Gut Microbiome Profiles Research Articles

Plant-oriented microbiome inoculum modulates age-related maturation of gut-mucosal expression of innate immune and barrier function genes in suckling and weaned piglets.

In the immediate time after weaning, piglets often show symptoms of gut inflammation. The change to a plant-based diet, lack of sow milk, and the resulting novel gut microbiome and metabolite profile in digesta may be causative factors for the observed inflammation. We used the intestinal loop perfusion assay (ILPA) to investigate jejunal and colonic expression of genes for antimicrobial secretion, oxidative stress, barrier function, and inflammatory signaling in suckling and weaned piglets when exposed to "plant-oriented" microbiome (POM) representing postweaning digesta with gut-site specific microbial and metabolite composition. Two serial ILPA were performed in two replicate batches, with 16 piglets preweaning (days 24 to 27) and 16 piglets postweaning (days 38 to 41). Two jejunal and colonic loops were perfused with Krebs-Henseleit buffer (control) or with the respective POM for 2 h. Afterward, RNA was isolated from the loop tissue to determine the relative gene expression. Age-related effects in jejunum included higher expression of genes for antimicrobial secretions and barrier function as well as reduced expression of pattern-recognition receptors post- compared to preweaning (P < 0.05). Age-related effects in the colon comprised downregulation of the expression of pattern-recognition receptors post- compared to preweaning (P < 0.05). Likewise, age reduced the colonic expression of genes encoding for cytokines, antimicrobial secretions, antioxidant enzymes, and tight-junction proteins post- compared to preweaning. Effect of POM in the jejunum comprised an increased the expression of toll-like receptors compared to the control (P < 0.05), demonstrating a specific response to microbial antigens. Similarly, POM administration upregulated the jejunal expression of antioxidant enzymes (P < 0.05). The POM perfusion strongly upregulated the colonic expression of cytokines and altered the expression of barrier function genes, fatty acid receptors and transporters, and antimicrobial secretions (P < 0.05). In conclusion, results indicated that POM signaled via altering the expression of pattern-recognition receptors in the jejunum, which in turn activated the secretory defense and decreased mucosal permeability. In the colon, POM may have acted pro-inflammatory via upregulated cytokine expression. Results are valuable for the formulation of transition feeds for the immediate time after weaning to maintain mucosal immune tolerance towards the novel digesta composition.

Abstract C099: Examining the role of a whole food, plant-based diet on the gut microbiome, intramuscular fat, adiposity and risk for breast cancer in obese, post-menopausal African American women

Abstract Background: Obese postmenopausal African American (AA) women are at increased risk of breast cancer. Obesity and aging have been associated with changes in the gut microbiota. Plant-based diets, such as soy isoflavone consumption may protect against breast cancer development. Methods: Participant inclusion and exclusion criteria informed active recruitment and sampling methods for study participants. A mixed-methods, lifestyle study was conducted to evaluate adoption of a culturally tailored, whole food, plant-based diet and changes in risk for breast cancer among obese, post-menopausal African American women. In the qualitative phase, we examined acceptability and feasibility of adopting a whole-food, plant-based diet. In the clinical research, we examined the effect of the whole food, plant-based diet on reductions in weight, adiposity, inflammation, circulating estrogen, and gut microbiome profile. Results: In five focus group discussions, 20 African American women described their knowledge of breast cancer risk factors, attitude toward a plant-based diet, experience with making lifestyle changes, and preferences for plant-based alternatives to traditional meat-based meals. Twelve African American women completed the 4-week whole food, plant-based diet feeding study, regular DEXA body composition scans, and provided blood and stool samples analyses. Women in the qualitative phase, reported being pleased with the whole-food, plant-based selections; document adherence to the meals with food tracking; and experience an average of a five pound weight loss. Biomarker examination is underway. Conclusion: This study addresses knowledge gaps in breast cancer risk and behavioral modifications affecting risk among an aging, obese, post-menopausal African American female population. Discussion will be provided describing how the outcomes of adiposity, inflammation, circulating estrogen, and gut microbiome profile are measured. As well, explanation of the relationship between these mechanistic outcomes will be explored. Lessons learned and insight regarding attrition and the number of women who dropped out or declined to participate will be shared. The long-term goal of this program of research will shed light on the mechanisms involved in the gut microbiome, tissue integrity and function with aging, and the contributions of changes to these systems on declining health and function in obese, postmenopausal African American women at risk for breast cancer. Citation Format: Desiree Rivers, Kay Umeakunne, Muhammad Mubasher, Brian Rivers, Tiffany Carson. Examining the role of a whole food, plant-based diet on the gut microbiome, intramuscular fat, adiposity and risk for breast cancer in obese, post-menopausal African American women [abstract]. In: Proceedings of the 15th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2022 Sep 16-19; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr C099.

