Bacterial associations in the healthy human gut microbiome across populations

Nutritional optimization of fecal microbiota transplantation in humans: a scoping review

Diet constitutes a major source of nutrient flow to the gut microbes. As such, it can be used to help shape the gut microbiome. Fecal microbiota transplantation (FMT) is an increasingly promising therapy in disease states beyond recurrent Clostridioides difficile infection, but diet is largely overlooked for its potential to help optimize this therapy. Therefore, the aim of this scoping review is to present the literature landscape that captures pre- and post-FMT dietary intake in humans, identify research gaps, and provide recommendations for future research. A comprehensive search strategy was developed and searches were run in five databases. Studies were included if they discussed adults who underwent FMT for any recognized treatment indication and had dietary intake as a study objective, this search encompassed studies with interventions that included foods and dietary supplements. The initial screening identified a total of 7721 articles, of which 18 met the inclusion criteria for this review. Studies were heterogeneous, but taken together, they introduce a framework that defines important nutritional considerations for both donors and FMT recipients in the period around FMT dosing. This framework is summarized with this review and highlights the opportunities available to develop FMT-based precision nutrition strategies to optimize its clinical efficacy.

Sorghum rhizosphere bacteriome studies and generation of multistrain beneficial bacterial consortia

The plant rhizosphere microbiome plays a crucial role in plant growth and health. Within this microbiome, bacteria dominate, exhibiting traits that benefit plants, such as facilitating nutrient acquisition, fixing nitrogen, controlling pathogens, and promoting root growth. This study focuses on designing synthetic bacterial consortia using key bacterial strains which have been mapped and then isolated from the sorghum rhizosphere microbiome. A large set of samples of the rhizosphere bacteriome of Sorghum bicolor was generated and analyzed across various genotypes and geographical locations. We assessed the taxonomic composition and structure of the sorghum root-associated bacterial community identifying the most prevalent and keystone taxa. A set of 321 bacterial strains was then isolated, and three multi-strain consortia were designed making use of the bacteriome data generated using culture independent methodology. Subsequently, co-existence and plant-growth promoting ability of three bacterial consortia were tested both in vitro and in planta. Consortia 3 promoted plant growth in growth-chamber conditions while Consortia 1 and 2 performed better in field-plot experiments. Despite these differences, bacterial community profiling confirmed the colonization of the inoculated consortia in the sorghum rhizosphere without significant alterations to the overall bacterial community compared to the non inoculated ones. In summary, this study focused on a method, using root bacteriome data, to design and test bacterial consortia for plant beneficial effects with the aim of translating microbiome knowledge into applications.

PyBootNet: a python package for bootstrapping and network construction

Background

Network analysis has emerged as a tool for investigating interactions among species in a community, interactions among genes or proteins within cells, or interactions across different types of data (e.g., genes and metabolites). Two aspects of networks that are difficult to assess are the statistical robustness of the network and whether networks from two different biological systems or experimental conditions differ.

Methods

PyBootNet is a user-friendly Python package that integrates bootstrapping analysis and correlation network construction. The package offers functions for generating bootstrapped network metrics, statistically comparing network metrics among datasets, and visualizing bootstrapped networks. PyBootNet is designed to be accessible and efficient with minimal dependencies and straightforward input requirements. To demonstrate its functionality, we applied PyBootNet to compare correlation networks derived from study using a mouse model to investigate the impacts of Polycystic Ovary Syndrome (PCOS) on the gut microbiome. PyBootNet includes functions for data preprocessing, bootstrapping, correlation matrix calculation, network statistics computation, and network visualization.

Results

We show that PyBootNet generates robust bootstrapped network metrics and identifies significant differences in one or more network metrics between pairs of networks. Our analysis of the previously published PCOS gut microbiome data also showed that our network analysis uncovered patterns and treatment effects missed in the original study. PyBootNet provides a powerful and extendible Python bioinformatics solution for bootstrapping analysis and network construction that can be applied to microbes, genes, metabolites and other biological data appropriate for network correlation comparison and analysis.

How the early life microbiome shapes immune programming in childhood asthma and allergies

Despite advances in our understanding of their diagnosis and treatment, pediatric allergies impose substantial burdens on affected children, families, and healthcare systems. Further, the prevalence of allergic diseases has dramatically increased over the past half-century, leading to additional concerns and concerted efforts to identify the origins, potential predictors and preventions, and therapies of allergic diseases. Together with the increase in allergic diseases, changes in lifestyle and early-life environmental influences have corresponded with changes in colonization patterns of the infant gut microbiome. The gut microbiome plays a key role in developing the immune system, thus greatly influencing the development of allergic disease. In this review, we specifically highlight the importance of the proper maturation and composition of the gut microbiome as an essential step in healthy child development or disease progression. By exploring the intertwined development of the immune system and microbiome across pediatric allergic diseases, we provide insights into potential novel strategies for their prevention and management.

