Genetic manipulation of the human gut bacterium Eggerthella lenta reveals a widespread family of transcriptional regulators

Metatranscriptomics-guided discovery and characterization of a polyphenol-metabolizing gut microbial enzyme

Gut microbial catechol dehydroxylases are a largely uncharacterized family of metalloenzymes that potentially impact human health by metabolizing dietary polyphenols. Here, we use metatranscriptomics (MTX) to identify highly transcribed catechol-dehydroxylase-encoding genes in human gut microbiomes. We discover a prevalent, previously uncharacterized catechol dehydroxylase (Gp Hcdh) from Gordonibacter pamelaeae that dehydroxylates hydrocaffeic acid (HCA), an anti-inflammatory gut microbial metabolite derived from plant-based foods. Further analyses suggest that the activity of Gp Hcdh may reduce anti-inflammatory benefits of polyphenol-rich foods. Together, these results show the utility of combining MTX analysis and biochemical characterization for gut microbial enzyme discovery and reveal a potential link between host inflammation and a specific polyphenol-metabolizing gut microbial enzyme.

Iron-sulfur cluster-dependent enzymes and molybdenum-dependent reductases in the anaerobic metabolism of human gut microbes

Metalloenzymes play central roles in the anaerobic metabolism of human gut microbes. They facilitate redox and radical-based chemistry that enables microbial degradation and modification of various endogenous, dietary, and xenobiotic nutrients in the anoxic gut environment. In this review, we highlight major families of iron-sulfur (Fe-S) cluster-dependent enzymes and molybdenum cofactor-containing enzymes used by human gut microbes. We describe the metabolic functions of 2-hydroxyacyl-CoA dehydratases, glycyl radical enzyme activating enzymes, Fe-S cluster-dependent flavoenzymes, U32 oxidases, and molybdenum-dependent reductases and catechol dehydroxylases in the human gut microbiota. We demonstrate the widespread distribution and prevalence of these metalloenzyme families across 5000 human gut microbial genomes. Lastly, we discuss opportunities for metalloenzyme discovery in the human gut microbiota to reveal new chemistry and biology in this important community.

Microbial transformation of dietary xenobiotics shapes gut microbiome composition

Diet is a major determinant of gut microbiome composition, and variation in diet-microbiome interactions may contribute to variation in their health consequences. To mechanistically understand these relationships, here we map interactions between ∼150 small-molecule dietary xenobiotics and the gut microbiome, including the impacts of these compounds on community composition, the metabolic activities of human gut microbes on dietary xenobiotics, and interindividual variation in these traits. Microbial metabolism can toxify and detoxify these compounds, producing emergent interactions that explain community-specific remodeling by dietary xenobiotics. We identify the gene and enzyme responsible for detoxification of one such dietary xenobiotic, resveratrol, and demonstrate that this enzyme contributes to interindividual variation in community remodeling by resveratrol. Together, these results systematically map interactions between dietary xenobiotics and the gut microbiome and connect toxification and detoxification to interpersonal differences in microbiome response to diet.

Identification of age-associated microbial changes via long-read 16S sequencing

BACKGROUND

Age-related gut microbial changes have been widely investigated over the past decade. Most of the previous age-related microbiome studies were conducted on the Western population, and the short-read sequencing (e.g., 16S V4 or V3-V4 region) was the most common microbiota profiling method. We evaluated the gut compositional differences using the long-read sequencing approach (i.e., PacBio sequencing targeting the full-length V1-V9 regions) to enable a deeper taxonomic resolution and better characterize the gut microbiome of Singaporeans from different age groups.

RESULTS

A total of 83 research participants were included in this study. Although no significant differences were detected in alpha and beta diversity, our study demonstrated several bacterial taxa with abundances that were significantly different across age groups. With young individuals as the reference group, Eggerthella lenta and Bacteroides uniformis were found to be significantly altered in the middle-aged group, while Catenibacterium mitsuokai and Bacteroides plebeius were significantly altered in the elderly group. These age-related differences in the gut microbiome were associated with aberrations in several predicted functional pathways, including dysregulations of pathways related to lipopolysaccharide and tricarboxylic acid cycle in older adults.

