Alteration in the gut microbiome is associated with changes in bone metabolism after laparoscopic sleeve gastrectomy.

Laparoscopic sleeve gastrectomy (LSG), the most common bariatric surgical procedure, leads to durable weight loss and improves obesity-related comorbidities. However, it induces abnormalities in bone metabolism. One unexplored potential contributor is the gut microbiome, which influences bone metabolism and is altered after surgery. We characterized the relationship between the gut microbiome and skeletal health in severe obesity and after LSG. In a prospective cohort study, 23 adults with severe obesity underwent skeletal health assessment and stool collection preoperatively and 6 mo after LSG. Gut microbial diversity and composition were characterized using 16S rRNA gene sequencing, and fecal concentrations of short-chain fatty acids (SCFA) were measured with LC-MS/MS. Spearman's correlations and PERMANOVA analyses were applied to assess relationships between the gut microbiome and bone health measures including serum bone turnover markers (C-terminal telopeptide of type 1 collagen [CTx] and procollagen type 1 N-terminal propeptide [P1NP]), areal BMD, intestinal calcium absorption, and calciotropic hormones. Six months after LSG, CTx and P1NP increased (by median 188% and 61%, P < .01) and femoral neck BMD decreased (mean -3.3%, P < .01). Concurrently, there was a decrease in relative abundance of the phylum Firmicutes. Although there were no change in overall microbial diversity or fecal SCFA concentrations after LSG, those with greater within-subject change in gut community microbial composition (ß-diversity) postoperatively had greater increases in P1NP level (ρ = 0.48, P = .02) and greater bone loss at the femoral neck (ρ = -0.43, P = .04). In addition, within-participant shifts in microbial richness/evenness (α-diversity) were associated with changes in IGF-1 levels (ρ = 0.56, P < .01). The lower the postoperative fecal butyrate concentration, the lower the IGF-1 level (ρ = 0.43, P = .04). Meanwhile, the larger the decrease in butyrate concentration, the higher the postoperative CTx (ρ = -0.43, P = .04). These findings suggest that LSG-induced gut microbiome alteration may influence skeletal outcomes postoperatively, and microbial influences on butyrate formation and IGF-1 are possible mechanisms.

Laparoscopic sleeve gastrectomy (LSG), the most common bariatric surgical procedure, is a highly effective treatment for obesity because it produces dramatic weight loss and improves obesity-related medical conditions. However, it also results in abnormalities in bone metabolism. It is important to understand how LSG affects the skeleton, so that bone loss after surgery might be prevented. We studied adult men and women before and 6 mo after LSG, and we explored the relationship between the altered gut bacteria and bone metabolism changes. We found that: Those with greater shifts in their gut bacterial composition had more bone loss.Butyrate, a metabolite produced by gut bacteria from fermentation of dietary fiber, was associated with less bone breakdown and higher IGF-1 level (a bone-building hormone). We conclude that changes in the gut bacteria may contribute to the negative skeletal impact of LSG and reduced butyrate production by the gut bacteria leading to lower IGF-1 levels is a possible mechanism.

Simvastatin induces human gut bacterial cell surface genes.

Drugs intended to target mammalian cells can have broad off-target effects on the human gut microbiota with potential downstream consequences for drug efficacy and side effect profiles. Yet, despite a rich literature on antibiotic resistance, we still know very little about the mechanisms through which commensal bacteria evade non-antibiotic drugs. Here, we focus on statins, one of the most prescribed drug types in the world and an essential tool in the prevention and treatment of high circulating cholesterol levels. Prior work in humans, mice, and cell culture support an off-target effect of statins on human gut bacteria; however, the genetic determinants of statin sensitivity remain unknown. We confirmed that simvastatin inhibits the growth of diverse human gut bacterial strains grown in communities and in pure cultures. Drug sensitivity varied between phyla and was dose-dependent. We selected two representative simvastatin-sensitive species for more in-depth analysis: Eggerthella lenta (phylum: Actinobacteriota) and Bacteroides thetaiotaomicron (phylum: Bacteroidota). Transcriptomics revealed that both bacterial species upregulate genes in response to simvastatin that alter the cell membrane, including fatty acid biogenesis (E. lenta) and drug efflux systems (B. thetaiotaomicron). Transposon mutagenesis identified a key efflux system in B. thetaiotaomicron that enables growth in the presence of statins. Taken together, these results emphasize the importance of the bacterial cell membrane in countering the off-target effects of host-targeted drugs. Continued mechanistic dissection of the various mechanisms through which the human gut microbiota evades drugs will be essential to understand and predict the effects of drug administration in human cohorts and the potential downstream consequences for health and disease.

SIMMER employs similarity algorithms to accurately identify human gut microbiome species and enzymes capable of known chemical transformations.

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.

Human gut bacterial metabolism drives Th17 activation and colitis.

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.

Phage-delivered CRISPR-Cas9 for strain-specific depletion and genomic deletions in the gut microbiome.

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.

Western Diet and Psoriatic-Like Skin and Joint Diseases: A Potential Role for the Gut Microbiota.

