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PubMed2025

Exploring Potential Causality and Molecular Mechanisms between Heart Failure and Renal Failure: Insights from Mendelian Randomization Studies, the MIMIC-IV Database and the Gene Expression Omnibus Database.

UNLABELLED: Introduction: Heart failure (HF) and renal failure (RF) frequently coexist as cardiorenal syndrome, but their underlying causal mechanisms remain poorly defined. METHODS: This study applied Mendelian randomization (MR) using genome-wide association study (GWAS) datasets to investigate the causal effect of HF on RF. The inverse variance weighted method assessed causality, and summary-data-based MR (SMR) was used to identify therapeutic targets. Additional analyses included 211 gut microbiota traits and 1,400 serum metabolites. Validation was performed using the MIMIC-IV database. Transcriptomic data were analyzed to identify differentially expressed genes (DEGs) and key transcription factors (TFs). RESULTS: This study found that HF significantly increases the risk of RF (OR = 1.54, 95% CI: 1.07-2.23, p = 0.020). SMR analysis identified SURF1 and MAP3K11 as potential therapeutic targets for HF and RF. One gut microbiota genus and one serum metabolite showed causal associations with both diseases. MIMIC-IV data supported the HF-RF association (OR = 2.94, 95% CI: 2.81-3.07, p < 0.001). A total of 11 overlapping DEGs were enriched in the MAPK cascade, with RELA identified as a key TF. CONCLUSION: This study provides genetic and molecular evidence supporting a causal role of HF in RF, highlighting microbial, metabolic, and immune mechanisms as potential therapeutic targets. .

Humans↗

PubMed2025

SetBERT: the deep learning platform for contextualized embeddings and explainable predictions from high-throughput sequencing.

MOTIVATION: High-throughput sequencing (HTS) is a modern sequencing technology used to profile microbiomes by sequencing thousands of short genomic fragments from the microorganisms within a given sample. This technology presents a unique opportunity for artificial intelligence to comprehend the underlying functional relationships of microbial communities. However, due to the unstructured nature of HTS data, nearly all computational models are limited to processing DNA sequences individually. This limitation causes them to miss out on key interactions between microorganisms, significantly hindering our understanding of how these interactions influence the microbial communities as a whole. Furthermore, most computational methods rely on post-processing of samples which could inadvertently introduce unintentional protocol-specific bias. RESULTS: Addressing these concerns, we present SetBERT, a robust pre-training methodology for creating generalized deep learning models for processing HTS data to produce contextualized embeddings and be fine-tuned for downstream tasks with explainable predictions. By leveraging sequence interactions, we show that SetBERT significantly outperforms other models in taxonomic classification with genus-level classification accuracy of 95%. Furthermore, we demonstrate that SetBERT is able to accurately explain its predictions autonomously by confirming the biological-relevance of taxa identified by the model. AVAILABILITY AND IMPLEMENTATION: All source code is available at https://github.com/DLii-Research/setbert. SetBERT may be used through the q2-deepdna QIIME 2 plugin whose source code is available at https://github.com/DLii-Research/q2-deepdna.

Deep Learning↗

PubMed2025

Microbiology Galaxy Lab: The first community-driven gateway for reproducible and FAIR analysis of microbial data.

The explosion of microbial omics data has outpaced the ability of many researchers to analyze it, with complex tools and limited computational resources creating barriers to discovery. To address this gap, we present the Microbiology Galaxy Lab: a free, globally accessible, community-supported platform that combines state-of-the-art analytical power with user-friendly accessibility. Supported by the Galaxy and global microbiology communities, this platform integrates over 315 tool suites and 115 curated workflows, enabling comprehensive metabarcoding, (meta)genomic, (meta)transcriptomic, and (meta)proteomic data analysis within a FAIR-aligned environment. It also supports research in the health and infectious disease sectors, as well as in environmental microbiology. The platform's utility is exemplified through various use cases, including antimicrobial resistance tracking, biomarker prediction, microbiome classification, and functional annotation of key microbes. Built on reproducibility and community engagement, it supports creation, sharing, and updating of best-practice workflows. Over 35 tutorials and learning paths empower scientists, fostering an ecosystem that keeps resources at the forefront of microbial science. The Microbiology Galaxy Lab enables collective analysis, democratising research, thereby accelerating discovery across the global microbiology community (microbiology.usegalaxy.org, .eu, .org.au, .fr).