Slight Changes in the Gut Microbiome in Early-stage Chronic Kidney Disease of Unknown Etiology.

Gut dysbiosis and changes in short-chain fatty acids (SCFAs) occur in end-stage chronic kidney disease (CKD); however, the degree of these changes in the gut microbiome and serum SCFA profiles in the early stages of CKD,‍ ‍particularly in‍ ‍CKD‍ ‍of unknown etiology (CKDu), is unclear. We herein investigated the gut microbiome and SCFA profiles of early-stage CKD patients (CKD stages 1-3) in a community in Khon Kaen Province, Thailand. Seventy-two parasite-free participants were distributed among a healthy control group (HC, n=18) and three patient groups (an underlying disease group [UD, n=18], early-stage CKD with underlying disease [CKD-UD, n=18], and early-stage CKD of unknown etiology, [CKDu, n=18]). Fecal DNA was individually extracted and pooled for groups of six individuals (three pools in each group) to examine the composition of the gut microbiome using next-generation sequencing. A SCFA ana-lysis was performed on serum samples from each individual using gas chromatography-mass spectrometry. The results revealed that microbial abundance differed between the healthy group and all patient groups (UD, CKD-UD, and CKDu). [Eubacterium]_coprostanoligenes_group was more abundant in the CKDu group than in the HC and CKD-UD groups. Furthermore, serum concentrations of acetate, a major SCFA component, were significantly lower in all patient groups than in the HC group. The present results indicate that minor changes in the gut microbiome and a significant decrease in serum acetate concentrations occur in early-stage CKDu, which may be important for the development of prevention strategies for CKD patients.

Stability of human gut microbiome: Comparison of ecological modelling and observational approaches

The gut microbiome plays a pivotal role in the human body, and perturbations in its composition have been linked to various disorders. Stability is an essential property of a healthy human gut microbiome, which allows it to maintain its functional richness under the external influences. This property has been explored through two distinct methodologies - mathematical modelling based on ecological principles and statistical analysis drawn from observations in interventional studies. Here we conducted a meta-analysis aimed to compare the two approaches utilising the data from 9 interventional and time series studies encompassing 3512 gut microbiome profiles obtained via 16S rRNA gene sequencing. By employing the previously published compositional Lotka-Volterra method, we modelled the dynamics of the microbial community and evaluated ecological stability measures. These measures were compared to those based on observed microbiome changes. There was a substantial correlation between the outcomes of the two approaches. Particularly, local stability assessed within the ecological paradigm was positively correlated with observational stability measures accounting for the compositional nature of microbiome data. Additionally, we were able to reproduce the previously reported inverse relationship between the community's robustness to microorganism loss and local stability, attributed to the distinct impacts of coefficient characterising the network decomposition on these two stability assessments. Our findings demonstrate harmonisation between the ecological and observational approaches to microbiome analysis, advancing the understanding of healthy gut microbiome concept. This paves the way to develop efficient microbiome-targeting interventions for disease prevention and treatment.

Gut Microbiome and Serum Metabolome Profiles of Capsaicin with Cognitive Benefits in APP/PS1 Mice

Capsaicin, a natural bioactive component, has been reported to improve cognition and ameliorate the pathology of Alzheimer's disease (AD). Studies have linked AD to alterations in gut microbiota composition and serum metabolites. In the present study, we examined the alterations in serum metabolome and gut microbiome in APPswe/PS1dE9 (APP/PS1) mice treated with capsaicin. Capsaicin treatments resulted in a significant increase in the abundance of Akkermansia, Faecalibaculum, Unclassified_f_Atopobiaceae, and Gordonibacter and a significant decrease in the abundance of Adlercreutzia, Peptococcaceae, Alistipes, Oscillibacter and Erysipelatoclostridium. Furthermore, the species Akkermansia muciniphila (A. muciniphila) was significantly enriched in capsaicin-treated APP/PS1 mice (p = 0.0002). Serum metabolomic analysis showed that capsaicin-treated APP/PS1 mice had a significant higher level of tryptophan (Trp) metabolism and a significantly lower level of lipid metabolism compared with vehicle-treated mice. Capsaicin altered serum metabolites, including Kynurenine (Kyn), 5-Hydroxy-L-tryptophan (5-HIT), 5-Hydroxyindoleacetic acid (5-HIAA), indoxylsulfuric acid, lysophosphatidyl cholines (LysoPCs), and lysophosphatidyl ethanolamine (LysoPE). Significant correlations were observed between the gut bacteria and serum metabolite. With regard to the increased abundance of A. muciniphila and the ensuing rise in tryptophan metabolites, our data show that capsaicin alters both the gut microbiota and blood metabolites. By altering the gut microbiome and serum metabolome, a diet high in capsaicin may reduce the incidence and development of AD.

