A new genomic blueprint of the human gut microbiota

Probiotic-fermented traditional Chinese herbal medicine, a promising approach to maintaining the intestinal microecology

ETHNOPHARMACOLOGICAL RELEVANCE

Traditional Chinese herbal medicines (TCHM) have been extensively used in China and other East and Southeast Asian countries. Due to the low content of bioactive components in most TCHM and the potential toxicity of some herbal ingredients to humans, researchers have turned to probiotic fermentation to enhance the efficacy, mitigate the toxic or side effects and improve the taste of TCHM. Both probiotics and certain TCHM benefit the intestinal microbiota and intestinal barrier of human body, demonstrating synergistic effects on in intestinal microecology.

AIM OF THE STUDY

This review aims to provide an overview of the development of fermentation technology, commonly used probiotic strains for TCHM fermentation, the advantages of probiotic fermentation and the challenges and limitations of probiotic-fermented TCHM. Additionally, it summarises and discusses the impact of probiotic-fermented TCHM on the intestinal barrier and microbiota, as well as the possible mechanisms involved.

MATERIALS AND METHODS

An extensive search of primary literature was conducted using various databases including PubMed, Google Scholar, Web of Science, Elsevier, SpringerLink, ScienceDirect, CNKI, and others. All the plant names have been checked with World Flora Online (http://www.worldfloraonline.org) on August 7, 2024.

RESULTS

The literature mentioned above was analyzed and summarized comprehensively. Probiotic-fermented TCHM can improve the intestinal barrier, modulate gut microbiota, and maintain homeostasis of the intestinal microecology. Modulating intestinal microecology by probiotic-fermented TCHM may be a crucial mechanism for its beneficial effects.

CONCLUSIONS

This article establishes a theoretical basis for further research on the relationship between probiotic-fermented TCHM and the intestinal microecology, with the hope of inspiring innovative concepts for the development of TCHM and exploring the potential of probiotic-fermented TCHM as a promising strategy for maintaining intestinal microecological balance.

Global challenges and microbial biofilms: Identification of priority questions in biofilm research, innovation and policy

Priority question exercises are increasingly used to frame and set future research, innovation and development agendas. They can provide an important bridge between the discoveries, data and outputs generated by researchers, and the information required by policy makers and funders. Microbial biofilms present huge scientific, societal and economic opportunities and challenges. In order to identify key priorities that will help to advance the field, here we review questions from a pool submitted by the international biofilm research community and from practitioners working across industry, the environment and medicine. To avoid bias we used computational approaches to group questions and manage a voting and selection process. The outcome of the exercise is a set of 78 unique questions, categorized in six themes: (i) Biofilm control, disruption, prevention, management, treatment (13 questions); (ii) Resistance, persistence, tolerance, role of aggregation, immune interaction, relevance to infection (10 questions); (iii) Model systems, standards, regulatory, policy education, interdisciplinary approaches (15 questions); (iv) Polymicrobial, interactions, ecology, microbiome, phage (13 questions); (v) Clinical focus, chronic infection, detection, diagnostics (13 questions); and (vi) Matrix, lipids, capsule, metabolism, development, physiology, ecology, evolution environment, microbiome, community engineering (14 questions). The questions presented are intended to highlight opportunities, stimulate discussion and provide focus for researchers, funders and policy makers, informing future research, innovation and development strategy for biofilms and microbial communities.

NOD2 protects against allergic lung inflammation in obese female mice

Obesity is associated with compartmentalized changes in immune responses that can be protective or pathogenic. It has been proposed that obesity-related changes in the microbiota influence allergic lung inflammation. We hypothesized that sensors of the bacterial cell wall influenced allergenic lung inflammation during obesity. Ovalbumin (OVA)-induced lung inflammation was similar in female Nod1-/- and wild-type mice during high-fat-diet-induced obesity, but allergic lung inflammation was higher in obese, high-fat-diet-fed female Nod2-/- mice. Obese Nod2-/- mice had higher inflammatory cell infiltration in the bronchial alveolar lavage (BAL) and lungs, pulmonary fibrosis, mucus levels, hypertrophy and hyperplasia of goblet cells, M2 alveolar macrophage infiltration, interleukin-4 (IL-4), IL-5, IL-6, and lower CXCL1 and IL-22. Therefore, Nod2 protects against excessive lung inflammation and is a bacterial sensor that relays protective responses to allergenic lung inflammation in obese female mice.

Exploring nature's battlefield: organismic interactions in the discovery of bioactive natural products

Covering: up to March 2024.Microbial natural products have historically been a cornerstone for the discovery of therapeutic agents. Advanced (meta)genome sequencing technologies have revealed that microbes harbor far greater biosynthetic capabilities than previously anticipated. However, despite the application of CRISPR/Cas-based gene editing and high-throughput technologies to activate silent biosynthetic gene clusters, the rapid identification of new natural products has not led to a proportional increase in the discovery rate of lead compounds or drugs. A crucial issue in this gap may be insufficient knowledge about the inherent biological and physiological functions of microbial natural products. Addressing this gap necessitates recognizing that the generation of functional natural products is deeply rooted in the interactions between the producing microbes and other (micro)organisms within their ecological contexts, an understanding that is essential for harnessing their potential therapeutic benefits. In this review, we highlight the discovery of functional microbial natural products from diverse niches, including those associated with humans, nematodes, insects, fungi, protozoa, plants, and marine animals. Many of these findings result from an organismic-interaction-guided strategy using multi-omic approaches. The current importance of this topic lies in its potential to advance drug discovery in an era marked by increasing antimicrobial resistance.

Multi-omics illuminates the functional significance of previously unknown species in a full-scale landfill leachate treatment plant

Landfill leachate treatment plants (LLTPs) harbor a vast reservoir of uncultured microbes, yet limited studies have systematically unraveled their functional potentials within LLTPs. Combining 36 metagenomic and 18 metatranscriptomic datasets from a full-scale LLTP, we unveiled a double-edged sword role of unknown species in leachate biotreatment and environmental implication. We identified 655 species-level genome bins (SGBs) spanning 47 bacterial and 3 archaeal phyla, with 75.9 % unassigned to any known species. Over 90 % of up-regulated functional genes in biotreatment units, compared to the leachate influent, were carried by unknown species and actively participated in carbon, nitrogen, and sulfur cycles. Approximately 79 % of the 37,366 carbohydrate active enzymes (CAZymes), with ∼90 % novelty and high expression, were encoded by unknown species, exhibiting great potential in biodegrading carbohydrate compounds linked to human meat-rich diets. Unknown species offered a valuable genetic resource of thousands of versatile, abundant, and actively expressed metabolic gene clusters (MGCs) and biosynthetic gene clusters (BGCs) for enhancing leachate treatment. However, unknown species may contribute to the emission of hazardous N2O/H2S and represented significant reservoirs for antibiotic-resistant pathogens that posed environmental safety risks. This study highlighted the significance of considering both positive and adverse effects of LLTP microbes to optimize LLTP performance.

MAGqual: a stand-alone pipeline to assess the quality of metagenome-assembled genomes

BACKGROUND

Metagenomics, the whole genome sequencing of microbial communities, has provided insight into complex ecosystems. It has facilitated the discovery of novel microorganisms, explained community interactions and found applications in various fields. Advances in high-throughput and third-generation sequencing technologies have further fuelled its popularity. Nevertheless, managing the vast data produced and addressing variable dataset quality remain ongoing challenges. Another challenge arises from the number of assembly and binning strategies used across studies. Comparing datasets and analysis tools is complex as it requires the quantitative assessment of metagenome quality. The inherent limitations of metagenomic sequencing, which often involves sequencing complex communities, mean community members are challenging to interrogate with traditional culturing methods leading to many lacking reference sequences. MIMAG standards aim to provide a method to assess metagenome quality for comparison but have not been widely adopted.

RESULTS

To address the need for simple and quick metagenome quality assignation, here we introduce the pipeline MAGqual (Metagenome-Assembled Genome qualifier) and demonstrate its effectiveness at determining metagenomic dataset quality in the context of the MIMAG standards.

