gutMGene: a comprehensive database for target genes of gut microbes and microbial metabolites

Butyrate-producing Faecalibacterium prausnitzii suppresses natural killer/T-cell lymphoma by dampening the JAK-STAT pathway

BACKGROUND

Natural killer/T-cell lymphoma (NKTCL) is a highly aggressive malignancy with a dismal prognosis, and gaps remain in understanding the determinants influencing disease outcomes.

OBJECTIVE

To characterise the gut microbiota feature and identify potential probiotics that could ameliorate the development of NKTCL.

DESIGN

This cross-sectional study employed shotgun metagenomic sequencing to profile the gut microbiota in two Chinese NKTCL cohorts, with validation conducted in an independent Korean cohort. Univariable and multivariable Cox proportional hazards analyses were applied to assess associations between identified marker species and patient outcomes. Tumour-suppressing effects were investigated using comprehensive in vivo and in vitro models. In addition, metabolomics, RNA sequencing, chromatin immunoprecipitation sequencing, Western blot analysis, immunohistochemistry and lentiviral-mediated gene knockdown system were used to elucidate the underlying mechanisms.

RESULTS

We first unveiled significant gut microbiota dysbiosis in NKTCL patients, prominently marked by a notable reduction in Faecalibacterium prausnitzii which correlated strongly with shorter survival among patients. Subsequently, we substantiated the antitumour properties of F. prausnitzii in NKTCL mouse models. Furthermore, F. prausnitzii culture supernatant demonstrated significant efficacy in inhibiting NKTCL cell growth. Metabolomics analysis revealed butyrate as a critical metabolite underlying these tumour-suppressing effects, validated in three human NKTCL cell lines and multiple tumour-bearing mouse models. Mechanistically, butyrate suppressed the activation of Janus kinase-signal transducer and activator of transcription pathway through enhancing histone acetylation, promoting the expression of suppressor of cytokine signalling 1.

CONCLUSION

These findings uncover a distinctive gut microbiota profile in NKTCL and provide a novel perspective on leveraging the therapeutic potential of F. prausnitzii to ameliorate this malignancy.

Gut microbiome-derived metabolites and their impact on gene regulatory networks in gestational diabetes

This study explored the therapeutic potential of gut microbiota metabolites in managing Gestational Diabetes Mellitus (GDM). Using network pharmacology, molecular docking, and dynamics simulations, we identified key targets and pathways involved in GDM. We screened 135 gut-derived metabolites, with 8 meeting drug-likeness and pharmacokinetic criteria. Analysis revealed significant overlap with GDM-related targets, including AKT1, IL6, TNF, and STAT3. Functional enrichment analysis highlighted the AGE-RAGE signaling and inflammatory pathways as crucial in GDM pathogenesis. Molecular docking and dynamics simulations showed strong binding affinities and stable interactions between two metabolites, luteolin and naringenin chalcone, and the target protein AKT1. These findings suggest that gut microbiota-derived metabolites, particularly luteolin and naringenin chalcone, may effectively modulate key pathways in GDM, offering promising avenues for novel treatment strategies.

Systematic druggable genome-wide Mendelian randomization to identify therapeutic targets and dominant flora for ulcerative colitis

The relationship and mechanism among gut microbiota (GM), metabolites and active ulcerative colitis (UC) are unclear. This study aims to infer the causal relationship between druggable-genes and active UC using Mendelian randomization (MR) and bioinformatics methods. The "microbiota-target" and "microbiota- metabolite" network was constructed to screen the microorganisms and metabolites associated with active UC, and the mechanism of GM, metabolites and active-UC was analyzed. These findings were verified through molecular docking, molecular dynamics (MD) simulations and co-localization analysis. Subsequently, the effects of key GM and targets on mice with UC induced by dextran sulfate sodium (DSS) was investigated. Our findings indicated that four drug targets (IFN-γ, IL24, CXCR6, PRKCZ) are closely associated with the risk of active UC, with IL24 specifically found to be colocalized with UC. These four targets were significantly correlated with differences of immune cell infiltration in active-UC. Faecalibacterium prausnitzii (F. prausnitzii) was predicted to inhibit IFN-γ and promote the remission of active UC. Additionally, seven GM were identified to be associated with the risk of active UC. Molecular docking and MD further confirmed the stable interactions between IFN-γ and metabolites of F. prausnitzii. We also verified the alleviating effect of F. prausnitzii on DSS-induced UC mice. The result indicated that F. prausnitzii can reduce inflammatory cell infiltration and goblet cell death in the colon, lower myeloperoxidase activity, and downregulate IFN-γ expression levels. This study revealed that GM can modify the immune microenvironment of active UC, providing new ideas for the prevention and treatment of UC.

Hypothesizing mechanistic links between microbes and disease using knowledge graphs

Knowledge graphs have been a useful tool for many biomedical applications because of their effective representation of biological concepts. Plentiful evidence exists linking the gut microbiome to disease in a correlative context, but uncovering the mechanistic explanation for those associations remains a challenge. Here we demonstrate the potential of knowledge graphs to hypothesize plausible mechanistic accounts of host-microbe interactions in disease. We have constructed a knowledge graph of linked microbes, genes and metabolites called MGMLink, and, using a shortest path or template-based search through the graph and a novel path-prioritization methodology based on the structure of the knowledge graph, we show that this knowledge supports inference of mechanistic hypotheses that explain observed relationships between microbes and disease phenotypes. We discuss specific applications of this methodology in inflammatory bowel disease and Parkinson's disease. This approach enables mechanistic hypotheses surrounding the complex interactions between gut microbes and disease to be generated in a scalable and comprehensive manner.

Fecal microbiota transplantation: transitioning from chaos and controversial realm to scientific precision era

With the popularization of modern lifestyles, the spectrum of intestinal diseases has become increasingly diverse, presenting significant challenges in its management. Traditional pharmaceutical interventions have struggled to keep pace with these changes, leaving many patients refractory to conventional pharmaceutical treatments. Fecal microbiota transplantation (FMT) has emerged as a promising therapeutic approach for enterogenic diseases. Still, controversies persist regarding its active constituents, mechanism of action, scheme of treatment evaluation, indications, and contraindications. In this review, we investigated the efficacy of FMT in addressing gastrointestinal and extraintestinal conditions, drawing from follow-up data on over 8000 patients. We systematically addressed the controversies surrounding FMT's clinical application. We delved into key issues such as its technical nature, evaluation methods, microbial restoration mechanisms, and impact on the host-microbiota interactions. Additionally, we explored the potential colonization patterns of FMT-engrafted new microbiota throughout the entire intestine and elucidated the specific pathways through which the new microbiota modulates host immunity, metabolism, and genome.

Exploring the "Microbe-Metabolite-Gene" regulatory mechanism of compound probiotics on antioxidant function in heat-stressed broilers

Nutritional modification strategies have become pivotal in addressing heat stress in poultry farming. Probiotics are increasingly recognized as a sustainable additive by researchers. The enhancement of antioxidant capacity is critical for improving the overall health and productivity of broilers. However, the molecular mechanisms by which probiotics influence antioxidant functions in heat-stressed broilers remain unclear. Consequently, this study raised 400 one day old Arbor Acres broilers until 28 d. Among them, 300 broilers, showing no significant weight differences (P > 0.05), were randomly divided into three groups (control, heat stress, and heat stress with probiotics), each consisting of five replicates with 20 broilers per replicate. The heat stress conditions were maintained at 32 ± 1°C from 9:00 to 17:00, and at 21 ± 1°C during the remaining hours, identical to the control conditions. The heat stress with probiotics group received a basal diet supplemented with 10 g/kg of compound probiotics (Lactobacillus casei: Lactobacillus acidophilus: Bifidobacterium at a ratio of 1:1:2). All groups had ad libitum access to food and water over the 14-day experimental period. Results indicated that the compound probiotics enhanced growth performance and antioxidant capacity, increasing levels of glutathione peroxidase and catalase. Weighted correlation network analysis (WGCNA) identified Hub genes (e.g., PANK2, FAM167A, ABCG8, DYDC2), Hub metabolites (e.g., l-glutamine, ornithine), and Hub microorganisms (e.g., Burkholderia, Macromonas) that regulate antioxidant functions in heat-stressed broilers. By integrating the WGCNA results, we constructed a "microbe-metabolite-gene" regulatory network centered around co-enriched pathways, illustrating the interrelationships between molecules. Notably, NT5C1A, GDA,l-glutamine, and guanosine were notably enriched in purine metabolism, whereas ABCG8,l-arginine, allose, and deoxyguanosine were prominently enriched in ABC transporters pathways, highlighting their crucial involvement in orchestrating the antioxidant response to heat stress. This study elucidates the molecular mechanisms by which compound probiotics enhance antioxidant functions in heat-stressed broilers, offering a theoretical foundation for probiotic applications in poultry.

