Characterization Of Gut Microbiota Research Articles

Gut Microbiota Diversity and Composition Is Associated with High-Risk Myeloma

Background: High-dose melphalan conditioning followed by autologous stem cell transplantation (ASCT) is standard therapy for newly diagnosed multiple myeloma (MM) patients with standard-risk (SR) or high-HR disease. Despite significant improvements in the depth of response by induction therapy prior to ASCT and consolidation and maintenance therapy post-ASCT, most MM patients with HR cytogenetics relapse quickly post-ASCT with a median PFS ranging between 1 and 2 years. It remains unclear as to why patients identified as relapse earlier than those with SR disease (SR). We ask whether the variability in outcome is due to differences in the diversity and composition of the gut microbiome given its role as a predictor and modulator of tumorigenesis, anti-tumor immunity and response to therapy. Two preclinical studies have shown progression of MM is accelerated by a T H17-associated gut commensal (Calcinotto et al. Nat Commun, 2018) and nitrogen-cycling bacteria (Zhu et al. Microbiome, 2020). In MM patients, lower peri-engraftment diversity predicted inferior outcomes after ASCT (Khan et al. Blood, 2021) and sustained minimal residual disease-negativity was associated with butyrate-producing bacteria and plant-based diets (Shah et al. Clin Cancer Res, 2022). In this study, we analyzed the association between gut microbiota and HR cytogenetic features in MM patients in the ASCT setting. We also determined the cause-and-effect relationship between gut microbiota and disease progression in the 5TGM1 mouse model. Methods and Results: We prospectively collected stool samples from 55 MM patients at time of melphalan conditioning (pre-ASCT), engraftment (72hrs of absolute neutrophil count ≥ 500/mcL), and 60-120 days post-ASCT. The median age of patients (35 males, 20 females) was 63.1 (range 45.6-77.2) years. After a median follow-up of 54 months (range 1-81), the median PFS of patients was 37 months (range 1- 76). We profiled the gut microbiome of SR (n=29) and HR (n=26) MM patients using 16S rRNA sequencing of the V4-V5 region. Amplicon sequence variants (ASV) were annotated against the NCBI 16S database using BLAST. We chose to identify gut microbiota features associated with HR cytogenetics in samples during peri-engraftment when microbial dysbiosis and impaired immune function is most pronounced. The alpha diversity was higher in HR patients using Inverse Diversity index (p<0.020). However, we did not observe significant differences in beta-diversity between groups. Random forest prediction classifier method and Shapley Additive exPlanations (SHAP), a post-hoc explainability algorithm identified Schaalia, Streptococcus, Bifidobacterium with the highest variable of importance (Fig. 1A) These findings were further validated by linear discriminant analysis of effect size (LEfSe). To establish causality, we generated two isogenic C57BL/KaLwRij strains that harbor differences in the complexity and richness of naturally occurring gut microbiota, using complex microbiota targeted rederivation. We observed significant baseline differences in richness (p<0.001), β-diversity (Bray Curtis, p=0.0001), hierarchical clustering and relative abundance among GM1 (low richness), GM4 (intermediate richness) and our original (Orig) GM strain (high richness) bred in our vivarium. The median overall survival for MM-bearing GM1, GM4 Orig GM mice were 31, 35 and 39 days post-5TGM1 transplantation (Fig. 1B). Conclusions: These studies suggest that distinct gut microbiota signatures are associated with HR and SR disease. Furthermore, our preclinical findings suggest a causal relationship between gut microbiota and severity of disease further highlighting the influence of the gut microbiome on progression of disease. Taken together, these findings provide the rationale for microbiota-centered interventions to improve immune-mediated control of MM and overturn resistance to therapy.

TIGIT+ NK cells in combination with specific gut microbiota features predict response to checkpoint inhibitor therapy in melanoma patients

