at a rate of 1 scan per sec. Precursor ions were selected by the quadrupole and fragmented via collision-induced dissociation (CID) with nitrogen at collision energies of 10, 20, or 40 eV. To provide a level-1 identification, the MS/MS spectra of the targeted metabolites were compared to spectra of commercial standards. Additionally, LC retention time comparisons between the targeted MF and the respective standard were made. A retention time window of ±5 sec was applied as a cutoff for identification. The MS/MS spectra of selected serum metabolites were compared to spectra in the Metlin database for a level-2 identification.
Example 7Metabolic pathway analysis in the examples above was performed by MetaboAnalyst. The experimentally obtained monoisotopic masses corresponding to the MFs of the 261 biosignature list were searched against HMDB using a 15 ppm window. The resulting list of potential metabolite structures were applied to the MetaboAnalyst pathway analysis tool (28) Settings for pathway analysis included applying Homo sapiens pathway library; the Hypergeometric Test for the over-representation analysis and Relative-betweenness centrality to estimate node importance in the pathway topology.
Example 8Methods for statistical analyses and classification modeling are described in this example. Methods to filter the list of MFs and to normalize abundances are described in the section on biosignature development. Prior to analysis, the normalized abundances were log 2 transformed and each MF was scaled to have a mean of zero and standard deviation of 1. Statistical analyses were performed using R software (60).
For classification modeling, Training- and Test-Set samples were used as previously described (27, 50) and as shown in
A linear discriminant analysis was performed with the 82 MFs selected by the three-way LASSO model using linear discriminant analysis function in R. MF abundance data included in the linear discriminant analysis were from healthy controls from Colorado, Florida, and New York, and from STARI patients from North Carolina, Missouri, and other states. Before linear discriminant analysis data were transformed by taking the log 2 value and standardizing to the mean 0 and variance 1 within each MF. Samples were differentiated by healthy and STARI.
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Claims
1. A method for treating a subject in need thereof, wherein the subject is suspected as having Lyme disease or STARI, the method comprising: TABLE A Predicted Retention Chemical m/z Time Compound Structure (based Metabolite MF (positive (see Predicted on accurate Class or # Name ion) Mass examples) Formula mass) Pathway 1 CSU/ 166.0852 165.078 1.86 C9H11NO2 Phenylalanine Phenylalanine CDC-001 metabolism 2 CSU/ 270.3156 269.3076 18.02 C18H39N — — CDC-012 3 CSU/ 284.3314 283.3236 18.13 C19H41N — — CDC-013 4 CSU/ 300.6407 599.268 18.27 C33H37N5O6 Asp Phe Arg Tyr Peptide CDC-014 (SEQ ID NO: 1) 5 CSU/ 300.2892 299.2821 19.66 C18H37NO2 Palmitoyl N-acyl CDC-019 ethanolamide ethanolamine metabolism 6 CSU/ 734.5079 1449.9753 17.81 — — — CDC-039 7 CSU/ 370.1837 369.1757 19.7 C19H23N5O3 — — CDC-062 8 CSU/ 811.1942 810.1869 12.07 C42H30N6O12 — — CDC-066 9 CSU/ 947.7976 946.7936 14.55 C62H106O6 TAG (59:7) Triacylglycerol CDC-067 metabolism 10 CSU/ 410.2033 409.196 17.18 — — — CDC-072 11 CSU/ 1487.0005 1485.9987 18.17 — — — CDC-075 12 CSU/ 137.0463 136.0378 1.37 C4H8O5 Threonate Sugar CDC-086 metabolite 13 CSU/ 811.7965 810.7882 12.07 — — — CDC-107 14 CSU/ 616.1776 615.1699 15.43 — — — CDC-132 15 CSU/ 713.4492 712.4391 19.35 C38H65O10P PG (32:5) Glycero- CDC-152 phospholipid metabolism 16 CSU/ 502.3376 484.3039 19.87 C27H40N4O4 Gln Leu Pro Lys Peptide CDC-155 (SEQ ID NO: 2) 17 CSU/ 415.3045 414.2978 20.19 — — — CDC-158 18 CSU/ 366.3729 365.3655 22.79 — — — CDC-164 19 CSU/ 333.1446 332.1373 12.89 C12H20N4O7 Glu Gln Gly Peptide CDC-166 20 CSU/ 241.1069 240.0996 14.7 C12H16O5 3-Carboxy-4- Fatty acid CDC-205 methyl-5-propyl-2- metabolism furanpropanoic acid (CMPF) 21 CSU/ 464.1916 463.1849 13.05 C16H29N7O7S Arg Asp Cys Ala Peptide CDC-211 (SEQ ID NO: 3) 22 CSU/ 1249.2045 1248.1993 15.31 — — — CDC-212 23 CSU/ 1248.9178 1247.9141 15.3 — — — CDC-213 24 CSU/ 158.1539 157.1466 15.36 — — — CDC-219 25 CSU/ 529.3381 528.3296 16.89 C24H44N6O7 Gln Val Leu Leu Peptide CDC-227 Gly (SEQ ID NO: 4) 26 CSU/ 282.2776 264.2456 20.56 C18H32O — — CDC-229 27 CSU/ 190.1260 189.1187 14.12 C9H19NOS 8- 2- CDC-235 