Background

Clinicians have known for centuries that there is substantial variability between patients in their response to medications-some individuals exhibit a miraculous recovery while others fail to respond at all. Still others experience dangerous side effects. The hunt for the factors responsible for this variation has been aided by the ability to sequence the human genome, but this just provides part of the picture. Here, we discuss the emerging field of study focused on the human microbiome and how it may help to better predict drug response and improve the treatment of human disease.

Discussion

Various clinical disciplines characterize drug response using either continuous or categorical descriptors that are then correlated to environmental and genetic risk factors. However, these approaches typically ignore the microbiome, which can directly metabolize drugs into downstream metabolites with altered activity, clearance, and/or toxicity. Variations in the ability of each individual's microbiome to metabolize drugs may be an underappreciated source of differences in clinical response. Complementary studies in humans and animal models are necessary to elucidate the mechanisms responsible and to test the feasibility of identifying microbiome-based biomarkers of treatment outcomes. We propose that the predictive power of genetic testing could be improved by taking a more comprehensive view of human genetics that encompasses our human and microbial genomes. Furthermore, unlike the human genome, the microbiome is rapidly altered by diet, pharmaceuticals, and other interventions, providing the potential to improve patient care by re-shaping our associated microbial communities.

In the past three decades, extraordinary advances have been made in the understanding of the pathogenesis of, and treatment options for, inflammatory arthritides, including rheumatoid arthritis and spondyloarthritis. The use of methotrexate and subsequently biologic therapies (such as TNF inhibitors, among others) and oral small molecules have substantially improved clinical outcomes for many patients with inflammatory arthritis; for others, however, these agents do not substantially improve their symptoms. The emerging field of pharmacomicrobiomics, which investigates the effect of variations within the human gut microbiome on drugs, has already provided important insights into these therapeutics. Pharmacomicrobiomic studies have demonstrated that human gut microorganisms and their enzymatic products can affect the bioavailability, clinical efficacy and toxicity of a wide array of drugs through direct and indirect mechanisms. This discipline promises to facilitate the advent of microbiome-based precision medicine approaches in inflammatory arthritis, including strategies for predicting response to treatment and for modulating the microbiome to improve response to therapy or reduce drug toxicity.

Bacteria within the gut microbiota possess the ability to metabolize a wide array of human drugs, foods, and toxins, but the responsible enzymes for these chemical events remain largely uncharacterized due to the time-consuming nature of current experimental approaches. Attempts have been made in the past to computationally predict which bacterial species and enzymes are responsible for chemical transformations in the gut environment, but with low accuracy due to minimal chemical representation and sequence similarity search schemes. Here, we present an in silico approach that employs chemical and protein Similarity algorithms that Identify MicrobioMe Enzymatic Reactions (SIMMER). We show that SIMMER accurately predicts the responsible species and enzymes for a queried reaction, unlike previous methods. We demonstrate SIMMER use cases in the context of drug metabolism by predicting previously uncharacterized enzymes for 88 drug transformations known to occur in the human gut. We validate these predictions on external datasets and provide an in vitro validation of SIMMER's predictions for metabolism of methotrexate, an anti-arthritic drug. After demonstrating its utility and accuracy, we made SIMMER available as both a command-line and web tool, with flexible input and output options for determining chemical transformations within the human gut. We present SIMMER as a computational addition to the microbiome researcher's toolbox, enabling them to make informed hypotheses before embarking on the lengthy laboratory experiments required to characterize novel bacterial enzymes that can alter human ingested compounds.

Bacterial activation of T helper 17 (Th17) cells exacerbates mouse models of autoimmunity, but how human-associated bacteria impact Th17-driven disease remains elusive. We show that human gut Actinobacterium Eggerthella lenta induces intestinal Th17 activation by lifting inhibition of the Th17 transcription factor Rorγt through cell- and antigen-independent mechanisms. E. lenta is enriched in inflammatory bowel disease (IBD) patients and worsens colitis in a Rorc-dependent manner in mice. Th17 activation varies across E. lenta strains, which is attributable to the cardiac glycoside reductase 2 (Cgr2) enzyme. Cgr2 is sufficient to induce interleukin (IL)-17a, a major Th17 cytokine. cgr2+ E. lenta deplete putative steroidal glycosides in pure culture; related compounds are negatively associated with human IBD severity. Finally, leveraging the sensitivity of Cgr2 to dietary arginine, we prevented E. lenta-induced intestinal inflammation in mice. Together, these results support a role for human gut bacterial metabolism in driving Th17-dependent autoimmunity.

Alterations to the gut microbiome have been reported between children with autism spectrum disorders (ASDs) and typically developing (TD) children. Characterizing these differences has led to the proposal of new treatments for ASD, such as probiotic interventions and fecal matter transplants. However, no study to date has characterized the gut microbiome or metabolome in Pitt Hopkins syndrome (PTHS), a severe ASD with a high incidence of gastrointestinal (GI) disturbances such as constipation. Here, we surveyed the gut microbiome and metabolome in a cohort of PTHS individuals and their unaffected parents. We focused our analysis on Clostridium bolteae, a microbe previously associated with ASD known to chemically modify bile acids in the gut. PTHS individuals carry a higher load of C. bolteae than their parents as well as both ASD and non-ASD individuals from the American Gut Project cohort. Specific metabolites were associated with PTHS, including bile acids and sphingosines. With a metadata reanalysis tool, we found that PTHS-associated metabolites have previously been identified in inflammatory bowel disease and obesity patients. These results suggest microbial involvement in PTHS, but further research must be performed to clarify the exact mechanisms through which microbes may act. Furthermore, new associations between PTHS-specific metabolites and other conditions may lead to additional therapeutic options for PTHS individuals. IMPORTANCE GI disturbances in ASD such as severe constipation can be medically significant and often require medication. This is especially true for individuals with PTHS, suggesting that the gut microbiome may be involved in PTHS's pathology. Revealing associations between specific gut microbes and PTHS may allow the development of new therapeutics or the application of existing therapeutics to ease day-to-day challenges encountered by PTHS individuals. In this study, we characterized an association between C. bolteae and PTHS, in addition to metabolites linked to both PTHS and C. bolteae. We also identified other microbiome-involved medical conditions where PTHS-associated metabolites have been isolated. Utilizing common metabolites to identify conditions with similar phenotypes may suggest new therapeutic options for GI-related symptoms.