BackgroundComposition of the intestinal microbiota has been correlated to therapeutic efficacy of immune checkpoint inhibitors (ICI) in various cancer entities including melanoma. Prediction of the outcome of such therapy, however, is still unavailable. This prospective, non-interventional study was conducted in order to achieve an integrated assessment of the connection between a specific intestinal microbiota profile and antitumor immune response to immune checkpoint inhibitor therapy (anti-PD-1 and/or anti-CTLA-4) in melanoma patients.MethodsWe assessed blood and stool samples of 29 cutaneous melanoma patients who received immune checkpoint inhibitor therapy. For functional and phenotypical immune analysis, 12-color flow cytometry and FluoroSpot assays were conducted. Gut microbiome was analyzed with shotgun metagenomics sequencing. To combine clinical, microbiome and immune variables, we applied the Random Forest algorithm.ResultsA total of 29 patients was analyzed in this study, among whom 51.7% (n = 15) reached a durable clinical benefit. The Immune receptor TIGIT is significantly upregulated in T cells (p = 0.0139) and CD56high NK cells (p = 0.0037) of responders. Several bacterial taxa were associated with response (e.g. Ruminococcus torques) or failure (e.g. Barnesiella intestinihominis) to immune therapy. A combination of two microbiome features (Barnesiella intestinihominis and the Enterobacteriaceae family) and one immune feature (TIGIT+ CD56high NK cells) was able to predict response to ICI already at baseline (AUC = 0.85; 95% CI: 0.841–0.853).ConclusionsOur results reconfirm a link between intestinal microbiota and response to ICI therapy in melanoma patients and furthermore point to TIGIT as a promising target for future immunotherapies.

Multi-omics data reveals aberrant gut microbiota-host glycerophospholipid metabolism in association with neuroinflammation in APP/PS1 mice

ABSTRACT Numerous studies have described the notable impact of gut microbiota on the brain in Alzheimer’s disease (AD) via the gut – brain axis. However, the molecular mechanisms underlying the involvement of gut microbiota in the development of AD are limited. This study aimed to explore the potential mechanisms of gut microbiota in AD by integrating multi-omics data. In this study, APP/PS1 and WT mice at nine months of age were used as study mouse model. Cognitive function was assessed using the Morris water maze test. The levels of Aβ plaque and neuroinflammation in the brain were detected using immunofluorescence and PET/CT. In addition, we not only used 16S rRNA gene sequencing and metabolomics to explore the variation characteristics of gut microbiota and serum metabolism abundance, but also combined spatial metabolomics and transcriptomics to explore the change in the brain and identify their potential correlation. APP/PS1 mice showed significant cognitive impairment and amyloid-β deposits in the brain. The abundance of gut microbiota was significantly changed in APP/PS1 mice, including decreased Desulfoviobrio, Enterococcus, Turicibacter, and Ruminococcus and increased Pseudomonas. The integration of serum untargeted metabolomics and brain spatial metabolomics showed that glycerophospholipid metabolism was a common alteration pathway in APP/PS1 mice. Significant proliferation and activation of astrocyte and microglia were observed in APP/PS1 mice, accompanied by alterations in immune pathways. Integration analysis and fecal microbiota transplantation (FMT) intervention revealed potential association of gut microbiota, host glycerophospholipid metabolism, and neuroinflammation levels in APP/PS1 mice.

Сhanges in the composition of gut microbiota in patients with chronic hepatitis С, non-alcoholic fatty liver disease at different stages of liver disease: a review