Methylthiooctanal oxocarboxylic doxime acid metabolism 28 CSU/ 382.3675 381.3603 20.23 C24H47NO2 Erucicoyl N-acyl CDC-238 ethanolamide ethanolamine metabolism 29 CSU/ 477.2968 476.2898 22.79 C31H40O4 Lys Lys Thr Thr Peptide CDC-244 (SEQ ID NO: 5) 30 CSU/ 459.3968 458.3904 19.08 — — — CDC-248 31 CSU/ 342.2635 341.2565 15.62 C19H35NO4 — — CDC-253 32 CSU/ 529.3827 1022.6938 17.86 — — — CDC-254 33 CSU/ 459.2502 458.2429 19.02 C23H39O7P Lyso PA (20:4) Glycero- CDC-258 hospholipid metabolism 34 CSU/ 239.0919 238.0844 11.66 C12H14O5 Trans-2, 3, 4- Phenylpropanoid CDC-002 trimethoxy- and cinnamate polyketide metabolism 35 CSU/ 389.2174 388.2094 15.47 C19H32O8 Methyl Fatty acid CDC-028 10,12,13,15- metabolism bisepidioxy-16- hydroperoxy-8E- octadecenoate 36 CSU/ 285.2065 284.1991 16.02 C16H28O4 — — CDC-182 37 CSU/ 279.1693 278.1629 11.05 C15H22N2O3 Phe Leu Dipeptide CDC-204 38 CSU/ 714.6967 1427.3824 11.76 — — — CDC-247 TABLE C Predicted Retention Chemical m/z Time Compound Structure Metabolite MF (positive (see Predicted (based on Class or # Name ion) Mass examples) Formula accurate mass) Pathway 1 CSU/ 166.0852 165.078 1.86 C9H11NO2 Phenylalanine Phenylalanine CDC-001 metabolism 2 CSU/ 270.3156 269.3076 18.02 C18H39N — — CDC-012 3 CSU/ 284.3314 283.3236 18.13 C19H41N — — CDC-013 4 CSU/ 300.6407 599.268 18.27 C33H37N5O6 Asp Phe Arg Tyr Peptide CDC-014 (SEQ ID NO: 1) 5 CSU/ 300.2892 299.2821 19.66 C18H37NO2 Palmitoyl N-acyl CDC-019 ethanolamide ethanolamine metabolism 6 CSU/ 734.5079 1449.9753 17.81 — — — CDC-039 7 CSU/ 370.1837 369.1757 19.7 C19H23N5O3 — — CDC-062 8 CSU/ 811.1942 810.1869 12.07 C42H30N6O12 — — CDC-066 9 CSU/ 947.7976 946.7936 14.55 C62H106O6 TAG (59:7) Triacylglycerol CDC-067 metabolism 10 CSU/ 410.2033 409.196 17.18 — — — CDC-072 11 CSU/ 1487.0005 1485.9987 18.17 — — — CDC-075 12 CSU/ 137.0463 136.0378 1.37 C4H8O5 Threonate Sugar CDC-086 metabolite 13 CSU/ 811.7965 810.7882 12.07 — — — CDC-107 14 CSU/ 616.1776 615.1699 15.43 — — — CDC-132 15 CSU/ 713.4492 712.4391 19.35 C38H65O10P PG (32:5) Glycero- CDC-152 phospholipid metabolism 16 CSU/ 502.3376 484.3039 19.87 C27H40N4O4 Gln Leu Pro Lys Peptide CDC-155 (SEQ ID NO: 2) 17 CSU/ 415.3045 414.2978 20.19 — — — CDC-158 18 CSU/ 366.3729 365.3655 22.79 — — — CDC-164 19 CSU/ 333.1446 332.1373 12.89 C12H20N4O7 Glu Gln Gly Peptide CDC-166 20 CSU/ 241.1069 240.0996 14.7 C12H16O5 3-Carboxy-4- Fatty acid CDC-205 methy1-5-propyl- metabolism 2-furanpropanoic acid (CMPF) 21 CSU/ 464.1916 463.1849 13.05 C16H29N7O7S Arg Asp Cys Ala Peptide CDC-211 (SEQ ID NO: 3) 22 CSU/ 1249.2045 1248.1993 15.31 — — — CDC-212 23 CSU/ 1248.9178 1247.9141 15.3 — — — CDC-213 24 CSU/ 158.1539 157.1466 15.36 — — — CDC-219 25 CSU/ 529.3381 528.3296 16.89 C24H44N6O7 Gln Val Leu Leu Peptide CDC-227 Gly (SEQ ID NO: 4) 26 CSU/ 282.2776 264.2456 20.56 C18H32O — — CDC-229 27 CSU/ 190.1260 189.1187 14.12 C9H19NOS 8- 2- CDC-235 Methylthiooctanal oxocarboxylic doxime acid metabolism 28 CSU/ 382.3675 381.3603 20.23 C24H47NO2 Erucicoyl N-acyl CDC-238 ethanolamide ethanolamine metabolism 29 CSU/ 477.2968 476.2898 22.79 C31H40O4 Lys Lys Thr Thr Peptide CDC-244 (SEQ ID NO: 5) 30 CSU/ 459.3968 458.3904 19.08 — — — CDC-248 31 CSU/ 342.2635 341.2565 15.62 C19H35NO4 — — CDC-253 32 CSU/ 529.3827 1022.6938 17.86 — — — CDC-254 33 CSU/ 459.2502 458.2429 19.02 C23H39O7P Lyso PA (20:4) Glycero- CDC-258 hospholipid metabolism 34 CSU/ 886.4296 1770.8438 12.18 — — — CDC-003 35 CSU/ 181.0859 180.0788 14.7 C10H12O3 5′-(3′-Methoxy-4′- Endogenous CDC-004 hydroxphenyl)- metabolite gamma- associated valerolactone with microbiome 36 CSU/ 286.1444 285.1371 16.08 C17H19NO3 Piperine Alkaloid CDC-006 metabolism 37 CSU/ 463.2339 462.2248 16.36 C25H34O8 Ala Lys Met Asn Peptide CDC-008 (SEQ ID NO: 6) 38 CSU/ 242.2844 241.2772 17.1 C16H35N — — CDC-009 39 CSU/ 590.4237 589.4194 19.24 — — — CDC-017 40 CSU/ 553.3904 552.3819 23.38 C35H52O5 Furohyperforin Endogenous CDC-026 metabolite- derived from food 41 CSU/ 399.2364 398.2313 16.23 — — — CDC-030 42 CSU/ 580.4144 1158.8173 18.26 — — — CDC-042 43 CSU/ 704.4985 1372.925 18.7 — — — CDC-052 44 CSU/ 623.4521 1210.8362 19.55 — — — CDC-061 45 CSU/ 389.2178 388.2099 15.52 C19H32O8 — — CDC-070 CSU/ 1111.6690 1110.6656 17.89 — — — 46 CDC-074 47 CSU/ 482.4040 481.3976 19.99 — — — CDC-083 48 CSU/ 533.1929 532.1854 20.84 C23H28N6O9 Asp His Phe Asp Peptide CDC-084 (SEQ ID NO: 7) 49 CSU/ 466.3152 465.3085 14.73 C26H43NO6 Glycocholic acid Bile acid CDC-087 metabolism 50 CSU/ 683.4728 