Multi-omics gut microbiome signatures in obese women: role of diet and uncontrolled eating behavior

BackgroundObesity and related co-morbidities represent a major health challenge nowadays, with a rapidly increasing incidence worldwide. The gut microbiome has recently emerged as a key modifier of human health that can affect the development and progression of obesity, largely due to its involvement in the regulation of food intake and metabolism. However, there are still few studies that have in-depth explored the functionality of the human gut microbiome in obesity and even fewer that have examined its relationship to eating behaviors.MethodsIn an attempt to advance our knowledge of the gut-microbiome-brain axis in the obese phenotype, we thoroughly characterized the gut microbiome signatures of obesity in a well-phenotyped Italian female cohort from the NeuroFAST and MyNewGut EU FP7 projects. Fecal samples were collected from 63 overweight/obese and 37 normal-weight women and analyzed via a multi-omics approach combining 16S rRNA amplicon sequencing, metagenomics, metatranscriptomics, and lipidomics. Associations with anthropometric, clinical, biochemical, and nutritional data were then sought, with particular attention to cognitive and behavioral domains of eating.ResultsWe identified four compositional clusters of the gut microbiome in our cohort that, although not distinctly associated with weight status, correlated differently with eating habits and behaviors. These clusters also differed in functional features, i.e., transcriptional activity and fecal metabolites. In particular, obese women with uncontrolled eating behavior were mostly characterized by low-diversity microbial steady states, with few and poorly interconnected species (e.g., Ruminococcus torques and Bifidobacterium spp.), which exhibited low transcriptional activity, especially of genes involved in secondary bile acid biosynthesis and neuroendocrine signaling (i.e., production of neurotransmitters, indoles and ligands for cannabinoid receptors). Consistently, high amounts of primary bile acids as well as sterols were found in their feces.ConclusionsBy finding peculiar gut microbiome profiles associated with eating patterns, we laid the foundation for elucidating gut-brain axis communication in the obese phenotype. Subject to confirmation of the hypotheses herein generated, our work could help guide the design of microbiome-based precision interventions, aimed at rewiring microbial networks to support a healthy diet-microbiome-gut-brain axis, thus counteracting obesity and related complications.

Gut microbial indicators of metabolic health underlie age-related differences in obesity and diabetes risk among Native Hawaiians and Pacific Islanders.

Native Hawaiians and Pacific Islanders (NHPIs) suffer from higher prevalence of and mortality to type 2 diabetes mellitus (T2DM) than any other major race/ethnic group in Hawaii. Health inequities in this indigenous population was further exacerbated by the SARS-CoV-2 pandemic. T2DM progression and medical complications exacerbated by COVID-19 are partially regulated by the gut microbiome. However, there is limited understanding of the role of gut bacteria in the context of inflammation-related diseases of health disparities including T2DM and obesity. To address these gaps, we used a community-based research approach from a cohort enriched with NHPI residents on the island of Oahu, Hawaii (N=138). Gut microbiome profiling was achieved via 16s rDNA metagenomic sequencing analysis from stool DNA. Gut bacterial capacity for butyrate-kinase (BUK)-mediated fiber metabolism was assessed using quantitative PCR to measure the abundance of BUK DNA and RNA relative to total bacterial load per stool sample. In our cohort, age positively correlated with hemoglobin A1c (%; R=0.39; P<0.001) and body mass index (BMI; R=0.28; P<0.001). The relative abundance of major gut bacterial phyla significantly varied across age groups, including Bacteroidetes (P<0.001), Actinobacteria (P=0.007), and Proteobacteria (P=0.008). A1c was negatively correlated with the relative levels of BUK DNA copy number (R=-0.17; P=0.071) and gene expression (R=-0.33; P=0.003). Interestingly, we identified specific genera of gut bacteria potentially mediating the effects of diet on metabolic health in this cohort. Additionally, α-diversity among gut bacterial genera significantly varied across T2DM and BMI categories. Together, these results provide insight into age-related differences in gut bacteria that may influence T2DM and obesity in NHPIs. Furthermore, we observed overlapping patterns between gut bacteria and T2DM risk factors, indicating more nuanced, interdependent interactions among these factors as partial determinants of health outcomes. This study adds to the paucity of NHPI-specific data to further elucidate the biological characteristics associated with pre-existing health inequities in this racial/ethnic group that is significantly underrepresented in biomedical research.

