A compendium of 32,277 metagenome-assembled genomes and over 80 million genes from the early-life human gut microbiome

Digging deeper into necrotizing enterocolitis: bridging clinical, microbial, and molecular perspectives

Necrotizing Enterocolitis (NEC) is a severe, life-threatening inflammatory condition of the gastrointestinal tract, especially affecting preterm infants. This review consolidates evidence from various biomedical disciplines to elucidate the complex pathogenesis of NEC, integrating insights from clinical, microbial, and molecular perspectives. It emphasizes the modulation of NEC-associated inflammatory pathways by probiotics and novel biologics, highlighting their therapeutic potential. We further critically examine dysbiotic alterations within the gut microbiota, with a particular focus on imbalances in bacterial and viral communities, which may contribute to the onset of NEC. The intricate interactions among toll-like receptor 4 (TLR4), microvascular integrity, immune activation, and the inflammatory milieu are meticulously summarized, offering a sophisticated understanding of NEC pathophysiology. This academic review aims to enhance the etiological comprehension of NEC, promote the development of targeted therapeutic interventions, and impart the significant impact of perinatal factors on the formulation of preventive and curative strategies for the disease.

Pangenome Analysis of Clostridium scindens: A Collection of Diverse Bile Acid- and Steroid-Metabolizing Commensal Gut Bacterial Strains

Clostridium scindens is a commensal gut bacterium capable of forming the secondary bile acids as well as converting glucocorticoids to androgens. Historically, only two strains, C. scindens ATCC 35704 and C. scindens VPI 12708, have been characterized to any significant extent. The formation of secondary bile acids is important in the etiology of cancers of the GI tract and in the prevention of Clostridioides difficile infection. We determined the presence and absence of bile acid inducible (bai) and steroid-17,20-desmolase (des) genes among C. scindens strains and the features of the pangenome of 34 cultured strains of C. scindens and a set of 200 metagenome-assembled genomes (MAGs) to understand the variability among strains. The results indicate that the C. scindens cultivars have an open pangenome with 12,720 orthologous gene groups and a core genome with 1630 gene families, in addition to 7051 and 4039 gene families in the accessory and unique (i.e., strain-exclusive) genomes, respectively. The pangenome profile including the MAGs also proved to be open. Our analyses reveal that C. scindens strains are distributed into two clades, indicating the possible onset of C. scindens separation into two species, as suggested by gene content, phylogenomic, and average nucleotide identity (ANI) analyses. This study provides insight into the structure and function of the C. scindens pangenome, offering a genetic foundation of significance for many aspects of research on the intestinal microbiota and bile acid metabolism.

Lineage-specific microbial protein prediction enables large-scale exploration of protein ecology within the human gut

Microbes use a range of genetic codes and gene structures, yet these are often ignored during metagenomic analysis. This causes spurious protein predictions, preventing functional assignment which limits our understanding of ecosystems. To resolve this, we developed a lineage-specific gene prediction approach that uses the correct genetic code based on the taxonomic assignment of genetic fragments, removes incomplete protein predictions, and optimises prediction of small proteins. Applied to 9634 metagenomes and 3594 genomes from the human gut, this approach increased the landscape of captured expressed microbial proteins by 78.9%, including previously hidden functional groups. Optimised small protein prediction captured 3,772,658 small protein clusters, which form an improved microbial protein catalogue of the human gut (MiProGut). To enable the ecological study of a protein's prevalence and association with host parameters, we developed InvestiGUT, a tool which integrates both the protein sequences and sample metadata. Accurate prediction of proteins is critical to providing a functional understanding of microbiomes, enhancing our ability to study interactions between microbes and hosts.

Gut microbiome evolution from infancy to 8 years of age

The human gut microbiome is most dynamic in early life. Although sweeping changes in taxonomic architecture are well described, it remains unknown how, and to what extent, individual strains colonize and persist and how selective pressures define their genomic architecture. In this study, we combined shotgun sequencing of 1,203 stool samples from 26 mothers and their twins (52 infants), sampled from childbirth to 8 years after birth, with culture-enhanced, deep short-read and long-read stool sequencing from a subset of 10 twins (20 infants) to define transmission, persistence and evolutionary trajectories of gut species from infancy to middle childhood. We constructed 3,995 strain-resolved metagenome-assembled genomes across 399 taxa, and we found that 27.4% persist within individuals. We identified 726 strains shared within families, with Bacteroidales, Oscillospiraceae and Lachnospiraceae, but not Bifidobacteriaceae, vertically transferred. Lastly, we identified weaning as a critical inflection point that accelerates bacterial mutation rates and separates functional profiles of genes accruing mutations.

Characterization of vaginal microbiomes in clinician-collected bacterial vaginosis diagnosed samples

Bacterial vaginosis (BV) is a type of vaginal inflammation caused by bacterial overgrowth, upsetting the healthy microbiome of the vagina. Existing clinical testing for BV is primarily based upon physical and microscopic examination of vaginal secretions. Modern PCR-based clinical tests target panels of BV-associated microbes, such as the Labcorp NuSwab test that targets Atopobium (Fannyhessea) vaginae, Megasphaera-1, and Bacterial Vaginosis Associated Bacterium (BVAB)-2. Remnant clinician-collected NuSwab vaginal swabs underwent DNA extraction and 16S V3-V4 rRNA gene sequencing to profile microbes in addition to those included in the Labcorp NuSwab test. Community state types (CSTs) were determined using the most abundant taxon detected in each sample. PCR results for NuSwab panel microbial targets were compared against the corresponding microbiome profiles. Metabolic pathway abundances were characterized via metagenomic prediction from amplicon sequence variants (ASVs). 16S V3-V4 rRNA gene sequencing of 75 remnant vaginal swabs yielded 492 unique 16S V3-V4 ASVs, identifying 83 unique genera. NuSwab microbe quantification was strongly concordant with quantification by sequencing (P < 0.01). Samples in CST-I (18 of 18, 100%), CST-II (three of three, 100%), CST-III (15 of 17, 88%), and CST-V (one of one, 100%) were largely categorized as BV-negative via the NuSwab panel, while most CST-IV samples (28 of 36, 78%) were BV-positive or BV-indeterminate. BV-associated microbial and predicted metabolic signatures were shared across multiple CSTs. These findings highlight robust sequencing-based quantification of Labcorp NuSwab BV microbes, accurate discrimination of vaginal microbiome CSTs dominated by distinct Lactobacilli, and expanded the identification of BV-associated bacterial and metabolic biomarkers.IMPORTANCEBacterial vaginosis (BV) poses a significant health burden for women during reproductive years and onward. Current BV diagnostics rely on either panels of select microbes or on physical and microscopic evaluations by technicians. Here, we sequenced the microbiome profiles of samples previously diagnosed by the Labcorp NuSwab test to better understand disruptions to the vaginal microbiome during BV. We show that microbial sequencing can faithfully reproduce targeted PCR diagnostic results and can improve our knowledge of healthy and BV-associated microbial and metabolic biomarkers. This work highlights a robust, agnostic BV classification scheme with potential for future development of sequencing-based BV diagnostic tools.

