Purpose:Despite adequate glycemic control, a proportion of patients develop diabetic retinopathy (DR), suggesting the contribution of other mechanisms. To explore genetic associations with DR, we conducted a whole-exome sequencing (WES) study of DR using a dual-control design in individuals of European ancestry. Methods:Leveraging UK Biobank WES data, we implemented a dual-control design to identify candidate genes associated with DR, comparing cases with both diabetes controls and the general population. Rare variant associations were tested at gene-level using SAIGE-GENE+, and common variants were analyzed via PLINK2. Significant findings were assessed through the exclusion of self-reported cases, leave-one-variant-out (LOVO) analysis, time-to-event analysis, transcriptomics analysis, virtual knockout experiments, proteomics, and protein-protein interaction (PPI) analysis. Results:Exome-wide gene-based association analysis identified FRZB (frizzled-related protein) as a candidate gene potentially associated with DR in comparisons with diabetic controls (odds ratio [OR] = 1.09; 95% confidence interval [CI], 1.04-1.15; P = 2.55 × 10-7). The observed association showed generally consistent patterns across several sensitivity analyses, including exclusion of self-reported cases, LOVO analyses, and Cox proportional hazards models. In addition, transcriptomic, proteomic, virtual knockout, and PPI analyses provided complementary exploratory evidence supporting a possible involvement of FRZB in DR-related biological processes. Additionally, single-variant analysis revealed two loci, FAM160A1 (4:151662612:C:G) and HK1 (10:69300854:A:G), associated with DR in the general population (OR = 1.82 and OR = 1.50, respectively). However, multiomics support for these signals was limited. Conclusions:This multilayered genetic study identified FRZB as a candidate gene potentially associated with DR. However, this finding should be interpreted cautiously, and further experimental studies and independent replication are needed to clarify the potential relevance of FRZB to DR.

PLXNA3 is a transmembrane protein essential for axon guidance and synapse formation, gaining attention in cancer research for its roles in cell migration, invasion, and signal transduction. However, its biological significance in breast cancer remains underexplored. Here, bulk RNA sequencing and survival analysis revealed that elevated PLXNA3 levels were correlated with increased malignancy and poorer survival in breast cancer. Single-cell RNA sequencing demonstrated that PLXNA3 was predominantly enriched in tumor cell clusters within the tumor immune microenvironment, rather than in immune cell clusters. In vitro experiments further demonstrated that knockdown and overexpression of PLXNA3 significantly altered the proliferative, invasive, and migratory capacities of breast cancer cells. Additionally, the overexpression of PLXNA3 was negatively correlated with immune activation status and served as a predictor of poor response to anti-PD1 immunotherapy. Our findings suggested that high expression of PLXNA3 is associated with poor prognosis in breast cancer and plays a crucial role in cancer immunity, making it a promising novel target for intervention in breast cancer.

The gut microbiome and its metabolomic potential in primary Sjögren syndrome (pSS) remain largely unexplored. Here, we perform whole-metagenome shotgun sequencing of fecal samples from 206 pSS patients and 355 non-pSS controls, integrating compositional and functional profiling with serum and fecal metabolomes. pSS is associated with extensive multi-kingdom alterations, including 49 bacterial (e.g., Streptococcus parasanguinis, Ligilactobacillus salivarius, and Veillonella parvula), 19 fungal (notably Candida albicans), and 1,323 viral species. These signatures form robust inter-kingdom correlations and achieve high diagnostic accuracy in an independent validation cohort. Functional and metabolomic analyses reveal enrichment of toxin-related and aromatic pathways and depletion of protective metabolites in patients. pSS-enriched bacteria harbor abundant immunogenic epitopes, virulence factors, and antimicrobial resistance genes, and induce proinflammatory responses ex vivo. Together, these findings outline a multi-faceted microbial framework for pSS and suggest mechanistic links between gut dysbiosis and immune dysregulation.