In situ dressing based on a D-π-A structured aggregation-induced emission photosensitizer for healing infected wounds

Photodynamic antimicrobial therapy (aPDT) can effectively kill bacteria without promoting drug resistance. However, the phototoxicity of photosensitizers in aPDT against normal cells hinders their practical applications. In this work, we report the utilization of an aggregation-induced emission (AIE)-active photosensitizer, DTTPB, to develop antibacterial dressing for effective eradication of both Gram-positive and Gram-negative bacteria. The D-π-A structure of DTTPB facilitates efficient ROS generation in the aggregate state, addressing the limitations of a traditional photosensitizer. Notably, DTTPB demonstrates good biocompatibility towards normal cells, which minimizes its phototoxicity to normal tissues. To demonstrate its practical implications, DTTPB is combined with Carbomer 940 to create an injectable hydrogel dressing (DTTPB@gel). DTTPB@gel not only adheres to wounds but also maintains the antimicrobial properties of DTTPB, which together contributes to its enhanced wound-healing performance. Biocompatibility and toxicity assessments confirm the safety of this novel material, highlighting its potential as a practical and effective treatment for bacterial infections in wounds. The results underscore the importance of innovative antimicrobial strategies in fighting against antibiotic resistance, paving the way for safer and more effective therapeutic options.

Continental-scale insights into the sugarbeet diffusion juice microbiomes

Bacterial contamination of raw diffusion juice poses unique challenges during the sugar extraction process. This study profiled bacterial communities by using full-length 16S rRNA amplicon sequencing and quantified the carbohydrate concentrations in raw diffusion juice samples received from sugar factory regions across the USA and Canada. Juice samples were collected at four time points during the 2021 and 2022 processing campaigns. Firmicutes was the dominant phylum from the raw diffusion juice samples collected during both campaigns and comprised 85.5% of total bacterial abundance. Lactic acid bacteria such as Leuconostoc and Lactobacillus were among the core genera which also dominated the bacterial community in raw diffusion juice. Positive correlations in the abundance of functionally and taxonomically related bacterial communities were identified. During the 2021 campaign, 44 bacterial genera were differentially abundant in raw diffusion juice extracted from sugarbeet roots in Periods 1 to 4. This number declined sixfold during the 2022 campaign to three genera. The concentration of raffinose in raw diffusion juice positively correlated to the relative abundance of Leuconostoc. Furthermore, an in vitro assay was performed to assess the growth dynamics of Leuconostoc mesenteroides in sucrose or raffinose-rich medium and observed the rapid consumption of both carbohydrates by this bacterium. This finding is important for deciphering microbial growth dynamics in raw diffusion juice that can be useful in minimizing sugar loss during the factory processing.IMPORTANCEFindings additionally provide baseline information that can be used to develop mitigation strategies that reduce losses due to microbial contamination of sucrose processing streams.

Dysbiosis-epigenetics-immune system interaction and ageing health problems

Background. The growing interest in microbiota-epigenetics-immune system research stems from the understanding that microbiota, a group of micro-organisms colonized in the human body, can influence the gene expression through epigenetic mechanisms and interaction with the immune system. Epigenetics refers to changes in gene activity that are not caused by the alteration in the DNA sequence itself.Discussion. The clinical significance of this research lies in the potential to develop new therapies for diseases linked to the imbalance of these microbial species (dysbiosis), such as cancer and neurodegenerative diseases. The intricate interaction between microbiota and epigenetics involves the production of metabolites and signalling molecules that can impact our health by influencing immune responses, metabolism and inflammation. Understanding these interactions could lead to novel therapeutic strategies targeting microbiota-epigenetic pathways to improve health outcomes.Conclusion. In this context, we aim to review and emphasize the current knowledge and key concepts that link the microbiota to epigenetics and immune system function, exploring their relevance to the development and maintenance of homeostasis and susceptibility to different diseases later in life. We aim to elucidate key concepts concerning the interactions and potential effects among the human gut microbiota, epigenetics, the immune system and ageing diseases linked to dysbiosis.

Microbial DNA extraction method for avian feces and preen oil from diverse species

As DNA sequencing technology continues to rapidly improve, studies investigating the microbial communities of host organisms (i.e., microbiota) are becoming not only more popular but also more financially accessible. Across many taxa, microbiomes can have important impacts on organismal health and fitness. To evaluate the microbial community composition of a particular microbiome, microbial DNA must be successfully extracted. Fecal samples are often easy to collect and are a good source of gut microbial DNA. Additionally, interest in the avian preen gland microbiome is rapidly growing, due to the importance of preen oil for many aspects of avian life. Microbial DNA extractions from avian fecal and preen oil samples present multiple challenges, however. Here, we describe a modified PrepMan Ultra Sample Preparation Reagent microbial DNA extraction method that is less expensive than other commonly used methodologies and is highly effective for both fecal and preen oil samples collected from a broad range of avian species. We expect our method will facilitate microbial DNA extractions from multiple avian microbiome reservoirs, which have previously proved difficult and expensive. Our method therefore increases the feasibility of future studies of avian host microbiomes.

Selective microbial attachment to LDPE plastic beads during passage through the wastewater network

Urban wastewater treatment plants (WWTP) represent key point-source discharges of microplastics (MP) into the environment, however, little is known about the microbial carrying capacity of plastics travelling through them. The purpose of this study was to quantify the number of cells that become associated with MP at different locations within a WWTP, and to assess differences in microbiome communities. We conducted a field experiment incubating low density polyethylene (LDPE) MP beads in WWTP influent and effluent, as well as tracking free floating beads during passage in wastewater from a large municipal hospital to an urban WWTP, where they were subsequently recovered. Using two cell counting methods - automated flow cytometric true absolute cell counts and indirect cell quantification via protein content based on a model E. coli cell - we quantified cell attachment to LDPE beads. LDPE associated counts ranged from 350 × 103 cells cm-2 after incubation in wastewater effluent, and 990 × 103 cells cm-2 after incubation in wastewater influent. 16S rRNA gene amplicon sequencing was used to determine the bacterial community structure of the plastic-associated microbiomes. Our results showed that distinct bacterial communities developed on the LDPE MP following exposure to each wastewater type. Influent (untreated) wastewater LDPE-associated microbiomes were dominated by Bacillota whereas the microbes that attached in wastewater effluent (tertiary treated) were dominated by Pseudomonadota. In conclusion, this study provides clear evidence that microplastics migrating through the sewer network and WWTP rapidly accumulate microbiomes with unique microbial community structures varying from sewage influent to effluent. These findings demonstrate the differential microbiological risk from MP associated with routine wastewater discharges to those released from intermittent combined sewer overflows (CSOs) during storm events.