Seed2LP: seed inference in metabolic networks for reverse ecology applications

MOTIVATION

A challenging problem in microbiology is to determine nutritional requirements of microorganisms and culture them, especially for the microbial dark matter detected solely with culture-independent methods. The latter foster an increasing amount of genomic sequences that can be explored with reverse ecology approaches to raise hypotheses on the corresponding populations. Building upon genome-scale metabolic networks (GSMNs) obtained from genome annotations, metabolic models predict contextualized phenotypes using nutrient information.

RESULTS

We developed the tool Seed2LP, addressing the inverse problem of predicting source nutrients, or seeds, from a GSMN and a metabolic objective. The originality of Seed2LP is its hybrid model, combining a scalable and discrete Boolean approximation of metabolic activity, with the numerically accurate flux balance analysis (FBA). Seed inference is highly customizable, with multiple search and solving modes, exploring the search space of external and internal metabolites combinations. Application to a benchmark of 107 curated GSMNs highlights the usefulness of a logic modelling method over a graph-based approach to predict seeds, and the relevance of hybrid solving to satisfy FBA constraints. Focusing on the dependency between metabolism and environment, Seed2LP is a computational support contributing to address the multifactorial challenge of culturing possibly uncultured microorganisms.

AVAILABILITY AND IMPLEMENTATION

Seed2LP is available on https://github.com/bioasp/seed2lp.

Benchmarking metagenomic binning tools on real datasets across sequencing platforms and binning modes

Metagenomic binning is a culture-free approach that facilitates the recovery of metagenome-assembled genomes by grouping genomic fragments. However, there remains a lack of a comprehensive benchmark to evaluate the performance of metagenomic binning tools across various combinations of data types and binning modes. In this study, we benchmark 13 metagenomic binning tools using short-read, long-read, and hybrid data under co-assembly, single-sample, and multi-sample binning, respectively. The benchmark results demonstrate that multi-sample binning exhibits optimal performance across short-read, long-read, and hybrid data. Moreover, multi-sample binning outperforms other binning modes in identifying potential antibiotic resistance gene hosts and near-complete strains containing potential biosynthetic gene clusters across diverse data types. This study also recommends three efficient binners across all data-binning combinations, as well as high-performance binners for each combination.

MAGs-based genomic comparison of gut significantly enriched microbes in obese individuals pre- and post-bariatric surgery across diverse locations

Introduction

Obesity, a pressing global health issue, is intricately associated with distinct gut microbiota profiles. Bariatric surgeries, such as Laparoscopic Sleeve Gastrectomy (LSG), Sleeve Gastrectomy (SG), and Roux-en-Y Gastric Bypass (RYGB), induce substantial weight loss and reshape gut microbiota composition and functionality, yet their comparative impacts remain underexplored.

Methods

This study integrated four published metagenomic datasets, encompassing 500 samples, and employed a unified bioinformatics workflow for analysis. We assessed gut microbiota α-diversity, identified species biomarkers using three differential analysis approaches, and constructed high-quality Metagenome-Assembled Genomes (MAGs). Comparative genomic, functional profiling and KEGG pathway analyses were performed, alongside estimation of microbial growth rates via Peak-to-Trough Ratios (PTRs).

Results

RYGB exhibited the most pronounced enhancement of gut microbiota α-diversity compared to LSG and SG. Cross-cohort analysis identified 39 species biomarkers: 27 enriched in the non-obesity group (NonOB_Enrich) and 12 in the obesity group (OB_Enrich). Among the MAGs, 177 were NonOB_Enrich and 14 were OB_Enrich. NonOB_Enrich MAGs displayed enriched carbohydrate degradation profiles (e.g., GH105, GH2, GH23, GH43, and GT0 families) and higher gene diversity in fatty acid biosynthesis and secondary metabolite pathways, alongside significant enrichment in amino acid metabolism (KEGG analysis). Post-surgery, Akkermansia muciniphila and Bacteroides uniformis showed elevated growth rates based on PTRs.

Discussion

These findings underscore RYGB's superior impact on gut microbiota diversity and highlight distinct microbial functional adaptations linked to weight loss, offering insights for targeted therapeutic strategies.

A review of neural networks for metagenomic binning

One of the main goals of metagenomic studies is to describe the taxonomic diversity of microbial communities. A crucial step in metagenomic analysis is metagenomic binning, which involves the (supervised) classification or (unsupervised) clustering of metagenomic sequences. Various machine learning models have been applied to address this task. In this review, the contributions of artificial neural networks (ANN) in the context of metagenomic binning are detailed, addressing both supervised, unsupervised, and semi-supervised approaches. 34 ANN-based binning tools are systematically compared, detailing their architectures, input features, datasets, advantages, disadvantages, and other relevant aspects. The findings reveal that deep learning approaches, such as convolutional neural networks and autoencoders, achieve higher accuracy and scalability than traditional methods. Gaps in benchmarking practices are highlighted, and future directions are proposed, including standardized datasets and optimization of architectures, for third-generation sequencing. This review provides support to researchers in identifying trends and selecting suitable tools for the metagenomic binning problem.

The gut microbiome across the lifespan: how diet modulates our microbial ecosystem from infancy to the elderly

This comprehensive review examines the impact of dietary patterns on gut microbiome composition and diversity from infancy to old age, linking these changes to age-related health outcomes. It investigates how the gut microbiome develops and changes across life stages, focusing on the influence of dietary factors. The review explores how early-life feeding practices, including breastfeeding and formula feeding, shape the infant gut microbiota and have lasting effects. In elderly individuals, alterations in the gut microbiome are associated with increased susceptibility to infections, chronic inflammation, metabolic disorders and cognitive decline. The critical role of diet in modulating the gut microbiome throughout life is emphasised, particularly the potential benefits of probiotics and fortified foods in promoting healthy ageing. By elucidating the mechanisms connecting food systems to gut health, this review provides insights into interventions that could enhance gut microbiome resilience and improve health outcomes across the lifespan.

A genome-scale metabolic reconstruction resource of 247,092 diverse human microbes spanning multiple continents, age groups, and body sites

Genome-scale modeling of microbiome metabolism enables the simulation of diet-host-microbiome-disease interactions. However, current genome-scale reconstruction resources are limited in scope by computational challenges. We developed an optimized and highly parallelized reconstruction and analysis pipeline to build a resource of 247,092 microbial genome-scale metabolic reconstructions, deemed APOLLO. APOLLO spans 19 phyla, contains >60% of uncharacterized strains, and accounts for strains from 34 countries, all age groups, and multiple body sites. Using machine learning, we predicted with high accuracy the taxonomic assignment of strains based on the computed metabolic features. We then built 14,451 metagenomic sample-specific microbiome community models to systematically interrogate their community-level metabolic capabilities. We show that sample-specific metabolic pathways accurately stratify microbiomes by body site, age, and disease state. APOLLO is freely available, enables the systematic interrogation of the metabolic capabilities of largely still uncultured and unclassified species, and provides unprecedented opportunities for systems-level modeling of personalized host-microbiome co-metabolism.

