Modulation of Gut Microbiota in Korean Navy Trainees following a Healthy Lifestyle Change

Alteration of the gut microbiome in patients with heart failure: A systematic review and meta-analysis

Recent research has revealed that alterations of the gut microbiome (GM) play a comprehensive role in the pathophysiology of HF. However, findings in this field remain controversial. In this study, we focus on differences in GM diversity and abundance between HF patients and non-HF people, based on previous 16 S ribosomal RNA (16rRNA) gene sequencing. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we conducted a comprehensive search of PubMed, Web of Science, Embase, Cochrane Library, and Ovid databases using the keyword "Heart failure" and "Gastrointestinal Microbiome". A significant decrease in alpha diversity was observed in the HF patients (Chao1, I2 = 87.5 %, p < 0.001; Shannon index, I2 = 62.8 %, p = 0.021). At the phylum level, the HF group exhibited higher abundances of Proteobacteria (I2 = 92.0 %, p = 0.004) and Actinobacteria (I2 = 82.5 %, p = 0.010), while Bacteroidetes (I2 = 45.1 %, p = 0.017) and F/B ratio (I2 = 0.0 %, p＜0.001) were lower. The Firmicutes showed a decreasing trend but did not reach statistical significance (I2 = 82.3 %, p = 0.127). At the genus level, the relative abundances of Streptococcus, Bacteroides, Alistipes, Bifidobacterium, Escherichia-Shigella, Enterococcus and Klebsiella were increased in the HF group, whereas Ruminococcus, Faecalibacterium, Dorea and Megamona exhibited decreased relative abundances. Dialister, Blautia and Prevotella showed decreasing trends but without statistical significance. This observational meta-analysis suggests that GM changes are associated with HF, manifesting as alterations in GM abundance, disruptions in the production of short-chain fatty acids (SCFAs) bacteria, and an increase in trimethylamine N-oxide (TMAO) producing bacteria.

Effects of Exercise on Gut Microbiota of Adults: A Systematic Review and Meta-Analysis

BACKGROUND

The equilibrium between gut microbiota (GM) and the host plays a pivotal role in maintaining overall health, influencing various physiological and metabolic functions. Emerging research suggests that exercise modulates the abundance and functionality of gut bacteria, yet the comprehensive effects on GM diversity remain to be synthesized.

OBJECTIVES AND DESIGN

The study aims to quantitatively examine the effect of exercise on the diversity of gut microbiota of adults using a systemic review and meta-analysis approach.

METHODS

PubMed, Ebsco, Embase, Web of Science, Cochrane Central Register of Controlled Trials, the China National Knowledge Infrastructure, and Wanfang Data were searched from their inception to September 2023. Exercise intervention studies with a control group that describe and compare the composition of GM in adults, using 16S rRNA gene sequencing, were included in this meta-analysis.

RESULTS

A total of 25 studies were included in this meta-analysis with a total of 1044 participants. Based on a fixed-effects model [Chi2 = 29.40, df = 20 (p = 0.08); I2 = 32%], the pooled analysis showed that compared with the control group, exercise intervention can significantly increase the alpha diversity of adult GM, using the Shannon index as an example [WMD = 0.05, 95% CI (0.00, 0.09); Z = 1.99 (p = 0.05)]. In addition, exercise interventions were found to significantly alter GM, notably decreasing Bacteroidetes and increasing Firmicutes, indicating a shift in the Firmicutes/Bacteroidetes ratio. The subgroup analysis indicates that females and older adults appear to exhibit more significant changes in the Shannon Index and observed OTUs.

CONCLUSIONS

Exercise may be a promising way to improve GM in adults. In particular, the Shannon index was significantly increased after exercise. Distinct responses in GM diversity to exercise interventions based on gender and age implicated that more research was needed.

The relationship between sleep, gut microbiota, and metabolome in patients with depression and anxiety: A secondary analysis of the observational study

BACKGROUND

Growing attention is paid to the association between alterations in the gut microbiota and their metabolites in patients with psychiatric disorders. Our study aimed to determine how gut microbiota and metabolomes are related to the sleep quality among patients with depression and anxiety disorders by analyzing the datasets of our previous study.

METHODS

Samples were collected from 40 patients (depression: 32 patients [80.0%]); anxiety disorders: 8 patients [20.0%]) in this study. Gut microbiomes were analyzed using 16S rRNA gene sequencing and gut metabolomes were analyzed by a mass spectrometry approach. Based on the Pittsburgh Sleep Quality Index (PSQI), patients were categorized into two groups: the insomnia group (PSQI score ≥ 9, n = 20) and the non-insomnia group (PSQI score < 9, n = 20).