Structure characterization of an agavin-type fructan isolated from Polygonatum cyrtonema and its effect on the modulation of the gut microbiota in vitro

The herbal medicine Polygonatum cyrtonema is highly regarded in China for its medicinal and dietary properties. However, further research is needed to elucidate the structure of its polysaccharide and understand how it promotes human health by modulating the gut microbiota. This study aims to investigate a homogeneous polysaccharide (PCP95-1-1) from Polygonatum cyrtonema and assess its susceptibility to digestion as well as its utilization by intestinal microbiota. The results confirmed that PCP95-1-1 is an agavin-type fructan, which possesses two fructose chains, namely β-(2 → 6) and β-(2 → 1) fructosyl-fructose, attached to the sucrose core, and has branches of β-D-Fruf residues. Moreover, PCP95-1-1 demonstrated resistance to digestion and maintained its reducing sugar content throughout the digestive system, indicating it could reach the gut without being digested. In vitro fermentation of PCP95-1-1 significantly decreased the pH value (p < 0.05) while notably increasing the production of short-chain fatty acids (SCFAs), confirming its utilization by human gut microbiota. Additionally, PCP95-1-1 exhibited a significant ability (p < 0.05) to beneficial bacteria such as Megamonas and Bifidobacterium, while reducing the presence of facultative or conditional pathogens such as Escherichia-Shigella and Klebsiella at the genus level. Consequently, PCP95-1-1 has the potential to positively influence physical well-being by modulating the gut microbiota environment and can be developed as a functional food.

BactInt: A domain driven transfer learning approach for extracting inter-bacterial associations from biomedical text

BACKGROUND

The healthy as well as dysbiotic state of an ecosystem like human body is known to be influenced not only by the presence of the bacterial groups in it, but also with respect to the associations within themselves. Evidence reported in biomedical text serves as a reliable source for identifying and ascertaining such inter bacterial associations. However, the complexity of the reported text as well as the ever-increasing volume of information necessitates development of methods for automated and accurate extraction of such knowledge.

METHODS

A BioBERT (biomedical domain specific language model) based information extraction model for bacterial associations is presented that utilizes learning patterns from other publicly available datasets. Additionally, a specialized sentence corpus has been developed to significantly improve the prediction accuracy of the 'transfer learned' model using a fine-tuning approach.

RESULTS

The final model was seen to outperform all other variations (non-transfer learned and non-fine-tuned models) as well as models trained on BioGPT (a domain trained Generative Pre-trained Transformer). To further demonstrate the utility, a case study was performed using bacterial association network data obtained from experimental studies.

CONCLUSION

This study attempts to demonstrate the applicability of transfer learning in a niche field of life sciences where understanding of inter bacterial relationships is crucial to obtain meaningful insights in comprehending microbial community structures across different ecosystems. The study further discusses how such a model can be further improved by fine tuning using limited training data. The results presented and the datasets made available are expected to be a valuable addition in the field of medical informatics and bioinformatics.

Uncovering the complexity of childhood undernutrition through strain-level analysis of the gut microbiome

BACKGROUND

Undernutrition (UN) is a critical public health issue that threatens the lives of children under five in developing countries. While evidence indicates the crucial role of the gut microbiome (GM) in UN pathogenesis, the strain-level inspection and bacterial co-occurrence network investigation in the GM of UN children are lacking.

RESULTS

This study examines the strain compositions of the GM in 61 undernutrition patients (UN group) and 36 healthy children (HC group) and explores the topological features of GM co-occurrence networks using a complex network strategy. The strain-level annotation reveals that the differentially enriched species between the UN and HC groups are due to discriminated strain compositions. For example, Prevotella copri is mainly composed of P. copri ASM1680343v1 and P. copri ASM345920v1 in the HC group, but it is composed of P. copri ASM346549v1 and P. copri ASM347465v1 in the UN group. In addition, the UN-risk model constructed at the strain level demonstrates higher accuracy (AUC = 0.810) than that at the species level (AUC = 0.743). With complex network analysis, we further discovered that the UN group had a more complex GM co-occurrence network, with more hub bacteria and a higher clustering coefficient but lower information transfer efficiencies. Moreover, the results at the strain level suggested the inaccurate and even false conclusions obtained from species level analysis.

CONCLUSIONS

Overall, this study highlights the importance of examining the GM at the strain level and investigating bacterial co-occurrence networks to advance our knowledge of UN pathogenesis.