Priority effects, nutrition and milk glycan-metabolic potential drive Bifidobacterium longum subspecies dynamics in the infant gut microbiome

Background

The initial colonization of the infant gut is a complex process that defines the foundation for a healthy microbiome development. Bifidobacterium longum is one of the first colonizers of newborns' gut, playing a crucial role in the healthy development of both the host and its microbiome. However, B. longum exhibits significant genomic diversity, with subspecies (e.g., Bifidobacterium longum subsp. infantis and subsp. longum) displaying distinct ecological and metabolic strategies including differential capabilities to break down human milk glycans (HMGs). To promote healthy infant microbiome development, a good understanding of the factors governing infant microbiome dynamics is required.

Methodology

We analyzed newly sequenced gut microbiome samples of mother-infant pairs from the Amsterdam Infant Microbiome Study (AIMS) and four publicly available datasets to identify important environmental and bifidobacterial features associated with the colonization success and succession outcomes of B. longum subspecies. Metagenome-assembled genomes (MAGs) were generated and assessed to identify characteristics of B. longum subspecies in relation to early-life gut colonization. We further implemented machine learning tools to identify significant features associated with B. longum subspecies abundance.

Results

B. longum subsp. longum was the most abundant and prevalent gut Bifidobacterium at one month, being replaced by B. longum subsp. infantis at six months of age. By utilizing metagenome-assembled genomes (MAGs), we reveal significant differences between and within B. longum subspecies in their potential to break down HMGs. We further combined strain-tracking, meta-pangenomics and machine learning to understand these abundance dynamics and found an interplay of priority effects, milk-feeding type and HMG-utilization potential to govern them across the first six months of life. We find higher abundances of B. longum subsp. longum in the maternal gut microbiome, vertical transmission, breast milk and a broader range of HMG-utilizing genes to promote its abundance at one month of age. Eventually, we find B. longum subsp. longum to be replaced by B. longum subsp. infantis at six months of age due to a combination of nutritional intake, HMG-utilization potential and a diminishment of priority effects.

Discussion

Our results establish a strain-level ecological framework explaining early-life abundance dynamics of B. longum subspecies. We highlight the role of priority effects, nutrition and significant variability in HMG-utilization potential in determining the predictable colonization and succession trajectories of B. longum subspecies, with potential implications for promoting infant health and well-being.

Clostridium scindens: history and current outlook for a keystone species in the mammalian gut involved in bile acid and steroid metabolism

Clostridium scindens is a keystone bacterial species in the mammalian gut that, while low in abundance, has a significant impact on bile acid and steroid metabolism. Numerous studies indicate that the two most studied strains of C. scindens (i.e. ATCC 35704 and VPI 12708) are important for a myriad of physiological processes in the host. We focus on both historical and current microbiological and molecular biology work on the Hylemon-Björkhem pathway and the steroid-17,20-desmolase pathway that were first discovered in C. scindens. Our most recent analysis now calls into question whether strains currently defined as C. scindens represent two separate taxonomic groups. Future directions include developing genetic tools to further explore the physiological role of bile acid and steroid metabolism by strains of C. scindens and the causal role of these pathways in host physiology and disease.

Recovery of metagenome-assembled genomes from the rumen and fecal microbiomes of Bos indicus beef cattle

Nelore is a Bos indicus beef breed that is well-adapted to tropical environments and constitutes most of the world's largest commercial cattle herd: the Brazilian bovine herd. Despite its significance, microbial genome recovery from ruminant microbiomes has largely excluded representatives from Brazilian Nelore cattle. To address this gap, this study presents a comprehensive dataset of microbial genomes recovered from the rumen and feces of 52 Brazilian Nelore bulls. A total of 1,526 non-redundant metagenome-assembled genomes (MAGs) were recovered from their gastrointestinal tract, with 497 ruminal and 486 fecal classified as high-quality. Phylogenetic analysis revealed that the bacterial MAGs fall into 12 phyla, with Firmicutes and Bacteroidota being the most predominant, while all archaeal MAGs belong to the genus Methanobrevibacter. The exploration of these microbial genomes will provide valuable insights into the metabolic potential and functional roles of individual microorganisms within host-microbiome interactions, contributing to a better understanding of the microbiome's roles in bovine performance.

Exploring the early life gut microbiome with MAGIC

In this issue of Cell Host & Microbe, Peng et al. provide the MAGIC catalog as a resource for studying bacterial and viral diversity of the global gut microbiome in early life. By addressing gaps in geographic and age representation, this database enhances our understanding of early microbiome dynamics.

A metagenome-assembled genome inventory for children reveals early-life gut bacteriome and virome dynamics

Existing microbiota databases are biased toward adult samples, hampering accurate profiling of the infant gut microbiome. Here, we generated a metagenome-assembled genome inventory for children (MAGIC) from a large collection of bulk and viral-like particle-enriched metagenomes from 0 to 7 years of age, encompassing 3,299 prokaryotic and 139,624 viral species-level genomes, 8.5% and 63.9% of which are unique to MAGIC. MAGIC improves early-life microbiome profiling, with the greatest improvement in read mapping observed in Africans. We then identified 54 candidate keystone species, including several Bifidobacterium spp. and four phages, forming guilds that fluctuated in abundance with time. Their abundances were reduced in preterm infants and were associated with childhood allergies. By analyzing the B. longum pangenome, we found evidence of phage-mediated evolution and quorum sensing-related ecological adaptation. Together, the MAGIC database recovers genomes that enable characterization of the dynamics of early-life microbiomes, identification of candidate keystone species, and strain-level study of target species.

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.

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.

Pangenome analysis of Clostridium scindens : a collection of diverse bile acid and steroid metabolizing commensal gut bacterial strains

Clostridium scindens is a commensal gut bacterium capable of forming the secondary bile acids deoxycholic acid and lithocholic acid from the primary bile acids cholic acid and chenodeoxycholic acid, respectively, as well as converting glucocorticoids to androgens. Historically, only two strains, C. scindens ATCC 35704 and C. scindens VPI 12708, have been characterized in vitro and in vivo to any significant extent. The formation of secondary bile acids is important in maintaining normal gastrointestinal function, in regulating the structure of the gut microbiome, in the etiology of such diseases such as cancers of the GI tract, and in the prevention of Clostridium difficile infection. We therefore wanted to determine the pangenome of 34 cultured strains of C. scindens and a set of 200 metagenome-assembled genomes (MAGs) to understand the variability among strains. The results indicate that the 34 strains of C. scindens have an open pangenome with 12,720 orthologous gene groups, and a core genome with 1,630 gene families, in addition to 7,051 and 4,039 gene families in the accessory and unique (i.e., strain-exclusive) genomes, respectively. The core genome contains 39% of the proteins with predicted metabolic function, and, in the unique genome, the function of storage and processing of information prevails, with 34% of the proteins being in that category. The pangenome profile including the MAGs also proved to be open. The presence of bile acid inducible ( bai ) and steroid-17,20-desmolase ( des ) genes was identified among groups of strains. The analysis reveals that C. scindens strains are distributed into two clades, indicating the possible onset of C. scindens separation into two species, confirmed by gene content, phylogenomic, and average nucleotide identity (ANI) analyses. This study provides insight into the structure and function of the C. scindens pangenome, offering a genetic foundation of significance for many aspects of research on the intestinal microbiota and bile acid metabolism.