The gut microbiome dysbiosis is recovered by restoring anormal diet in hypercholesterolemic pigs.

Gut microbiota is thought to modulate cardiovascular risk. However, the effect of cardiovascular primary prevention strategies on gut microbiota remains largely unknown. This study investigates the impact of diet and rosuvastatin interventions on gut microbiota composition in hypercholesterolemic pigs and associated potential changes in host metabolic pathways. Diet-induced hypercholesterolemic pigs (n=32) were randomly distributed to receive one of the following 30-day interventions: (I) continued hypercholesterolemic diet (HCD; n=9), (II) normocholesterolemic diet (NCD; n=8), (III) continued HCD plus 40 mg rosuvastatin/daily (n=7), or (IV) NCD plus 40 mg rosuvastatin/daily (n=8). Faeces were collected at study endpoint for characterisation of the gut microbiome and metabolic profile prediction (PICRUSt2). TMAO levels and biochemical parameters were determined. Principal coordinate analyses (beta-diversity) showed clear differences in the microbiota of NCD vs HCD pigs (PERMANOVA, p=.001). NCD-fed animals displayed significantly higher alpha-diversity, which inversely correlated with total cholesterol and LDL-cholesterol levels (p < .0003). NCD and HCD animals differed in the abundance of 12 genera (ANCOM; p=.001 vs HCD), and PICRUSt2 analysis revealed detrimental changes in HCD-related microbiota metabolic capacities. These latter findings were associated with a significant fivefold increase in TMAO levels in HCD-fed pigs (p < .0001 vs NCD). The addition of a 30-day rosuvastatin treatment to either of the diets exerted no effects in microbiota nor lipid profile. In hypercholesterolemic animals, the ingestion of a low-fat diet for 30 days modifies gut microbiota composition in favour of alpha-diversity and towards a healthy metabolic profile, whereas rosuvastatin treatment for this period exerts no effects.

High-risk genotypes for type 1 diabetes are associated with the imbalance of gut microbiome and serum metabolites.

The profile of gut microbiota, serum metabolites, and lipids of type 1 diabetes (T1D) patients with different human leukocyte antigen (HLA) genotypes remains unknown. We aimed to explore gut microbiota, serum metabolites, and lipids signatures in individuals with T1D typed by HLA genotypes. We did a cross-sectional study that included 73 T1D adult patients. Patients were categorized into two groups according to the HLA haplotypes they carried: those with any two of three susceptibility haplotypes (DR3, DR4, DR9) and without any of the protective haplotypes (DR8, DR11, DR12, DR15, DR16) were defined as high-risk HLA genotypes group (HR, n=30); those with just one or without susceptibility haplotypes as the non-high-risk HLA genotypes group (NHR, n=43). We characterized the gut microbiome profile with 16S rRNA gene amplicon sequencing and analyzed serum metabolites with liquid chromatography-mass spectrometry. Study individuals were 32.5 (8.18) years old, and 60.3% were female. Compared to NHR, the gut microbiota of HR patients were characterized by elevated abundances of Prevotella copri and lowered abundances of Parabacteroides distasonis. Differential serum metabolites (hypoxanthine, inosine, and guanine) which increased in HR were involved in purine metabolism. Different lipids, phosphatidylcholines and phosphatidylethanolamines, decreased in HR group. Notably, Parabacteroides distasonis was negatively associated (p ≤ 0.01) with hypoxanthine involved in purine metabolic pathways. The present findings enabled a better understanding of the changes in gut microbiome and serum metabolome in T1D patients with HLA risk genotypes. Alterations of the gut microbiota and serum metabolites may provide some information for distinguishing T1D patients with different HLA risk genotypes.

Gut and lung microbiome profiles in pregnant mice.