CONCLUSIONS

The utilization of long-read sequencing facilitated the identification of species- and strain-level differences across age groups, which was challenging with the partial 16S rRNA sequencing approach. Nevertheless, replication studies are warranted to confirm our findings, and if confirmed, further in vitro and in vivo studies are crucial to better understand the impact of the altered levels of age-related bacterial taxa. Additionally, the modest performance of strain-level taxonomic classification using 16S-ITS-23S gene sequences, likely due to the limited depth of currently available alignment databases, highlights the need for optimization and refinement in curating these databases for the long-read sequencing approach.

Ligify: Automated Genome Mining for Ligand-Inducible Transcription Factors

Prokaryotic transcription factors can be repurposed into biosensors for the ligand-inducible control of gene expression, but the landscape of chemical ligands for which biosensors exist is extremely limited. To expand this landscape, we developed Ligify, a web application that leverages information in enzyme reaction databases to predict transcription factors that may be responsive to user-defined chemicals. Candidate transcription factors are then incorporated into automatically generated plasmid sequences that are designed to express GFP in response to the target chemical. Our benchmarking analyses demonstrated that Ligify correctly predicted 31/100 previously validated biosensors and highlighted strategies for further improvement. We then used Ligify to build a panel of genetic circuits that could induce a 47-fold, 5-fold, 9-fold, and 27-fold change in fluorescence in response to D-ribose, L-sorbose, isoeugenol, and 4-vinylphenol, respectively. Ligify should enhance the ability of researchers to quickly develop biosensors for an expanded range of chemicals and is publicly available at https://ligify.groov.bio.

The gut-airway microbiome axis in health and respiratory diseases

Communication between the gut and remote organs, such as the brain or the cardiovascular system, has been well established and recent studies provide evidence for a potential bidirectional gut-airway axis. Observations from animal and human studies indicate that respiratory insults influence the activity of the gut microbiome and that microbial ligands and metabolic products generated by the gut microbiome shape respiratory immunity. Information exchange between these two large mucosal surface areas regulates microorganism-immune interactions, with significant implications for the clinical and treatment outcomes of a range of respiratory conditions, including asthma, chronic obstructive pulmonary disease and lung cancer. In this Review, we summarize the most recent data in this field, offering insights into mechanisms of gut-airway crosstalk across spatial and temporal gradients and their relevance for respiratory health.

Gut microbiota as a sensor of autoimmune response and treatment for rheumatoid arthritis

Rheumatoid arthritis (RA) is considered a multifactorial condition where interaction between the genetic and environmental factors lead to immune dysregulation causing autoreactivity. While among the various genetic factors, HLA-DR4 and DQ8, have been reported to be the strongest risk factors, the role of various environmental factors has been unclear. Though events initiating autoreactivity remain unknown, a mucosal origin of RA has gained attention based on the recent observations with the gut dysbiosis in patients. However, causality of gut dysbiosis has been difficult to prove in humans. Mouse models, especially mice expressing RA-susceptible and -resistant HLA class II genes have helped unravel the complex interactions between genetic factors and gut microbiome. This review describes the interactions between HLA genes and gut dysbiosis in sex-biased preclinical autoreactivity and discusses the potential use of endogenous commensals as indicators of treatment efficacy as well as therapeutic tool to suppress pro-inflammatory response in rheumatoid arthritis.

Identification of volatile metabolites produced from levodopa metabolism by different bacteria strains of the gut microbiome

Interspecies pathways in the gut microbiome have been shown to metabolize levodopa, the primary treatment for Parkinson's disease, and reduce its bioavailability. While the enzymatic reactions have been identified, the ability to establish the resulting macromolecules as biomarkers of microbial metabolism remains technically challenging. In this study, we leveraged an untargeted mass spectrometry-based approach to investigate volatile organic compounds (VOCs) produced during levodopa metabolism by Enterococcus faecalis, Clostridium sporogenes, and Eggerthella lenta. We cultured these organisms with and without their respective bioactive metabolites and detected levodopa-induced shifts in VOC profiles. We then utilized bioinformatics to identify significant differences in 2,6-dimethylpyrazine, 4,6-dimethylpyrimidine, and 4,5-dimethylpyrimidine associated with its biotransformation. Supplementing cultures with inhibitors of levodopa-metabolizing enzymes revealed specific modulation of levodopa-associated diazines, verifying their relationship to its metabolism. Furthermore, functional group analysis depicts strain-specific VOC profiles that reflect interspecies differences in metabolic activity that can be leveraged to assess microbiome functionality in individual patients. Collectively, this work identifies previously uncharacterized metabolites of microbe-mediated levodopa metabolism to determine potential indicators of this activity and further elucidate the metabolic capabilities of different gut bacteria.