CONCLUSIONS

The MAGqual pipeline offers an accessible way to evaluate metagenome quality and generate metadata on a large scale. MAGqual is built in Snakemake to ensure readability and scalability, and its open-source nature promotes accessibility, community development, and ease of updates. MAGqual is built in Snakemake, R, and Python and is available under the MIT license on GitHub at https://github.com/ac1513/MAGqual . Video Abstract.

Incomplete human reference genomes can drive false sex biases and expose patient-identifying information in metagenomic data

As next-generation sequencing technologies produce deeper genome coverages at lower costs, there is a critical need for reliable computational host DNA removal in metagenomic data. We find that insufficient host filtration using prior human genome references can introduce false sex biases and inadvertently permit flow-through of host-specific DNA during bioinformatic analyses, which could be exploited for individual identification. To address these issues, we introduce and benchmark three host filtration methods of varying throughput, with concomitant applications across low biomass samples such as skin and high microbial biomass datasets including fecal samples. We find that these methods are important for obtaining accurate results in low biomass samples (e.g., tissue, skin). Overall, we demonstrate that rigorous host filtration is a key component of privacy-minded analyses of patient microbiomes and provide computationally efficient pipelines for accomplishing this task on large-scale datasets.

Microbes: Drivers of Chenpi manufacturing, biotransformation, and physiological effects

Chenpi holds a rich history of both edible and medicinal applications worldwide, garnering increased attention from researchers in recent years due to its diverse physiological effects. While current research predominantly exploresed its chemical composition and physiological effects, there remains a notable gap in knowledge concerning its manufacturing, characteristic chemical substances, and the underlying mechanisms driving its physiological effects. In this review, the impacts of microbes on the manufacturing, biotransformation, and physiological effects of Chenpi were summarized, as well as the present status of product development. Furthermore, this review engaged in an in-depth discussion highlighting the challenges and shortcomings in recent research, while proposing potential directions and prospects. Additionally, the claim that "The longer the aging, the better the quality" of Chenpi was scientifically evaluated for the first time, providing a solid theoretical foundation for advancing the Chenpi industry.

CHAMP delivers accurate taxonomic profiles of the prokaryotes, eukaryotes, and bacteriophages in the human microbiome

Introduction

Accurate taxonomic profiling of the human microbiome composition is crucial for linking microbial species to health outcomes. Therefore, we created the Clinical Microbiomics Human Microbiome Profiler (CHAMP), a comprehensive tool designed for the profiling of prokaryotes, eukaryotes, and viruses across all body sites.

Methods

CHAMP uses a reference database derived from 30,382 human microbiome samples, covering 6,567 prokaryotic and 244 eukaryotic species, as well as 64,003 viruses. We benchmarked CHAMP against established profiling tools (MetaPhlAn 4, Bracken 2, mOTUs 3, and Phanta) using a diverse set of in silico metagenomes and DNA mock communities.

Results

CHAMP demonstrated unparalleled species recall, F1 score, and significantly reduced false positives compared to all other tools benchmarked. The false positive relative abundance (FPRA) for CHAMP was, on average, 50-fold lower than the second-best performing profiler. CHAMP also proved to be more robust than other tools at low sequencing depths, highlighting its application for low biomass samples.

Discussion

Taken together, this establishes CHAMP as a best-in-class human microbiome profiler of prokaryotes, eukaryotes, and viruses in diverse and complex communities across low and high biomass samples. CHAMP profiling is offered as a service by Clinical Microbiomics A/S and is available for a fee at https://cosmosidhub.com.

Metagenomic discovery of microbial eukaryotes in stool microbiomes

Host-associated microbiota form complex microbial communities that are increasingly associated with host behavior and disease. While these microbes include bacterial, archaeal, viral, and eukaryotic constituents, most studies have focused on bacteria due to their dominance in the human host and available tools for investigation. Accumulating evidence suggests microbial eukaryotes in the microbiome play pivotal roles in host health, but our understandings of these interactions are limited to a few readily identifiable taxa because of technical limitations in unbiased eukaryote exploration. Here, we combined cell sorting, optimized eukaryotic cell lysis, and shotgun sequencing to accelerate metagenomic discovery and analysis of host-associated microbial eukaryotes. Using synthetic communities with a 1% microbial eukaryote representation, the eukaryote-optimized cell lysis and DNA recovery method alone yielded a 38-fold increase in eukaryotic DNA. Automated sorting of eukaryotic cells from stool samples of healthy adults increased the number of microbial eukaryote reads in metagenomic pools by up to 28-fold compared to commercial kits. Read frequencies for identified fungi increased by 10,000× on average compared to the Human Microbiome Project and allowed for the identification of novel taxa, de novo assembly of contigs from previously unknown microbial eukaryotes, and gene prediction from recovered genomic segments. These advances pave the way for the unbiased inclusion of microbial eukaryotes in deciphering determinants of health and disease in the host-associated microbiome.IMPORTANCEMicrobial eukaryotes are common constituents of the human gut where they can contribute to local ecology and host health, but they are often overlooked in microbiome studies. The lack of attention is due to current technical limitations that are heavily biased or poorly recovered DNA from microbial eukaryotes. We developed a method to increase the representation of these eukaryotes in metagenomic sequencing of microbiome samples that allows to improve their detection compared to prior methods and allows for the identification of new species. Application of the technique to gut microbiome samples improved detection of fungi, protists, and helminths. New eukaryotic taxa and their encoded genes could be identified by sequencing a small number of samples. This approach can improve the inclusion of eukaryotes into microbiome research.

Hidden Links Between Skin Microbiome and Skin Imaging Phenome

Despite the skin microbiome has been linked to skin health and diseases, its role in modulating human skin appearance remains understudied. Using a total of 1244 face imaging phenomes and 246 cheek metagenomes, we first established three skin age indices by machine learning, including skin phenotype age (SPA), skin microbiota age (SMA), and skin integration age (SIA) as surrogates of phenotypic aging, microbial aging, and their combination, respectively. Moreover, we found that besides aging and gender as intrinsic factors, skin microbiome might also play a role in shaping skin imaging phenotypes (SIPs). Skin taxonomic and functional α diversity was positively linked to melanin, pore, pigment, and ultraviolet spot levels, but negatively linked to sebum, lightening, and porphyrin levels. Furthermore, certain species were correlated with specific SIPs, such as sebum and lightening levels negatively correlated with Corynebacterium matruchotii, Staphylococcus capitis, and Streptococcus sanguinis. Notably, we demonstrated skin microbial potential in predicting SIPs, among which the lightening level presented the least error of 1.8%. Lastly, we provided a reservoir of potential mechanisms through which skin microbiome adjusted the SIPs, including the modulation of pore, wrinkle, and sebum levels by cobalamin and heme synthesis pathways, predominantly driven by Cutibacterium acnes. This pioneering study unveils the paradigm for the hidden links between skin microbiome and skin imaging phenome, providing novel insights into how skin microbiome shapes skin appearance and its healthy aging.

Rapid species-level metagenome profiling and containment estimation with sylph

Profiling metagenomes against databases allows for the detection and quantification of microorganisms, even at low abundances where assembly is not possible. We introduce sylph, a species-level metagenome profiler that estimates genome-to-metagenome containment average nucleotide identity (ANI) through zero-inflated Poisson k-mer statistics, enabling ANI-based taxa detection. On the Critical Assessment of Metagenome Interpretation II (CAMI2) Marine dataset, sylph was the most accurate profiling method of seven tested. For multisample profiling, sylph took >10-fold less central processing unit time compared to Kraken2 and used 30-fold less memory. Sylph's ANI estimates provided an orthogonal signal to abundance, allowing for an ANI-based metagenome-wide association study for Parkinson disease (PD) against 289,232 genomes while confirming known butyrate-PD associations at the strain level. Sylph took <1 min and 16 GB of random-access memory to profile metagenomes against 85,205 prokaryotic and 2,917,516 viral genomes, detecting 30-fold more viral sequences in the human gut compared to RefSeq. Sylph offers precise, efficient profiling with accurate containment ANI estimation even for low-coverage genomes.