INTRODUCTION. The human gut microbiota is a set of microorganisms that are in symbiosis with the host organism. The predominance of bacteria of Bacteroidetes, Firmicutes, Actinobacteria types is a characteristic feature of human gut microbiota. However, the composition of the gut microbiota is subject to change under the influence of a number of factors. The influence of the gut-liver axis on the pathogenesis of many chronic liver diseases of various etiologies, both infectious and metabolic, has been proven. At the same time, in the analyzed literature, the data on the relationship between the nature of GM changes and the stage of liver damage are quite controversial.
 OBJECTIVE. Updating and systematization of ideas about the nature of changes in the composition of intestinal microbiota in chronic hepatitis C (CHC) and non-alcoholic fatty liver disease (NAFLD) at different stages of the disease.
 MATERIALS AND METHODS. We analyzed the scientific literature on GM changes in chronic liver diseases with regard to their etiology and disease stage, in particular in chronic viral hepatitis C and NAFLD. The search was conducted in PubMed, Google Scholar, and eLIBRARY databases for 2000-2023 using the following keywords: gut microbiota; microbiota in hepatitis C; microbiota in NAFLD; microbiota in liver fibrosis; liver fibrosis progression. Sixty scientific articles were analyzed and 42 sources were selected, of which more than 60% were published within the last five years.
 RESULTS. This review presents the results of original studies describing the main taxonomic differences between the microbiomes of healthy individuals and patients with CHC complicated by the formation of liver cirrhosis and hepatocellular carcinoma (HCC). Thus, patients with viral cirrhosis showed impoverishment of intestinal commensals Lachnospiraceae and Ruminococcaceae and significant enrichment of Streptococcaceae, Lactobacillaceae and Enterobacteriaceae. In patients with non-alcoholic steatohepatitis (NASH) and FII, enrichment of GM Phylum Bacteroides and Proteobacteria, Families Lachnospiraceae and Enterobacteriaceae, Genera Blautia and Escherichia, with a concomitant decrease of Prevotella was detected.
 DISCUSSION. The analysis of GM taxonomic units in the previously described studies did not reveal significant differences depending on the etiology of the underlying disease. However, a decrease in the representative of the phylum Firmicutes - Ruminococcaceae was observed in CHC in contrast to NAFLD. A more distinct relationship can be observed in the changes of intestinal microbiota in F(III-IV) irrespective of etiology. Thus, in advanced liver fibrosis and cirrhosis of both CHC and NAFLD etiologies, enrichment of GM has been described due to an increase in phylum Proteobacteria, in particular the genus Escherichia and phylum Firmicutes (Blautia, Veillonellaceae). In the studies analyzed, at F(III-IV), an enrichment of the microbiota is observed due to an increase in the phylum Bacteroidetes. Regarding the genus Prevotella, contradictory results were found. In addition, there are ambiguous data regarding the genera Lactobacillus, Ruminococcaceae and Bifidobacteria.
 CONCLUSION. Based on the results of the literature review, we obtained contradictory data on GM composition in patients with CHC and NAFLD. This may be due to the size of samples, heterogeneity of initial data during the selection of patients in the study. The relationship between the composition of microbiota and persistent inflammatory process, expressed liver fibrosis is observed. GM reflects not only functional disorders in chronic liver diseases of various etiologies, but can also serve as a diagnostic indicator of their progression.

Intestinal microbiome dysbiosis in alcohol-dependent patients and its effect on rat behaviors.

Intestinal microbiome dysbiosis is associated with psychiatric disease through the "microbiota-gut-brain" axis. Here, we revealed that there was obvious intestinal microbiome (including bacterial and fungal) dysbiosis in alcohol-dependent patients. Alcohol consumption seriously disturbs the gut equilibrium between bacteria and fungi, reduces the interactions among bacterial-fungal trans-kingdom, and increases intestinal permeability. Gut microbiota should be considered as a whole to study the development of alcohol dependence. The gut microbiome of alcohol-dependent patients increased the anxiety- and depression-like behavior in rats. The gut microbiota dysbiosis may promote the development of alcohol dependence by regulating the endogenous cholecystokinin (CCK) and related receptors. Hence, regulating the balance of gut microbiota and the endogenous CCK may be a potential strategy for reducing the risk of relapse in alcohol addiction patients.

Association of patient clinical and gut microbiota features with vancomycin-resistant enterococci environmental contamination in nursing homes: a retrospective observational study

Preventing transmission is crucial for reducing infections with multidrug-resistant organisms (MDROs) in nursing homes. To identify resident characteristics associated with MDRO spread, we investigated associations between patient characteristics and contamination of their proximate room surfaces with vancomycin-resistant enterococci (VRE). In this retrospective observational study, we used demographic and clinical data (including data on comorbidities, physical independence, catheter use within the past 30 days, and antibiotic exposure within the past 30 days) and surveillance cultures of patient body sites and room surfaces at enrolment and during weekly follow-up visits within the first month, and monthly thereafter (up to 6 months), in six US nursing homes collected in a previous clinical trial (September, 2016, to August, 2018). We did 16S rRNA gene sequencing on perirectal surveillance swabs to investigate the association between the gut microbiota and the culture status of participants and their rooms. We included 245 participants (mean age 72·5 years [SD 13·6]; 111 [45%] were men, 134 [55%] were women, 132 [54%] were non-Hispanic white, and 112 [46%] were African American). We collected 2802 participant samples and 5592 environmental samples. At baseline, VRE colonisation was present in 49 (20%) participants, with environmental surfaces being contaminated in 36 (73%) of these patients. Hand contamination among VRE-colonised participants was more common in those with environmental contamination compared with those without (50 [51%] of 99 vs seven [13%] of 55; p<0·0001). We found a correlation between hand contamination and both groin and perirectal colonisation and contamination of various high-touch room surfaces (Cohen's κ 0·43). We found participant microbiota composition to be associated with antibiotic receipt within the past 30 days (high-risk antibiotics p=0·011 and low-risk antibiotics p=0·0004) and participant VRE colonisation status, but not environmental contamination among VRE-colonised participants (participant only vs uncolonised p=0·071, both participant and environment vs uncolonised p=0·025, and participant only vs participant and environment p=0·29). Multivariable analysis to identify independent factors associated with VRE-colonised participants contaminating their environment identified antibiotic exposure (adjusted odds ratio 2·75 [95% CI 1·22-6·16]) and male sex (2·75 [1·24-6·08]) as being associated with increased risk of environmental contamination, and physical dependence as being associated with a reduced risk of environmental contamination (0·91 [0·83-0·99]). Our data support antibiotic use and interaction with proximal surfaces by physically independent nursing home residents as under-appreciated drivers of environmental contamination among VRE-colonised residents. Integrating resident hand-hygiene education and antimicrobial stewardship will strengthen efforts to reduce MDROs in nursing homes. US Centers for Disease Control and Prevention, National Institute of Health, Canadian Institutes of Health Research, and University of Michigan.

