PREVENTION OF NSAID ENTEROPATHY WITH MICROBIOTA-DERIVED TRYPTOPHAN-METABOLITE

This disclosure relates to methods and compositions for addressing conditions of dysbiosis and/or inflammation, such as enteropathy, associated with administration of non-steroidal anti-inflammatory drug (NSAID). The disclosure includes methods comprising administering an effective amount of a tryptophan derived microbiota metabolite (TDMM) to a subject that has also been administered, or is expected to have administered, a NSAID.

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Description
CROSS-REFERENCES TO RELATED APPLICATIONS

This application claims the benefit of Provisional Application No. 62/310,606, filed Mar. 18, 2016, Provisional Application No. 62/310,648, filed Mar. 18, 2016, Provisional Application No. 62/310,643, filed Mar. 18, 2016, and Provisional Application No. 62/310,630, filed Mar. 18, 2016, each of which is expressly incorporated herein by reference in its entirety.

STATEMENT OF GOVERNMENT LICENSE RIGHTS

This invention was made with Government support under GM106251 awarded by The National Institutes of Health, National Institute of General Medical Sciences; AI110642 awarded by The National Institutes of Health, National Institute of Allergy and Infectious Disease; A095788 awarded by The National Institutes of Health, National Institute of Allergy and Infectious Disease; and MCB-1120827, awarded by the National Science Foundation. The Government has certain rights in the invention.

FIELD OF THE INVENTION

This disclosure relates to methods and compositions for addressing conditions of dysbiosis and/or inflammation, such as enteropathy, associated with administration of non-steroidal anti-inflammatory drug (NSAID).

BACKGROUND

Non-steroidal anti-inflammatory drugs (NSAIDs) are among the most frequently used medications worldwide for routine relief of pain or fever, to manage various forms of arthritis and inflammatory intestinal disorders, and to prevent or treat alimentary cancers. Despite their effectiveness for managing these varied and highly prevalent conditions, NSAIDs cause damage to the gastrointestinal (GI) tract. Although methods for diagnosis and effective treatment of NSAID-induced lesions of the proximal GI tract (i.e., gastropathy) have been well documented, the pathogenesis, diagnosis, and treatment of NSAID-induced damage of the GI tract distal to the duodenum (known as NSAID enteropathy, primarily affecting the distal jejunum and ileum) remain unclear. The magnitude of the problem of NSAID enteropathy is alarmingly high. In the United States, NSAID enteropathy results in approximately 100,000 hospitalizations and 16,500 deaths each year. Additionally, two-thirds of both short- and long-term NSAID users develop distal small intestinal lesions. Although the use of either NSAIDs considered to be safer for the GI tract or other ancillary therapies have reduced the incidence and severity of NSAID-induced gastropathy, the incidence of NSAID enteropathy has remained constant or has increased.

In view of the foregoing, there is a need for compositions and methods to ameliorate or prevent side effects, such as enteropathy, associated with NSAID administration. The present disclosure addresses this and related needs.

SUMMARY

This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This summary is not intended to identify key features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.

In one aspect, the disclosure provides a method of treating dysbiosis of commensal microbiota in a subject. The method comprises administering an effective amount of a tryptophan derived microbiota metabolite (TDMM), or a precursor, prodrug, or acceptable salt thereof, to a subject in need thereof.

In one embodiment, the subject has received or is expected to receive administration of an agent suspected to cause dysbiosis. In one embodiment, the agent causes a reduction in a gram-positive component of the microbiota. In one embodiment, the agent causes an increase in a gram-negative component of the microbiota. In one embodiment, the agent is a non-steroid anti-inflammatory drug (NSAID). In one embodiment, the TDMM is indole. In one embodiment, the TDMM, or a precursor, prodrug, or acceptable salt thereof, is co-administered with the agent. In one embodiment, the TDMM, or a precursor, prodrug, or acceptable salt thereof, is administered in a pharmaceutical composition that also comprises the agent. In one embodiment, the TDMM is administered by intra-peritoneal (IP), intravenous (IV), topical, parenteral, intradermal, transdermal, oral (e.g., via liquid or pill), or respiratory (e.g., intranasal mist) routes. In one embodiment, the subject is a mammal, such as a human or rodent. In one embodiment, treating the dysbiosis prevents or ameliorates enteropathy.

In another aspect, the disclosure provides a method of treating enteropathy associated with NSAID in a subject. The method comprises administering an effective amount of a tryptophan derived microbiota metabolite (TDMM), or a precursor, prodrug, or acceptable salt thereof, to a subject in need thereof. In one embodiment, the NSAID causes a reduction in a gram-positive component of the microbiota. In one embodiment, the NSAID causes an increase in a gram-negative component of the microbiota. In one embodiment, the NSAID is selected from aspirin, salsalate, celecoxib, diclofenac, etodolac, ibuprofen, indomethacin, ketoprofen, ketorolac, nabumetone, naproxen, oxaprozin, piroxicam, sulindac, meloxicam, tolmetin, and the like. In one embodiment, the TDMM is indole. In one embodiment, the effective amount of indole is at least about 5 mg/kg. In one embodiment, the TDMM, or a precursor, prodrug, or acceptable salt thereof, is co-administered with the NSAID. In one embodiment, the TDMM, or a precursor, prodrug, or acceptable salt thereof, is administered in a pharmaceutical composition that also comprises the NSAID. In one embodiment, the TDMM is administered by intra-peritoneal (IP), intravenous (IV), topical, parenteral, intradermal, transdermal, oral (e.g., via liquid or pill), or respiratory (e.g., intranasal mist) routes. In one embodiment, the subject is a mammal, such as a human or rodent.

In another aspect, the disclosure provides a method of treating a condition characterized by inflammation in the GI tract. The method comprises administering an effective amount of a tryptophan derived microbiota metabolite (TDMM), or a precursor, prodrug, or acceptable salt thereof, to a subject in need thereof. In one embodiment, the inflammation is associated with administration of an NSAID to the subject.

In another aspect, the disclosure provides a pharmaceutical composition comprising: at least one TDMM, or a precursor, prodrug, or acceptable salt thereof; at least one NSAID composition, or a precursor, prodrug, or acceptable salt thereof and a pharmaceutically acceptable carrier. In one embodiment, the TDMM is indole.

DESCRIPTION OF THE DRAWINGS

The foregoing aspects and many of the attendant advantages of this invention will become more readily appreciated as the same become better understood by reference to the following detailed description, when taken in conjunction with the accompanying drawings, wherein:

FIGS. 1A-1F illustrate that indole attenuates severity of non-steroidal anti-inflammatory drug (NSAID) enteropathy. FIG. 1A graphically illustrates fecal calprotectin levels were determined by enzyme-linked immunosorbent assay (ELISA) on fecal samples on Day 0 (white bar) and then again after 6 days of therapy (black bar) with indomethacin, indole, or the combination. Day 6 fecal calprotectin values were significantly different from day 0 calprotectin only for the animals that received indomethacin alone (P=0.0095). FIG. 1B graphically illustrates microscopic pathology scores from analysis of Swiss-rolled H & E stained small intestinal sections (n=5/group). Error bars represent 95% confidence intervals about the mean score. Groups with different letters differed significantly (P<0.05): control and indole-treated mice are not different from one another; NSAID alone results in a significant difference from control, indole alone, and from NSAID+indole; the NSAID+indole group was significantly higher than control and indole alone groups, but significantly lower than the NSAID group. FIG. 1C shows representative H & E stained sections of small intestine. The microscopic appearance of both the control and indole appear normal, whereas the small intestine of NSAID-treated mice showed ulceration, inflammation in the lamina propria and epithelium, and thickening and blunting of the villi. These pathological findings were ameliorated by co-administration of indole with NSAID (i.e., NSAID+indole). FIG. 1D graphically illustrates the ratio of villus height to crypt depth taken from small intestinal mucosa; relative to the controls, this ratio was only significantly decreased for mice in the NSAID group. FIG. 1E graphically illustrates the percentage of CD11b- and GR-1-positive cells (i.e., neutrophils) in the spleen after 7 days of treatment. Groups with different letters differed significantly. Indole significantly attenuated the NSAID-induced increase in splenic neutrophils. FIG. 1F graphically illustrates the percentage of CD11b positive and GR-1-positive cells (i.e., neutrophils) in the mesenteric lymph nodes (MLN) after 7 days of treatment. Groups with different letters differed significantly. Indole significantly attenuated the NSAID-induced increase in MLN neutrophils.

FIGS. 2A-2F illustrate that indole increases abundance of Clostridiales and prevents NSAID-induced shift of the microbiota and inferred metagenome. FIG. 2A illustrates a principal component analysis (PCA) plots of 16S rRNA sequencing of the fecal microbiota at the phylum level with biplot overlay and analysis of similarity (ANOSIM) based on the unweighted Unifrac distance metric (located in the lower right quadrant) of the operational taxonomic unit (OTU) table from day 0 revealed no significant differences among the groups at day 0.The area of the gray shaded shapes reflects the variation among individuals in a group: the larger the shaded area, the more variation among the individuals in that group. FIG. 2B illustrates a principal component analysis (PCA) plots of 16S rRNA sequencing of the fecal microbiota at the phylum level with biplot overlay and ANOSIM based on the unweighted Unifrac distance metric (located in the lower right quadrant) of the OTU table from day 7 reveal that there is a significant difference among the groups at day 7. After 7 days, the NSAID group is significantly shifted from the other 3 groups, and this shift was associated with a qualitative increase in the phylum Bacteroidetes and loss of Firmicutes. FIG. 2C graphically illustrates the abundance of Clostridales from Day 0 (orange) and Day 7 (green) and Bacteroidales Day 0 (white) and Day 7 (black). Error bars represent the upper bound of the symmetrical 95% confidence intervals about the mean of the 5 animals in each group and P values displayed on the graph are corrected for multiplicity of comparisons by the method of Sidak. Clostridiales were significantly increased on Day 7 relative to Day 0 by co-administration of indole with the NSAID indomethacin. FIG. 2D graphically illustrates the mean abundance of several families of Clostridiales at Day 0 and Day 7 for each of the treatment groups depicting reduction of these families following NSAID therapy but expansion indole is co-administered. FIG. 2E illustrates PCA plots of inferred metagenome of the fecal microbiota from Day 0. ANOSIM based on the Bray Curtis distance measure revealed no significant difference among the groups at day 0. FIG. 2F illustrates PCA plots of inferred metagenome of the fecal microbiota from Day 7. ANOSIM based on the Bray Curtis distance measure revealed that, consistent with the microbiota data, the NSAID group visually separated from the other groups; however, this apparent difference was not significant (P>0.05 by ANOSIM based on Bray Curtis dissimilarity measure).

FIGS. 3A-3D illustrate that NSAID enteropathy results in upregulation of innate immune response pathways and the co-administration of indole regulates this response. FIG. 3A is a heatmap showing upregulated (red) and down regulated (blue) pathways in the small intestinal mucosa from NSAID-treated mice compared with control animals. FIG. 3B is a 2-column heatmap showing upregulated (red) and down-regulated (blue) pathways in the small intestinal mucosa from NSAID-treated mice compared with control mice (left column) and NSAID+indole versus control mice (right column). FIG. 3C is a heatmap of the Z-scores of the canonical pathways to which the differentially expressed genes between control vs. NSAID-treated animals (left column) and control vs. NSAID+indole (right column) were mapped. FIG. 3D is a heat map of the Z-scores of the upstream regulators to which the differentially expressed genes between control vs. NSAID-treated animals (left column) and control vs. NSAID+indole (right column) were mapped.

FIG. 4 schematically illustrates that co-administration of indole with indomethacin attenuates NSAID enteropathy and indole may exert these beneficial effects through several possible mechanisms. NSAID enteropathy is characterized by a loss of barrier function resulting in an influx of luminal contents and a massive innate immune response. Indole is known to have direct effects on intestinal epithelial cells including upregulation of tight-cell junctional proteins, effects on immune responses including inhibition of nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB), and regulating the overall innate immune response. Moreover, co-administration of indole with NSAID administration resulted in an increase in the abundance of Firmicutes, principally Costridales, known to be important in maintaining intestinal homeostasis, and appeared to prevent any increase in Bacteroidales.

