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PubMed2025

Decreased intestinal abundance of Akkermansia muciniphila is associated with metabolic disorders among people living with HIV.

BACKGROUND: Previous studies have shown changes in gut microbiota after human immunodeficiency virus (HIV) infection, but there is limited research linking the gut microbiota of people living with HIV (PLWHIV) to metabolic diseases. METHODS: A total of 103 PLWHIV were followed for 48 weeks of anti-retroviral therapy (ART), with demographic and clinical data collected. Gut microbiome analysis was conducted using metagenomic sequencing of fecal samples from 12 individuals. Nonalcoholic fatty liver disease (NAFLD) was diagnosed based on controlled attenuation parameter (CAP) values of 238 dB/m from liver fibro-scans. Participants were divided based on the presence of metabolic disorders, including NAFLD, overweight, and hyperlipidemia. Akkermansia abundance in stool samples was measured using RT-qPCR, and Pearson correlation and logistic regression were applied for analysis. RESULTS: Metagenomic sequencing revealed a significant decline in gut Akkermansia abundance in PLWHIV with NAFLD. STAMP analysis of public datasets confirmed this decline after HIV infection, while KEGG pathway analysis identified enrichment of metabolism-related genes. A prospective cohort study with 103 PLWHIV followed for 48 weeks validated these findings. Akkermansia abundance was significantly lower in participants with NAFLD, overweight, and hyperlipidemia at baseline, and it emerged as an independent predictor of NAFLD and overweight. Negative correlations were observed between Akkermansia abundance and both CAP values and body mass index (BMI) at baseline and at week 48. At the 48-week follow-up, Akkermansia remained a predictive marker for NAFLD. CONCLUSIONS: Akkermansia abundance was reduced in PLWHIV with metabolic disorders and served as a predictive biomarker for NAFLD progression over 48 weeks of ART.

Humans↗

PubMed2006

Improved serial analysis of V1 ribosomal sequence tags (SARST-V1) provides a rapid, comprehensive, sequence-based characterization of bacterial diversity and community composition.

Serial analysis of ribosomal sequence tags (SARST) is a recently developed technology that can generate large 16S rRNA gene (rrs) sequence data sets from microbiomes, but there are numerous enzymatic and purification steps required to construct the ribosomal sequence tag (RST) clone libraries. We report here an improved SARST method, which still targets the V1 hypervariable region of rrs genes, but reduces the number of enzymes, oligonucleotides, reagents, and technical steps needed to produce the RST clone libraries. The new method, hereafter referred to as SARST-V1, was used to examine the eubacterial diversity present in community DNA recovered from the microbiome resident in the ovine rumen. The 190 sequenced clones contained 1055 RSTs and no less than 236 unique phylotypes (based on > or = 95% sequence identity) that were assigned to eight different eubacterial phyla. Rarefaction and monomolecular curve analyses predicted that the complete RST clone library contains 99% of the 353 unique phylotypes predicted to exist in this microbiome. When compared with ribosomal intergenic spacer analysis (RISA) of the same community DNA sample, as well as a compilation of nine previously published conventional rrs clone libraries prepared from the same type of samples, the RST clone library provided a more comprehensive characterization of the eubacterial diversity present in rumen microbiomes. As such, SARST-V1 should be a useful tool applicable to comprehensive examination of diversity and composition in microbiomes and offers an affordable, sequence-based method for diversity analysis.

Bacteria↗

PubMed2026

High dietary fiber is associated with improved outcomes in patients with melanoma and sarcoma treated with immunotherapy regardless of gut microbiome dysbiosis and social vulnerability.