The Characteristics, Mechanisms and Therapeutics: Exploring the Role of Gut Microbiota in Obesity.

Presently, obesity has emerged as a significant global public health concern due to its escalating prevalence and incidence rates. The gut microbiota, being a crucial environmental factor, has emerged as a key player in the etiology of obesity. Nevertheless, the intricate and specific interactions between obesity and gut microbiota, along with the underlying mechanisms, remain incompletely understood. This review comprehensively summarizes the gut microbiota characteristics in obesity, the mechanisms by which it induces obesity, and explores targeted therapies centered on gut microbiota restoration.

Gut Microbiome (Bacteria, Fungi, and Viruses) and HIV Infection: Revealing Novel Treatment Strategies.

Plenty of research on microbial-viral interactions has revealed that some commensal microorganisms in the gut, including bacteria, fungi, and viruses, can resist or promote viral infection, whereas other microorganisms are involved in pathogenicity. Therefore, the balance between commensal microorganisms and human organisms is a key factor for determining infection and disease progression, and commensal microorganisms have become a hot research area in the medical field. In this review, the compositional characteristics of gut microbiota (bacteria, fungi, and viruses) during HIV infection are reviewed and changes in gut microbiota among different HIV-infected populations are described. Furthermore, the latest progress of potential microbial therapeutic methods, including a) probiotics, prebiotics, and synbiotics, b) fecal microbiota transplantation (FMT), c) phage therapy, and d) antifungal strategy, microbial enzyme inhibition, and dietary therapeutics, is analyzed based on gut bacteria, fungi, and viruses in the field of HIV infection. This study aims to provide a useful reference for developing novel strategies for the prevention and treatment of HIV infection based on commensal microorganisms.

Characteristics of gut microbiota and metabolic phenotype in patients with major depressive disorder based on multi-omics analysis

Depression is a chronic, relapsing mental illness, often accompanied by loss of appetite, increased fatigue, insomnia and poor concentration. Here, we performed serum and urine metabolomics and fecal 16S rDNA sequencing studies on 57 unmedicated patients with major depressive disorder (MDD) and 57 healthy controls to characterize the metabolic and flora profile of MDD patients. We observed significant differences in serum and urinary metabolome between MDD patients and healthy individuals. Specifically, glycerophospholipid metabolism, primary bile acid biosynthesis and linoleic acid metabolism were significantly disordered in serum, and aminoacyl-tRNA biosynthesis, arginine biosynthesis, purine metabolism, phenylalanine metabolism, alanine, aspartate and glutamate metabolism, and pyrimidine metabolism were significantly impaired in urine. On this basis, we identified four potential diagnostic biomarkers for carnitine and four fatty acid classes in serum and urine, respectively. In addition, we observed significant disturbances of the gut microbiota in MDD patients. Spearman correlation analysis showed that imbalances in the gut microbiota were associated with metabolic disturbances, suggesting an important role of the gut microbiota in the pathogenesis of MDD. Our study provides a theoretical basis for further understanding of the pathogenesis of depression and for future clinical diagnosis and screening, as well as a basis for targeting the gut flora to optimize its structure for the prevention and treatment of depression.

The association between BMI and serum uric acid is partially mediated by gut microbiota.