Objective

Previous studies have shown that differential DNA methylation is associated with SLE susceptibility. How DNA methylation affects the clinical heterogeneity of SLE has not been fully defined. We conducted this study to identify differentially methylated CpG sites associated with nephritis among women with SLE.

Methods

The methylation status of 428 229 CpG sites across the genome was characterised for peripheral blood cells from 322 women of European descent with SLE, 80 of whom had lupus nephritis, using the Illumina HumanMethylation450 BeadChip. Multivariable linear regression adjusting for population substructure and leucocyte cell proportions was used to identify differentially methylated sites associated with lupus nephritis. The influence of genetic variation on methylation status was investigated using data from a genome-wide association study of SLE. Pathway analyses were used to identify biological processes associated with lupus nephritis.

Results

We identified differential methylation of 19 sites in 18 genomic regions that was associated with nephritis among patients with SLE (false discovery rate q<0.05). Associations for four sites in HIF3A, IFI44 and PRR4 were replicated when examining methylation data derived from CD4+ T cells collected from an independent set of patients with SLE. These associations were not driven by genetic variation within or around the genomic regions. In addition, genes associated with lupus nephritis in a prior genome-wide association study were not differentially methylated in this epigenome-wide study. Pathway analysis indicated that biological processes involving type 1 interferon responses and the development of the immune system were associated with nephritis in patients with SLE.

Conclusions

Differential methylation of genes regulating the response to tissue hypoxia and interferon-mediated signalling is associated with lupus nephritis among women with SLE. These findings have not been identified in genetic studies of lupus nephritis, suggesting that epigenome-wide association studies can help identify the genomic differences that underlie the clinical heterogeneity of SLE.

Mechanistic insights into the role of the human microbiome in the predisposition to and treatment of disease are limited by the lack of methods to precisely add or remove microbial strains or genes from complex communities. Here, we demonstrate that engineered bacteriophage M13 can be used to deliver DNA to Escherichia coli within the mouse gastrointestinal (GI) tract. Delivery of a programmable exogenous CRISPR-Cas9 system enables the strain-specific depletion of fluorescently marked isogenic strains during competitive colonization and genomic deletions that encompass the target gene in mice colonized with a single strain. Multiple mechanisms allow E. coli to escape targeting, including loss of the CRISPR array or even the entire CRISPR-Cas9 system. These results provide a robust and experimentally tractable platform for microbiome editing, a foundation for the refinement of this approach to increase targeting efficiency, and a proof of concept for the extension to other phage-bacterial pairs of interest.

Immunomodulatory drugs can inhibit bacterial growth, yet their mechanism of action, spectrum, and clinical relevance remain unknown. Methotrexate (MTX), a first-line rheumatoid arthritis (RA) treatment, inhibits mammalian dihydrofolate reductase (DHFR), but whether it directly impacts gut bacteria is unclear. We show that MTX broadly alters the human gut microbiota. Drug sensitivity varied across strains, but the mechanism of action against DHFR appears conserved between mammalian and bacterial cells. RA patient microbiotas were sensitive to MTX, and changes in gut bacterial taxa and gene family abundance were distinct between responders and non-responders. Transplantation of post-treatment samples into germ-free mice given an inflammatory trigger led to reduced immune activation relative to pre-treatment controls, enabling identification of MTX-modulated bacterial taxa associated with intestinal and splenic immune cells. Thus, conservation in cellular pathways across domains of life can result in broad off-target drug effects on the human gut microbiota with consequences for immune function.

OBJECTIVE: Although oral methotrexate (MTX) remains the anchor drug for rheumatoid arthritis (RA), up to 50% of patients do not achieve a clinically adequate outcome. In addition, there is a lack of prognostic tools for treatment response prior to drug initiation. This study was undertaken to investigate whether interindividual differences in the human gut microbiome can aid in the prediction of MTX efficacy in new-onset RA. METHODS: We performed 16S ribosomal RNA gene and shotgun metagenomic sequencing on the baseline gut microbiomes of drug-naive patients with new-onset RA (n = 26). Results were validated in an additional independent cohort (n = 21). To gain insight into potential microbial mechanisms, we conducted ex vivo experiments coupled with metabolomics analysis to evaluate the association between microbiome-driven MTX depletion and clinical response. RESULTS: Our analysis revealed significant associations of the abundance of gut bacterial taxa and their genes with future clinical response (q < 0.05), including orthologs related to purine and MTX metabolism. Machine learning techniques were applied to the metagenomic data, resulting in a microbiome-based model that predicted lack of response to MTX in an independent group of patients. Finally, MTX levels remaining after ex vivo incubation with distal gut samples from pretreatment RA patients significantly correlated with the magnitude of future clinical response, suggesting a possible direct effect of the gut microbiome on MTX metabolism and treatment outcomes. CONCLUSION: Taken together, these findings are the first step toward predicting lack of response to oral MTX in patients with new-onset RA and support the value of the gut microbiome as a possible prognostic tool and as a potential target in RA therapeutics.