Background:Osteoporosis (OP) is a multifactorial skeletal disorder influenced by host metabolism, inflammation, and gut microbiota-derived metabolites such as S-equol. However, the interplay between intestinal microbiota, S-equol production, and host metabolic profiles in OP remains incompletely understood. Objective:To conduct a preliminary multi-omics investigation integrating metagenomic and metabolomic analyses to identify gut microbiota and metabolite biomarkers associated with serum S-equol levels in older adults with OP. Methods:A cross-sectional study was conducted in 39 community-dwelling adults aged ≥50 years in Haikou, China. Participants were grouped into OP and control groups based on lumbar spine T-scores, using a cut-off value of ≤ - 2.5 to define osteoporosis. Serum biomarkers (S-equol, inflammatory cytokines, oxidative stress indicators) were assessed by ELISA. Fecal samples underwent metagenomic sequencing and untargeted metabolomics. LEfSe, Spearman correlation, machine learning, and KEGG enrichment were used to explore microbiota-metabolite-bone health axes. Results:Serum S-equol levels were significantly lower in the OP group compared to controls (3,561 ± 304 vs. 3,855 ± 469 pg/mL, p = 0.026), whereas most inflammatory markers were comparable between groups, apart from a modest increase in IL-1β in OP. Metagenomic analysis revealed a lower relative abundances of key SCFA-producing taxa in OP (e.g., Faecalibacterium prausnitzii, Roseburia hominis, Bacteroides uniformis). Metabolomic profiling identified distinct alterations in amino acid and tryptophan pathways, with KEGG analysis highlighting disruptions in glycerophospholipid, glycine-serine-threonine, and choline metabolism. Discriminative metabolites (e.g., Gln-Val-Ile-Asp., 5-oxooctanoic acid) showed diagnostic potential (AUC > 0.75). S-equol levels positively correlated with these beneficial microbes and with amino acid-related metabolites (e.g., D-tryptophan, 3-indoleacrylic acid, N-methylglutamate). Network and heatmap analyses illustrated differences in microbial-metabolite association patterns between groups. Conclusion:In conclusion, low levels of serum S-equol in older adults with osteoporosis were associated with distinct changes in gut microbiota composition and fecal metabolic profiles in this pilot study.

BackgroundChronic functional constipation (CFC) is a common gastrointestinal disorder increasingly linked to gut microbiome dysbiosis. However, multi-kingdom metagenomic characterization of bacterial, fungal, and viral communities in CFC remains limited.MethodsFecal samples from 53 CFC patients and 48 healthy controls were analyzed using whole-metagenome shotgun sequencing. Microbial composition, function, cross-kingdom interactions, and diagnostic potential were evaluated using diversity analyses, KEGG annotation, network analysis, and random forest modeling.ResultsCompared with healthy controls, CFC patients exhibited marked alterations across multiple microbial kingdoms. The gut bacteriome showed significant community-structure shifts despite comparable α-diversity, characterized by depletion of health-associated Firmicutes (e.g., Faecalibacterium and Roseburia) and enrichment of Proteobacteria (e.g., Klebsiella). The mycobiome displayed selective changes in diversity and composition, with several potentially pathogenic fungal taxa enriched in CFC (e.g., Fusarium sp. c181). In the virome, community composition differed significantly between groups, with higher viral richness in CFC and widespread depletion of diverse bacteriophages in CFC patients. Functional profiling suggested feature-level functional differences without a clear global shift, including reduced carbohydrate transport and utilization pathways and relatively higher abundance of stress-response and metabolic adaptation modules in CFC. Cross-kingdom network analysis demonstrated substantially denser microbial interactions in CFC, dominated by viral associations, with Faecalibacterium prausnitzii and Faecalibacterium_SGB15346 acting as central hubs. Machine-learning models showed strong discriminatory power for CFC classification based on bacterial and viral features, whereas fungal features contributed less.ConclusionsCFC is associated with coordinated multi-kingdom gut microbiome dysbiosis involving bacteria, fungi, and viruses, accompanied by functional shifts and intensified cross-kingdom interactions. Bacterial and viral signatures show strong potential as microbiome-based biomarkers for CFC, highlighting the importance of integrating multi-kingdom analyses to better understand disease-associated gut ecosystem alterations.