In recent years, microbiome research has expanded from the gastrointestinal tract to other host sites previously thought to be abacterial such as the lungs. Yet, the effects of pregnancy in the lung and gut microbiome remains unclear. Here we examined the changes in the gut and lung microbiome in mice at 14 days of gestation. Lung tissue and stool samples were collected from pregnant and non-pregnant female BALB/c mice, DNA was isolated, amplified, and bacterial specific V4 16S rRNA gene was sequenced. Using an in-house bioinformatic pipeline we assessed the microbial composition of each organ using stool and lung tissue samples. The stool data showed that Lachnospiraceae and Lactobacillaceae were more abundant in the pregnant mice. Likewise, Lactobacillaceae were dominant in the lungs of pregnant mice. However, Streptococcaceae were dominant in the lungs of non-pregnant mice with a low microbial abundance in the pregnant mice. A permutation test showed that pregnancy significantly contributes to the variance in both the lung and stool microbiome. At the same time, we estimate that 49% of the total detected operational taxonomic units were shared between the stool and lung data. After removing common stool-associated bacteria from the lung dataset, no microbial differential abundance was detected between the pregnant and non-pregnant lung microbial community. Thus, pregnancy contributes to variance to the lung and stool microbiome but not in the unique lung microbiota.

Dietary Impacts on Gestational Diabetes: Connection between Gut Microbiome and Epigenetic Mechanisms.

Gestational diabetes mellitus (GDM) is one of the most common obstetric complications due to an increased level of glucose intolerance during pregnancy. The prevalence of GDM increases due to the obesity epidemic. GDM is also associated with an increased risk of gestational hypertension and preeclampsia resulting in elevated maternal and perinatal morbidity and mortality. Diet is one of the most important environmental factors associated with etiology of GDM. Studies have shown that the consumption of certain bioactive diets and nutrients before and during pregnancy might have preventive effects against GDM leading to a healthy pregnancy outcome as well as beneficial metabolic outcomes later in the offspring's life. Gut microbiome as a biological ecosystem bridges the gap between human health and diseases through diets. Maternal diets affect maternal and fetal gut microbiome and metabolomics profiles, which consequently regulate the host epigenome, thus contributing to later-life metabolic health in both mother and offspring. This review discusses the current knowledge regarding how epigenetic mechanisms mediate the interaction between maternal bioactive diets, the gut microbiome and the metabolome leading to improved metabolic health in both mother and offspring.

Multiomics signatures of type 1 diabetes with and without albuminuria.

To identify novel pathophysiological signatures of longstanding type 1 diabetes (T1D) with and without albuminuria we investigated the gut microbiome and blood metabolome in individuals with T1D and healthy controls (HC). We also mapped the functional underpinnings of the microbiome in relation to its metabolic role. One hundred and sixty-one individuals with T1D and 50 HC were recruited at the Steno Diabetes Center Copenhagen, Denmark. T1D cases were stratified based on levels of albuminuria into normoalbuminuria, moderate and severely increased albuminuria. Shotgun sequencing of bacterial and viral microbiome in stool samples and circulating metabolites and lipids profiling using mass spectroscopy in plasma of all participants were performed. Functional mapping of microbiome into Gut Metabolic Modules (GMMs) was done using EggNog and KEGG databases. Multiomics integration was performed using MOFA tool. Measures of the gut bacterial beta diversity differed significantly between T1D and HC, either with moderately or severely increased albuminuria. Taxonomic analyses of the bacterial microbiota identified 51 species that differed in absolute abundance between T1D and HC (17 higher, 34 lower). Stratified on levels of albuminuria, 10 species were differentially abundant for the moderately increased albuminuria group, 63 for the severely increased albuminuria group while 25 were common and differentially abundant both for moderately and severely increased albuminuria groups, when compared to HC. Functional characterization of the bacteriome identified 23 differentially enriched GMMs between T1D and HC, mostly involved in sugar and amino acid metabolism. No differences in relation to albuminuria stratification was observed. Twenty-five phages were differentially abundant between T1D and HC groups. Six of these varied with albuminuria status. Plasma metabolomics indicated differences in the steroidogenesis and sugar metabolism and circulating sphingolipids in T1D individuals. We identified association between sphingolipid levels and Bacteroides sp. abundances. MOFA revealed reduced interactions between gut microbiome and plasma metabolome profiles albeit polar metabolite, lipids and bacteriome compositions contributed to the variance in albuminuria levels among T1D individuals. Individuals with T1D and progressive kidney disease stratified on levels of albuminuria show distinct signatures in their gut microbiome and blood metabolome.