Journal Article↗

PubMed2025

Metagenomic analyses reveal E. coli-derived siderophores as potential signatures for breast cancer.

BACKGROUND: Breast cancer remains a leading cause of cancer-related mortality in women. Recent evidence implicates the gut microbiome and metabolites in breast cancer pathogenesis. This study explores associations between gut microbial species, their predicted metabolites, and breast cancer to uncover potential mechanistic insights. METHODS: Comprehensive metagenomic analyses were conducted on the gut microbiome of pre- and postmenopausal breast cancer patients, where microbial species were profiled through AMPHORA2 and metabolites were predicted through antiSMASH. Multivariate association analysis was used to identify significant associations between specific microbial species, predicted metabolites, and breast cancer status. A custom ensemble machine learning classifier was developed to classify pre- and postmenopausal breast cancer cases and controls based on microbial and predicted metabolite features. Additionally, a synthetic microbiome dataset was generated through MIDASim to validate the reproducibility of the ML results. Using our results, we explored the underlying dynamics of identified taxa and metabolite in breast cancer through literature and statistical support. RESULTS: Our analysis identified 471 microbial species and predicted 40 key metabolites in the metagenomic data. Multivariate analysis identified significant positive associations (p-value < 0.05) of E. coli, siderophore, and thiopeptide with breast cancer. The custom ensemble model achieved accuracy and AUC as high as 78% and 90%, respectively, in classifying pre- and postmenopausal cases and controls. The high-ranking features i.e., E. coli, siderophore, and thiopeptide were consistent with the results of the multivariate association analysis, thereby substantiating their biological significance. Using these findings, we propose a mechanistic model in which E. coli secretes siderophores under iron-limited conditions in breast cancer patients, for iron sequestration from the host, which can potentially promote angiogenesis and tumor progression. CONCLUSION: Our findings suggest that microbial iron acquisition mechanisms may play a critical role in breast cancer pathophysiology. Functional validation of these mechanisms is needed to assess therapeutic potential. This study highlights gut microbiota and their metabolites as promising targets for breast cancer research and intervention.

Breast Neoplasms↗

PubMed2026

Malaria driven mechanisms shaping cancer risk and aggressiveness in African populations.

Malaria and cancer represent intersecting public health challenges in sub-Saharan Africa, where malaria remains endemic and cancer incidence is rapidly increasing. Emerging evidence indicates that chronic or recurrent malaria infection may influence carcinogenesis and tumour aggressiveness through complex biological mechanisms. This narrative review critically synthesizes data from PubMed, Scopus, and Web of Science to elucidate the mechanistic intersections between malaria and cancer risk, progression, and therapeutic response. The review highlights five principal axes linking malaria to oncogenesis: malaria-induced oxidative stress and chronic inflammation driving genomic instability; gut microbiome dysbiosis altering systemic immunity and tumour microenvironment; exploitation of shared molecular targets such as the endothelial protein C receptor (EPCR) and oncofetal chondroitin sulfate by Plasmodium parasites and cancer cells; cooperative interactions between malaria and oncogenic viruses like Epstein-Barr virus in lymphomagenesis; and malaria-associated vitamin D deficiency impairing immune surveillance. Furthermore, pharmacological evidence reveals that several antimalarial agents, including artemisinin derivatives, chloroquine, and quinacrine, possess anticancer properties, while some anticancer drugs exhibit antimalarial activity, underscoring opportunities for dual-action or repurposed therapeutics. The convergence of malaria and cancer biology underscores the urgent need for integrative, multidisciplinary research spanning molecular epidemiology, immunology, and pharmacology. Unveiling these mechanisms may unveil novel biomarkers and therapeutic targets, guiding context-specific interventions to reduce the disproportionate cancer burden in malaria-endemic African populations.

Humans↗

PubMed2025

Cardiovascular Complications Are Increased in Inflammatory Bowel Disease: A Path Toward Achievement of a Personalized Risk Estimation.