BACKGROUND: Social vulnerability, dietary fiber, and the gut microbiome have been individually implicated in clinical outcomes for melanoma and sarcoma patients. This study hypothesized that increasing social vulnerability is associated with insufficient dietary fiber intake and negatively associated with microbiome composition and clinical outcomes. METHODS: Clinicopathologic data, baseline fiber intake, and gut microbiome profiles were assessed in 153 patients with melanoma or sarcoma treated with immune checkpoint blockade (ICB) and prospectively followed. Patients' social vulnerability index (SVI) and fiber intake were evaluated for associations with microbiome composition, treatment response, and overall survival (OS). RESULTS: SVI percentile was 0.4 (interquartile ratio [IQR], 0.2-0.7), and median dietary fiber intake was 17 (IQR, 15-20) g/day. SVI was inversely correlated with dietary fiber intake (r, -0.18, p = .0398). Gut microbiome analyses revealed community and compositional differences by SVI, including inverse associations with α-diversity and the relative abundance of favorable bacteria such as Bifidobacterium longum (p < .001), contrasting the positive associations observed between fiber and these microbial markers. Increased dietary fiber intake was associated with measurable response to ICB. A difference in OS was not observed in more socially vulnerable patients (SVI, not reached vs. 81.7 months), however, a survival advantage was evident with higher dietary fiber intake (not reached, 58.9 months). CONCLUSIONS: Increased social vulnerability was associated with a less favorable gut microbiome composition but not worse OS among melanoma and sarcoma patients treated with ICB. Consistent with prior findings, high dietary fiber intake emerged as a potentially modifiable pathway to improve outcomes in patients initiating ICB, particularly those with increased SVI.

Humans↗

PubMed2025

Alterations of gut microbiome in chronic rhinosinusitis: insights from a mendelian randomization study.

OBJECTIVE: Gut microbiome dysbiosis is associated with various diseases. Causal association between Chronic Rhinosinusitis (CRS) and gut microbiome is yet unknown. This study aimed to investigate the potential causal relationship between CRS and gut microbiome dysbiosis. METHODS: We used Genome-Wide Association Study (GWAS) data from FinnGen database for CRS. The Dutch Microbiome Project study provided data on gut microbiota species. A total of 334,182 individuals were included. Two-sample bidirectional Mendelian Randomization (MR) analysis was used to investigate causal relationship between CRS and gut microbiome. The main methods of evaluation were Inverse Variance Weighting (IVW), weighted median, weighted mode, and MR-Egger regression. Sensitivity analyses were performed to assess heterogeneity and pleiotropy. RESULTS: Forward MR analysis indicated CRS is potentially linked to decreased risk of Haemophilus parainfluenzae (OR = 0.79, 95% CI 0.66‒0.94, p = 0.009) and increased risk of Bilophila's (OR = 1.14, 95% CI 1.02-1.27, p = 0.023) within the gut. Reduced risks in gut microbiota-related pathways like UDP-N-acetyl-d-glucosamine biosynthesis I (OR = 0.85, 95% CI 0.77‒0.94, p = 0.002) and increased risk in pathway NAD biosynthesis I from aspartate (OR = 1.14, 95% CI 1.03-1.27, p = 0.010) were also linked to CRS. Reverse MR analyses, we obtained no positive results (p > 0.05/412). CONCLUSION: This study reveals CRS exerts a causal impact on shifts within the composition of the gut microbiome and also links to the changes of gut microbiota-related metabolic pathways. The risk of changes in gut microbiota should be of greater concern in patients with CRS than in the general population. LEVEL OF EVIDENCE: Mendelian Randomized (MR) studies are second only to randomized controlled trials in terms of the level of evidence.

Humans↗

PubMed2026

A novel transformer model of protein domains for viral taxonomy classification.