AIMS:Individuals with abnormal glucose metabolism are at a significantly higher risk of developing heart failure (HF). However, strategies for early identification of HF in this high-risk population remain inadequate. This study aimed to identify plasma protein biomarkers associated with HF development and construct predictive models to identify at-risk individuals. METHODS AND RESULTS:We analyzed HF development in abnormal glucose metabolism population using data from 6517 participants in discovery cohort and 2783 in validation cohort, all from the UK Biobank, with no prior history of HF. Proteomic profiling was performed, and Lasso-Cox regression was used to identify protein associations, followed by Cox regression to develop predictive models. The model incorporated four proteins (NTproBNP, LTBP2, REN, GDF15) and clinical factors to create a protein-panel-clinical-factors (PPCF) model. For comparison, the model's performance was also evaluated in individuals with normal glucose metabolism. Over a median follow-up of 13.90 years, 555 incident HF cases were recorded in discovery cohort. The PPCF model achieved an AUC of 0.823 (95% CI: 0.785-0.860) in validation cohort, improving predictive performance by 0.05 (P < 0.001) compared with clinical factors-only model. In general population of 23 107 individuals, PPCF model obtained an AUC of 0.807 (95% CI: 0.786-0.829). Both protein panel model and PPCF model demonstrated superior net benefits over clinical factors model in abnormal glucose metabolism population. CONCLUSION:This study identified plasma protein biomarkers linked to HF development in abnormal glucose metabolism population and established the predictive models. These findings support early identification in high-risk populations.

Background:Obesity is a major global health challenge, linked to cardiometabolic and neuropsychiatric disorders through mechanisms such as inflammation and insulin resistance. However, little is known about how adiposity and its longitudinal changes interact with glycemic status to shape neuropsychiatric health and brain structural vulnerability. Clarifying these relationships is of high importance, as both obesity and dysglycemia are modifiable risk factors that may jointly accelerate psychiatric disorder and brain aging. Methods:Using UK Biobank data (n = 423,750, with 32,551 having brain MRI), we examined associations between obesity indicators (body mass index [BMI], waist circumference [WC], body fat percentage [BFP]) and changes in obesity status with incident neuropsychiatric disorders (stroke, dementia, Parkinson's disease, depression, anxiety) and brain structural measures. Participants were stratified by glycemic status-normal glucose regulation (NGR), prediabetes (Pre-DM), and diabetes (DM)-based on American Diabetes Association criteria. Cox proportional hazards and linear regression models were used. Results:Higher BMI, WC, and BFP were associated with increased risks of depression and anxiety across all glycemic groups, particularly in NGR. Abdominal obesity was linked to Parkinson's disease risk in NGR. Conversely, BMI showed an inverse association with dementia in NGR, possibly due to reverse causality. Persistent obesity and weight gain were associated with higher depression and anxiety risks in NGR. In diabetes, higher BFP was strongly linked to reduced grey matter, thalamus, and hippocampus volumes and increased WMHs. This association with BFP represented the most robust imaging signal, highlighting the pronounced vulnerability of brain structure to excess adiposity in diabetes. Similar but weaker patterns were observed in prediabetes and NGR. Conclusion:Obesity, particularly persistent or increasing adiposity, adversely affects neuropsychiatric health and brain structure, and these effects are significantly modified by glycemic status. Our findings underscore the importance of considering glucose metabolism when assessing obesity-related brain risks, and suggest that early weight management and metabolic control may have broad benefits for preventing neuropsychiatric disorders and mitigating brain aging.

The gut microbiome has been implicated in the development of autoimmune diseases, including gout. However, the role of the gut virome in gout pathogenesis remains underexplored. We employed a reference-dependent virome approach to analyze fecal metagenomic data from 102 gout patients (77 in the discovery cohort and 25 in the validation cohort) and 86 healthy controls (HCs) (63 and 23 in each cohort). A subset of gout patients in the discovery cohort provided longitudinal samples at Weeks 2, 4, and 24. Our analysis revealed significant alterations in the gut virome of gout patients, including reduced viral richness and shifts in viral family composition. Notably, Siphoviridae, Myoviridae, and Podoviridae were depleted, while Quimbyviridae, Retroviridae, and Schitoviridae were enriched in gout patients. We identified 359 viral operational taxonomic units (vOTUs) associated with gout. Enriched vOTUs in gout patients predominantly consisted of Fusobacteriaceae, Bacteroidaceae, and Selenomonadaceae phages, while control-enriched vOTUs included Ruminococcaceae, Oscillospiraceae, and Enterobacteriaceae phages. Longitudinal analysis revealed that a substantial proportion of these virome signatures remained stable over 6 months. Functional profiling highlighted the enrichment of viral auxiliary metabolic genes, suggesting potential metabolic interactions between viruses and host bacteria. Notably, gut virome signatures effectively discriminated gout patients from HCs, with high classification performance in the validation cohort. This study provides the first comprehensive characterization of the gut virome in gout, revealing its potential role in disease pathogenesis and highlighting virome-based signatures as promising biomarkers for gout diagnosis and future therapeutic strategies.