Integrated large-scale metagenome assembly and multi-kingdom network analyses identify sex differences in the human nasal microbiome

BACKGROUND

Respiratory diseases impose an immense health burden worldwide. Epidemiological studies have revealed extensive disparities in the incidence and severity of respiratory tract infections between men and women. It has been hypothesized that there might also be a nasal microbiome axis contributing to the observed sex disparities.

RESULTS

Here, we study the nasal microbiome of healthy young adults in the largest cohort to date with 1593 individuals, using shotgun metagenomic sequencing. We compile the most comprehensive reference catalog for the nasal bacterial community containing 4197 metagenome-assembled genomes and integrate the mycobiome, to provide a valuable resource and a more holistic perspective for the understudied human nasal microbiome. We systematically evaluate sex differences and reveal extensive sex-specific features in both taxonomic and functional levels in the nasal microbiome. Through network analyses, we capture markedly higher ecological stability and antagonistic potentials in the female nasal microbiome compared to the male's. The analysis of the keystone bacteria reveals that the sex-dependent evolutionary characteristics might have contributed to these differences.

CONCLUSIONS

In summary, we construct the most comprehensive catalog of metagenome-assembled-genomes for the nasal bacterial community to provide a valuable resource for the understudied human nasal microbiome. On top of that, comparative analysis in relative abundance and microbial co-occurrence networks identify extensive sex differences in the respiratory tract community, which may help to further our understanding of the observed sex disparities in the respiratory diseases.

Hidden army within: Harnessing the microbiome to improve cancer treatment outcomes

The gut microbiome has emerged as a critical player in cancer pathogenesis and treatment response. Dysbiosis, an imbalance in the gut microbial community, impacts tumor initiation, progression, and therapy outcomes. Specific bacterial species have been associated with either promoting or inhibiting tumor growth, offering potential targets for therapeutic intervention. The gut microbiome influences the efficacy and toxicity of conventional treatments and cutting-edge immunotherapies, highlighting its potential as a therapeutic target in cancer care. However, translating microbiome research into clinical practice requires addressing challenges such as standardizing methodologies, validating microbial biomarkers, and ensuring ethical considerations. Here, we provide a comprehensive overview of the gut microbiome's role in cancer highlighting the need for ongoing research, collaboration, and innovation to harness its full potential for improving patient outcomes in oncology. The current editorial aims to explore these insights and emphasizes the need for standardized methodologies, validation of microbial biomarkers, and interdisciplinary collaboration to translate microbiome research into clinical applications. Furthermore, it underscores ethical considerations and regulatory challenges surrounding the use of microbiome-based therapies. Together, this article advocates for ongoing research, collaboration, and innovation to realize the full potential of microbiome-guided oncology in improving patient care and outcomes.

Establishing human microbial observatory programs in low- and middle-income countries

Studies of the human microbiome are progressing rapidly but have largely focused on populations living in high-income countries. With increasing evidence that the microbiome contributes to the pathogenesis of diseases that affect infants, children, and adults in low- and middle-income countries (LMICs), and with profound and rapid ongoing changes occurring in our lifestyles and biosphere, understanding the origins of and developing microbiome-directed therapeutics for treating a number of global health challenges requires the development of programs for studying human microbial ecology in LMICs. Here, we discuss how the establishment of long-term human microbial observatory programs in selected LMICs could provide one timely approach.

Efficient and easy-to-use capturing three-dimensional metagenome interactions with GutHi-C

Hi-C can obtain three-dimensional chromatin structure information and is widely used for genome assembly. We constructed the GutHi-C technology. As shown in the graphical abstract, it is a highly efficient and quick-to-operate method and can be widely used for human, livestock, and poultry gut microorganisms. It provides a reference for the Hi-C methodology of the microbial metagenome. DPBS, Dulbecco's phosphate-buffered saline; Hi-C, high-through chromatin conformation capture; LB, Luria-Bertani; NGS, next-generation sequencing; PCR, polymerase chain reaction; QC, quality control.

Comparing microbial communities in mucilage and seawater samples: Metagenomic insights into mucilage formation in the Marmara Sea

Marine environments are subject to various naturally occurring phenomena, including marine snow and mucilage. In 2021, the rapid emergence of mucilage in the Marmara Sea raised concerns about its environmental impact. This study investigates the microbial communities in mucilage and seawater samples from the Marmara Sea using metagenomic-scale comparative analyses. The results indicate significant differences in microbial composition and diversity, with mucilage samples showing higher levels of polysaccharide biosynthesis-related enzymes. Over 50% of reads in mucilage samples remained unclassified (dark matter), highlighting unknown microbial taxa. Clean seawater was characterized by a higher presence of Euryarchaeota, Proteobacteria, and Rhodothermaeota, while Chlamydiae and Fusobacteria were dominant in mucilage. The study underscores the necessity for comprehensive metagenomic analyses to understand microbial roles in mucilage formation and persistence. Early detection of microbial shifts could serve as a warning system for mucilage outbreaks, aiding in the development of management strategies.

Comprehensive data optimization and risk prediction framework: machine learning methods for inflammatory bowel disease prediction based on the human gut microbiome data

Over the past decade, the prevalence of inflammatory bowel disease (IBD) has significantly increased, making early detection crucial for improving patient survival rates. Medical research suggests that changes in the human gut microbiome are closely linked to IBD onset, playing a critical role in its prediction. However, the current gut microbiome data often exhibit missing values and high dimensionality, posing challenges to the accuracy of predictive algorithms. To address these issues, we proposed the comprehensive data optimization and risk prediction framework (CDORPF), an ensemble learning framework designed to predict IBD risk based on the human gut microbiome, aiding early diagnosis. The framework comprised two main components: data optimization and risk prediction. The data optimization module first employed triple optimization imputation (TOI) to impute missing data while preserving the biological characteristics of the microbiome. It then utilized importance-weighted variational autoencoder (IWVAE) to reduce redundant information from the high-dimensional microbiome data. This process resulted in a complete, low-dimensional representation of the data, laying the foundation for improved algorithm efficiency and accuracy. In the risk prediction module, the optimized data was classified using a random forest (RF) model, and hyperparameters were globally optimized using improved aquila optimizer (IAO), which incorporated multiple strategies. Experimental results on IBD-related gut microbiome datasets showed that the proposed framework achieved classification accuracy, recall, and F1 scores exceeding 0.9, outperforming comparison models and serving as a valuable tool for predicting IBD onset risk.

Feature sequence-based genome mining uncovers the hidden diversity of bacterial siderophore pathways

Microbial secondary metabolites are a rich source for pharmaceutical discoveries and play crucial ecological functions. While tools exist to identify secondary metabolite clusters in genomes, precise sequence-to-function mapping remains challenging because neither function nor substrate specificity of biosynthesis enzymes can accurately be predicted. Here, we developed a knowledge-guided bioinformatic pipeline to solve these issues. We analyzed 1928 genomes of Pseudomonas bacteria and focused on iron-scavenging pyoverdines as model metabolites. Our pipeline predicted 188 chemically different pyoverdines with nearly 100% structural accuracy and the presence of 94 distinct receptor groups required for the uptake of iron-loaded pyoverdines. Our pipeline unveils an enormous yet overlooked diversity of siderophores (151 new structures) and receptors (91 new groups). Our approach, combining feature sequence with phylogenetic approaches, is extendable to other metabolites and microbial genera, and thus emerges as powerful tool to reconstruct bacterial secondary metabolism pathways based on sequence data.

MBCN: A novel reference database for Effcient Metagenomic analysis of human gut microbiome

Metagenomic shotgun sequencing data can identify microbes and their proportions. But metagenomic shotgun data profiling results obtained from multiple projects using different reference databases are difficult to compare and apply meta-analysis. Our work aims to create a novel collection of human gut prokaryotic genomes, named Microbiome Collection Navigator (MBCN). 2379 human gut metagenomic samples are screened, and 16,785 metagenome-assembled genomes (MAGs) are assembled using a standardized pipeline. In addition, MAGs are combined with the representative genomes from public prokaryotic genomes collections to cluster, and pan-genomes for each cluster's genomes are constructed to build Kraken2 and Bracken databases. The databases built by MBCN are more comprehensive and accurate for profiling metagenomic reads comparing with other collections on simulated reads and virtual bio-projects. We profile 1082 human gut metagenomic samples with MBCN database and organize profiles and metadata on the web program. Meanwhile, using MBCN as a reference database, we also develop a unified, standardized, and systematic metagenomic analysis pipeline and platform, named MicrobiotaCN (http://www.microbiota.cn) and common statistical and visualization tools for microbiome research are integrated into the web program. Taken together, MBCN and MicrobiotaCN can be a valuable resource and a powerful tool that allows researchers to perform metagenomic analysis by a unified pipeline efficiently.