MOTIVATION: Viruses with carefully curated taxonomic assignments (such as those in the ICTV taxonomy) still represent only a small fraction of viruses identified through sequencing data from virome or microbiome projects. It is therefore critical to develop methods that can assign viruses at multiple taxonomic ranks, so that a virus deemed novel at a given rank may still be placed into a higher-level taxon. Sequence-similarity-based approaches can classify viruses that share substantial genomic similarity with known viruses (e.g. those belonging to the same species or genus); however, their performance drops significantly when applied to more divergent viruses. Recent deep learning models, such as ViTax, which utilize DNA language models, aim to address these limitations, but their performance also degrades when applied to novel viruses lacking genus-level similarity to known references. Proteins are more conserved than genomic sequences, and the multiple proteins encoded by a virus can be leveraged to reveal evolutionary relationships among viruses. RESULTS: We propose a new tool, D2T (Domain-to-Taxonomy), that leverages recent advances in protein language models to improve viral taxonomic assignment. D2T represents a virus as a sequence of protein domain tokens and learns a transformer-based model for taxonomic classification. Experiments on multiple closed-set and open-set datasets show that D2T excels at assigning higher-level taxonomic labels (family and above). Furthermore, by combining D2T with Kraken2, which performs well at the genus level, the hybrid method (K+D2T) achieves accurate viral taxonomic classification across multiple taxonomic ranks. AVAILABILITY AND IMPLEMENTATION: D2T is available as a GitHub repository at https://github.com/mgtools/D2T.

Viruses↗

PubMed2026

Enteral Nutrition Is Associated with a Distinct Gut Microbiome Composition and Fermentation Capacity Profile After Acute Colonic Injury in Rats.

Enteral nutrition (EN) is known to promote mucosal healing in inflammatory bowel disease, and multi-omics data suggest that the gut microbiome mediates its therapeutic effects. However, the impact of EN and its components on the gut community during recovery from acute epithelial injury remains incompletely understood. We used whole-genome metagenomic sequencing to investigate the effect of an EN formula based on extruded amaranth flour and pea protein on the gut microbiome in a dextran sulfate sodium (DSS) rat model of acute colonic injury. Three groups were compared, as follows: an unchallenged control (n = 9) with standard chow, a colonic injury (5% DSS; n = 9) group with standard chow, and a colonic injury (5% DSS; n = 9) group with EN. Injury was confirmed histologically (median MCHI score was 2, indicating epithelial damage without inflammation). DSS caused significant weight loss. Animals receiving EN regained baseline weight faster, by day 14, whereas animals on standard chow achieved recovery only by day 21. Differences in energy intake should be further investigated to validate the effect of EN on body weight recovery. At day 21, both injury groups demonstrated higher relative abundances of Bacteroidaceae and Erysipelotrichaceae, including the mucin-degrader Allobaculum mucilyticum, compared with the control group. Conversely, Lactobacillus abundance, notably Lactobacillus acidophilus, was higher in the EN group than in both other groups, as was the inferred capacity for lactate-producing fermentation. These findings suggest that EN is associated with a distinct microbial composition and inferred metabolic profile during the post-injury period, with lactobacilli as one of the potential mediators of its effects.

Animals↗

PubMed2026

Exploring the role of gut microbiota in coronary atherosclerosis through lipoprotein-mediated cholesterol transport and distribution: A Mendelian randomization analysis.

We employed Mendelian randomization (MR) to explore causal relationships between gut microbiota (GM), coronary atherosclerotic heart disease (CAHD), and potential metabolic mediators. We utilized summary statistics from genome-wide association studies (GWAS), encompassing data on 473 GM traits from comprehensive microbiome GWAS, 61 lipoprotein-mediated cholesterol transport and distribution data from large-scale metabolic biomarker studies, and coronary atherosclerosis (CA) data from the GWAS catalog (study accession GCST90043957) involving 456,348 European participants. Bidirectional MR analyses were conducted to investigate the causal relationships between GM and CA. Two-sample Mendelian randomization analyses were performed to identify potential mediating metabolites and quantify the mediation proportion. Ultimately, the GM GCA-900066755, identified through MR as having a potential causal relationship, was selected to investigate its potential effects on CA by influencing cholesterol transport and distribution. Our results indicated that GCA-900066755 was positively associated with an increased risk of CA (odds ratio = 1.156). CA did not significantly affect the levels of GCA-900066755 (odds ratio = 1.009). GCA-900066755 was negatively correlated with total cholesterol levels in medium high-density lipoprotein, which reduced CA risk, and was positively correlated with total cholesterol levels in low-density lipoprotein (LDL), large LDL, medium LDL, and small LDL, which were positively associated with CA. Mediation analysis showed 7 data points mediating the association between GCA-900066755 and CA. Our MR study supports a causal relationship between specific GM groups and the risk of CAHD, highlighting that cholesterol traits are not merely outcomes associated with the relationship between GM and CAHD, but are important mediating factors. Understanding the biological mechanisms of these traits can provide a concrete foundation for future targeted interventions.