The Gut Microbiota and Diabetes: Research, Translation, and Clinical Applications-2023 Diabetes, Diabetes Care, and Diabetologia Expert Forum

This article summarizes the state of the science on the role of the gut microbiota (GM) in diabetes from a recent international expert forum organized by Diabetes, Diabetes Care, and Diabetologia, which was held at the European Association for the Study of Diabetes 2023 Annual Meeting in Hamburg, Germany. Forum participants included clinicians and basic scientists who are leading investigators in the field of the intestinal microbiome and metabolism. Their conclusions were as follows: 1) the GM may be involved in the pathophysiology of type 2 diabetes, as microbially produced metabolites associate both positively and negatively with the disease, and mechanistic links of GM functions (e.g., genes for butyrate production) with glucose metabolism have recently emerged through the use of Mendelian randomization in humans; 2) the highly individualized nature of the GM poses a major research obstacle, and large cohorts and a deep-sequencing metagenomic approach are required for robust assessments of associations and causation; 3) because single-time point sampling misses intraindividual GM dynamics, future studies with repeated measures within individuals are needed; and 4) much future research will be required to determine the applicability of this expanding knowledge to diabetes diagnosis and treatment, and novel technologies and improved computational tools will be important to achieve this goal.

The gut microbiota and diabetes: research, translation, and clinical applications - 2023 Diabetes, Diabetes Care, and Diabetologia Expert Forum

This article summarises the state of the science on the role of the gut microbiota (GM) in diabetes from a recent international expert forum organised by Diabetes, Diabetes Care, and Diabetologia, which was held at the European Association for the Study of Diabetes 2023 Annual Meeting in Hamburg, Germany. Forum participants included clinicians and basic scientists who are leading investigators in the field of the intestinal microbiome and metabolism. Their conclusions were as follows: (1) the GM may be involved in the pathophysiology of type 2 diabetes, as microbially produced metabolites associate both positively and negatively with the disease, and mechanistic links of GM functions (e.g. genes for butyrate production) with glucose metabolism have recently emerged through the use of Mendelian randomisation in humans; (2) the highly individualised nature of the GM poses a major research obstacle, and large cohorts and a deep-sequencing metagenomic approach are required for robust assessments of associations and causation; (3) because single time point sampling misses intraindividual GM dynamics, future studies with repeated measures within individuals are needed; and (4) much future research will be required to determine the applicability of this expanding knowledge to diabetes diagnosis and treatment, and novel technologies and improved computational tools will be important to achieve this goal.

The Gut Microbiota and Diabetes: Research, Translation, and Clinical Applications-2023 Diabetes, Diabetes Care, and Diabetologia Expert Forum

This article summarizes the state of the science on the role of the gut microbiota (GM) in diabetes from a recent international expert forum organized by Diabetes, Diabetes Care, and Diabetologia, which was held at the European Association for the Study of Diabetes 2023 Annual Meeting in Hamburg, Germany. Forum participants included clinicians and basic scientists who are leading investigators in the field of the intestinal microbiome and metabolism. Their conclusions were as follows: 1) the GM may be involved in the pathophysiology of type 2 diabetes, as microbially produced metabolites associate both positively and negatively with the disease, and mechanistic links of GM functions (e.g., genes for butyrate production) with glucose metabolism have recently emerged through the use of Mendelian randomization in humans; 2) the highly individualized nature of the GM poses a major research obstacle, and large cohorts and a deep-sequencing metagenomic approach are required for robust assessments of associations and causation; 3) because single-time point sampling misses intraindividual GM dynamics, future studies with repeated measures within individuals are needed; and 4) much future research will be required to determine the applicability of this expanding knowledge to diabetes diagnosis and treatment, and novel technologies and improved computational tools will be important to achieve this goal.

Clostridium scindens : an endocrine keystone species in the mammalian gut

Clostridium scindens is a keystone human gut microbial taxonomic group that, while low in abundance, has a disproportionate effect on bile acid and steroid metabolism in the mammalian gut. Numerous studies indicate that the two most studied strains of C. scindens (i.e., ATCC 35704 and VPI 12708) are important for a myriad of physiological processes in the host. We focus on both historical and current microbiological and molecular biology work on the Hylemon-Björkhem pathway and the steroid-17,20-desmolase pathway that were first discovered in C. scindens. Our most recent analysis now calls into question whether strains currently defined as C. scindens represent two separate taxonomic groups. Future directions include developing genetic tools to further explore the physiological role bile acid and steroid metabolism by strains of C. scindens , and the causal role of these pathways in host physiology and disease.

From soil to sequence: filling the critical gap in genome-resolved metagenomics is essential to the future of soil microbial ecology

Soil microbiomes are heterogeneous, complex microbial communities. Metagenomic analysis is generating vast amounts of data, creating immense challenges in sequence assembly and analysis. Although advances in technology have resulted in the ability to easily collect large amounts of sequence data, soil samples containing thousands of unique taxa are often poorly characterized. These challenges reduce the usefulness of genome-resolved metagenomic (GRM) analysis seen in other fields of microbiology, such as the creation of high quality metagenomic assembled genomes and the adoption of genome scale modeling approaches. The absence of these resources restricts the scale of future research, limiting hypothesis generation and the predictive modeling of microbial communities. Creating publicly available databases of soil MAGs, similar to databases produced for other microbiomes, has the potential to transform scientific insights about soil microbiomes without requiring the computational resources and domain expertise for assembly and binning.

Metagenome-assembled genomes (MAGs) suggest an acetate-driven protective role in gut microbiota disrupted by Clostridioides difficile

Clostridioides difficile may have a negative impact on gut microbiota composition in terms of diversity and abundance, thereby triggering functional changes supported by the differential presence of genes involved in significant metabolic pathways, such as short-chain fatty acids (SCFA). This work has evaluated shotgun metagenomics data regarding 48 samples from four groups classified according to diarrhea acquisition site (community- and healthcare facility-onset) and positive or negative Clostridioides difficile infection (CDI) result. The metagenomic-assembled genomes (MAGs) obtained from each sample were taxonomically assigned for preliminary comparative analysis concerning differences in composition among groups. The predicted genes involved in metabolism, transport, and signaling remained constant in microbiota members; characteristic patterns were observed in MAGs and genes involved in SCFA butyrate and acetate metabolic pathways for each study group. A decrease in genera and species, as well as relative MAG abundance with the presence of the acetate metabolism-related gene, was evident in the HCFO/- group. Increased antibiotic resistance markers (ARM) were observed in MAGs along with the genes involved in acetate metabolism. The results highlight the need to explore the role of acetate in greater depth as a potential protector of the imbalances produced by CDI, as occurs in other inflammatory intestinal diseases.

