Predictive Metagenomic Analysis of Autoimmune Disease Identifies Robust Autoimmunity and Disease Specific Microbial Signatures

Bronchoalveolar Lavage Fluid Microbiota is Associated with the Diagnosis and Prognosis Evaluation of Lung Cancer

The gut microbiota and cancer have been demonstrated to be closely related. However, few studies have explored the bronchoalveolar lavage fluid (BALF) microbiota in patients with lung cancer (LC), specifically the microbiota related to progression-free survival (PFS) in LC. A total of 216 BALF samples were collected including 166 LC and 50 benign pulmonary disease (N-LC) samples, and further sequenced using 16S rRNA amplicon sequencing. Enrolled LC patients were followed up, the therapeutic efficacy was assessed, and PFS was calculated. The associated clinical and microbiota sequencing data were deeply analysed. Distinct differences in the microbial profiles were evident in the lower airways of patients with LC and N-LC, which was also found between non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). A combined random forest model was built to distinguish NSCLC from SCLC and reached area under curves (AUCs) of 0.919 (95% CI 86.69-97.1%) and 0.893 (95% CI 79.39-99.29%) in the training and test groups, respectively. The lower alpha diversity of the BALF microbiota in NSCLC patients was significantly associated with reduced PFS, although this link was not observed in SCLC. Specifically, NSCLC with a higher abundance of f_Lachnospiraceae, s_Prevotella nigrescens and f_[Mogibacteriaceae] achieved longer PFS. The enrichment of o_Streptophyta and g_Prevotella was observed in SCLC with worse PFS. This study provided a detailed description of the characteristics of BALF microbiota in patients with NSCLC and SCLC simultaneously and provided insights into the role of the diagnosis and prognosis evaluation.

Supplementary Information

The online version contains supplementary material available at 10.1007/s43657-023-00135-9.

Tripterygium hypoglaucum extract ameliorates adjuvant-induced arthritis in mice through the gut microbiota

Traditionally, Tripterygium hypoglaucum (Levl.) Hutch (THH) are widely used in Chinese folk to treat rheumatoid arthritis (RA). This study aimed to investigate whether the anti-RA effect of THH is related with the gut microbiota. The main components of prepared THH extract were identified by HPLC-MS. C57BL/6 mice with adjuvant-induced arthritis (AIA) were treated with THH extract by gavage for one month. THH extract significantly alleviated swollen ankle, joint cavity exudation, and articular cartilage destruction in AIA mice. The mRNA and protein levels of inflammatory mediators in muscles and plasma indicated that THH extract attenuated inflammatory responses in the joint by blocking TLR4/MyD88/MAPK signaling pathways. THH extract remarkably restored the dysbiosis of the gut microbiota in AIA mice, featuring the increases of Bifidobacterium, Akkermansia, and Lactobacillus and the decreases of Butyricimonas, Parabacteroides, and Anaeroplasma. Furthermore, the altered bacteria were closely correlated with physiological indices and drove metabolic changes of the intestinal microbiota. In addition, antibiotic-induced pseudo germ-free mice were employed to verify the role of the intestinal flora. Strikingly, THH treatment failed to ameliorate the arthritis symptoms and signaling pathways in pseudo germ-free mice, which validates the indispensable role of the intestinal flora. For the first time, we demonstrated that THH extract protects joint inflammation by manipulating the intestinal flora and regulating the TLR4/MyD88/MAPK signaling pathway. Therefore, THH extract may serve as a microbial modulator to recover RA in clincial practice.ver RA in clincial practice.

Decoding Applications of Artificial Intelligence in Rheumatology

Artificial intelligence (AI) is not a newcomer in medicine. It has been employed for image analysis, disease diagnosis, drug discovery, and improving overall patient care. ChatGPT (Chat Generative Pre-trained Transformer, Inc., Delaware) has renewed interest and enthusiasm in artificial intelligence. Algorithms, machine learning, deep learning, and data analysis are some of the complex terminologies often encountered when health professionals try to learn AI. In this article, we try to review the practical applications of artificial intelligence in vernacular language in the fields of medicine and rheumatology in particular. From the standpoint of the everyday physician, we have endeavored to encapsulate the influence of AI on the cutting edge of medical practice and the potential revolutionary shift in the realm of rheumatology.

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.