A Bioinformatic Analysis of Gut Microbiota Related with Immune Cell Infiltration in Colorectal Cancer

OBJECTIVE

The composition of microbiota which correlates with infiltrating immune cells and clinical signatures is not clarified in CRC.

METHODS

We applied 4 kinds of bioinformatic tools GSVA (version: 1.42.0), ESTIMATE (version: 1.0.13), CIBERSORT (version: 2.0), and immune-related genes.

RESULTS

We found that a total of 8 types of microbiotas appeared in the three immune correlation analyses. Among these microbiotas, significant enrichments in relative abundances associated with immune cell infiltration can be found for the dominant phyla Proteobacteria, Firmicutes, and Actinobacteria. Moreover, there existed correlations between some of the 8 microbiotas and clinical-related indicators.

CONCLUSION

We identified some novel microbiotas involved in immune regulation in CRC.

Human gut Actinobacteria boost drug absorption by secreting P-glycoprotein ATPase inhibitors

Drug efflux transporters are a major determinant of drug efficacy and toxicity. A canonical example is P-glycoprotein (P-gp), an efflux transporter that controls the intestinal absorption of diverse compounds. Despite a rich literature on the dietary and pharmaceutical compounds that impact P-gp activity, its sensitivity to gut microbial metabolites remains an open question. Surprisingly, we found that the cardiac drug-metabolizing gut Actinobacterium Eggerthella lenta increases drug absorption in mice. Experiments in cell culture revealed that E. lenta produces a soluble factor that post-translationally inhibits P-gp ATPase efflux activity. P-gp inhibition is conserved in the Eggerthellaceae family but absent in other Actinobacteria. Comparative genomics identified genes associated with P-gp inhibition. Finally, activity-guided biochemical fractionation coupled to metabolomics implicated a group of small polar metabolites with P-gp inhibitory activity. These results highlight the importance of considering the broader relevance of the gut microbiome for drug disposition beyond first-pass metabolism.

Gut bacteria convert glucocorticoids into progestins in the presence of hydrogen gas

Recent studies suggest that human-associated bacteria interact with host-produced steroids, but the mechanisms and physiological impact of such interactions remain unclear. Here, we show that the human gut bacteria Gordonibacter pamelaeae and Eggerthella lenta convert abundant biliary corticoids into progestins through 21-dehydroxylation, thereby transforming a class of immuno- and metabo-regulatory steroids into a class of sex hormones and neurosteroids. Using comparative genomics, homologous expression, and heterologous expression, we identify a bacterial gene cluster that performs 21-dehydroxylation. We also uncover an unexpected role for hydrogen gas production by gut commensals in promoting 21-dehydroxylation, suggesting that hydrogen modulates secondary metabolism in the gut. Levels of certain bacterial progestins, including allopregnanolone, better known as brexanolone, an FDA-approved drug for postpartum depression, are substantially increased in feces from pregnant humans. Thus, bacterial conversion of corticoids into progestins may affect host physiology, particularly in the context of pregnancy and women's health.

Mapping multi-omics characteristics related to short-term PM2.5 trajectory and their impact on type 2 diabetes in middle-aged and elderly adults in Southern China