Mendelian Randomization Analysis↗

PubMed2024

Genetic modification of the shikimate pathway to reduce lignin content in switchgrass (Panicum virgatum L.) significantly impacts plant microbiomes.

UNLABELLED: Switchgrass (Panicum virgatum L.) is considered a sustainable biofuel feedstock, given its fast-impact growth, low input requirements, and high biomass yields. Improvements in bioenergy conversion efficiency of switchgrass could be made by reducing its lignin content. Engineered switchgrass that expresses a bacterial 3-dehydroshikimate dehydratase (QsuB) has reduced lignin content and improved biomass saccharification due to the rerouting of the shikimate pathway towards the simple aromatic protocatechuate at the expense of lignin biosynthesis. However, the impacts of this QsuB trait on switchgrass microbiome structure and function remain unclear. To address this, wild-type and QsuB-engineered switchgrass were grown in switchgrass field soils, and samples were collected from inflorescences, leaves, roots, rhizospheres, and bulk soils for microbiome analysis. We investigated how QsuB expression influenced switchgrass-associated fungal and bacterial communities using high-throughput Illumina MiSeq amplicon sequencing of ITS and 16S rDNA. Compared to wild-type, QsuB-engineered switchgrass hosted different microbial communities in roots, rhizosphere, and leaves. Specifically, QsuB-engineered plants had a lower relative abundance of arbuscular mycorrhizal fungi (AMF). Additionally, QsuB-engineered plants had fewer Actinobacteriota in root and rhizosphere samples. These findings may indicate that changes in the plant metabolism impact both AMF and Actinobacteriota similarly or potential interactions between AMF and the bacterial community. This study enhances understanding of plant-microbiome interactions by providing baseline microbial data for developing beneficial bioengineering strategies and by assessing nontarget impacts of engineered plant traits on the plant microbiome. IMPORTANCE: Bioenergy crops provide an important strategy for mitigating climate change. Reducing the lignin in bioenergy crops could improve fermentable sugar yields for more efficient conversion into bioenergy and bioproducts. In this study, we assessed how switchgrass engineered for low lignin impacted aboveground and belowground switchgrass microbiome. Our results show unexpected reductions in mycorrhizas and actinobacteria in belowground tissues, raising questions on the resilience and function of genetically engineered plants in agricultural systems.

Panicum↗

PubMed2026

Host-independent metagenomics reveal gut bacteria contribution to Delia antiqua growth by vitamin B6 provision.

Insect guts host a diverse and abundant array of microorganisms. These microbes improve host fitness by extensively involving in a range of crucial physiological processes, which have mainly been revealed by high-throughput sequencing, particularly metagenomics. However, it is almost impossible to make an accurate and complete distinction between the genetic functions of microbial symbionts and insect hosts without host genome data. By comparing metagenomic data from gut germ-free and nonaxenic larvae, we accurately identified the data belonging to the gut microbiome of the onion maggot Delia antiqua (Diptera: Anthomyiidae). Besides, a correlation between bacteria of the genus Wohlfahrtiimonas (Gammaproteobacteria: Pseudomonadaceae) and vitamin B6 metabolism was detected through collinearity analysis. Furthermore, in vitro tests confirmed that the gut bacterium Wohlfahrtiimonas larvae contributed to the growth of D. antiqua larvae via the independent synthesis of vitamin B6. This study provides a comprehensive view of the gut bacterial diversity in D. antiqua and reveals a functional profile that is strictly specific to the gut microbiota of this species. It has preliminarily revealed the functional differentiation between insect hosts and their symbiotic microorganisms. This study also offers a technical reference for the study of microbial symbiotic functions in other insect-microbe symbioses without host genomic data.