SV4GD: a comprehensive structural variation database specially for genetic diseases

Structural variations (SVs) contribute to a large extent to genomic diversity and are highly relevant for various human genetic diseases. The sensitivity and specificity of SV identification have significantly improved with the development and widespread application of high-throughput sequencing, making clinical diagnosis and treatment more accurate. Therefore, the SV4GD (Structural Variation for Genetic Diseases, https://bio-computing.hrbmu.edu.cn/SV4GD/), a manually curated database, was constructed to provide a comprehensive, standardized and user-friendly data resource for selective batch browsing, searching, downloading and comparing those genetic disease-relevant SVs. This database compiles 10 305 records of germline structural variants from 58 human neoplastic diseases and 232 non-neoplastic genetic diseases, including 2695 disease-related SVs, and other 7610 pending research SVs detected from patients. SV4GD provides a browser and search engine to query for the detailed information of SVs, human genetic diseases and the clinical information of patients, providing an easy-to-use online tool for clinical and molecular genetics research.

gutMGene v2.0: an updated comprehensive database for target genes of gut microbes and microbial metabolites

The gut microbiota is essential for various physiological functions in the host, primarily through the metabolites it produces. To support researchers in uncovering how gut microbiota contributes to host homeostasis, we launched the gutMGene database in 2022. In this updated version, we conducted an extensive review of previous papers and incorporated new papers to extract associations among gut microbes, their metabolites, and host genes, carefully classifying these as causal or correlational. Additionally, we performed metabolic reconstructions for representative gut microbial genomes from both human and mouse. gutMGene v2.0 features an upgraded web interface, providing users with improved accessibility and functionality. This upgraded version is freely available at http://bio-computing.hrbmu.edu.cn/gutmgene. We believe that this new version will greatly advance research in the gut microbiota field by offering a comprehensive resource.

Discovering geroprotectors through the explainable artificial intelligence-based platform AgeXtend

Aging involves metabolic changes that lead to reduced cellular fitness, yet the role of many metabolites in aging is unclear. Understanding the mechanisms of known geroprotective molecules reveals insights into metabolic networks regulating aging and aids in identifying additional geroprotectors. Here we present AgeXtend, an artificial intelligence (AI)-based multimodal geroprotector prediction platform that leverages bioactivity data of known geroprotectors. AgeXtend encompasses modules that predict geroprotective potential, assess toxicity and identify target proteins and potential mechanisms. We found that AgeXtend accurately identified the pro-longevity effects of known geroprotectors excluded from training data, such as metformin and taurine. Using AgeXtend, we screened ~1.1 billion compounds and identified numerous potential geroprotectors, which we validated using yeast and Caenorhabditis elegans lifespan assays, as well as exploring microbiome-derived metabolites. Finally, we evaluated endogenous metabolites predicted as senomodulators using senescence assays in human fibroblasts, highlighting AgeXtend's potential to reveal unidentified geroprotectors and provide insights into aging mechanisms.

The Intricate Interplay: Microbial Metabolites and the Gut-Liver-Brain Axis in Parkinson's Disease

Parkinson's Disease (PD) is a neurodegenerative disorder marked by the depletion of dopaminergic neurons. Recent studies highlight the gut-liver-brain (GLB) axis and its role in PD pathogenesis. The GLB axis forms a dynamic network facilitating bidirectional communication between the gastrointestinal tract, liver, and central nervous system. Dysregulation within this axis, encompassing gut dysbiosis and microbial metabolites, is emerging as a critical factor influencing PD progression. Our understanding of PD was traditionally centered on neurodegenerative processes within the brain. However, examining PD through the lens of the GLB axis provides new insights. This review provides a comprehensive analysis of microbial metabolites, such as short-chain fatty acids (SCFAs), trimethylamine-N-oxide (TMAO), kynurenine, serotonin, bile acids, indoles, and dopamine, which are integral to PD pathogenesis by modulation of the GLB axis. Our extensive research included a comprehensive literature review and database searches utilizing resources such as gutMGene and gutMDisorder. These databases have been instrumental in identifying specific microbes and their metabolites, shedding light on the intricate relationship between the GLB axis and PD. This review consolidates existing knowledge and underscores the potential for targeted therapeutic interventions based on the GLB axis and its components, which offer new avenues for future PD research and treatment strategies. While the GLB axis is not a novel concept, this review is the first to focus specifically on its role in PD, highlighting the importance of integrating the liver and microbial metabolites as central players in the PD puzzle.

The unfolded features on the synchronized fashion of gut microbiota and Drynaria rhizome against obesity via integrated pharmacology

Drynaria rhizome (DR) is used as a natural remedy to ameliorate obesity (OB) in East Asia; in parallel, the gut microbiota (GM) might exert a positive impact on OB through their metabolites. This study elucidates the orchestrated effects of DR and GM on OB. DR-GM, - a key signaling pathway-target-metabolite (DGSTM) networks were used to unveil the relationship between DR and GM, and Molecular Docking Test (MDT) and Density Functional Theory (DFT) were adopted to underpin the uppermost molecules. The NR1H3 (target) - 3-Epicycloeucalenol (ligand), and PPARG (target) - Clionasterol (ligand) conjugates from DR, FABP3 (target) - Ursodeoxycholic acid, FABP4 (target) - Lithocholic acid (ligand) or Deoxycholic acid (ligand), PPARA (target) - Equol (ligand), and PPARD (target) - 2,3-Bis(3,4-dihydroxybenzyl)butyrolactone (ligand) conjugates from GM formed the most stable conformers via MDT and DFT. Overall, these findings suggest that DR-GM might be a promising ameliorator on PPAR signaling pathway against OB.

Predicting Disease-Metabolite Associations Based on the Metapath Aggregation of Tripartite Heterogeneous Networks

The exploration of the interactions between diseases and metabolites holds significant implications for the diagnosis and treatment of diseases. However, traditional experimental methods are time-consuming and costly, and current computational methods often overlook the influence of other biological entities on both. In light of these limitations, we proposed a novel deep learning model based on metapath aggregation of tripartite heterogeneous networks (MAHN) to explore disease-related metabolites. Specifically, we introduced microbes to construct a tripartite heterogeneous network and employed graph convolutional network and enhanced GraphSAGE to learn node features with metapath length 3. Additionally, we utilized node-level and semantic-level attention mechanisms, a more granular approach, to aggregate node features with metapath length 2. Finally, the reconstructed association probability is obtained by fusing features from different metapaths into the bilinear decoder. The experiments demonstrate that the proposed MAHN model achieved superior performance in five-fold cross-validation with Acc (91.85%), Pre (90.48%), Recall (93.53%), F1 (91.94%), AUC (97.39%), and AUPR (97.47%), outperforming four state-of-the-art algorithms. Case studies on two complex diseases, irritable bowel syndrome and obesity, further validate the predictive results, and the MAHN model is a trustworthy prediction tool for discovering potential metabolites. Moreover, deep learning models integrating multi-omics data represent the future mainstream direction for predicting disease-related biological entities.

Potential of gut microbiota metabolites in treating COPD: network pharmacology and Mendelian randomization approaches

Objective

The gut microbiota and its metabolites exert a significant influence on COPD, yet the underlying mechanisms remain elusive. We aim to holistically evaluate the role and mechanisms of the gut microbiota and its metabolites in COPD through network pharmacology and Mendelian randomization approaches.

Methods

Employing network pharmacology, we identified the gut microbiota and its metabolites' impact on COPD-related targets, elucidating the complex network mechanisms involving the gut microbiota, its metabolites, targets, and signaling pathways in relation to COPD. Further, promising gut microbiota metabolites and microbiota were pinpointed, with their causal relationships inferred through Mendelian randomization.