FIGS. 5A and 5B illustrate the noninvasive identification of 6 key genes altered between NSAID and NSAID+Indole treatments. FIG. 5A is a network of the top 15 upstream regulator, identified by 2-feature LDA in the context of NSAID vs control animals with FDR P-values and fold changes overlaid. FIG. 5B is the same network with P-values and fold changes of NSAID+indole overlaid reveal 11 genes that were altered (opposite fold-change either realized or predicted) by the co-administration of indole.

FIGS. 6A and 6B illustrate that Indole attenuates small intestinal mural thickening associated with NSAID enteropathy. FIG. 6A is a diagram showing the location of representative measures for calculating villus height, crypt depth, and mural thickness (indicated in black lines). Three sets of measurements from three separate sections were recorded for each animal by an observer blinded to treatment group. FIG. 6B graphically illustrates representative mural thickness among all groups.

FIGS. 7A and 7B illustrate that co-administration of indole reduces NSAID-induced neutrophilic infiltration. FIG. 7A provides representative dot-plots of double-positive CD11b/GR-1 cells (neutrophils) from spleen; and FIG. 7B provides mesenteric lymph nodes.

FIGS. 8A-8D. FIG. 8A graphically illustrates alpha rarefaction curves for each treatment group showing numbers of observed species (richness) at each sampling depth on the y-axis and sequences/sample on the x-axis up to a sampling depth of 10,800 sequences/sample at Day 0. FIG. 8B graphically illustrates the alpha rarefaction curves at Day 7. FIG. 8C graphically illustrates estimated Good's coverage at an even sampling depth of 10,800 reads/sample at Day 0. FIG. 8D graphically illustrates estimated Good's coverage at Day 7.

FIGS. 9A and 9B. FIG. 9A graphically illustrates abundance of members of the phylum Bacteroidetes in fecal 16S rRNA sequences on Day 0 (white bar) and Day 7 (black bar). FIG. 9A graphically illustrates abundance of members of the phylum Firmicutes in fecal 16S rRNA sequences on Day 0 (white bar) and Day 7 (black bar).

FIGS. 10A and 10B are bar graphs of log fold-change and false discovery rate (FDR) P value of several pro-inflammatory cytokines (FIG. 10A) and chemokines (FIG. 10B) from NSAID+indole versus control mice (white bar) and NSAID vs. control animals (black bar) from the small intestinal mucosal transcriptome after 7 days of therapy.

DETAILED DESCRIPTION

The pathophysiology of NSAID enteropathy is complex and incompletely understood. It appears to involve deleterious effects of NSAIDs on the intestinal mucosa including enterocyte cell death, increased mucosal permeability, and interaction of the damaged mucosa with luminal contents including bacteria (GI microbiota) and bacterial products or components such as lipopolysaccharide (LPS). The GI microbiota has been implicated as an important contributor to NSAID enteropathy. For example, administration of NSAIDs causes a dysbiosis characterized by a reduction of the predominately gram-positive phylum Firmicutes and a corresponding increase of gram-negative bacteria. Germ-free rats lacking intestinal microbiota do not develop NSAID enteropathy, whereas they develop NSAID-induced intestinal lesions when colonized with gram-negative bacteria. Concurrent administration of NSAIDs and antimicrobials targeting gram-negative bacteria reduces the severity of NSAID-induced gastrointestinal lesions in rats.

In view of the role of some components of the microbiota in exacerbating NSAID enteropathy, the inventors investigated the whether there might be microbiota-derived metabolites that could mitigate gut inflammation and other pathology that characterizes NSAID enteropathy. As described in more detail below, the inventors examined the interaction of the host, the NSAID indomethacin, and a microbiota-derived metabolite, indole, in a murine model of NSAID enteropathy. The inventors surprisingly demonstrated that co-administration of indole attenuated NSAID-induced intestinal damage via a number of classic metrics. Notably, the inventors observed that co-administration of indole prevented the classic NSAID-induced shift of the microbiota, an important aspect of the pathophysiology of NSAID enteropathy. Additionally, the inventors performed RNA-sequencing of the small intestinal mucosa (representing an in vivo analysis of the transcriptome of NSAID enteropathy) and examined how the co-administration of indole altered this transcriptome. This dataset is available to the public (NCBI accession number PRJNA290483).

Accordingly, in view of the foregoing, in one aspect the disclosure provides a method of treating dysbiosis of commensal microbiota in a subject. The method comprises administering an effective amount of a tryptophan derived microbiota metabolite (TDMM), or a precursor or prodrug thereof.

As used herein, the term “treating” means preventing, reducing, slowing, or ameliorating symptoms associated with the indicated condition.

As used herein, the term “dysbiosis” refers to an imbalance of the diverse microbial community that inhabits the intestinal tract of a subject (e.g., vertebrate host). The commensal microbial community is also referred to herein as “microbiota”. In some embodiments, the dysbiosis leads to or is correlated with enteropathy. As used herein, the term “enteropathy” refers to the presence of lesions in the region of the gastrointestinal (GI) tract that is generally distal to the duodenum. In some embodiments, this region includes at least a portion of the distal jejunum and ileum. Accordingly, in some embodiments of this aspect, treating dysbiosis refers to preventing, reducing, slowing, or ameliorating the symptoms associated with enteropathy. In some embodiments, the enteropathy is associated with the agent, such as with administration of an NSAID.

In some embodiments, the dysbiosis is associated with inflammation and/or other pathologies similar to enteropathy, such as experienced in disorders such as colitis, inflammatory bowel disease (IBD), psoriasis, rheumatoid arthritis, multiple sclerosis, and the like. While the methods generally disclosed herein are described mostly in the context of NSAID enteropathy, it is known that several signaling target that have a role in enteropathy, such as stat3, akt, and mTor, also play a role in such other inflammatory diseases. Accordingly, such other diseases and conditions are encompassed by the present disclosure.

As used herein, the term “effective amount” or “therapeutically effective” amount refers to a sufficient quantify or concentration of the agent, e.g., TDMM, so as to elicit an intended result, such as prevention or reduction of dysbiosis. Effective amounts can be readily determined and optimized per normal

As used herein, the term “tryptophan derived microbiota metabolite” (“TDMM”) refers to metabolites produced by microorganisms of the commensal microbiota that resides in the intestinal tract. The individuals of the microbiota inhabit the space of the intestines and exist in homeostasis with the healthy vertebrate host. Thus, presumably, the metabolite products produced by the individuals of the microbiota have been selected over time to promote a stable environment beneficial to both the microbiota and host. For example, such beneficial environment is one that is adverse to colonization by pathogens or that avoid inflammatory conditions in the mucosa. Specific metabolites referred to by the term TDMM are derivatives of tryptophan and include compounds such as indole, hydroxyindole (e.g., 2-hydroxyindole, 3 -hydroxyindole, 5-hydroxyindole, 7-hydroxyindole), 1-(2-carboxyphenylamino)-1-deoxy-D-ribulose-5-phosphate, 5-Hydroxy-L-tryptophan, indoleglycerol phosphate, indolepyruvate, N-(5-Phospho-D-ribosyl)anthranilate, tryptamine, indole-3-acetate, L-formylkynurenine, L-Tryptophanyl-tRNA(Trp), indole-3-acetamide, indole-3-pyruvate, indole-3-lactic acid, tryptophol, indole-3-acetaldehyde, indole-3-aldehyde, isatin (indole-2,3-dione), isoindigo, indirubin, indoxyl-sulfate, and 2-oxyindole. The TDMM encompasses such molecules that have been directly obtained from microbiota individuals. Such TDMMs can be isolated, purified, or partially purified through well-established methods. However, the term TDMM, while referring to the feature of a “microbiota metabolite,” is not necessarily limited to the specific source of one or more individual organisms from the commensal microbiota. Instead, it merely refers to the fact that commensal microorganisms of the microbiota are known to produce such a compound. Thus, the TDMM used as part of this disclosure can also be obtained from an organism that is not typically considered to be a member of a commensal microbiota of a vertebrate organism (whether the vertebrate individual is the source of the naïve T cell or not). Instead, the TDMM can be produced by known methods, such as typical recombinant approaches, in preferred laboratory strains of bacteria, and the like. Furthermore, the TDMM can be produced synthetically, as appropriate through known methods of synthetic chemistry. In any of the above embodiments, the subject can be administered with a composition that is a precursor to or prodrug of any of the above listed TDMM compound embodiments.

In one embodiment, the TDMM is indole, which is typically represented with the following structure:

In some embodiments, the subject has received or is expected to receive an administration of an agent, e.g., drug, with a suspected side-effect of causing dysbiosis. As described above, some agents associated with dysbiosis have been shown to result in the reduction of gram-positive bacteria in the phylum Firmicutes. Accordingly, in some embodiments, the agent is known to cause a relative decrease in a gram-positive component of the microbiota, such as bacteria in the phylum Firmicutes. In some embodiments, the agent is known to cause a relative increase in a gram-negative component in the microbiota.

In some embodiments, the agent is a nonsteroidal anti-inflammatory drug (NSAID).

The term “nonsteroidal anti-inflammatory drug” or the abbreviation “NSAID” refers to a class of drugs that are typically used for analgesic and antipyretic purposes, although they are also used in higher doses for anti-inflammatory effects. Due to non-addictive characteristics, NSAIDs are often used as alternatives to narcotic drugs. Most NSAIDs inhibit the activity of cyclooxygenase-1 (COX-1) and cyclooxygenase-2 (COX-2), and thereby, the synthesis of prostaglandins and thromboxanes. It is thought that inhibiting COX-2 leads to the anti-inflammatory, analgesic and antipyretic effects. Most relevant to the present disclosure are NSAIDs that cause adverse reactions in the intestinal tract, especially dysbiosis in the microbiota and enteropathy. Exemplary, non-limiting NSAIDs relevant to the present disclosure are set forth in TABLE 1.

TABLE 1 Exemplary, non-limiting list of NSAIDs relevant to the present disclosure NSAID category Examples Salicylates Aspirin (acetylsalicylic acid) Diflunisal (Dolobid) Salicylic acid and other salicylates Salsalate (Disalcid) Propionic acid Ibuprofen[64] derivatives Dexibuprofen Naproxen Fenoprofen Ketoprofen Dexketoprofen Flurbiprofen Oxaprozin Loxoprofen Acetic acid Indomethacin derivatives Tolmetin Sulindac Etodolac Ketorolac Diclofenac Aceclofenac Nabumetone Enolic acid Piroxicam (Oxicam) Meloxicam derivatives Tenoxicam Droxicam Lornoxicam Isoxicam Phenylbutazone (Bute) Anthranilic acid Mefenamic acid derivatives Meclofenamic acid (Fenamates) Flufenamic acid Tolfenamic acid Selective COX-2 Celecoxib inhibitors Rofecoxib (Coxibs) Valdecoxib Parecoxib (FDA withdrawn, licensed in the EU) Lumiracoxib (TGA cancelled registration) Etoricoxib (not FDA approved, licensed in the EU) Firocoxib (used in dogs and horses) Sulfonanilides Nimesulide Others Clonixin Licofelone (5-LOX/COX inhibitor) H-harpagide (in Figwort or Devil's Claw)

Temporally, the TDMM can be administered prior to, concurrently with, or after administration of the agent. It is within the skill of the ordinary artisan to determine and optimize the most efficacious regimen of pre-, co-, and/or post-administration of the TDMM relative to the agent. Furthermore, it will be appreciated that multiple administrations of the TDMM relative to one or more exposures to the agent are encompassed by the present disclosure. Again, determining and optimizing such administration regimens is within the skill of the ordinary artisan.

Administration of the TDMM can be in any appropriate route of administration. For example, the TDMM can be administered by intra-peritoneal (IP), intravenous (IV), topical, parenteral, intradermal, transdermal, oral (e.g., via liquid or pill), rectal, or respiratory (e.g., intranasal mist) routes. In preferred embodiments, the TDMM is ingested, e.g., via liquid or pill, etc. to facilitate delivery of the TDMM to the intestinal tract where the microbiota reside.