RESULTS

The insomnia group showed a lower alpha diversity in the Chao1 and Shannon indices than the non-insomnia group after the false discovery rate (FDR) correction. The relative abundance of genus Bacteroides showed a positive correlation with PSQI scores in the non-insomnia group. The concentrations of glucosamine and N-methylglutamate were significantly higher in the insomnia group than in the non-insomnia group.

CONCLUSIONS

Our findings suggest that specific taxa could affect the sleep quality among patients with depression and anxiety disorders. Further studies are needed to elucidate the impact of sleep on specific gut microbiota and metabolomes in depression and anxiety disorders.

Impact of nicotine and cotinine on macrophage inflammatory plasticity via vesicular modifications in gastrointestinal bacteria

OBJECTIVES

This study aimed to elucidate mechanistic explanation(s) for compositional changes to enteric microbiota by determining the impacts of continuous nicotine/cotinine exposure on representative gastrointestinal bacteria and how these alterations impact innate immune cell plasticity.

METHODS

In vitro cultures of the gastrointestinal bacteria (Bacteroides fragilis 25285, Prevotella bryantii B14, and Acetoanaerobium sticklandii SR) were continuously exposed to nicotine or cotinine. Supernatant samples were collected for fermentation acid analysis. Vesicles were collected and analyzed for physiological changes in number, size, and total protein cargo. Cultured macrophages were stimulated to a tolerogenic phenotype, exposed to control or altered (nicotine or cotinine - exposed) vesicles, and inflammatory plasticity assessed via inflammatory cytokine production.

RESULTS

Nicotine/cotinine exposure differentially affected metabolism of all bacteria tested in a Gram (nicotine) and concentration-dependent (cotinine) manner. Physiological studies demonstrated changes in vesiculation number and protein cargo following nicotine/cotinine exposures. Continuous exposure to 1 μM nicotine and 10 μM cotinine concentrations reduced total protein cargo of Gram (-) - 25285 and B14 vesicles, while cotinine generally increased total protein in Gram (+) - SR vesicles. We found that theses physiological changes to the vesicles of 25285 and SR formed under nicotine and cotinine, respectively, challenged the plasticity of tolerogenic macrophages. Tolerogenic macrophages exposed to vesicles from 1 μM nicotine, and 5 or 10 μΜ cotinine cultures produced significantly less IL-12p70, TNFα, or KC/GRO, regardless of macrophage exposure to nicotine/cotinine.

CONCLUSIONS

Nicotine/cotinine exposure differentially alters bacterial metabolism and vesicle physiology, ultimately impacting the inflammatory response of tolerogenic macrophages.

Effects of vermicompost leachate on nitrogen, phosphorus, and microbiome in a food waste bioponic system

Food waste is rich in nutrients, such as nitrogen and phosphorus, and can be integrated with bioponics, a closed-loop agricultural system that combines hydroponics with biological nutrient recovery. Vermicompost leachate (VCL) supplementation has been shown to improve the co-composting of organic waste (i.e., compost quality) and the biodegradation of organic compounds. Thus, VCL has high potential for enhancing nutrient availability in bioponics from food waste. However, the understanding of nitrogen and phosphorus availability in food waste-based bioponics is limited, both with and without VCL. In this study, food waste derived from cafeteria vegetable waste was used as the substrate (500 g dry wt./system) in bioponics to grow lettuce (Lactuca sativa L.) for two consecutive cycles (35 days/cycle) without substrate replacement. VCL was applied weekly (1-5% v/v) and compared to the control without VCL. The results showed that the food waste in bioponics provided nitrogen and phosphorus for plant growth (15.5-65.8 g/lettuce head). Organic-degrading and nutrient-transforming bacteria (Hydrogenispora, Clostridium_sensu_stricto_1, Ruminiclostridium_1, Cellvibrio, Thauera, Hydrogenophaga, and Bacillus) were predominantly found in plant roots and residual food waste. VCL addition significantly increased nitrate, phosphate, and chemical oxygen demand levels in bioponics, owing to the nutrients in VCL and the enhancement of keystone microorganisms responsible for organic degradation and nutrient cycling (e.g., Ellin6067, Actinomyces, and Pirellula). These findings suggest that nitrogen, phosphorus, and organic carbon concentrations in an ecosystem of nutrient-transforming and organic-degrading microbes are key in managing nutrient recovery from food waste in bioponics.

Cross-talks between gut microbiota and tobacco smoking: a two-sample Mendelian randomization study

BACKGROUND

Considerable evidence has been reported that tobacco use could cause alterations in gut microbiota composition. The microbiota-gut-brain axis also in turn hinted at a possible contribution of the gut microbiota to smoking. However, population-level studies with a higher evidence level for causality are lacking.