Integrative genomic reconstruction of carbohydrate utilization networks in bifidobacteria: global trends, local variability, and dietary adaptation

Bifidobacteria are among the earliest colonizers of the human gut, conferring numerous health benefits. While multiple Bifidobacterium strains are used as probiotics, accumulating evidence suggests that the individual responses to probiotic supplementation may vary, likely due to a variety of factors, including strain type(s), gut community composition, dietary habits of the consumer, and other health/lifestyle conditions. Given the saccharolytic nature of bifidobacteria, the carbohydrate composition of the diet is one of the primary factors dictating the colonization efficiency of Bifidobacterium strains. Therefore, a comprehensive understanding of bifidobacterial glycan metabolism at the strain level is necessary to rationally design probiotic or synbiotic formulations that combine bacterial strains with glycans that match their nutrient preferences. In this study, we systematically reconstructed 66 pathways involved in the utilization of mono-, di-, oligo-, and polysaccharides by analyzing the representation of 565 curated metabolic functional roles (catabolic enzymes, transporters, transcriptional regulators) in 2973 non-redundant cultured Bifidobacterium isolates and metagenome-assembled genomes (MAGs). Our analysis uncovered substantial heterogeneity in the predicted glycan utilization capabilities at the species and strain level and revealed the presence of a yet undescribed phenotypically distinct subspecies-level clade within the Bifidobacterium longum species. We also identified Bangladeshi isolates harboring unique gene clusters tentatively implicated in the breakdown of xyloglucan and human milk oligosaccharides. Predicted carbohydrate utilization phenotypes were experimentally characterized and validated. Our large-scale genomic analysis considerably expands the knowledge of carbohydrate metabolism in bifidobacteria and provides a foundation for rationally designing single- or multi-strain probiotic formulations of a given bifidobacterial species as well as synbiotic combinations of bifidobacterial strains matched with their preferred carbohydrate substrates.

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.

Gut microbial genomes with paired isolates from China illustrate probiotic and cardiometabolic effects

The gut microbiome displays genetic differences among populations, and characterization of the genomic landscape of the gut microbiome in China remains limited. Here, we present the Chinese Gut Microbial Reference (CGMR) set, comprising 101,060 high-quality metagenomic assembled genomes (MAGs) of 3,707 nonredundant species from 3,234 fecal samples across primarily rural Chinese locations, 1,376 live isolates mainly from lactic acid bacteria, and 987 novel species relative to worldwide databases. We observed region-specific coexisting MAGs and MAGs with probiotic and cardiometabolic functionalities. Preliminary mouse experiments suggest a probiotic effect of two Faecalibacillus intestinalis isolates in alleviating constipation, cardiometabolic influences of three Bacteroides fragilis_A isolates in obesity, and isolates from the genera Parabacteroides and Lactobacillus in host lipid metabolism. Our study expands the current microbial genomes with paired isolates and demonstrates potential host effects, contributing to the mechanistic understanding of host-microbe interactions.

The role of the gut microbiota in tumor, immunity, and immunotherapy

In recent years, with the deepening understanding of the gut microbiota, it has been recognized to play a significant role in the development and progression of diseases. Particularly in gastrointestinal tumors, the gut microbiota influences tumor growth by dysbiosis, release of bacterial toxins, and modulation of host signaling pathways and immune status. Immune checkpoint inhibitors (ICIs) have greatly improved cancer treatment efficacy by enhancing immune cell responses. Current clinical and preclinical studies have demonstrated that the gut microbiota and its metabolites can enhance the effectiveness of immunotherapy. Furthermore, certain gut microbiota can serve as biomarkers for predicting immunotherapy responses. Interventions targeting the gut microbiota for the treatment of gastrointestinal diseases, especially colorectal cancer (CRC), include fecal microbiota transplantation, probiotics, prebiotics, engineered bacteria, and dietary interventions. These approaches not only improve the efficacy of ICIs but also hold promise for enhancing immunotherapy outcomes. In this review, we primarily discuss the role of the gut microbiota and its metabolites in tumors, host immunity, and immunotherapy.

Construction of high-quality genomes and gene catalogue for culturable microbes of sugarcane (Saccharum spp.)

Microbes living inside or around sugarcane (Saccharum spp.) are crucial for their resistance to abiotic and biotic stress, growth, and development. Sequences of microbial genomes and genes are helpful to understand the function of these microbes. However, there is currently a lack of such knowledge in sugarcane. Here, we combined Nanopore and Illumina sequencing technologies to successfully construct the first high-quality metagenome-assembled genomes (MAGs) and gene catalogues of sugarcane culturable microbes (GCSCMs), which contained 175 species-level genome bins (SGBs), and 7,771,501 non-redundant genes. The SGBs included 79 novel culturable bacteria genomes, and 3 bacterial genomes with nitrogen-fixing gene clusters. Four single scaffold near-complete circular MAGs (cMAGs) with 0% contamination were obtained from Nanopore sequencing data. In conclusion, we have filled a research gap in the genomes and gene catalogues of culturable microbes of sugarcane, providing a vital data resource for further understanding the genetic basis and functions of these microbes. In addition, our methodology and results can provide guidance and reference for other plant microbial genome and gene catalogue studies.

Genome plasticity shapes the ecology and evolution of Phocaeicola dorei and Phocaeicola vulgatus

Phocaeicola dorei and Phocaeicola vulgatus are very common and abundant members of the human gut microbiome and play an important role in the infant gut microbiome. These species are closely related and often confused for one another; yet, their genome comparison, interspecific diversity, and evolutionary relationships have not been studied in detail so far. Here, we perform phylogenetic analysis and comparative genomic analyses of these two Phocaeicola species. We report that P. dorei has a larger genome yet a smaller pan-genome than P. vulgatus. We found that this is likely because P. vulgatus is more plastic than P. dorei, with a larger repertoire of genetic mobile elements and fewer anti-phage defense systems. We also found that P. dorei directly descends from a clade of P. vulgatus¸ and experienced genome expansion through genetic drift and horizontal gene transfer. Overall, P. dorei and P. vulgatus have very different functional and carbohydrate utilisation profiles, hinting at different ecological strategies, yet they present similar antimicrobial resistance profiles.

Genomics and physiology of Catenibacillus, human gut bacteria capable of polyphenol C-deglycosylation and flavonoid degradation

The genus Catenibacillus (family Lachnospiraceae, phylum Bacillota) includes only one cultivated species so far, Catenibacillus scindens, isolated from human faeces and capable of deglycosylating dietary polyphenols and degrading flavonoid aglycones. Another human intestinal Catenibacillus strain not taxonomically resolved at that time was recently genome-sequenced. We analysed the genome of this novel isolate, designated Catenibacillus decagia, and showed its ability to deglycosylate C-coupled flavone and xanthone glucosides and O-coupled flavonoid glycosides. Most of the resulting aglycones were further degraded to the corresponding phenolic acids. Including the recently sequenced genome of C. scindens and ten faecal metagenome-assembled genomes assigned to the genus Catenibacillus, we performed a comparative genome analysis and searched for genes encoding potential C-glycosidases and other polyphenol-converting enzymes. According to genome data and physiological characterization, the core metabolism of Catenibacillus strains is based on a fermentative lifestyle with butyrate production and hydrogen evolution. Both C. scindens and C. decagia encode a flavonoid O-glycosidase, a flavone reductase, a flavanone/flavanonol-cleaving reductase and a phloretin hydrolase. Several gene clusters encode enzymes similar to those of the flavonoid C-deglycosylation system of Dorea strain PUE (DgpBC), while separately located genes encode putative polyphenol-glucoside oxidases (DgpA) required for C-deglycosylation. The diversity of dgpA and dgpBC gene clusters might explain the broad C-glycoside substrate spectrum of C. scindens and C. decagia. The other Catenibacillus genomes encode only a few potential flavonoid-converting enzymes. Our results indicate that several Catenibacillus species are well-equipped to deglycosylate and degrade dietary plant polyphenols and might inhabit a corresponding, specific niche in the gut.