Abstract A030: Gut microbiome as a promising biomarker for colorectal cancer diagnosis and immunotherapy response prediction

Abstract Background and objective: The association between human intestinal microbes and disease is an emerging topic in microbiome research, with potential implications in cancer diagnosis. Meanwhile, immune checkpoint inhibitor (ICI) therapy has been extensively applied to cancer patients but with relatively low overall response rate owing to pathological complexity. Thus, accurate prediction of cancer onset as well as patients’ response to ICI treatment through non-invasive methods, such as gut microbiome profiling, could greatly facilitate cancer diagnosis and treatment practice, which essentially improves patients’ quality of life. With the rapid accumulation of microbiome sequencing data and advanced machine learning algorithms, the use of gut microbiome as predictive biomarkers presents a feasible approach to address these problems. Methods and results: The first model we built is a predictive model for classifying healthy people from patients with colon cancer (CRC). The data (originated from Curated Metagenomics Data of Human Microbiome) consists of 166 fecal samples from healthy population and equal number of CRC patients from two studies (PRJEB24748, PRJEB6070). The data were divided into training and testing sets with a 3:1 ratio. Using the training dataset, we developed a Gradient Boosting Decision Tree model. To demonstrate the model's generalizability, we evaluated it using the testing dataset and the model achieved an AUC score of 0.93 (accuracy: 89%). Moreover, using an external independent cohort (which included 52 CRC patients from PRJEB12449 and 52 healthy individuals), we obtained an AUC score of 0.94 (accuracy: 88%). We also detected bacteria highly related to CRC pathology including Solobacterium_moorei, which could be potential biomarkers for CRC diagnosis. In addition, to predict CRC patients’ response to ICI, we built a random forest model based on patients’ fecal microbiome data before the ICI treatment. In total, stool metagenomics data from 63 CRC patients were used, including 30 patients responding to ICI treatment and 33 non-responders. With five-fold cross-validation, we achieved training and testing AUC score of 0.89 and 0.72, respectively. With an external data with 11 CRC patients’ samples, the classifier achieved an AUC of 0.78. Several microbial taxa were listed as important features contributing to the classification, e.g. Eubacterium hallii – a butyrate producer, which previously report to have a positive association with ICI clinical benefits. Conclusion: Using the non-invasive human gut microbiome data and advanced machine learning techniques, we present here two models with great performance to predict the occurrence of CRC and clinical response to ICI treatment in CRC patients. Similar strategy could be readily transferred to other cancer types to improve early diagnosis and patient stratification. Moreover, the microbial taxa identified to be essential features in the model could be developed as potential therapeutic targets. Citation Format: Die Dai, Xiaomin Xu, Wei Shen, Zhengnong Zhu, Fang Li, Xiaochen Yin, Yan Kou. Gut microbiome as a promising biomarker for colorectal cancer diagnosis and immunotherapy response prediction [abstract]. In: Proceedings of the AACR Special Conference on Colorectal Cancer; 2022 Oct 1-4; Portland, OR. Philadelphia (PA): AACR; Cancer Res 2022;82(23 Suppl_1):Abstract nr A030.

Different Characteristics in Gut Microbiome between Advanced Adenoma Patients and Colorectal Cancer Patients by Metagenomic Analysis.

The occurrence and development of colorectal cancer (CRC) and advanced adenoma (AA) are closely related to the gut microbiome, and AA has a high cancerization progression rate to CRC. Current studies have revealed that bacteriological analysis cannot identify CRC from AA. The objective was to explore microbial targets that could identify CRC and AA from a microecological perspective and to figure out the best way to identify CRC based on fecal microbes. The metagenomic sequencing data were used to describe the gut microbiome profile and analyze the differences between microbial abundance and microbial single nucleotide polymorphism (SNP) characteristics in AA and CRC patients. It was found that there were no significant differences in the diversity between the two groups. The abundance of bacteria (e.g., Firmicutes, Clostridia, and Blautia), fungi (Hypocreales), archaea (Methanosarcina, Methanoculleus, and Methanolacinia), and viruses (Alphacoronavirus, Sinsheimervirus, and Gammaretrovirus) differed between AA and CRC patients. Multiple machine-learning algorithms were used to establish prediction models, aiming to identify CRC and AA. The accuracy of the random forest (RF) model based on the gut microbiome was 86.54%. Nevertheless, the accuracy of SNP was 92.31% in identifying CRC from AA. In conclusion, using microbial SNP was the best method to identify CRC, it was superior to using the gut microbiome, and it could provide new targets for CRC screening. IMPORTANCE There are differences in characteristic microorganisms between AA and CRC. However, current studies have indicated that bacteriological analysis cannot identify CC from AA, and thus, we wondered if there were some other targets that could be used to identify CRC from AA in the gut microbiome. The differences of SNPs in the gut microbiota of intraindividuals were significantly smaller than those of interindividuals. In addition, compared with intestinal microbes, SNP was less affected by time with certain stability. It was discovered that microbial SNP was better than the gut microbiome for identifying CRC from AA. Therefore, screening characteristic microbial SNP could provide a new research direction for identifying CRC from AA.