METHODS

This study utilized the summary-level data of respective genome-wide association study (GWAS) for 211 gut microbial taxa and five smoking phenotypes to reveal the causal association between the gut microbiota and tobacco smoking. Two-sample bidirectional Mendelian randomization (MR) design was deployed and comprehensively sensitive analyses were followed to validate the robustness of results. We further performed multivariable MR to evaluate the effect of neurotransmitter-associated metabolites on observed associations.

RESULTS

Our univariable MR results confirmed the effects of smoking on three taxa (Intestinimonas, Catenibacterium, and Ruminococcaceae, observed from previous studies) with boosted evidence level and identified another 13 taxa which may be causally affected by tobacco smoking. As for the other direction, we revealed that smoking behaviors could be potential consequence of specific taxa abundance. Combining with existing observational evidence, we provided novel insights regarding a positive feedback loop of smoking through Actinobacteria and indicated a potential mechanism for the link between parental smoking and early smoking initiation of their children driven by Bifidobacterium. The multivariable MR results suggested that neurotransmitter-associated metabolites (tryptophan and tyrosine, also supported by previous studies) probably played a role in the action pathway from the gut microbiota to smoking, especially for Actinobacteria and Peptococcus.

CONCLUSIONS

In summary, the current study suggested the role of the specific gut microbes on the risk for cigarette smoking (likely involving alterations in metabolites) and in turn smoking on specific gut microbes. Our findings highlighted the hazards of tobacco use for gut flora dysbiosis and shed light on the potential role of specific gut microbiota for smoking behaviors.

Review of the Current State of Freely Accessible Web Tools for the Analysis of 16S rRNA Sequencing of the Gut Microbiome

Owing to the emergence and improvement of high-throughput technology and the associated reduction in costs, next-generation sequencing (NGS) technology has made large-scale sampling and sequencing possible. With the large volume of data produced, the processing and downstream analysis of data are important for ensuring meaningful results and interpretation. Problems in data analysis may be encountered if researchers have little experience in using programming languages, especially if they are clinicians and beginners in the field. A strategy for solving this problem involves ensuring easy access to commercial software and tools. Here, we observed the current status of free web-based tools for microbiome analysis that can help users analyze and handle microbiome data effortlessly. We limited our search to freely available web-based tools and identified MicrobiomeAnalyst, Mian, gcMeta, VAMPS, and Microbiome Toolbox. We also highlighted the various analyses that each web tool offers, how users can analyze their data using each web tool, and noted some of their limitations. From the abovementioned list, gcMeta, VAMPS, and Microbiome Toolbox had several issues that made the analysis more difficult. Over time, as more data are generated and accessed, more users will analyze microbiome data. Thus, the availability of free and easily accessible web tools can enable the easy use and analysis of microbiome data, especially for those users with less experience in using command-line interfaces.

Physical activity induced alterations of gut microbiota in humans: a systematic review

Background

Gut microbiota is considered to have a great impact on human health and disease. While it is widely recognized that the gut microbiota of healthy individuals differs from those with obesity, inflammatory bowel disease, metabolic syndrome, and other chronic diseases, the alterations of gut microbiota with physical activity are not fully understood. Accordingly, we performed this systematic review to address the question regarding the effects of mild and intense exercise on the gut microbiota in humans.

Methods

The comparative analyses of gut microbiota were conducted following the PRISMA protocol to determine the differences in the active vs. non-active individuals (phenotypes) (n = 11), including the influence of physical activity intervention on the human gut microbiota (n = 13); the differences in the gut microbiota of athletes vs. non-athletes (n = 8); and the microbiota status at different stages of athletic performance or intervention (n = 7), with various of physical activities, sport disciplines, and activity duration. Literature searches were completed using four databases: PubMed, Web of Science, Scopus, and EBSCO, and 2090 articles were retrieved by using appropriate keywords. The low heterogeneity of the studies hasn’t allowed us to prepare a meta-analysis. After excluding 2052 articles, we ultimately selected 38 articles that met the eligibility criteria for this review.

Results

The data analyses revealed that in non-athletes rising physical activity markedly influenced the relative abundance of short-chain fatty acid (SCFA). Aerobic training that lasted 60 min, and physical activity that characterized 60% HRmax or more also influenced beta diversity indexes. The results showed that athletes harbor a more diverse type of intestinal microflora than non-athletes, but with a relatively reduced abundance of SCFA- and lactic acid-producing bacteria, thereby suggesting an adverse effect of intense exercise on the population of gut microbiota.