The relationship between PM2.5 and metabolic diseases, including type 2 diabetes (T2D), has become increasingly prominent, but the molecular mechanism needs to be further clarified. To help understand the mechanistic association between PM2.5 exposure and human health, we investigated short-term PM2.5 exposure trajectory-related multi-omics characteristics from stool metagenome and metabolome and serum proteome and metabolome in a cohort of 3267 participants (age: 64.4 ± 5.8 years) living in Southern China. And then integrate these features to examine their relationship with T2D. We observed significant differences in overall structure in each omics and 193 individual biomarkers between the high- and low-PM2.5 groups. PM2.5-related features included the disturbance of microbes (carbohydrate metabolism-associated Bacteroides thetaiotaomicron), gut metabolites of amino acids and carbohydrates, serum biomarkers related to lipid metabolism and reducing n-3 fatty acids. The patterns of overall network relationships among the biomarkers differed between T2D and normal participants. The subnetwork membership centered on the hub nodes (fecal rhamnose and glycylproline, serum hippuric acid, and protein TB182) related to high-PM2.5, which well predicted higher T2D prevalence and incidence and a higher level of fasting blood glucose, HbA1C, insulin, and HOMA-IR. Our findings underline crucial PM2.5-related multi-omics biomarkers linking PM2.5 exposure and T2D in humans.

Microbial Features Linked to Medication Strategies in Cardiometabolic Disease Management

Human gut microbiota are recognized as critical players in both metabolic disease and drug metabolism. However, medication-microbiota interactions in cardiometabolic diseases are not well understood. To gain a comprehensive understanding of how medication intake impacts the gut microbiota, we investigated the association of microbial structure with the use of single or multiple medications in a cohort of 134 middle-aged adults diagnosed with cardiometabolic disease, recruited from Alberta's Tomorrow Project. Predominant cardiometabolic prescription medication classes (12 total) were included in our analysis. Multivariate Association with Linear Model (MaAsLin2) was employed and results were corrected for age, BMI, sex, and diet to evaluate the relationship between microbial features and single- or multimedication use. Highly individualized microbiota profiles were observed across participants, and increasing medication use was negatively correlated with α-diversity. A total of 46 associations were identified between microbial composition and single medications, exemplified by the depletion of Akkermansia muciniphila by β-blockers and statins, and the enrichment of Escherichia/Shigella and depletion of Bacteroides xylanisolvens by metformin. Metagenomics prediction further indicated alterations in microbial functions associated with single medications such as the depletion of enzymes involved in energy metabolism encoded by Eggerthella lenta due to β-blocker use. Specific dual medication combinations also had profound impacts, including the depletion of Romboutsia and Butyriciocccus by statin plus metformin. Together, these results show reductions in bacterial diversity as well as species and microbial functional potential associated with both single- and multimedication use in cardiometabolic disease.

Characterization of the gut microbiota and fecal and blood metabolomes under various factors in urban children from Northwest China

Introduction

Children have regional dynamics in the gut microbiota development trajectory. Hitherto, the features and influencing factors of the gut microbiota and fecal and plasma metabolites in children from Northwest China remain unclear.

Methods

Shotgun metagenomic sequencing and untargeted metabolomics were performed on 100 healthy volunteers aged 2-12 years.

Results

Age, body mass index (BMI), regular physical exercise (RPE), and delivery mode (DM) significantly affect gut microbiota and metabolites. Lactobacillus, Butyricimonas, Prevotella, Alistipes, and predicted pathway propanoate production were significantly increased with age while Bifidobacterium breve, B. animalis, B. pseudocatenulatum, Streptococcus infantis, and carbohydrate degradation were decreased. Fecal metabolome revealed that the metabolism of caffeine, amino acids, and lipid significantly increased with age while galactose metabolism decreased. Noticeably, BMI was positively associated with pathogens including Erysipelatoclostridium ramosum, Parabacteroides distasonis, Ruminococcus gnavus, and amino acid metabolism but negatively associated with beneficial Akkermansia muciniphila, Alistipes finegoldii, Eubacterium ramulus, and caffeine metabolism. RPE has increased probiotic Faecalibacterium prausnitzii and Anaerostipes hadrus, acetate and lactate production, and major nutrient metabolism in gut and plasma, but decreased pathobiont Bilophila wadsworthia, taurine degradation, and pentose phosphate pathway. Interestingly, DM affects the gut microbiota and metabolites throughout the whole childhood. Bifidobacterium animalis, Lactobacillus mucosae, L. ruminis, primary bile acid, and neomycin biosynthesis were enriched in eutocia, while anti-inflammatory Anaerofustis stercorihominis, Agathobaculum butyriciproducens, Collinsella intestinalis, and pathogenic Streptococcus salivarius, Catabacter hongkongensis, and amino acid metabolism were enriched in Cesarean section children.