Multi-omic approaches for host-microbiome data integration

The gut microbiome interacts with the host through complex networks that affect physiology and health outcomes. It is becoming clear that these interactions can be measured across many different omics layers, including the genome, transcriptome, epigenome, metabolome, and proteome, among others. Multi-omic studies of the microbiome can provide insight into the mechanisms underlying host-microbe interactions. As more omics layers are considered, increasingly sophisticated statistical methods are required to integrate them. In this review, we provide an overview of approaches currently used to characterize multi-omic interactions between host and microbiome data. While a large number of studies have generated a deeper understanding of host-microbiome interactions, there is still a need for standardization across approaches. Furthermore, microbiome studies would also benefit from the collection and curation of large, publicly available multi-omics datasets.

Enhancing recovery from gut microbiome dysbiosis and alleviating DSS-induced colitis in mice with a consortium of rare short-chain fatty acid-producing bacteria

The human gut microbiota is a complex community comprising hundreds of species, with a few present in high abundance and the vast majority in low abundance. The biological functions and effects of these low-abundant species on their hosts are not yet fully understood. In this study, we assembled a bacterial consortium (SC-4) consisting of B. paravirosa, C. comes, M. indica, and A. butyriciproducens, which are low-abundant, short-chain fatty acid (SCFA)-producing bacteria isolated from healthy human gut, and tested its effect on host health using germ-free and human microbiota-associated colitis mouse models. The selection also favored these four bacteria being reduced in abundance in either Ulcerative Colitis (UC) or Crohn's disease (CD) metagenome samples. Our findings demonstrate that SC-4 can colonize germ-free (GF) mice, increasing mucin thickness by activating MUC-1 and MUC-2 genes, thereby protecting GF mice from Dextran Sodium Sulfate (DSS)-induced colitis. Moreover, SC-4 aided in the recovery of human microbiota-associated mice from DSS-induced colitis, and intriguingly, its administration enhanced the alpha diversity of the gut microbiome, shifting the community composition closer to control levels. The results showed enhanced phenotypes across all measures when the mice were supplemented with inulin as a dietary fiber source alongside SC-4 administration. We also showed a functional redundancy existing in the gut microbiome, resulting in the low abundant SCFA producers acting as a form of insurance, which in turn accelerates recovery from the dysbiotic state upon the administration of SC-4. SC-4 colonization also upregulated iNOS gene expression, further supporting its ability to produce an increasing number of goblet cells. Collectively, our results provide evidence that low-abundant SCFA-producing species in the gut may offer a novel therapeutic approach to IBD.

OBMeta: a comprehensive web server to analyze and validate gut microbial features and biomarkers for obesity-associated metabolic diseases

MOTIVATION

Gut dysbiosis is closely associated with obesity and related metabolic diseases including type 2 diabetes (T2D) and nonalcoholic fatty liver disease (NAFLD). The gut microbial features and biomarkers have been increasingly investigated in many studies, which require further validation due to the limited sample size and various confounding factors that may affect microbial compositions in a single study. So far, it lacks a comprehensive bioinformatics pipeline providing automated statistical analysis and integrating multiple independent studies for cross-validation simultaneously.

RESULTS

OBMeta aims to streamline the standard metagenomics data analysis from diversity analysis, comparative analysis, and functional analysis to co-abundance network analysis. In addition, a curated database has been established with a total of 90 public research projects, covering three different phenotypes (Obesity, T2D, and NAFLD) and more than five different intervention strategies (exercise, diet, probiotics, medication, and surgery). With OBMeta, users can not only analyze their research projects but also search and match public datasets for cross-validation. Moreover, OBMeta provides cross-phenotype and cross-intervention-based advanced validation that maximally supports preliminary findings from an individual study. To summarize, OBMeta is a comprehensive web server to analyze and validate gut microbial features and biomarkers for obesity-associated metabolic diseases.

AVAILABILITY AND IMPLEMENTATION

OBMeta is freely available at: http://obmeta.met-bioinformatics.cn/.

Metagenomic and metabolomic analyses reveal the role of gut microbiome-associated metabolites in diarrhea calves

IMPORTANCE

Calf diarrhea is of great concern to the global dairy industry as it results in significant economic losses due to lower conception rates, reduced milk production, and early culling. Although there is evidence of an association between altered gut microbiota and diarrhea, remarkably little is known about the microbial and metabolic mechanisms underlying the link between gut microbiota dysbiosis and the occurrence of calf diarrhea. Here, we used fecal metagenomic and metabolomic analyses to demonstrate that gut microbiota-driven metabolic disorders of purine or arachidonic acid were associated with calf diarrhea. These altered gut microbiotas play vital roles in diarrhea pathogenesis and indicate that gut microbiota-targeted therapies could be useful for both prevention and treatment of diarrhea.

A Pipeline for Constructing Reference Genomes for Large Cohort-Specific Metagenome Compression

Metagenomic data compression is very important as metagenomic projects are facing the challenges of larger data volumes per sample and more samples nowadays. Reference-based compression is a promising method to obtain a high compression ratio. However, existing microbial reference genome databases are not suitable to be directly used as references for compression due to their large size and redundancy, and different metagenomic cohorts often have various microbial compositions. We present a novel pipeline that generated simplified and tailored reference genomes for large metagenomic cohorts, enabling the reference-based compression of metagenomic data. We constructed customized reference genomes, ranging from 2.4 to 3.9 GB, for 29 real metagenomic datasets and evaluated their compression performance. Reference-based compression achieved an impressive compression ratio of over 20 for human whole-genome data and up to 33.8 for all samples, demonstrating a remarkable 4.5 times improvement than the standard Gzip compression. Our method provides new insights into reference-based metagenomic data compression and has a broad application potential for faster and cheaper data transfer, storage, and analysis.