1347.9062 17.56 — — — CDC-091 51 CSU/ 227.0897 204.1002 9.68 C9H16O5 — — CDC-095 52 CSU/ 183.1016 182.0943 10.89 C10H14O3 — — CDC-098 53 CSU/ 476.3055 475.2993 11.09 C26H41N3O5 — — CDC-099 54 CSU/ 215.1283 214.1209 12.32 C11H18O4 alpha-Carboxy- Endogenous CDC-112 delta- metabolite- decalactone derived from food 55 CSU/ 519.1881 518.1813 12.33 C20H30N4O12 Poly-g-D- Poly D- CDC-115 glutamate glutamate metabolism 56 CSU/ 1086.1800 2170.3435 15.38 — — — CDC-128 57 CSU/ 285.2061 284.1993 15.99 C16H28O4 — — CDC-133 58 CSU/ 357.1363 356.1284 15.98 C20H20O6 Xanthoxylol Endogenous CDC-134 metabolite- derived from food 59 CSU/ 299.1853 298.1781 16.24 C16H26O5 Tetranor-PGE1 Prostaglandin CDC-136 metabolism 60 CSU/ 334.2580 333.2514 16.36 — — — CDC-137 61 CSU/ 317.2317 316.2254 16.63 — — — CDC-138 62 CSU/ 331.2471 330.2403 17.26 C18H34O5 11,12,13- Fatty acid CDC-141 trihydroxy-9- metabolism octadecenoic acid 63 CSU/ 583.3480 582.3379 18.04 C27H46N6O8 Leu Lys Glu Pro Peptide CDC-144 Pro (SEQ ID NO: 8) 64 CSU/ 648.4672 647.4609 19.98 C34H66NO8P PE (29:1) Glycero- CDC-157 phospholipid metabolism 65 CSU/ 445.2880 854.5087 12.48 C45H74O15 (3b,21b)-12- Endogenous CDC-165 Oleanene- metabolite- 3,21,28-triol 28- derived from [arabinosyl-(1- food >3)-arabinosyl- (1->3)- arabinoside] 66 CSU/ 1486.7386 2971.4668 14.97 — — — CDC-181 67 CSU/ 668.4686 1317.8969 18.04 C16H28O4 Omphalotin A Endogenous CDC-183 metabolite- derived from food 68 CSU/ 454.2924 436.2587 18.1 C21H41O7P Lyso-PA (18:1) Glycero- CDC-184 phospholipid metabolism 69 CSU/ 607.9324 606.9246 19.01 — — — CDC-186 70 CSU/ 521.4202 503.3858 21.06 — — — CDC-188 71 CSU/ 176.0746 175.0667 2.31 — — — CDC-193 72 CSU/ 596.9082 1191.8033 19.1 — — — CDC-194 73 CSU/ 532.5606 531.5555 18.38 — — — CDC-203 74 CSU/ 337.1667 336.1599 20.67 C12H24N4O7 — — CDC-206 75 CSU/ 415.1634 207.0784 12.2 C8H9N5O2 6-Amino-9H- Endogenous CDC-210 purine-9- metabolite- propanoic acid derived from food 76 CSU/ 364.3407 346.3068 20.72 — — — CDC-218 77 CSU/ 989.5004 1976.9858 12.03 — — — CDC-222 78 CSU/ 819.6064 1635.8239 12.06 — — — CDC-224 79 CSU/ 286.2737 285.2666 19.08 C17H35NO2 Pentadecanoyl N-acyl CDC-237 ethanolamide ethanolamine metabolism 80 CSU/ 614.4833 613.4772 19.78 — — — CDC-245 81 CSU/ 298.2740 297.2668 16.44 C18H35NO2 3-Ketospingosine Sphingolipid CDC-250 metabolism 82 CSU/ 1003.7020 1002.696 18.46 — — — CDC-252 TABLE D Predicted Retention Chemical m/z Time Compound Structure Metabolite MF (positive (see Predicted (based on Class or # Name ion) Mass examples) Formula accurate mass) Pathway 1 CSU/ 166.0852 165.07 8 1.86 C9H11NO2 Phenylalanine Phenylalanine CDC-001 metabolism 2 CSU/ 270.3156 269.3076 18.02 C18H39N — — CDC-012 3 CSU/ 284.3314 283.3236 18.13 C19H41N — — CDC-013 4 CSU/ 300.6407 599.268 18.27 C33H37N5O6 Asp Phe Arg Tyr Peptide CDC-014 (SEQ ID NO: 1) 5 CSU/ 300.2892 299.2821 19.66 C18H37NO2 Palmitoyl N-acyl CDC-019 ethanolamide ethanolamine metabolism 6 CSU/ 734.5079 1449.9753 17.81 — — — CDC-039 7 CSU/ 370.1837 369.1757 19.7 C19H23N5O3 — — CDC-062 8 CSU/ 811.1942 810.1869 12.07 C42H30N6O12 — — CDC-066 9 CSU/ 947.7976 946.7936 14.55 C62H106O6 TAG (59:7) Triacylglycerol CDC-067 metabolism 10 CSU/ 410.2033 409.196 17.18 — — — CDC-072 11 CSU/ 1487.0005 1485.9987 18.17 — — — CDC-075 12 CSU/ 137.0463 136.0378 1.37 C4H8O5 Threonate Sugar CDC-086 metabolite 13 CSU/ 811.7965 810.7882 12.07 — — — CDC-107 14 CSU/ 616.1776 615.1699 15.43 — — — CDC-132 15 CSU/ 713.4492 712.4391 19.35 C38H65O10P PG (32:5) Glycero- CDC-152 phospholipid metabolism 16 CSU/ 502.3376 484.3039 19.87 C27H40N4O4 Gln Leu Pro Lys Peptide CDC-155 (SEQ ID NO: 2) 17 CSU/ 415.3045 414.2978 20.19 — — — CDC-158 18 CSU/ 366.3729 365.3655 22.79 — — — CDC-164 19 CSU/ 333.1446 332.1373 12.89 C12H20N4O7 Glu Gln Gly Peptide CDC-166 20 CSU/ 241.1069 240.0996 14.7 C12H16O5 3-Carboxy-4- Fatty acid CDC-205 methy1-5-propyl- metabolism 2-furanpropanoic acid (CMPF) 21 CSU/ 464.1916 463.1849 13.05 C16H29N7O7S Arg Asp Cys Ala Peptide CDC-211 (SEQ ID NO: 3) 22 CSU/ 1249.2045 1248.1993 15.31 — — — CDC-212 23 CSU/ 1248.9178 1247.9141 15.3 — — — CDC-213 24 CSU/ 158.1539 157.1466 15.36 — — — CDC-219 25 CSU/ 529.3381 528.3296 16.89 C24H44N6O7 Gln Val Leu Leu Gly Peptide CDC-227 (SEQ ID NO: 4) 26 CSU/ 282.2776 264.2456 20.56 C18H32O — — CDC-229 CSU/ 190.1260 189.1187 14.12 C9H19NOS 8- 2- CDC-235 Methylthiooctanal oxocarboxylic 27 doxime acid metabolism 28 CSU/ 382.3675 381.3603 20.23 C24H47NO2 Erucicoyl N-acyl CDC-238 ethanolamide