Discussion

Our results provided theoretical and data foundation for the gut microbiota and metabolites in preadolescent children's growth and development in Northwest China.

Bioinformatic Analysis of Sulfotransferases from an Unexplored Gut Microbe, Sutterella wadsworthensis 3_1_45B: Possible Roles towards Detoxification via Sulfonation by Members of the Human Gut Microbiome

Sulfonation, primarily facilitated by sulfotransferases, plays a crucial role in the detoxification pathways of endogenous substances and xenobiotics, promoting metabolism and elimination. Traditionally, this bioconversion has been attributed to a family of human cytosolic sulfotransferases (hSULTs) known for their high sequence similarity and dependence on 3'-phosphoadenosine 5'-phosphosulfate (PAPS) as a sulfo donor. However, recent studies have revealed the presence of PAPS-dependent sulfotransferases within gut commensals, indicating that the gut microbiome may harbor a diverse array of sulfotransferase enzymes and contribute to detoxification processes via sulfation. In this study, we investigated the prevalence of sulfotransferases in members of the human gut microbiome. Interestingly, we stumbled upon PAPS-independent sulfotransferases, known as aryl-sulfate sulfotransferases (ASSTs). Our bioinformatics analyses revealed that members of the gut microbial genus Sutterella harbor multiple asst genes, possibly encoding multiple ASST enzymes within its members. Fluctuations in the microbes of the genus Sutterella have been associated with various health conditions. For this reason, we characterized 17 different ASSTs from Sutterella wadsworthensis 3_1_45B. Our findings reveal that SwASSTs share similarities with E. coli ASST but also exhibit significant structural variations and sequence diversity. These differences might drive potential functional diversification and likely reflect an evolutionary divergence from their PAPS-dependent counterparts.

Perturbations in gut and respiratory microbiota in COVID-19 and influenza patients: a systematic review and meta-analysis

Objectives

Coronavirus disease-19 (COVID-19)/influenza poses unprecedented challenges to the global economy and healthcare services. Numerous studies have described alterations in the microbiome of COVID-19/influenza patients, but further investigation is needed to understand the relationship between the microbiome and these diseases. Herein, through systematic comparison between COVID-19 patients, long COVID-19 patients, influenza patients, no COVID-19/influenza controls and no COVID-19/influenza patients, we conducted a comprehensive review to describe the microbial change of respiratory tract/digestive tract in COVID-19/influenza patients.

Methods

We systematically reviewed relevant literature by searching the PubMed, Embase, and Cochrane Library databases from inception to August 12, 2023. We conducted a comprehensive review to explore microbial alterations in patients with COVID-19/influenza. In addition, the data on α-diversity were summarized and analyzed by meta-analysis.

Results

A total of 134 studies comparing COVID-19 patients with controls and 18 studies comparing influenza patients with controls were included. The Shannon indices of the gut and respiratory tract microbiome were slightly decreased in COVID-19/influenza patients compared to no COVID-19/influenza controls. Meanwhile, COVID-19 patients with more severe symptoms also exhibited a lower Shannon index versus COVID-19 patients with milder symptoms. The intestinal microbiome of COVID-19 patients was characterized by elevated opportunistic pathogens along with reduced short-chain fatty acid (SCFAs)-producing microbiota. Moreover, Enterobacteriaceae (including Escherichia and Enterococcus) and Lactococcus, were enriched in the gut and respiratory tract of COVID-19 patients. Conversely, Haemophilus and Neisseria showed reduced abundance in the respiratory tract of both COVID-19 and influenza patients.

Conclusion

In this systematic review, we identified the microbiome in COVID-19/influenza patients in comparison with controls. The microbial changes in influenza and COVID-19 are partly similar.