Establishment and analysis of a disease risk prediction model for the systemic lupus erythematosus with random forest

Systemic lupus erythematosus (SLE) is a latent, insidious autoimmune disease, and with the development of gene sequencing in recent years, our study aims to develop a gene-based predictive model to explore the identification of SLE at the genetic level. First, gene expression datasets of SLE whole blood samples were collected from the Gene Expression Omnibus (GEO) database. After the datasets were merged, they were divided into training and validation datasets in the ratio of 7:3, where the SLE samples and healthy samples of the training dataset were 334 and 71, respectively, and the SLE samples and healthy samples of the validation dataset were 143 and 30, respectively. The training dataset was used to build the disease risk prediction model, and the validation dataset was used to verify the model identification ability. We first analyzed differentially expressed genes (DEGs) and then used Lasso and random forest (RF) to screen out six key genes (OAS3, USP18, RTP4, SPATS2L, IFI27 and OAS1), which are essential to distinguish SLE from healthy samples. With six key genes incorporated and five iterations of 10-fold cross-validation performed into the RF model, we finally determined the RF model with optimal mtry. The mean values of area under the curve (AUC) and accuracy of the models were over 0.95. The validation dataset was then used to evaluate the AUC performance and our model had an AUC of 0.948. An external validation dataset (GSE99967) with an AUC of 0.810, an accuracy of 0.836, and a sensitivity of 0.921 was used to assess the model's performance. The external validation dataset (GSE185047) of all SLE patients yielded an SLE sensitivity of up to 0.954. The final high-throughput RF model had a mean value of AUC over 0.9, again showing good results. In conclusion, we identified key genetic biomarkers and successfully developed a novel disease risk prediction model for SLE that can be used as a new SLE disease risk prediction aid and contribute to the identification of SLE.

Mining the microbiota to identify gut commensals modulating neuroinflammation in a mouse model of multiple sclerosis

BACKGROUND

The gut microbiome plays an important role in autoimmunity including multiple sclerosis and its mouse model called experimental autoimmune encephalomyelitis (EAE). Prior studies have demonstrated that the multiple sclerosis gut microbiota can contribute to disease, hence making it a potential therapeutic target. In addition, antibiotic treatment has been shown to ameliorate disease in the EAE mouse model of multiple sclerosis. Yet, to this date, the mechanisms mediating these antibiotic effects are not understood. Furthermore, there is no consensus on the gut-derived bacterial strains that drive neuroinflammation in multiple sclerosis.

RESULTS

Here, we characterized the gut microbiome of untreated and vancomycin-treated EAE mice over time to identify bacteria with neuroimmunomodulatory potential. We observed alterations in the gut microbiota composition following EAE induction. We found that vancomycin treatment ameliorates EAE, and that this protective effect is mediated via the microbiota. Notably, we observed increased abundance of bacteria known to be strong inducers of regulatory T cells, including members of Clostridium clusters XIVa and XVIII in vancomycin-treated mice during the presymptomatic phase of EAE, as well as at disease peak. We identified 50 bacterial taxa that correlate with EAE severity. Interestingly, several of these taxa exist in the human gut, and some of them have been implicated in multiple sclerosis including Anaerotruncus colihominis, a butyrate producer, which had a positive correlation with disease severity. We found that Anaerotruncus colihominis ameliorates EAE, and this is associated with induction of RORγt⁺ regulatory T cells in the mesenteric lymph nodes.

CONCLUSIONS

We identified vancomycin as a potent modulator of the gut-brain axis by promoting the proliferation of bacterial species that induce regulatory T cells. In addition, our findings reveal 50 gut commensals as regulator of the gut-brain axis that can be used to further characterize pathogenic and beneficial host-microbiota interactions in multiple sclerosis patients. Our findings suggest that elevated Anaerotruncus colihominis in multiple sclerosis patients may represent a protective mechanism associated with recovery from the disease. Video Abstract.

Artificial Intelligence in Rheumatoid Arthritis: Current Status and Future Perspectives: A State-of-the-Art Review