Results

A complex biological network was constructed, comprising 39 gut microbiota, 20 signaling pathways, 19 targets, and 23 metabolites associated with COPD. Phenylacetylglutamine emerged as a potentially promising metabolite for COPD treatment, with Mendelian randomization analysis revealing a causal relationship with COPD.

Conclusion

This study illuminates the intricate associations between the gut microbiota, its metabolites, and COPD. Phenylacetylglutamine may represent a novel avenue for COPD treatment. These findings could aid in identifying individuals at high risk for COPD, offering insights into early prevention and treatment strategies.

Protective effect of Shenqi Wenfei Formula against lipopolysaccharide/cigarette smoke-induced COPD in Rat based on gut microbiota and network pharmacology analysis

Introduction

The incidence of chronic obstructive pulmonary disease (COPD) appears to be increasing and evidence suggests that the intestinal flora may play a causative role in its development. Previous studies found that the Shenqi Wenfei Formula (SQWF) can regulate pyroptosis via the NLRP3/GSDMD pathway, thereby reducing the inflammatory response in the lungs of COPD model rats. However, there is no information on whether the drug's effects are associated with intestinal flora. Therefore, this study investigates whether the effects of SQWF are mediated through the regulation of intestinal flora, aiming to elucidate the underlying mechanisms of its therapeutic impact on COPD.

Methods

COPD was induced in rats using lipopolysaccharide and cigarette smoke, followed by intragastric administration of SQWF or physiological saline The targets of SQWF, associated signaling pathways, and key bacterial groups were investigated using 16S rRNA sequencing, network pharmacology, and bioinformatics techniques. The prediction results were validated using quantitative reverse transcription PCR, western blotting, and immunofluorescence, among other methods.

Results

SQWF treatment was found to alleviate COPD in model rats. Treatment was also observed to restore the balance of the intestinal flora in the rats, especially by reducing the abundance of g_Parabacteroides. Bioinformatics predictions identified g_Parabacteroides metabolites, RelA, HDAC1, and enriched neutrophil extracellular trap formation pathways as core targets of SQWF in COPD. qRT-PCR and Western blotting results showed that SQWF treatment reduced ReLA and HDAC1 mRNA and protein expression, along with decreased myeloperoxidase and neutrophil elastase levels in the nucleus.

Conclusion

Treatment with SQWF was found to restore the imbalance of intestinal g_Parabacteroides in COPD and also regulate the expression of the ReLA and HDAC1 genes, thereby reducing pulmonary neutrophil extracellular traps and alleviating lung inflammation.

Prediction of YY1 loop anchor based on multi-omics features

The three-dimensional structure of chromatin is crucial for the regulation of gene expression. YY1 promotes enhancer-promoter interactions in a manner analogous to CTCF-mediated chromatin interactions. However, little is known about which YY1 binding sites can form loop anchors. In this study, the LightGBM model was used to predict YY1-loop anchors by integrating multi-omics data. Due to the large imbalance in the number of positive and negative samples, we use AUPRC to reflect the quality of the classifier. The results show that the LightGBM model exhibits strong predictive performance (AUPRC≥0.93). To verify the robustness of the model, the dataset was divided into training and test sets at a 4:1 ratio. The results show that the model performs well for YY1-loop anchor prediction on both the training and independent test sets. Additionally, we ranked the importance of the features and found that the formation of YY1-loop anchors is primarily influenced by the co-binding of transcription factors CTCF, SMC3, and RAD21, as well as histone modifications and sequence context.

Exploring bacterial metabolites in microbe-human host dialogue and their therapeutic potential in Alzheimer's diseases

Neurological dysfunction in association with aging, dementia, and cognitive impairment is the major cause of Alzheimer's disease (AD). Current AD therapies often yield unsatisfactory results due to their poor mechanism in treating the underlying mechanism of the disease. Recent studies suggested that metabolites from the gut microbiota facilitate brain-gut communication. A systematic network pharmacology study and the structure- and analog-based approaches are employed to investigate the metabolites produced by gut microbiota to treat AD. The microbiota metabolites available in the gutMGene database were considered in this study. Two servers, namely Swiss Target Prediction (STP) and Similarity Ensemble Approach (SEA), were used to identify the possible AD targets for the selected metabolites. Detailed KEGG pathway and Gene Ontology (GO) analysis on identified hub genes highlighted the importance of IL6, AKT1, and GSK3B in AD pathophysiology. MMTSp (Microbiota Metabolites Target Signaling pathways) network analysis elucidated that there is a strong relationship with microbiota (Paraprevotella xylaniphila YIT 11841, Bifidobacterium dentium, Paraprevotella clara YIT 11840, Enterococcus sp. 45, Bacteroides sp. 45, Bacillus sp. 46, Escherichia sp. 33, Enterococcus casseliflavus, Bacteroides uniformis, Alistipes indistinctus YIT 12060, Bacteroides ovatus, Escherichia sp. 12, and Odoribacter laneus YIT 12061) and AD pathogenesis. In addition to this, we performed molecular docking to study the metabolite interactions in the AD drug targets. The ADME/T properties of these metabolites were also calculated and the results are discussed in detail.

Disentangling the molecular mystery of tumour-microbiota interactions: Microbial metabolites

The profound impact of the microbiota on the initiation and progression of cancer has been a focus of attention. In recent years, many studies have shown that microbial metabolites serve as key hubs that connect the microbiome and cancer progression, but the underlying molecular mechanisms have not been fully elucidated. Multiple mechanisms that influence tumour development and therapy resistance, including disrupting cellular signalling pathways, triggering oxidative stress, inducing metabolic reprogramming and reshaping tumour immune microenvironment, are reviewed. Focusing on recent advancements in this field, this review also summarises the methodological framework of studies regarding microbial metabolites. In this review, we outline the current state of research on tumour-associated microbial metabolites and describe the challenges in future scientific research and clinical applications. KEY POINTS: Metabolites derived from both gut and intratumoural microbiota play important roles in cancer initiation and progression. The dual roles of microbial metabolites pose an obstacle for clinical translations. Absolute quantification and tracing techniques of microbial metabolites are essential for addressing the gaps in studies on microbial metabolites. Integrating microbial metabolomics with multi-omics transcends current research paradigms.

Potential Target Metabolites From Gut Microbiota Against Hepatocellular Carcinoma: A Network Pharmacology and Molecular Docking Study

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths worldwide, posing significant challenges and economic burdens on healthcare systems. Gut microbiota metabolites have shown promise in cancer treatment, but the specific active metabolites and their key targets remain unclear. This study employed a network pharmacology-based approach to identify potent metabolites of gut microbiota and their key targets. Active metabolites produced by gut microbiota were retrieved using the database gutMGene, and targets associated with these metabolites were identified using the Swiss Target Prediction tool. HCC-related targets were obtained from the GeneCards database, and overlapping targets were selected through a Venn diagram tool. An integrated metabolites-target-pathway network was analyzed to identify active inhibitors against HCC, including p-cresol glucuronide, secoisolariciresinol, glycocholic acid, enterodiol, and citric acid. Molecular docking tests were performed to validate the findings and assess the binding affinity of the metabolites with their target proteins. The study identified AKT1, EGFR, ALB, and TNF genes as potential therapeutic targets against hepatic cancer. The metabolites, p-cresol glucuronide, secoisolariciresinol, glycocholic acid, enterodiol, and citric acid, exhibited significant binding affinity with their respective target proteins. The study also revealed multiple signaling pathways and biological processes associated with the metabolites, demonstrating their preventive effect against HCC. This research utilizes a network pharmacology-based approach to identify potent metabolites of gut microbiota and their key targets for the treatment of HCC. The findings were validated through molecular docking tests, providing a foundation for future studies on anti-HCC metabolites and their mechanisms of action. Furthermore, this study offers insights into the development of novel anti-HCC drugs utilizing gut microbiota metabolites.