Background/Objectives: The global burden of inflammatory bowel diseases (IBDs) continues to rise, with up to 50% of patients experiencing extraintestinal manifestations. Cardiovascular diseases (CVDs) are of particular concern, ranking as the second leading cause of mortality in this population. Despite a comparatively lower prevalence of traditional cardiovascular (CV) risk factors, the persistent inflammatory milieu and immune dysregulation inherent to IBD may contribute to heightened CVD risk. In this study, following a review of the current literature, an ongoing prospective trial designed to clarify CV risk profiles in IBD patients is detailed. Methods: A cohort of patients with IBD is being enrolled for comprehensive baseline evaluation of CV risk factors, lifestyle metrics, and disease characteristics. The incidence of major adverse cardiovascular events (MACEs) will be tracked and contrasted with a gender- and age-matched non-IBD cohort over a 2-year follow-up period. In cases of MACE occurrence, a multi-omics analysis-including genomic, proteomic, transcriptomic, and microbiome profiling-will be performed, along with a parallel evaluation in matched IBD controls without MACE. An artificial intelligence (AI) framework will support the analysis of this complex dataset. Results: To date, over 150 patients with IBD have been enrolled, and detailed phenotypic data and biological samples have been collected. Conclusions: We aim to introduce an IBD-specific correction factor for existing CV risk scores upon study completion. This is particularly relevant for individuals under 40 years of age, who are often inadequately assessed by current risk stratification models.

Crohn’s disease↗

PubMed2025

Microbiota and kidney disease: the road ahead.

More than 850 million individuals worldwide, accounting for 10-15% of the adult population, are estimated to have chronic kidney disease. Each of these individuals is host to tens of trillions of microorganisms that are collectively referred to as microbiota - a dynamic ecosystem that both influences host health and is itself influenced by changes in the host. Available evidence supports the existence of functional connections between resident microorganisms and kidney health that are altered in the context of specific kidney diseases, including acute kidney injury, chronic kidney disease and renal stone disease. Moreover, promising data from preclinical studies suggest that targeting of gut microbial pathways may provide new therapeutic opportunities for the treatment of kidney disease. This Roadmap describes current understanding of the mechanisms by which microorganisms regulate host organ function, the effects of kidney disease on the gut microbiome, and how these insights may contribute to the development of microbe-targeted therapeutics. We highlight key knowledge gaps that remain to be addressed and strategies for addressing these, outlining both the promise and the potential pitfalls of leveraging our understanding of the gut microbiota to better understand and treat kidney disease.

Humans↗

PubMed2025

Metagenomic and Transcriptomic Datasets of Plateau Brown Frogs (Rana kukunoris) from the Helan Mountains.

Global climate change has become a primary driving factor behind the biodiversity crisis in amphibians, making it crucial to understand how climate change affects species and their potential responses. The plateau brown frog (Rana kukunoris) is often regarded as an ideal ecological indicator species, yet research on its environmental adaptation mechanisms based on transcriptomic and microbiomic studies remains limited. Therefore, this study investigates the adaptation strategies of the plateau brown frog to environmental changes, providing extensive transcriptomic and the first comprehensive metagenomic dataset from two distinctly different environmental regions (eastern and western slopes of the Helan Mountains). We gathered transcriptomic data from three tissues (blood, liver, and muscle), resulting in 294,962 unigenes and 570,192 transcripts. Metagenomic sequencing identified major bacterial groups, including Firmicutes, Proteobacteria, Bacteroidetes, Spirochetes, and Actinobacteria. In summary, the results of this study can be used to further explore the associations among microbiota, host, and environment, which are crucial for comprehending the mechanisms of environmental adaptation in this species and contributing to the conservation of amphibian biodiversity.

Animals↗

PubMed2025

Effects of commonly used antibiotics on children's developing gut microbiomes and resistomes in peri-urban Lima, Peru.

BACKGROUND: The effects of antibiotic use on children's gut microbiomes and resistomes are not well characterized in middle-income countries, where antibiotic consumption is exceptionally common. OBJECTIVES: We characterized the effects of antibiotics commonly used by Peruvian children (i.e. amoxicillin, azithromycin, cefalexin, trimethoprim/sulfamethoxazole) on the α-diversity, β-diversity and abundance of gut genera and antibiotic resistance genes (ARGs) from 3 to 16 months. METHODS: This study included 54 children from a prospective cohort of enteric infections in peri-urban Lima, 2016-19. Stools collected at 3, 6, 7, 9, 12 and 16 months underwent DNA extraction and short-read metagenomic sequencing. We profiled the taxonomy of stool metagenomes and assessed ARG abundance by aligning reads to the ResFinder database. We used daily surveillance data (40 662 observations) to tabulate the number of antibiotic courses consumed in the 30 days prior to stool sampling. Using linear mixed models, we examined associations of recent antibiotic use with richness, diversity and abundance of gut genera and ARGs over time. RESULTS: Each additional recent antibiotic course decreased Bifidobacterium and Dialister abundance and increased Veillonella abundance, although gut richness and diversity were not affected. Recent use of amoxicillin, azithromycin, cefalexin or trimethoprim/sulfamethoxazole, specifically, did not impact gut microbiome measures. Amoxicillin, azithromycin and trimethoprim/sulfamethoxazole significantly enriched multiple ARGs and amoxicillin use significantly increased total ARGs. CONCLUSIONS: Common antibiotics like amoxicillin and azithromycin appear to be key drivers of the paediatric gut resistome. Resistome perturbations appeared to be stronger, or persist for longer, than gut microbiome effects in this middle-income country setting.