Capturing the dynamics of microbial interactions through individual-specific networks

Longitudinal analysis of multivariate individual-specific microbiome profiles over time or across conditions remains dauntin. Most statistical tools and methods that are available to study microbiomes are based on cross-sectional data. Over the past few years, several attempts have been made to model the dynamics of bacterial species over time or across conditions. However, the field needs novel views on handling microbial interactions in temporal analyses. This study proposes a novel data analysis framework, MNDA, that combines representation learning and individual-specific microbial co-occurrence networks to uncover taxon neighborhood dynamics. As a use case, we consider a cohort of newborns with microbiomes available at 6 and 9 months after birth, and extraneous data available on the mode of delivery and diet changes between the considered time points. Our results show that prediction models for these extraneous outcomes based on an MNDA measure of local neighborhood dynamics for each taxon outperform traditional prediction models solely based on individual-specific microbial abundances. Furthermore, our results show that unsupervised similarity analysis of newborns in the study, again using the notion of a taxon's dynamic neighborhood derived from time-matched individual-specific microbial networks, can reveal different subpopulations of individuals, compared to standard microbiome-based clustering, with potential relevance to clinical practice. This study highlights the complementarity of microbial interactions and abundances in downstream analyses and opens new avenues to personalized prediction or stratified medicine with temporal microbiome data.

Universal gut microbial relationships in the gut microbiome of wild baboons

Ecological relationships between bacteria mediate the services that gut microbiomes provide to their hosts. Knowing the overall direction and strength of these relationships is essential to learn how ecology scales up to affect microbiome assembly, dynamics, and host health. However, whether bacterial relationships are generalizable across hosts or personalized to individual hosts is debated. Here, we apply a robust, multinomial logistic-normal modeling framework to extensive time series data (5534 samples from 56 baboon hosts over 13 years) to infer thousands of correlations in bacterial abundance in individual baboons and test the degree to which bacterial abundance correlations are 'universal'. We also compare these patterns to two human data sets. We find that, most bacterial correlations are weak, negative, and universal across hosts, such that shared correlation patterns dominate over host-specific correlations by almost twofold. Further, taxon pairs that had inconsistent correlation signs (either positive or negative) in different hosts always had weak correlations within hosts. From the host perspective, host pairs with the most similar bacterial correlation patterns also had similar microbiome taxonomic compositions and tended to be genetic relatives. Compared to humans, universality in baboons was similar to that in human infants, and stronger than one data set from human adults. Bacterial families that showed universal correlations in human infants were often universal in baboons. Together, our work contributes new tools for analyzing the universality of bacterial associations across hosts, with implications for microbiome personalization, community assembly, and stability, and for designing microbiome interventions to improve host health.

Phage-bacterial evolutionary interactions: experimental models and complications

Phage treatment of bacterial infections has offered some hope even as the crisis of antimicrobial resistance continues to be on the rise. However, bacterial resistance to phage is another looming challenge capable of undermining the effectiveness of phage therapy. Moreover, the consideration of including phage therapy in modern medicine calls for more careful research around every aspect of phage study. In an attempt to adequately prepare for the events of phage resistance, many studies have attempted to experimentally evolve phage resistance in different bacterial strains, as well as train phages to evolve counter-infectivity of resistant bacterial mutants, in view of answering such questions as coevolutionary dynamics between phage and bacteria, mechanisms of phage resistance, fitness costs of phage resistance on bacteria, etc. In this review, we summarised many such studies and by careful examination, highlighted critical issues to the outcome of phage therapy. We also discuss the insufficiency of many of these in vitro studies to represent actual disease conditions during phage application, alongside other complications that exist in phage-bacterial evolutionary interactions. Conclusively, we present the exploitation of phage-bacterial interactions for successful infection managements, as well as some future perspectives to direct phage research.

Inferring Bacterial Community Interactions and Functionalities Associated with Osteopenia and Osteoporosis in Taiwanese Postmenopausal Women

Growing evidence suggests that the gut microbiota and their metabolites are associated with bone homeostasis and fragility. However, this association is limited to microbial taxonomic differences. This study aimed to explore whether gut bacterial community associations, composition, and functions are associated with osteopenia and osteoporosis. We compared the gut bacterial community composition and interactions of healthy postmenopausal women with normal bone density (n = 8) with those of postmenopausal women with osteopenia (n = 18) and osteoporosis (n = 21) through 16S rRNA sequencing coupled with network biology and statistical analyses. The results of this study showed reduced alpha diversity in patients with osteoporosis, followed by that in patients with osteopenia, then in healthy controls. Taxonomic analysis revealed that significantly enriched bacterial genera with higher abundance was observed in patients with osteoporosis and osteopenia than in healthy subjects. Additionally, a co-occurrence network revealed that, compared to healthy controls, bacterial interactions were higher in patients with osteoporosis, followed by those with osteopenia. Further, NetShift analysis showed that a higher number of bacteria drove changes in the microbial community structure of patients with osteoporosis than osteopenia. Correlation analysis revealed that most of these driver bacteria had a significant positive relationship with several significant metabolic pathways. Further, ordination analysis revealed that height and T-score were the primary variables influencing the gut microbial community structure. Taken together, this study evaluated that microbial community interaction is more important than the taxonomic differences in knowing the critical role of gut microbiota in postmenopausal women associated with osteopenia and osteoporosis. Additionally, the significantly enriched bacteria and functional pathways might be potential biomarkers for the prognosis and treatment of postmenopausal women with osteopenia and osteoporosis.