ethanolamine metabolism 29 CSU/ 477.2968 476.2898 22.79 C31H40O4 Lys Lys Thr Thr Peptide CDC-244 (SEQ ID NO: 5) 30 CSU/ 459.3968 458.3904 19.08 — — — CDC-248 31 CSU/ 342.2635 341.2565 15.62 C19H35NO4 — — CDC-253 32 CSU/ 529.3827 1022.6938 17.86 — — — CDC-254 33 CSU/ 459.2502 458.2429 19.02 C23H39O7P Lyso PA (20:4) Glycero- CDC-258 hospholipid metabolism 34 CSU/ 239.0919 238.0844 11.66 C12H14O5 Trans-2, 3, 4- Phenyl- CDC-002 trimethoxycinnamate propanoid and polyketide metabolism CSU/ 389.2174 388.2094 15.47 C19H32O8 Methyl Fatty acid CDC-028 10,12,13,15- metabolism 35 bisepidioxy-16- hydroperoxy-8E- octadecenoate 36 CSU/ 285.2065 284.1991 16.02 C16H28O4 — — CDC-182 37 CSU/ 279.1693 278.1629 11.05 C15H22N2O3 Phe Leu Dipeptide CDC-204 38 CSU/ 714.6967 1427.3824 11.76 — — — CDC-247 39 CSU/ 886.4296 1770.8438 12.18 — — — CDC-003 40 CSU/ 181.0859 180.0788 14.7 C10H12O3 5′-(3′-Methoxy-4′- Endogenous CDC-004 hydroxyphenyl)- metabolite gamma- associated valerolactone with microbiome 41 CSU/ 286.1444 285.1371 16.08 C17H19NO3 Piperine Alkaloid CDC-006 metabolism 42 CSU/ 463.2339 462.2248 16.36 C25H34O8 Ala Lys Met Asn Peptide CDC-008 (SEQ ID NO: 6) 43 CSU/ 242.2844 241.2772 17.1 C16H35N — — CDC-009 44 CSU/ 590.4237 589.4194 19.24 — — — CDC-017 45 CSU/ 553.3904 552.3819 23.38 C35H52O5 Furohyperforin Endogenous CDC-026 metabolite- derived from food 46 CSU/ 399.2364 398.2313 16.23 — — — CDC-030 47 CSU/ 580.4144 1158.8173 18.26 — — — CDC-042 48 CSU/ 704.4985 1372.925 18.7 — — — CDC-052 49 CSU/ 623.4521 1210.8362 19.55 CDC-061 — — — 50 CSU/ 389.2178 388.2099 15.52 C19H32O8 — — CDC-070 51 CSU/ 1111.6690 1110.6656 17.89 — — — CDC-074 52 CSU/ 482.4040 481.3976 19.99 — — — CDC-083 53 CSU/ 533.1929 532.1854 20.84 C23H28N6O9 Asp His Phe Asp Peptide CDC-084 (SEQ ID NO: 7) 54 CSU/ 466.3152 465.3085 14.73 C26H43NO6 Glycocholic acid Bile acid CDC-087 metabolism 55 CSU/ 683.4728 1347.9062 17.56 — — — CDC-091 56 CSU/ 227.0897 204.1002 9.68 C9H16O5 — — CDC-095 57 CSU/ 183.1016 182.0943 10.89 C10H14O3 — — CDC-098 58 CSU/ 476.3055 475.2993 11.09 C26H41N3O5 — — CDC-099 59 CSU/ 215.1283 214.1209 12.32 C11H18O4 alpha-Carboxy- Endogenous CDC-112 delta- metabolite- decalactone derived from food 60 CSU/ 519.1881 518.1813 12.33 C20H30N4O12 Poly-g-D- Poly D- CDC-115 glutamate glutamate metabolism 61 CSU/ 1086.1800 2170.3435 15.38 — — — CDC-128 62 CSU/ 285.2061 284.1993 15.99 C16H28O4 — — CDC-133 63 CSU/ 357.1363 356.1284 15.98 C20H20O6 Xanthoxylol Endogenous CDC-134 metabolite- derived from food 64 CSU/ 299.1853 298.1781 16.24 C16H26O5 Tetranor-PGE1 Prostaglandin CDC-136 metabolism 65 CSU/ 334.2580 333.2514 16.36 — — — CDC-137 66 CSU/ 317.2317 316.2254 16.63 — — — CDC-138 67 CSU/ 331.2471 330.2403 17.26 C18H34O5 11,12,13- Fatty acid CDC-141 trihydroxy-9- metabolism octadecenoic acid 68 CSU/ 583.3480 582.3379 18.04 C27H46N6O8 Leu Lys Glu Pro Pro Peptide CDC-144 (SEQ ID NO: 8) 69 CSU/ 648.4672 647.4609 19.98 C34H66NO8P PE (29:1) Glycero- CDC-157 phospholipid metabolism 70 CSU/ 445.2880 854.5087 12.48 C45H74O15 (3b,21b)-12- Endogenous CDC-165 Oleanene- metabolite- 3,21,28-triol 28- derived from [arabinosyl-(1- food >3)-arabinosyl- (1->3)- arabinoside] 71 CSU/ 1486.7386 2971.4668 14.97 — — — CDC-181 72 CSU/ 668.4686 1317.8969 18.04 C16H28O4 Omphalotin A Endogenous CDC-183 metabolite- derived from food 73 CSU/ 454.2924 436.2587 18.1 C21H41O7P Lyso-PA (18:1) Glycero- CDC-184 phospholipid metabolism 74 CSU/ 607.9324 606.9246 19.01 — — — CDC-186 75 CSU/ 521.4202 503.3858 21.06 — — — CDC-188 76 CSU/ 176.0746 175.0667 2.31 — — — CDC-193 77 CSU/ 596.9082 1191.8033 19.1 — — — CDC-194 78 CSU/ 532.5606 531.5555 18.38 — — — CDC-203 79 CSU/ 337.1667 336.1599 20.67 C12H24N4O7 — — CDC-206 80 CSU/ 415.1634 207.0784 12.2 C8H9N5O2 6-Amino-9H- Endogenous CDC-210 purine-9- metabolite- propanoic acid derived from food 81 CSU/ 364.3407 346.3068 20.72 — — — CDC-218 82 CSU/ 989.5004 1976.9858 12.03 — — — CDC-222 83 CSU/ 819.6064 1635.8239 12.06 — — — CDC-224 84 CSU/ 286.2737 285.2666 19.08 C17H35NO2 Pentadecanoyl N-acyl CDC-237 ethanolamide ethanolamine metabolism 85 CSU/ 614.4833 613.4772 19.78 — — — CDC-245 86 CSU/ 298.2740 297.2668 16.44 C18H35NO2 3-Ketospingosine Sphingolipid CDC-250 metabolism 87 CSU/ 1003.7020 1002.696 18.46 — — — CDC-252 88 CSU/ 223.0968 222.0895 14.69 C12H14O4 — — CDC-005 89 CSU/ 286.1437 285.1364 16.06 C17H19NO3 — — CDC-007 90 CSU/ 1112.6727 1111.6663 17.86 — — — CDC-010 91 CSU/ 454.2923 453.2867 18.08 C21H44NO7P Glycerophospho- N-acyl