Considering the distinct etiological pathways and molecular characteristics of different lung cancer subtypes, it is crucial to develop subtype-specific prevention strategies and therapeutic targets. This study aimed to identify protein biomarkers and potential therapeutic targets for specific subtypes of lung cancer by integrating population-based observational studies and Mendelian randomisation (MR) analyses. The cohort study was conducted in the UK Biobank, including about 47,000 participants whose blood samples were measured for 2,923 unique proteins and who were followed for the development of lung cancer. Two-sample MR was performed leveraging publicly available data from genome-wide association studies (GWAS) and protein quantitative trait loci (pQTL). Proteins were prioritised based on consistent associations across logistic regression, MR, transcriptomic validation and sensitivity analyses. Tier 1 proteins passed all evaluations, including GP1BA (squamous cell carcinoma) and ACADSB (small cell carcinoma). Tier 2 proteins, supported by transcriptomic evidence but not sensitivity analyses, included AGRN, ITGB2, SEPTIN3 (adenocarcinoma) and DPP10 (squamous cell carcinoma). Tier 3 proteins, supported by logistic regression and MR only, included CD5L, GNPDA, ACAN, C7, DMP1, HEPH, CEACAM6, COX6B1, CPXM2 and IL12RB2. Druggability evaluation suggests that existing drugs targeting ITGB2, GP1BA, ACADSB and COX6B1 could potentially be repurposed for the treatment of specific lung cancer subtypes.

BACKGROUND:The stress hyperglycemia ratio (SHR) has been linked to adverse outcomes in various conditions, yet its association with Parkinson's disease (PD) remains unclear. This study investigates the relationship between SHR and PD risk across sex and glucose metabolism statuses using data from the UK Biobank. METHODS:In this prospective cohort study, 406,271 participants without baseline PD from the UK Biobank were included. SHR was calculated as [FPG (mmol/L)]/[1.59 × HbA1c (%)-2.59] and divided into tertiles. Incident PD cases were identified via linked medical records. Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs), with analyses stratified by sex and diabetes status (nondiabetic, prediabetic, diabetic). RESULTS:Over a median follow-up of 9 years, 2837 PD cases were identified. In men, elevated SHR was associated with increased PD risk, with the highest tertile (T3) showing a significantly higher risk compared with the lowest (T1) (HR: 1.20, 95% CI: 1.06-1.37). This association was strongest in nondiabetic men (T3 vs. T1: HR: 1.25, 95% CI: 1.08-1.45). No significant associations were observed in women or in prediabetic or diabetic men, either across tertiles or as a continuous variable. CONCLUSION:Elevated SHR is independently linked to an increased PD risk in men, particularly those without diabetes, but not in women or other glucose metabolism groups. These findings suggest a sex-specific role of acute metabolic stress in PD pathogenesis and emphasize the need to consider glucose metabolism status in PD risk assessment.

Introduction:Hydroxysafflor yellow A (HSYA), its primary bioactive metabolite of Carthamus tinctorius L. (safflower), has shown therapeutic potential in various inflammatory diseases. However, its role in alleviating inflammation and oxidative stress in non-alcoholic fatty liver disease (NAFLD) remains unclear. This study investigates the therapeutic effects of HSYA in mice with NAFLD, focusing on its impact on gut microbiota and serum non-targeted metabolomics to elucidate the mechanisms underlying its efficacy. Methods:NAFLD was induced in mice using a high-fat diet (HFD), followed by intragastric administration of hydroxysafflor yellow A (HSYA). Serum levels of alanine aminotransferase (ALT), aspartate aminotransferase (AST), total cholesterol (TC), and triglycerides (TG) were quantified to evaluate liver function and lipid metabolism. Oxidative stress markers, including superoxide dismutase (SOD) activity and malondialdehyde (MDA) concentration, were also assessed. The pro-inflammatory cytokines IL-6, TNF-α, and IL-1β in serum were measured using ELISA. The hepatic expression of NLRP3 inflammasome and its downstream effector, Caspase-1, was analyzed by Western blot. Histopathological examination of liver tissues was performed using hematoxylin and eosin (H&E) staining to evaluate structural damage. Furthermore, alterations in the gut microbiota composition were characterized via 16S rDNA sequencing of fecal samples. Untargeted metabolomics was conducted to identify serum metabolic variations and elucidate enriched metabolic pathways associated with HSYA treatment. Results:HSYA significantly inhibited HFD-induced weight gain and alleviated liver inflammation. It reduced serum levels of alanine aminotransferase (ALT), aspartate aminotransferase (AST) and triglycerides (TG) (P < 0.05). HSYA administration decreased hepatic mRNA and protein expression of nucleotide binding oligomerization domain like receptor protein 3 (NLRP3), Caspase-1 and interleukin - 1β (IL-1β) while increasing superoxide dismutase (SOD) activity (P < 0.05). Gut microbiota analysis revealed a significant increase in the abundance of Turicibacter, while a reduction of Ruminococcus. Serum metabolomics identified a reduction in inflammation-associated metabolites, such as phenylalanine and tyrosine, alongside enhanced phenylalanine and tyrosine biosynthesis pathways. Discussion:HSYA demonstrates potent anti-inflammatory and antioxidant effects, effectively mitigating liver inflammation and oxidative stress in NAFLD mice. Its therapeutic mechanisms may involve modulating gut microbiota and regulating serum phenylalanine and tyrosine metabolism, offering insights into its potential as a treatment for NAFLD.