The human intestinal bacterium Eggerthella lenta influences gut metabolomes in gnotobiotic mice

The intestinal microbiota and its metabolites are known to influence host metabolic health. However, little is known about the role of specific microbes. In this work, we used the minimal consortium Oligo-Mouse-Microbiota (OMM12) to study the function of Coriobacteriia under defined conditions in gnotobiotic mice. OMM12 mice with or without the addition of the dominant gut bacterium Eggerthella lenta (E. lenta) were fed with diets varying in fat content and primary bile acids. E. lenta stably colonised the mouse caecum at high relative abundances (median: 27.5%). This was accompanied by decreased occurrence of Akkermansia muciniphila and Enterococcus faecalis, but results did not reach statistical significance in all groups depending on diet and inter-individual differences. Changes in host parameters (anthropometry, blood glucose, and cholesterol) and liver proteomes were primarily due to diet. In contrast, metabolomes in colon content differed significantly between the colonisation groups. The presence of E. lenta was associated with elevated levels of latifolicinin C acid and decreased creatine, sarcosine, N,N-dimethylarginine, and N-Acetyl-DL-methionine. In conclusion, E. lenta altered specific metabolites in the colon but did not have significant effects on the mice or liver proteomes under the conditions tested due to marked inter-individual differences.

Dietary- and host-derived metabolites are used by diverse gut bacteria for anaerobic respiration

Respiratory reductases enable microorganisms to use molecules present in anaerobic ecosystems as energy-generating respiratory electron acceptors. Here we identify three taxonomically distinct families of human gut bacteria (Burkholderiaceae, Eggerthellaceae and Erysipelotrichaceae) that encode large arsenals of tens to hundreds of respiratory-like reductases per genome. Screening species from each family (Sutterella wadsworthensis, Eggerthella lenta and Holdemania filiformis), we discover 22 metabolites used as respiratory electron acceptors in a species-specific manner. Identified reactions transform multiple classes of dietary- and host-derived metabolites, including bioactive molecules resveratrol and itaconate. Products of identified respiratory metabolisms highlight poorly characterized compounds, such as the itaconate-derived 2-methylsuccinate. Reductase substrate profiling defines enzyme-substrate pairs and reveals a complex picture of reductase evolution, providing evidence that reductases with specificities for related cinnamate substrates independently emerged at least four times. These studies thus establish an exceptionally versatile form of anaerobic respiration that directly links microbial energy metabolism to the gut metabolome.

Endogenous CRISPR-Cas mediated in situ genome editing: State-of-the-art and the road ahead for engineering prokaryotes

The CRISPR-Cas systems have shown tremendous promise as heterologous tools for genome editing in various prokaryotes. However, the perturbation of DNA homeostasis and the inherent toxicity of Cas9/12a proteins could easily lead to cell death, which led to the development of endogenous CRISPR-Cas systems. Programming the widespread endogenous CRISPR-Cas systems for in situ genome editing represents a promising tool in prokaryotes, especially in genetically intractable species. Here, this review briefly summarizes the advances of endogenous CRISPR-Cas-mediated genome editing, covering aspects of establishing and optimizing the genetic tools. In particular, this review presents the application of different types of endogenous CRISPR-Cas tools for strain engineering, including genome editing and genetic regulation. Notably, this review also provides a detailed discussion of the transposon-associated CRISPR-Cas systems, and the programmable RNA-guided transposition using endogenous CRISPR-Cas systems to enable editing of microbial communities for understanding and control. Therefore, they will be a powerful tool for targeted genetic manipulation. Overall, this review will not only facilitate the development of standard genetic manipulation tools for non-model prokaryotes but will also enable more non-model prokaryotes to be genetically tractable.

Protein Language Models Uncover Carbohydrate-Active Enzyme Function in Metagenomics

In metagenomics, the pool of uncharacterized microbial enzymes presents a challenge for functional annotation. Among these, carbohydrate-active enzymes (CAZymes) stand out due to their pivotal roles in various biological processes related to host health and nutrition. Here, we present CAZyLingua, the first tool that harnesses protein language model embeddings to build a deep learning framework that facilitates the annotation of CAZymes in metagenomic datasets. Our benchmarking results showed on average a higher F1 score (reflecting an average of precision and recall) on the annotated genomes of Bacteroides thetaiotaomicron, Eggerthella lenta and Ruminococcus gnavus compared to the traditional sequence homology-based method in dbCAN2. We applied our tool to a paired mother/infant longitudinal dataset and revealed unannotated CAZymes linked to microbial development during infancy. When applied to metagenomic datasets derived from patients affected by fibrosis-prone diseases such as Crohn's disease and IgG4-related disease, CAZyLingua uncovered CAZymes associated with disease and healthy states. In each of these metagenomic catalogs, CAZyLingua discovered new annotations that were previously overlooked by traditional sequence homology tools. Overall, the deep learning model CAZyLingua can be applied in combination with existing tools to unravel intricate CAZyme evolutionary profiles and patterns, contributing to a more comprehensive understanding of microbial metabolic dynamics.