The effective amount of TDMM, or a precursor or prodrug thereof, can depend on factors pertaining to the subject and the route of administration, etc., and can be readily determined according to the skill of the ordinary artisan. In some embodiments, a single administration (of potentially one or multiple administrations) comprises at least about 1 mg/kg, 5 mg/kg, 15 mg/kg, 20 mg/kg, 25 mg/kg, 30 mg/kg, 35 mg/kg, 40 mg/kg, 45 mg/kg, 50 mg/kg, 55 mg/kg, 60 mg/kg, 65 mg/kg, 70 mg/kg, 75 mg/kg, 80 mg/kg, 85 mg/kg, 90 mg/kg, 95 mg/kg, 100 mg/kg, or more. Dosing can be readily determined for any particular subject based on routine cell and animal toxicity studies so as to avoid deleterious effects of high concentrations, while still achieving the desired effect.

In some embodiments, the microbiota of the subject is induced to produce elevated levels of the appropriate TDMM, such as indole. In this regard, additional microbiota organisms that are known to produce high levels of the TDMM can be administered to the subject. In some embodiments, the administered microbiota organisms can be artificially selected or genetically engineered to produce higher levels of the TDMM when residing in the GI tract of the subject. For example, in some embodiments the genetically engineered microorganisms are engineered to express, stably or transiently, higher levels of a tnaA gene, which encodes the enzyme responsible for tryptophan metabolism and production of indole. Alternatively or additionally, the subject can be provided with elevated levels of tryptophan, such as in dietary supplements, so as to allow the microbiota to produce higher levels of TDMMs.

The subject can be any organism for which maintenance of a balance of commensal microbiota in the intestinal tract is desired. The subject can be any vertebrate, including birds (e.g., chickens) and mammals, including primates (such as humans); rodents (such as mouse, rat, and guinea pig); cat; dog; cow; horse; sheep; pig; and the like.

In another aspect, the disclosure provides a method of treating an inflammatory disease or condition. In some embodiments, the condition is characterized by inflammation in the gut. In some embodiments, the disease or condition is enteropathy associated with NSAID (i.e., “NSAID-enteropathy”) in a subject. As described above, other diseases or conditions involve inflammation and/or other pathologies similar to enteropathy, such as experienced in disorders such as colitis, inflammatory bowel disease (IBD), psoriasis, rheumatoid arthritis, multiple sclerosis, and the like. It is known that several signaling target that have a role in enteropathy, such as stat3, akt, and mTor, also play a role in such other inflammatory diseases. Accordingly, such other diseases and conditions are encompassed by the present disclosure.

In this aspect, the method comprises administering an effective amount of a TDMM, or TDMM precursor, to the subject, as described above. As described, in some embodiments, at least some components of the microbiota are induced to produce higher levels of the TDMM.

In some embodiments, administration of the NSAID to the subject has caused or is suspected of being capable of causing enteropathy in the subject. In some embodiments, the NSAID has caused or is suspected of causing the relative decrease of a gram-positive component in the microbiota, and/or conversely the NSAID has caused or is suspected of causing the relative increase of a gram-negative component of the microbiota.

Representative NSAIDs and TDMMs, and their administrations, are described above.

In another aspect, the disclosure provides a pharmaceutical composition comprising at least one TDMM, or precursor or prodrug thereof, as described above. If derived from microbiota or a bacterial source, the TDMM is preferably isolated or substantially isolated from the bacterial source. In some embodiments, the pharmaceutical composition also comprises at least one NSAID composition, as described above. Accordingly, the disclosure provides for a pharmaceutical composition with co-formulation of an NSAID and a TDMM appropriate for a single, effective dose in a subject that reduces the chance or severity of enteropathy in the subject due to the NSAID.

As the present disclosure has established that administration of a TDMM has ameliorative and/or therapeutic effects for inflammation of the gut, for example, as caused by NSAID administration, the disclosure also provides a method of treating a condition characterized by inflammation in the GI tract. The method comprises administering an effective amount of a tryptophan derived microbiota metabolite (TDMM), or a precursor, prodrug, or acceptable salt thereof, to a subject in need thereof. The inflammation can associated with administration of an NSAID to the subject. Alternatively, the inflammatory condition can have a similar onset or presentation as NSAID induced inflammation in the GI tract.

The pharmaceutical composition can be formulated appropriately using routine and conventional aspects of the art for appropriate routes of administration. Exemplary routes of administration appropriate for the contemplated applications include intra peritoneal (IP), intravenous (IV), topical, parenteral, intradermal, transdermal, oral (e.g., via liquid or pill), and respiratory (e.g., intranasal mist) routes.

The disclosed compositions are preferably pharmaceutically acceptable and can contain pharmaceutically acceptable carriers, concentrations of salt, buffering agents, preservatives, other immune modulators, and optionally other therapeutic ingredients. The term “pharmaceutically-acceptable carrier” as used herein, means one or more compatible solid or liquid filler, dilutants or encapsulating substances which are suitable for administration to a human or other animal. The term “carrier” denotes an organic or inorganic ingredient, natural or synthetic, with which the active ingredient is combined to facilitate the application. The components of the pharmaceutical compositions also are capable of being comingled with active agents of the present invention, in a manner such that there is no interaction which would substantially impair the desired pharmaceutical efficacy.

Unless specifically defined herein, all terms used herein have the same meaning as they would to one skilled in the art of the present invention. Practitioners are particularly directed to Sambrook J., et al. (eds.) Molecular Cloning: A Laboratory Manual, 3rd ed., Cold Spring Harbor Press, Plainsview, N.Y. (2001); Ausubel F. M., et al. (eds.), Current Protocols in Molecular Biology, John Wiley & Sons, New York (2010); and Coligan J. E., et al. (eds.), Current Protocols in Immunology, John Wiley & Sons, New York (2010) for definitions and terms of art.

The use of the term “or” in the claims is used to mean “and/or” unless explicitly indicated to refer to alternatives only or the alternatives are mutually exclusive, although the disclosure supports a definition that refers to only alternatives and “and/or.”

Following long-standing patent law, the words “a” and “an,” when used in conjunction with the word “comprising” in the claims or specification, denotes one or more, unless specifically noted.

Unless the context clearly requires otherwise, throughout the description and the claims, the words “comprise,” “comprising,” and the like, are to be construed in an inclusive sense as opposed to an exclusive or exhaustive sense; that is to indicate, in the sense of “including, but not limited to.” Words using the singular or plural number also include the plural and singular number, respectively. Additionally, the words “herein,” “above,” and “below,” and words of similar import, when used in this application, shall refer to this application as a whole and not to any particular portions of the application.

Disclosed are materials, compositions, and components that can be used for, can be used in conjunction with, can be used in preparation for, or are products of the disclosed methods and compositions. It is understood that, when combinations, subsets, interactions, groups, etc., of these materials are disclosed, each of various individual and collective combinations is specifically contemplated, even though specific reference to each and every single combination and permutation of these compounds may not be explicitly disclosed. This concept applies to all aspects of this disclosure including, but not limited to, steps in the described methods. Thus, specific elements of any foregoing embodiments can be combined or substituted for elements in other embodiments. For example, if there are a variety of additional steps that can be performed, it is understood that each of these additional steps can be performed with any specific method steps or combination of method steps of the disclosed methods, and that each such combination or subset of combinations is specifically contemplated and should be considered disclosed. Additionally, it is understood that the embodiments described herein can be implemented using any suitable material such as those described elsewhere herein or as known in the art.

Publications cited herein and the subject matter for which they are cited are hereby specifically incorporated by reference in their entireties.

The following describes a study demonstrating that microbiota-derived metabolite indole decreases mucosal inflammation and injury in a murine model of NSAID enteropathy.

Brief Summary

Non-steroidal anti-inflammatory drugs (NSAIDs) are one of the most frequently used classes of medications in the world. Unfortunately, NSAIDs induce an enteropathy associated with high morbidity and mortality. Although the pathophysiology of this condition involves the interaction of the gut epithelium, microbiota, and NSAIDs, the precise mechanisms by which microbiota influence NSAID enteropathy are unclear. It was proposed that a possible mechanism is that the microbiota may attenuate the severity of disease by specific metabolite-mediated regulation of host inflammation and injury. The microbiota-derived tryptophan-metabolite indole is abundant in the healthy mammalian gut and otherwise positively influences intestinal health. Thus the effects of indole administration on NSAID enteropathy were examined.

Mice (n=5 per group) were treated once daily for 7 days with an NSAID (indomethacin; 5 mg/kg), indole (20 mg/kg), indomethacin plus indole, or vehicle only (control). Outcomes compared among groups included: microscopic pathology; fecal calprotectin concentration; proportion of neutrophils in the spleen and mesenteric lymph nodes; fecal microbiota composition and diversity; small intestinal mucosal transcriptome; and, fecal tryptophan metabolites.

It was observed that co-administration of indole with indomethacin significantly reduced mucosal pathology scores, fecal calprotectin concentrations, and neutrophilic infiltration of the spleen and mesenteric lymph nodes induced by indomethacin. Additionally, co-administration of indole with indomethacin modulated NSAID-induced perturbation of the microbiota, fecal metabolites, and inferred metagenome. Finally, co-administration of indole with indomethacin abrogated a pro-inflammatory gene expression profile in the small intestinal mucosa induced by indomethacin.

These data demonstrate that the microbiota-derived metabolite indole attenuated multiple deleterious effects of NSAID enteropathy, including modulating inflammation mediated by innate immune responses and altering indomethacin-induced shift of the microbiota.

Introduction

As described above, it was hypothesized that aspects of a healthy microbiota can prevent or ameliorate the negative side effects of NSAID administration, such as enteropathy. A mechanism by which the microbiota might influence NSAID-induced intestinal mucosal damage is by producing metabolites that protect intestinal epithelial cells. Previously, the inventors identified tryptophan metabolites, including indole, as an important class of GI microbiota-derived compounds. Indole is a quorum-sensing molecule produced by bacterial metabolism of L-tryptophan that mediates communication among bacterial population and inter-kingdom signaling between the host and microbe. Indole improves barrier function and decreases intestinal inflammation in vitro and in vivo. Moreover, several other tryptophan metabolites reportedly exert similar salutary effects on the intestinal epithelium. The inventors investigated whether indole might mitigate the severity of NSAID enteropathy. Toward this end, indole was co-administered with the NSAID indomethacin and demonstrated a reduction in severity of mucosal injury caused by indomethacin alone. To determine whether the protective effects of indole were associated with alterations in the GI microbiota, the effects of administration of NSAIDs, indole, and their co-administration on the composition and diversity of the fecal microbiota were characterized. It was observed that the co-administration of indole with indomethacin resulted in maintenance of or even an increase of an important member of the Firmicutes phylum. Finally, to better understand the mechanisms of NSAID enteropathy and the effects of indole on these processes, RNA sequencing (RNA-Seq) was performed on the distal small intestinal mucosa in mice that were untreated or treated with indomethacin, indole alone, or the combination of indomethacin and indole. Pro-inflammatory pathways associated with innate immune responses were up-regulated by indomethacin administration relative to control mice, and co-administration of indole significantly mitigated the up-regulation of these pathways concomitant with reduced GI pathology.

Results Indole Reduced the Severity of NSAID Enteropathy

Calprotectin enzyme-linked immunosorbent assay (ELISA) of fecal samples collected on days 0 and 6 revealed that co-administration of indole with indomethacin significantly decreased fecal calprotectin levels (FIG. 1A). Moreover, attenuation of NSAID-induced small intestinal damage was confirmed by microscopic pathology scores (FIGS. 1B and 1C) of mice in which indole was co-administered with indomethacin. The reduction in mean microscopic pathology scores were corroborated by morphological parameters which revealed maintenance of villus height:crypt depth ratio (FIG. 1D) and thickness of the submucosal layers (FIGS. 6A and 6B) in mice in which indole was co-administered. Indomethacin had a negligible effect on the large intestine (data not shown), consistent with evidence that indomethacin induces small intestinal ulcerations in mice in a location similar to where NSAIDs injure people.