Intestinal Microbiota Interventions to Enhance Athletic Performance-A Review

Recent years have witnessed an uptick in research highlighting the gut microbiota's role as a primary determinant of athletes' health, which has piqued interest in the hypothesis that it correlates with athletes' physical performance. Athletes' physical performances could be impacted by the metabolic activity of the assortment of microbes found in their gut. Intestinal microbiota impacts multiple facets of an athlete's physiology, including immune response, gut membrane integrity, macro- and micronutrient absorption, muscle endurance, and the gut-brain axis. Several physiological variables govern the gut microbiota; hence, an intricately tailored and complex framework must be implemented to comprehend the performance-microbiota interaction. Emerging evidence underscores the intricate relationship between the gut microbiome and physical fitness, revealing that athletes who engage in regular physical activity exhibit a richer diversity of gut microbes, particularly within the Firmicutes phylum, e.g., Ruminococcaceae genera, compared to their sedentary counterparts. In elite sport, it is challenging to implement an unconventional strategy whilst simultaneously aiding an athlete to accomplish feasible, balanced development. This review compiles the research on the effects of gut microbiota modulation on performance in sports and illustrates how different supplementation strategies for gut microbiota have the ability to improve athletic performance by enhancing physical capacities. In addition to promoting athletes' overall health, this study evaluates the existing literature in an effort to shed light on how interventions involving the gut microbiota can dramatically improve performance on the field. The findings should inform both theoretical and practical developments in the fields of sports nutrition and training.

Mining human microbiomes reveals an untapped source of peptide antibiotics

Drug-resistant bacteria are outpacing traditional antibiotic discovery efforts. Here, we computationally screened 444,054 previously reported putative small protein families from 1,773 human metagenomes for antimicrobial properties, identifying 323 candidates encoded in small open reading frames (smORFs). To test our computational predictions, 78 peptides were synthesized and screened for antimicrobial activity in vitro, with 70.5% displaying antimicrobial activity. As these compounds were different compared with previously reported antimicrobial peptides, we termed them smORF-encoded peptides (SEPs). SEPs killed bacteria by targeting their membrane, synergizing with each other, and modulating gut commensals, indicating a potential role in reconfiguring microbiome communities in addition to counteracting pathogens. The lead candidates were anti-infective in both murine skin abscess and deep thigh infection models. Notably, prevotellin-2 from Prevotella copri presented activity comparable to the commonly used antibiotic polymyxin B. Our report supports the existence of hundreds of antimicrobials in the human microbiome amenable to clinical translation.

Advancing Source Tracking: Systematic Review and Source-Specific Genome Database Curation of Fecally Shed Prokaryotes

Advancements within fecal source tracking (FST) studies are complicated by a lack of knowledge regarding the genetic content and distribution of fecally shed microbial populations. To address this gap, we performed a systematic literature review and curated a large collection of genomes (n = 26,018) representing fecally shed prokaryotic species across broad and narrow source categories commonly implicated in FST studies of recreational waters (i.e., cats, dogs, cows, seagulls, chickens, pigs, birds, ruminants, human feces, and wastewater). We find that across these sources the total number of prokaryotic genomes recovered from materials meeting our initial inclusion criteria varied substantially across fecal sources: from none in seagulls to 9,085 in pigs. We examined genome sequences recovered from these metagenomic and isolation-based studies extensively via comparative genomic approaches to characterize trends across source categories and produce a finalized genome database for each source category which is available online (n = 12,730). On average, 81% of the genomes representing species-level populations occur only within a single source. Using fecal slurries to test the performance of each source database, we report read capture rates that vary with fecal source alpha diversity and database size. We expect this resource to be useful to FST-related objectives, One Health research, and sanitation efforts globally.

Metagenomic analysis of colonic tissue and stool microbiome in patients with colorectal cancer in a South Asian population

BACKGROUND

The gut microbiome is thought to play an important role in the development of colorectal cancer (CRC). However, as the gut microbiome varies widely based on diet, we sought to investigate the gut microbiome changes in patients with CRC in a South Asian population.

METHODS

The gut microbiome was assessed by 16s metagenomic sequencing targeting the V4 hypervariable region of the bacterial 16S rRNA in stool samples (n = 112) and colonic tissue (n = 36) in 112 individuals. The cohort comprised of individuals with CRC (n = 24), premalignant lesions (n = 10), healthy individuals (n = 50) and in those with diabetes (n = 28).

RESULTS

Overall, the relative abundances of genus Fusobacterium (p < 0.001), Acinetobacter (p < 0.001), Escherichia-Shigella (p < 0.05) were significantly higher in gut tissue, while Romboutsia (p < 0.01) and Prevotella (p < 0.05) were significantly higher in stool samples. Bacteroides and Fusobacterium were the most abundant genera found in stool samples in patients with CRC. Patients with pre-malignant lesions had significantly high abundances of Christensenellaceae, Enterobacteriaceae, Mollicutes and Ruminococcaceae (p < 0.001) compared to patients with CRC, and healthy individuals. Romboutsia was significantly more abundant (p < 0.01) in stool samples in healthy individuals compared to those with CRC and diabetes.

CONCLUSION

Despite marked differences in the Sri Lankan diet compared to the typical Western diet, Bacteroides and Fusobacterium species were the most abundant in those with CRC, with Prevotella species, being most abundant in many individuals. We believe these results pave the way for possible dietary interventions for prevention of CRC in the South Asian population.

A streamlined culturomics case study for the human gut microbiota research

Bacterial culturomics is a set of techniques to isolate and identify live bacteria from complex microbial ecosystems. Despite its potential to revolutionize microbiome research, bacterial culturomics has significant challenges when applied to human gut microbiome studies due to its labor-intensive nature. Therefore, we established a streamlined culturomics approach with minimal culture conditions for stool sample preincubation. We evaluated the suitability of non-selective medium candidates for maintaining microbial diversity during a 30-day incubation period based on 16S rRNA gene amplicon analysis. Subsequently, we applied four culture conditions (two preincubation media under an aerobic/anaerobic atmosphere) to isolate gut bacteria on a large scale from eight stool samples of healthy humans. We identified 8141 isolates, classified into 263 bacterial species, including 12 novel species candidates. Our analysis of cultivation efficiency revealed that seven days of aerobic and ten days of anaerobic incubation captured approximately 91% and 95% of the identified species within each condition, respectively, with a synergistic effect confirmed when selected preincubation media were combined. Moreover, our culturomics findings expanded the coverage of gut microbial diversity compared to 16S rRNA gene amplicon sequencing results. In conclusion, this study demonstrated the potential of a streamlined culturomics approach for the efficient isolation of gut bacteria from human stool samples. This approach might pave the way for the broader adoption of culturomics in human gut microbiome studies, ultimately leading to a more comprehensive understanding of this complex microbial ecosystem.

Integrating functional metagenomics to decipher microbiome-immune interactions

Microbial metabolites can be viewed as the cytokines of the microbiome, transmitting information about the microbial and metabolic environment of the gut to orchestrate and modulate local and systemic immune responses. Still, many immunology studies focus solely on the taxonomy and community structure of the gut microbiota rather than its functions. Early sequencing-based microbiota profiling approaches relied on PCR amplification of small regions of bacterial and fungal genomes to facilitate identification of the microbes present. However, recent microbiome analysis methods, particularly shotgun metagenomic sequencing, now enable culture-independent profiling of microbiome functions and metabolites in addition to taxonomic characterization. In this review, we showcase recent advances in functional metagenomics methods and applications and discuss the current limitations and potential avenues for future development. Importantly, we highlight a few examples of key areas of opportunity in immunology research where integrating functional metagenomic analyses of the microbiome can substantially enhance a mechanistic understanding of microbiome-immune interactions and their contributions to health and disease states.