Humans↗

PubMed2026

Bridging Organ-on-a-Chip and Omics: A Multi-Dimensional Frontier in Biomedical Research.

Organ-on-a-Chip (OOC) technology offers a powerful platform for replicating human tissue-specific microenvironments, thereby narrowing the translational gap between conventional biomedical models and actual human physiology. Concurrently, omics technologies deliver comprehensive molecular-level insights into biological systems. This review highlights the transformative potential of integrating OOC platforms with high-throughput omics methodologies. We systematically examine the classification, structural configurations, and engineering principles underlying OOC systems, alongside the defining attributes of key omics domains-genomics, transcriptomics, proteomics, and metabolomics. The convergence of dynamic OOC models with advanced omics technologies enables high-resolution, multi-dimensional analyses across numerous biomedical applications, including drug metabolism, disease mechanisms, environmental toxicity assessments, and host-microbiome interactions. This interdisciplinary integration is driving a paradigm shift in precision and translational medicine. However, several challenges remain to be addressed, such as the development of whole-organ mimetics, adaptation of sample collection techniques, and real-time artificial intelligence-based integration of biosensor data with multi-omics datasets. Addressing these hurdles will be vital for unlocking the full potential of this technological synergy in biomedical science.

Multiomics↗

PubMed2026

MHASS: Microbiome HiFi Amplicon Sequencing Simulator.

SUMMARY: Microbiome HiFi Amplicon Sequence Simulator (MHASS) creates realistic synthetic PacBio HiFi amplicon sequencing datasets for microbiome studies, by integrating genome-aware abundance modeling, realistic dual-barcoding strategies, and empirically derived pass-number distributions from actual sequencing runs. MHASS generates datasets tailored for rigorous benchmarking and validation of long-read microbiome analysis workflows, including ASV clustering and taxonomic assignment. AVAILABILITY AND IMPLEMENTATION: Implemented in Python with automated dependency management, the source code for MHASS is freely available at https://github.com/rhowardstone/MHASS along with installation instructions. Our code is also published on Zenodo at https://doi.org/10.5281/zenodo.17486364. The data underlying this article are available on GitHub at https://github.com/rhowardstone/MHASS_evaluation/.

Software↗

PubMed2026

Inclusion of Multi-Omic Biomarkers Improves Prediction Accuracy of Response, Relapse, and Overall Survival in Acute Myeloid Leukemia Patients Receiving High-Intensity Induction Chemotherapy.

BACKGROUND: Despite advancements in genetic markers for acute myeloid leukemia (AML) risk stratification, outcome prediction remains challenging due to disease heterogeneity and dynamic genetic changes, highlighting the need for reliable biomarkers to improve AML treatment strategies and patient outcomes. To refine outcome predictions, we investigated the use of microbial-derived biomarkers to predict composite complete remission (CRc), relapse, and survival for patients on high- and low-intensity regimens, and to integrate those variables into the widely clinically utilized European Leukemia Network (ELN-2022) genetic risk classification model for high-intensity-treated patients. METHODS: We first developed machine learning models that integrate baseline fecal metabolomics, 16S rRNA-based stool microbiome features, and clinical metadata (sex, antibiotic administration, AML somatic mutations, and cytogenetics) from two cohorts of AML patients (n = 83) undergoing remission induction chemotherapy. Univariate tests and sparse canonical correlation analysis were employed for variable selection and to explore fecal metabolite-microbe relationships. A robust machine learning approach using XGBoost was employed, with 100 stratified data splits (80% training, 20% testing) and coarse-to-fine hyperparameter optimization. Variable importance was aggregated across all models to select key predictors. RESULTS: For high-intensity-treated patients, XGBoost models achieved aggregated AUROC scores of 0.719, 0.729, and 0.65 for CRc, relapse, and overall survival, respectively. For low-intensity-treated patients, these models achieved aggregate AUROC scores of 0.945, 0.724, and 0.768 for these same outcomes, respectively. Integrating the biomarkers identified in the high-intensity machine-learning models with the current ELN-2022 AML risk stratification system effectively stratified patients into risk categories, which obtained higher concordance indices and likelihood ratios, demonstrating improved prognostic accuracy for each outcome compared to ELN-2022 alone. CONCLUSIONS: The inclusion of microbial-derived biomarkers serves as a robust prognostic tool to improve outcome prediction in AML patients, highlighting the potential of its integration into AML risk assessment and paving the way for personalized treatment strategies and improved patient outcomes.