CDC-011 N-Palmitoyl ethanolamine Ethanolamine metabolism 92 CSU/ 522.3580 521.3483 18.5 C26H52NO7P PC (18:1) Glycero- CDC-015 phospholipid metabolism 93 CSU/ 363.2192 362.2132 18.58 C21H30O5 4,50- Sterol CDC-016 dihydrocortisone metabolism 94 CSU/ 388.3939 387.3868 19.53 — — — CDC-018 95 CSU/ 256.2632 255.2561 20.08 C16H33NO Palmitic amide Primary Fatty CDC-020 Acid Amide Metabolism 96 CSU/ 394.3515 376.3171 20.09 — — — CDC-021 97 CSU/ 228.1955 227.1885 20.99 — — — CDC-022 98 CSU/ 284.2943 283.2872 21.15 C18H37NO Stearamide Primary Fatty CDC-023 Acid Amide Metabolism 99 CSU/ 338.3430 337.3344 22.14 C22H43NO 13Z- Primary Fatty CDC-024 Docosenamide Acid Amide (Erucamide) Metabolism 100 CSU/ 689.5604 688.5504 22.52 C38H77N2O6P SM(d18:1-15:0)/ Sphingolipid CDC-025 SM(d18:1/14:1-OH) metabolism 101 CSU/ 432.2803 431.2727 10.8 C25H37NO5 Ala Ile Lys Thr Peptide CDC-027 (SEQ ID NO: 9) 102 CSU/ 385.2211 384.2147 15.84 C16H28N6O5 Lys His Thr Peptides CDC-029 103 CSU/ 449.3261 879.6122 17.07 C46H89NO12S C22-OH Sphingolipid CDC-031 Sulfatide metabolism 104 CSU/ 467.3821 444.2717 17.1 C24H40O8 2-glyceryl-6-keto- Prostaglandin CDC-032 PGF1α metabolism 105 CSU/ 836.5936 835.5845 17.15 C44H85NO11S C20 Sulfatide Sphingolipid CDC-033 metabolism 106 CSU/ 792.5646 791.5581 17.17 C42H82NO10P PS (36:0) Glycero- CDC-034 phospholipid metabolism 107 CSU/ 356.2802 355.2722 17.35 — — — CDC-035 108 CSU/ 806.5798 805.5746 17.71 C43H84NO10P PS (37:0) Glycero- CDC-036 phospholipid metabolism 109 CSU/ 762.5582 761.5482 17.79 C41H80NO9P PS-O (35:1) Glycero- CDC-037 phospholipid metabolism 110 CSU/ 718.5308 700.4946 17.88 C39H73O8P PA (36:2) Glycero- CDC-038 phospholipid metabolism 111 CSU/ 690.4825 1361.924 17.95 — — — CDC-040 112 CSU/ 426.1798 425.1725 18.03 — — — CDC-041 113 CSU/ 741.5154 1481.0142 18.24 C83H150O17P2 CL (74:6) Glycero- CDC-043 phospholipid metabolism 114 CSU/ 864.6245 863.6166 18.17 C46H89NO11S C22 Sulfatide Sphingolipid CDC-044 metabolism 115 CSU/ 558.4017 1080.7347 18.28 — — — CDC-045 116 CSU/ 719.5012 1402.9377 18.26 — — — CDC-046 117 CSU/ 536.3897 1053.7382 18.36 — — — CDC-047 118 CSU/ 538.8674 1058.696 18.4 — — — CDC-048 119 CSU/ 653.4619 1270.8593 18.43 — — — CDC-049 120 CSU/ 732.5450 714.5092 18.47 C40H75O8P PA (37:2) Glycero- CDC-050 phospholipid metabolism 121 CSU/ 748.5232 1478.0059 18.58 — — — CDC-051 122 CSU/ 682.4841 1328.9008 18.77 — — — CDC-053 123 CSU/ 360.3615 359.3555 18.89 — — — CDC-054 124 CSU/ 441.2412 440.2325 19.09 C20H132N4O7 Pro Asp Pro Leu Peptide CDC-055 (SEQ ID NO: 10) 125 CSU/ 638.4554 1240.847 18.92 — — — CDC-056 126 CSU/ 755.5311 1474.9941 18.94 C83H144O17P2 CL (74:9) Glycero- CDC-057 phospholipid metabolism 127 CSU/ 711.5023 1386.9417 19.09 — — — CDC-058 128 CSU/ 784.5530 1567.0908 19.27 — — — CDC-059 129 CSU/ 645.4660 1271.8896 19.36 — — — CDC-060 130 CSU/ 300.2886 282.2569 19.84 C18H34O2 13Z- Fatty acid CDC-063 octadecenoic metabolism acid 131 CSU/ 309.0981 308.0913 2.06 C15H16O7 — — CDC-064 132 CSU/ 561.2965 1120.5778 11.7 C54H88O24 Camellioside D Endogenous CDC-065 metabolite- derived from food 133 CSU/ 1106.2625 2209.5193 14.53 — — CDC-068 134 CSU/ 371.2070 370.1997 15.52 C15H26N6O7 His Ser Lys Peptide CDC-069 135 CSU/ 443.2649 442.256 15.52 C19H34N6O6 Pro Gln Ala Lys Peptide CDC-071 (SEQ ID NO: 11) 136 CSU/ 850.6093 849.6009 17.63 C48H84NO9P PS-O (42:6) Glycero- CDC-073 phospholipid metabolism 137 CSU/ 697.4896 1358.909 18.32 — — — CDC-076 138 CSU/ 439.8234 877.6325 18.71 — — — CDC-077 139 CSU/ 567.8897 566.8818 18.73 — — — CDC-078 140 CSU/ 435.2506 434.243 19 C21H39O7P Lyso-PA (18:2) Glycero- CDC-079 phospholipid metabolism 141 CSU/ 834.6136 833.6057 18.83 C45H88NO10P PS (39:0) Glycero- CDC-080 phospholipid metabolism 142 CSU/ 534.8834 533.8771 18.82 — — — CDC-081 143 CSU/ 468.8441 467.8373 19.13 — — — CDC-082 144 CSU/ 312.3259 311.319 22.05 — — — CDC-085 145 CSU/ 228.1955 227.1884 15.22 — — — CDC-088 146 CSU/ 385.2211 384.2143 15.83 C20H32O7 Lys His Thr Peptide CDC-089 147 CSU/ 403.2338 402.2253 15.84 C16H30N6O6 Lys Gln Gln Peptide CDC-090 148 CSU/ 675.4753 1348.9377 18.37 — — — CDC-092 149 CSU/ 682.4841 1345.9257 18.76 — — — CDC-093 150 CSU/ 762.5401 1506.0367 19.36 — — — CDC-094 151 CSU/ 189.1122 188.1049 12.27 C9H14O4 Nonanedioic Acid Fatty acid CDC-177 metabolism 152 CSU/ 169.0860 168.0786 9.94 C9H12O3 2,6-Dimethoxy-4- Endogenous CDC-097 methylphenol metabolite- derived from food 153 CSU/ 276.1263 275.1196 11.16 C15H17NO4 — — CDC-100 154 CSU/ 314.0672 