Obesity is a risk factor for the development of hyperuricemia, both of which were related to gut microbiota. However, whether alterations in the gut microbiota lie in the pathways mediating obesity's effects on hyperuricemia is less clear. Body mass index (BMI) and serum uric acid (SUA) were separately important indicators of obesity and hyperuricemia. Our study aims to investigate whether BMI-related gut microbiota characteristics would mediate the association between BMI and SUA levels. A total of 6,280 participants from Guangdong Gut Microbiome Project were included in this study. Stool samples were collected for 16S rRNA gene sequencing. The results revealed that BMI was significantly and positively associated with SUA. Meanwhile, BMI was significantly associated with the abundance of 102 gut microbial genera, 16 of which were also significantly associated with SUA. The mediation analysis revealed that the association between BMI and SUA was partially mediated by the abundance of Proteobacteria (proportion mediated: 0.94%, P < 0.05). At the genus level, 25 bacterial genera, including Ralstonia, Oscillospira, Faecalibacterium, etc., could also partially mediate the association of BMI with SUA (the highest proportion is mediated by Ralstonia, proportion mediated: 2.76%, P < 0.05). This study provided evidence for the associations among BMI, gut microbiota, and SUA, and the mediation analysis suggested that the association of BMI with SUA was partially mediated by the gut microbiota. IMPORTANCE Using 16S rRNA sequencing analysis, local interpretable machine learning technique analysis and mediation analysis were used to explore the association between BMI with SUA, and the mediating effects of gut microbial dysbiosis in the association were investigated.

Tumor-associated microbiota in colorectal cancer with vascular tumor thrombus and neural invasion and association with clinical prognosis.

Neural invasion (NI) and vascular tumor thrombus (VT) are associated with poor prognosis in patients with colorectal cancer (CRC). In this study, we apply 16S rRNA amplicon sequencing to tumor tissues and adjacent normal tissues in patients with CRC to determine the microbial differences. A discovery cohort, including 30 patients with NI, 23 with VT, and 35 with double-negative CRC tissue, is utilized. Then, we analyze the relationship between the specific bacterial taxa and indicators of different dimensions in separate cohorts. In the discovery cohort, the diversity and composition of the gut microbiome distinctly differ between the tumor and nontumor tissues in the NI and VT groups. A high abundance of Cupriavidus is found to be related to a short survival time of NI CRC, while Herbaspirillum is a potential microbial biomarker predicting the prognosis of patients with CRC with NI or VT. Moreover, the abundance of Cupriavidus or Herbaspirillum is associated with some clinical patient characteristics and prognosis, respectively. In conclusion, this study is the first to comprehensively elaborate the differences in the gut microbiota of patients with CRC with different invasion statuses and to prove the relationship between some gut microbiota and clinical patient characteristics.

The link between the gut microbiome, inflammation, and Parkinson's disease.

As our society ages, the growing number of people with Parkinson's disease (PD) puts tremendous pressure on our society. Currently, there is no effective treatment for PD, so there is an urgent need to find new treatment options. In recent years, increasing studies have shown a strong link between gut microbes and PD. In this review, recent advances in research on gut microbes in PD patients were summarized. Increased potential pro-inflammatory microbes and decreased potential anti-inflammatory microbes are prominent features of gut microbiota in PD patients. These changes may lead to an increase in pro-inflammatory substances (such as lipopolysaccharide and H2S) and a decrease in anti-inflammatory substances (such as short-chain fatty acids) to promote inflammation in the gut. This gut microbiota-mediated inflammation will lead to pathological α-synuclein accumulation in the gut, and the inflammation and α-synuclein can spread to the brain via the microbiota-gut-brain axis, thereby promoting neuroinflammation, apoptosis of dopaminergic neurons, and ultimately the development of PD. This review also showed that therapies based on gut microbiota may have a bright future for PD. However, more research and new approaches are still needed to clarify the causal relationship between gut microbes and PD and to determine whether therapies based on gut microbiota are effective in PD patients. KEY POINTS: • There is a strong association between gut microbes and PD. • Inflammation mediated by gut microbes may promote the development of PD. • Therapies based on the gut microbiome provide a promising strategy for PD prevention.

The gut microbiota as a potential biomarker for methamphetamine use disorder: evidence from two independent datasets.