Investigation of the potential applications of artificial intelligence (AI), including machine learning (ML) and deep learning (DL) techniques, is an exponentially growing field in medicine and healthcare. These methods can be critical in providing high-quality care to patients with chronic rheumatological diseases lacking an optimal treatment, like rheumatoid arthritis (RA), which is the second most prevalent autoimmune disease. Herein, following reviewing the basic concepts of AI, we summarize the advances in its applications in RA clinical practice and research. We provide directions for future investigations in this field after reviewing the current knowledge gaps and technical and ethical challenges in applying AI. Automated models have been largely used to improve RA diagnosis since the early 2000s, and they have used a wide variety of techniques, e.g., support vector machine, random forest, and artificial neural networks. AI algorithms can facilitate screening and identification of susceptible groups, diagnosis using omics, imaging, clinical, and sensor data, patient detection within electronic health record (EHR), i.e., phenotyping, treatment response assessment, monitoring disease course, determining prognosis, novel drug discovery, and enhancing basic science research. They can also aid in risk assessment for incidence of comorbidities, e.g., cardiovascular diseases, in patients with RA. However, the proposed models may vary significantly in their performance and reliability. Despite the promising results achieved by AI models in enhancing early diagnosis and management of patients with RA, they are not fully ready to be incorporated into clinical practice. Future investigations are required to ensure development of reliable and generalizable algorithms while they carefully look for any potential source of bias or misconduct. We showed that a growing body of evidence supports the potential role of AI in revolutionizing screening, diagnosis, and management of patients with RA. However, multiple obstacles hinder clinical applications of AI models. Incorporating the machine and/or deep learning algorithms into real-world settings would be a key step in the progress of AI in medicine.

A Systematic Review of Artificial Intelligence and Machine Learning Applications to Inflammatory Bowel Disease, with Practical Guidelines for Interpretation

BACKGROUND

Inflammatory bowel disease (IBD) is a gastrointestinal chronic disease with an unpredictable disease course. Computational methods such as machine learning (ML) have the potential to stratify IBD patients for the provision of individualized care. The use of ML methods for IBD was surveyed, with an additional focus on how the field has changed over time.

METHODS

On May 6, 2021, a systematic review was conducted through a search of MEDLINE and Embase databases, with the search structure ("machine learning" OR "artificial intelligence") AND ("Crohn* Disease" OR "Ulcerative Colitis" OR "Inflammatory Bowel Disease"). Exclusion criteria included studies not written in English, no human patient data, publication before 2001, studies that were not peer reviewed, nonautoimmune disease comorbidity research, and record types that were not primary research.

RESULTS

Seventy-eight (of 409) records met the inclusion criteria. Random forest methods were most prevalent, and there was an increase in neural networks, mainly applied to imaging data sets. The main applications of ML to clinical tasks were diagnosis (18 of 78), disease course (22 of 78), and disease severity (16 of 78). The median sample size was 263. Clinical and microbiome-related data sets were most popular. Five percent of studies used an external data set after training and testing for additional model validation.

DISCUSSION

Availability of longitudinal and deep phenotyping data could lead to better modeling. Machine learning pipelines that consider imbalanced data and that feature selection only on training data will generate more generalizable models. Machine learning models are increasingly being applied to more complex clinical tasks for specific phenotypes, indicating progress towards personalized medicine for IBD.

Fecal Microbiota Transplantation Ameliorates Active Ulcerative Colitis by Downregulating Pro-inflammatory Cytokines in Mucosa and Serum

Background

Ulcerative colitis (UC) is a multi-factor disease characterized by alternating remission periods and repeated occurrence. It has been shown that fecal microbiota transplantation (FMT) is an emerging and effective approach for UC treatment. Since most existing studies chose adults as donors for fecal microbiota, we conducted this study to determine the long-term efficacy and safety of the microbiota from young UC patient donors and illustrate its specific physiological effects.

Methods

Thirty active UC patients were enrolled and FMT were administered with the first colonoscopy and two subsequent enema/transendoscopic enteral tubing (TET) practical regimens in The First Affiliated Hospital of Anhui Medical University in China. Disease activity and inflammatory biomarkers were assessed 6 weeks/over 1 year after treatment. The occurrence of adverse events was also recorded. The samples from blood and mucosa were collected to detect the changes of inflammatory biomarkers and cytokines. The composition of gut and oral microbiota were also sampled and sequenced to confirm the alteration of microbial composition.

Results

Twenty-seven patients completed the treatment, among which 16 (59.3%) achieved efficacious clinical response and 11 (40.7%) clinical remission. Full Mayo score and calprotectin dropped significantly and remained stable over 1 year. FMT also significantly reduced the levels of C-reactive protein (CRP), interleukin-1 beta (IL-1β), and interleukin-6 (IL-6). The gut microbiota altered significantly with increased bacterial diversity and decreased metabolic diversity in responsive patients. The pro-inflammatory enterobacteria decreased after FMT and the abundance of Collinsella increased. Accordingly, the altered metabolic functions, including antigen synthesis, amino acids metabolism, short chain fatty acid production, and vitamin K synthesis of microbiota, were also corrected by FMT.