Indole Reduced Indomethacin-Induced Neutrophilic Infiltration of Spleen and Mesenteric Lymph Node (MLN)

Neutrophilic inflammation is primarily responsible for NSAID enteropathy, therefore, we quantified the abundance of neutrophils in the spleen and MLNs as measures of systemic neutrophilic response and trafficking of neutrophils though the GI tract, respectively.29-30 Indomethacin treatment resulted in a significant increase in neutrophils (defined as both CD11b- and GR-1 double-positive; FIG. 2) in both the spleen (FIG. 1E) and MLNs (FIG. 1F), and co-administration of indole significantly decreased neutrophilic infiltration of these tissues. Taken together, these data demonstrate that indomethacin induced small intestinal injury in mice that was accompanied by neutrophilic infiltration of the spleen and mesenteric lymph node and that co-administration of indole attenuated the small intestinal injury and limited neutrophilic infiltration of the spleen and mesenteric lymph nodes caused by indomethacin.

Indole Prevented Indomethacin-Induced Fecal Microbiota Shift and Alterations of the Inferred Metagenome

Because NSAIDs alter the intestinal microbiota, we evaluated the composition and diversity of the fecal microbiota in the four groups of mice. To adjust for uneven sequencing depth among the samples, each sample was rarefied to an even sequencing depth of 10,000 reads per sample prior to analysis. Alpha rarefaction curves and Good's coverage index estimates indicated that over 90% of the species were represented across all samples at this sequencing depth (FIGS. 8A-8D). Using analysis of similarities (ANOSIM), no significant difference in the unweighted Unifrac distance metric among the groups was observed on day 0 (R=0.10; P=0.11); however, by day 7 there was a significant difference among the groups (R=0.3007; P=0.0031). Pairwise comparisons between groups revealed that a significant difference in the Unifrac distance metric existed only between NSAID and control animals (TABLE 2) and that co-administration of indole with indomethacin attenuated this change in the beta diversity of the fecal microbiota.

TABLE 2 Pairwise analysis of similarity (ANOSIM) R-values based on the unweighted Unifrac distance metric at day 7. Only the difference between control and NSAIDs was significantly different than 0. Pairwise AOSIM R-values Control NSAID Indole NSAID + Indole Control 0.5487* 0.2 0.2813 NSAID 0.5487* 0.2615 0.4444 Indole 0.2 0.2615 0.1938 NSAID + Indole 0.2813 0.4444 0.1938 *R value is significantly (P < 0.05) different than a value of 0.

The primary gram-positive and gram-negative phyla found in murine feces are Firmicutes and Bacteroidetes, respectively. At the phylum level, principal component analysis (PCA) revealed a separation of NSAID-treated mice from the other groups characterized by increase in members of the phyla Bacteroidetes in the NSAID-treated animals between day 0 and day 7 (FIGS. 2A and 2B). Interestingly, PCA revealed qualitatively that co-administration of indole counteracted the increase in Bacteroidetes and instead appeared to shift the group closer to the phylum Firmicutes. Based on the results of PCA, we compared the abundance of members of the phyla Firmicutes and Bacteroidetes and found no significant difference after treatment with NSAIDs, indole, or their combination (FIGS. 9A and 9B). At lower taxonomic levels, however, significant differences were observed. For example, co-administration of indole with indomethacin prevented a decrease in Clostridiales and instead this group had a significant increase in several members of the Clostridales order (FIG. 2C). Furthermore, similarity percentage (SIMPER) based on the Bray Curtis dissimilarity measure at phylum, order, and family levels further confirmed that indomethacin treatment resulted in the gain of members of the Bacteroidales S24-7 family, a major family of Bacteroidetes found in murine feces, indicating that gain of this family contributed to the dissimilarity between NSAID and the other groups.33 Co-administration of indole prevented this increase in Bacteroidales and instead resulted in an increase in Clostridiales with marginal increases in several other members of the Firmicutes phyla (TABLES 3A-5 and FIG. 2D).

PCA of the inferred metagenome revealed clustering and separation of the NSAID-treated mice from the other groups (FIGS. 2E and 2F), although analysis of similarity (ANOSIM) based on the Bray Curtis dissimilarity metric indicated this difference was not significant (P>0.05). Consistent with the microbiota data, the distance among groups was greatest between the NSAID group and the NSAID+indole group. The major up- and down-regulated inferred functional pathways between NSAID and NSAID+indole groups were tabulated (TABLE 6).

TABLES 3A-3C disclose the results of a pair-wise similarity percentage analysis (SIMPER) analysis based on the bray Curtis dissimilarity measure of fecal 16S rRNA data at the phylum level of each treatment group compared to the control group based on day 7 feces.
TABLE 3A: The first column identifies the bacterial phylum explained by that row, the second column shows % contribution of that phylum to the Bray Curtis dissimilarity measure between the 2 groups, the third column tallies the cumulative Bray Curtis dissimilarity measure thus far represented in the table, and the last 2 columns show mean abundance in control mice and mean abundance for NSAID-treated mice;
TABLE 3B: Control mice and NSAID+indole-treated mice, and
TABLE 3C: control mice and indole-treated mice.

A) % Contribution % Cumulative Control mean NSAID mean Phylum to dissimilarity dissimilarity % abundance % abundance k_Bacteria; p_Firmicutes 49.22 49.22 32.2 17.1 k_Bacteria; p_Bacteroidetes 47.32 96.54 67.1 81.2 k_Bacteria; p_Tenericutes 3.215 99.75 0.611 1.66 k_Bacteria; p_Actinobacteria 0.1128 99.87 0.0552 0.0476 k_Bacteria; p_Proteobacteria 0.05056 99.92 0.0171 0.00317 Unassigned; Other 0.04668 99.96 0.0171 0.00317 k_Bacteria; p_Cyanobacteria 0.01555 99.98 0.0038 0.00634 k_Bacteria; p_Verrucomicrobia 0.01167 99.99 0.0038 0 k_Bacteria; p_Acidobacteria 0.009721 100 0 0.00317 k_Bacteria; Other 0 100 0 0

NSAID + Indole B) % Contribution % Cumulative Control mean mean Phylum to dissimilarity dissimilarity % abundance % abundance k_Bacteria; p_Firmicutes 49.41 49.41 32.2 44.5 k_Bacteria; p_Bacteroidetes 49.19 98.6 67.1 54.7 k_Bacteria; p_Tenericutes 1.174 99.77 0.611 0.671 k_Bacteria; p_Actinobacteria 0.09981 99.87 0.0552 0.0452 k_Bacteria; p_Proteobacteria 0.06521 99.94 0.0171 0.0262 Unassigned; Other 0.03194 99.97 0.0171 0.0143 k_Bacteria; p_Cyanobacteria 0.01197 99.98 0.0038 0.00238 k_Bacteria; p_Verrucomicrobia 0.01197 99.99 0.0038 0.00238 k_Bacteria; Other 0.006652 100 0 0.00238 k_Bacteria; p_Acidobacteria 0 100 0 0

C) % Contribution to % Cumulative Control mean Indole mean Phylum dissimilarity dissimilarity % abundance % abundance k_Bacteria; p_Bacteroidetes 49.01 98.22 32.2 24.6 k_Bacteria; p_Firmicutes 49.21 49.21 67.1 74.7 k_Bacteria; p_Tenericutes 1.37 99.59 0.611 0.656 k_Bacteria; p_Actinobacteria 0.1894 99.78 0.0552 0.0381 k_Bacteria; p_Proteobacteria 0.09238 99.87 0.0171 0.0285 Unassigned; Other 0.06467 99.94 0.0171 1.90E−03 k_Bacteria; p_Cyanobacteria 0.02925 99.97 3.80E−03 9.51E−03 k_Bacteria; p_Verrucomicrobia 0.0169 99.98 3.80E−03 1.90E−03 k_Bacteria; Other 0.0154 100 0 3.81E−03 k_Bacteria; p_Acidobacteria 0 100 0 0

TABLES 4A and 4B disclose the results of a pair-wise SIMPER analysis based on the Bray Curtis dissimilarity measure of fecal 16S rRNA data at the order level of NSAID and NSAID+indole groups compared to the control group based on day 7 feces.
TABLE 4A: The first column identifies the bacterial order explained by that row, the second column shows % contribution of that order to the Bray Curtis dissimilarity measure between the two groups, the third column tallies the cumulative Bray Curtis dissimilarity measure thus far represented in the table, and the last two columns show mean abundance in control mice and mean abundance for NSAID-treated mice; and
TABLE 4B: Control mice and NSAID+indole-treated mice.

A) % Contribution % Cumulative Control mean NSAID mean Order to dissimilarity dissimilarity % abundance % abundance k_Bacteria; p_Bacteroidetes; c_Bacteroidia; o_Bacteroidales 45.9 45.9 67.1 81.2 k_Bacteria; p_Firmicutes; c_Clostridia; o_Clostridiales 32.46 78.36 25.1 14.2 k_Bacteria; p_Firmicutes; c_Bacilli; o_Lactobacillales 12.33 90.69 5.2 2.65 k_Bacteria; p_Firmicutes; c_Bacilli; o_Turicibacterales 4.948 95.64 1.7 0.0412 k_Bacteria; p_Tenericutes; c_Mollicutes; o_RF39 3.273 98.91 0.523 1.62 k_Bacteria; p_Firmicutes: c_Erysipelotrichi; 0.4641 99.38 0.211 0.136 o_Erysipelotrichales k_Bacteria; p_Tenericutes; c_Mollicutes; 0.2339 99.61 0.0875 0.0317 o_Anaeroplasmatales k_Bacteria; p_Actinobacteria; c_Coriobacteriia; 0.1056 99.72 0.0533 0.0381 o_Coriobacteriales k_Bacteria; p_Firmicutes; c_Bacilli; o_Bacillales 0.06603 99.78 0.0228 0.0412 Unassigned; Other; Other; Other 0.04528 99.83 0.0171 0.00317 k_Bacteria; p_Proteobacteria; c_Alphaproteobacteria; 0.0283 99.85 0.00951 0 o_Rickettsiales k_Bacteria; p_Actinobacteria; c_Actinobacteria; 0.02641 99.88 0.0019 0.00951 o_Actinomycetales k_Bacteria; p_Bacteroidetes; c_Cytophagia; 0.01132 99.89 0.00381 0 o_Cytophagales k_Bacteria; p_Firmicutes; c_Bacilli; Other 0.01132 99.9 0.00381 0

NSAID + indole B) % Contribution % Cumulative Control mean mean Order to dissimilarity dissimilarity % abundance % abundance k_Bacteria; p_Firmicutes; c_Clostridia; o_Clostridiales 46.65 46.65 25.1 41.8 k_Bacteria; p_Bacteroidetes; c_Bacteroidia; 38.49 85.13 67.1 54.7 o_Bacteroidales k_Bacteria; p_Firmicutes; c_Bacilli; o_Lactobacillales 9.715 94.85 5.2 1.33 k_Bacteria; p_Firmicutes; c_Bacilli; o_Turicibacterales 3.667 98.52 1.7 1.28 k_Bacteria; p_Tenericutes; c_Mollicutes; o_RF39 0.8235 99.34 0.523 0.587 k_Bacteria; p_Firmicutes; c_Erysipelotrichi; 0.2041 99.54 0.211 0.119 o_Erysipelotrichales k_Bacteria; p_Tenericutes; c_Mollicutes; 0.1822 99.73 0.0875 0.0832 o_Anaeroplasmatales k_Bacteria; p_Actinobacteria; c_Coriobacteriia; 0.07184 99.8 0.0533 0.0404 o_Coriobacteriales k_Bacteria; p_Proteobacteria; c_Alphaproteobacteria; 0.04164 99.84 0.00951 0.019 o_Rickettsiales k_Bacteria; p_Firmicutes; c_Bacilli; o_Bacillales 0.03228 99.8 0.0228 0.0166 Unassigned; Other; Other; Other 0.02499 99.9 0.0171 0.0143

TABLES 5A and 5B disclose the results of a pair-wise SIMPER analysis based on the Bray Curtis dissimilarity measure of fecal 16S rRNA data at the family level of NSAID and NSAID+indole groups compared to the control group based on day 7 feces. TABLE 5A: The first column identifies the bacterial family explained by that row, the second column shows % contribution of that family to the Bray Curtis dissimilarity measure between the 2 groups, the third column tallies the cumulative Bray Curtis dissimilarity measure thus far represented in the table, and the last 2 columns show mean abundance in control mice and mean abundance for NSAID-treated mice; and

TABLE 5B: Control mice and NSAID+indole-treated mice.