Animals↗

PubMed2024

Longitudinal effects of elexacaftor/tezacaftor/ivacaftor on the oropharyngeal metagenome in adolescents with cystic fibrosis.

BACKGROUND: Triple modulator therapy elexacaftor/tezacaftor/ivacaftor (ETI) improves lung function and impacts upon the respiratory microbiome in people with Cystic fibrosis (pwCF) with advanced lung disease. However, adolescents with cystic fibrosis (CF) are less colonized with bacterial pathogens than adult pwCF but their microbiota already differs from healthy individuals. The aim of this study was to longitudinally analyze the impact of ETI on the respiratory metagenome in adolescents with predominantly mild CF lung disease. METHODS: In this prospective observational study, we included pwCF aged 12-20 years with at least one F508del mutation, who collected oropharyngeal swabs before and after initiation of ETI therapy twice per week to biweekly over three months. We performed whole metagenome shotgun sequencing, followed by host DNA filtering and taxonomic profiling. We used linear and additive mixed effects models adjusted for known confounders and corrected for multiple testing to study longitudinal development of the microbiome. We analyzed bacterial diversity, abundance, and strain-level phylogeny. RESULTS: We analyzed the metagenomic data of 297 swabs of 20 pwCF. Microbiome composition changed after initiation of ETI therapy. We observed a slight diversification of the microbiome over time (Inv Simpson, Coef 0.085, 95 %CI 0.003, 0.17, p = 0.04). Strain-level analysis and clustering showed that strain retention of the most frequent bacterial species is predominant even during ETI therapy. CONCLUSIONS: During three months of ETI therapy, commensal bacteria increased, which may help to prevent overgrowth of bacterial pathogens.

Humans↗

PubMed2026

Microbial partnerships and molecular mechanisms in plant stress physiology for climate-resilient and sustainable farming.

Plant-microbial partnerships and their underlying molecular mechanisms are indispensable, natural drivers of improved nutrient acquisition and stress tolerance in the face of climate-driven environmental challenges. Modern multi-omics tools, when coupled with artificial intelligence and synthetic biology, enable the precise design of targeted bioinoculants and synthetic microbial consortia. Translating these advanced microbiome-based strategies into scalable, field-level agricultural applications provides a sustainable path toward securing global food production while maintaining soil health. Global climate change imposes multifaceted abiotic and biotic stresses on crops, disrupting physiological and molecular processes and threatening agricultural productivity. Plant-associated microbes represent an underexplored yet powerful ally in enhancing crop resilience. This review presents current knowledge of plant-microbe interactions and the molecular mechanisms governing plant stress physiology, with an emphasis on climate-resilient and sustainable farming. Hence, ever-changing environmental cues pose a significant burden on agricultural productivity, and plant-associated microbial communities modulate a cascade of physiological and molecular responses, including production of phytohormones, signaling, regulation of reactive oxygen species homeostasis, and activation of plant immune responses to help plants withstand stress and enhance productivity. Moreover, root exudates, phytohormones, and quorum sensing mediate the central communication networks, facilitating plant-microbe cross talk. Additionally, the advances in OMICs approaches aid in disentangling the molecular underpinnings of these interactions by providing mechanistic insights and potential candidate gene targets for crop improvement and stress resilience. In the post-genomic era, integrating artificial intelligence and big data analysis to optimize microbiome-based strategies for sustainable agriculture is a new frontier for disentangling plant-microbe symbiosis to improve soil health, enhance crop yields, and improve stress tolerance. Thus, by integrating the ecological, physiological, and molecular perspectives, this review highlights the transformative potential of harnessing plant-microbe symbiosis for climate-resilient and sustainable agriculture.

Stress, Physiological↗

PubMed2024

Shotgun metagenomic analysis of saliva microbiome suggests Mogibacterium as a factor associated with chronic bacterial osteomyelitis.