Methamphetamine use disorder (MUD) poses a considerable public health threat, and its identification remains challenging due to the subjective nature of the current diagnostic system that relies on self-reported symptoms. Recent studies have suggested that MUD patients may have gut dysbiosis and that gut microbes may be involved in the pathological process of MUD. We aimed to examine gut dysbiosis among MUD patients and generate a machine-learning model utilizing gut microbiota features to facilitate the identification of MUD patients. Fecal samples from 78 MUD patients and 50 sex- and age-matched healthy controls (HCs) were analyzed by 16S rDNA sequencing to identify gut microbial characteristics that could help differentiate MUD patients from HCs. Based on these microbial features, we developed a machine learning model to help identify MUD patients. We also used public data to verify the model; these data were downloaded from a published study conducted in Wuhan, China (with 16 MUD patients and 14 HCs). Furthermore, we explored the gut microbial features of MUD patients within the first three months of withdrawal to identify the withdrawal period of MUD patients based on microbial features. MUD patients exhibited significant gut dysbiosis, including decreased richness and evenness and changes in the abundance of certain microbes, such as Proteobacteria and Firmicutes. Based on the gut microbiota features of MUD patients, we developed a machine learning model that demonstrated exceptional performance with an AUROC of 0.906 for identifying MUD patients. Additionally, when tested using an external and cross-regional dataset, the model achieved an AUROC of 0.830. Moreover, MUD patients within the first three months of withdrawal exhibited specific gut microbiota features, such as the significant enrichment of Actinobacteria. The machine learning model had an AUROC of 0.930 for identifying the withdrawal period of MUD patients. In conclusion, the gut microbiota is a promising biomarker for identifying MUD and thus represents a potential approach to improving the identification of MUD patients. Future longitudinal studies are needed to validate these findings.

Responses of the gut microbiota to environmental heavy metal pollution in tree sparrow (Passer montanus) nestlings

Gut microbiota plays a critical role in regulating the health and adaptation of wildlife. However, our understanding of how exposure to environmental heavy metals influences the gut microbiota of wild birds, particularly during the vulnerable and sensitive nestling stage, remains limited. In order to investigate the relationship between heavy metals and the gut microbiota, we analyzed the characteristics of gut microbiota and heavy metals levels in tree sparrow nestlings at different ages (6, 9 and 12-day-old). The study was conducted in two distinct areas: Baiyin (BY), which is heavily contaminated with heavy metals, and Liujiaxia (LJX), a relatively unpolluted area. Our result reveled a decrease in gut microbiota diversity and increased inter-individual variation among nestlings in BY. However, we also observed an increase in the abundance of bacterial groups and an up-regulation of bacterial metabolic functions associated with resistance to heavy metals toxicity in BY. Furthermore, we identified a metal-associated shift in the relative abundance of microbial taxa in 12-day-old tree sparrow nestlings in BY, particularly involving Aeromonadaceae, Ruminococcaceae and Pseudomonadaceae. Moreover, a significant positive correlation was found between the body condition of tree sparrow nestlings and the abundance of Bifidobacteriaceae in BY. Collectively, our findings indicate that the gut microbiota of tree sparrow nestlings is susceptible to heavy metals during early development. However, the results also highlight the presence of adaptive responses that enable them to effectively cope with environmental heavy metal pollution.

Impact of clozapine monotherapy on gut microbiota and metabolism in people with schizophrenia.

Clozapine is considered one of the most effective antipsychotic drugs, but it is most likely to cause metabolic abnormalities. Researchers have studied the causes of metabolic abnormalities caused by clozapine from multiple perspectives, but the reasons remain unclear. Characterize the gut microbiota of people with schizophrenia taking clozapine, exploring the association between gut microbiota and glucose lipid metabolic markers in schizophrenia patients taking clozapine. Sixty-one long-term inpatients with schizophrenia in clozapine monotherapy were selected as study subjects. We got four subgroups by sex and the presence of metabolic syndrome. 16s analysis technology was applied at the genus level to determine the classification of gut microbiota. Then we compared the characteristics of gut microbiota and the association of gut microbiota with glucose lipid metabolic markers in each group. We found differences in the diversity of gut microbiota among groups. The association between gut microbiota and glucose lipid metabolic markers was complicated. Gender was an important differentiating factor. Oscillibacter has a low abundance. However, it was the only genus associated with glycemic or lipids in each group. Among metabolic syndromes, Gemmiger was positively correlated with most lipids in females but negatively correlated in males, showing gender differences. In female non-metabolic syndromes, Bifidobacterium lost its probiotic character; instead, showing pathogenicity, which has strong positive correlations with fasting blood glucose and low-density lipoprotein but negative correlations with Apolipoprotein A1. Maybe schizophrenia, taking clozapine, and gender factors influenced the gut microbiota, which complicated our findings. The significance of the results remains to be determined by in-depth studies.