A) % Contribution % Cumulative Control mean NSAID mean Family to dissimilarity dissimilarity % abundance % abundance k_Bacteria; p_Bacteroidetes; c_Bacteroidia; o_Bacteroidales; 43.49 43.49 67.1 81.2 f_S24-7 k_Bacteria; p_Firmicutes; c_Clostridia; o_Clostridiales; 16.7 60.19 7.24 1.31 f_Lachnosoiraceae k_Bacteria; p_Firmicutes; c_Bacilli; o_Lactobacillales; 11.57 71.77 5.19 2.48 f_Lactobacillaceae k_Bacteria; p_Firmicutes; c_Clostridia; o_Clostridiales; f_ 11.12 82.88 11 7.67 k_Bacteria; p_Firmicutes; c_Clostridia; o_Clostridiales; 5.042 87.93 5.04 3.53 f_Ruminococcaceae k_Bacteria; p_Firmicutes; c_Bacilli; o_Turicibacterales; 4.688 92.61 1.7 0.0412 f_Turicibacteraceae k_Bacteria; p_Tenericutes; c_Mollicutes; o_RF 39; f_ 3.101 95.72 0.523 1.62 k_Bacteria; p_Firmicutes; c_Clostridia; o_Clostridiales; 2.452 98.17 1.7 1.68 f_Clostridiaceae k_Bacteria; p_Firmicutes; c_Bacilli; o_Lactobacillales; 0.4826 98.65 0.00381 0.171 f_Enterococcaceae k_Bacteria; p_Firmicutes; c_Erysipelotrichi; o_Erysipelotrichales; 0.4397 99.09 0.211 0.136 f_Erysipelotrichaceae k_Bacteria; p_Tenericutes; c_Mollicutes; o_Anaeroplasmatales; 0.2216 99.31 0.0875 0.0317 f_Anaeroplasmataceae k_Bacteria; p_Firmicutes; c_Clostridia; o_Clostridiales; 0.1108 99.42 0.0457 0.00634 f_Dehalobacteriaceae k_Bacteria; p_Actinobacteria; c_Coriobacteriia; o_Coriobacteriales; 0.1001 99.52 0.0533 0.0381 f_Coriobacteriaceae k_Bacteria; p_Firmicutes; c_Bacilli; o_Bacillales; 0.05005 99.57 0.0038 0.019 f_Staphylococcaceae k_Bacteria; p_Firmicutes; c_Bacilli; o_Bacillales; Other 0.05004 99.62 0.00381 0.019 Unassigned; Other; Other; Other; Other 0.0429 99.67 0.0171 0.00317 k_Bacteria; p_Firmicutes; c_Clostridia; o_Clostridiales; Other 0.0429 99.71 0.0171 0.00317 k_Bacteria; p_Firmicutes; c_Bacilli; o_Bacillales; 0.03217 99.74 0.0133 0.00317 f_Bacillaceae k_Bacteria; p_Proteobacteria; c_Alphaproteobacteria; 0.02681 99.77 0.00951 0 o_Rickettsiales; f_mitochondria k_Bacteria; p_Actinobacteria; c_Actinobacteria; 0.02502 99.79 0.0019 0.00951 o_Actinomycetales; f_Micrococcaceae k_Bacteria; p_Firmicutes; c_Clostridia; o_Clostridiales; 0.02145 99.81 0.00381 0.00634 f_[Mogibacteriaceae] k_Bacteria; p_Firmicutes; c_Bacilli; o_Lactobacillales; 0.01608 99.83 0.00571 0 f_Streptococcaceae k_Bacteria; p_Bacteroidetes; c_Bacteroidia; o_Bacteroidale; 0.01608 99.85 0.0019 0.00634 f_Bacteroidaceae k_Bacteria; p_Firmacutes; c_Bacilli; o_Lactobacillales; 0.01608 99.86 0.00571 0 f_Aerococcaceae k_Bacteria; p_Bacteroidetes; c_Bacteroidia; o_Bacteroidales; 0.01429 99.88 0.00571 0.00317 f_Prevotellaceae k_Bacteria; p_Bacteroidetes; c_Cytophagla; o_Cytophagales; 0.01072 99.89 0.00381 0 f_Cytophagacae k_Bacteria; p_Firmacutes; c_Bacilli; Other; Other 0.01072 99.9 0.00381 0

NSAID + indole B) % Contribution % Cumulative Control mean mean Family to dissimilarity dissimilarity % abundance % abundance k_Bacteria; p_Bacteroidetes; c_Bacteroidia; o_Bacteroidales; 34.58 34.58 67.1 54.7 f_S24-7 k_Bacteria; p_Firmicutes; c_Clostridia; o_Clostridiales; f_ 22.57 57.15 11 19.4 k_Bacteria; p_Firmicutes; c_Clostridia; o_Clostridiales; 13.69 70.83 5.04 9.99 f_Ruminococcaceae k_Bacteria; p_Firmicutes; c_Clostridia; o_Clostridiales; 10.45 81.29 7.24 9.12 f_Lachnosoiraceae k_Bacteria; p_Firmicutes; c_Bacilli; o_Lactobacillales; 8.725 90.01 5.19 1.31 f_ Lactobacillaceae k_Bacteria; p_Firmicutes; c_Clostridia; o_Clostridiales; 4.964 94.98 1.7 3.05 f_Clostridiaceae k_Bacteria; p_Firmicutes; c_Bacilli; o_Turicibacterales; 3.296 98.27 1.7 1.28 f_Turicibacteraceae k_Bacteria; p_Tenericutes; c_Mollicutes; o_RF 39; f_ 0.7401 99.01 0.523 0.587 k_Bacteria; p_Firmicutes; c_Erysipelotrichi; o_Erysipelotrichales; 0.1834 99.2 0.211 0.119 f_Erysipelotrichaceae k_Bacteria; p_Tenericutes; c_Mollicutes; o_Anaeroplasmatales; 0.1637 99.36 0.0875 0.0832 f_Anaeroplasmataceae k_Bacteria; p_Firmicutes; c_Clostridia; o_Clostridiales; Other 0.1628 99.52 0.0171 0.0999 k_Bacteria; p_—Firmicutes; c_Clostridia; o_Clostridiales; 0.09731 99.62 0.0457 0.0571 f_Dehalobacteriaceae k_Bacteria; p_Actinobacteria; c_Coriobacteriia; o_Coriobacteriales; 0.06456 99.68 0.0533 0.0404 f_Coriobacterlaceae k_Bacteria; p_Firmicutes; c_Bacilli; o_Lactobacillales; 0.03743 99.72 0.00381 0.019 f_Enterococcaceae k_Bacteria; p_Proteobacteria; c_Alphaproteobacteria; 0.03743 99.76 0.00951 0.019 o_Rickettsiales; f_mitochondria Unassigned; Other; Other; Other; Other 0.02246 99.78 0.0171 0.0143 k_Bacteria; p_Firmicutes; c_Bacilli; o_Bacillales; 0.02058 99.8 0.0133 0.00475 f_Bacillaceae k_Bacteria; p_Firmicutes; c_Bacilli; o_Bacillales; Other 0.01871 99.82 0.00381 0.00951 k_Bacteria; p_Firmicutes; c_Clostridia; o_Clostridiales; 0.01403 99.83 0.0019 0.00713 f_Peptostreptococcaceae k_Bacteria; p_Proteobacteria; c_Gammaproteobacteria; 0.01123 99.85 0.0019 0.00476 o_Aeromonadales; f_Succinivibrionaceae k_Bacteria; p_Bacteroidetes; c_Bacteroidia; o_Bacteroidales; 0.01122 99.86 0.00571 0 f_Prevotellaceae k_Bacteria; p_Firmicutes; c_Bacilli; o_Lactobacillales; 0.01122 99.87 0.00571 0 f_Streptococcaceae k_Bacteria; p_Firmacutes; c_Bacilli; o_Lactobacillales; 0.01122 99.88 0.00571 0 f_Aerococcaceae k_Bacteria; p_Firmicutes; c_Clostridia; o_Clostridiales; 0.01029 99.89 0.00381 0.00238 f_[Mogibacteriacaea] k_Bacteria; p_Firmicutes; c_Clostridia; o_Clostridiales; 0.009357 99.9 0 0.00476 f_[Tissierellaeae]

NSAID NSAID + NSAID + Control NSAIDmean vs. indole indole vs. % Contribution to mean % % Control mean % Control Taxon dissimilarity abundance abundance Direction abundance Direction Environmental Information 13.88 5.29% 3.82% 6.35% Processing; Membrane Transport; Transporters Environmental Information 6.132 2.36% 1.71% 2.76% Processing; Membrane Transport; ABC transporters Genetic Information Processing; 3.171 1.14% 0.31% 1.38% Transcription; Transcription factors Cellular Processes; Cell Motility; 2.895 0.61% 0.81% 0.93% Bacterial motility proteins Genetic Information Processing; 2.536 2.70% 2.94% 2.45% Translation; Ribosome Unclassified; Cellular Processes and 1.971 0.40% 1.00% 0.55% Signalling; Sporulation Metabolism; Energy Metabolism; 1.944 1.46% 1.65% 1.28% Oxidative phosphorylation Environmental Information 1.746 1.19% 0.20% 1.37% Processing; Signal Transduction; Two-component system Unclassified; Cellular Processes and 1.578 0.75% 0.17% 0.62% Signalling; Membrane and intracellular structural molecules Genetic Information Processing; 1.572 2.98% 3.12% 2.81% Replication and Repair DNA repair and recombination proteins Cellular Processes; Cell Motility; 1.5 0.33% 0.91% 0.50% Flagellar assembly Environmental Information 1.486 0.27% 0.18% 0.22% Processing; Membrane Transport; Phosphotransferase system (PTS) Metabolism; Nucleotide 1.47 2.11% 2.24% 1.97% Metabolism; Pyrimidine metabolism Metabolism; Energy Metabolism; 1.327 1.18% 0.40% 1.06% Carbon fixation pathways in prokaryotes Cellular Processes; Cell Motility; 1.322 0.31% 1.30% 0.46% Bacterial chemotaxis Metabolism; Metabolism of 1.24 0.53% 2.42% 0.66% Cofactors and Vitamins; Porphyrin and chlorophyll metabolism Metabolism; Nucleotide 1.191 2.31% 0.90% 2.17% Metabolism; Purine metabolism Unclassified; Metabolism; Energy 1.178 1.14% 1.25% 1.04% metabolism Metabolism; Carbohydrate 1.162 0.79% 1.25% 0.68% Metabolism; Citrate cycle (TCA cycle) Genetic Information Processing; 1.159 1.15% 0.61% 1.05% Folding, Sorting and Degradation; Chaperones and folding catalysts Metabolism; Glycan Biosynthesis 1.114 0.50% 1.48% 0.42% and Metabolism; Lipopolysaccharide biosynthesis proteins Genetic Information Processing; 1.078 1.39% 0.50% 1.29% Replication and Repair; DNA replication proteins Metabolism; Glycan Biosynthesis 1.073 0.40% 1.55% 0.32% and Metabolism; Lipopolysaccharide biosynthesis Metabolism; Amino Acid 0.9791 1.18% 1.14% 1.10% Metabolism; Alanine, aspartate and glutamate metabolism Metabolism; Glycan Biosynthesis 0.9663 0.64% 1.27% 0.60% and Metabolism; Other glycan degradation Genetic Information Processing; 0.9311 1.05% 2.12% 0.97% Replication and Repair; Homologous recombination Metabolism; Amino Acid 0.9101 1.57% 1.65% 1.49% Metabolism; Amino Acid related enzymes Genetic Information Processing; 0.8961 1.47% 0.70% 1.37% Translation Ribosome Biogenesis Unclassified; Cellular Processes and 0.8215 0.41% 3.54% 0.34% Signaling; Pores ion channels Metabolism; Metabolism of 0.7986 0.77% 0.49% 0.71% Cofactors and Vitamins; One carbon pool by folate Unclassified; Metabolism; Others 0.7909 0.79% 0.84% 0.85% Metabolism; Metabolism of 0.77 0.47% 1.66% 0.41% Cofactors and Vitamins; Folate biosynthesis

TABLE 6 discloses the results of a pair-wise SIMPER analysis based on the Bray Curtis dissimilarity measure of the inferred metagenome of the fecal microbiota of NSAID and NSAID+indole groups compared to the control group based on day 7 feces. The first column identifies the KEGG pathway explained by that row, the second column shows % contribution of that pathway to the Bray Curtis dissimilarity measure between the 2 groups, the third column tallies the cumulative Bray Curtis dissimilarity measure thus far represented in the table, and the last 4 columns show mean abundance in control mice and mean abundance for NSAID-treated mice, direction of change for that comparison, NSAID+indole-treated mice and directional change for that comparison versus control mice.