Osteomyelitis of the jaw is a severe inflammatory disorder that affects bones, and it is categorized into two main types: chronic bacterial and nonbacterial osteomyelitis. Although previous studies have investigated the association between these diseases and the oral microbiome, the specific taxa associated with each disease remain unknown. In this study, we conducted shotgun metagenome sequencing (≥10 Gb from ≥66,395,670 reads per sample) of bulk DNA extracted from saliva obtained from patients with chronic bacterial osteomyelitis (N = 5) and chronic nonbacterial osteomyelitis (N = 10). We then compared the taxonomic composition of the metagenome in terms of both taxonomic and sequence abundances with that of healthy controls (N = 5). Taxonomic profiling revealed a statistically significant increase in both the taxonomic and sequence abundance of Mogibacterium in cases of chronic bacterial osteomyelitis; however, such enrichment was not observed in chronic nonbacterial osteomyelitis. We also compared a previously reported core saliva microbiome (59 genera) with our data and found that out of the 74 genera detected in this study, 47 (including Mogibacterium) were not included in the previous meta-analysis. Additionally, we analyzed a core-genome tree of Mogibacterium from chronic bacterial osteomyelitis and healthy control samples along with a reference complete genome and found that Mogibacterium from both groups was indistinguishable at the core-genome and pan-genome levels. Although limited by the small sample size, our study provides novel evidence of a significant increase in Mogibacterium abundance in the chronic bacterial osteomyelitis group. Moreover, our study presents a comparative analysis of the taxonomic and sequence abundances of all genera detected using deep salivary shotgun metagenome data. The distinct enrichment of Mogibacterium suggests its potential as a marker to distinguish between patients with chronic nonbacterial osteomyelitis and chronic bacterial osteomyelitis, particularly at the early stages when differences are unclear.

Humans↗

PubMed2026

Identifying fundamental gaps in functional metagenomics: a step towards unlocking microbiome research potential.

Incomplete functional annotation limits biological interpretation in microbiome studies and their translational potential. Poor annotation arises from multiple causes, with incomplete gene-protein-reaction mapping being one tractable yet under-examined contributor. We address this gap by developing a comprehensive hierarchical framework that systematically integrates gene families in UniRef, proteins in UniProt, and metabolic reactions in MetaCyc and BioCyc through UniProtKB accession, EC number, and Pfam-domain matching. Applied to a human gut metagenome dataset via HUMAnN3, our MetaCyc-based mapping recovers up to 2.3-fold more unique reaction identifiers than the default pipeline and increases reaction prevalence across samples from ≈32% to 52% core reactions, addressing the data sparsity that limits statistical and machine-learning applications in microbiome research. Biological plausibility for the tested functions was supported by positive and negative controls: gut-microbial hormone-metabolism reactions previously linked to this dataset were recovered, while vertebrate-specific hormone-metabolism reactions remained correctly undetected. These gains derive from systematic database integration alone, without predictive algorithms, indicating that a tractable, mapping-related component of functional dark matter and data sparsity in microbiome studies is directly addressable. Because Pfam- and BioCyc-derived mappings trade specificity for coverage, confidence in any individual reaction assignment depends on the supporting evidence tier and source database.

Humans↗

PubMed2024

Metagenomic analysis demonstrates distinct changes in the gut microbiome of Kawasaki diseases children.

