Specific enterotype of gut microbiota predicted clinical effect of methotrexate in patients with rheumatoid arthritis

The impact of the human gut microbiome on the treatment of autoimmune disease

Autoimmune (or rheumatic) diseases are increasing in prevalence but selecting the best therapy for each patient proceeds in trial-and-error fashion. This strategy can lead to ineffective therapy resulting in irreversible damage and suffering; thus, there is a need to bring the promise of precision medicine to patients with autoimmune disease. While host factors partially determine the therapeutic response to immunosuppressive drugs, these are not routinely used to tailor therapy. Thus, non-host factors likely contribute. Here, we consider the impact of the human gut microbiome in the treatment of autoimmunity. We propose that the gut microbiome can be manipulated to improve therapy and to derive greater benefit from existing therapies. We focus on the mechanisms by which the human gut microbiome impacts treatment response, provide a framework to interrogate these mechanisms, review a case study of a widely-used anti-rheumatic drug, and discuss challenges with studying multiple complex systems: the microbiome, the human immune system, and autoimmune disease. We consider open questions that remain in the field and speculate on the future of drug-microbiome-autoimmune disease interactions. Finally, we present a blue-sky vision for how the microbiome can be used to bring the promise of precision medicine to patients with rheumatic disease.

Modulation of mercaptopurine intestinal toxicity and pharmacokinetics by gut microbiota

The interaction between the gut microbiota and mercaptopurine (6-MP), a crucial drug used in pediatric acute lymphoblastic leukemia (ALL) treatment, has not been extensively studied. Here we reveal the significant perturbation of gut microbiota after 2-week 6-MP treatment in beagles and mice followed by the functional prediction that showed impairment of SCFAs production and altered amino acid synthesis. And the targeted metabolomics in plasma also showed changes in amino acids. Additionally, targeted metabolomics analysis of feces showed changes in amino acids and SCFAs. Furthermore, ablating the intestinal microbiota by broad-spectrum antibiotics exacerbated the imbalance of amino acids, particularly leading to a significant decrease in the concentration of S-adenosylmethionine (SAM). Importantly, the depletion of gut microbiota worsened the damage of small intestine caused by 6-MP, resulting in increased intestinal permeability. Considering the relationship between toxicity and 6-MP metabolites, we conducted a pharmacokinetic study in pseudo germ-free rats to confirm that gut microbiota depletion altered the methylation metabolites of 6-MP. Specifically, the concentration of MeTINs, a secondary methylation metabolite, showed a negative correlation with SAM, the pivotal methyl donor. Additionally, we observed a strong correlation between Alistipes and SAM levels in both feces and plasma. In conclusion, our study demonstrates that 6-MP disrupts the gut microbiota, and depleting the gut microbiota exacerbates 6-MP-induced intestinal toxicity. Moreover, SAM derived from microbiota plays a crucial role in influencing plasma SAM and the methylation of 6-MP. These findings underscore the importance of comprehending the role of the gut microbiota in 6-MP metabolism and toxicity.

Dupilumab therapy improves gut microbiome dysbiosis and tryptophan metabolism in Chinese patients with atopic dermatitis

BACKGROUND

Dupilumab has demonstrate its potential to orchestrate inflammatory skin microenvironment, enhance skin barrier and shift skin microbiome dysbiosis, collectively contributing to clinical improvement in patients with atopic dermatitis (AD). As the second genome of human body, growing evidence suggests that the gut microbiome might relate to the host response to treatments. Little is known about the association between dupilumab treatment and gut microbiome in AD patients.

OBJECTIVE

We aimed to characterize the gut microbiome among Chinese subjects with or without AD and determine the potential effect of dupilumab on the gut microbiome.

RESULTS

The 16 s rRNA gene sequencing was conducted on 48 healthy controls (HC), 44 AD patients and 27 AD patients who received dupilumab for 16 weeks. Prior to treatment, we identified the changed beta-diversity, increased Firmicutes/Bacteroidetes ratio, decreased Bifidobacterium and expanded Faecalibacterium among the AD patients compared to HC. After 16 weeks of dupilumab treatment, gut microbiome dysbiosis of the AD patients improved with reversed beta-diversity, closer bacterial connections, increased colonization of Bifidobacterium, Ruminococcus gnavus, and Coprococcus, which were negatively correlated with disease severity indicators. This shift was largely independent of the degree of clinical improvement. Bacterial function analysis revealed further metabolic alterations following dupilumab treatment, including up-regulated expression of genes involved in the indole pathway of tryptophan metabolism, corroborated by quantitative UHPLC-MS/MS analysis.

CONCLUSION

Dupilumab treatment tends to help shift the gut microbial dysbiosis in AD patients to a healthier state, along with improved intestinal tryptophan metabolism, suggesting the gut flora and its metabolites may mediate part of the synergistic therapeutic effects on the host.

Pathophysiology and stratification of treatment-resistant rheumatoid arthritis

Early diagnosis and timely therapeutic intervention are clinical challenges of rheumatoid arthritis (RA), especially for treatment-resistant or difficult-to-treat patients. Little is known about the immunological mechanisms involved in refractory RA. In this review, we summarize previous research findings on the immunological mechanisms of treatment-resistant RA. Genetic prediction of treatment-resistant RA is challenging. Patients with and without anti-cyclic citrullinated peptide autoantibodies are considered part of distinct subgroups, especially regarding long-term clinical prognosis and treatment responses. B cells, T cells and other immune cells and fibroblasts are of pathophysiological importance and are associated with treatment responses. Finally, we propose a new hypothesis that stratifies patients with RA into two subgroups with distinct immunological pathologies based on our recent immunomics analysis of RA. One RA subgroup with a favorable prognosis is characterized by increased interferon signaling. Another subgroup with a worse prognosis is characterized by enhanced acquired immune responses. Increases in dendritic cell precursors and diversified autoreactive anti-modified protein antibodies may have pathophysiological roles, especially in the latter subgroup. These findings that improve treatment response predictions might contribute to future precision medicine for RA.

From hype to hope: Considerations in conducting robust microbiome science

Microbiome science has been one of the most exciting and rapidly evolving research fields in the past two decades. Breakthroughs in technologies including DNA sequencing have meant that the trillions of microbes (particularly bacteria) inhabiting human biological niches (particularly the gut) can be profiled and analysed in exquisite detail. This microbiome profiling has profound impacts across many fields of research, especially biomedical science, with implications for how we understand and ultimately treat a wide range of human disorders. However, like many great scientific frontiers in human history, the pioneering nature of microbiome research comes with a multitude of challenges and potential pitfalls. These include the reproducibility and robustness of microbiome science, especially in its applications to human health outcomes. In this article, we address the enormous promise of microbiome science and its many challenges, proposing constructive solutions to enhance the reproducibility and robustness of research in this nascent field. The optimisation of microbiome science spans research design, implementation and analysis, and we discuss specific aspects such as the importance of ecological principals and functionality, challenges with microbiome-modulating therapies and the consideration of confounding, alternative options for microbiome sequencing, and the potential of machine learning and computational science to advance the field. The power of microbiome science promises to revolutionise our understanding of many diseases and provide new approaches to prevention, early diagnosis, and treatment.

Gut microbiota landscape and potential biomarker identification in female patients with systemic lupus erythematosus using machine learning

Objectives

Systemic Lupus Erythematosus (SLE) is a complex autoimmune disease that disproportionately affects women. Early diagnosis and prevention are crucial for women's health, and the gut microbiota has been found to be strongly associated with SLE. This study aimed to identify potential biomarkers for SLE by characterizing the gut microbiota landscape using feature selection and exploring the use of machine learning (ML) algorithms with significantly dysregulated microbiotas (SDMs) for early identification of SLE patients. Additionally, we used the SHapley Additive exPlanations (SHAP) interpretability framework to visualize the impact of SDMs on the risk of developing SLE in females.

Methods

Stool samples were collected from 54 SLE patients and 55 Negative Controls (NC) for microbiota analysis using 16S rRNA sequencing. Feature selection was performed using Elastic Net and Boruta on species-level taxonomy. Subsequently, four ML algorithms, namely logistic regression (LR), Adaptive Boosting (AdaBoost), Random Forest (RF), and eXtreme gradient boosting (XGBoost), were used to achieve early identification of SLE with SDMs. Finally, the best-performing algorithm was combined with SHAP to explore how SDMs affect the risk of developing SLE in females.