Unraveling the gut microbiota and short-chain fatty acids characteristics and associations in a cancer cachexia mouse model

ObjectiveCachexia is a common pathological condition in cancer patients, affecting prognosis and treatment outcomes. The relationship between cachexia and gut microbiota and short-chain fatty acids (SCFAs) remains understudied. This research aimed to establish a cachexia mouse model and explore the gut microbiota-SCFAs connection. The study provides fundamental insights into the regulatory mechanisms of cancer cachexia and potential therapeutic strategies. MethodsA cachexia mouse model was created using C26 cells, with relevant indicators measured. Histological and immunohistochemical analyses assessed muscle structure and protein expression. ELISA was performed to detect the levels of IL-1β, IL-6, TNF-α, and LPS in serum to evaluate inflammation.16S rDNA sequencing and GC-MS quantified gut microbiota and SCFAs. Bioinformatics analysis identified indicator species and explored microbiota-SCFAs correlations.ROC analysis was performed to assess the potential of gut microbiota and SCFAs in identifying cachexia. ResultsThe cachexia mouse model exhibited weight loss, muscle atrophy, and elevated inflammatory factors. Gut microbiota in cachexia mice showed decreased diversity and imbalance. Fourteen bacterial genera were identified as potential cachexia indicators. Functional prediction indicated alterations in the functional composition of gut microbial communities in cachexia mice, particularly in carbohydrate and lipid metabolism pathways. Four SCFAs showed significant changes, potentially serving as diagnostic factors. Specific microbial taxa were positively or negatively correlated with changes in SCFAs, and these microbial taxa and differential SCFAs were also correlated with inflammatory cytokines. ConclusionOur study uncovers the gut microbiota and SCFAs features in a cachexia mouse model, revealing novel correlations between them. These newfound insights into the interplay between cachexia, gut microbiota, and SCFAs provide a crucial foundation for understanding the mechanisms behind cancer cachexia development and potential therapeutic approaches.

Revealing the role of gut microbiota in immune regulation and organ damage in sepsis using 16s rRNA and untargeted metabolomics

To analyze the composition and metabolites of gut microbiota in septic rats by fecal 16s rRNA sequencing and untargeted metabolomics, and to preliminarily explore the effect and potential mechanism of gut microbiota and its metabolites on inflammatory response and multiple organ damage in sepsis. Ten males healthy male Wistar rats were randomly divided into a sham operated group (Sham group) and sepsis model group (CLP group) using a random number table method, with 5 rats in each group. A rat sepsis model was established by cecal ligation and perforation (CLP) method. The animals were sacrificed 24 hours after modeling, the levels of tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6) in peripheral blood were detected by enzyme-linked immunosorbent assay (ELISA). Hematoxylin-eosin (HE) staining was used to observe the pathological changes of lung and kidney tissues, and the pathological scores were evaluated. Fecal samples were collected, and 16s rRNA high-throughput sequencing and non-targeted metabolomics were used to screen microbiota, metabolites and potential signal pathways that may play an important role in disease outcomes. Spearman correlation analysis was conducted to jointly analyze the gut microbiota and non-targeted metabolism. Compared with the Sham group, the degree of pathological damage to lung and kidney tissues in the CLP group was significantly increased (lung tissue score: 3.60±0.80 vs. 0.00±0.00, kidney tissue score: 2.40±0.80 vs. 0.00±0.00, both P < 0.01), the level of IL-6 and TNF-α in peripheral blood significantly increased [TNF-α (ng/L): 248.12±55.98 vs. 143.28±36.57, IL-6 (ng/L): 260.26±39.47 vs. 116.01±26.43, both P < 0.05], the species diversity of intestinal flora of rats in the CLP group was significantly reduced, the relative abundance of Morganella, Bacteroides and Escherichia-Shigella were significantly increased, and the relative abundance of Lachnospiraceae NK4A136, Ruminococcus, Romboutsia and Roseburia were significantly reduced. In addition, the biosynthesis and bile secretion of phenylalanine, tyrosine, and tryptophan in the gut microbiota of the CLP group were significantly increased, while the biosynthesis of secondary bile acids was significantly reduced. There was a significant correlation between differential metabolites and differential microbiota. Sepsis can cause significant changes in the characteristics of gut microbiota and fecal metabolites in rats, which provides a basis for translational research to seek new targets for the treatment of sepsis.