BACKGROUND: Kawasaki disease (KD) has been considered as the most common required pediatric cardiovascular diseases among the world. However, the molecular mechanisms of KD were not fully underlined, leading to a confused situation in disease management and providing precious prognosis prediction. The disorders of gut microbiome had been identified among several cardiovascular diseases and inflammation conditions. Therefore, it is urgent to elucidate the characteristics of gut microbiome in KD and demonstrate its potential role in regulating intravenous immunoglobulin (IVIG) resistance and coronary artery injuries. METHODS: A total of 96 KD children and 62 controls were enrolled in the study. One hundred forty fecal samples had been harvested from KD patients, including individuals before or after IVIG treatment, with or without early coronary artery lesions and IVIG resistance. Fecal samples had been collected before and after IVIG administration and stored at -80°C. Then, metagenomic analysis had been done using Illumina NovaSeq 6000 platform. After that, the different strains and functional differences among comparisons were identified. RESULTS: First, significant changes had been observed between KD and their controls. We found that the decrease of Akkermansia muciniphila, Faecalibacterium prausnitzii, Bacteroides uniformis, and Bacteroides ovatus and the increase of pathogenic bacteria Finegoldia magna, Abiotrophia defectiva, and Anaerococcus prevotii perhaps closely related to the incidence of KD. Then, metagenomic and responding functional analysis demonstrated that short-chain fatty acid pathways and related strains were associated with different outcomes of therapeutic efficacies. Among them, the reduction of Bacteroides thetaiotaomicron, the enrichment of Enterococcus faecalis and antibiotic resistance genes had been found to be involved in IVIG resistance of KD. Moreover, our data also revealed several potential pathogenetic microbiome of that KD patients with coronary artery lesions. CONCLUSION: These results strongly proved that distinct changes in the gut microbiome of KD and the dysfunction of gut microbiomes should be responsible for the pathogenesis of KD and significantly impact the prognosis of KD.

Humans↗

PubMed2024

metaExpertPro: A Computational Workflow for Metaproteomics Spectral Library Construction and Data-Independent Acquisition Mass Spectrometry Data Analysis.

Analysis of large-scale data-independent acquisition mass spectrometry metaproteomics data remains a computational challenge. Here, we present a computational pipeline called metaExpertPro for metaproteomics data analysis. This pipeline encompasses spectral library generation using data-dependent acquisition MS, protein identification and quantification using data-independent acquisition mass spectrometry, functional and taxonomic annotation, as well as quantitative matrix generation for both microbiota and hosts. By integrating FragPipe and DIA-NN, metaExpertPro offers compatibility with both Orbitrap and timsTOF MS instruments. To evaluate the depth and accuracy of identification and quantification, we conducted extensive assessments using human fecal samples and benchmark tests. Performance tests conducted on human fecal samples indicated that metaExpertPro quantified an average of 45,000 peptides in a 60-min diaPASEF injection. Notably, metaExpertPro outperformed three existing software tools by characterizing a higher number of peptides and proteins. Importantly, metaExpertPro maintained a low factual false discovery rate of approximately 5% for protein groups across four benchmark tests. Applying a filter of five peptides per genus, metaExpertPro achieved relatively high accuracy (F-score = 0.67-0.90) in genus diversity and showed a high correlation (rSpearman = 0.73-0.82) between the measured and true genus relative abundance in benchmark tests. Additionally, the quantitative results at the protein, taxonomy, and function levels exhibited high reproducibility and consistency across the commonly adopted public human gut microbial protein databases IGC and UHGP. In a metaproteomic analysis of dyslipidemia patients, metaExpertPro revealed characteristic alterations in microbial functions and potential interactions between the microbiota and the host.

Proteomics↗

PubMed2026

Metagenomic polymorphic toxin effector and immunity profiling predicts microbiome development and disease-related dysbiosis.