313.06 11.56 C10H12N5O5P — — CDC-101 155 CSU/ 201.1122 200.1047 11.56 C10H16O4 Decenedioic acid Fatty acid CDC-102 metabolism 156 CSU/ 115.0391 114.0318 11.57 C5H6O3 2-Hydroxy-2,4- Phenylalanine CDC-103 pentadienoate metabolism 157 CSU/ 491.1569 490.1504 11.56 C24H26O11 — — CDC-104 158 CSU/ 241.1054 218.1157 11.57 C10H18O5 3-Hydroxy- Fatty acid CDC-105 sebacic acid metabolism 159 CSU/ 105.0914 104.0841 11.57 — — — CDC-106 160 CSU/ 311.1472 328.1391 12.22 C18H20N2O4 Phe Tyr Peptide CDC-108 161 CSU/ 271.1543 270.1464 12.24 — — — CDC-109 162 CSU/ 169.0860 168.0787 12.24 C9H12O3 2,6-Dimethoxy-4- Endogenous CDC-110 methylphenol metabolite- derived from food 163 CSU/ 187.0967 186.0889 12.24 C9H14O4 — — CDC-111 164 CSU/ 475.1635 474.1547 12.25 C25H22N4O6 His Cys Asp Thr Peptide CDC-113 (SEQ ID NO: 12) 165 CSU/ 129.0547 128.0474 12.33 C6H8O3 (4E)-2- Fatty acid CDC-114 Oxohexenoic metabolism acid 166 CSU/ 125.0599 124.0527 13.12 C7H8O2 4-Methylcatechol Catechol CDC-116 metabolism 167 CSU/ 247.1550 246.1469 13.13 C12H22O5 3-Hydroxy- Fatty acid CDC-117 dodecanedioic metabolism acid 168 CSU/ 517.2614 516.2544 13.13 C21H36N6O9 Gln Glu Gln Ile Peptide CDC-118 (SEQ ID NO: 13) 169 CSU/ 301.0739 300.0658 13.14 C16H12O6 Chrysoeriol Endogenous CDC-119 metabolite- derived from food 170 CSU/ 327.1773 304.1885 14.17 C16H24N4O2 — — CDC-120 171 CSU/ 387.2023 386.1935 14.51 C19H30O8 Citroside A Endogenous CDC-121 metabolite- derived from food 172 CSU/ 875.8451 1749.684 14.55 — — — CDC-122 173 CSU/ 737.5118 736.5056 14.52 C42H73O8P PA (39:5) Glycero- CDC-123 phospholipid metabolism 174 CSU/ 1274.3497 1273.3481 14.96 — — — CDC-124 175 CSU/ 1274.2092 1273.2 14.96 — — — CDC-125 176 CSU/ 1486.5728 2971.1328 14.95 — — — CDC-126 177 CSU/ 965.3818 964.3727 15.37 — — — CDC-127 129 CSU/ 1086.0562 2170.0908 15.38 C97H167N5O48 NeuAcalpha2- Sphingolipid CDC-178 3Galbeta1- metabolism 3GalNAcbeta1-4 (9-0Ac- NeuAcalpha2- 8NeuAcalpha2- 3)Galbeta1- 4Glcbeta- Cer(d18:1/18:0) 179 CSU/ 1086.4344 2169.8474 15.39 — — — CDC-130 180 CSU/ 1240.7800 1239.7712 15.38 — — — CDC-131 181 CSU/ 317.1956 316.1885 16.24 C12H24N6O4 Arg Ala Ala Peptide CDC-135 182 CSU/ 299.2219 298.2148 16.64 C17H30O4 8E- Fatty acid CDC-139 Heptadecenedioic metabolism acid 183 CSU/ 748.5408 747.5317 17.23 C40H78NO9P PS-O (34:1) Glycero- CDC-140 phospholipid metabolism 184 CSU/ 712.4935 1422.9749 17.82 C79H140P17P2 CL (70:7) Glycero- CDC-142 phospholipid metabolism 185 CSU/ 674.5013 673.4957 17.99 C37H72NP7P PE-P (32:1) Glycero- CDC-143 phospholipid metabolism 186 CSU/ 677.9537 676.9478 18.36 — — — CDC-145 187 CSU/ 531.3522 530.3457 18.4 C35H46O4 — — CDC-146 188 CSU/ 585.2733 584.2649 18.39 C33H36N4O6 15,16- Bilirubin CDC-147 Dihydrobiliverdin breakdown products- Porphyrin metabolism 189 CSU/ 513.3431 512.3352 18.4 — — — CDC-148 190 CSU/ 611.9156 610.9073 18.59 — — — CDC-149 191 CSU/ 549.0538 531.0181 18.38 — — — CDC-150 192 CSU/ 755.5311 1509.0457 18.93 — — — CDC-151 193 CSU/ 599.4146 598.4079 19.59 C40H54O4 Isomytiloxanthin Isoflavinoid CDC-153 194 CSU/ 762.5029 761.4919 19.66 C43H72NO8P PE (38:7) Glycero- CDC-154 phospholipid metabolism 195 CSU/ 741.4805 740.4698 19.96 C40H69O10P PG (34:5) Glycero- CDC-156 phospholipid metabolism 196 CSU/ 516.3532 498.3199 20.27 C23H42N6O6 Ala Leu Ala Pro Lys Peptide CDC-159 (SEQ ID NO: 14) 197 CSU/ 769.5099 768.5018 20.53 C42H73O10P PG (36:5) Glycero- CDC-160 phospholipid metabolism 198 CSU/ 862.5881 861.5818 20.86 — — — CDC-161 199 CSU/ 837.5358 836.5274 21.11 C53H72O8 Amitenone Endogenous CDC-162 metabolite- derived from food 200 CSU/ 558.3995 540.367 21.44 C26H48N6O6 Leu Ala Pro Lys Ile Peptide CDC-163 (SEQ ID NO: 15) 201 CSU/ 1105.9305 2209.8462 14.53 — — — CDC-167 202 CSU/ 329.1049 328.0976 14.61 C18H16O6 2-Oxo-3- Phenylalanine CDC-168 phenylpropanoic metabolism acid 203 CSU/ 1241.2053 1240.2 15.38 — — — CDC-169 204 CSU/ 1088.6731 1087.6676 17.85 — — — CDC-170 205 CSU/ 667.4391 666.4323 20.35 C37H63O8P PA (24:5) Glycero- CDC-171 phospholipid metabolism 206 CSU/ 133.0497 132.0423 11.57 C5H8O4 2-Acetolactic Pantothenate CDC-172 acid and CoA Biosynthesis Pathway 207 CSU/ 259.1540 258.1469 11.75 — — — CDC-173 208 CSU/ 311.1472 288.1574 12.23 C10H20N6O4 Asn Arg Dipeptide CDC-174 209 CSU/ 147.0652 146.0579 12.33 C6H10O4 α-Ketopantoic Pantothenate CDC-175 acid and CoA Biosynthesis Pathway 210 CSU/ 169.0860 168.0788 12.29 C9H12O3 Epoxyoxophorone Endogenous CDC-176 metabolite- derived from food 211 CSU/ 187.0965 186.0894 9.93 C9H14O4 5- Endogenous