Co-Administration of Indole Prevents the Indomethacin-Induced Tryptophan-Derived Metabolite Disruption in Feces

Multiple tryptophan-derived metabolites produced by the microbiota predicted to be bioactive and exert effects on the host have been identified (Sridharan G V, Choi K, Klemashevich C, Wu C, Prabakaran D, Pan L B, Steinmeyer S, Mueller C, Yousofshahi M, Alaniz R C, et al. Prediction and quantification of bioactive microbiota metabolites in the mouse gut. Nature Communications 2014; 5:5492, incorporated herein by reference in its entirety). Given the importance of the microbiota in NSAID enteropathy, the effects of indole on the intestinal epithelium and microbiota were examined (FIGS. 2A-2F) and were correlated with tryptophan metabolites. No single tryptophan metabolite was significantly correlated with fecal calprotectin (data not shown). Only the metabolite tyramine was significantly (P<0.01) correlated with microscopic pathology scores (TABLE 7). Examination of the fecal profile of all tryptophan metabolites using PCA revealed a visible separation of the NSAID-treated mice from the remaining groups on day 7, while co-administration of indole appeared to prevent this separation (TABLE 7); however, ANOSIM of the Bray Curtis dissimilarity measure indicated this apparent difference was not significant (P=0.55). Examination of tryptophan metabolite concentrations by treatment group revealed a significant increase in tyramine in the NSAID group that was attenuated by co-administration of indole (TABLE 2); moreover, several other tryptophan metabolites tended to be increased only in the feces of mice treated exclusively with indomethacin, indicating that NSAID treatment caused differences that were attenuated by co-administration of indole (TABLE 8). Relative to controls, mice in the indole-only and indomethacin-only groups had significantly more indole; although fecal indole concentrations were generally higher in the NSAID+indole mice, this difference was not significant.

TABLE 7 discloses a correlation of fecal tryptophan metabolites with histopathology scores≤2 or>2. Only the tryptophan metabolite tyramine correlated with microscopic pathology score.

Metabolite Score < 2 Median (Range) Score > 2 Median (Range) P value Hydroxytryptophan 11.9 (3.5-24.5) 27.3 (6.0-28.4) 0.3097 Hydroxyindole 23.7 (15.7-58.1) 38.3 (18.0-78.6) 0.3301 Arginine 11.2 (1.9-241.6) 8.9 (4.9-24.6) 1 Glutamic Acid 404 (56-4,857) 345 (77-1,814) 0.953 Indole-3-acetamide 0.001 (<0.001-0.004) 0.001 (<0.001-0.002) 0.5941 Indole-3-actetate 0.021 (0-0.080) 0 (0-0.238) 0.3879 Indole-3-carboxaldehyde 0.324 (0.162-0.867) 0.441 (0.179-0.582) 0.5135 Indole 21.5 (0-157.2) 97.1 (53.3-131.3) 0.0922 Kyneurine 0 (0-0.184) 0 (0 to 0.061) 0.6287 Ornithine 6.2 (2.0-9.6) 13.3 (6.3-32.8) 0.3277 Phenylalanine 2053 (1109-6247) 3,108 (1,192-3,957) 0.8591 Serotonin 1.28 (0.62-1.73) 2.60 (0.60-3.32) 0.0992 Tryptamine 0.076 (0.050-0.314) 0.094 (0.053-0.166) 0.2065 Tryptophan 3.38 (1.33-19.22) 4.66 (1.64-7.95) 0.5135 Tyramine 2.32 (1.44-11.66) 22.77 (2.68-208.17) 0.008 Tyrosine 48.0 (20.9-231.0) 24.88 (10.52-134.37) 0.206

TABLE 8 Group effects of tryptophan-derived metabolites identifying the direction and magnitude of change in fecal concentration between Days 0 and 7. Several tryptophan-derived metabolites tended to be increased in the NSAID treated mice (5-hydroxytryptamine, 5-hydroxyindole, indole, serotonin, tyramine). Fecal concentrations of indole increased in the groups in which indole was administered. Metabolite Group effect Magnitude (95% CI) P value 5-Hydroxytryptamine ↑ NSAID only 13.9 (−1.4 to 29.3) 0.1029 5-Hydroxyindole ↑ NSAID only 27.2 (−2.2 to 56.6) 0.0657 Arginine Glutamine Indole-3-acetamide Indole-3-acetate Indole-3-carboxaldehyde Indole ↑ NSAID 6.7 (1.6 to 28.2) 0.0255 ↑Indole 5.3 (1.1 to 24.7) 0.0364 ↑NSAID + Indole 4.7 (0.9 to 24.7) 0.0641 Kyneurine Ornithine Phenylalanine Serotonin ↑ NSAID only 1.6 (−0.3 to 3.5) 0.0895 Tryptamine Tryptophan Tyramine ↑ NSAID only 6.7 (1.3 to 33.6) 0.0024 Tyrosine

Co-Administration of Indole Attenuates NSAID-Induced Pro-Inflammatory Mucosal Transcriptomic Changes

RNA sequencing (RNA-Seq) of the distal small intestinal mucosa was performed to examine the in vivo transcriptomic changes associated with NSAID enteropathy and to gain insight into how the co-administration of indole altered gene expression. The top genes that were significantly up- or down-regulated (fold change≥2) in NSAID-treated mice relative to control mice, indole-treated mice relative to control mice, and NSAID+indole-treated mice relative to control mice were tabulated. The Ingenuity Pathway Analysis software package was used to identify pathways represented by differentially expressed genes. Several canonical pathways were altered in NSAID-treated mice relative to controls (FIG. 3A). Several of the pathways that were modulated by NSAID administration were either shifted to the opposite direction (i.e., inhibited or activated, respectively), or the degree of activation or inhibition was markedly attenuated by co-administration of indole (FIG. 3B).

Moreover, transcription of specific pro-inflammatory cytokines (interleukin [IL]-1α, IL-1β, tumor necrosis factor (TNF), IL-6) and chemokines (chemokine C-X-C motif [CXC]L1, CXCL3, CXCL2, CXCL5, CCL2, CCL7) was significantly up-regulated in NSAID-treated mice; however, when indole was co-administered the degree of up-regulation was not significantly different than control mice (FIGS. 10A and 10B). Based on our results, a proposed schema of the interaction of NSAIDs, the host mucosal epithelium, the microbiota, and indole is presented (FIG. 4).

It was also observed that the exfoliated intestinal epithelial cells (IEC) and tissue-level gene expression profiles were similar (not shown). Thus, the differences in the exfoliated IEC transcriptome between control versus indomethacin-treated animals and control versus indole+indomethacin-treated animals were examined. For this purpose, the top 40 canonical pathways altered in exfoliated IECs were sorted based on differences in Z-scores between the 2 treatments (FIG. 3C). Similarly, the top 50 upstream regulators altered in exfoliated IECs are shown in FIG. 3D. While these data reveal pathways consistent with reduced inflammation, it is unclear how this anti-inflammatory effect occurred and which of these upstream regulators mediate indomethacin induced GI injury and the protective effects observed by the co-administration of indole. To address this question, the network of the top 15 upstream regulators identified by our novel data reduction approach was mapped and analyzed with 2-feature LDA in the context of NSAID vs control animals (FIG. 5A). The fold change and false discovery rate (FDR) P-values of these same molecules from the analysis of control was overlaid versus NSAID+indole (FIG. 5B), allowing identification of the molecules were altered (opposite fold-change either realized or predicted). This resulted in the identification of 11 genes that were altered between NSAID administration and NSAID+indole administration.

Discussion

Co-administration of indole attenuated small intestinal mucosal damage induced by administration of indomethacin in mice as manifested by reduced microscopic pathology and fecal calprotectin concentration. Fecal calprotectin is a well-established, non-invasive indicator of intestinal mucosal injury induced by NSAIDs in human patients and animal models, and correlates well with 4-day fecal excretion of 111Indium-labelled leukocytes. The findings of decreased fecal calprotectin and decreased microscopic pathology have important clinical implications. A variety of NSAIDs are used widely for an array of clinical conditions ranging from pain relief for minor injuries to management of rheumatoid arthritis or cancer. The relatively low cost, high effectiveness, and lack of alternatives to NSAIDs indicate that their use will continue to be highly prevalent. Consequently, agents that might be co-administered with NSAIDs to diminish NSAID enteropathy would be clinically important. Further evaluation of indole to ameliorate NSAID enteropathy in animal models and naturally occurring disease is warranted by our findings.

Administration of NSAIDs increases the proportion of gram-negative organisms at the expense of gram-positive organisms in the intestinal microbiota, and this shift has been shown to contribute to NSAID-induced intestinal injury. Specifically, NSAID administration decreases various members of the class Clostridia and increases members of the class Bacteroidia. Mice treated with indole and indomethacin did not have a change in the abundance of Bacteroidia but did have an increase in several members of the gram-positive family Clostridiales in concert with diminution of the severity of intestinal mucosal damage. Evidence exists that the microbiota plays an important role in the development of NSAID enteropathy. Germ-free rats treated with NSAIDs develop less severe enteropathy than specific-pathogen-free rats or germ-free rats that have been colonized with gram-negative bacteria.39 Toll-like receptor (TLR) 4-deficient mice develop less severe lesions than isogeneic TLR4-competent strains. Dramatic NSAID-induced alterations of the gut microbiota are well-documented and most often characterized by a loss of gram-positive bacteria with a concurrent increase in gram-negative bacteria. Moreover, this particular shift in the microbiota has been associated with increased severity of intestinal mucosal injury, and preventing this shift can reduce mucosal injury. The classic indomethacin-induced increase in types of Bacteroidia we observed in our study were not significant, but this was likely attributable to limited power to detect a statistically significant difference resulting from our small sample size. It is not clear why increased abundance of gram-negative bacteria worsens the severity of NSAID enteropathy, but direct effects of LPS and the host innate immune response to LPS appear to be important. It is also possible that loss of beneficial gram-positive bacteria is important. Commensal Clostridia have been shown to be critically important in gut homeostasis, specifically members of Clostridium cluster XIVa and Clostridium cluster IV. Interestingly, several members of these two clostridial clusters were increased in the NSAID-treated animals in which indole was co-administered.