Identification of metabolites from the gut microbiota in hypertension via network pharmacology and molecular docking

Hypertension is the most prevalent cardiovascular disease, affecting one-third of adults. All antihypertensive drugs have potential side effects. Gut metabolites influence hypertension. The objective of this study was to identify antihypertensive gut metabolites through network pharmacology and molecular docking techniques and to validate their antihypertensive mechanisms via in vitro experiments. A total of 10 core antihypertensive targets and 18 gut metabolites that act on hypertension were identified. Four groups of protein metabolites, namely, CXCL8-baicalein, CXCL8-baicalin, CYP1A1-urolithin A, and PTGS2-equol, which have binding energies of - 7.7, - 8.5, - 7.2, and - 8.8 kcal-mol-1, respectively, were found to have relatively high affinities. Based on its drug-likeness properties in silico and toxicological properties, equol was identified as a potential antihypertensive metabolite. On the basis of the results of network pharmacology and molecular docking, equol may exert antihypertensive effects by regulating the IL-17 signaling pathway and PTGS2. A phenylephrine-induced H9c2 cell model was subsequently utilized to verify that equol inhibits cell hypertrophy (P < 0.05) by inhibiting the IL-17 signaling pathway and PTGS2 (P < 0.05). This study demonstrated that equol has the potential to be developed as a novel therapeutic agent for the treatment of hypertension.

Host-diet-microbiota interplay in intestinal nutrition and health

The intestine is populated by a complex and dynamic assortment of microbes, collectively called gut microbiota, that interact with the host and contribute to its metabolism and physiology. Diet is considered a key regulator of intestinal microbiota, as ingested nutrients interact with and shape the resident microbiota composition. Furthermore, recent studies underscore the interplay of dietary and microbiota-derived nutrients, which directly impinge on intestinal stem cells regulating their turnover to ensure a healthy gut barrier. Although advanced sequencing methodologies have allowed the characterization of the human gut microbiome, mechanistic studies assessing diet-microbiota-host interactions depend on the use of genetically tractable models, such as Drosophila melanogaster. In this review, we first discuss the similarities between the human and fly intestines and then we focus on the effects of diet and microbiota on nutrient-sensing signaling cascades controlling intestinal stem cell self-renewal and differentiation, as well as disease. Finally, we underline the use of the Drosophila model in assessing the role of microbiota in gut-related pathologies and in understanding the mechanisms that mediate different whole-body manifestations of gut dysfunction.

The role of KLF5 in gut microbiota and lung adenocarcinoma: unveiling programmed cell death pathways and prognostic biomarkers

Lung adenocarcinoma (LUAD) is the most important subtype of lung cancer. It is well known that the gut microbiome plays an important role in the pathophysiology of various diseases, including cancer, but little research has been done on the intestinal microbiome associated with LUAD. Utilizing bioinformatics tools and data analysis, we identified novel potential prognostic biomarkers for LUAD. To integrate differentially expressed genes and clinical significance modules, we used a weighted correlation network analysis system. According to the Peryton database and the gutMGene database, the composition and structure of gut microbiota in LUAD patients differed from those in healthy individuals. LUAD was associated with 150 gut microbiota and 767 gut microbiota targets, with Krüppel-like factor 5 (KLF5) being the most closely related. KLF5 was associated with immune status and correlated well with the prognosis of LUAD patients. The identification of KLF5 as a potential prognostic biomarker suggests its utility in improving risk stratification and guiding personalized treatment strategies for LUAD patients. Altogether, KLF5 could be a potential prognostic biomarker in LUAD.

Analysis of gut microbiota-derived metabolites regulating pituitary neuroendocrine tumors through network pharmacology

Pituitary neuroendocrine tumors (PitNETs) are a special class of tumors of the central nervous system that are closely related to metabolism, endocrine functions, and immunity. In this study, network pharmacology was used to explore the metabolites and pharmacological mechanisms of PitNET regulation by gut microbiota. The metabolites of the gut microbiota were obtained from the gutMGene database, and the targets related to the metabolites and PitNETs were determined using public databases. A total of 208 metabolites were mined from the gutMGene database; 1,192 metabolite targets were screened from the similarity ensemble approach database; and 2,303 PitNET-related targets were screened from the GeneCards database. From these, 392 overlapping targets were screened between the metabolite and PitNET-related targets, and the intersection between these overlapping and gutMGene database targets (223 targets) were obtained as the core targets (43 targets). Using the protein-protein interaction (PPI) network analysis, Kyoto encyclopedia of genes and genomes (KEGG) signaling pathway and metabolic pathway analysis, CXCL8 was obtained as a hub target, tryptophan metabolism was found to be a key metabolic pathway, and IL-17 signaling was screened as the key KEGG signaling pathway. In addition, molecular docking analysis of the active metabolites and target were performed, and the results showed that baicalin, baicalein, and compound K had good binding activities with CXCL8. We also describe the potential mechanisms for treating PitNETs using the information on the microbiota (Bifidobacterium adolescentis), signaling pathway (IL-17), target (CXCL8), and metabolites (baicalin, baicalein, and compound K); we expect that these will provide a scientific basis for further study.

The orchestrated feature of Cornus kousa fruit and gut microbiota against obesity via integrated pharmacology

Cornus kousa fruit (CKF) has been utilized as anti‐obesity supplementation in East Asia, including Korea, and gut microbiota (GM) might have synergistic effects on obesity (OB) via its interplay. We aimed to decode molecule(s), mechanism(s), and target(s) on interplay between CKF and GM via network pharmacology analysis. The final targets were analyzed by protein–protein interaction (PPI) networks and a bubble plot. The GM interacted with significant targets identified by the gutMGene database. The relationships among CKF or GM, signaling pathways, targets, and molecules (CGSTM) were plotted by R package. Finally, molecular docking assay and density functional theory (DFT) were performed to validate its affinity. The final targets (22) were selected on OB‐responded targets, indicating that interleukin‐6 (IL6) was the most crucial protein‐coding target on PPI networks. A bubble plot and CGSTM networks suggested that the advanced glycation end‐receptor for advanced glycation end products signaling pathway in diabetic complications is inhibited by CKF and peroxisome proliferator–activated receptor (PPAR) signaling pathway is activated by GM. As the most stable conformers, IL6‐equol complex was attributed to GM, and PPAR alpha‐linoleic acid, PPAR delta‐stearic acid, and fatty acid–binding protein 4‐dimethyl 2,3‐bis(1,3‐dimethylindol‐2‐yl) fumarate complex were attributed to CKF. Noticeably, stearic acid was removed by DFT analysis; all other three molecules were proposed as good electron donators with the higher electronegativity compared with a standard drug (Orlistat). This study shows that integrated pharmacological analysis can enable to decode the unknown relationships between CKF and GM. Overall, this study reveals that the combination of CKF and favorable GM might exert dual therapeutic effects on OB.

Dietary succinate reduces fat deposition through gut microbiota and lipid metabolism in broilers

Succinate has been shown to be a potentially beneficial nutritional supplement with a diverse range of physiological functions. However, it remains unknown whether succinate supplementation regulates lipid metabolism in chickens. The aim of this study was to explore how succinate affects fat deposition and the underlying mechanism involved in broilers and to determine the most appropriate level of succinate supplementation in the diet. A total of 640 one-day-old male yellow-feathered broilers were randomly divided into 4 groups with 8 replicates and 20 broilers per replicate. A basal diet was provided to the control group (CON). The experimental broilers were fed diets containing 0.2% (L), 0.4% (M), or 0.6% (H) succinate and the study was lasted for 21 d. The linear (l) and quadratic (q) effects of succinate addition were determined. The results indicated that supplementation with 0.4% succinate reduced ADFI, serum triglycerides (l, q; P < 0.05), glucose (q; P < 0.05), and increased high-density lipidprotein cholesterol (l, q; P < 0.05) concentrations in broilers. Moreover, 0.4% succinate affects lipid metabolism by decreasing the abdominal fat percentage and adipocyte surface area, the expression of genes that promote liposynthesis in the abdominal fat and liver, as well as increasing the expression of genes that promote lipolysis in the abdominal fat and liver. In addition, increased cecal propionic acid content (q, P < 0.05) was found in the M group compared to the CON group. The 16S rRNA sequence analysis showed that group M altered cecum microbial composition by increasing the abundance of genera such as Blautia and Sellimonas (P < 0.05). LC-MS metabolomic analysis revealed that the differential metabolites between the M and CON groups were enriched in amino acid-related pathways. In conclusion, the optimum level of succinate added to broiler diets in the present study was 0.4%. Succinate can potentially reduce fat accumulation in broilers by modulating the composition of the gut flora and amino acid metabolism related to lipid metabolism.