CDC-096 Butyltetrahydro- metabolite- 2-oxo-3- derived from furancarboxylic food acid 212 CSU/ 139.1116 138.1044 12.95 C9H14O4 3,6-Nonadienal Endogenous CDC-178 metabolite- derived from food 213 CSU/ 515.2811 514.2745 13.14 C26H42O10 Cofaryloside Endogenous CDC-179 metabolite- derived from food 214 CSU/ 283.1522 282.1444 13.93 C25H42N2O7S Epidihydrophaseic Endogenous CDC-180 acid metabolite - derived from food 215 CSU/ 706.9750 705.9684 18.7 — — — CDC-185 216 CSU/ 834.5575 833.5502 20.32 — — — CDC-187 217 CSU/ 683.4727 1364.9294 17.54 — — — CDC-189 218 CSU/ 728.9890 1455.9633 18.63 — — — CDC-190 219 CSU/ 726.5104 1451.0035 18.64 C18H144O17P2 CL (72:7) Glycero- CDC-191 phospholipid metabolism 220 CSU/ 633.9280 632.9206 18.47 — — — CDC-192 221 CSU/ 209.0784 208.0713 9.92 C17H24O3 Benzylsuccinate Phenylpropanoic CDC-195 acid metabolism 222 CSU/ 792.5483 1566.055 18.46 — — — CDC-196 223 CSU/ 618.9221 1218.8083 19.02 — — — CDC-197 224 CSU/ 549.0543 531.0189 18.37 — — — CDC-198 225 CSU/ 553.7262 552.7188 18.74 — — — CDC-199 226 CSU/ 756.0320 755.0266 18.95 — — — CDC-200 227 CSU/ 639.6307 638.6205 19.58 — — — CDC-201 228 CSU/ 753.4414 730.4513 19.37 C42H67O8P PA (39:8) Glycero- CDC-202 phospholipid metabolism 229 CSU/ 328.3204 327.3148 20.72 C20H41NO2 Stearoyl N-acyl CDC-207 ethanolamide ethanolamine metabolism 230 CSU/ 514.3718 1009.7122 18.42 C56H99NO14 3-O-acetyl- Sphingolipid CDC-208 sphingosine- metabolism 2,3,4,6-tetra-O- acetyl- GalCer(d18:1/h2 2:0) 231 CSU/ 630.4594 1241.8737 19.95 — — — CDC-209 232 CSU/ 244.2270 243.22 17.17 C14H29NO2 Lauroyl N-acyl CDC-214 ethanolamide ethanolamine metabolism 233 CSU/ 463.3426 924.6699 18.08 — — — CDC-215 234 CSU/ 468.3892 450.3553 19.17 C31H46O2 — — CDC-216 235 CSU/ 438.3787 420.3453 19.93 — — — CDC-217 236 CSU/ 792.0006 790.995 12.04 — — — CDC-220 237 CSU/ 792.2025 791.1947 12.04 — — — CDC-221 238 CSU/ 791.6016 790.594 12.04 — — — CDC-223 239 CSU/ 1115.5593 2228.1028 14.95 — — — CDC-225 240 CSU/ 1486.9176 2970.7976 14.96 — — — CDC-226 241 CSU/ 430.3161 412.2845 20.23 C23H40O6 — — CDC-228 242 CSU/ 297.2793 296.2734 20.66 C19H36O2 Methyl oleate Oleic acid CDC-230 ester 243 CSU/ 714.3655 1426.718 11.73 — — — CDC-231 244 CSU/ 714.5306 1427.0479 11.76 — — — CDC-232 245 CSU/ 989.7499 1977.4865 12.03 — — — CDC-233 246 CSU/ 221.0744 220.0672 13.7 C7H12N2O6 L-beta-aspartyl- Peptide CDC-234 L-serine 247 CSU/ 313.2734 312.2663 18.91 C19H36O3 2-oxo- Fatty acid CDC-236 nonadecanoic metabolism acid 248 CSU/ 337.2712 314.282 20.66 C19H38O3 2-Hydroxy- Fatty acid CDC-239 nonadecanoic metabolism acid 249 CSU/ 441.3687 440.3614 21.26 C30H48O2 4,4-Dimethyl- Sterol CDC-240 14a-formyl-5a- metabolism cholesta-8,24- dien-3b-ol 250 CSU/ 425.3735 424.3666 21.5 C30H48O Butyrospermone Sterol CDC-241 metabolism 251 CSU/ 356.3517 355.3448 21.67 C22H45NO2 Eicosanoyl N-acyl CDC-242 ethanolamide ethanolamine metabolism 252 CSU/ 393.2970 370.3082 22.46 C22H42O4 — — CDC-243 253 CSU/ 167.9935 166.9861 13.2 C7H5NS2 — — CDC-246 254 CSU/ 677.6170 676.6095 20.71 C47H80O2 Cholesterol ester Sterol CDC-249 (20:2) metabolism 255 CSU/ 460.2695 459.2627 16.87 C26H37NO6 — — CDC-251 CSU/ 630.4765 612.4417 18.11 — — — 256 CDC-255 257 CSU/ 514.3734 1026.7281 18.41 — — — CDC-256 258 CSU/ 667.4754 1315.916 19.28 — — — CDC-257 259 CSU/ 516.8549 1031.6945 18.43 — — — CDC-259 260 CSU/ 740.5242 1479.0334 19.4 C83H148O17P2 CL (74:7) Glycero- CDC-260 hospholipid metabolism 261 CSU/ 1104.0614 2206.1096 15.2 — — — CDC-261 wherein each molecular feature is identified by its mass to charge ratio; and
- (a) obtaining a disease score from a mass spectrometry based test;
- (b) diagnosing the subject with Lyme disease or STARI based on the disease score; and
- (c) if the subject is diagnosed with Lyme disease, administering a treatment to the subject diagnosed with Lyme disease based on the disease score, wherein the treatment is a pharmacological treatment for Lyme disease selected from an antibiotic, an antibacterial agent, an immune modulator, an anti-inflammatory agent, or a combination thereof;
- (d) if the subject is diagnosed with STARI, optionally administering a treatment to the subject diagnosed with STARI based on the disease score, wherein the treatment is a pharmacological treatment is selected from an antibiotic, an antibacterial agent, an anti-inflammatory agent, or a combination thereof, wherein the mass spectrometry-based test comprises:
- (i) deproteinizing a blood sample from a subject to produce a metabolite extract, wherein the subject has at least one symptom that is associated with Lyme disease or STARI;
- (ii) performing liquid chromatography coupled to mass spectrometry on a sample of the metabolite extract;
- (iii) providing abundance values for each molecular feature in Table A, Table C, or Table D:
- (iv) inputting the abundance values from step (iii) into a classification model trained with samples of metabolite extracts derived from suitable controls, wherein if the molecular features of Table A or Table C are provided, the classification model is Least Absolute Shrinkage and Selection Operator (LASSO) and if the molecular features of Table D are provided, the classification model is Random Forest (RF), and wherein the classification model produces a disease score and the disease score distinguishes subjects with Lyme disease and STARI.
2. The method of claim 1, wherein an area under the curve (AUC) value for an ROC curve of the classification model is about 0.8 or greater.
3. The method of claim 1, wherein the classification model has a sensitivity from about 0.8 to about 1 and/or a specificity from about 0.8 to about 1, and optionally an area under the curve (AUC) value for an ROC curve that is about 0.8 or greater.
4. The method of claim 1, wherein the classification model has a sensitivity from about 0.85 to about 1 and/or a specificity from about 0.85 to about 1, and optionally an area under the curve (AUC) value for an ROC curve that is about 0.8 or greater.
5. The method of claim 1, wherein the classification model has a sensitivity from about 0.9 to about 1 and/or a specificity from about 0.9 to about 1, and optionally an area under the curve (AUC) value for an ROC curve that is about 0.8 or greater.
6. The method of claim 5, wherein the classification model has an accuracy of at least 97% for detecting a sample from a subject with Lyme disease and an accuracy of at least 87% for detecting a sample from a subject with STARI.
7. The method of claim 1, wherein:
- abundance values are provided for each molecular feature in Table A or Table D; and
- the suitable controls comprise a blood sample from a subject known to be positive for Lyme disease, and a blood sample from a subject known to be positive for STARI.
8. The method of claim 7, wherein the classification model has an accuracy of at least 95% for detecting a sample from a subject with Lyme disease and an accuracy of at least 85% for detecting a sample from a subject with STARI.
9. The method of claim 7, wherein the classification model has an accuracy of at least 97% for detecting a sample from a subject with Lyme disease and an accuracy of at least 87% for detecting a sample from a subject with STARI.
10. The method of claim 1, wherein:
- abundance values are provided for each molecular feature in Table C or Table D;
- the suitable controls include a blood sample from a subject known to be positive for Lyme disease, a blood sample from a subject known to be positive for STARI, and a blood sample from a healthy subject.
11. The method of claim 10, wherein the classification model has an accuracy of at least 85% for detecting a sample from a subject with Lyme disease, an accuracy of at least 85% for detecting a sample from a subject with STARI, and an accuracy of at least 85% for detecting a sample from a healthy subject.
12. The method of claim 10, wherein the classification model has an accuracy of at least 85% for detecting a sample from a subject with Lyme disease, an accuracy of at least 90% for detecting a sample from a subject with STARI, and an accuracy of at least 90% for detecting a sample from a healthy subject.
13. The method of claim 1, wherein the blood sample is a serum sample.
14. The method of claim 1, wherein the subject has at least one symptom that is associated with both Lyme disease and STARI.
15. The method of claim 1, wherein the subject has an erythema migrans rash.
16. The method of claim 1, wherein the subject's serum is negative for antibodies to Lyme disease-causing Borrelia species.
Type: Application
Filed: Nov 23, 2021
Publication Date: Jun 2, 2022
Applicants: Colorado State University Research Foundation (Fort Collins, CO), The United States of America, as represented by the secretary, Department of Health and Human Servic (Bethesda, MD)
Inventors: John T. Belisle (Fort Collins, CO), Claudia R. Molins (Bethesda, MD), Gary P. Wormser (New York, NY)
Application Number: 17/534,170