Systems Biology of Human Microbiome for the Prediction of Personal Glycaemic Response

The human gut microbiota is increasingly recognized as a pivotal factor in diabetes management, playing a significant role in the body's response to treatment. However, it is important to understand that long-term usage of medicines like metformin and other diabetic treatments can result in problems, gastrointestinal discomfort, and dysbiosis of the gut flora. Advanced sequencing technologies have improved our understanding of the gut microbiome's role in diabetes, uncovering complex interactions between microbial composition and metabolic health. We explore how the gut microbiota affects glucose metabolism and insulin sensitivity by examining a variety of -omics data, including genomics, transcriptomics, epigenomics, proteomics, metabolomics, and metagenomics. Machine learning algorithms and genome-scale modeling are now being applied to find microbiological biomarkers associated with diabetes risk, predicted disease progression, and guide customized therapy. This study holds promise for specialized diabetic therapy. Despite significant advances, some concerns remain unanswered, including understanding the complex relationship between diabetes etiology and gut microbiota, as well as developing user-friendly technological innovations. This mini-review explores the relationship between multiomics, precision medicine, and machine learning to improve our understanding of the gut microbiome's function in diabetes. In the era of precision medicine, the ultimate goal is to improve patient outcomes through personalized treatments.

Improving the odds: Artificial intelligence and the great plate count anomaly

Next-generation DNA sequencing has shown that the great plate count anomaly, that is, the difference between bacteria present in the environment and those that can be obtained in culture from that environment, is even greater and more persisting than initially thought. This hampers fundamental understanding of bacterial physiology and biotechnological application of the unculture majority. With big sequence data as foundation, artificial intelligence (AI) may be a game changer in bacterial isolation efforts and provide directions for the cultivation media and conditions that are most promising and as such be used to canalize limited human and financial resources. This opinion paper discusses how AI is or can be used to improve the success of bacterial isolation.

Harmony unveiled: Intricate the interplay of dietary factor, gut microbiota, and colorectal cancer-A narrative review

Diet plays a critical role in shaping the gut microbiome, which in turn regulates molecular activities in the colonic mucosa. The state and composition of the gut microbiome are key factors in the development of colorectal cancer. An altered gut microbiome, linked to weakened immune responses and the production of carcinogenic substances, is a significant contributor to colorectal cancer pathogenesis. Dietary changes that involve low-fiber and phytomolecule intake, coupled with higher consumption of red meat, can raise the risk of colorectal cancer. Salutary filaments, which reach the colon undigested, are metabolized by the gut microbiome, producing short-chain fatty acids. Short-chain fatty acids possess beneficial anti-inflammatory and antiproliferative properties that promote colon health. A well-balanced microbiome, supported by beneficial fibers and phytochemicals, can regulate the activation of proto-oncogenes and oncogenic pathways, thereby reducing cell proliferation. Recent research suggests that an overabundance of specific microbes, such as Fusobacterium nucleatum, may contribute to adverse changes in the colonic mucosa. Positive lifestyle adjustments have been demonstrated to effectively inhibit the growth of harmful opportunistic organisms. Synbiotics, which combine probiotics and prebiotics, can protect the intestinal mucosa by enhancing immune responses and decreasing the production of harmful metabolites, oxidative stress, and cell proliferation. This narrative review provides a concise understanding of evolving evidence regarding how diet influences the gut microbiome, leading to the restoration of the colonic epithelium. It underscores the importance of a healthy, plant-based diet and associated supplements in preventing colorectal cancer by enhancing gut microbiome health.

Engineered minimal type I CRISPR-Cas system for transcriptional activation and base editing in human cells

Type I CRISPR-Cas systems are widespread and have exhibited high versatility and efficiency in genome editing and gene regulation in prokaryotes. However, due to the multi-subunit composition and large size, their application in eukaryotes has not been thoroughly investigated. Here, we demonstrate that the type I-F2 Cascade, the most compact among type I systems, with a total gene size smaller than that of SpCas9, can be developed for transcriptional activation in human cells. The efficiency of the engineered I-F2 tool can match or surpass that of dCas9. Additionally, we create a base editor using the I-F2 Cascade, which induces a considerably wide editing window (~30 nt) with a bimodal distribution. It can expand targetable sites, which is useful for disrupting functional sequences and genetic screening. This research underscores the application of compact type I systems in eukaryotes, particularly in the development of a base editor with a wide editing window.

Immunomodulation aspects of gut microbiome-related interventional strategies in colorectal cancer

Colorectal cancer (CRC), the third most common cancer worldwide, develops mainly due to the accumulation of genetic and epigenetic changes over many years. Substantial evidence suggests that gut microbiota plays a significant role in the initiation, progression, and control of CRC, depending on the balance between beneficial and pathogenic microorganisms. Nonetheless, gut microbiota composition by regulating the host immune response may either promote or inhibit CRC. Thus, modification of gut microbiota potentially impacts clinical outcomes of immunotherapy. Previous studies have indicated that therapeutic strategies such as probiotics, prebiotics, and postbiotics enhance the intestinal immune system and improve the efficacy of immunotherapeutic agents, potentially serving as a complementary strategy in cancer immunotherapy. This review discusses the role of the gut microbiota in the onset and development of CRC in relation to the immune response. Additionally, we focus on the effect of strategies manipulating gut microbiome on the immune response and efficacy of immunotherapy against CRC. We demonstrate that manipulation of gut microbiome can enhance immune response and outcomes of immunotherapy through downregulating Treg cells and other immunosuppressive cells while improving the function of T cells within the tumor; however, further research, especially clinical trials, are needed to evaluate its efficacy in cancer treatment.

Microbiome alterations are associated with apolipoprotein E mutation in Octodon degus and humans with Alzheimer's disease

Gut microbiome dysbiosis is linked to many neurological disorders including Alzheimer's disease (AD). A major risk factor for AD is polymorphism in the apolipoprotein E (APOE) gene, which affects gut microbiome composition. To explore the gut-brain axis in AD, long-lived animal models of naturally developing AD-like pathologies are needed. Octodon degus (degu) exhibit spontaneous AD-like symptoms and ApoE mutations, making them suitable for studying the interplay between AD genetic determinants and gut microbiome. We analyzed the association between APOE genotype and gut microbiome in 50 humans and 32 degu using16S rRNA gene amplicon sequencing. Significant associations were found between the degu ApoE mutation and gut microbial changes in degu, notably a depletion of Ruminococcaceae and Akkermansiaceae and an enrichment of Prevotellaceae, mirroring patterns seen in people with AD. The altered taxa were previously suggested to be involved in AD, validating the degu as an unconventional model for studying the AD/microbiome crosstalk.

Capturing clinically relevant Campylobacter attributes through direct whole genome sequencing of stool

Campylobacter is the leading bacterial cause of infectious intestinal disease, but the pathogen typically accounts for a very small proportion of the overall stool microbiome in each patient. Diagnosis is even more difficult due to the fastidious nature of Campylobacter in the laboratory setting. This has, in part, driven a change in recent years, from culture-based to rapid PCR-based diagnostic assays which have improved diagnostic detection, whilst creating a knowledge gap in our clinical and epidemiological understanding of Campylobacter genotypes - no isolates to sequence. In this study, direct metagenomic sequencing approaches were used to assess the possibility of replacing genome sequences with metagenome sequences; metagenomic sequencing outputs were used to describe clinically relevant attributes of Campylobacter genotypes. A total of 37 diarrhoeal stool samples with Campylobacter and five samples with an unknown pathogen result were collected and processed with and without filtration, DNA was extracted, and metagenomes were sequenced by short-read sequencing. Culture-based methods were used to validate Campylobacter metagenome-derived genome (MDG) results. Sequence output metrics were assessed for Campylobacter genome quality and accuracy of characterization. Of the 42 samples passing quality checks for analysis, identification of Campylobacter to the genus and species level was dependent on Campylobacter genome read count, coverage and genome completeness. A total of 65% (24/37) of samples were reliably identified to the genus level through Campylobacter MDG, 73% (27/37) by culture and 97% (36/37) by qPCR. The Campylobacter genomes with a genome completeness of over 60% (n=21) were all accurately identified at the species level (100%). Of those, 72% (15/21) were identified to sequence types (STs), and 95% (20/21) accurately identified antimicrobial resistance (AMR) gene determinants. Filtration of stool samples enhanced Campylobacter MDG recovery and genome quality metrics compared to the corresponding unfiltered samples, which improved the identification of STs and AMR profiles. The phylogenetic analysis in this study demonstrated the clustering of the metagenome-derived with culture-derived genomes and revealed the reliability of genomes from direct stool sequencing. Furthermore, Campylobacter genome spiking percentages ranging from 0 to 2% total metagenome abundance in the ONT MinION sequencer, configured to adaptive sequencing, exhibited better assembly quality and accurate identification of STs, particularly in the analysis of metagenomes containing 2 and 1% of Campylobacter jejuni genomes. Direct sequencing of Campylobacter from stool samples provides clinically relevant and epidemiologically important genomic information without the reliance on cultured genomes.