Deciphering the gut microbiota's (Coprococcus and Subdoligranulum) impact on depression: Network pharmacology and molecular dynamics simulation

Depression, a prevalent mental health condition, significantly impacts global mental impairment rates. While antidepressants are commonly used, treatment-resistant depression (TRD) poses a challenge. Emerging research highlights the role of the gut microbiota in depression through the gut-brain axis. This study identifies key genes associated with depression influenced by specific gut microbiota, Coprococcus and Subdoligranulum. Using bioinformatics tools, potential targets were elucidated, and molecular docking studies were performed. Furthermore, gene expression analysis identified hub-genes related to depression, intersecting with metabolite targets. Protein-protein interaction analysis revealed pivotal targets such as PTGS2 and MMP9. Molecular docking demonstrated 3-Indolepropionic acid's superior affinity over (R)-3-(4-Hydroxyphenyl)lactate. Physicochemical properties and toxicity profiles were compared, suggesting favorable attributes for 3-Indolepropionic acid. Molecular dynamics simulations confirmed stability and interactions of compounds with target proteins. This comprehensive approach sheds light on the complex interplay between gut microbiota, genes, and depression, emphasizing the potential for microbiota-targeted interventions in mental health management.

Identification of renal protective gut microbiome derived-metabolites in diabetic chronic kidney disease: An integrated approach using network pharmacology and molecular docking

Metabolites from the gut microbiota define molecules in the gut-kidney cross talks. However, the mechanistic pathway by which the kidneys actively sense gut metabolites and their impact on diabetic chronic kidney disease (DCKD) remains unclear. This study is an attempt to investigate the gut microbiome metabolites, their host targeting genes, and their mechanistic action against DCKD. Gut microbiome, metabolites, and host targets were extracted from the gutMgene database and metabolites from the PubChem database. DCKD targets were identified from DisGeNET, GeneCard, NCBI, and OMIM databases. Computational examination such as protein-protein interaction networks, enrichment pathway, identification of metabolites for potential targets using molecular docking, hubgene-microbes-metabolite-samplesource-substrate (HMMSS) network architecture were executed using Network analyst, ShinyGo, GeneMania, Cytoscape, Autodock tools. There were 574 microbial metabolites, 2861 DCKD targets, and 222 microbes targeting host genes. After screening, we obtained 27 final targets, which are used for computational examination. From enrichment analysis, we found NF-ΚB1, AKT1, EGFR, JUN, and RELA as the main regulators in the DCKD development through mitogen activated protein kinase (MAPK) pathway signalling. The (HMMSS) network analysis found F.prausnitzi, B.adolescentis, and B.distasonis probiotic bacteria that are found in the intestinal epithelium, colonic region, metabolize the substrates like tryptophan, other unknown substrates might have direct interaction with the NF-kB1 and epidermal growth factor receptor (EGFR) targets. On docking of these target proteins with 3- Indole propionic acid (IPA) showed high binding energy affinity of -5.9 kcal/mol and -7.4kcal/mol. From this study we identified, the 3 IPA produced by F. prausnitzi A2-165 was found to have renal sensing properties inhibiting MAPK/NF-KB1 inflammatory pathway and would be useful in treating CKD in diabetics.

Emerging importance of stool preservation methods in OMICS studies with special focus on cancer biology

The intricate consortium of microorganisms in the human gut plays a crucial role in different physiological functions. The complex known-unknown elements of the gut microbiome are perplexing and the absence of standardized procedures for collecting and preserving samples has hindered continuous research in comprehending it. The technological bias produced because of lack of standard protocols has affected the reproducibility of results. The complex nature of diseases like colorectal cancer, gastric cancer, hepatocellular carcinoma and breast cancer require a thorough understanding of its etiology for an efficient and timely diagnosis. The designated protocols for collection and preservation of stool specimens have great variance, hence generate inconsistencies in OMICS studies. Due to the complications associated to the nature of sample, it is important to preserve the sample to be studied later in a laboratory or to be used in the future research purpose. Stool preservation is gaining importance due to the increased use of treatment options like fecal microbiota transplantation to cure conditions like recurrent Clostridium difficile infections and for OMICS studies including metagenomics, metabolomics and culturomics. This review provides an insight into the importance of omics studies for the identification and development of novel biomarkers for quick and noninvasive diagnosis of various diseases.

Knowledge graph-derived feed efficiency analysis via pig gut microbiota

Feed efficiency (FE) is essential for pig production, has been reported to be partially explained by gut microbiota. Despite an extensive body of research literature to this topic, studies regarding the regulation of feed efficiency by gut microbiota remain fragmented and mostly confined to disorganized or semi-structured unrestricted texts. Meanwhile, structured databases for microbiota analysis are available, yet they often lack a comprehensive understanding of the associated biological processes. Therefore, we have devised an approach to construct a comprehensive knowledge graph by combining unstructured textual intelligence with structured database information and applied it to investigate the relationship between pig gut microbes and FE. Firstly, we created the pgmReading knowledge base and the domain ontology of pig gut microbiota by annotating, extracting, and integrating semantic information from 157 scientific publications. Secondly, we created the pgmPubtator by utilizing PubTator to expand the semantic information related to microbiota. Thirdly, we created the pgmDatabase by mapping and combining the ADDAGMA, gutMGene, and KEGG databases based on the ontology. These three knowledge bases were integrated to form the Pig Gut Microbial Knowledge Graph (PGMKG). Additionally, we created five biological query cases to validate the performance of PGMKG. These cases not only allow us to identify microbes with the most significant impact on FE but also provide insights into the metabolites produced by these microbes and the associated metabolic pathways. This study introduces PGMKG, mapping key microbes in pig feed efficiency and guiding microbiota-targeted optimization.

Metagenomic and culture-dependent analysis of Rhinopithecius bieti gut microbiota and characterization of a novel genus of Sphingobacteriaceae

Culture-dependent and metagenomic binning techniques were employed to gain an insight into the diversification of gut bacteria in Rhinopithecius bieti, a highly endangered primate endemic to China. Our analyses revealed that Bacillota_A and Bacteroidota were the dominant phyla. These two phyla species are rich in carbohydrate active enzymes, which could provide nutrients and energy for their own or hosts' survival under different circumstances. Among the culturable bacteria, one novel bacterium, designated as WQ 2009T, formed a distinct branch that had a low similarity to the known species in the family Sphingobacteriaceae, based on the phylogenetic analysis of its 16S rRNA gene sequence or phylogenomic analysis. The ANI, dDDH and AAI values between WQ 2009T and its most closely related strains S. kitahiroshimense 10CT, S. pakistanense NCCP-246T and S. faecium DSM 11690T were significantly lower than the accepted cut-off values for microbial species delineation. All results demonstrated that WQ 2009T represent a novel genus, for which names Rhinopithecimicrobium gen. nov. and Rhinopithecimicrobium faecis sp. nov. (Type strain WQ 2009T = CCTCC AA 2021153T = KCTC 82941T) are proposed.

Prediction of systemic lupus erythematosus-related genes based on graph attention network and deep neural network

Systemic lupus erythematosus (SLE) is an autoimmune disorder intricately linked to genetic factors, with numerous approaches having identified genes linked to its development, diagnosis and prognosis. Despite genome-wide association analysis and gene knockout experiments confirming some genes associated with SLE, there are still numerous potential genes yet to be discovered. The search for relevant genes through biological experiments entails significant financial and human resources. With the advancement of computational technologies like deep learning, we aim to identify SLE-related genes through deep learning methods, thereby narrowing down the scope for biological experimentation. This study introduces SLEDL, a deep learning-based approach that leverages DNN and graph neural networks to effectively identify SLE-related genes by capturing relevant features in the gene interaction network. The above steps transform the identification of SLE related genes into a binary classification problem, ultimately solved through a fully connected layer. The results demonstrate the superiority of SLEDL, achieving higher AUC (0.7274) and AUPR (0.7599), further validated through case studies.