Bacteria use antagonistic interbacterial weapons, such as polymorphic toxin secretion systems (TSS), to compete for niches in the human gut microbiome. We hypothesized that TSS influence gut microbiome development and disease-related dysbiosis. We developed a bioinformatic marker gene approach (PolyProf) to quantify TSS including ~200 effector and immunity genes and applied it to ~15,000 publicly available human metagenomes. PolyProf alpha and beta diversity readily distinguished 12 different human disease states and enabled the construction of highly accurate linear regression classifier machine learning models. Elastic net machine learning models integrating bacterial taxonomy with PolyProf had strong predictive value for 12 disease states, outperforming models utilizing taxonomy alone. During microbiome development in the first year of life, PolyProf alpha diversity increases, and beta diversity becomes increasingly like the maternal microbiome, influenced by vertical transfer, delivery mode, and breastfeeding. PolyProf is related to strain sharing among adults through social interactions. In summary, TSS genes strongly correlate with microbiome development and interpersonal strain sharing, suggesting roles for interbacterial antagonism. Since PolyProf distinguishes diverse adult disease statuses, these dynamics may contribute to non-genetic inheritance.IMPORTANCEPrevious research has demonstrated that bacteria compete within the gut microbiome using toxin secretion systems (TSS). How TSS contribute to human microbiome development and the microbiome alterations observed in human diseases is not known. This study develops a new bioinformatic tool for profiling TSS-related genes in metagenomic data. Application of this approach to large-scale human fecal metagenomic data demonstrates the dynamic association of TSS during microbiome development, including the exchange of strains among social contacts. TSS gene abundance patterns are highly predictive of 12 disease states. This study advances the field by enabling TSS profiling in metagenomes and by identifying disease and microbiome development biomarkers that provide hypotheses for future mechanistic studies and may be useful for disease diagnosis.

Dysbiosis↗

PubMed2026

Integrating multi-omics technologies to decipher microbiome functions.

Multi-omics approaches have revolutionized our understanding of microbial communities by enabling simultaneous interrogation of genomic, transcriptomic, proteomic, and metabolomic data. The systematic integration and analysis of these deep datasets help decipher the functional roles of microbiomes, providing critical insights into microbial activities, interactions, and dynamics across diverse environments. Biological complexity makes multi-omics analysis of a single, isolated organism demanding but highly informative, yet this complexity increases further when samples comprise hundreds to thousands of individual species. As microbiome research continues to expand into clinical, environmental, and engineered systems, standardized workflows, benchmarked datasets, and community-driven initiatives are essential to ensure reproducibility, standardization and interpretability. Establishing and disseminating best practices for experimental design, data processing, and integrative analyses will be critical for maximizing comparability and scientific rigor across studies. This perspective highlights recent advances in multi-omics microbiome research, outlines key obstacles in data integration and metadata harmonization, and proposes a collaborative roadmap for scalable, FAIR-compliant multi-omics investigations and potentially disruptive Artificial Intelligence (AI) advances comparable to those of AlphaFold in the field of microbiome science.

Multiomics↗

PubMed2025

Unveiling Potato Cultivars With Microbiome Interactive Traits for Sustainable Agricultural Production.

Root traits significantly shape rhizosphere microbiomes, yet their interaction with microbes is often overlooked in plant breeding programs. Here, we propose that selecting modern cultivars based on microbiome interactive trait (MIT), such as root biomass, exudate patterns and the rhizosphere microbiome, can enhance agricultural sustainability by interacting effectively with soil microbiomes, which in turn, promotes plant growth and resistance to stress, thereby reducing reliance on synthetic crop protectants. Through a stepwise selection process (in silico and in vitro) that started with approximately 1000 potato genotypes, we chose 51 potato cultivars based on known phenotypical properties and distinct root exudate patterns. We conducted a greenhouse experiment to evaluate their capacity to interact with the soil microbiome and to assess their MIT scores. Our findings revealed that cultivars significantly influence plant growth, metabolite profiles, and rhizosphere fungal community composition. Moreover, we observed a positive correlation between microbial community diversity and root biomass. Additionally, leaf metabolites were correlated with rhizosphere bacterial composition, supporting the plant holobiont framework. Utilising z-scores, we aggregated all data related to plant growth, metabolomes, and microbiomes, creating a classification of 51 cultivars based on a gradient of MIT scores. By examining the distribution of low, intermediate, and high MIT, we identified a group of 11 potato cultivars suitable for further studies to assess their resilience and productivity under low-input production systems. This study provides an in-depth correlation between microbiome and several plant traits across 51 cultivars, offering tools to facilitate and expedite the incorporation of microbiome traits into breeding goals to support sustainable agriculture.

Solanum tuberosum↗