The host response to the microbiota might be more important than the microbiota itself in the pathogenesis of NSAID enteropathy. Neutrophils are key effector cells of innate immunity and are critically important in the pathogenesis of NSAID enteropathy. Neutrophils are recruited to the site of injury by the influx of luminal contents following increased mucosal permeability. The resident innate immune cells present in the epithelium and lamina propria release cytokines and chemokines that attract circulating neutrophils. These neutrophils, along with other innate immune cells, then release pro-inflammatory cytokines, typically characterized by an abundance of the IL-1 super family, TNF-α, IL-6, and others, that are responsible for the damage to the lower GI tract. This critical role for neutrophils in NSAID enteropathy is supported by our findings of neutrophilic infiltration of both the spleen and MLN following NSAID administration and reduced neutrophil concentrations in these tissues with co-administration of indole. These data also affirm the importance of the innate immune response in the pathophysiology of NSAID enteropathy because many of the most up-regulated pathways identified by RNA-Seq reflected the innate immune response (viz., NF-κB pathway, TLR signaling pathways, and the LPS/IL-1 response). Moreover, at the individual gene level, several of the classic pro-inflammatory cytokines associated with neutrophil activation and known to be important in NSAID enteropathy (e.g., TNF-α, IL-1, and IL-6) were up-regulated among NSAID-treated mice. Co-administration of indole, however, attenuated or reversed up-regulation of genes associated with innate immunity and inflammation that contribute to the pathogenesis of NSAID enteropathy. In addition, several chemokines that attract neutrophils were up-regulated in the NSAID-treated mice and this up-regulation was dramatically attenuated by the co-administration of indole. These data indicate that indole can mitigate the host innate immune response to the influx of luminal contents across injured epithelia. Indole has been shown to inhibit nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) signaling in intestinal epithelial cells. It is thus plausible that indole exerts similar effects on neutrophils and other innate immune cells, thereby reducing cytokines dependent upon NF-κB signaling. Although NF-κB signaling was activated by administration of both NSAID and NSAID+indole, activation of this pathway was greatly diminished by co-administration of indole with NSAIDs, suggesting indole might mitigate NSAID damage in part by inhibiting NF-κB. It is important to note that the mRNA for RNA-seq was isolated from mucosal scrapings. These scrapings likely contain epithelial cells as well as cells residing in the lamina propria (i.e., immune cells recruited to the inflamed gut due to loss of barrier function) and therefore we cannot be sure the exact source of gene expression profiles. NSAID enteropathy is characterized as a mucosal injury and we felt examining the transcriptome of this location would be more informative than examining the transcriptome of whole tissue. The observed anti-inflammatory effects of indole may be due to indole's effects on epithelial cells, immune cells in the lamina propria, or both.

It is unclear how indole prevented the characteristic shifts in the composition of the microbiota associated with NSAID administration. Indole has long been recognized as a quorum-sensing molecule so it is possible that indole directly affected the microbial community by acting as an intra-kingdom signaling molecule. It is, however, also plausible that changes in the microbiota associated with NSAID use occur secondary to mucosal inflammation because mucosal inflammation has been shown to alter the luminal environment. Indole might have attenuated mucosal damage, which in turn prevented the expected changes in the microbiota. Finally, it has been shown that GI microbiota can vary with environment including cage-dependent variation and cage-dependent clustering. In order to mitigate this phenomenon, after acclimation and immediately prior to starting the study (Day 0), mice were randomly assigned into treatment groups and then were moved into cages based on the group to which they were randomly assigned. There were no significant differences in the fecal beta diversity among the groups at Day 0. It is possible, however, that were some cage-dependent microbiota changes that occurred over the 7 days of treatment that might have influenced the composition of the microbiota after treatment.

Indole is present in the GI tract of humans and animals at relatively high concentrations (˜250-1,100 μM). It has been speculated that, because intestinal epithelial cells are continually exposed to indole, indole may act as an interkingdom signaling molecule. Indeed the inventors have shown, in vitro, that indole does behave in this manner and has anti-inflammatory effects on intestinal epithelial cells and upregulates expression of genes associated with tight cell junctions. It was tested, then, whether increasing the concentration of indole within the lumen of the GI tract might mitigate NSAID enteropathy because of the in vitro effects of indole on intestinal epithelial cells. As expected, mice gavaged with indole (alone or in combination with indomethacin) were observed to have increased fecal concentration of indole. Interestingly, the NSAID-only treated mice also had increased fecal concentrations of indole. Although both gram-negative and gram-positive bacteria produce indole, the list of gram-negative bacteria known to produce indole is much larger than gram-positive bacteria. Thus, the observed increase in gram-negative bacteria in the NSAID treated mice might explain their increased fecal concentrations of indole. The beneficial effects of indole in this study were observed at the distal small intestine, but the concentration of indole and the microbiota diversity were determined using fecal samples. Fecal metabolite and microbiota characterization do not always correlate well with those of more proximal mucosal locations in the GI tract. Therefore, it is possible that indole was present in higher concentrations in the distal small intestine of mice treated with NSAID combined with indole compared with mice treated with the NSAID alone, thus contributing to the attenuation of NSAID-induced injury by indole administration. Interestingly, several tryptophan metabolites that act as neurotransmitters including serotonin and 5-hydroxytryptamine were also increased in NSAID-treated mice. Hypermotility of the GI tract is induced by NSAIDs and contributes to the pathophysiology of NSAID enteropathy. The microbiota shift induced by NSAIDs might result in production of prokinetic metabolites that contribute to GI hypermotility.

In summary, indole supplementation of mice attenuates the deleterious effects of NSAIDs on the distal small intestine and modulates NSAID-induced alterations in the composition of the fecal microbiota. The major events in the onset of NSAID enteropathy are intestinal epithelial cell death, increased mucosal permeability, influx of luminal contents, and host innate immune response to the microbiota. Indole likely reduces intestinal injury induced by NSAIDs at multiple levels, including neutrophilic infiltration, NSAID-induced dysbiosis, and pro-inflammatory pathways in the distal small intestine. Future work will focus on interrogating the potential mechanisms by which indole exerts this beneficial effect in order to further elucidate means to control or prevent NSAID enteropathy.

Materials and Methods

Animal protocols were approved by the Texas A & M Institutional Animal Care and Use Committee in accordance with appropriate institutional and regulatory bodies' guidelines.

Mice and Treatments

Eight- to 10-week-old specific-pathogen-free C57BL/6J mice were purchased and allowed to acclimate for 2 weeks. Mice were fed standardized laboratory rodent diet and sterile water ad libitum. Mice were randomly divided into the following 4 groups (n=5 mice/group): 1) NSAID (indomethacin); 2) indole; 3) NSAID+indole; and, 4) untreated controls. Mice were then rehoused on the basis of treatment group assignment, with 5 animals/group-cage. To induce NSAID enteropathy, mice in group 1 were gavaged once daily with indomethacin (5 mg/kg; for 7 days; Sigma Aldrich, St. Louis, Mo.) dissolved in dimethylysulfoxide (DMSO) (Sigma Aldrich, St. Louis, Mo.) and further diluted in phosphate buffered saline (PBS). Mice in group 2 received indole by gavage (20 mg/kg; once daily for 7 days; Sigma Aldrich, St. Louis, Mo.) dissolved in sterile water warmed to 55° C. Mice in group 3 received indole co-administered with indomethacin by gavage at the dosages described above. All mice were gavaged with equal volumes (200 μL) and equal concentrations of DMSO (0.001%).

Sample Collection

Feces were collected daily by placing individual animals in sterile plastic cups that were RNase- and DNase-free until they passed feces. The mice were immediately returned to their home cages and the feces immediately flash frozen at −80° C. All animals were euthanized via CO2 asphyxiation on day 8 (i.e., after 7 days of treatment). The small intestine was harvested, opened longitudinally, rinsed with ice-cold PBS, and the distal ⅓ of the intestinal mucosa was scraped for tissue gene expression analysis. The remaining small intestine was fixed in 4% paraformaldehyde, Swiss-rolled, paraffin-embedded, and stained with hematoxylin and eosin. The spleen and mesenteric lymph nodes (MLN) were harvested, and immediately placed in ice cold RPMI-1640-c+10% fetal calf serum (FCS: Life Technologies, Carlsbad, Calif.), homogenized, and prepared as a single cell suspension for flow cytometric analysis as previously described (Wang N, Strugnell R, Wijburg O, Brodnicki T. Measuring bacterial load and immune responses in mice infected with Listeria monocytogenes. Journal of Visualized Experiments: JoVE 2011).

Fecal Calprotectin ELISA

A murine calprotectin ELISA kit (HK214, Hycult Biotech, Plymouth Meeting, Pa.) was used according to the manufacturer's protocol with slight modifications. Briefly, 100 mg of feces was homogenized in extraction buffer (0.1 M Tris, 0.15 M NaCl, 1.0 M urea, 10 mM CaCl2, 0.1 M citric acid monohydrate, 5 g/l bovine serum albumin (BSA) and 0.25 mM thimerosal [pH 8.0]). The homogenate was centrifuged at 10,000×g at 4° C. for 20 minutes and the supernatant used as directed in manufacturer's protocol.

Tissue RNA Extraction, Sequencing, and Processing

RNA was extracted from the mucosal scrapings using an RNeasy mini kit (QIAGEN, Redwood City, Calif.) following the manufacturer's instructions and including on-column DNase treatment. RNA quantity was determined using a Nanodrop spectrophotometer (Fisher Thermoscientific) and the quality was assessed using the Nano6000 chip on a Bioanalyzer 2100(Agilent Technologies). Only RNA with an integrity number (RIN)≥8 was used. The samples were randomized before beginning the RNA-Seq library preparation. Sequencing libraries were made using 250 ng of RNA and the TruSeq RNA Sample Preparation kit (Illumina) following the manufacturer's instructions. A volume of 2.5 μl of ERCC spike-in RNA control mix (Life Technologies) was added to the starting RNA at a dilution of 1:1000. The libraries were pooled and sequenced on an Illumina HiSeq 2500 at the Texas AgriLife Genomics and Bioinformatics Services Core Facility (College Station, Tex.). Sequencing data were provided in a de-multiplexed format and aligned using Spliced Transcripts Alignment to a Reference (STAR) software with default parameters and referenced against the genome of Mus musculus (Ensembl version GRCm38) (Dobin A, Davis C A, Schlesinger F, Drenkow J, Zaleski C, Jha S, Batut P, Chaisson M, Gingeras T R. STAR: ultrafast universal RNA-seq aligner. Bioinformatics (Oxford, England) 2013; 29:15-21). Differentially expressed genes were determined using EdgeR based on the matrix of gene counts (Robinson M D, McCarthy D J, Smyth G K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics (Oxford, England) 2010; 26:139-40). Gene lists were analyzed through use of QIAGEN's Ingenuity® Pathway Analysis (IPA, QIAGEN, Redwood City, Calif. http://www.qiagen.com/ingenuity). Sequence data were uploaded into NCBI small reads archive (Accession number PRJNA290483).

Microbiota DNA Extraction, Sequencing, and Processing

Microbiota 16S rRNA gene sequencing methods were adapted from the methods developed for the NIH-Human Microbiome Project (A framework for human microbiome research. Nature 2012; 486:215-21; Structure, function and diversity of the healthy human microbiome. Nature 2012; 486:207-14). Briefly, bacterial genomic DNA was extracted using MO BIO PowerSoil DNA Isolation Kit (MO BIO Laboratories) according to the manufacturer's protocol. The 16S rDNA V4 region was amplified by PCR and sequenced in the MiSeq platform (Illumina) using the 2×250-bp paired-end protocol yielding paired-end reads that overlap almost completely (Caporaso J G, Lauber C L, Walters W A, Berg-Lyons D, Huntley J, Fierer N, Owens S M, Betley J, Fraser L, Bauer M, et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. The ISME Journal 2012; 6:1621-4). The primers used for amplification contain adapters for MiSeq sequencing and dual-index barcodes so that the PCR products can be pooled and sequenced directly. The software suite Quantitative Insights Into Microbial Ecology (QIIME v1.9[http://qiime.sourceforge.net]) was used for data processing and analysis (Caporaso J G, Kuczynski J, Stombaugh J, Bittinger K, Bushman F D, Costello E K, Fierer N, Pena A G, Goodrich J K, Gordon J I, et al. QIIME allows analysis of high-throughput community sequencing data. Nature Methods 2010; 7:335-6). The raw sequence data were de-multiplexed, and low-quality reads were filtered using the database's default parameters. Chimeric sequences were detected using Uchime and removed prior to further analysis.64 Sequences were then assigned to operational taxonomic units (OTUs) using an open-reference OTU picking protocol [http://qiime.org/scripts/pick_open_reference_otus.html] with UCLUST software in QIIME based on 97% identity with the Greengenes database (v13_5) (Edgar R C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics (Oxford, England) 2010; 26:2460-1; DeSantis T Z, Hugenholtz P, Larsen N, Rojas M, Brodie E L, Keller K, Huber T, Dalevi D, Hu P, Andersen G L. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Applied and Environmental Microbiology 2006; 72:5069-72; McDonald D, Price M N, Goodrich J, Nawrocki E P, DeSantis T Z, Probst A, Andersen G L, Knight R, Hugenholtz P. An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. The ISMS Journal 2012; 6:610-8). To adjust for uneven sequencing depth among the samples, each sample was rarefied to an even sequencing depth (10,512 reads/sample) prior to further analysis.