Machine learning and network analysis of the gut microbiome from patients with schizophrenia and non-psychiatric subject controls reveal behavioral risk factors and bacterial interactions

Recent findings have supported an association between deviations in gut microbiome composition and schizophrenia. However, the extent to which the gut microbiota contributes to schizophrenia remains unclear. Moreover, studies have yet to explore variations in ecological associations among bacterial types in subjects with schizophrenia, which can reveal differences in community interactions and gut stability. We examined the dataset collected by Nguyen et al. (2021) to investigate the similarities and differences in gut microbial constituents between 48 subjects with schizophrenia and 48 matched non-psychiatric comparison cases. We re-analyzed alpha- and beta-diversity differences and completed modified differential abundance analyses and confirmed the findings of Nguyen et al. (2021) that there was little variation in alpha-diversity but significant differences in beta-diversity between individuals with schizophrenia and non-psychiatric subjects. We also conducted mediation analysis, developed a machine learning (ML) model to predict schizophrenia, and completed network analysis to examine community-level interactions among bacterial taxa. Our study offers new insights, suggesting that the gut microbiome mediates the effects between schizophrenia and smoking status, BMI, anxiety score, and depression score. Our differential abundance and network analysis findings suggest that the differential abundance of Lachnospiraceae and Ruminococcaceae taxa fosters a decrease in stabilizing competitive interactions in the gut microbiome of subjects with schizophrenia. Loss of this competition may promote ecological instability and dysbiosis, altering gut-brain axis interactions in these subjects.

Ecological shifts of salivary microbiota associated with metabolic-associated fatty liver disease

Introduction

Metabolic-associated fatty liver disease (MAFLD) is the most common chronic liver disease related to metabolic syndrome. However, ecological shifts in the saliva microbiome in patients with MAFLD remain unknown. This study aimed to investigate the changes to the salivary microbial community in patients with MAFLD and explore the potential function of microbiota.

Methods

Salivary microbiomes from ten MAFLD patients and ten healthy participants were analyzed by 16S rRNA amplicon sequencing and bioinformatics analysis. Body composition, plasma enzymes, hormones, and blood lipid profiles were assessed with physical examinations and laboratory tests.

Results

The salivary microbiome of MAFLD patients was characterized by increased α-diversity and distinct β-diversity clustering compared with control subjects. Linear discriminant analysis effect size analysis showed a total of 44 taxa significantly differed between the two groups. Genera Neisseria, Filifactor, and Capnocytophaga were identified as differentially enriched genera for comparison of the two groups. Co-occurrence networks suggested that the salivary microbiota from MAFLD patients exhibited more intricate and robust interrelationships. The diagnostic model based on the salivary microbiome achieved a good diagnostic power with an area under the curve of 0.82(95% CI: 0.61-1). Redundancy analysis and spearman correlation analysis revealed that clinical variables related to insulin resistance and obesity were strongly associated with the microbial community. Metagenomic predictions based on Phylogenetic Investigation of Communities by Reconstruction of Unobserved States revealed that pathways related to metabolism were more prevalent in the two groups.

Conclusions

Patients with MAFLD manifested ecological shifts in the salivary microbiome, and the saliva microbiome-based diagnostic model provides a promising approach for auxiliary MAFLD diagnosis.

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.

Some results on the Gaussian Markov Random Field construction problem based on the use of invariant subgraphs

The study of Gaussian Markov Random Fields has attracted the attention of a large number of scientific areas due to its increasing usage in several fields of application. Here, we consider the construction of Gaussian Markov Random Fields from a graph and a positive-definite matrix, which is closely related to the problem of finding the Maximum Likelihood Estimator of the covariance matrix of the underlying distribution. In particular, it is simultaneously required that the variances and the covariances between variables associated with adjacent nodes in the graph are fixed by the positive-definite matrix and that pairs of variables associated with non-adjacent nodes in the graph are conditionally independent given all other variables. The solution to this construction problem exists and is unique up to the choice of a vector of means. In this paper, some results focusing on a certain type of subgraphs (invariant subgraphs) and a representation of the Gaussian Markov Random Field as a Multivariate Gaussian Markov Random Field are presented. These results ease the computation of the solution to the aforementioned construction problem.

Bacterial infections in cancer: A bilateral relationship

Bacteria share a long commensal relationship with the human body. New findings, however, continue to unravel many complexities associated with this old alliance. In the past decades, the dysbiosis of human microbiome has been linked to tumorigenesis, and more recently to spontaneous colonization of existing tumors. The topic, however, remains open for debate as the claims for causative-prevailing dual characteristics of bacteria are mostly based on epidemiological evidence rather than robust mechanistic models. There are also no reviews linking the collective impact of bacteria in tumor microenvironments to the efficacy of cancer drugs, mechanisms of pathogen-initiated cancer and bacterial colonization, personalized nanomedicine, nanotechnology, and antimicrobial resistance. In this review, we provide a holistic overview of the bilateral relationship between cancer and bacteria covering all these aspects. Our collated evidence from the literature does not merely categorize bacteria as cancer causative or prevailing agents, but also critically highlights the gaps in the literature where more detailed studies may be required to reach such a conclusion. Arguments are made in favor of dual drug therapies that can simultaneously co-target bacteria and cancer cells to overcome drug resistance. Also discussed are the opportunities for leveraging the natural colonization and remission power of bacteria for cancer treatment. This article is categorized under: Therapeutic Approaches and Drug Discovery > Nanomedicine for Infectious Disease Nanotechnology Approaches to Biology > Nanoscale Systems in Biology Therapeutic Approaches and Drug Discovery > Emerging Technologies.