Two novel sesquiterpenoids, wenyujinone J and wenyujinone K, alongside a new natural product (3), and three known sesquiterpenoids analogs (4-6), were isolated and identified from Curcuma wenyujin. The structures of the novel sesquiterpenoids were elucidated through comprehensive spectroscopic analysis and chemical methods. Their absolute stereochemistry was determined via electronic circular dichroism (ECD) spectroscopy and computational ECD analysis. The protective effect of all compounds (50-400 mu M) against high glucose-induced apoptosis in pulmonary microvascular endothelial cells (PMVECs) were evaluated. Compounds 4, 5 and 6, particularly compound 5, exhibited significant dose-dependent protective effects. Furthermore, treatment with compound 5 markedly upregulated the nuclear protein expression of phosphorylated protein kinase B (p-AKT).

ETHNOPHARMACOLOGICAL RELEVANCE:Idiopathic pulmonary fibrosis (IPF) is a progressive and fatal disease. Baihe Gujin decoction (BHGJ), a traditional Chinese medicine consisting of ten medicine food homology herbs, has shown therapeutic effects in various lung diseases; however, its efficacy in ameliorating IPF and the underlying mechanisms remain unclear. AIM OF THE STUDY:This study aimed to evaluate the effects of BHGJ on IPF and investigate its potential mechanisms. MATERIALS AND METHODS:We established a bleomycin (BLM)-induced IPF model and performed proteomic analysis. The therapeutic effects of BHGJ on IPF were assessed by measuring lung index, hydroxyproline (HYP) content, lung function parameters, and histopathological changes. Mechanistic insights were further explored using Western blot and RT-qPCR analyses. RESULTS:Our results demonstrated that BHGJ significantly alleviated BLM-induced IPF, improved lung function, reduced histopathological damage, and decreased collagen deposition. BHGF reduced apoptosis and inhibited EMT in TGF-β-induced A549 cells. Proteomic analysis revealed that its effects were associated with the modulation of the proline metabolism pathway. CONCLUSIONS:BHGJ effectively attenuated IPF progression via regulating proline metabolism, providing a potential therapeutic strategy for pulmonary fibrosis.

The gut viral community has been increasingly recognized for its role in human physiology and health; however, our understanding of its genetic makeup, functional potential, and disease associations remains incomplete. In this study, we collected 11,286 bulk or viral metagenomes from fecal samples across large-scale Chinese populations to establish a Chinese Gut Virus Catalogue (cnGVC) using a de novo virus identification approach. We then examined the diversity and compositional patterns of the gut virome in relation to common diseases by analyzing 6311 bulk metagenomes representing 28 disease or unhealthy states. The cnGVC contains 93,462 nonredundant viral genomes, with over 70