Caffeine-Induced Sleep Restriction Alters the Gut Microbiome and Fecal Metabolic Profiles in Mice

Insufficient sleep is becoming increasingly common and contributes to many health issues. To combat sleepiness, caffeine is consumed daily worldwide. Thus, caffeine consumption and sleep restriction often occur in succession. The gut microbiome can be rapidly affected by either one's sleep status or caffeine intake, whereas the synergistic effects of a persistent caffeine-induced sleep restriction remain unclear. In this study, we investigated the impact of a chronic caffeine-induced sleep restriction on the gut microbiome and its metabolic profiles in mice. Our results revealed that the proportion of Firmicutes and Bacteroidetes was not altered, while the abundance of Proteobacteria and Actinobacteria was significantly decreased. In addition, the content of the lipids was abundant and significantly increased. A pathway analysis of the differential metabolites suggested that numerous metabolic pathways were affected, and the glycerophospholipid metabolism was most significantly altered. Combined analysis revealed that the metabolism was significantly affected by variations in the abundance and function of the intestinal microorganisms and was closely relevant to Proteobacteria and Actinobacteria. In conclusion, a long-term caffeine-induced sleep restriction affected the diversity and composition of the intestinal microbiota in mice, and substantially altered the metabolic profiles of the gut microbiome. This may represent a novel mechanism by which an unhealthy lifestyle such as mistimed coffee breaks lead to or exacerbates disease.

A Narrative Review: Exercise Induces Adipose Browning by Altering the Gut Microbiome Profile

Obesity is a condition that is highly associated with chronic diseases. The number of obese individuals is increasing on a global scale every year. Energy intake that is imbalanced with energy expenditure causes the accumulation of excess fat. Regular exercise is part of a lifestyle that can reduce the risk of obesity by inducing adipose browning. One of the mechanisms of exercise in inducing browning is by modifying the gut microbiome profile. This profile may promote adipose browning by maintaining the integrity of the intestinal barrier, glucose homeostasis, regulating energy absorption, and appetite-regulating hormones.

Altered fecal microbiome and metabolome in adult patients with non-cystic fibrosis bronchiectasis

BackgroundEmerging experimental and epidemiological evidence highlights a crucial cross-talk between the intestinal flora and the lungs, termed the “gut-lung axis”. However, the function of the gut microbiota in bronchiectasis remains undefined. In this study, we aimed to perform a multi-omics-based approach to identify the gut microbiome and metabolic profiles in patients with bronchiectasis.MethodsFecal samples collected from non-CF bronchiectasis patients (BE group, n = 61) and healthy volunteers (HC group, n = 37) were analyzed by 16 S ribosomal RNA (rRNA) sequencing. The BE group was divided into two groups based on their clinical status: acute exacerbation (AE group, n = 31) and stable phase (SP group, n = 30). Further, metabolome (lipid chromatography-mass spectrometry, LC-MS) analyses were conducted in randomly selected patients (n = 29) and healthy volunteers (n = 31).ResultsDecreased fecal microbial diversity and differential microbial and metabolic compositions were observed in bronchiectasis patients. Correlation analyses indicated associations between the differential genera and clinical parameters such as bronchiectasis severity index (BSI). Disease-associated gut microbiota was screened out, with eight genera exhibited high accuracy in distinguishing SP patients from HCs in the discovery cohort and validation cohort using a random forest model. Further correlation networks were applied to illustrate the relations connecting disease-associated genera and metabolites.ConclusionThe study uncovered the relationships among the decreased fecal microbial diversity, differential microbial and metabolic compositions in bronchiectasis patients by performing a multi-omics-based approach. It is the first study to characterize the gut microbiome and metabolome in bronchiectasis, and to uncover the gut microbiota’s potentiality as biomarkers for bronchiectasis.Trial registration: This study is registered with ClinicalTrials.gov, number NCT04490447.