Eggerthella lenta augments preclinical autoantibody production and metabolic shift mimicking senescence in arthritis

Although the etiology of rheumatoid arthritis (RA) is unknown, a strong genetic predisposition and the presence of preclinical antibodies before the onset of symptoms is documented. An expansion of Eggerthella lenta is associated with severe disease in RA. Here, using a humanized mouse model of collagen-induced arthritis, we determined the impact of E. lenta abundance on RA severity. Naïve mice gavaged with E. lenta produce preclinical rheumatoid factor and, when induced for arthritis, develop severe disease. The augmented antibody response was much higher in female mice, and among patients with RA, women had higher average load of E. lenta. Expansion of E. lenta increased CXCL5 and CD4 T cells, and both interleukin-17- and interferon-γ-producing B cells. Further, E. lenta gavage caused gut dysbiosis and decline in amino acids and nicotinamide adenine dinucleotide with an increase in microbe-dependent bile acids and succinyl carnitine causing systemic senescent-like inflammation.

Characteristics of Intestinal Microbiota in Japanese Patients with Mild Cognitive Impairment and a Risk-Estimating Method for the Disorder

Intestinal microbiota may play a significant role in the development and progression of mild cognitive impairment (MCI). In addition, sex differences in the prevalence of MCI and intestinal microbiota are likely to exist. Therefore, this study investigated the association between MCI and intestinal microbiota by comparing Japanese patients in their 70s with MCI (11 males and 18 females) and disease-free controls (17 males and 23 females), taking sex into account. In both sexes, Clostridium_XVIII, Eggerthella, Erysipelatoclostridium, Flavonifractor, and Ruminococcus 2 were the more abundant taxa in the MCI group, whereas Megasphaera, Oscillibacter, Prevotella, Roseburia, and Victivallis were less abundant. Based on these characteristics, it was hypothesized that the composition of the intestinal microbiota in the MCI group leads to dysregulation of the intestinal microbiota, increased intestinal and blood-brain barrier permeability, and increased chronic neuroinflammation, with the long-term persistence of these abnormalities ultimately leading to cognitive decline. Furthermore, risk estimation models for MCI based on intestinal microbiota data were developed using structural equation modeling. These tests discriminated between the MCI and control groups. Incorporating these factors into intestinal microbiota testing using stool samples may be an efficient method to screen individuals with MCI.

Robust cross-cohort gut microbiome associations with COVID-19 severity

Although many recent studies have examined associations between the gut microbiome and COVID-19 disease severity in individual patient cohorts, questions remain on the robustness across international cohorts of the biomarkers they reported. Here, we performed a meta-analysis of eight shotgun metagenomic studies of COVID-19 patients (comprising 1,023 stool samples) and 23 > 16S rRNA gene amplicon sequencing (16S) cohorts (2,415 total stool samples). We found that disease severity (as defined by the WHO clinical progression scale) was associated with taxonomic and functional microbiome differences. This alteration in gut microbiome configuration peaks at days 7-30 post diagnosis, after which the gut microbiome returns to a configuration that becomes more similar to that of healthy controls over time. Furthermore, we identified a core set of species that were consistently associated with disease severity across shotgun metagenomic and 16S cohorts, and whose abundance can accurately predict disease severity category of SARS-CoV-2 infected subjects, with Actinomyces oris abundance predicting population-level mortality rate of COVID-19. Additionally, we used relational diet-microbiome databases constructed from cohort studies to predict microbiota-targeted diet patterns that would modulate gut microbiota composition toward that of healthy controls. Finally, we demonstrated the association of disease severity with the composition of intestinal archaeal, fungal, viral, and parasitic communities. Collectively, this study has identified robust COVID-19 microbiome biomarkers, established accurate predictive models as a basis for clinical prognostic tests for disease severity, and proposed biomarker-targeted diets for managing COVID-19 infection.