The aim of this study was to develop a machine learning-assisted rapid determination methodology for traditional Chinese Medicine Constitution. Based on the Constitution in Chinese Medicine Questionnaire (CCMQ), the most applied diagnostic instrument for assessing individuals’ constitutions, we employed automated supervised machine learning algorithms (i.e., Tree-based Pipeline Optimization Tool; TPOT) on all the possible item combinations for each subscale and an unsupervised machine learning algorithm (i.e., variable clustering; varclus) on the whole scale to select items that can best predict body constitution (BC) classifications or BC scores. By utilizing subsets of items selected based on TPOT and corresponding machine learning algorithms, the accuracies of BC classifications prediction ranged from 0.819 to 0.936, with the root mean square errors of BC scores prediction stabilizing between 6.241 and 9.877. Overall, the results suggested that the automated machine learning algorithms performed better than the varclus algorithm for item selection. Additionally, based on an automated machine learning item selection procedure, we provided the top three ranked item combinations with each possible subscale length, along with their corresponding algorithms for predicting BC classification and severity. This approach could accommodate the needs of different practitioners in traditional Chinese medicine for rapid constitution determination.

Background: Senna leaf is a commonly used medication for treating constipation, and long-term use can cause damage to the intestinal mucosa and lead to drug dependence. But the exact mechanism remains unclear. Objective: Using non-targeted metabolomics technology to study the mechanism of senna leaf ethanol extract (EESL) inducing inflammation and oxidative stress in mice and causing side effects. Methods: EESL was administered to mice by gavage to detect inflammation and oxidative stressrelated factors in mice, and the EESL components and differential metabolites in mouse plasma were analyzed using non-targeted metabolome techniques. Results: 23 anthraquinone compounds were identified in the EESL, including sennoside and their derivatives. Administration of EESL to mice resulted in a significant increase in pro-inflammatory factors, IL-1β, and IL-6 in the plasma, while the levels of IgA significantly decreased. The levels of oxidative stress significantly increased, and the intestinal mucosal integrity was impaired. 21 endogenous in plasma metabolites were identified as differential metabolites related with taurine and taurine metabolism, glycerophospholipid metabolism, arachidonic acid metabolism, tryptophan metabolism, and sphingolipid metabolism. These metabolic pathways are related to oxidative stress and inflammation. Conclusion: Senna leaf can inhibit the expression of tight junction proteins in the intestinal mucosa and disrupt intestinal mucosal barrier integrity, exacerbating oxidative stress and inflammation induced by bacterial LPS entering the bloodstream. In addition, the impact of Senna leaf on tryptophan metabolism may be linked to the occurrence of drug dependence.

The gut microbiome has been implicated in various human diseases, though findings across studies have shown considerable variability. In this study, we reanalyzed 6314 publicly available fecal metagenomes from 36 case-control studies on different diseases to investigate microbial diversity and disease-shared signatures. Using a unified analysis pipeline, we observed reduced microbial diversity in many diseases, while some exhibited increased diversity. Significant alterations in microbial communities were detected across most diseases. A meta-analysis identified 277 disease-associated gut species, including numerous opportunistic pathogens enriched in patients and a depletion of beneficial microbes. A random forest classifier based on these signatures achieved high accuracy in distinguishing diseased individuals from controls (AUC = 0.776) and high-risk patients from controls (AUC = 0.825), and it also performed well in external cohorts. These results offer insights into the gut microbiome’s role in common diseases in the Chinese population and will guide personalized disease management strategies.

IntroductionThe gut microbiota is believed to be directly involved in the etiology and development of chronic liver diseases. However, the holistic characterization of the gut bacteriome, mycobiome, and virome in patients with chronic hepatitis B-related liver fibrosis (CHB-LF) remains unclear.MethodsIn this study, we analyzed the multi-kingdom gut microbiome (i.e., bacteriome, mycobiome, and virome) of 25 CHB-LF patients and 28 healthy individuals through whole-metagenome shotgun sequencing of their stool samples.ResultsWe found that the gut bacteriome, mycobiome, and virome of CHB-LF patients were fundamentally altered, characterized by a panel of 110 differentially abundant bacterial species, 16 differential fungal species, and 90 differential viruses. The representative CHB-LF-enriched bacteria included members of Blautia_A (e.g., B. wexlerae, B. massiliensis, and B. obeum), Dorea (e.g., D. longicatena and D. formicigenerans), Streptococcus, Erysipelatoclostridium, while some species of Bacteroides (e.g., B. finegoldii and B. thetaiotaomicron), Faecalibacterium (mainly F. prausnitzii), and Bacteroides_A (e.g., B. plebeius_A and B. coprophilus) were depleted in patients. Fungi such as Malassezia spp. (e.g., M. japonica and M. sympodialis), Candida spp. (e.g., C. parapsilosis), and Mucor circinelloides were more abundant in CHB-LF patients, while Mucor irregularis, Phialophoraverrucosa, Hortaea werneckii, and Aspergillus fumigatus were decreases. The CHB-LF-enriched viruses contained 18 Siphoviridae, 12 Myoviridae, and 1 Podoviridae viruses, while the control-enriched viruses included 16 Siphoviridae, 9 Myoviridae, 2 Quimbyviridae, and 1 Podoviridae_crAss-like members. Moreover, we revealed that the CHB-LF-associated gut multi-kingdom signatures were tightly interconnected, suggesting that they may act together on the disease. Finally, we showed that the microbial signatures were effective in discriminating the patients from healthy controls, suggesting the potential of gut microbiota in the prediction of CHB-LF and related diseases.DiscussionIn conclusion, our findings delineated the fecal bacteriome, mycobiome, and virome landscapes of the CHB-LF microbiota and provided biomarkers that will aid in future mechanistic and clinical intervention studies.