Alpha rarefaction, beta diversity measures, richness, taxonomic summaries, and tests for significance were calculated and plotted using QIIME. The weighted and unweighted Unifrac distances were calculated for comparison of beta diversity. Differences in microbial communities among the treatment groups were investigated by visual assessment of clustering on principal component analysis (PCA) and by analysis of similarity (ANOSIM) calculated on unweighted UniFrac distance metrics (Clark K. Non-parametric multivariate analyses of changes in community structure. Australian Journal of Ecology 1993; 18:117-43; Lozupone C, Knight R. UniFrac: a new phylogenetic method for comparing microbial communities. Applied and Environmental Microbiology 2005; 71:8228-35; Bray J R, Curtis J T. An ordination of upland forest communities of southern Wisconsin. Ecological Monographs 1957:325-49). ANOSIM is a non-parametric test of difference between 2 or more groups based on a distance metric. This test gives an R value between −1 and 1 where large positive R values indicate a large magnitude of dissimilarity between the groups and small R values indicate small magnitudes of dissimilarity; the P value provides statistical significance (Clark K. Non-parametric multivariate analyses of changes in community structure. Australian Journal of Ecology 1993; 18:117-43). When ANOSIM identified significant differences among groups, then pairwise ANOSIM was performed to determine which groups differed significantly and similarity percentage (SIMPER) was used to examine which features contributed to the differences among groups. ANOSIM, SIMPER, and PCA plots were performed with PAST v3.05 (Hammer O, Harper D, Ryan P D. PAST: Paleon Statictics Software Package for Education and Data Analysis. Palaeontologica Electronica 2001; 4:9).

The software Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) was used to predict the metagenome (Langille M G, Zaneveld J, Caporaso J G, McDonald D, Knights D, Reyes J A, Clemente J C, Burkepile D E, Vega Thurber R L, Knight R, et al. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nature Biotechnology 2013; 31:814-21). Sequencing data were prepared as described above, but sequences were then clustered into OTUs using a closed-reference OTU picking protocol at the 97% sequencing identity level [http://qiime.org/scripts/pick_closed_reference_otus.html]. The resulting OTU table was normalized by the expected copy number(s) of the 16s rRNA gene in each OTU. PICRUSt was then used to predict the metagenome [https://picrust.github.io/picrust/tutorials/metagenome_prediction.html#metagenome-prediction-tutorial]. Each sample was rarefied to an even sequencing depth to adjust for uneven sequencing depth prior to further analysis. Differences in the metagenomes among the groups were investigated by visual assessment of clustering on PCA and by analysis of similarity (ANOSIM) calculated on Bray Curtis dissimilarity metric.

Metabolite Extraction from Fecal Samples

Metabolites from the fecal contents were extracted using a solvent-based method as previously described (Sridharan G V, Choi K, Klemashevich C, Wu C, Prabakaran D, Pan L B, Steinmeyer S, Mueller C, Yousofshahi M, Alaniz R C, et al. Prediction and quantification of bioactive microbiota metabolites in the mouse gut. Nature Communications 2014; 5:5492). Briefly, fecal pellets were homogenized using a homogenizer (Omni International) with equal volume of cold methanol and half volume of chloroform. The samples were then centrifuged at 10,000 g at 4° C. (Thermo Fisher Scientific) for 10 min. Supernatant was passed through a 70-μm sterile nylon cell strainer (Falcon) and 0.6 ml of ice cold water was added. The samples were vortex and centrifuged again at 10,000 g for 5 minutes. The upper phase and lower phase were collected and 400 μl of upper phase was dried to a pellet using a vacufuge (Eppendorf, Hauppauge, N.Y.), and then reconstituted in 50 μl of methanol/water (1:1, v/v). The samples were stored at −80° C. until analysis. Tryptophan metabolites in the samples were detected and quantified on a triple quadrupole linear ion trap mass spectrometer (3200 QTRAP, AB SCIEX, Foster City, Calif.) coupled to a binary pump HPLC (Prominence LC-20, Shimazu, Concord, Ontario, Canada).

Flow Cytometry

Spleens and MLNs were processed individually to single-cell suspensions with frosted glass slides in RPMI-c+10% FCS, and spleen cells underwent red blood cell lysis (Charles N, Hardwick D, Daugas E, Illei G G, Rivera J. Basophils and the T helper 2 environment can promote the development of lupus nephritis. Nature Medicine 2010; 16:701-7). Cell suspensions were plated in individual wells, washed with 0.5% BSA in PBS, surface-stained for CD11b-AlexaFluor488 (eBioscience cat. #53-0112-82) and Gr-1-biotin (BD cat. #553125), followed by streptavidin-PE (eBioscience cat. #12-4317), fixed with 0.4% paraformaldehyde, and samples were acquired on a BD FACS Aria II in the College of Medicine Cell Analysis Facility (COM-CAF) at the Texas A & M Health Science Center.

Histology and small intestinal morphometric measurements

The stained sections of the small intestine were analyzed by a board-certified veterinary pathologist (BRW) blinded to treatment group. The slides were scored as previously described for intestinal inflammation (Jia Q, Lupton J R, Smith R, Weeks B R, Callaway E, Davidson L A, Kim W, Fan Y Y, Yang P, Newman R A, et al. Reduced colitis-associated colon cancer in Fat-1 (n-3 fatty acid desaturase) transgenic mice. Cancer Research 2008; 68:3985-91). Briefly, mucosal injury was determined by the following parameters scored from 0 (no evidence) to 3 (marked): mucosal ulceration, mucosal erosion, and presence of squamified epithelium. Inflammatory changes were scored similarly based on the following parameters: lymphocytic infiltration, plasma cell infiltration, and neutrophilic infiltration. Finally, an overall evidence of injury score was used to document total injury graded from 0 (none) to 4 (marked). Morphological parameters were obtained from digitally scanned slides using SPOT vr 5.0 software. Three sets of measurements from 3 separate sections were recorded for each animal by an observer blinded to treatment group. Measurements consisted of villus height, crypt depth, and submucosal mural thickness. The ratio of the villus height to crypt depth was calculated (FIGS. 6A and 6B)

Data Analysis

Results were expressed as mean±95% confidence interval unless indicated otherwise. For all analyses, significance was set P≤0.05. Data were analyzed using S-PLUS statistical software (Version 8.2, TIBCO Inc., Seattle, Wash.) unless otherwise noted. Histology scores, the proportion of neutrophils in the spleen and MLNs, ratios of villus height to crypt depth, submucosal thicknesses, paired differences between Day 7 and Day 0 in phyla and families, and fecal tryptophan metabolites were compared among treatment groups using a generalized linear model with post hoc testing for pairwise differences among groups using the method of Sidak (Šidák Z K. Rectangular confidence regions for the means of multivariate normal distributions. J Am Stat Assoc 1967; 62:626-33). To meet statistical assumptions underlying the generalized linear model, the histology scores were converted to ranks and the neutrophil data were log10 transformed prior to analysis. Ratios of villus height to crypt depth and submucosal thicknesses were compared among groups using a generalized linear model with post hoc testing for pairwise differences among groups using the method of Sidak. Fecal calprotectin concentrations were analyzed as a function of treatment group, time (Day 0 [baseline] and Day 7), and their interaction using linear mixed-effects modeling with treatment group and time modeled as fixed, categorical effects and individual mouse modeled as a random effect to account for repeated measures on individual mice. Paired differences between Day 0 and Day 7 in phyla and families were compared among groups using a generalized linear model and post hoc testing for pairwise differences among groups using the method of Sidak.

While illustrative embodiments have been illustrated and described, it will be appreciated that various changes can be made therein without departing from the spirit and scope of the invention.

Claims

1. A method of treating dysbiosis of commensal microbiota in a subject, comprising administering an effective amount of a tryptophan derived microbiota metabolite (TDMM), or a precursor, prodrug, or acceptable salt thereof.

2. The method of claim 1, wherein the subject has received or is expected to receive administration of an agent suspected to cause dysbiosis.

3. The method of claim 2, wherein the agent causes a reduction in a gram-positive component of the microbiota.

4. The method of claim 2, wherein the agent causes an increase in a gram-negative component of the microbiota.

5. The method of claim 2, wherein the agent is a non-steroid anti-inflammatory drug (NSAID).

6. The method of claim 1, wherein the TDMM is indole.

7. The method of claim 2, wherein the TDMM, or a precursor, prodrug, or acceptable salt thereof, is co-administered with the agent.

8. The method of claim 2, wherein the TDMM, or a precursor, prodrug, or acceptable salt thereof, is administered in a pharmaceutical composition that also comprises the agent.

9. The method of claim 1, wherein the TDMM is administered by intra-peritoneal (IP), intravenous (IV), topical, parenteral, intradermal, transdermal, oral (e.g., via liquid or pill), or respiratory (e.g., intranasal mist) routes.

10. The method of claim 1, wherein the subject is a mammal, such as a human or rodent.

11. The method of claim 1, wherein treating dysbiosis prevents or ameliorates enteropathy.

12. A method of treating enteropathy associated with NSAID in a subject, comprising administering an effective amount of a tryptophan derived microbiota metabolite (TDMM), or a precursor, prodrug, or acceptable salt thereof.

13. The method of claim 12, wherein the NSAID causes a reduction in a gram-positive component of the microbiota.

14. The method of claim 12, wherein the NSAID causes an increase in a gram-negative component of the microbiota.

15. The method of claim 12, wherein the NSAID is selected from aspirin, salsalate, celecoxib, diclofenac, etodolac, ibuprofen, indomethacin, ketoprofen, ketorolac, nabumetone, naproxen, oxaprozin, piroxicam, sulindac, meloxicam, tolmetin, and the like.

16. The method of claim 12, wherein the TDMM is indole.

17. The method of claim 16, wherein the effective amount of indole is at least about 5 mg/kg.

18. The method of claim 12, wherein the TDMM, or a precursor, prodrug, or acceptable salt thereof, is co-administered with the NSAID.

19. The method of claim 12, wherein the TDMM, or a precursor, prodrug, or acceptable salt thereof, is administered in a pharmaceutical composition that also comprises the NSAID.

20. The method of claim 12, wherein the TDMM is administered by intra-peritoneal (IP), intravenous (IV), topical, parenteral, intradermal, transdermal, oral (e.g., via liquid or pill), or respiratory (e.g., intranasal mist) routes.

21. The method of claim 12, wherein the subject is a mammal, such as a human or rodent.

22. A method of treating a condition characterized by inflammation in the GI tract, comprising administering an effective amount of a tryptophan derived microbiota metabolite (TDMM), or a precursor, prodrug, or acceptable salt thereof, to a subject in need thereof.

23. The method of claim 22, wherein the inflammation is associated with administration of an NSAID to the subject.

24. A pharmaceutical composition comprising:

at least one TDMM, or a precursor, prodrug, or acceptable salt thereof;
at least one NSAID composition, or a precursor, prodrug, or acceptable salt thereof; and
a pharmaceutically acceptable carrier.

25. The pharmaceutical composition of claim 24, wherein the TDMM is indole.

Patent History
Publication number: 20190083462
Type: Application
Filed: Mar 17, 2017
Publication Date: Mar 21, 2019
Applicants: THE TEXAS A&M UNIVERSITY SYSTEM (College Station, TX), TRUSTEES OF TUFTS COLLEGE (Medford, MA)
Inventors: Robert C. Alaniz (College Station, TX), Arul Jayaraman (College Station, TX), Kyongbum Lee (Winchester, MA), Canaan Whitfield (College Station, TX), Noah Cohen (College Station, TX)
Application Number: 16/085,896
Classifications
International Classification: A61K 31/405 (20060101); A61K 45/06 (20060101); A61P 1/00 (20060101);