Preclinical Atopic Dermatitis Skin in Infants: An Emerging Research Area

Whereas clinically apparent atopic dermatitis (AD) can be confirmed by validated diagnostic criteria, the preclinical phenotype of infants who eventually develop AD is less well-characterized. Analogous to unaffected or nonlesional skin in established AD, clinically normal-appearing skin in infants who will develop clinical AD has distinct changes. Prospective studies have revealed insights into this preclinical AD phenotype. In this study, we review the structural, immunologic, and microbiome nature of the preclinical AD phenotype. Determination of markers that predict the development of AD will facilitate targeting of interventions to prevent the development or reduce the severity of AD in infants.

Infant age inversely correlates with gut carriage of resistance genes, reflecting modifications in microbial carbohydrate metabolism during early life

The infant gut microbiome is increasingly recognized as a reservoir of antibiotic resistance genes, yet the assembly of gut resistome in infants and its influencing factors remain largely unknown. We characterized resistome in 4132 metagenomes from 963 infants in six countries and 4285 resistance genes were observed. The inherent resistome pattern of healthy infants (N = 272) could be distinguished by two stages: a multicompound resistance phase (Months 0-7) and a tetracycline-mupirocin-β-lactam-dominant phase (Months 8-14). Microbial taxonomy explained 40.7% of the gut resistome of healthy infants, with Escherichia (25.5%) harboring the most resistance genes. In a further analysis with all available infants (N = 963), we found age was the strongest influencer on the resistome and was negatively correlated with the overall resistance during the first 3 years (p < 0.001). Using a random-forest approach, a set of 34 resistance genes could be used to predict age (R 2 = 68.0%). Leveraging microbial host inference analyses, we inferred the age-dependent assembly of infant resistome was a result of shifts in the gut microbiome, primarily driven by changes in taxa that disproportionately harbor resistance genes across taxa (e.g., Escherichia coli more frequently harbored resistance genes than other taxa). We performed metagenomic functional profiling and metagenomic assembled genome analyses whose results indicate that the development of gut resistome was driven by changes in microbial carbohydrate metabolism, with an increasing need for carbohydrate-active enzymes from Bacteroidota and a decreasing need for Pseudomonadota during infancy. Importantly, we observed increased acquired resistance genes over time, which was related to increased horizontal gene transfer in the developing infant gut microbiome. In summary, infant age was negatively correlated with antimicrobial resistance gene levels, reflecting a composition shift in the gut microbiome, likely driven by the changing need for microbial carbohydrate metabolism during early life.

Lipid Metabolites as Potential Regulators of the Antibiotic Resistome in Tetramorium caespitum

Antibiotic resistance genes (ARGs) are ancient but have become a modern critical threat to health. Gut microbiota, a dynamic reservoir for ARGs, transfer resistance between individuals. Surveillance of the antibiotic resistome in the gut during different host growth phases is critical to understanding the dynamics of the resistome in this ecosystem. Herein, we disentangled the ARG profiles and the dynamic mechanism of ARGs in the egg and adult phases of Tetramorium caespitum. Experimental results showed a remarkable difference in both gut microbiota and gut resistome with the development of T. caespitum. Meta-based metagenomic results of gut microbiota indicated the generalizability of gut antibiotic resistome dynamics during host development. By using Raman spectroscopy and metabolomics, the metabolic phenotype and metabolites indicated that the biotic phase significantly changed lipid metabolism as T. caespitum aged. Lipid metabolites were demonstrated as the main factor driving the enrichment of ARGs in T. caespitum. Cuminaldehyde, the antibacterial lipid metabolite that displayed a remarkable increase in the adult phase, was demonstrated to strongly induce ARG abundance. Our findings show that the gut resistome is host developmental stage-dependent and likely modulated by metabolites, offering novel insights into possible steps to reduce ARG dissemination in the soil food chain.

A metagenomic catalog of the early-life human gut virome

Early-life human gut microbiome is a pivotal driver of gut homeostasis and infant health. However, the viral component (known as "virome") remains mostly unexplored. Here, we establish the Early-Life Gut Virome (ELGV), a catalog of 160,478 non-redundant DNA and RNA viral sequences from 8130 gut virus-like particles (VLPs) enriched or bulk metagenomes in the first three years of life. By clustering, 82,141 viral species are identified, 68.3% of which are absent in existing databases built mainly from adults, and 64 and 8 viral species based on VLPs-enriched and bulk metagenomes, respectively, exhibit potentials as biomarkers to distinguish infants from adults. With the largest longitudinal population of infants profiled by either VLPs-enriched or bulk metagenomic sequencing, we track the inherent instability and temporal development of the early-life human gut virome, and identify differential viruses associated with multiple clinical factors. The mother-infant shared virome and interactions between gut virome and bacteriome early in life are further expanded. Together, the ELGV catalog provides the most comprehensive and complete metagenomic blueprint of the early-life human gut virome, facilitating the discovery of pediatric disease-virome associations in future.

Synergistic horizontal transfer of antibiotic resistance genes and transposons in the infant gut microbial genome

Transposons, plasmids, bacteriophages, and other mobile genetic elements facilitate horizontal gene transfer in the gut microbiota, allowing some pathogenic bacteria to acquire antibiotic resistance genes (ARGs). Currently, the relationship between specific ARGs and specific transposons in the comprehensive infant gut microbiome has not been elucidated. In this study, ARGs and transposons were annotated from the Unified Human Gastrointestinal Genome (UHGG) and the Early-Life Gut Genomes (ELGG). Association rules mining was used to explore the association between specific ARGs and specific transposons in UHGG, and the robustness of the association rules was validated using the external database in ELGG. Our results suggested that ARGs and transposons were more likely to be relevant in infant gut microbiota compared to adult gut microbiota, and nine robust association rules were identified, among which Klebsiella pneumoniae, Enterobacter hormaechei_A, and Escherichia coli_D played important roles in this association phenomenon. The emphasis of this study is to investigate the synergistic transfer of specific ARGs and specific transposons in the infant gut microbiota, which can contribute to the study of microbial pathogenesis and the ARG dissemination dynamics.IMPORTANCEThe transfer of transposons carrying antibiotic resistance genes (ARGs) among microorganisms accelerates antibiotic resistance dissemination among infant gut microbiota. Nonetheless, it is unclear what the relationship between specific ARGs and specific transposons within the infant gut microbiota. K. pneumoniae, E. hormaechei_A, and E. coli_D were identified as key players in the nine robust association rules we discovered. Meanwhile, we found that infant gut microorganisms were more susceptible to horizontal gene transfer events about specific ARGs and specific transposons than adult gut microorganisms. These discoveries could enhance the understanding of microbial pathogenesis and the ARG dissemination dynamics within the infant gut microbiota.