Long-term fasting: Multi-system adaptations in humans (GENESIS) study-A single-arm interventional trial.

[ClinicalTrials.gov], identifier [NCT05031598].

Potential mechanism of pyrotinib-induced diarrhea was explored by gut microbiome and ileum metabolomics.

Pyrotinib is a novel epidermal growth factor receptor/human epidermal growth factor receptor-2 (HER2) tyrosine kinase inhibitor that exhibited clinical efficacy in patients with HER2-positive breast cancer and HER2-mutant/amplified lung cancer. However, severe diarrhea adverse responses preclude its practical use. At present, the mechanism of pyrotinib-induced diarrhea is unknown and needs further study. First, to develop a suitable and reproducible animal model, we compared the effects of different doses of pyrotinib (20, 40, 60 and 80 mg/kg) in Wistar rats. Second, we used this model to examine the intestinal toxicity of pyrotinib. Finally, the mechanism underlying pyrotinib-induced diarrhea was fully studied using gut microbiome and host intestinal tissue metabolomics profiling. Reproducible diarrhea occurred in rats when they were given an 80 mg/kg daily dose of pyrotinib. Using the pyrotinib-induced model, we observed that Lachnospiraceae and Acidaminococcaceae decreased in the pyrotinib groups, whereas Enterobacteriaceae, Helicobacteraceae and Clostridiaceae increased at the family level by 16S rRNA gene sequence. Multiple bioinformatics methods revealed that glycocholic acid, ursodeoxycholic acid and cyclic AMP increased in the pyrotinib groups, whereas kynurenic acid decreased, which may be related to the pathogenesis of pyrotinib-induced diarrhea. Additionally, pyrotinib-induced diarrhea may be associated with a number of metabolic changes mediated by the gut microbiome, such as Primary bile acid biosynthesis. We reported the establishment of a reproducible pyrotinib-induced animal model for the first time. Furthermore, we concluded from this experiment that gut microbiome imbalance and changes in related metabolites are significant contributors to pyrotinib-induced diarrhea.

Design of New Personalized Therapeutic Approaches for Diffuse Large B-Cell Lymphoma through Gut Microbiome Profiling

Although front-line R-CHOP (rituximab-cyclophosphamide, doxorubicin, vincristine, prednisone) can improve clinical outcomes in diffuse large B-cell lymphoma (DLBCL), 20%-25% of patients relapse after initial response, within two years. Recent research on the gut microbiome (GM) in cancer has highlighted its importance in hematology. Indeed, GM can impact the onset and progression of cancer, as well as the outcomes of anticancer therapies and drug side effects.Fifty DLBCL patients undergoing front-line R-CHOP therapy were enrolled (ClinicalTrials.gov Identifier: NCT03797170, RF-2016-02363730). Feces were collected at baseline, before each therapy cycle and at response assessment (during both therapeutic course and follow-up). GM was profiled by 16S rRNA amplicon sequencing. Patients responding to first line will be followed up for two years after the end of treatment by fecal sampling every 6 months, in correspondence with disease re-evaluation. The GM of DLBCL patients before starting R-CHOP was compared with already-published data from healthy controls matched by geographical origin (Italy), gender and age. Patients showed less alpha diversity and some changes in composition, including notably an increase in Collinsella and a decrease in typically health-associated taxa, such as Faecalibacterium, Ruminococcus, Blautia and Bacteroides (Figure 1A-D). Concerning the GM dynamics though the R-CHOP therapy cycles (Figure 1E), no significant changes were observed but an overall reduction of several taxa starting from the fourth cycle. The compositional differences reported at baseline persisted until response assessment (following the last therapy cycle), while they partially reversed at the first follow-up (6 months after the end of therapy), with the recovery of healthy-like proportions for Bacteroides, Blautia, Faecalibacterium, Ruminococcus and Collinsella. All but two patients responded positively to therapy.The GM of DLBCL patients showed a significant alteration even before starting R-CHOP therapy, with reduced richness and dysbiotic compositional features. This imbalance was maintained until the fourth cycle, after which we detected an overall GM shrinkage. Six months after the end of therapy, the dysbiotic signatures were largely reversed, suggesting that GM resilience may be involved in the response to therapy. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal