Identification and assembly of genomes and genetic elements in complex metagenomic samples without using reference genomes. Nat Biotechnol. 2014;32:822-828. [PMID: 24997787 DOI: 10.1038/nbt.2939]

Immune regulation and the tumor microenvironment in anti-PD-1/PDL-1 and anti-CTLA-4 therapies for cancer immune evasion: A bibliometric analysis

This study aims to conduct a bibliometric analysis, employing visualization tools to examine literature pertaining to tumor immune evasion related to anti-CTLA-4 and anti-PD-1/PD-L1 therapy from 1999 to 2022. A special emphasis is placed on the interplay between tumor microenvironment, signaling pathways, immune cells and immune evasion, with data sourced from the Web of Science core collection (WoSCC). Advanced tools, including VOSviewer, Citespace, and Scimago Graphica, were utilized to analyze various parameters, such as co-authorship/co-citation patterns, regional contributions, journal preferences, keyword co-occurrences, and significant citation bursts. Out of 4778 publications reviewed, there was a marked increase in research focusing on immune evasion, with bladder cancer being notably prominent. Geographically, China, the USA, and Japan were the leading contributors. Prestigious institutions like MD Anderson Cancer Center, Harvard Medical School, Fudan University, and Sun Yat Sen University emerged as major players. Renowned journals in this domain included Frontiers in Immunology, Cancers, and Frontiers in Oncology. Ehen LP and Wang W were identified as prolific authors on this topic, while Topalian SL stood out as one of the most cited. Research current situation is notably pivoting toward challenges like immunotherapy resistance and the intricate signaling pathways driving drug resistance. This bibliometric study seeks to provide a comprehensive overview of past and current research trends, emphasizing the potential role of tumor microenvironment, signaling pathways and immune cells in the context of immune checkpoint inhibitors (ICIs) and tumor immune evasion.

Noninvasive, microbiome-based diagnosis of inflammatory bowel disease

Despite recent progress in our understanding of the association between the gut microbiome and inflammatory bowel disease (IBD), the role of microbiome biomarkers in IBD diagnosis remains underexplored. Here we developed a microbiome-based diagnostic test for IBD. By utilization of metagenomic data from 5,979 fecal samples with and without IBD from different geographies and ethnicities, we identified microbiota alterations in IBD and selected ten and nine bacterial species for construction of diagnostic models for ulcerative colitis and Crohn's disease, respectively. These diagnostic models achieved areas under the curve >0.90 for distinguishing IBD from controls in the discovery cohort, and maintained satisfactory performance in transethnic validation cohorts from eight populations. We further developed a multiplex droplet digital polymerase chain reaction test targeting selected IBD-associated bacterial species, and models based on this test showed numerically higher performance than fecal calprotectin in discriminating ulcerative colitis and Crohn's disease from controls. Here we discovered universal IBD-associated bacteria and show the potential applicability of a multibacteria biomarker panel as a noninvasive tool for IBD diagnosis.

Bidirectional Link Between Exercise and the Gut Microbiota

Exercise is well known to exert beneficial changes to the gut microbiota. An emerging area is how the gut microbiota may regulate exercise tolerance. This review will summarize the current evidence on how exercise influences gut microbial communities, with emphasis on how disruptions or depletion of an intact gut microbiota impacts exercise tolerance as well as future directions.

An expanded database and analytical toolkit for identifying bacterial virulence factors and their associations with chronic diseases

Virulence factor genes (VFGs) play pivotal roles in bacterial infections and have been identified within the human gut microbiota. However, their involvement in chronic diseases remains poorly understood. Here, we establish an expanded VFG database (VFDB 2.0) consisting of 62,332 nonredundant orthologues and alleles of VFGs using species-specific average nucleotide identity ( https://github.com/Wanting-Dong/MetaVF_toolkit/tree/main/databases ). We further develop the MetaVF toolkit, facilitating the precise identification of pathobiont-carried VFGs at the species level. A thorough characterization of VFGs for 5452 commensal isolates from healthy individuals reveals that only 11 of 301 species harbour these factors. Further analyses of VFGs within the gut microbiomes of nine chronic diseases reveal both common and disease-specific VFG features. Notably, in type 2 diabetes patients, long HiFi sequencing confirms that shared VF features are carried by pathobiont strains of Escherichia coli and Klebsiella pneumoniae. These findings underscore the critical importance of identifying and understanding VFGs in microbiome-associated diseases.

The DAV132 colon-targeted adsorbent does not interfere with plasma concentrations of antibiotics but prevents antibiotic-related dysbiosis: a randomized phase I trial in healthy volunteers

The deleterious impact of antibiotics (ATB) on the microbiome negatively influences immune checkpoint inhibitors (ICI) response in patients with cancer. We conducted a randomized phase I study (EudraCT:2019-A00240-57) with 148 healthy volunteers (HV) to test two doses of DAV132, a colon-targeted adsorbent, alongside intravenous ceftazidime-avibactam (CZA), piperacillin-tazobactam (PTZ) or ceftriaxone (CRO) and a group without ATB. The primary objective of the study was to assess the effect of DAV132 on ATB plasma concentrations and both doses of DAV132 did not alter ATB levels. Secondary objectives included safety, darkening of the feces, and fecal ATB concentrations. DAV132 was well tolerated, with no severe toxicity and similar darkening at both DAV132 doses. DAV132 led to significant decrease in CZA or PTZ feces concentration. When co-administered with CZA or PTZ, DAV132 preserved microbiome diversity, accelerated recovery to baseline composition and protected key commensals. Fecal microbiota transplantation (FMT) in preclinical cancer models in female mice from HV treated with CZA or PTZ alone inhibited anti-PD-1 response, while transplanted samples from HV treated with ATB + DAV132 circumvented resistance to anti-PD-1. This effect was linked to activated CD8+ T cell populations in the tumor microenvironment. DAV132 represents a promising strategy for overcoming ATB-related dysbiosis and further studies are warranted to evaluate its efficacy in cancer patients.

Engineered probiotic ameliorates ulcerative colitis by restoring gut microbiota and redox homeostasis

Probiotics are potential treatments for ulcerative colitis (UC), but their efficacy is frequently compromised by gastrointestinal conditions that limit adhesion and activity. Here, we use machine learning and bioinformatics to confirm that patients with UC have decreased prevalence of Lactobacillus genus and increased oxidative stress, which correlate with inflammation severity. Accordingly, we developed a probiotic-based therapeutic that synergistically restores intestinal redox and microbiota homeostasis. Lactobacillus casei (Lac) were induced to form a pericellular film, providing a polysaccharide network for spatially confined crystallization of ultrasmall but highly active selenium dots (Se-Lac). Upon oral administration, the selenium dot-embedded pericellular film efficiently enhanced gastric acid resistance and intestinal mucoadhesion of Lac cells. At the lesion site, the selenium dots scavenged reactive oxygen species, while Lac modulated the gut microbiota. In multiple mouse models and non-human primates, this therapeutic effectively relieved inflammation and reduced colonic damage, thus showing promise as a UC treatment.

Metagenome comparison (MC): A new framework for detecting unique/enriched OMUs (operational metagenomic units) derived from whole-genome sequencing reads

BACKGROUND

Current methods for comparing metagenomes, derived from whole-genome sequencing reads, include top-down metrics or parametric models such as metagenome-diversity, and bottom-up, non-parametric, model-free machine learning approaches like Naïve Bayes for k-mer-profiling. However, both types are limited in their ability to effectively and comprehensively identify and catalogue unique or enriched metagenomic genes, a critical task in comparative metagenomics. This challenge is significant and complex due to its NP-hard nature, which means computational time grows exponentially, or even faster, with the problem size, rendering it impractical for even the fastest supercomputers without heuristic approximation algorithms.

METHOD

In this study, we introduce a new framework, MC (Metagenome-Comparison), designed to exhaustively detect and catalogue unique or enriched metagenomic genes (MGs) and their derivatives, including metagenome functional gene clusters (MFGC), or more generally, the operational metagenomic unit (OMU) that can be considered the counterpart of the OTU (operational taxonomic unit) from amplicon sequencing reads. The MC is essentially a heuristic search algorithm guided by pairs of new metrics (termed MG-specificity or OMU-specificity, MG-specificity diversity or OMU-specificity diversity). It is further constrained by statistical significance (P-value) implemented as a pair of statistical tests.

RESULTS

We evaluated the MC using large metagenomic datasets related to obesity, diabetes, and IBD, and found that the proportions of unique and enriched metagenomic genes ranged from 0.001% to 0.08 % and 0.08%-0.82 % respectively, and less than 10 % for the MFGC.

CONCLUSION

The MC provides a robust method for comparing metagenomes at various scales, from baseline MGs to various function/pathway clusters of metagenomes, collectively termed OMUs.

Gut microbiome shifts in people with type 1 diabetes are associated with glycaemic control: an INNODIA study

AIMS/HYPOTHESIS

The gut microbiome is implicated in the disease process leading to clinical type 1 diabetes, but less is known about potential changes in the gut microbiome after the diagnosis of type 1 diabetes and implications in glucose homeostasis. We aimed to analyse potential associations between the gut microbiome composition and clinical and laboratory data during a 2 year follow-up of people with newly diagnosed type 1 diabetes, recruited to the Innovative approaches to understanding and arresting type 1 diabetes (INNODIA) study. In addition, we analysed the microbiome composition in initially unaffected family members, who progressed to clinical type 1 diabetes during or after their follow-up for 4 years.

METHODS

We characterised the gut microbiome composition of 98 individuals with newly diagnosed type 1 diabetes (ND cohort) and 194 autoantibody-positive unaffected family members (UFM cohort), representing a subgroup of the INNODIA Natural History Study, using metagenomic sequencing. Participants from the ND cohort attended study visits within 6 weeks from the diagnosis and 3, 6, 12 and 24 months later for stool sample collection and laboratory tests (HbA1c, C-peptide, diabetes-associated autoantibodies). Participants from the UFM cohort were assessed at baseline and 6, 12, 18, 24 and 36 months later.

RESULTS

We observed a longitudinal increase in 21 bacterial species in the ND cohort but not in the UFM cohort. The relative abundance of Faecalibacterium prausnitzii was inversely associated with the HbA1c levels at diagnosis (p=0.0019). The rate of the subsequent disease progression in the ND cohort, as assessed by change in HbA1c, C-peptide levels and insulin dose, was associated with the abundance of several bacterial species. Individuals with rapid decrease in C-peptide levels in the ND cohort had the lowest gut microbiome diversity. Nineteen individuals who were diagnosed with type 1 diabetes in the UFM cohort had increased abundance of Sutterella sp. KLE1602 compared with the undiagnosed UFM individuals (p=1.2 × 10-4).

CONCLUSIONS/INTERPRETATION

Our data revealed associations between the gut microbiome composition and the disease progression in individuals with recent-onset type 1 diabetes. Future mechanistic studies as well as animal studies and human trials are needed to further validate the significance and causality of these associations.

Exploring microbial worlds: a review of whole genome sequencing and its application in characterizing the microbial communities

The classical microbiology techniques have inherent limitations in unraveling the complexity of microbial communities, necessitating the pivotal role of sequencing in studying the diversity of microbial communities. Whole genome sequencing (WGS) enables researchers to uncover the metabolic capabilities of the microbial community, providing valuable insights into the microbiome. Herein, we present an overview of the rapid advancements achieved thus far in the use of WGS in microbiome research. There was an upsurge in publications, particularly in 2021 and 2022 with the United States, China, and India leading the metagenomics research landscape. The Illumina platform has emerged as the widely adopted sequencing technology, whereas a significant focus of metagenomics has been on understanding the relationship between the gut microbiome and human health where distinct bacterial species have been linked to various diseases. Additionally, studies have explored the impact of human activities on microbial communities, including the potential spread of pathogenic bacteria and antimicrobial resistance genes in different ecosystems. Furthermore, WGS is used in investigating the microbiome of various animal species and plant tissues such as the rhizosphere microbiome. Overall, this review reflects the importance of WGS in metagenomics studies and underscores its remarkable power in illuminating the variety and intricacy of the microbiome in different environments.

Metagenome reveals the midgut microbial community of Haemaphysalis qinghaiensis ticks collected from yaks and Tibetan sheep

BACKGROUND

Haemaphysalis qinghaiensis is a tick species distributed only in China. Due to its ability to transmit a variety of pathogens, including species of the genera Anaplasma, Rickettsia, Babesia, and Theileria, it seriously endangers livestock husbandry. However, the microbial community of the midgut of H. qinghaiensis females collected from yaks and Tibetan sheep has not yet been characterized using metagenomic sequencing technology.

METHODS

Haemaphysalis qinghaiensis were collected from the skins of yaks and Tibetan sheep in Gansu Province, China. Genomic DNA was extracted from the midguts and midgut contents of fully engorged H. qinghaiensis females collected from the two hosts. Metagenomic sequencing technology was used to analyze the microbial community of the two groups.

RESULTS

Fifty-seven phyla, 483 genera, and 755 species were identified in the two groups of samples. The ticks from the two hosts harbored common and unique microorganisms. At the phylum level, the dominant common phyla were Proteobacteria, Firmicutes, and Mucoromycota. At the genus level, the dominant common genera were Anaplasma, Ehrlichia, and Pseudomonas. At the species level, bacteria including Anaplasma phagocytophilum, Ehrlichia minasensis, and Pseudomonas aeruginosa along with eukaryotes such as Synchytrium endobioticum and Rhizophagus irregularis, and viruses such as the orf virus, Alphadintovirus mayetiola, and Parasteatoda house spider adintovirus were detected in both groups. In addition, the midgut of H. qinghaiensis collected from yaks had unique microbial taxa including two phyla, eight genera, and 23 species. Unique microorganisms in the midgut of H. qinghaiensis collected from Tibetan sheep included two phyla, 14 genera, and 32 species. Kyoto Encyclopedia of Genes and Genomes enrichment analysis revealed that the functional genes of the microbiome of H. qinghaiensis were annotated to six pathways, and the metabolic pathways included 11 metabolic processes, in which the genes involved in carbohydrate metabolism were the most abundant, followed by the genes involved in lipid metabolism.

CONCLUSIONS

These findings indicate that most of the microbial species in the collected H. qinghaiensis ticks were the same in both hosts, but there were also slight differences. The analytical data from this study have enhanced our understanding of the midgut microbial composition of H. qinghaiensis collected from different hosts. The database of H. qinghaiensis microbe constructed from this study will lay the foundation for predicting tick-borne diseases. Furthermore, a comprehensive understanding of tick microbiomes will be useful for understanding vector competency and interactions with ticks and midgut microorganisms.

Meta-analysis of the human gut microbiome uncovers shared and distinct microbial signatures between diseases

Microbiome studies have revealed gut microbiota's potential impact on complex diseases. However, many studies often focus on one disease per cohort. We developed a meta-analysis workflow for gut microbiome profiles and analyzed shotgun metagenomic data covering 11 diseases. Using interpretable machine learning and differential abundance analysis, our findings reinforce the generalization of binary classifiers for Crohn's disease (CD) and colorectal cancer (CRC) to hold-out cohorts and highlight the key microbes driving these classifications. We identified high microbial similarity in disease pairs like CD vs ulcerative colitis (UC), CD vs CRC, Parkinson's disease vs type 2 diabetes (T2D), and schizophrenia vs T2D. We also found strong inverse correlations in Alzheimer's disease vs CD and UC. These findings, detected by our pipeline, provide valuable insights into these diseases.

IMPORTANCE

Assessing disease similarity is an essential initial step preceding a disease-based approach for drug repositioning. Our study provides a modest first step in underscoring the potential of integrating microbiome insights into the disease similarity assessment. Recent microbiome research has predominantly focused on analyzing individual diseases to understand their unique characteristics, which by design excludes comorbidities in individuals. We analyzed shotgun metagenomic data from existing studies and identified previously unknown similarities between diseases. Our research represents a pioneering effort that utilizes both interpretable machine learning and differential abundance analysis to assess microbial similarity between diseases.

Major depletion of insulin sensitivity-associated taxa in the gut microbiome of persons living with HIV controlled by antiretroviral drugs

BACKGROUND

Persons living with HIV (PWH) harbor an altered gut microbiome (higher abundance of Prevotella and lower abundance of Bacillota and Ruminococcus lineages) compared to non-infected individuals. Some of these alterations are linked to sexual preference and others to the HIV infection. The relationship between these lineages and metabolic alterations, often present in aging PWH, has been poorly investigated.

METHODS

In this study, we compared fecal metagenomes of 25 antiretroviral-treatment (ART)-controlled PWH to three independent control groups of 25 non-infected matched individuals by means of univariate analyses and machine learning methods. Moreover, we used two external datasets to validate predictive models of PWH classification. Next, we searched for associations between clinical and biological metabolic parameters with taxonomic and functional microbiome profiles. Finally, we compare the gut microbiome in 7 PWH after a 17-week ART switch to raltegravir/maraviroc.

RESULTS

Three major enterotypes (Prevotella, Bacteroides and Ruminococcaceae) were present in all groups. The first Prevotella enterotype was enriched in PWH, with several of characteristic lineages associated with poor metabolic profiles (low HDL and adiponectin, high insulin resistance (HOMA-IR)). Conversely butyrate-producing lineages were markedly depleted in PWH independently of sexual preference and were associated with a better metabolic profile (higher HDL and adiponectin and lower HOMA-IR). Accordingly with the worst metabolic status of PWH, butyrate production and amino-acid degradation modules were associated with high HDL and adiponectin and low HOMA-IR. Random Forest models trained to classify PWH vs. control on taxonomic abundances displayed high generalization performance on two external holdout datasets (ROC AUC of 80-82%). Finally, no significant alterations in microbiome composition were observed after switching to raltegravir/maraviroc.

CONCLUSION

High resolution metagenomic analyses revealed major differences in the gut microbiome of ART-controlled PWH when compared with three independent matched cohorts of controls. The observed marked insulin resistance could result both from enrichment in Prevotella lineages, and from the depletion in species producing butyrate and involved into amino-acid degradation, which depletion is linked with the HIV infection.

Enhancing insights into diseases through horizontal gene transfer event detection from gut microbiome

Horizontal gene transfer (HGT) phenomena pervade the gut microbiome and significantly impact human health. Yet, no current method can accurately identify complete HGT events, including the transferred sequence and the associated deletion and insertion breakpoints from shotgun metagenomic data. Here, we develop LocalHGT, which facilitates the reliable and swift detection of complete HGT events from shotgun metagenomic data, delivering an accuracy of 99.4%-verified by Nanopore data-across 200 gut microbiome samples, and achieving an average F1 score of 0.99 on 100 simulated data. LocalHGT enables a systematic characterization of HGT events within the human gut microbiome across 2098 samples, revealing that multiple recipient genome sites can become targets of a transferred sequence, microhomology is enriched in HGT breakpoint junctions (P-value = 3.3e-58), and HGTs can function as host-specific fingerprints indicated by the significantly higher HGT similarity of intra-personal temporal samples than inter-personal samples (P-value = 4.3e-303). Crucially, HGTs showed potential contributions to colorectal cancer (CRC) and acute diarrhoea, as evidenced by the enrichment of the butyrate metabolism pathway (P-value = 3.8e-17) and the shigellosis pathway (P-value = 5.9e-13) in the respective associated HGTs. Furthermore, differential HGTs demonstrated promise as biomarkers for predicting various diseases. Integrating HGTs into a CRC prediction model achieved an AUC of 0.87.

Shotgun Metagenomics Reveals Taxonomic and Functional Patterns of the Microbiome Associated with Barbour's Seahorse (Hippocampus barbouri)

This study aimed to investigate the taxonomic and functional patterns of the microbiome associated with Barbour's seahorse (Hippocampus barbouri) using a combination of shotgun metagenomics and bioinformatics. The analyses revealed that Pseudomonadota and Bacillota were the dominant phyla in the seahorse skin microbiome, whereas Pseudomonadota and, to a lesser extent, Bacillota and Bacteroidota were the dominant phyla in the seahorse gut microbiome. Several metabolic pathway categories were found to be enriched in the skin microbiome, including amino acid metabolism, carbohydrate metabolism, cofactor and vitamin metabolism, energy metabolism, nucleotide metabolism, as well as membrane transport, signal transduction, and cellular community-prokaryotes. In contrast, the gut microbiome exhibited enrichment in metabolic pathways associated with the metabolism of terpenoids and polyketides, biosynthesis of other secondary metabolites, xenobiotics biodegradation and metabolism, and quorum sensing. Additionally, although the relative abundance of bacteriocins in the skin and gut was slightly similar, notable differences were observed at the class level. Specifically, class I bacteriocins were found to be more abundant in the skin microbiome, whereas class III bacteriocins were more abundant in the gut microbiome. To the best of our knowledge, this study represents the first comprehensive examination of the taxonomic and functional patterns of the skin and gut microbiome in Barbour's seahorse. These findings can greatly contribute to a deeper understanding of the seahorse-associated microbiome, which can play a pivotal role in predicting and controlling bacterial infections, thereby contributing to the success of aquaculture and health-promoting initiatives.

Bacteroidales reduces growth rate through serum metabolites and cytokines in Chinese Ningdu yellow chickens

Increasing evidence has indicated that the gut microbiome plays an important role in chicken growth traits. However, the cecal microbial taxa associated with the growth rates of the Chinese Ningdu yellow chickens are unknown. In this study, shotgun metagenomic sequencing was used to identify cecal bacterial species associated with the growth rate of the Chinese Ningdu yellow chickens. We found that nine cecal bacterial species differed significantly between high and low growth rate chickens, including three species (Succinatimonas hippei, Phocaeicola massiliensis, and Parabacteroides sp. ZJ-118) that were significantly enriched in high growth rate chickens. We identified six Bacteroidales that were significantly enriched in low growth rate chickens, including Barnesiella sp. An22, Barnesiella sp. ET7, and Bacteroidales bacterium which were key biomarkers in differentiating high and low growth rate chickens and were associated with alterations in the functional taxa of the cecal microbiome. Untargeted serum metabolome analysis revealed that 8 metabolites showing distinct enrichment patterns between high and low growth rate chickens, including triacetate lactone and N-acetyl-a-neuraminic acid, which were at higher concentrations in low growth rate chickens and were positively and significantly correlated with Barnesiella sp. An22, Barnesiella sp. ET7, and Bacteroidales bacterium. Furthermore, the results suggest that serum cytokines, such as IL-5, may reduce growth rate and are related to changes in serum metabolites and gut microbes (e.g., Barnesiella sp. An22 and Barnesiella sp. ET7). These results provide important insights into the effects of the cecal microbiome, serum metabolism and cytokines in Ningdu yellow chickens.

Breaking the Cycle: A Yeast Mannan-Rich Fraction Beneficially Modulates Egg Quality and the Antimicrobial Resistome Associated with Layer Hen Caecal Microbiomes under Commercial Conditions

Antibiotics and antibiotic growth promoters have been extensively employed in poultry farming to enhance growth performance, maintain bird health, improve nutrient uptake efficiency, and mitigate enteric diseases at both sub-therapeutic and therapeutic doses. However, the extensive use of antimicrobials in poultry farming has led to the emergence of antimicrobial resistance (AMR) in microbial reservoirs, representing a significant global public health concern. In response, non-antibiotic dietary interventions, such as yeast mannan-rich fraction (MRF), have emerged as a promising alternative to modulate the gut microbiota and combat the AMR crisis. This study investigated whether a yeast mannan-rich fraction containing feed supplement impacted the performance of laying hens, their microbiomes, and the associated carriage of antimicrobial resistance genes under commercial conditions. High-throughput DNA sequencing was utilised to profile the bacterial community and assess changes in the antibiotic resistance genomes detected in the metagenome, the "resistome", in response to MRF supplementation. It was found that supplementation favourably influenced laying hen performance and microbial composition. Notably, there was a compositional shift in the MRF supplemented group associated with a lower relative abundance of pathobionts, e.g., Escherichia, Brachyspira and Trueperella, and their AMR-encoded genes, relative to beneficial microbes. Overall, the findings further demonstrate the ability of prebiotics to improve laying hen performance through changes associated with their microbiome and resistome.

Solving genomic puzzles: computational methods for metagenomic binning

Metagenomics involves the study of genetic material obtained directly from communities of microorganisms living in natural environments. The field of metagenomics has provided valuable insights into the structure, diversity and ecology of microbial communities. Once an environmental sample is sequenced and processed, metagenomic binning clusters the sequences into bins representing different taxonomic groups such as species, genera, or higher levels. Several computational tools have been developed to automate the process of metagenomic binning. These tools have enabled the recovery of novel draft genomes of microorganisms allowing us to study their behaviors and functions within microbial communities. This review classifies and analyzes different approaches of metagenomic binning and different refinement, visualization, and evaluation techniques used by these methods. Furthermore, the review highlights the current challenges and areas of improvement present within the field of research.

Resistome Signature and Antibiotic Resistance Mechanisms in Rhizospheric Soil Bacteriomes of Mecca Region, Saudi Arabia: Insights into Impact on Human Health

The objective of this investigation is to ascertain the distinctive profile of the rhizospheric soil resistome within the Mecca region, while also evaluating the potential risks associated with the horizontal transfer of resistome determinants to the open environment and human clinical isolates. We have made metagenomic whole-genome shotgun sequencing for rhizospheric microbiomes of two endemic plants, namely Moringa oleifera and Abutilon fruticosum. The rhizospheric resistomes of the two plants and the abundance of antibiotic resistance genes (ARGs) were identified by cross-referencing encoded proteins with the comprehensive antibiotic resistance database (CARD). The identified ARGs were then analyzed for their antimicrobial resistance (AMR) mechanisms. Predominantly within this soil are the two bacterial species Pseudomonas aeruginosa and Mycobacterium tuberculosis. These opportunistic human pathogens are implicated in respiratory infections and are correlated with heightened mortality rates. The most prevalent array of ARGs existing in this soil comprises mexA, mexC, mexE, and cpxR, associated with mechanisms of antibiotic active efflux, along with ACC(2), ACC(3), AAC(6), and APH(6), in addition to arr1, arr3, arr4, iri, rphA, and rphB, implicated in antibiotic inactivation. Furthermore, vanS, vanR, and vanJ are identified for antibiotic target alteration, while rpoB2 and RbpA are noted for antibiotic target replacement and protection, respectively. These mechanisms confer resistance against a diverse spectrum of drug classes encompassing fluoroquinolones, aminoglycosides, glycopeptides, and rifampicins. This study underscores the potential hazards posed to human health by the presence of these pathogenic bacteria within the rhizospheric soil of the Mecca region, particularly in scenarios where novel ARGs prevalent in human populations are harbored and subsequently transmitted through the food chain to human clinical isolates. Consequently, stringent adherence to good agricultural and food transportation practices is imperative, particularly with regard to edible plant parts and those utilized in folkloric medicine.

Gut microbiome model predicts response to neoadjuvant immunotherapy plus chemoradiotherapy in rectal cancer

BACKGROUND

Accurate evaluation of the response to preoperative treatment enables the provision of a more appropriate personalized therapeutic schedule for locally advanced rectal cancer (LARC), which remains an enormous challenge, especially neoadjuvant immunotherapy plus chemoradiotherapy (nICRT).

METHODS

This prospective, multicenter cohort study enrolled patients with LARC from 6 centers who received nICRT. The dynamic variation in the gut microbiome during nICRT was evaluated. A species-level gut microbiome prediction (SPEED) model was developed and validated to predict the pathological complete response (pCR) to nICRT.

FINDINGS

A total of 50 patients were enrolled, 75 fecal samples were collected from 33 patients at different time points, and the pCR rate reached 42.4% (14/33). Lactobacillus and Eubacterium were observed to increase after nICRT. Additionally, significant differences in the gut microbiome were observed between responders and non-responders at baseline. Significantly higher abundances of Lachnospiraceaebacterium and Blautiawexlerae were found in responders, while Bacteroides, Prevotella, and Porphyromonas were found in non-responders. The SPEED model showcased a superior predictive performance with areas under the curve of 98.80% (95% confidence interval [CI]: 95.67%-100%) in the training cohort and 77.78% (95% CI: 65.42%-88.29%) in the validation cohort.

CONCLUSIONS

Programmed death 1 (PD-1) blockade plus concurrent long-course CRT showed a favorable pCR rate and is well tolerated in microsatellite-stable (MSS)/mismatch repair-proficient (pMMR) patients with LARC. The SPEED model can be used to predict the pCR to nICRT based on the baseline gut microbiome with high robustness and accuracy, thereby assisting clinical physicians in providing individualized management for patients with LARC.

FUNDING

This research was funded by the China National Natural Science Foundation (82202884).

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.

Gut microbiota differences in five-year-old children that were born preterm with a history of necrotizing enterocolitis: A pilot trial

The study explores the long-term effects of necrotizing enterocolitis (NEC) on gut microbiota in preterm infants by analyzing stool samples from 5-year-old children using shotgun metagenomic sequencing. It compares children with a history of NEC, treated surgically or medically, to preterm controls without NEC. Findings reveal persistent gut microbiota dysbiosis in NEC children, with reduced species diversity and evenness, especially in those treated surgically. The surgical NEC group had a lower Shannon index, indicating less microbial diversity. Significant differences in taxonomic profiles were observed, mainly influenced by surgical treatment. These results underscore the lasting impact of NEC and its treatment on gut microbiota, suggesting a need for strategies addressing long-term dysbiosis.

Enhanced microbiota profiling in patients with quiescent Crohn's disease through comparison with paired healthy first-degree relatives

Prior studies indicate no correlation between the gut microbes of healthy first-degree relatives (HFDRs) of patients with Crohn's disease (CD) and the development of CD. Here, we utilize HFDRs as controls to examine the microbiota and metabolome in individuals with active (CD-A) and quiescent (CD-R) CD, thereby minimizing the influence of genetic and environmental factors. When compared to non-relative controls, the use of HFDR controls identifies fewer differential taxa. Faecalibacterium, Dorea, and Fusicatenibacter are decreased in CD-R, independent of inflammation, and correlated with fecal short-chain fatty acids (SCFAs). Validation with a large multi-center cohort confirms decreased Faecalibacterium and other SCFA-producing genera in CD-R. Classification models based on these genera distinguish CD from healthy individuals and demonstrate superior diagnostic power than models constructed with markers identified using unrelated controls. Furthermore, these markers exhibited limited discriminatory capabilities for other diseases. Finally, our results are validated across multiple cohorts, underscoring their robustness and potential for diagnostic and therapeutic applications.

AFITbin: a metagenomic contig binning method using aggregate l-mer frequency based on initial and terminal nucleotides

BACKGROUND

Using next-generation sequencing technologies, scientists can sequence complex microbial communities directly from the environment. Significant insights into the structure, diversity, and ecology of microbial communities have resulted from the study of metagenomics. The assembly of reads into longer contigs, which are then binned into groups of contigs that correspond to different species in the metagenomic sample, is a crucial step in the analysis of metagenomics. It is necessary to organize these contigs into operational taxonomic units (OTUs) for further taxonomic profiling and functional analysis. For binning, which is synonymous with the clustering of OTUs, the tetra-nucleotide frequency (TNF) is typically utilized as a compositional feature for each OTU.

RESULTS

In this paper, we present AFIT, a new l-mer statistic vector for each contig, and AFITBin, a novel method for metagenomic binning based on AFIT and a matrix factorization method. To evaluate the performance of the AFIT vector, the t-SNE algorithm is used to compare species clustering based on AFIT and TNF information. In addition, the efficacy of AFITBin is demonstrated on both simulated and real datasets in comparison to state-of-the-art binning methods such as MetaBAT 2, MaxBin 2.0, CONCOT, MetaCon, SolidBin, BusyBee Web, and MetaBinner. To further analyze the performance of the purposed AFIT vector, we compare the barcodes of the AFIT vector and the TNF vector.

CONCLUSION

The results demonstrate that AFITBin shows superior performance in taxonomic identification compared to existing methods, leveraging the AFIT vector for improved results in metagenomic binning. This approach holds promise for advancing the analysis of metagenomic data, providing more reliable insights into microbial community composition and function.

AVAILABILITY

A python package is available at: https://github.com/SayehSobhani/AFITBin .

Discovery of disease-adapted bacterial lineages in inflammatory bowel diseases

Gut bacteria are implicated in inflammatory bowel disease (IBD), but the strains driving these associations are unknown. Large-scale studies of microbiome evolution could reveal the imprint of disease on gut bacteria, thus pinpointing the strains and genes that may underlie inflammation. Here, we use stool metagenomes of thousands of IBD patients and healthy controls to reconstruct 140,000 strain genotypes, revealing hundreds of lineages enriched in IBD. We demonstrate that these strains are ancient, taxonomically diverse, and ubiquitous in humans. Moreover, disease-associated strains outcompete their healthy counterparts during inflammation, implying long-term adaptation to disease. Strain genetic differences map onto known axes of inflammation, including oxidative stress, nutrient biosynthesis, and immune evasion. Lastly, the loss of health-associated strains of Eggerthella lenta was predictive of fecal calprotectin, a biomarker of disease severity. Our work identifies reservoirs of strain diversity that may impact inflammatory disease and can be extended to other microbiome-associated diseases.

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.

The Conservation Implications of the Gut Microbiome for Protecting the Critically Endangered Gray Snub-Nosed Monkey (Rhinopithecus brelichi)

The gut microbiota plays a crucial role in regulating energy metabolism, facilitating nutrient absorption, and supporting immune function, thereby assisting the host in adapting to seasonal dietary changes. Here, we compare the gut microbiome composition of wild gray snub-nosed monkeys during winter (from October to December) and spring (from January to March) to understand differences in seasonal nutrient intake patterns. Snub-nosed monkeys are foregut fermenters and consume difficult-to-digest carbohydrates and lichen. To examine the digestive adaptations of gray snub-nosed monkeys, we collected 14 fresh fecal samples for DNA analysis during the winter and spring. Based on 16S rRNA sequencing, metagenomic sequencing, and functional metagenomic analyses, we identified that Firmicutes, Actinobacteria, Verrucomicrobia, and Bacteroidetes constitute a keystone bacterial group in the gut microbiota during winter and spring and are responsible for degrading cellulose. Moreover, the transition in dietary composition from winter to spring was accompanied by changes in gut microbiota composition, demonstrating adaptive responses to varying food sources and availability. In winter, the bacterial species of the genera Streptococcus were found in higher abundance. At the functional level, these bacteria are involved in fructose and mannose metabolism and galactose metabolism c-related pathways, which facilitate the breakdown of glycogen, starch, and fiber found in fruits, seeds, and mature leaves. During spring, there was an increased abundance of bacteria species from the Prevotella and Lactobacillus genera, which aid the digestion of protein-rich buds. Combined, these findings reveal how the gut microbiota adjusts to fluctuations in energy balance and nutrient intake across different seasons in this critically endangered species. Moreover, we also identified Pseudomonas in two samples; the presence of potential pathogens within the gut could pose a risk to other troop members. Our findings highlight the necessity of a conservation plan that focuses on protecting vegetation and implementing measures to prevent disease transmission for this critically endangered species.

Shifts in Microbial Community Structure and Co-occurrence Network along a Wide Soil Salinity Gradient

The response of microbiomes to salinity has been clarified in different geographic scales or ecosystems. However, how soil microbial community structure and interaction respond to salinity across wide salinity range and climatic region is still unclearly resolved. To address this issue, we examined the microbial community's composition in saline soils from two climatic regions (coastal wetland and arid desert). Our research confirms that soil salinity had a negative effect on soil nutrient content. Salinity decreased the relative abundance of bacteria, but increased archaea abundance, leading to the shifts from bacteria dominant community to archaea dominant community. Low-water medium-salinity soil (LWMS) had the most complex archaeal community network, whereas for bacteria, the most complex bacterial community network was observed in low-water high-salinity soils (LWHS). Key microbial taxa differed in three salinity gradients. Salinity, soil water content, pH, total nitrogen (TN), and soil organic carbon (SOC) were the main driving factors for the composition of archaeal and bacterial community. Salinity directly affected archaeal community, but indirectly influenced bacteria community through SOC; pH affected archaeal community indirectly through TN, but directly affected bacterial community. Our study suggests that soil salinity dramatically influences diversity, composition, and interactions within the microbial community.

Salinity affects microbial function genes related to nutrient cycling in arid regions

Introduction

Salinization damages soil system health and influences microbial communities structure and function. The response of microbial functions involved in the nutrient cycle to soil salinization is a valuable scientific question. However, our knowledge of the microbial metabolism functions in salinized soil and their response to salinity in arid desert environments is inadequate.

Methods

Here, we applied metagenomics technology to investigate the response of microbial carbon (C), nitrogen (N), phosphorus (P), and sulfur (S) cycling and the key genes to salinity, and discuss the effects of edaphic variables on microbial functions.

Results

We found that carbon fixation dominated the carbon cycle. Nitrogen fixation, denitrification, assimilatory nitrate reduction (ANRA), and nitrogen degradation were commonly identified as the most abundant processes in the nitrogen cycle. Organic phosphorus dissolution and phosphorus absorption/transport were the most enriched P metabolic functions, while sulfur metabolism was dominated by assimilatory sulfate reduction (ASR), organic sulfur transformation, and linkages between inorganic and organic sulfur transformation. Increasing salinity inhibited carbon degradation, nitrogen fixation, nitrogen degradation, anammox, ANRA, phosphorus absorption and transport, and the majority of processes in sulfur metabolism. However, some of the metabolic pathway and key genes showed a positive response to salinization, such as carbon fixation (facA, pccA, korAB), denitrification (narG, nirK, norBC, nosZ), ANRA (nasA, nirA), and organic phosphorus dissolution processes (pstABCS, phnCD, ugpAB). High salinity reduced the network complexity in the soil communities. Even so, the saline microbial community presented highly cooperative interactions. The soil water content had significantly correlations with C metabolic genes. The SOC, N, and P contents were significantly correlated with C, N, P, and S network complexity and functional genes. AP, NH4+, and NO3- directly promote carbon fixation, denitrification, nitrogen degradation, organic P solubilization and mineralization, P uptake and transport, ASR, and organic sulfur transformation processes.

Conclusion

Soil salinity in arid region inhibited multiple metabolic functions, but prompted the function of carbon fixation, denitrification, ANRA, and organic phosphorus dissolution. Soil salinity was the most important factor driving microbial functions, and nutrient availability also played important roles in regulating nutrient cycling.

A Randomized, Double-Blind, Phase 3 Safety and Efficacy Study of Ridinilazole Versus Vancomycin for Treatment of Clostridioides difficile Infection: Clinical Outcomes With Microbiome and Metabolome Correlates of Response

BACKGROUND

Exposure to antibiotics predisposes to dysbiosis and Clostridioides difficile infection (CDI) that can be severe, recurrent (rCDI), and life-threatening. Nonselective drugs that treat CDI and perpetuate dysbiosis are associated with rCDI, in part due to loss of microbiome-derived secondary bile acid (SBA) production. Ridinilazole is a highly selective drug designed to treat CDI and prevent rCDI.

METHODS

In this phase 3 superiority trial, adults with CDI, confirmed with a stool toxin test, were randomized to receive 10 days of ridinilazole (200 mg twice daily) or vancomycin (125 mg 4 times daily). The primary endpoint was sustained clinical response (SCR), defined as clinical response and no rCDI through 30 days after end of treatment. Secondary endpoints included rCDI and change in relative abundance of SBAs.

RESULTS

Ridinilazole and vancomycin achieved an SCR rate of 73% versus 70.7%, respectively, a treatment difference of 2.2% (95% CI: -4.2%, 8.6%). Ridinilazole resulted in a 53% reduction in recurrence compared with vancomycin (8.1% vs 17.3%; 95% CI: -14.1%, -4.5%; P = .0002). Subgroup analyses revealed consistent ridinilazole benefit for reduction in rCDI across subgroups. Ridinilazole preserved microbiota diversity, increased SBAs, and did not increase the resistome. Conversely, vancomycin worsened CDI-associated dysbiosis, decreased SBAs, increased Proteobacteria abundance (∼3.5-fold), and increased the resistome.

CONCLUSIONS

Although ridinilazole did not meet superiority in SCR, ridinilazole greatly reduced rCDI and preserved microbiome diversity and SBAs compared with vancomycin. These findings suggest that treatment of CDI with ridinilazole results in an earlier recovery of gut microbiome health. Clinical Trials Registration.Ri-CoDIFy 1 and 2: NCT03595553 and NCT03595566.

A commensal-derived sugar protects against metabolic disease

Obesity is a worsening global epidemic that is regulated by the microbiota through unknown bacterial factors. We discovered a human-derived commensal bacterium, Clostridium immunis , that protects against metabolic disease by secreting a phosphocholine-modified exopolysaccharide. Genetic interruption of the phosphocholine biosynthesis locus ( licABC ) results in a functionally inactive exopolysaccharide, which demonstrates the critical requirement for this phosphocholine moiety. This C. immunis exopolysaccharide acts via group 3 innate lymphoid cells and modulating IL-22 levels, which results in a reduction in serum triglycerides, body weight, and visceral adiposity. Importantly, phosphocholine biosynthesis genes are less abundant in humans with obesity or hypertriglyceridemia, findings that suggest the role of bacterial phosphocholine is conserved across mice and humans. These results define a bacterial molecule-and its key structural motif-that regulates host metabolism. More broadly, they highlight how small molecules, such as phosphocholine, may help fine-tune microbiome- immune-metabolism interactions.

A genomic compendium of cultivated human gut fungi characterizes the gut mycobiome and its relevance to common diseases

The gut fungal community represents an essential element of human health, yet its functional and metabolic potential remains insufficiently elucidated, largely due to the limited availability of reference genomes. To address this gap, we presented the cultivated gut fungi (CGF) catalog, encompassing 760 fungal genomes derived from the feces of healthy individuals. This catalog comprises 206 species spanning 48 families, including 69 species previously unidentified. We explored the functional and metabolic attributes of the CGF species and utilized this catalog to construct a phylogenetic representation of the gut mycobiome by analyzing over 11,000 fecal metagenomes from Chinese and non-Chinese populations. Moreover, we identified significant common disease-related variations in gut mycobiome composition and corroborated the associations between fungal signatures and inflammatory bowel disease (IBD) through animal experimentation. These resources and findings substantially enrich our understanding of the biological diversity and disease relevance of the human gut mycobiome.

Evaluation of normalization methods for predicting quantitative phenotypes in metagenomic data analysis

Genotype-to-phenotype mapping is an essential problem in the current genomic era. While qualitative case-control predictions have received significant attention, less emphasis has been placed on predicting quantitative phenotypes. This emerging field holds great promise in revealing intricate connections between microbial communities and host health. However, the presence of heterogeneity in microbiome datasets poses a substantial challenge to the accuracy of predictions and undermines the reproducibility of models. To tackle this challenge, we investigated 22 normalization methods that aimed at removing heterogeneity across multiple datasets, conducted a comprehensive review of them, and evaluated their effectiveness in predicting quantitative phenotypes in three simulation scenarios and 31 real datasets. The results indicate that none of these methods demonstrate significant superiority in predicting quantitative phenotypes or attain a noteworthy reduction in Root Mean Squared Error (RMSE) of the predictions. Given the frequent occurrence of batch effects and the satisfactory performance of batch correction methods in predicting datasets affected by these effects, we strongly recommend utilizing batch correction methods as the initial step in predicting quantitative phenotypes. In summary, the performance of normalization methods in predicting metagenomic data remains a dynamic and ongoing research area. Our study contributes to this field by undertaking a comprehensive evaluation of diverse methods and offering valuable insights into their effectiveness in predicting quantitative phenotypes.

Vaginal microbiota are associated with in vitro fertilization during female infertility

The vaginal microbiome plays an essential role in the reproductive health of human females. As infertility increases worldwide, understanding the roles that the vaginal microbiome may have in infertility and in vitro fertilization (IVF) treatment outcomes is critical. To determine the vaginal microbiome composition of 1411 individuals (1255 undergoing embryo transplantation) and their associations with reproductive outcomes, clinical and biochemical features are measured, and vaginal samples are 16S rRNA sequenced. Our results suggest that both too high and too low abundance of Lactobacillus is not beneficial for pregnancy; a moderate abundance is more beneficial. A moderate abundance of Lactobacillus crispatus and Lactobacillus iners (~80%) (with a pregnancy rate of I-B: 54.35% and III-B: 57.73%) is found beneficial for pregnancy outcomes compared with a higher abundance (>90%) of Lactobacillus (I-A: 44.81% and III-A: 51.06%, respectively). The community state type (CST) IV-B (contains a high to moderate relative abundance of Gardnerella vaginalis) shows a similar pregnant ratio (48.09%) with I-A and III-A, and the pregnant women in this CST have a higher abundance of Lactobacillus species. Metagenome analysis of 71 samples shows that nonpregnant women are detected with more antibiotic-resistance genes, and Proteobacteria and Firmicutes are the main hosts. The inherent differences within and between women in different infertility groups suggest that vaginal microbes might be used to detect infertility and potentially improve IVF outcomes.

Hyperbaric oxygen therapy ameliorates intestinal and systematic inflammation by modulating dysbiosis of the gut microbiota in Crohn's disease

BACKGROUND

Dysbiosis of the gut microbiota is pivotal in Crohn's disease (CD) and modulated by host physiological conditions. Hyperbaric oxygen therapy (HBOT) is a promising treatment for CD that can regulate gut microbiota. The relationship between HBOT and the gut microbiota in CD remains unknown.

METHODS

CD patients were divided into an HBOT group (n = 10) and a control group (n = 10) in this open-label prospective interventional study. The fecal samples before and after HBOT were used for 16 S rRNA gene sequencing and fecal microbiota transplantation (FMT). A colitis mouse model was constructed using dextran sulfate sodium, and intestinal and systematic inflammation was evaluated. The safety and long-term effect of HBOT were observed.

RESULTS

HBOT significantly reduced the level of C-reactive protein (CRP) (80.79 ± 42.05 mg/L vs. 33.32 ± 18.31 mg/L, P = 0.004) and the Crohn's Disease Activity Index (CDAI) (274.87 ± 65.54 vs. 221.54 ± 41.89, P = 0.044). HBOT elevated the declined microbial diversity and ameliorated the altered composition of gut microbiota in patients with CD. The relative abundance of Escherichia decreased, and that of Bifidobacterium and Clostridium XIVa increased after HBOT. Mice receiving FMT from donors after HBOT had significantly less intestinal inflammation and serum CRP than the group before HBOT. HBOT was safe and well-tolerated by patients with CD. Combined with ustekinumab, more patients treated with HBOT achieved clinical response (30%vs.70%, P = 0.089) and remission (20%vs.50%, P = 0.160) at week 4.

CONCLUSIONS

HBOT modulates the dysbiosis of gut microbiota in CD and ameliorates intestinal and systematic inflammation. HBOT is a safe option for CD and exhibits a promising auxiliary effect to ustekinumab.

TRIAL REGISTRATION

Chinese Clinical Trial Registry, ChiCTR2200061193. Registered 15 June 2022, https://www.chictr.org.cn/showproj.html?proj=171605 .

Reducing transmission of high-risk antibiotic resistance genes in whole-crop corn silage through lactic acid bacteria inoculation and increasing ensiling temperature

The microbial hosts of antibiotic resistance genes (ARGs) found epiphytically on plant materials could grow and flourish during silage fermentation. This study employed metagenomic analysis and elucidated the occurrence and transmission mechanisms of ARGs and their microbial hosts in whole-crop corn silage inoculated with homofermentative strain Lactiplantibacillus plantarum or heterofermentative strain Lentilactobacillus buchneri ensiled under different temperature (20 and 30 °C). The results revealed that the corn silage was dominated by Lactobacillus, Leuconostoc, Lentilactobacillus, and Latilactobacillus. Both the ensiling temperature and inoculation had greatly modified the silage microbiota. However, regardless of the ensiling temperature, L. buchneri had significantly higher ARGs, while it only exhibited significantly higher mobile genetic elements (MGEs) in low temperature treatments. The microbial community of the corn silage hosted highly diverse form of ARGs, which were primarily MacB, RanA, bcrA, msbA, TetA (58), and TetT and mainly corresponded to macrolides and tetracyclines drug classes. Plasmids were identified as the most abundant MGEs with significant correlation with some high-risk ARGs (tetM, TolC, mdtH, and NorA), and their abundances have been reduced by ensiling process. Furthermore, higher temperature and L. buchneri reduced abundances of high-risk ARGs by modifying their hosts and reduced their transmission in the silage. Therefore, ensiling, L. buchneri inoculation and higher storage temperature could improve the biosafety of corn silage.

Raman cell sorting for single-cell research

Cells constitute the fundamental units of living organisms. Investigating individual differences at the single-cell level facilitates an understanding of cell differentiation, development, gene expression, and cellular characteristics, unveiling the underlying laws governing life activities in depth. In recent years, the integration of single-cell manipulation and recognition technologies into detection and sorting systems has emerged as a powerful tool for advancing single-cell research. Raman cell sorting technology has garnered attention owing to its non-labeling, non-destructive detection features and the capability to analyze samples containing water. In addition, this technology can provide live cells for subsequent genomics analysis and gene sequencing. This paper emphasizes the importance of single-cell research, describes the single-cell research methods that currently exist, including single-cell manipulation and single-cell identification techniques, and highlights the advantages of Raman spectroscopy in the field of single-cell analysis by comparing it with the fluorescence-activated cell sorting (FACS) technique. It describes various existing Raman cell sorting techniques and introduces their respective advantages and disadvantages. The above techniques were compared and analyzed, considering a variety of factors. The current bottlenecks include weak single-cell spontaneous Raman signals and the requirement for a prolonged total cell exposure time, significantly constraining Raman cell sorting technology's detection speed, efficiency, and throughput. This paper provides an overview of current methods for enhancing weak spontaneous Raman signals and their associated advantages and disadvantages. Finally, the paper outlines the detailed information related to the Raman cell sorting technology mentioned in this paper and discusses the development trends and direction of Raman cell sorting.

Neural network establishes co-occurrence links between transformation products of the contaminant and the soil microbiome

It remains challenging to establish reliable links between transformation products (TPs) of contaminants and corresponding microbes. This challenge arises due to the sophisticated experimental regime required for TP discovery and the compositional nature of 16S rRNA gene amplicon sequencing and mass spectrometry datasets, which can potentially confound statistical inference. In this study, we present a new strategy by combining the use of 2H-labeled Stable Isotope-Assisted Metabolomics (2H-SIAM) with a neural network-based algorithm (i.e., MMvec) to explore links between TPs of pyrene and the soil microbiome. The links established by this novel strategy were further validated using different approaches. Briefly, a metagenomic study provided indirect evidence for the established links, while the identification of pyrene degraders from soils, and a DNA-based stable isotope probing (DNA-SIP) study offered direct evidence. The comparison among different approaches, including Pearson's and Spearman's correlations, further confirmed the superior performance of our strategy. In conclusion, we summarize the unique features of the combined use of 2H-SIAM and MMvec. This study not only addresses the challenges in linking TPs to microbes but also introduces an innovative and effective approach for such investigations. Environmental Implication: Taxonomically diverse bacteria performing successive metabolic steps of the contaminant were firstly depicted in the environmental matrix.

Sesame bacterial wilt significantly alters rhizosphere soil bacterial community structure, function, and metabolites in continuous cropping systems

Bacterial wilt is the leading disease of sesame and alters the bacterial community composition, function, and metabolism of sesame rhizosphere soil. However, its pattern of change is unclear. Here, the purpose of this study was to investigate how these communities respond to three differing severities of bacterial wilt in mature continuously cropped sesame plants by metagenomic and metabolomic techniques, namely, absence (WH), moderate (WD5), and severe (WD9) wilt. The results indicated that bacterial wilt could significantly change the bacterial community structure in the rhizosphere soil of continuously cropped sesame plants. The biomarker species with significant differences will also change with increasing disease severity. In particular, the gene expression levels of Ralstonia solanacearum in the WD9 and WD5 treatments increased by 25.29% and 33.61%, respectively, compared to those in the WH treatment (4.35 log10 copies g-1). The occurrence of bacterial wilt significantly altered the functions of the bacterial community in rhizosphere soil. KEEG and CAZy functional annotations revealed that the number of significantly different functions in WH was greater than that in WD5 and WD9. Bacterial wilt significantly affected the relative content of metabolites, especially acids, in the rhizosphere soil, and compared with those in the rhizosphere soil from WH, 10 acids (including S-adenosylmethionine, N-acetylleucine, and desaminotyrosine, etc.) in the rhizosphere soil from WD5 or WD9 significantly increased. In comparison, the changes in the other 10 acids (including hypotaurine, erucic acid, and 6-hydroxynicotinic acid, etc.) were reversed. The occurrence of bacterial wilt also significantly inhibited metabolic pathways such as ABC transporter and amino acid biosynthesis pathways in rhizosphere soil and had a significant impact on two key enzymes (1.1.1.11 and 2.6.1.44). In conclusion, sesame bacterial wilt significantly alters the rhizosphere soil bacterial community structure, function, and metabolites. This study enhances the understanding of sesame bacterial wilt mechanisms and lays the groundwork for future prevention and control strategies against this disease.

Identifying Gut Microbiome Features that Predict Responsiveness Toward a Prebiotic Capable of Increasing Calcium Absorption: A Pilot Study

Previously, we demonstrated that prebiotics may provide a complementary strategy for increasing calcium (Ca) absorption in adolescents which may improve long-term bone health. However, not all children responded to prebiotic intervention. We determine if certain baseline characteristics of gut microbiome composition predict prebiotic responsiveness. In this secondary analysis, we compared differences in relative microbiota taxa abundance between responders (greater than or equal to 3% increase in Ca absorption) and non-responders (less than 3% increase). Dual stable isotope methodologies were used to assess fractional Ca absorption at the end of crossover treatments with placebo, 10, and 20 g/day of soluble corn fiber (SCF). Microbial DNA was obtained from stool samples collected before and after each intervention. Sequencing of the 16S rRNA gene was used to taxonomically characterize the gut microbiome. Machine learning techniques were used to build a predictive model for identifying responders based on baseline relative taxa abundances. Model output was used to infer which features contributed most to prediction accuracy. We identified 19 microbial features out of the 221 observed that predicted responsiveness with 96.0% average accuracy. The results suggest a simplified prescreening can be performed to determine if a subject's bone health may benefit from a prebiotic. Additionally, the findings provide insight and prompt further investigation into the metabolic and genetic underpinnings affecting calcium absorption during pubertal bone development.

The European livestock resistome

Metagenomic sequencing has proven to be a powerful tool in the monitoring of antimicrobial resistance (AMR). Here, we provide a comparative analysis of the resistome from pigs, poultry, veal calves, turkey, and rainbow trout, for a total of 538 herds across nine European countries. We calculated the effects of per-farm management practices and antimicrobial usage (AMU) on the resistome in pigs, broilers, and veal calves. We also provide an in-depth study of the associations between bacterial diversity, resistome diversity, and AMR abundances as well as co-occurrence analysis of bacterial taxa and antimicrobial resistance genes (ARGs) and the universality of the latter. The resistomes of veal calves and pigs clustered together, as did those of avian origin, while the rainbow trout resistome was different. Moreover, we identified clear core resistomes for each specific food-producing animal species. We identified positive associations between bacterial alpha diversity and both resistome alpha diversity and abundance. Network analyses revealed very few taxa-ARG associations in pigs but a large number for the avian species. Using updated reference databases and optimized bioinformatics, previously reported significant associations between AMU, biosecurity, and AMR in pig and poultry farms were validated. AMU is an important driver for AMR; however, our integrated analyses suggest that factors contributing to increased bacterial diversity might also be associated with higher AMR load. We also found that dispersal limitations of ARGs are shaping livestock resistomes, and future efforts to fight AMR should continue to emphasize biosecurity measures.IMPORTANCEUnderstanding the occurrence, diversity, and drivers for antimicrobial resistance (AMR) is important to focus future control efforts. So far, almost all attempts to limit AMR in livestock have addressed antimicrobial consumption. We here performed an integrated analysis of the resistomes of five important farmed animal populations across Europe finding that the resistome and AMR levels are also shaped by factors related to bacterial diversity, as well as dispersal limitations. Thus, future studies and interventions aimed at reducing AMR should not only address antimicrobial usage but also consider other epidemiological and ecological factors.

Integrative metagenomic analysis reveals distinct gut microbial signatures related to obesity

Obesity is a metabolic disorder closely associated with profound alterations in gut microbial composition. However, the dynamics of species composition and functional changes in the gut microbiome in obesity remain to be comprehensively investigated. In this study, we conducted a meta-analysis of metagenomic sequencing data from both obese and non-obese individuals across multiple cohorts, totaling 1351 fecal metagenomes. Our results demonstrate a significant decrease in both the richness and diversity of the gut bacteriome and virome in obese patients. We identified 38 bacterial species including Eubacterium sp. CAG:274, Ruminococcus gnavus, Eubacterium eligens and Akkermansia muciniphila, and 1 archaeal species, Methanobrevibacter smithii, that were significantly altered in obesity. Additionally, we observed altered abundance of five viral families: Mesyanzhinovviridae, Chaseviridae, Salasmaviridae, Drexlerviridae, and Casjensviridae. Functional analysis of the gut microbiome indicated distinct signatures associated to obesity and identified Ruminococcus gnavus as the primary driver for function enrichment in obesity, and Methanobrevibacter smithii, Akkermansia muciniphila, Ruminococcus bicirculans, and Eubacterium siraeum as functional drivers in the healthy control group. Additionally, our results suggest that antibiotic resistance genes and bacterial virulence factors may influence the development of obesity. Finally, we demonstrated that gut vOTUs achieved a diagnostic accuracy with an optimal area under the curve of 0.766 for distinguishing obesity from healthy controls. Our findings offer comprehensive and generalizable insights into the gut bacteriome and virome features associated with obesity, with the potential to guide the development of microbiome-based diagnostics.

Metagenomic data of microbiota in mangrove soil from Lukut River, Malaysia

The mangrove ecosystem contains sediment microorganisms that play a crucial part in the decomposition of organic matter and the cycling of water and nutrients in the mangrove. Here we present the metagenomics whole genome shotgun (mWGS) sequence data analysis from three soil samples that were collected at the freshwater riverine mangrove at Lukut River, Negeri Sembilan, Malaysia. Data analysis shows different distributions of bacteria of the genera Bradyrhizobium, Methyloceanibacter and Desulfobacteaceae were detected in soil samples collected at freshwater riverine mangrove. In the data analysis, we report the existence of a large number of Carbohydrate-Active genes in metagenomes collected from mangrove soil. An in-depth exploration of functional annotation analysis based on the KEGG database also showed that the most abundant genes found in these three soils are those that function in carbon fixation pathways, followed by methane, nitrogen, sulfur metabolisms, atrazine and dioxin degradations.

Metabolic analysis of the CAZy class glycosyltransferases in rhizospheric soil fungiome of the plant species Moringa oleifera

The target of the present work is to study the most abundant carbohydrate-active enzymes (CAZymes) of glycosyltransferase (GT) class, which are encoded by fungiome genes present in the rhizospheric soil of the plant species Moringa oleifera. The datasets of this CAZy class were recovered using metagenomic whole shotgun genome sequencing approach, and the resultant CAZymes were searched against the KEGG pathway database to identify function. High emphasis was given to the two GT families, GT4 and GT2, which were the highest within GT class in the number and abundance of gene queries in this soil compartment. These two GT families harbor CAZymes playing crucial roles in cell membrane and cell wall processes. These CAZymes are responsible for synthesizing essential structural components such as cellulose and chitin, which contribute to the integrity of cell walls in plants and fungi. The CAZyme beta-1,3-glucan synthase of GT2 family accumulates 1,3-β-glucan, which provides elasticity as well as tensile strength to the fungal cell wall. Other GT CAZymes contribute to the biosynthesis of several compounds crucial for cell membrane and wall integrity, including lipopolysaccharide, e.g., lipopolysaccharide N-acetylglucosaminyltransferase, cell wall teichoic acid, e.g., alpha-glucosyltransferase, and cellulose, e.g., cellulose synthase. These compounds also play pivotal roles in ion homeostasis, organic carbon mineralization, and osmoprotection against abiotic stresses in plants. This study emphasizes the major roles of these two CAZy GT families in connecting the structure and function of cell membranes and cell walls of fungal and plant cells. The study also sheds light on the potential occurrence of tripartite symbiotic relationships involving the plant, rhizospheric bacteriome, and fungiome via the action of CAZymes of GT4 and GT2 families. These findings provide valuable insights towards the generation of innovative agricultural practices to enhance the performance of crop plants in the future.

Deep learning methods in metagenomics: a review

The ever-decreasing cost of sequencing and the growing potential applications of metagenomics have led to an unprecedented surge in data generation. One of the most prevalent applications of metagenomics is the study of microbial environments, such as the human gut. The gut microbiome plays a crucial role in human health, providing vital information for patient diagnosis and prognosis. However, analysing metagenomic data remains challenging due to several factors, including reference catalogues, sparsity and compositionality. Deep learning (DL) enables novel and promising approaches that complement state-of-the-art microbiome pipelines. DL-based methods can address almost all aspects of microbiome analysis, including novel pathogen detection, sequence classification, patient stratification and disease prediction. Beyond generating predictive models, a key aspect of these methods is also their interpretability. This article reviews DL approaches in metagenomics, including convolutional networks, autoencoders and attention-based models. These methods aggregate contextualized data and pave the way for improved patient care and a better understanding of the microbiome's key role in our health.

Towards a unified medical microbiome ecology of the OMU for metagenomes and the OTU for microbes

BACKGROUND

Metagenomic sequencing technologies offered unprecedented opportunities and also challenges to microbiology and microbial ecology particularly. The technology has revolutionized the studies of microbes and enabled the high-profile human microbiome and earth microbiome projects. The terminology-change from microbes to microbiomes signals that our capability to count and classify microbes (microbiomes) has achieved the same or similar level as we can for the biomes (macrobiomes) of plants and animals (macrobes). While the traditional investigations of macrobiomes have usually been conducted through naturalists' (Linnaeus & Darwin) naked eyes, and aerial and satellite images (remote-sensing), the large-scale investigations of microbiomes have been made possible by DNA-sequencing-based metagenomic technologies. Two major types of metagenomic sequencing technologies-amplicon sequencing and whole-genome (shotgun sequencing)-respectively generate two contrastingly different categories of metagenomic reads (data)-OTU (operational taxonomic unit) tables representing microorganisms and OMU (operational metagenomic unit), a new term coined in this article to represent various cluster units of metagenomic genes.