Microbial metabolites as modulators of host physiology

The gut microbiota is increasingly recognised as a key player in influencing human health and changes in the gut microbiota have been strongly linked with many non-communicable conditions in humans such as type 2 diabetes, obesity and cardiovascular disease. However, characterising the molecular mechanisms that underpin these associations remains an important challenge for researchers. The gut microbiota is a complex microbial community that acts as a metabolic interface to transform ingested food (and other xenobiotics) into metabolites that are detected in the host faeces, urine and blood. Many of these metabolites are only produced by microbes and there is accumulating evidence to suggest that these microbe-specific metabolites do act as effectors to influence human physiology. For example, the gut microbiota can digest dietary complex polysaccharides (such as fibre) into short-chain fatty acids (SCFA) such as acetate, propionate and butyrate that have a pervasive role in host physiology from nutrition to immune function. In this review we will outline our current understanding of the role of some key microbial metabolites, such as SCFA, indole and bile acids, in human health. Whilst many studies linking microbial metabolites with human health are correlative we will try to highlight examples where genetic evidence is available to support a specific role for a microbial metabolite in host health and well-being.

MAGICIAN: MAG simulation for investigating criteria for bioinformatic analysis

BACKGROUND

The possibility of recovering metagenome-assembled genomes (MAGs) from sequence reads allows for further insights into microbial communities and their members, possibly even analyzing such sequences with tools designed for single-isolate genomes. As result quality depends on sequence quality, performance of tools for single-isolate genomes on MAGs should be tested beforehand. Bioinformatics can be leveraged to quickly create varied synthetic test sets with known composition for this purpose.

RESULTS

We present MAGICIAN, a flexible, user-friendly pipeline for the simulation of MAGs. MAGICIAN combines a synthetic metagenome simulator with a metagenomic assembly and binning pipeline to simulate MAGs based on user-supplied input genomes, allowing users to test performance of tools on MAGs while having a ground truth to compare results to. Using MAGICIAN, we found that even very slight (1%) changes in depth of coverage can drastically affect whether a genome can be recovered. We also demonstrate the use of simulated MAGs by evaluating the suitability of such genomes obtained with MAGICIAN's current default pipeline for analysis with the antimicrobial resistance gene identification tool ResFinder.

CONCLUSIONS

Using MAGICIAN, it is possible to simulate MAGs which, while generally high in quality, reflect issues encountered with real-world data, thus providing realistic best-case data. Evaluating the results of ResFinder analysis of these genomes revealed a risk for plausible-looking false positives, which underlines the need for pipeline validation so that researchers are aware of the potential issues when interpreting real-world data. Furthermore, the effects of fluctuations in depth of coverage on genome recovery in our simulated "random sequencing" warrant further investigation and indicate random subsampling of reads may affect discovery of more genomes.

How to Obtain and Compare Metagenome-Assembled Genomes

Metagenome-assembled genomes, or MAGs, are genomes retrieved from metagenome datasets. In the vast majority of cases, MAGs are genomes from prokaryotic species that have not been isolated or cultivated in the lab. They, therefore, provide us with information on these species that are impossible to obtain otherwise, at least until new cultivation methods are devised. Thanks to improvements and cost reductions of DNA sequencing technologies and growing interest in microbial ecology, the rise in number of MAGs in genome repositories has been exponential. This chapter covers the basics of MAG retrieval and processing and provides a practical step-by-step guide using a real dataset and state-of-the-art tools for MAG analysis and comparison.

Influence of age, socioeconomic status, and location on the infant gut resistome across populations

Antibiotic resistance is a growing global concern, with many ecological niches showing a high abundance of antibiotic resistance genes (ARGs), including the human gut. With increasing indications of ARGs in infants, this study aims to investigate the gut resistome profile during early life at a wider geographic level. To achieve this objective, we utilized stool samples data from 26 studies involving subjects aged up to 3 years from different geographical locations. The 32,277 Metagenome Assembled Genomes (MAGs) previously generated from shotgun sequencing reads from these studies were used for resistome analysis using RGI with the CARD database. This analysis showed that the distribution of ARGs across the countries in our study differed in alpha diversity and compositionally. In particular, the abundance of ARGs was found to vary by socioeconomic status and healthcare access and quality (HAQ) index. Surprisingly, countries having lower socioeconomic status and HAQ indices showed lower ARG abundance, which was contradictory to previous reports. Gram-negative genera, including Escherichia, Enterobacter, Citrobacter, and Klebsiella harbored a particularly rich set of ARGs, which included antibiotics that belong to the Reserve, Access or Watch category, such as glycopeptides, fluoroquinolones, sulfonamides, macrolides, and tetracyclines. We showed that ARG abundance exponentially decreased with time during the first 3 years of life. Many highly ARG-abundant species including Escherichia, Klebsiella, Citrobacter species that we observed are well-known pathobionts found in the infant gut in early life. High abundance of these species and a diverse range of ARGs in their genomes point toward the infant gut, acting as an ARG reservoir. This is a concern and further studies are needed to examine the causal effect and its consequences on long-term health.

A genome catalog of the early-life human skin microbiome

BACKGROUND

Metagenome-assembled genomes have greatly expanded the reference genomes for skin microbiome. However, the current reference genomes are largely based on samples from adults in North America and lack representation from infants and individuals from other continents.

RESULTS

Here we use deep shotgun metagenomic sequencing to profile the skin microbiota of 215 infants at age 2-3 months and 12 months who are part of the VITALITY trial in Australia as well as 67 maternally matched samples. Based on the infant samples, we present the Early-Life Skin Genomes (ELSG) catalog, comprising 9483 prokaryotic genomes from 1056 species, 206 fungal genomes from 13 species, and 39 eukaryotic viral sequences. This genome catalog substantially expands the diversity of species previously known to comprise human skin microbiome and improves the classification rate of sequenced data by 21%. The protein catalog derived from these genomes provides insights into the functional elements such as defense mechanisms that distinguish early-life skin microbiome. We also find evidence for microbial sharing at the community, bacterial species, and strain levels between mothers and infants.

CONCLUSIONS

Overall, the ELSG catalog uncovers the skin microbiome of a previously underrepresented age group and population and provides a comprehensive view of human skin microbiome diversity, function, and development in early life.

Fast and robust metagenomic sequence comparison through sparse chaining with skani

Sequence comparison tools for metagenome-assembled genomes (MAGs) struggle with high-volume or low-quality data. We present skani ( https://github.com/bluenote-1577/skani ), a method for determining average nucleotide identity (ANI) via sparse approximate alignments. skani outperforms FastANI in accuracy and speed (>20× faster) for fragmented, incomplete MAGs. skani can query genomes against >65,000 prokaryotic genomes in seconds and 6 GB memory. skani unlocks higher-resolution insights for extensive, noisy metagenomic datasets.