Conclusion

It is concluded that the level of physical activity modulates the gastrointestinal microbiota in humans. For a long period, increasing the intensity and volume of exercise may lead to gut dysbiosis. Perhaps, proper supplementation should be considered to keep gut microbiota in large biodiversity and richness, especially under unfavorable gut conditions associated with intense exercise.

Trial registration

Prospero CRD42021264064.

Microbiome dataset of spontaneously fermented Ethiopian honey wine, Tej

This dataset contains raw and analyzed microbial data for the samples of spontaneously fermented Ethiopian honey wine, Tej, collected from three locations of Ethiopia. It was generated using culture independent amplicon sequencing technique. To gain a better understanding of microbial community variance and similarity across Tej samples from the same and different locations, the raw sequenced data obtained from the Illumina Miseq sequencer was subjected to a bioinformatics analysis. Lower diversity and richness of both bacterial and fungal communities were observed for all of the Tej samples. Besides, samples collected from Debre Markos area showed a significant discriminating tax for both bacterial and fungal communities. In nutshell, this amplicon sequencing dataset provides a useful collection of data for modernizing this spontaneous fermentation into a directed inoculated fermentation. Detail discussion on microbiome of Tej samples is given in [1].

Intestinal Collinsella may mitigate infection and exacerbation of COVID-19 by producing ursodeoxycholate

The mortality rates of COVID-19 vary widely across countries, but the underlying mechanisms remain unelucidated. We aimed at the elucidation of relationship between gut microbiota and the mortality rates of COVID-19 across countries. Raw sequencing data of 16S rRNA V3-V5 regions of gut microbiota in 953 healthy subjects in ten countries were obtained from the public database. We made a generalized linear model (GLM) to predict the COVID-19 mortality rates using gut microbiota. GLM revealed that low genus Collinsella predicted high COVID-19 mortality rates with a markedly low p-value. Unsupervised clustering of gut microbiota in 953 subjects yielded five enterotypes. The mortality rates were increased from enterotypes 1 to 5, whereas the abundances of Collinsella were decreased from enterotypes 1 to 5 except for enterotype 2. Collinsella produces ursodeoxycholate. Ursodeoxycholate was previously reported to inhibit binding of SARS-CoV-2 to angiotensin-converting enzyme 2; suppress pro-inflammatory cytokines like TNF-α, IL-1β, IL-2, IL-4, and IL-6; have antioxidant and anti-apoptotic effects; and increase alveolar fluid clearance in acute respiratory distress syndrome. Ursodeoxycholate produced by Collinsella may prevent COVID-19 infection and ameliorate acute respiratory distress syndrome in COVID-19 by suppressing cytokine storm syndrome.

Dynamics of Fecal Microbiota with and without Invasive Cervical Cancer and Its Application in Early Diagnosis

Simple Summary

The fecal microbiome has been suggested to be linked to invasive cervical cancer (ICC). Considering that ICC is common in women, it is important to identify bacterial signatures from fecal microbiota that contribute in classifying cervical cancer. Although previous studies have suggested possible biomarkers based on fecal microbiota, limited information exists in terms of the diagnostic ability using gut microbiota-derived signatures for detecting early ICC. The purpose of this study was to investigate the potential association between early ICC and fecal microbiota and to examine whether fecal microbiota-derived markers can be utilized as a non-invasive tool to diagnose early ICC using machine learning (ML) techniques. Further studies to incorporate quantitative and qualitative characterization of identified individual bacterial genus and validate our model in larger cohorts are imperative in terms of causality for the association between cervical cancer and microbes.

Abstract

The fecal microbiota is being increasingly implicated in the diagnosis of various diseases. However, evidence on changes in the fecal microbiota in invasive cervical cancer (ICC) remains scarce. Here, we aimed to investigate the fecal microbiota of our cohorts, develop a diagnostic model for predicting early ICC, and identify potential fecal microbiota-derived biomarkers using amplicon sequencing data. We obtained fecal samples from 29 healthy women (HC) and 17 women with clinically confirmed early ICC (CAN). Although Shannon’s diversity index was not reached at statistical significance, the Chao1 and Observed operational taxonomic units (OTUs) in fecal microbiota was significantly different between CAN and HC group. Furthermore, there were significant differences in the taxonomic profiles between HC and CAN; Prevotella was significantly more abundant in the CAN group and Clostridium in the HC group. Linear discriminant analysis effect size (LEfSe) analysis was applied to validate the taxonomic differences at the genus level. Furthermore, we identified a set of seven bacterial genera that were used to construct a machine learning (ML)-based classifier model to distinguish CAN from patients with HC. The model had high diagnostic utility (area under the curve [AUC] = 0.913) for predicting early ICC. Our study provides an initial step toward exploring the fecal microbiota and helps clinicians diagnose.