Conclusion

Fecal microbiota transplantation seems to be safe and effective for active UC patients who are nonresponsive to mesalazine or prednisone in the long-term. FMT could efficiently downregulate pro-inflammatory cytokines to ameliorate the inflammation.

Leveraging diet to engineer the gut microbiome

Autoimmune diseases, including inflammatory bowel disease, multiple sclerosis and rheumatoid arthritis, have distinct clinical presentations but share underlying patterns of gut microbiome perturbation and intestinal barrier dysfunction. Their potentially common microbial drivers advocate for treatment strategies aimed at restoring appropriate microbiome function, but individual variation in host factors makes a uniform approach unlikely. In this Perspective, we consolidate knowledge on diet-microbiome interactions in local inflammation, gut microbiota imbalance and host immune dysregulation. By understanding and incorporating the effects of individual dietary components on microbial metabolic output and host physiology, we examine the potential for diet-based therapies for autoimmune disease prevention and treatment. We also discuss tools targeting the gut microbiome, such as faecal microbiota transplantation, probiotics and orthogonal niche engineering, which could be optimized using custom dietary interventions. These approaches highlight paths towards leveraging diet for precise engineering of the gut microbiome at a time of increasing autoimmune disease.

Novel microbiota-related gene set enrichment analysis identified osteoporosis associated gut microbiota from autoimmune diseases

INTRODUCTION

Gut microbiota is now considered to be a hidden organ that interacts bidirectionally with cellular responses in numerous organs belonged to the immune, bone, and nervous systems. Here, we aimed to investigate the relationships between gut microbiota and complex diseases by utilizing multiple publicly available genome-wide association.

MATERIALS AND METHODS

We applied a novel microbiota-related gene set enrichment analysis approach to detect the associations between gut microbiota and complex diseases by processing genome-wide association studies (GWASs) data sets of six autoimmune diseases (including celiac disease (CeD), inflammatory bowel diseases (IBD), multiple sclerosis (MS), primary biliary cirrhosis (PBC), type 1 diabetes (T1D) and primary sclerosing cholangitis (PSC)) and osteoporosis (OP).

RESULTS

The family Oxalobacteraceae and genus Candidatus_Soleaferrea were found to be correlated with all of the six autoimmune diseases (FDR adjusted P < 0.05). Moreover, we observed that the six autoimmune diseases except PBC shared 3 overlapping features (including family Peptostreptococcaceae, order Gastranaerophilales and genus Romboutsia). For all of the six autoimmune diseases and BMDs (LS-BMD and TB-BMD), an association signal was observed for genus Candidatus_Soleaferrea (FDR adjusted P < 0.05). Notably, FA / FN-BMD shared the maximum number of overlapping microbial features (e.g., genus Ruminococcaceae_UCG009, Erysipelatoclostridium and Ruminococcaceae_UCG013).

CONCLUSION

Our study found that part of the gut microbiota could be novel regulators of BMDs and autoimmune diseases via the effects of its metabolites and may lead to a better understanding of the role played by gut microbiota in the communication of the microbiota-skeletal/immune-gut axis.

Changes in the Gut Bacteria Composition of Healthy Men with the Same Nutritional Profile Undergoing 10-Week Aerobic Exercise Training: A Randomized Controlled Trial

Nutrient consumption and body mass index (BMI) are closely related to the gut microbiota, and exercise effects on gut bacteria composition may be related to those variables. Thus, we aimed to investigate the effect of 10-week moderate aerobic exercise on the cardiorespiratory fitness and gut bacteria composition of non-obese men with the same nutritional profile. Twenty-four previously sedentary men (age 25.18 [SD 4.66] years, BMI 24.5 [SD 3.72] kg/m²) were randomly assigned into Control (CG; n = 12) or Exercise Groups (EG; n = 12). Body composition, cardiorespiratory parameters, blood markers, dietary habits and gut bacteria composition were evaluated. EG performed 150 min per week of supervised moderate (60–65% of VO₂peak) aerobic exercise, while CG maintained their daily routine. The V4 16S rRNA gene was sequenced and treated using QIIME software. Only EG demonstrated marked improvements in cardiorespiratory fitness (VO₂peak, p < 0.05; Effect Size = 0.971) without changes in other gut bacteria-affecting variables. Exercise did not promote clustering based on diversity indices (p > 0.05), although significant variations in an unclassified genus from Clostridiales order and in Streptococcus genus were observed (p < 0.05). Moreover, α-diversity was correlated with VO₂peak (Pearson’s R: 0.47; R² 0.23: 95%CI: 0.09 to 0.74, p = 0.02) and BMI (Pearson’s R: −0.50; R² 0.25: 95%CI: −0.75 to −0.12, p = 0.01). Roseburia, Sutterella and Odoribacter genera were associated with VO₂peak, while Desulfovibrio and Faecalibacterium genera were associated with body composition (p < 0.05). Our study indicates that aerobic exercise at moderate intensity improved VO₂peak and affected gut bacteria composition of non-obese men who maintained a balanced consumption of nutrients.

Utilizing machine learning with knockoff filtering to extract significant metabolites in Crohn's disease with a publicly available untargeted metabolomics dataset

Metabolomic data processing pipelines have been improving in recent years, allowing for greater feature extraction and identification. Lately, machine learning and robust statistical techniques to control false discoveries are being incorporated into metabolomic data analysis. In this paper, we introduce one such recently developed technique called aggregate knockoff filtering to untargeted metabolomic analysis. When applied to a publicly available dataset, aggregate knockoff filtering combined with typical p-value filtering improves the number of significantly changing metabolites by 25% when compared to conventional untargeted metabolomic data processing. By using this method, features that would normally not be extracted under standard processing would be brought to researchers’ attention for further analysis.

Concentrated Raw Fibers Enhance the Fiber-Degrading Capacity of a Synthetic Human Gut Microbiome

The consumption of prebiotic fibers to modulate the human gut microbiome is a promising strategy to positively impact health. Nevertheless, given the compositional complexity of the microbiome and its inter-individual variances, generalized recommendations on the source or amount of fiber supplements remain vague. This problem is further compounded by availability of tractable in vitro and in vivo models to validate certain fibers. We employed a gnotobiotic mouse model containing a 14-member synthetic human gut microbiome (SM) in vivo, characterized a priori for their ability to metabolize a collection of fibers in vitro. This SM contains 14 different strains belonging to five distinct phyla. Since soluble purified fibers have been a common subject of studies, we specifically investigated the effects of dietary concentrated raw fibers (CRFs)—containing fibers from pea, oat, psyllium, wheat and apple—on the compositional and functional alterations in the SM. We demonstrate that, compared to a fiber-free diet, CRF supplementation increased the abundance of fiber-degraders, namely Eubacterium rectale, Roseburia intestinalis and Bacteroides ovatus and decreased the abundance of the mucin-degrader Akkermansia muciniphila. These results were corroborated by a general increase of bacterial fiber-degrading α-glucosidase enzyme activity. Overall, our results highlight the ability of CRFs to enhance the microbial fiber-degrading capacity.

Gut Microbiome in Progressive Multiple Sclerosis

OBJECTIVE

This study was undertaken to investigate the gut microbiome in progressive multiple sclerosis (MS) and how it relates to clinical disease.

METHODS

We sequenced the microbiota from healthy controls and relapsing-remitting MS (RRMS) and progressive MS patients and correlated the levels of bacteria with clinical features of disease, including Expanded Disability Status Scale (EDSS), quality of life, and brain magnetic resonance imaging lesions/atrophy. We colonized mice with MS-derived Akkermansia and induced experimental autoimmune encephalomyelitis (EAE).

RESULTS

Microbiota β-diversity differed between MS patients and controls but did not differ between RRMS and progressive MS or differ based on disease-modifying therapies. Disease status had the greatest effect on the microbiome β-diversity, followed by body mass index, race, and sex. In both progressive MS and RRMS, we found increased Clostridium bolteae, Ruthenibacterium lactatiformans, and Akkermansia and decreased Blautia wexlerae, Dorea formicigenerans, and Erysipelotrichaceae CCMM. Unique to progressive MS, we found elevated Enterobacteriaceae and Clostridium g24 FCEY and decreased Blautia and Agathobaculum. Several Clostridium species were associated with higher EDSS and fatigue scores. Contrary to the view that elevated Akkermansia in MS has a detrimental role, we found that Akkermansia was linked to lower disability, suggesting a beneficial role. Consistent with this, we found that Akkermansia isolated from MS patients ameliorated EAE, which was linked to a reduction in RORγt+ and IL-17-producing γδ T cells.

INTERPRETATION

Whereas some microbiota alterations are shared in relapsing and progressive MS, we identified unique bacteria associated with progressive MS and clinical measures of disease. Furthermore, elevated Akkermansia in MS may be a compensatory beneficial response in the MS microbiome. ANN NEUROL 2021;89:1195-1211.