Prediction of cell-type-specific cohesin-mediated chromatin loops based on chromatin state

Chromatin loop is of crucial importance for the regulation of gene transcription. Cohesin is a type of chromatin-associated protein that mediates the interaction of chromatin through the loop extrusion. Cohesin-mediated chromatin interactions have strong cell-type specificity, posing a challenge for predicting chromatin loops. Existing computational methods perform poorly in predicting cell-type-specific chromatin loops. To address this issue, we propose a random forest model to predict cell-type-specific cohesin-mediated chromatin loops based on chromatin states identified by ChromHMM and the occupancy of related factors. Our results show that chromatin state is responsible for cell-type-specificity of loops. Using only chromatin states as features, the model achieved high accuracy in predicting cell-type-specific loops between two cell types and can be applied to different cell types. Furthermore, when chromatin states are combined with the occurrence frequency of CTCF, RAD21, YY1, and H3K27ac ChIP-seq peaks, more accurate prediction can be achieved. Our feature extraction method provides novel insights into predicting cell-type-specific chromatin loops and reveals the relationship between chromatin state and chromatin loop formation.

Deep learning and machine learning approaches to classify stomach distant metastatic tumors using DNA methylation profiles

Distant metastasis of cancer is a significant contributor to cancer-related complications, and early identification of unidentified stomach adenocarcinoma is crucial for a positive prognosis. Changes inDNA methylation are being increasingly recognized as a crucial factor in predicting cancer progression. Within this research, we developed machine learning and deep learning models for distinguishing distant metastasis in samples of stomach adenocarcinoma based on DNA methylation profile. Employing deep neural networks (DNN), support vector machines (SVM), random forest (RF), Naive Bayes (NB) and decision tree (DT), and models for forecasting distant metastasis in stomach adenocarcinoma. The results show that the performance of DNN is better than that of other models, AUC and AUPR achieving 99.9 % and 99.5 % respectively. Additionally, a weighted random sampling technique was utilized to address the issue of imbalanced datasets, enabling the identification of crucial methylation markers associated with functionally significant genes in stomach distant metastasis tumors with greater performance.

Glycyrrhiza polysaccharides may have an antitumor effect in γδT cells through gut microbiota and TLRs/NF-κB pathway in mice

Tumor immunotherapy can be a suitable cancer treatment option in certain instances. Here we investigated the potential immunomodulatory effect of oral glycyrrhiza polysaccharides (GCP) on the antitumor function of γδT cells in intestinal epithelial cells in mice. We found that GCP can inhibit tumor growth and was involved in the regulation of systemic immunosuppression. GCP administration also promoted the differentiation of gut epithelia γδT cells into IFN-γ-producing subtype through regulation of local cytokines in gut mucosa. GCP administration increased local cytokine levels through gut microbiota and the gut mucosa Toll-like receptors / nuclear factor kappa-B pathway. Taken together, our results suggest that GCP might be a suitable candidate for tumor immunotherapy, although further clinical research, including clinical trials, are required to validate these results.

Non-Targeted RNA Sequencing: Towards the Development of Universal Clinical Diagnosis Methods for Human and Veterinary Infectious Diseases

Metagenomics offers the potential to replace and simplify classical methods used in the clinical diagnosis of human and veterinary infectious diseases. Metagenomics boasts a high pathogen discovery rate and high specificity, advantages absent in most classical approaches. However, its widespread adoption in clinical settings is still pending, with a slow transition from research to routine use. While longer turnaround times and higher costs were once concerns, these issues are currently being addressed by automation, better chemistries, improved sequencing platforms, better databases, and automated bioinformatics analysis. However, many technical options and steps, each producing highly variable outcomes, have reduced the technology's operational value, discouraging its implementation in diagnostic labs. We present a case for utilizing non-targeted RNA sequencing (NT-RNA-seq) as an ideal metagenomics method for the detection of infectious disease-causing agents in humans and animals. Additionally, to create operational value, we propose to identify best practices for the "core" of steps that are invariably shared among many human and veterinary protocols. Reference materials, sequencing procedures, and bioinformatics standards should accelerate the validation processes necessary for the widespread adoption of this technology. Best practices could be determined through "implementation research" by a consortium of interested institutions working on common samples.

Riboswitch Distribution in the Human Gut Microbiome Reveals Common Metabolite Pathways

Riboswitches are widely distributed, conserved RNAs which regulate metabolite levels in bacterial cells through direct, noncovalent binding of their cognate metabolite. Various riboswitch families are highly enriched in gut bacteria, suggestive of a symbiotic relationship between the host and bacteria. Previous studies of the distribution of riboswitches have examined bacterial taxa broadly. Thus, the distribution of riboswitches associated with bacteria inhabiting the intestines of healthy individuals is not well understood. To address these questions, we survey the gut microbiome for riboswitches by including an international database of prokaryotic genomes from the gut samples. Using Infernal, a program that uses RNA-specific sequence and structural features, we survey this data set using existing riboswitch models. We identify 22 classes of riboswitches with vitamin cofactors making up the majority of riboswitch-associated pathways. Our finding is reproducible in other representative databases from the oral as well as the marine microbiomes, underscoring the importance of thiamine pyrophosphate, cobalamin, and flavin mononucleotide in gene regulation. Interestingly, riboswitches do not vary significantly across microbiome representatives from around the world despite major taxonomic differences; this suggests an underlying conservation. Further studies elucidating the role of bacterial riboswitches in the host metabolome are needed to illuminate the consequences of our finding.

Mulberry Leaf Compounds and Gut Microbiota in Alzheimer's Disease and Diabetes: A Study Using Network Pharmacology, Molecular Dynamics Simulation, and Cellular Assays

Recently, studies have reported a correlation that individuals with diabetes show an increased risk of developing Alzheimer's disease (AD). Mulberry leaves, serving as both a traditional medicinal herb and a food source, exhibit significant hypoglycemic and antioxidative properties. The flavonoid compounds in mulberry leaf offer therapeutic effects for relieving diabetic symptoms and providing neuroprotection. However, the mechanisms of this effect have not been fully elucidated. This investigation aimed to investigate the combined effects of specific mulberry leaf flavonoids (kaempferol, quercetin, rhamnocitrin, tetramethoxyluteolin, and norartocarpetin) on both type 2 diabetes mellitus (T2DM) and AD. Additionally, the role of the gut microbiota in these two diseases' treatment was studied. Using network pharmacology, we investigated the potential mechanisms of flavonoids in mulberry leaves, combined with gut microbiota, in combating AD and T2DM. In addition, we identified protein tyrosine phosphatase 1B (PTP1B) as a key target for kaempferol in these two diseases. Molecular docking and molecular dynamics simulations showed that kaempferol has the potential to inhibit PTP1B for indirect treatment of AD, which was proven by measuring the IC50 of kaempferol (279.23 μM). The cell experiment also confirmed the dose-dependent effect of kaempferol on the phosphorylation of total cellular protein in HepG2 cells. This research supports the concept of food-medicine homology and broadens the range of medical treatments for diabetes and AD, highlighting the prospect of integrating traditional herbal remedies with modern medical research.

Integrating biological knowledge for mechanistic inference in the host-associated microbiome

Advances in high-throughput technologies have enhanced our ability to describe microbial communities as they relate to human health and disease. Alongside the growth in sequencing data has come an influx of resources that synthesize knowledge surrounding microbial traits, functions, and metabolic potential with knowledge of how they may impact host pathways to influence disease phenotypes. These knowledge bases can enable the development of mechanistic explanations that may underlie correlations detected between microbial communities and disease. In this review, we survey existing resources and methodologies for the computational integration of broad classes of microbial and host knowledge. We evaluate these knowledge bases in their access methods, content, and source characteristics. We discuss challenges of the creation and utilization of knowledge bases including inconsistency of nomenclature assignment of taxa and metabolites across sources, whether the biological entities represented are rooted in ontologies or taxonomies, and how the structure and accessibility limit the diversity of applications and user types. We make this information available in a code and data repository at: https://github.com/lozuponelab/knowledge-source-mappings. Addressing these challenges will allow for the development of more effective tools for drawing from abundant knowledge to find new insights into microbial mechanisms in disease by fostering a systematic and unbiased exploration of existing information.