Metatranscriptomics: A Tool for Clinical Metagenomics

In the field of bioinformatics, amplicon sequencing of 16S rRNA genes has long been used to investigate community membership and taxonomic abundance in microbiome studies. As we can observe, shotgun metagenomics has become the dominant method in this field. This is largely owing to advancements in sequencing technology, which now allow for random sequencing of the entire genetic content of a microbiome. Furthermore, this method allows profiling both genes and the microbiome's membership. Although these methods have provided extensive insights into various microbiomes, they solely assess the existence of organisms or genes, without determining their active role within the microbiome. Microbiome scholarship now includes metatranscriptomics to decipher how a community of microorganisms responds to changing environmental conditions over a period of time. Metagenomic studies identify the microbes that make up a community but metatranscriptomics explores the diversity of active genes within that community, understanding their expression profile and observing how these genes respond to changes in environmental conditions. This expert review article offers a critical examination of the computational metatranscriptomics tools for studying the transcriptomes of microbial communities. First, we unpack the reasons behind the need for community transcriptomics. Second, we explore the prospects and challenges of metatranscriptomic workflows, starting with isolation and sequencing of the RNA community, then moving on to bioinformatics approaches for quantifying RNA features, and statistical techniques for detecting differential expression in a community. Finally, we discuss strengths and shortcomings in relation to other microbiome analysis approaches, pipelines, use cases and limitations, and contextualize metatranscriptomics as a tool for clinical metagenomics.

Gut microbiota regulates gut homeostasis, mucosal immunity and influences immune-related diseases

The intestinal microbiome constitutes a sophisticated and massive ecosystem pivotal for maintaining gastrointestinal equilibrium and mucosal immunity via diverse pathways. The gut microbiota is continuously reshaped by multiple environmental factors, thereby influencing overall wellbeing or predisposing individuals to disease state. Many observations reveal an altered microbiome composition in individuals with autoimmune conditions, coupled with shifts in metabolic profiles, which has spurred ongoing development of therapeutic interventions targeting the microbiome. This review delineates the microbial consortia of the intestine, their role in sustaining gastrointestinal stability, the association between the microbiome and immune-mediated pathologies, and therapeutic modalities focused on microbiome modulation. We emphasize the entire role of the intestinal microbiome in human health and recommend microbiome modulation as a viable strategy for disease prophylaxis and management. However, the application of gut microbiota modification for the treatment of immune-related diseases, such as fecal microbiota transplantation and probiotics, remain quite challenging. Therefore, more research is needed into the role and mechanisms of these therapeutics.

Global compositional and functional states of the human gut microbiome in health and disease

The human gut microbiota is of increasing interest, with metagenomics a key tool for analyzing bacterial diversity and functionality in health and disease. Despite increasing efforts to expand microbial gene catalogs and an increasing number of metagenome-assembled genomes, there have been few pan-metagenomic association studies and in-depth functional analyses across different geographies and diseases. Here, we explored 6014 human gut metagenome samples across 19 countries and 23 diseases by performing compositional, functional cluster, and integrative analyses. Using interpreted machine learning classification models and statistical methods, we identified Fusobacterium nucleatum and Anaerostipes hadrus with the highest frequencies, enriched and depleted, respectively, across different disease cohorts. Distinct functional distributions were observed in the gut microbiomes of both westernized and nonwesternized populations. These compositional and functional analyses are presented in the open-access Human Gut Microbiome Atlas, allowing for the exploration of the richness, disease, and regional signatures of the gut microbiota across different cohorts.

Anti-diabetic effect of dicaffeoylquinic acids is associated with the modulation of gut microbiota and bile acid metabolism

INTRODUCTION

The human gut microbiome plays a pivotal role in health and disease, notably through its interaction with bile acids (BAs). BAs, synthesized in the liver, undergo transformation by the gut microbiota upon excretion into the intestine, thus influencing host metabolism. However, the potential mechanisms of dicaffeoylquinic acids (DiCQAs) from Ilex kudingcha how to modulate lipid metabolism and inflammation via gut microbiota remain unclear.

OBJECTIVES AND METHODS

The objectives of the present study were to investigate the regulating effects of DiCQAs on diabetes and the potential mechanisms of action. Two mice models were utilized to investigate the anti-diabetic effects of DiCQAs. Additionally, analysis of gut microbiota structure and functions was conducted concurrently with the examination of DiCQAs' impact on gut microbiota carrying the bile salt hydrolase (BSH) gene, as well as on the enterohepatic circulation of BAs and related signaling pathways.

RESULTS

Our findings demonstrated that DiCQAs alleviated diabetic symptoms by modulating gut microbiota carrying the BSH gene. This modulation enhanced intestinal barrier integrity, increased enterohepatic circulation of conjugated BAs, and inhibited the farnesoid X receptor-fibroblast growth factor 15 (FGF15) signaling axis in the ileum. Consequently, the protein expression of hepatic FGFR4 fibroblast growth factor receptor 4 (FGFR4) decreased, accompanied by heightened BA synthesis, reduced hepatic BA stasis, and lowered levels of hepatic and plasma cholesterol. Furthermore, DiCQAs upregulated glucolipid metabolism-related proteins in the liver and muscle, including v-akt murine thymoma viral oncogene homolog (AKT)/glycogen synthase kinase 3-beta (GSK3β) and AMP-activated protein kinase (AMPK), thereby ameliorating hyperglycemia and mitigating inflammation through the down-regulation of the MAPK signaling pathway in the diabetic group.

CONCLUSION

Our study elucidated the anti-diabetic effects and mechanism of DiCQAs from I. kudingcha, highlighting the potential of targeting gut microbiota, particularly Acetatifactor sp011959105 and Acetatifactor muris carrying the BSH gene, as a therapeutic strategy to attenuate FXR-FGF15 signaling and ameliorate diabetes.

Impact of captivity and natural habitats on gut microbiome in Epinephelus akaara across seasons

BACKGROUND

The gut microbiota significantly influences the health and growth of red-spotted grouper (Epinephelus akaara), a well-known commercial marine fish from Fujian Province in southern China. However, variations in survival strategies and seasons can impact the stability of gut microbiota data, rendering it inaccurate in reflecting the state of gut microbiota. Which impedes the effective enhancement of aquaculture health through a nuanced understanding of gut microbiota. Inspired by this, we conducted a comprehensive analysis of the gut microbiota of wild and captive E. akaara in four seasons.

RESULTS

Seventy-two E. akaara samples were collected from wild and captive populations in Dongshan city, during four different seasons. Four sections of the gut were collected to obtain comprehensive information on the gut microbial composition and sequenced using 16S rRNA next-generation Illumina MiSeq. We observed the highest gut microbial diversity in both captive and wild E. akaara during the winter season, and identified strong correlations with water temperature using Mantel analysis. Compared to wild E. akaara, we found a more complex microbial network in captive E. akaara, as evidenced by increased abundance of Bacillaceae, Moraxellaceae and Enterobacteriaceae. In contrast, Vibrionaceae, Clostridiaceae, Flavobacteriaceae and Rhodobacteraceae were found to be more active in wild E. akaara. However, some core microorganisms, such as Firmicutes and Photobacterium, showed similar distribution patterns in both wild and captive groups. Moreover, we found the common community composition and distribution characteristics of top 10 core microbes from foregut to hindgut in E. akaara.