Shi et al. (2021) in "Short-term Western diet intake promotes IL-23-mediated skin and joint inflammation accompanied by changes to the gut microbiota in mice" show that Western diet (WD) exacerbates an IL-23 minicircle‒mediated model of psoriasis and psoriatic arthritis, with an expansion of IL-17A‒expressing γδ T cells and shifts to the gut microbial community. WD-associated inflammation is mitigated by diet manipulation or antibiotic administration. These results suggest that dietary manipulation may be useful in the treatment of IL-23‒mediated disease, possibly through the modulation of the gut microbiota.

Methotrexate impacts conserved pathways in diverse human gut bacteria leading to decreased host immune activation.

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.

Pharmacomicrobiomics in inflammatory arthritis: gut microbiome as modulator of therapeutic response.

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.

Mirror, mirror on the wall: which microbiomes will help heal them all?

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.

Genome-wide profiling identifies associations between lupus nephritis and differential methylation of genes regulating tissue hypoxia and type 1 interferon responses.

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.

Stress-induced changes in gene interactions in human cells.

Cells respond to variable environments by changing gene expression and gene interactions. To study how human cells response to stress, we analyzed the expression of >5000 genes in cultured B cells from nearly 100 normal individuals following endoplasmic reticulum stress and exposure to ionizing radiation. We identified thousands of genes that are induced or repressed. Then, we constructed coexpression networks and inferred interactions among genes. We used coexpression and machine learning analyses to study how genes interact with each other in response to stress. The results showed that for most genes, their interactions with each other are the same at baseline and in response to different stresses; however, a small set of genes acquired new interacting partners to engage in stress-specific responses. These genes with altered interacting partners are associated with diseases in which endoplasmic reticulum stress response or sensitivity to radiation has been implicated. Thus, our findings showed that to understand disease-specific pathways, it is important to identify not only genes that change expression levels but also those that alter interactions with other genes.

Polymorphic cis- and trans-regulation of human gene expression.

Expression levels of human genes vary extensively among individuals. This variation facilitates analyses of expression levels as quantitative phenotypes in genetic studies where the entire genome can be scanned for regulators without prior knowledge of the regulatory mechanisms, thus enabling the identification of unknown regulatory relationships. Here, we carried out such genetic analyses with a large sample size and identified cis- and trans-acting polymorphic regulators for about 1,000 human genes. We validated the cis-acting regulators by demonstrating differential allelic expression with sequencing of transcriptomes (RNA-Seq) and the trans-regulators by gene knockdown, metabolic assays, and chromosome conformation capture analysis. The majority of the regulators act in trans to the target (regulated) genes. Most of these trans-regulators were not known to play a role in gene expression regulation. The identification of these regulators enabled the characterization of polymorphic regulation of human gene expression at a resolution that was unattainable in the past.

Gene expression and genetic variation in response to endoplasmic reticulum stress in human cells.

The accumulation of unfolded or misfolded proteins in the endoplasmic reticulum (ER) results in the condition called "ER stress," which induces the unfolded protein response (UPR), a complex cellular process that includes changes in expression of many genes. Failure to restore homeostasis in the ER is associated with human diseases. To identify the underlying changes in gene expression in response to ER stress, we induced ER stress in human B cells and then measured gene expression at ten time points. We followed up those results by studying cells from 60 unrelated people. We rediscovered genes that were known to play a role in the ER-stress response and uncovered several thousand genes that are not known to be involved. Two of these are VLDLR and INHBE, which showed significant increase in expression after ER stress in B cells and in primary fibroblasts. To study the links between UPR and disease susceptibility, we identified ER-stress-responsive genes that are associated with human diseases and assessed individual differences in the ER-stress response. Many of the UPR genes are associated with Mendelian disorders, such as Wolfram syndrome, and complex diseases, including amyotrophic lateral sclerosis and diabetes. Data from two independent samples showed extensive individual variability in ER-stress response. Additional analyses with monozygotic twins revealed significant correlations within twin pairs in their responses to ER stress, thus showing evidence for heritable variation among individuals. These results have implications for basic understanding of ER function and its role in disease susceptibility.

Coexpression network based on natural variation in human gene expression reveals gene interactions and functions.

Genes interact in networks to orchestrate cellular processes. Analysis of these networks provides insights into gene interactions and functions. Here, we took advantage of normal variation in human gene expression to infer gene networks, which we constructed using correlations in expression levels of more than 8.5 million gene pairs in immortalized B cells from three independent samples. The resulting networks allowed us to identify biological processes and gene functions. Among the biological pathways, we found processes such as translation and glycolysis that co-occur in the same subnetworks. We predicted the functions of poorly characterized genes, including CHCHD2 and TMEM111, and provided experimental evidence that TMEM111 is part of the endoplasmic reticulum-associated secretory pathway. We also found that IFIH1, a susceptibility gene of type 1 diabetes, interacts with YES1, which plays a role in glucose transport. Furthermore, genes that predispose to the same diseases are clustered nonrandomly in the coexpression network, suggesting that networks can provide candidate genes that influence disease susceptibility. Therefore, our analysis of gene coexpression networks offers information on the role of human genes in normal and disease processes.