Inhibitory Bacterial Diversity and Mucosome Function Differentiate Susceptibility of Appalachian Salamanders to Chytrid Fungal Infection

Mucosal defenses are crucial in animals for protection against pathogens and predators. Host defense peptides (antimicrobial peptides, AMPs) as well as skin-associated microbes are key components of mucosal immunity, particularly in amphibians. We integrate microbiology, molecular biology, network-thinking, and proteomics to understand how host and microbially derived products on amphibian skin (referred to as the mucosome) serve as pathogen defenses. We studied defense mechanisms against chytrid pathogens, Batrachochytrium dendrobatidis (Bd) and B. salamandrivorans (Bsal), in four salamander species with different Batrachochytrium susceptibilities. Bd infection was quantified using qPCR, mucosome function (i.e., ability to kill Bd or Bsal zoospores in vitro), skin bacterial communities using 16S rRNA gene amplicon sequencing, and the role of Bd-inhibitory bacteria in microbial networks across all species. We explored the presence of candidate-AMPs in eastern newts and red-backed salamanders. Eastern newts had the highest Bd prevalence and mucosome function, while red-back salamanders had the lowest Bd prevalence and mucosome function, and two-lined salamanders and seal salamanders were intermediates. Salamanders with highest Bd infection intensity showed greater mucosome function. Bd infection prevalence significantly decreased as putative Bd-inhibitory bacterial richness and relative abundance increased on hosts. In co-occurrence networks, some putative Bd-inhibitory bacteria were found as hub-taxa, with red-backs having the highest proportion of protective hubs and positive associations related to putative Bd-inhibitory hub bacteria. We found more AMP candidates on salamanders with lower Bd susceptibility. These findings suggest that salamanders possess distinct innate mechanisms that affect chytrid fungi. IMPORTANCE How host mucosal defenses interact, and influence disease outcome is critical in understanding host defenses against pathogens. A more detailed understanding is needed of the interactions between the host and the functioning of its mucosal defenses in pathogen defense. This study investigates the variability of chytrid susceptibility in salamanders and the innate defenses each species possesses to mediate pathogens, thus advancing the knowledge toward a deeper understanding of the microbial ecology of skin-associated bacteria and contributing to the development of bioaugmentation strategies to mediate pathogen infection and disease. This study improves the understanding of complex immune defense mechanisms in salamanders and highlights the potential role of the mucosome to reduce the probability of Bd disease development and that putative protective bacteria may reduce likelihood of Bd infecting skin.

Network-based approaches for the investigation of microbial community structure and function using metagenomics-based data

Network-based approaches offer a powerful framework to evaluate microbial community organization and function as it relates to a variety of environmental processes. Emerging studies are exploring network theory as a method for data integration that is likely to be critical for the integration of 'omics' data using systems biology approaches. Intricacies of network theory and methodological and computational complexities in network construction, however, impede the use of these tools for translational science. We provide a perspective on the methods of network construction, interpretation and emerging uses for these techniques in understanding host-microbiota interactions.

Hypertension- and glycaemia-lowering effects of a grape-pomace-derived seasoning in high-cardiovascular risk and healthy subjects. Interplay with the gut microbiome

Purpose: Grape pomace (GP) is a winery by-product rich in polyphenols and dietary fibre. Some recent results suggest that GP-derived extracts could be promising additives in food, specially recommended for low-salt diets. The hypothesis tested in this paper is that the regular consumption of GP-derived seasonings could help in the control of hypertension and glycaemia. Methods: A randomized intervention study (6 weeks) was performed in high-risk cardiovascular subjects (n = 17) and in healthy subjects (n = 12) that were randomly allocated into intervention (2 g day^-1 of GP seasoning) or control (no seasoning consumed) groups. Blood samples, faeces, urine and blood pressure (BP) were taken at the baseline and at the end of the intervention. Faecal samples were analysed for microbiota composition (16S rRNA gene sequencing) and microbial-derived metabolites (short chain fatty acids and phenolic metabolites). Results: Among the clinical parameters studied, BP and fasting blood glucose significantly decreased (p < 0.05) after the seasoning intervention, but not for the control group. Notably, application of a novel approach based on ASV (Amplicon Sequence Variant) co-occurrence networks allowed us to identify some bacterial communities whose relative abundances were related with metadata. Conclusion: Our primary findings suggest that GP-seasoning may help in the modulation of cardiometabolic risk factors, mainly in the early stages. Furthermore, it evidences modulation of gut microbiota and functional bacterial communities by grape pomace, which might mediate the cardiometabolic effects of this by-product.