CONCLUSIONS

Collectively, the study provides relatively more comprehensive description of the gut microbiota in E. akaara, taking into account survival strategies and temporal dimensions, which yields valuable insights into the gut microbiota of E. akaara and provides a valuable reference to its aquaculture.

[Microbial metaproteomics--From sample processing to data acquisition and analysis]

Microorganisms are closely associated with human diseases and health. Understanding the composition and function of microbial communities requires extensive research. Metaproteomics has recently become an important method for throughout and in-depth study of microorganisms. However, major challenges in terms of sample processing, mass spectrometric data acquisition, and data analysis limit the development of metaproteomics owing to the complexity and high heterogeneity of microbial community samples. In metaproteomic analysis, optimizing the preprocessing method for different types of samples and adopting different microbial isolation, enrichment, extraction, and lysis schemes are often necessary. Similar to those for single-species proteomics, the mass spectrometric data acquisition modes for metaproteomics include data-dependent acquisition (DDA) and data-independent acquisition (DIA). DIA can collect comprehensive peptide information from a sample and holds great potential for future development. However, data analysis for DIA is challenged by the complexity of metaproteome samples, which hinders the deeper coverage of metaproteomes. The most important step in data analysis is the construction of a protein sequence database. The size and completeness of the database strongly influence not only the number of identifications, but also analyses at the species and functional levels. The current gold standard for metaproteome database construction is the metagenomic sequencing-based protein sequence database. A public database-filtering method based on an iterative database search has been proven to have strong practical value. The peptide-centric DIA data analysis method is a mainstream data analysis strategy. The development of deep learning and artificial intelligence will greatly promote the accuracy, coverage, and speed of metaproteomic analysis. In terms of downstream bioinformatics analysis, a series of annotation tools that can perform species annotation at the protein, peptide, and gene levels has been developed in recent years to determine the composition of microbial communities. The functional analysis of microbial communities is a unique feature of metaproteomics compared with other omics approaches. Metaproteomics has become an important component of the multi-omics analysis of microbial communities, and has great development potential in terms of depth of coverage, sensitivity of detection, and completeness of data analysis.

Genome-resolved metagenomics: a game changer for microbiome medicine

Recent substantial evidence implicating commensal bacteria in human diseases has given rise to a new domain in biomedical research: microbiome medicine. This emerging field aims to understand and leverage the human microbiota and derivative molecules for disease prevention and treatment. Despite the complex and hierarchical organization of this ecosystem, most research over the years has relied on 16S amplicon sequencing, a legacy of bacterial phylogeny and taxonomy. Although advanced sequencing technologies have enabled cost-effective analysis of entire microbiota, translating the relatively short nucleotide information into the functional and taxonomic organization of the microbiome has posed challenges until recently. In the last decade, genome-resolved metagenomics, which aims to reconstruct microbial genomes directly from whole-metagenome sequencing data, has made significant strides and continues to unveil the mysteries of various human-associated microbial communities. There has been a rapid increase in the volume of whole metagenome sequencing data and in the compilation of novel metagenome-assembled genomes and protein sequences in public depositories. This review provides an overview of the capabilities and methods of genome-resolved metagenomics for studying the human microbiome, with a focus on investigating the prokaryotic microbiota of the human gut. Just as decoding the human genome and its variations marked the beginning of the genomic medicine era, unraveling the genomes of commensal microbes and their sequence variations is ushering us into the era of microbiome medicine. Genome-resolved metagenomics stands as a pivotal tool in this transition and can accelerate our journey toward achieving these scientific and medical milestones.

Single-cell transcriptomics across 2,534 microbial species reveals functional heterogeneity in the rumen microbiome

Deciphering the activity of individual microbes within complex communities and environments remains a challenge. Here we describe the development of microbiome single-cell transcriptomics using droplet-based single-cell RNA sequencing and pangenome-based computational analysis to characterize the functional heterogeneity of the rumen microbiome. We generated a microbial genome database (the Bovine Gastro Microbial Genome Map) as a functional reference map for the construction of a single-cell transcriptomic atlas of the rumen microbiome. The atlas includes 174,531 microbial cells and 2,534 species, of which 172 are core active species grouped into 12 functional clusters. We detected single-cell-level functional roles, including a key role for Basfia succiniciproducens in the carbohydrate metabolic niche of the rumen microbiome. Furthermore, we explored functional heterogeneity and reveal metabolic niche trajectories driven by biofilm formation pathway genes within B. succiniciproducens. Our results provide a resource for studying the rumen microbiome and illustrate the diverse functions of individual microbial cells that drive their ecological niche stability or adaptation within the ecosystem.

Data-independent acquisition in metaproteomics

INTRODUCTION

Metaproteomics offers insights into the function of complex microbial communities, while it is also capable of revealing microbe-microbe and host-microbe interactions. Data-independent acquisition (DIA) mass spectrometry is an emerging technology, which holds great potential to achieve deep and accurate metaproteomics with higher reproducibility yet still facing a series of challenges due to the inherent complexity of metaproteomics and DIA data.

AREAS COVERED

This review offers an overview of the DIA metaproteomics approaches, covering aspects such as database construction, search strategy, and data analysis tools. Several cases of current DIA metaproteomics studies are presented to illustrate the procedures. Important ongoing challenges are also highlighted. Future perspectives of DIA methods for metaproteomics analysis are further discussed. Cited references are searched through and collected from Google Scholar and PubMed.

EXPERT OPINION

Considering the inherent complexity of DIA metaproteomics data, data analysis strategies specifically designed for interpretation are imperative. From this point of view, we anticipate that deep learning methods and de novo sequencing methods will become more prevalent in the future, potentially improving protein coverage in metaproteomics. Moreover, the advancement of metaproteomics also depends on the development of sample preparation methods, data analysis strategies, etc. These factors are key to unlocking the full potential of metaproteomics.

Effect of Probiotic Bacteria on the Gut Microbiome of Mice with Lipopolysaccharide-Induced Inflammation

The role of lipopolysaccharide (LPS) in the development of diseases is clear, but the specific mechanisms remain poorly understood. This study aimed to investigate the microbiome aberrations in the guts of mice against the background of LPS, as well as the anti-inflammatory effect of probiotic supplementation with Lactobacillus plantarum from the gut, a mix of commercial probiotic lactic acid bacteria, and Weissella confusa isolated from milk using next-generation sequencing. LPS injections were found to induce inflammatory changes in the intestinal mucosa. These morphological changes were accompanied by a shift in the microbiota. We found no significant changes in the microbiome with probiotic supplementation compared to the LPS group. However, when Lactobacillus plantarum and a mix of commercial probiotic lactic acid bacteria were used, the intestinal mucosa was restored. Weissella confusa did not contribute to the morphological changes of the intestinal wall or the microbiome. Changes in the microbiome were observed with probiotic supplementation of Lactobacillus plantarum and a mix of commercial probiotic lactic acid bacteria compared to the control group. In addition, when Lactobacillus plantarum was used, we observed a decrease in the enrichment of the homocysteine and cysteine interconversion pathways with an increase in the L-histidine degradation pathway.