Expanded microbiome niches of RAG-deficient patients

The complex interplay between microbiota and immunity is important to human health. To explore how altered adaptive immunity influences the microbiome, we characterize skin, nares, and gut microbiota of patients with recombination-activating gene (RAG) deficiency-a rare genetically defined inborn error of immunity (IEI) that results in a broad spectrum of clinical phenotypes. Integrating de novo assembly of metagenomes from RAG-deficient patients with reference genome catalogs provides an expansive multi-kingdom view of microbial diversity. RAG-deficient patient microbiomes exhibit inter-individual variation, including expansion of opportunistic pathogens (e.g., Corynebacterium bovis, Haemophilus influenzae), and a relative loss of body site specificity. We identify 35 and 27 bacterial species derived from skin/nares and gut microbiomes, respectively, which are distinct to RAG-deficient patients compared to healthy individuals. Underscoring IEI patients as potential reservoirs for viral persistence and evolution, we further characterize the colonization of eukaryotic RNA viruses (e.g., Coronavirus 229E, Norovirus GII) in this patient population.

Mediterranean diet and olive oil, microbiota, and obesity-related cancers. From mechanisms to prevention

Olive oil (OO) is the main source of added fat in the Mediterranean diet (MD). It is a mix of bioactive compounds, including monounsaturated fatty acids, phytosterols, simple phenols, secoiridoids, flavonoids, and terpenoids. There is a growing body of evidence that MD and OO improve obesity-related factors. In addition, obesity has been associated with an increased risk for several cancers: endometrial, oesophageal adenocarcinoma, renal, pancreatic, hepatocellular, gastric cardia, meningioma, multiple myeloma, colorectal, postmenopausal breast, ovarian, gallbladder, and thyroid cancer. However, the epidemiological evidence linking MD and OO with these obesity-related cancers, and their potential mechanisms of action, especially those involving the gut microbiota, are not clearly described or understood. The goals of this review are 1) to update the current epidemiological knowledge on the associations between MD and OO consumption and obesity-related cancers, 2) to identify the gut microbiota mechanisms involved in obesity-related cancers, and 3) to report the effects of MD and OO on these mechanisms.

Uncultured prokaryotic genomes in the spotlight: An examination of publicly available data from metagenomics and single-cell genomics

Owing to the ineffectiveness of traditional culture techniques for the vast majority of microbial species, culture-independent analyses utilizing next-generation sequencing and bioinformatics have become essential for gaining insight into microbial ecology and function. This mini-review focuses on two essential methods for obtaining genetic information from uncultured prokaryotes, metagenomics and single-cell genomics. We analyzed the registration status of uncultured prokaryotic genome data from major public databases and assessed the advantages and limitations of both the methods. Metagenomics generates a significant quantity of sequence data and multiple prokaryotic genomes using straightforward experimental procedures. However, in ecosystems with high microbial diversity, such as soil, most genes are presented as brief, disconnected contigs, and lack association of highly conserved genes and mobile genetic elements with individual species genomes. Although technically more challenging, single-cell genomics offers valuable insights into complex ecosystems by providing strain-resolved genomes, addressing issues in metagenomics. Recent technological advancements, such as long-read sequencing, machine learning algorithms, and in silico protein structure prediction, in combination with vast genomic data, have the potential to overcome the current technical challenges and facilitate a deeper understanding of uncultured microbial ecosystems and microbial dark matter genes and proteins. In light of this, it is imperative that continued innovation in both methods and technologies take place to create high-quality reference genome databases that will support future microbial research and industrial applications.

A comprehensive genomic catalog from global cold seeps

Cold seeps harbor abundant and diverse microbes with tremendous potential for biological applications and that have a significant influence on biogeochemical cycles. Although recent metagenomic studies have expanded our understanding of the community and function of seep microorganisms, knowledge of the diversity and genetic repertoire of global seep microbes is lacking. Here, we collected a compilation of 165 metagenomic datasets from 16 cold seep sites across the globe to construct a comprehensive gene and genome catalog. The non-redundant gene catalog comprised 147 million genes, and 36% of them could not be assigned to a function with the currently available databases. A total of 3,164 species-level representative metagenome-assembled genomes (MAGs) were obtained, most of which (94%) belonged to novel species. Of them, 81 ANME species were identified that cover all subclades except ANME-2d, and 23 syntrophic SRB species spanned the Seep-SRB1a, Seep-SRB1g, and Seep-SRB2 clades. The non-redundant gene and MAG catalog is a valuable resource that will aid in deepening our understanding of the functions of cold seep microbiomes.

SemiBin2: self-supervised contrastive learning leads to better MAGs for short- and long-read sequencing

MOTIVATION

Metagenomic binning methods to reconstruct metagenome-assembled genomes (MAGs) from environmental samples have been widely used in large-scale metagenomic studies. The recently proposed semi-supervised binning method, SemiBin, achieved state-of-the-art binning results in several environments. However, this required annotating contigs, a computationally costly and potentially biased process.

RESULTS

We propose SemiBin2, which uses self-supervised learning to learn feature embeddings from the contigs. In simulated and real datasets, we show that self-supervised learning achieves better results than the semi-supervised learning used in SemiBin1 and that SemiBin2 outperforms other state-of-the-art binners. Compared to SemiBin1, SemiBin2 can reconstruct 8.3-21.5% more high-quality bins and requires only 25% of the running time and 11% of peak memory usage in real short-read sequencing samples. To extend SemiBin2 to long-read data, we also propose ensemble-based DBSCAN clustering algorithm, resulting in 13.1-26.3% more high-quality genomes than the second best binner for long-read data.

AVAILABILITY AND IMPLEMENTATION

SemiBin2 is available as open source software at https://github.com/BigDataBiology/SemiBin/ and the analysis scripts used in the study can be found at https://github.com/BigDataBiology/SemiBin2_benchmark.

A genome catalog of the early-life human skin microbiome

Metagenome-assembled genomes have greatly expanded the reference genomes for skin microbiome. However, the current reference genomes are largely based on samples from adults in North America and lack representation from infants and individuals from other continents. Here we used ultra-deep shotgun metagenomic sequencing to profile the skin microbiota of 215 infants at age 2-3 months and 12 months who were part of the VITALITY trial in Australia as well as 67 maternally-matched samples. Based on the infant samples, we present the Early-Life Skin Genomes (ELSG) catalog, comprising 9,194 bacterial genomes from 1,029 species, 206 fungal genomes from 13 species, and 39 eukaryotic viral sequences. This genome catalog substantially expands the diversity of species previously known to comprise human skin microbiome and improves the classification rate of sequenced data by 25%. The protein catalog derived from these genomes provides insights into the functional elements such as defense mechanisms that distinguish early-life skin microbiome. We also found evidence for vertical transmission at the microbial community, individual skin bacterial species and strain levels between mothers and infants. Overall, the ELSG catalog uncovers the skin microbiome of a previously underrepresented age group and population and provides a comprehensive view of human skin microbiome diversity, function, and transmission in early life.