Introduction:Prolonged fasting is an intervention approach with potential benefits for individuals with obesity or metabolic disorders. Changes in gut microbiota during and after fasting may also have significant effects on the human body.Methods:Here we conducted a 7-days medically supervised water-only fasting for 46 obese volunteers and characterized their gut microbiota based on whole-metagenome sequencing of feces at five timepoints.Results:Substantial changes in the gut microbial diversity and composition were observed during fasting, with rapid restoration after fasting. The ecological pattern of the microbiota was also reassembled during fasting, reflecting the reduced metabolic capacity of diet-derived carbohydrates, while other metabolic abilities such as degradation of glycoproteins, amino acids, lipids, and organic acid metabolism, were enhanced. We identified a group of species that responded significantly to fasting, including 130 fasting-resistant (consisting of a variety of members of Bacteroidetes, Proteobacteria, and Fusobacteria) and 140 fasting-sensitive bacteria (mainly consisting of Firmicutes members). Functional comparison of the fasting-responded bacteria untangled the associations of taxon-specific functions (e.g., pentose phosphate pathway modules, glycosaminoglycan degradation, and folate biosynthesis) with fasting. Furthermore, we found that the serum and urine metabolomes of individuals were also substantially changed across the fasting procedure, and particularly, these changes were largely affected by the fasting-responded bacteria in the gut microbiota.Discussion:Overall, our findings delineated the patterns of gut microbiota alterations under prolonged fasting, which will boost future mechanistic and clinical intervention studies.

Objective The gut microbial composition has been linked to metabolic and autoimmune diseases, including arthritis. However, there is a dearth of knowledge on the gut bacteriome, mycobiome, and virome in patients with gouty arthritis (GA). Methods We conducted a comprehensive analysis of the multi-kingdom gut microbiome of 26 GA patients and 28 healthy controls, using whole-metagenome shotgun sequencing of their stool samples. Results Profound alterations were observed in the gut bacteriome, mycobiome, and virome of GA patients. We identified 1,117 differentially abundant bacterial species, 23 fungal species, and 4,115 viral operational taxonomic units (vOTUs). GA-enriched bacteria included Escherichia coli_D GENOME144544, Bifidobacterium infantis GENOME095938, Blautia_A wexlerae GENOME096067, and Klebsiella pneumoniae GENOME147598, while control-enriched bacteria comprised Faecalibacterium prausnitzii_G GENOME147678, Agathobacter rectalis GENOME143712, and Bacteroides_A plebeius_A GENOME239725. GA-enriched fungi included opportunistic pathogens like Cryptococcus neoformans GCA_011057565, Candida parapsilosis GCA_000182765, and Malassezia spp., while control-enriched fungi featured several Hortaea werneckii subclades and Aspergillus fumigatus GCA_000002655. GA-enriched vOTUs mainly attributed to Siphoviridae , Myoviridae , Podoviridae , and Microviridae , whereas control-enriched vOTUs spanned 13 families, including Siphoviridae , Myoviridae , Podoviridae , Quimbyviridae , Phycodnaviridae , and crAss-like . A co-abundance network revealed intricate interactions among these multi-kingdom signatures, signifying their collective influence on the disease. Furthermore, these microbial signatures demonstrated the potential to effectively discriminate between patients and controls, highlighting their diagnostic utility. Conclusions This study yields crucial insights into the characteristics of the GA microbiota that may inform future mechanistic and therapeutic investigations.