CoGSPro-net:A graph neural network based on protein-protein interaction for classifying lung cancer-relatrd proteins

This paper proposes a deep learning algorithm named CoGSPro for classifying lung cancer-related proteins. CoGSPro combines graph neural networks and attention mechanisms to extract key features from protein data and accurately classify proteins. It utilizes large-scale protein expression datasets to train and validate the model, enabling it to identify subtle patterns related to lung cancer. CoGSPro integrates protein-protein interaction network information to improve its predictive accuracy. The experimental results indicate that CoGSPro achieves cutting-edge performance, attaining an accuracy of 96.60% in the classification of lung cancer proteins, surpassing other baseline methods. Additionally, CoGSPro has uncovered new biomarkers for lung cancer, offering potential targets for early detection and treatment.

Graph neural network model GGDisnet for identifying genes in gastrointestinal cancer and single-cell analysis

Gastrointestinal cancer, a highly prevalent form of cancer, has been the subject of extensive research resulting in the identification of numerous pathogenic genes. However, validation and exploration of these findings often require traditional biological experiments, which are time-consuming and limit the ability to make extensive assessments promptly. To address this challenge, this paper introduces GGDisnet, a novel model for identifying genes associated with gastrointestinal cancer. GGDisnet efficiently screens human genes, providing a set of genes with a high correlation to gastrointestinal cancer for reference. Comparative analysis with other models demonstrates GGDisnet's superior performance. Furthermore, we conducted enrichment and single-cell analyses based on GGDisnet-predicted genes, offering valuable clinical insights.

MetagenomicKG: a knowledge graph for metagenomic applications

Motivation

The sheer volume and variety of genomic content within microbial communities makes metagenomics a field rich in biomedical knowledge. To traverse these complex communities and their vast unknowns, metagenomic studies often depend on distinct reference databases, such as the Genome Taxonomy Database (GTDB), the Kyoto Encyclopedia of Genes and Genomes (KEGG), and the Bacterial and Viral Bioinformatics Resource Center (BV-BRC), for various analytical purposes. These databases are crucial for genetic and functional annotation of microbial communities. Nevertheless, the inconsistent nomenclature or identifiers of these databases present challenges for effective integration, representation, and utilization. Knowledge graphs (KGs) offer an appropriate solution by organizing biological entities and their interrelations into a cohesive network. The graph structure not only facilitates the unveiling of hidden patterns but also enriches our biological understanding with deeper insights. Despite KGs having shown potential in various biomedical fields, their application in metagenomics remains underexplored.

Results

We present MetagenomicKG, a novel knowledge graph specifically tailored for metagenomic analysis. MetagenomicKG integrates taxonomic, functional, and pathogenesis-related information from widely used databases, and further links these with established biomedical knowledge graphs to expand biological connections. Through several use cases, we demonstrate its utility in enabling hypothesis generation regarding the relationships between microbes and diseases, generating sample-specific graph embeddings, and providing robust pathogen prediction.

Availability and Implementation

The source code and technical details for constructing the MetagenomicKG and reproducing all analyses are available at Github: https://github.com/KoslickiLab/MetagenomicKG. We also host a Neo4j instance: http://mkg.cse.psu.edu:7474 for accessing and querying this graph.

Potential of Gut Microbial Metabolites in Treating Osteoporosis and Obesity: A Network Pharmacology and Bioinformatics Approach

BACKGROUND The gut microbial metabolites demonstrate significant activity against metabolic diseases including osteoporosis (OP) and obesity, but active compounds, targets, and mechanisms have not been fully identified. Hence, the current investigation explored the mechanisms of active metabolites and targets against OP and obesity by using network pharmacology approaches. MATERIAL AND METHODS The gutMGene database was used to collect gut microbial targets-associated metabolites; DisGeNET and OMIM databases were used to identify targets relevant to OP and obesity. A total of 63 and 89 overlapped targets were considered the final OP and obesity targets after creating a Venn diagram of metabolites-related targets and disease-related targets. Furthermore, the top 20% of degrees, betweenness, and closeness were used to form the sub-network of protein-protein interaction of these targets. Finally, the biotransformation-increased receptors and biological mechanisms were identified and validated using ADMET properties analysis, molecular docking, and molecular dynamic simulation. RESULTS GO, KEGG pathway analysis, and protein-protein interactions were performed to establish metabolites and target networks. According to the enrichment analysis, OP and obesity are highly linked to the lipid and atherosclerosis pathways. Moreover, ADMET analysis depicts that the major metabolites have drug-likeliness activity and no or less toxicity. Following that, the molecular docking studies showed that compound K and TP53 target have a remarkable negative affinity (-8.0 kcal/mol) among all metabolites and targets for both diseases. Finally, the conformity of compound K against the targeted protein TP53 was validated by 250ns MD simulation. CONCLUSIONS Therefore, we summarized that compound K can regulate TP53 and could be developed as a therapy option for OP and obesity.

Metabolites: a converging node of host and microbe to explain meta-organism

Meta-organisms encompassing the host and resident microbiota play a significant role in combatting diseases and responding to stress. Hence, there is growing traction to build a knowledge base about this ecosystem, particularly to characterize the bidirectional relationship between the host and microbiota. In this context, metabolomics has emerged as the major converging node of this entire ecosystem. Systematic comprehension of this resourceful omics component can elucidate the organism-specific response trajectory and the communication grid across the ecosystem embodying meta-organisms. Translating this knowledge into designing nutraceuticals and next-generation therapy are ongoing. Its major hindrance is a significant knowledge gap about the underlying mechanisms maintaining a delicate balance within this ecosystem. To bridge this knowledge gap, a holistic picture of the available information has been presented with a primary focus on the microbiota-metabolite relationship dynamics. The central theme of this article is the gut-brain axis and the participating microbial metabolites that impact cerebral functions.

MicrobeTCM: A comprehensive platform for the interactions of microbiota and traditional Chinese medicine

Thanks to the advancements in bioinformatics, drugs, and other interventions that modulate microbes to treat diseases have been emerging continuously. In recent years, an increasing number of databases related to traditional Chinese medicine (TCM) or gut microbes have been established. However, a database combining the two has not yet been developed. To accelerate TCM research and address the traditional medicine and micro ecological system connection between short board, we have developed the most comprehensive micro-ecological database of TCM. This initiative includes the standardization of the following advantages: (1) A repeatable process achieved through the standardization of a retrieval strategy to identify literature. This involved identifying 419 experiment articles from PubMed and six authoritative databases; (2) High-quality data integration achieved through double-entry extraction of literature, mitigating uncertainties associated with natural language extraction; (3) Implementation of a similar strategy aiding in the prediction of mechanisms of action. Leveraging drug similarity, target entity similarity, and known drug-target entity association, our platform enables the prediction of the effects of a new herb or acupoint formulas using the existing data. In total, MicrobeTCM includes 171 diseases, 725 microbes, 1468 herb-formulas, 1032 herbs, 15780 chemical compositions, 35 acupoint-formulas, and 77 acupoints. For further exploration, please visit https://www.microbetcm.com.

The Role of Intestinal Microbiota and Diet as Modulating Factors in the Course of Alzheimer's and Parkinson's Diseases

Many researchers propose manipulating microbiota to prevent and treat related diseases. The brain-gut axis is an object that remains the target of modern research, and it is not without reason that many researchers enrich it with microbiota and diet in its name. Numerous connections and mutual correlations have become the basis for seeking answers to many questions related to pathology as well as human physiology. Disorders of this homeostasis as well as dysbiosis itself accompany neurodegenerative diseases such as Alzheimer's and Parkinson's. Heavily dependent on external factors, modulation of the gut microbiome represents an opportunity to advance the treatment of neurodegenerative diseases. Probiotic interventions, synbiotic interventions, or fecal transplantation can undoubtedly support the biotherapeutic process. A special role is played by diet, which provides metabolites that directly affect the body and the microbiota. A holistic view of the human organism is therefore essential.