Floria: fast and accurate strain haplotyping in metagenomes

SUMMARY

Shotgun metagenomics allows for direct analysis of microbial community genetics, but scalable computational methods for the recovery of bacterial strain genomes from microbiomes remains a key challenge. We introduce Floria, a novel method designed for rapid and accurate recovery of strain haplotypes from short and long-read metagenome sequencing data, based on minimum error correction (MEC) read clustering and a strain-preserving network flow model. Floria can function as a standalone haplotyping method, outputting alleles and reads that co-occur on the same strain, as well as an end-to-end read-to-assembly pipeline (Floria-PL) for strain-level assembly. Benchmarking evaluations on synthetic metagenomes show that Floria is  > 3× faster and recovers 21% more strain content than base-level assembly methods (Strainberry) while being over an order of magnitude faster when only phasing is required. Applying Floria to a set of 109 deeply sequenced nanopore metagenomes took <20 min on average per sample and identified several species that have consistent strain heterogeneity. Applying Floria's short-read haplotyping to a longitudinal gut metagenomics dataset revealed a dynamic multi-strain Anaerostipes hadrus community with frequent strain loss and emergence events over 636 days. With Floria, accurate haplotyping of metagenomic datasets takes mere minutes on standard workstations, paving the way for extensive strain-level metagenomic analyses.

AVAILABILITY AND IMPLEMENTATION

Floria is available at https://github.com/bluenote-1577/floria, and the Floria-PL pipeline is available at https://github.com/jsgounot/Floria_analysis_workflow along with code for reproducing the benchmarks.

Sequential Sampling of the Gastrointestinal Tract to Characterize the Entire Digestive Microbiome in Japanese Subjects

The gastrointestinal (GI) tract harbors trillions of microorganisms known to influence human health and disease, and next-generation sequencing (NGS) now enables the in-depth analysis of their diversity and functions. Although a significant amount of research has been conducted on the GI microbiome, comprehensive metagenomic datasets covering the entire tract are scarce due to cost and technical challenges. Despite the widespread use of fecal samples, integrated datasets encompassing the entire digestive process, beginning at the mouth and ending with feces, are lacking. With this study, we aimed to fill this gap by analyzing the complete metagenome of the GI tract, providing insights into the dynamics of the microbiota and potential therapeutic avenues. In this study, we delved into the complex world of the GI microbiota, which we examined in five healthy Japanese subjects. While samples from the whole GI flora and fecal samples provided sufficient bacteria, samples obtained from the stomach and duodenum posed a challenge. Using a principal coordinate analysis (PCoA), clear clustering patterns were identified; these revealed significant diversity in the duodenum. Although this study was limited by its small sample size, the flora in the overall GI tract showed unwavering consistency, while the duodenum exhibited unprecedented phylogenetic diversity. A visual heat map illustrates the discrepancy in abundance, with Fusobacteria and Bacilli dominating the upper GI tract and Clostridia and Bacteroidia dominating the fecal samples. Negativicutes and Actinobacteria were found throughout the digestive tract. This study demonstrates that it is possible to continuously collect microbiome samples throughout the human digestive tract. These findings not only shed light on the complexity of GI microbiota but also provide a basis for future research.

Sequencing-based analysis of microbiomes

Microbiomes occupy a range of niches and, in addition to having diverse compositions, they have varied functional roles that have an impact on agriculture, environmental sciences, and human health and disease. The study of microbiomes has been facilitated by recent technological and analytical advances, such as cheaper and higher-throughput DNA and RNA sequencing, improved long-read sequencing and innovative computational analysis methods. These advances are providing a deeper understanding of microbiomes at the genomic, transcriptional and translational level, generating insights into their function and composition at resolutions beyond the species level.

Gut Microbiome Interactions with Oxidative Stress: Mechanisms and Consequences for Health

Understanding how gut flora interacts with oxidative stress has been the subject of significant research in recent years. There is much evidence demonstrating the existence of the microbiome-oxidative stress interaction. However, the biochemical basis of this interaction is still unclear. In this narrative review, possible pathways of the gut microbiota and oxidative stress interaction are presented, among which genetic underpinnings play an important role. Trimethylamine-N-oxide, mitochondria, short-chain fatty acids, and melatonin also appear to play roles. Moreover, the relationship between oxidative stress and the gut microbiome in obesity, metabolic syndrome, chronic ethanol consumption, dietary supplements, and medications is considered. An investigation of the correlation between bacterial community features and OS parameter changes under normal and pathological conditions might provide information for the determination of new research methods. Furthermore, such research could contribute to establishing a foundation for determining the linkers in the microbiome-OS association.

Simultaneous application of oral and intravaginal probiotics for Helicobacter pylori and its antibiotic-therapy-induced vaginal dysbacteriosis

Helicobacter pylori is a prevalent bacterial pathogen globally, implicated in various gastrointestinal disorders. Current recommended antibiotic therapies for H. pylori infection have been proven to be therapeutically insufficient, with low eradication rates and high recurrence rates. Emerging evidence suggests that antibiotic therapy for H. pylori can lead to gastrointestinal and subsequent vaginal dysbiosis, posing challenges for conventional antibiotic approaches. Thus, this article proposes a novel probiotic therapy involving simultaneous oral and intra-vaginal probiotic administration alongside antibiotics for H. pylori treatment, aiming to enhance eradication rates and mitigate dysbiosis. We begin by providing an overview of gastrointestinal and vaginal microbiota and their interconnectedness through the vagina-gut axis. We then review the efficacy of current antibiotic regimens for H. pylori and discuss how antibiotic treatment impacts the vaginal microenvironment. To explore the feasibility of this approach, we evaluate the effectiveness of oral and intra-vaginal probiotics in restoring normal microbiota in the gastrointestinal and vaginal tracts, respectively. Additionally, we analyze the direct mechanisms by which oral and intra-vaginal probiotics act on their respective tracts and discuss potential cross-tract mechanisms. Considering the potential synergistic therapeutic effects of probiotics in both the gastrointestinal and vaginal tracts, dual-channel probiotic therapy holds promise as a more effective approach for H. pylori eradication and dysbiosis mitigation, presenting a novel concept in the collaborative treatment of gastrointestinal and genital disorders.

Microbiome modeling: a beginner's guide

Microbiomes, comprised of diverse microbial species and viruses, play pivotal roles in human health, environmental processes, and biotechnological applications and interact with each other, their environment, and hosts via ecological interactions. Our understanding of microbiomes is still limited and hampered by their complexity. A concept improving this understanding is systems biology, which focuses on the holistic description of biological systems utilizing experimental and computational methods. An important set of such experimental methods are metaomics methods which analyze microbiomes and output lists of molecular features. These lists of data are integrated, interpreted, and compiled into computational microbiome models, to predict, optimize, and control microbiome behavior. There exists a gap in understanding between microbiologists and modelers/bioinformaticians, stemming from a lack of interdisciplinary knowledge. This knowledge gap hinders the establishment of computational models in microbiome analysis. This review aims to bridge this gap and is tailored for microbiologists, researchers new to microbiome modeling, and bioinformaticians. To achieve this goal, it provides an interdisciplinary overview of microbiome modeling, starting with fundamental knowledge of microbiomes, metaomics methods, common modeling formalisms, and how models facilitate microbiome control. It concludes with guidelines and repositories for modeling. Each section provides entry-level information, example applications, and important references, serving as a valuable resource for comprehending and navigating the complex landscape of microbiome research and modeling.

Degradation and fermentation of hyaluronic acid by Bacteroides spp. from the human gut microbiota

Bacteroides spp. are prominent members of the human gut microbiota that play critical roles in the metabolism of complex carbohydrates from the daily diet. Hyaluronic acid (HA) is a multifunctional polysaccharide which has been extensively used in the food and biomedical industry. However, how HA is degraded and fermented by Bacteroides spp. has not been fully characterized. Here, we comprehensively investigated the detailed degradation profiles and fermentation characteristics of four different HAs with discrete molecular weight (Mw) by fourteen distinctive Bacteroides spp. from the human gut microbiota. Our results indicated that high-Mw HAs were more degradable and fermentable than low-Mw HAs. Interestingly, B. salyersiae showed the best degrading capability for both high-Mw and low-Mw HAs, making it a keystone species for HA degradation among Bacteroides spp.. Specifically, HA degradation by B. salyersiae produced significant amounts of unsaturated tetrasaccharide (udp4). Co-culture experiments indicated that the produced udp4 could be further fermented and utilized by non-proficient HA-degraders, suggesting a possible cross-feeding interaction in the utilization of HA within the Bacteroides spp.. Altogether, our study provides novel insights into the metabolism of HA by the human gut microbiota, which has considerable implications for the development of new HA-based nutraceuticals and medicines.