Results

Both alpha and beta diversity were found to be different in SLE group. Following feature selection, 68 and 21 microbiota were retained in Elastic Net and Boruta, respectively, with 16 microbiota overlapping between the two, i.e., SDMs for SLE. The four ML algorithms with SDMs could effectively identify SLE patients, with XGBoost performing the best, achieving Accuracy, Sensitivity, Specificity, Positive Predictive Value, Negative Predictive Value, and AUC values of 0.844, 0.750, 0.938, 0.923, 0.790, and 0.930, respectively. The SHAP interpretability framework showed a complex non-linear relationship between the relative abundance of SDMs and the risk of SLE, with Escherichia_fergusonii having the largest SHAP value.

Conclusions

This study revealed dysbiosis in the gut microbiota of female SLE patients. ML classifiers combined with SDMs can facilitate early identification of female patients with SLE, particularly XGBoost. The SHAP interpretability framework provides insight into the impact of SDMs on the risk of SLE and may inform future scientific treatment for SLE.

Advances in the implications of the gut microbiota on the treatment efficacy of disease-modifying anti-rheumatic drugs in rheumatoid arthritis

Alterations in the composition or function of the gut microbiota are associated with the etiology of human diseases. Drug-microbiota interactions can affect drug bioavailability, effectiveness, and toxicity through various routes. For instance, the direct effect of microbial enzymes on drugs can either boost or diminish their efficacy. Thus, considering its wide range of metabolic capabilities, the gut microbiota is a promising target for pharmacological modulation. Furthermore, drugs can alter the microbiota and the mechanisms by which they interact with their host. Individual variances in microbial profiles can also contribute to the different host responses to various drugs. However, the influence of interactions between the gut microbiota and drugs on treatment efficacy remains poorly elucidated. In this review, we will discuss the impact of microbiota dysbiosis in the pathogenesis of rheumatoid arthritis (RA), and we will attempt to elucidate the crosstalk between the gut microbiota and disease-modifying anti-rheumatic drugs (DMARDs), with an emphasis on how drug-microbiota interactions affect the treatment efficacy in RA. We speculate that improved knowledge of these critical interactions will facilitate the development of novel therapeutic options that use microbial markers for predicting or optimizing treatment outcomes.

Relationship between gut microbiota and rheumatoid arthritis: A bibliometric analysis

Introduction

Rheumatoid arthritis (RA) is a multifactorial autoimmune disease. Recently, growing evidence demonstrates that gut microbiota (GM) plays an important role in RA. But so far, no bibliometric studies pertaining to GM in RA have ever been published. This study attempts to depict the knowledge framework in this field from a holistic and systematic perspective based on the bibliometric analysis.

Methods

Literature related to the involvement of GM in RA was searched and picked from the Web of Science Core Collection (WOSCC) database. The annual output, cooperation, hotspots, research status and development trend of this field were analyzed by bibliometric software (VOSviewer and Bibliometricx).

Results

255 original research articles and 204 reviews were included in the analysis. The articles in this field that can be retrieved in WOSCC were first published in 2004 and increased year by year since then. 2013 is a growth explosion point. China and the United States are the countries with the most contributions, and Harvard University is the affiliation with the most output. Frontiers in Immunology (total citations = 603) is the journal with the most publications and the fastest growth rate. eLife is the journal with the most citations (total citations = 1248). Scher, Jose U. and Taneja, Veena are the most productive and cited authors. The research in this field is mainly distributed in the evidence, mechanism and practical application of GM participating in RA through the analysis of keywords and documents. There is sufficient evidence to prove the close relationship between GM and RA, which lays the foundation for this field. This extended two colorful and tender branches of mechanism research and application exploration, which have made some achievements but still have broad exploration space. Recently, the keywords "metabolites", "metabolomics", "acid", "b cells", "balance", "treg cells", "probiotic supplementation" appeared most frequently, which tells us that research on the mechanism of GM participating in RA and exploration of its application are the hotspots in recent years.

Discussion

Taken together, these results provide a data-based and objective introduction to the GM participating in RA, giving readers a valuable reference to help guide future research.