Gut microbial carbohydrate metabolism contributes to insulin resistance

Insulin resistance is the primary pathophysiology underlying metabolic syndrome and type 2 diabetes1,2. Previous metagenomic studies have described the characteristics of gut microbiota and their roles in metabolizing major nutrients in insulin resistance3–9. In particular, carbohydrate metabolism of commensals has been proposed to contribute up to 10% of the host’s overall energy extraction10, thereby playing a role in the pathogenesis of obesity and prediabetes3,4,6. Nevertheless, the underlying mechanism remains unclear. Here we investigate this relationship using a comprehensive multi-omics strategy in humans. We combine unbiased faecal metabolomics with metagenomics, host metabolomics and transcriptomics data to profile the involvement of the microbiome in insulin resistance. These data reveal that faecal carbohydrates, particularly host-accessible monosaccharides, are increased in individuals with insulin resistance and are associated with microbial carbohydrate metabolisms and host inflammatory cytokines. We identify gut bacteria associated with insulin resistance and insulin sensitivity that show a distinct pattern of carbohydrate metabolism, and demonstrate that insulin-sensitivity-associated bacteria ameliorate host phenotypes of insulin resistance in a mouse model. Our study, which provides a comprehensive view of the host–microorganism relationships in insulin resistance, reveals the impact of carbohydrate metabolism by microbiota, suggesting a potential therapeutic target for ameliorating insulin resistance.

Multi-Omics Analysis Reveals the Gut Microbiota Characteristics of Diarrheal Piglets Treated with Gentamicin.

The involvement of alterations in gut microbiota composition due to the use of antibiotics has been widely observed. However, a clear picture of the influences of gentamicin, which is employed for the treatment of bacterial diarrhea in animal production, are largely unknown. Here, we addressed this problem using piglet models susceptible to enterotoxigenic Escherichia coli (ETEC) F4, which were treated with gentamicin. Gentamicin significantly alleviated diarrhea and intestinal injury. Through 16s RNS sequencing, it was found that gentamicin increased species richness but decreased community evenness. Additionally, clear clustering was observed between the gentamicin-treated group and the other groups. More importantly, with the establishment of a completely different microbial structure, a novel metabolite composition profile was formed. KEGG database annotation revealed that arachidonic acid metabolism and vancomycin resistance were the most significantly downregulated and upregulated pathways after gentamicin treatment, respectively. Meanwhile, we identified seven possible targets of gentamicin closely related to these two functional pathways through a comprehensive analysis. Taken together, these findings demonstrate that gentamicin therapy for diarrhea is associated with the downregulation of arachidonic acid metabolism. During this process, intestinal microbiota dysbiosis is induced, leading to increased levels of the vancomycin resistance pathway. An improved understanding of the roles of these processes will advance the conception and realization of new therapeutic and preventive strategies.

Consistent features of the gut microbiota in response to diverse shrimp Litopenaeus vannamei diseases: A meta‐analysis

AbstractDysbiosis in the gut microbiota has been intimately implicated in shrimp (Litopenaeus vannamei, Penaeidae) diseases. However, considering the variety of shrimp diseases and the variability in experimental conditions, studies addressing common features of the gut microbiota−shrimp disease relationship are limited. Through an unbiased subject‐level meta‐analysis framework, 463 shrimp gut bacterial communities from 27 studies were re‐analysed, including six lifestages and eight diseases of shrimp, with the causal agents of viral, bacterial, eukaryotic, and unknown pathogens. Shrimp lifestages and diseases were the predominant factors governing the gut microbiota. After ruling out the top lifestage‐ and disease‐specific discriminatory amplicon sequence variants (ASVs) from the gut microbiota, the top 27 disease common‐discriminatory ASVs were identified, contributing to an overall accuracy of 95.9% in diagnosing shrimp health status. By using these optimisation procedures, the accuracy of our diagnosis model was unbiased by shrimp lifestage, specific disease, sampling size, hypervariable region and sequencing platform. The shrimp eight diseases consistently and significantly increased the relative importance of stochasticity, the relative abundance of pathogenic potentials and diversified core ASVs, whereas decreased the diversity and stability of gut microbiota. Collectively, these findings illustrate the effectiveness of meta‐analysis in determining the robust and common features of the shrimp gut microbiota in response to diverse diseases. In particular, disease common‐discriminatory ASVs could accurately diagnose shrimp health status, although the data are divergent in biotic and technical variances.