AgeAnnoMO: a knowledgebase of multi-omics annotation for animal aging

Aging entails gradual functional decline influenced by interconnected factors. Multiple hallmarks proposed as common and conserved underlying denominators of aging on the molecular, cellular and systemic levels across multiple species. Thus, understanding the function of aging hallmarks and their relationships across species can facilitate the translation of anti-aging drug development from model organisms to humans. Here, we built AgeAnnoMO (https://relab.xidian.edu.cn/AgeAnnoMO/#/), a knowledgebase of multi-omics annotation for animal aging. AgeAnnoMO encompasses an extensive collection of 136 datasets from eight modalities, encompassing 8596 samples from 50 representative species, making it a comprehensive resource for aging and longevity research. AgeAnnoMO characterizes multiple aging regulators across species via multi-omics data, comprehensively annotating aging-related genes, proteins, metabolites, mitochondrial genes, microbiotas and age-specific TCR and BCR sequences tied to aging hallmarks for these species and tissues. AgeAnnoMO not only facilitates a deeper and more generalizable understanding of aging mechanisms, but also provides potential insights of the specificity across tissues and species in aging process, which is important to develop the effective anti-aging interventions for diverse populations. We anticipate that AgeAnnoMO will provide a valuable resource for comprehending and integrating the conserved driving hallmarks in aging biology and identifying the targetable biomarkers for aging research.

MAMI: a comprehensive database of mother-infant microbiome and probiotic resources

Extensive evidence has demonstrated that the human microbiome and probiotics confer great impacts on human health, particularly during critical developmental stages such as pregnancy and infancy when microbial communities undergo remarkable changes and maturation. However, a major challenge in understanding the microbial community structure and interactions between mothers and infants lies in the current lack of comprehensive microbiome databases specifically focused on maternal and infant health. To address this gap, we have developed an extensive database called MAMI (Microbiome Atlas of Mothers and Infants) that archives data on the maternal and neonatal microbiome, as well as abundant resources on edible probiotic strains. By leveraging this resource, we can gain profound insights into the dynamics of microbial communities, contributing to lifelong wellness for both mothers and infants through precise modulation of the developing microbiota. The functionalities incorporated into MAMI provide a unique perspective on the study of the mother-infant microbiome, which not only advance microbiome-based scientific research but also enhance clinical practice. MAMI is publicly available at https://bioinfo.biols.ac.cn/mami/.

Model organism life extending therapeutics modulate diverse nodes in the drug-gene-microbe tripartite human longevity interactome

Advances in antiaging drug/lead discovery in animal models constitute a large body of literature on novel senotherapeutics and geroprotectives. However, with little direct evidence or mechanism of action in humans-these drugs are utilized as nutraceuticals or repurposed supplements without proper testing directions, appropriate biomarkers, or consistent in-vivo models. In this study, we take previously identified drug candidates that have significant evidence of prolonging lifespan and promoting healthy aging in model organisms, and simulate them in human metabolic interactome networks. Screening for drug-likeness, toxicity, and KEGG network correlation scores, we generated a library of 285 safe and bioavailable compounds. We interrogated this library to present computational modeling-derived estimations of a tripartite interaction map of animal geroprotective compounds in the human molecular interactome extracted from longevity, senescence, and dietary restriction-associated genes. Our findings reflect previous studies in aging-associated metabolic disorders, and predict 25 best-connected drug interactors including Resveratrol, EGCG, Metformin, Trichostatin A, Caffeic Acid and Quercetin as direct modulators of lifespan and healthspan-associated pathways. We further clustered these compounds and the functionally enriched subnetworks therewith to identify longevity-exclusive, senescence-exclusive, pseudo-omniregulators and omniregulators within the set of interactome hub genes. Additionally, serum markers for drug-interactions, and interactions with potentially geroprotective gut microbial species distinguish the current study and present a holistic depiction of optimum gut microbial alteration by candidate drugs. These findings provide a systems level model of animal life-extending therapeutics in human systems, and act as precursors for expediting the ongoing global effort to find effective antiaging pharmacological interventions.Communicated by Ramaswamy H. Sarma.

Prediction and related genes of cancer distant metastasis based on deep learning

Cancer metastasis is one of the main causes of cancer progression and difficulty in treatment. Genes play a key role in the process of cancer metastasis, as they can influence tumor cell invasiveness, migration ability and fitness. At the same time, there is heterogeneity in the organs of cancer metastasis. Breast cancer, prostate cancer, etc. tend to metastasize in the bone. Previous studies have pointed out that the occurrence of metastasis is closely related to which tissue is transferred to and genes. In this paper, we identified genes associated with cancer metastasis to different tissues based on LASSO and Pearson correlation coefficients. In total, we identified 45 genes associated with bone metastases, 89 genes associated with lung metastases, and 86 genes associated with liver metastases. Through the expression of these genes, we propose a CNN-based model to predict the occurrence of metastasis. We call this method MDCNN, which introduces a modulation mechanism that allows the weights of convolution kernels to be adjusted at different positions and feature maps, thereby adaptively changing the convolution operation at different positions. Experiments have proved that MDCNN has achieved satisfactory prediction accuracy in bone metastasis, lung metastasis and liver metastasis, and is better than other 4 methods of the same kind. We performed enrichment analysis and immune infiltration analysis on bone metastasis-related genes, and found multiple pathways and GO terms related to bone metastasis, and found that the abundance of macrophages and monocytes was the highest in patients with bone metastasis.

Catechin Protects against Lipopolysaccharide-induced Depressive-like Behaviour in Mice by Regulating Neuronal and Inflammatory Genes

BACKGROUND

Many studies have suggested that tea has antidepressant effects; however, the underlying mechanism is not fully studied. As the main anti-inflammatory polyphenol in tea, catechin may contribute to the protective role of tea against depression.

OBJECTIVE

The objective of this study is to prove that catechin can protect against lipopolysaccharide (LPS)-induced depressive-like behaviours in mice, and then explore the underlying molecular mechanisms.

METHODS

Thirty-one C57BL/6J mice were categorized into the normal saline (NS) group, LPS group, catechin group, and amitriptyline group according to their treatments. Elevated Plus Maze (EPM), Tail Suspension Test (TST), and Open Field Test (OFT) were employed to assess depressive- like behaviours in mice. RNA sequencing (RNA-seq) and subsequent Bioinformatics analyses, such as differential gene analysis and functional enrichment, were performed on the four mouse groups.

RESULTS

In TST, the mice in the LPS group exhibited significantly longer immobility time than those in the other three groups, while the immobility times for the other three groups were not significantly different. Similarly in EPM, LPS-treated mice exhibited a significantly lower percentage in the time/path of entering open arms than the mice in the other three groups, while the percentages of the mice in the other three groups were not significantly different. In OFT, LPS-treated mice exhibited significantly lower percentages in the time/path of entering the centre area than those in the other three groups. The results suggested that the LPS-induced depression models were established successfully and catechin can reverse (LPS)-induced depressive-like behaviours in mice. Finally, RNA-seq analyses revealed 57 differential expressed genes (DEGs) between LPS and NS with 19 up-regulated and 38 down-regulated. Among them, 13 genes were overlapped with the DEGs between LPS and cetechin (in opposite directions), with an overlapping p-value < 0.001. The 13 genes included Rnu7, Lcn2, C4b, Saa3, Pglyrp1, Gpx3, Lyz2, S100a8, S100a9, Tmem254b, Gm14288, Hbb-bt, and Tmem254c, which might play key roles in the protection of catechin against LPS-induced depressive-like behaviours in mice. The 13 genes were significantly enriched in defense response and inflammatory response, indicating that catechin might work through counteracting changes in the immune system induced by LPS.

CONCLUSION

Catechin can protect mice from LPS-induced depressive-like behaviours through affecting inflammatory pathways and neuron-associated gene ontologies.

Intestinal deguelin drives resistance to acetaminophen-induced hepatotoxicity in female mice

Acetaminophen (APAP) overdose is a leading cause of drug-induced liver injury (DILI), with gender-specific differences in susceptibility. However, the mechanism underlying this phenomenon remains unclear. Our study reveals that the gender-specific differences in susceptibility to APAP-induced hepatotoxicity are due to differences in the gut microbiota. Through microbial multi-omics and cultivation, we observed increased gut microbiota-derived deguelin content in both women and female mice. Administration of deguelin was capable of alleviating hepatotoxicity in APAP-treated male mice, and this protective effect was associated with the inhibition of hepatocyte oxidative stress. Mechanistically, deguelin reduced the expression of thyrotropin receptor (TSHR) in hepatocytes with APAP treatment through direct interaction. Pharmacologic suppression of TSHR expression using ML224 significantly increased hepatic glutathione (GSH) in APAP-treated male mice. These findings suggest that gut microbiota-derived deguelin plays a crucial role in reducing APAP-induced hepatotoxicity in female mice, offering new insights into therapeutic strategies for DILI.