RESULTS

The ecological science of microbiomes based on the OTU representing microbes has been unified with the classic ecology of macrobes (macrobiomes), but the unification based on OMU representing metagenomes has been rather limited. In a previous series of studies, we have demonstrated the applications of several classic ecological theories (diversity, composition, heterogeneity, and biogeography) to the studies of metagenomes. Here I push the envelope for the unification of OTU and OMU again by demonstrating the applications of metacommunity assembly and ecological networks to the metagenomes of human gut microbiomes. Specifically, the neutral theory of biodiversity (Sloan's near neutral model), Ning et al.stochasticity framework, core-periphery network, high-salience skeleton network, special trio-motif, and positive-to-negative ratio are applied to analyze the OMU tables from whole-genome sequencing technologies, and demonstrated with seven human gut metagenome datasets from the human microbiome project.

CONCLUSIONS

All of the ecological theories demonstrated previously and in this article, including diversity, composition, heterogeneity, stochasticity, and complex network analyses, are equally applicable to OMU metagenomic analyses, just as to OTU analyses. Consequently, I strongly advocate the unification of OTU/OMU (microbiomes) with classic ecology of plants and animals (macrobiomes) in the context of medical ecology.

Understanding the gut microbiota by considering human evolution: a story of fire, cereals, cooking, molecular ingenuity, and functional cooperation

SUMMARYThe microbial community inhabiting the human colon, referred to as the gut microbiota, is mostly composed of bacterial species that, through extensive metabolic networking, degrade and ferment components of food and human secretions. The taxonomic composition of the microbiota has been extensively investigated in metagenomic studies that have also revealed details of molecular processes by which common components of the human diet are metabolized by specific members of the microbiota. Most studies of the gut microbiota aim to detect deviations in microbiota composition in patients relative to controls in the hope of showing that some diseases and conditions are due to or exacerbated by alterations to the gut microbiota. The aim of this review is to consider the gut microbiota in relation to the evolution of Homo sapiens which was heavily influenced by the consumption of a nutrient-dense non-arboreal diet, limited gut storage capacity, and acquisition of skills relating to mastering fire, cooking, and cultivation of cereal crops. The review delves into the past to gain an appreciation of what is important in the present. A holistic view of "healthy" microbiota function is proposed based on the evolutionary pathway shared by humans and gut microbes.

Metagenomic insight into the pathogenic-related characteristics and resistome profiles within microbiome residing on the Angkor sandstone monuments in Cambodia

To reveal the characteristics of indigenous microbiome including the pathogenic-related ones on Angkor monuments in Cambodia and the distribution pattern of resistome at different locations, several sites, namely Angkor Wat, Bayon of Angkor Thom, and Prasat Preah Vihear with different exposure levels to tourists were selected to conduct the metagenomic analysis in this study. The general characteristics of the microbiome on these monuments were revealed, and the association between the environmental geo-ecological feature and the indigenous microbiome was delineated. The most common microbial groups included 6 phyla, namely Acidobacteria, Actinobacteria, Gemmatimonadetes, Nitrospirae, Proteobacteria and Verrucomicrobia on the monuments, but Firmicutes and Chlamydiae were the most dominant phyla found in bats droppings. The taxonomic family of Chitinophagaceae could serve as a signature microbial group for Preah Vihear, the less visited site. More importantly, the pathogenic-related characteristics of the microbiome residing on Angkor monuments were uncovered. A set of specific antibiotic resistance genes (ARGs) with cross-niches dispersal capacity (between the environmental microbiome and the microbiome within warm blood fauna) was identified to be high by the source tracking analysis based on ARGs profile varies in this study. Among the 10 ARG-types detected in this study, 6 of them are confined to resistance mechanism of antibiotic efflux-pump. The findings of this study provide new a new direction on public health management and implication globally at archaeological sites for tourism.

Comparison of the effectiveness of different normalization methods for metagenomic cross-study phenotype prediction under heterogeneity

The human microbiome, comprising microorganisms residing within and on the human body, plays a crucial role in various physiological processes and has been linked to numerous diseases. To analyze microbiome data, it is essential to account for inherent heterogeneity and variability across samples. Normalization methods have been proposed to mitigate these variations and enhance comparability. However, the performance of these methods in predicting binary phenotypes remains understudied. This study systematically evaluates different normalization methods in microbiome data analysis and their impact on disease prediction. Our findings highlight the strengths and limitations of scaling, compositional data analysis, transformation, and batch correction methods. Scaling methods like TMM show consistent performance, while compositional data analysis methods exhibit mixed results. Transformation methods, such as Blom and NPN, demonstrate promise in capturing complex associations. Batch correction methods, including BMC and Limma, consistently outperform other approaches. However, the influence of normalization methods is constrained by population effects, disease effects, and batch effects. These results provide insights for selecting appropriate normalization approaches in microbiome research, improving predictive models, and advancing personalized medicine. Future research should explore larger and more diverse datasets and develop tailored normalization strategies for microbiome data analysis.

Donor-recipient intermicrobial interactions impact transfer of subspecies and fecal microbiota transplantation outcome

Studies on fecal microbiota transplantation (FMT) have reported inconsistent connections between clinical outcomes and donor strain engraftment. Analyses of subspecies-level crosstalk and its influences on lineage transfer in metagenomic FMT datasets have proved challenging, as single-nucleotide polymorphisms (SNPs) are generally not linked and are often absent. Here, we utilized species genome bin (SGB), which employs co-abundance binning, to investigate subspecies-level microbiome dynamics in patients with autism spectrum disorder (ASD) who had gastrointestinal comorbidities and underwent encapsulated FMT (Chinese Clinical Trial: 2100043906). We found that interactions between donor and recipient microbes, which were overwhelmingly phylogenetically divergent, were important for subspecies transfer and positive clinical outcomes. Additionally, a donor-recipient SGB match was indicative of a high likelihood of strain transfer. Importantly, these ecodynamics were shared across FMT datasets encompassing multiple diseases. Collectively, these findings provide detailed insight into specific microbial interactions and dynamics that determine FMT success.

Multi-omics reveals the mechanism of rumen microbiome and its metabolome together with host metabolome participating in the regulation of milk production traits in dairy buffaloes

Recently, it has been discovered that certain dairy buffaloes can produce higher milk yield and milk fat yield under the same feeding management conditions, which is a potential new trait. It is unknown to what extent, the rumen microbiome and its metabolites, as well as the host metabolism, contribute to milk yield and milk fat yield. Therefore, we will analyze the rumen microbiome and host-level potential regulatory mechanisms on milk yield and milk fat yield through rumen metagenomics, rumen metabolomics, and serum metabolomics experiments. Microbial metagenomics analysis revealed a significantly higher abundance of several species in the rumen of high-yield dairy buffaloes, which mainly belonged to genera, such as Prevotella, Butyrivibrio, Barnesiella, Lachnospiraceae, Ruminococcus, and Bacteroides. These species contribute to the degradation of diets and improve functions related to fatty acid biosynthesis and lipid metabolism. Furthermore, the rumen of high-yield dairy buffaloes exhibited a lower abundance of methanogenic bacteria and functions, which may produce less methane. Rumen metabolome analysis showed that high-yield dairy buffaloes had significantly higher concentrations of metabolites, including lipids, carbohydrates, and organic acids, as well as volatile fatty acids (VFAs), such as acetic acid and butyric acid. Meanwhile, several Prevotella, Butyrivibrio, Barnesiella, and Bacteroides species were significantly positively correlated with these metabolites. Serum metabolome analysis showed that high-yield dairy buffaloes had significantly higher concentrations of metabolites, mainly lipids and organic acids. Meanwhile, several Prevotella, Bacteroides, Barnesiella, Ruminococcus, and Butyrivibrio species were significantly positively correlated with these metabolites. The combined analysis showed that several species were present, including Prevotella.sp.CAG1031, Prevotella.sp.HUN102, Prevotella.sp.KHD1, Prevotella.phocaeensis, Butyrivibrio.sp.AE3009, Barnesiella.sp.An22, Bacteroides.sp.CAG927, and Bacteroidales.bacterium.52-46, which may play a crucial role in rumen and host lipid metabolism, contributing to milk yield and milk fat yield. The "omics-explainability" analysis revealed that the rumen microbial composition, functions, metabolites, and serum metabolites contributed 34.04, 47.13, 39.09, and 50.14%, respectively, to milk yield and milk fat yield. These findings demonstrate how the rumen microbiota and host jointly affect milk production traits in dairy buffaloes. This information is essential for developing targeted feeding management strategies to improve the quality and yield of buffalo milk.