Exploring the universal healthy human gut microbiota around the World

The human gut holds a special place in the study of different microbial environments due to growing evidence that the gut microbiota is related to host health. However, despite extensive research, there is still a lack of knowledge about the core taxa forming the gut microbiota and, moreover, available information is biased towards western microbiomes in both genome databases and most core taxa studies. To tackle these limitations, we tested a database enrichment strategy and analyzed public datasets of whole-genome shotgun data, generated from 545 fecal samples, comprising three gradients of westernization. The NT database was selected as a baseline of biological diversity, subsequently being combined with various studies of interest related to the human microbiota. This enrichment strategy made it possible to improve classification capacity, compared to the original unenriched database, regarding the various lifestyles and populations studied. The effects of incomplete-taxonomy metagenome-assembled genomes on genome database enrichment were also examined, revealing that, while they are helpful, they should be used with caution depending on the taxonomic level of interest. Moreover, in terms of high prevalence, the core analysis revealed a conserved set of bacterial taxa in the healthy human gut microbiota worldwide, despite apparent lifestyle differences. Such taxa show a set of traits, metabolic roles, and ancestral status, making them suitable candidates for a hypothetical phylogenetic core of mutualistic microorganisms co-evolving with the human species.

Next Generation Microbiome Research: Identification of Keystone Species in the Metabolic Regulation of Host-Gut Microbiota Interplay

The community of the diverse microorganisms residing in the gastrointestinal tract, known as the gut microbiota, is exceedingly being studied for its impact on health and disease. This community plays a major role in nutrient metabolism, maintenance of the intestinal epithelial barrier but also in local and systemic immunomodulation. A dysbiosis of the gut microbiota, characterized by an unbalanced microbial ecology, often leads to a loss of essential functions that may be associated with proinflammatory conditions. Specifically, some key microbes that are depleted in dysbiotic ecosystems, called keystone species, carry unique functions that are essential for the balance of the microbiota. In this review, we discuss current understanding of reported keystone species and their proposed functions in health. We also elaborate on current and future bioinformatics tools needed to identify missing functions in the gut carried by keystone species. We propose that the identification of such keystone species functions is a major step for the understanding of microbiome dynamics in disease and toward the development of microbiome-based therapeutics.

Linking Inflammatory Bowel Disease Symptoms to Changes in the Gut Microbiome Structure and Function

Inflammatory bowel disease (IBD) is a chronic disease of the gastrointestinal tract that is often characterized by abdominal pain, rectal bleeding, inflammation, and weight loss. Many studies have posited that the gut microbiome may play an integral role in the onset and exacerbation of IBD. Here, we present a novel computational analysis of a previously published IBD dataset. This dataset consists of shotgun sequence data generated from fecal samples collected from individuals with IBD and an internal control group. Utilizing multiple external controls, together with appropriate techniques to handle the compositionality aspect of sequence data, our computational framework can identify and corroborate differences in the taxonomic profiles, bacterial association networks, and functional capacity within the IBD gut microbiome. Our analysis identified 42 bacterial species that are differentially abundant between IBD and every control group (one internal control and two external controls) with at least a twofold difference. Of the 42 species, 34 were significantly elevated in IBD, relative to every other control. These 34 species were still present in the control groups and appear to play important roles, according to network centrality and degree, in all bacterial association networks. Many of the species elevated in IBD have been implicated in modulating the immune response, mucin degradation, antibiotic resistance, and inflammation. We also identified elevated relative abundances of protein families related to signal transduction, sporulation and germination, and polysaccharide degradation as well as decreased relative abundance of protein families related to menaquinone and ubiquinone biosynthesis. Finally, we identified differences in functional capacities between IBD and healthy controls, and subsequently linked the changes in the functional capacity to previously published clinical research and to symptoms that commonly occur in IBD.

Bacterial community structure alterations within the colorectal cancer gut microbiome

BACKGROUND

Colorectal cancer is a leading cause of cancer-related deaths worldwide. The human gut microbiome has become an active area of research for understanding the initiation, progression, and treatment of colorectal cancer. Despite multiple studies having found significant alterations in the carriage of specific bacteria within the gut microbiome of colorectal cancer patients, no single bacterium has been unequivocally connected to all cases. Whether alterations in species carriages are the cause or outcome of cancer formation is still unclear, but what is clear is that focus should be placed on understanding changes to the bacterial community structure within the cancer-associated gut microbiome.

RESULTS

By applying a novel set of analyses on 252 previously published whole-genome shotgun sequenced fecal samples from healthy and late-stage colorectal cancer subjects, we identify taxonomic, functional, and structural changes within the cancer-associated human gut microbiome. Bacterial association networks constructed from these data exhibited widespread differences in the underlying bacterial community structure between healthy and colorectal cancer associated gut microbiomes. Within the cancer-associated ecosystem, bacterial species were found to form associations with other species that are taxonomically and functionally dissimilar to themselves, as well as form modules functionally geared towards potential changes in the tumor-associated ecosystem. Bacterial community profiling of these samples revealed a significant increase in species diversity within the cancer-associated gut microbiome, and an elevated relative abundance of species classified as originating from the oral microbiome including, but not limited to, Fusobacterium nucleatum, Peptostreptococcus stomatis, Gemella morbillorum, and Parvimonas micra. Differential abundance analyses of community functional capabilities revealed an elevation in functions linked to virulence factors and peptide degradation, and a reduction in functions involved in amino-acid biosynthesis within the colorectal cancer gut microbiome.

CONCLUSIONS

We utilize whole-genome shotgun sequenced fecal samples provided from a large cohort of late-stage colorectal cancer and healthy subjects to identify a number of potentially important taxonomic, functional, and structural alterations occurring within the colorectal cancer associated gut microbiome. Our analyses indicate that the cancer-associated ecosystem influences bacterial partner selection in the native microbiota, and we highlight specific oral bacteria and their associations as potentially relevant towards aiding tumor progression.