Humans↗

PubMed2026

Unveiling the Molecular Secrets of Seaweeds: A Comprehensive Review of Bioinformatics Applications in Algal Research.

Recent advances in high-throughput sequencing, bioinformatics, and multi-omics technologies have transformed seaweed research by overcoming long-standing challenges associated with complex genomes, diverse life cycles, and limited genomic resources. This review provides a comprehensive overview of bioinformatics approaches used to investigate seaweed genomics, transcriptomics, proteomics, metabolomics, microbiomes, and functional genomics, with emphasis on the computational tools and databases that support these analyses. Applications of bioinformatics in phylogenetics, drug discovery, microbiome characterization, and the development of biofuels, nutraceuticals, pharmaceuticals, and sustainable agriculture are also discussed. Particular attention is given to emerging strategies involving multi-omics integration, genome editing, artificial intelligence, machine learning, and synthetic biology that are reshaping seaweed research. The review further examines current challenges, including incomplete genomic resources, data standardization, and the need for experimental validation of computational predictions. Collectively, these advances highlight the growing role of bioinformatics in enabling systems-level understanding of seaweed biology and accelerating their translation into sustainable biotechnological and marine bioeconomy applications.

macroalgal genomics↗

PubMed2026

Ancient DNA as a temporal lens: reconstructing evolution, migration, and disease dynamics.

Ancient DNA (aDNA) has transformed evolutionary biology and anthropology by providing direct, chronologically validated genetic evidence over millennia. This review synthesizes significant findings from the paleogenomic era (2010-2025), demonstrating how ancient DNA has resolved persistent debates across four interconnected themes: (i) human migration and admixture, revealing complex population transitions from archaic hominins to Holocene expansions; (ii) adaptation, tracking allele frequency changes during domestication and selection; (iii) pathogen history, clarifying the origins of pandemics and the evolution of microbiomes; and (iv) ecosystem dynamics, identifying extinction causes through sedimentary DNA and conservation genomics. We contend that scientific rigor and ethical stewardship are crucial for accurate conclusions, given ancient DNA study requires the destructive collection of culturally significant remains. This review argues that continued advancement will depend on the integration of genomic data with archaeological, isotopic, and proteomic evidence, and highlights the necessity for equitable involvement with descendant communities. By conceptualizing the past as a continuum of dynamic processes rather than static events, ancient DNA provides a revised historical narrative and insights relevant to contemporary concerns in conservation, health, and social justice.

Evolution↗

PubMed2026

MetagenomicKG: a knowledge graph for metagenomic applications.

MOTIVATION: The sheer volume and variety of genomic content within microbial communities makes metagenomics a field rich in biomedical knowledge. To traverse these complex communities and their vast unknowns, metagenomic studies often depend on distinct reference databases, such as the Genome Taxonomy Database (GTDB), the Kyoto Encyclopedia of Genes and Genomes (KEGG), and the Bacterial and Viral Bioinformatics Resource Center (BV-BRC), for various analytical purposes. These databases are crucial for the genetic and functional annotation of microbial communities. Nevertheless, the inconsistent nomenclature or identifiers of these databases present challenges for effective integration, representation, and utilization. Knowledge graphs (KGs) offer an appropriate solution by organizing biological entities from different databases to standardized identifiers, allowing their interrelations to be captured into a cohesive network regardless of the naming conventions used in each source. The graph structure not only facilitates the unveiling of hidden patterns but also enriches our biological understanding with deeper insights. Despite KGs having shown potential in various biomedical fields, their application in metagenomics remains underexplored. RESULTS: We present MetagenomicKG, a novel knowledge graph specifically tailored for metagenomic analysis. MetagenomicKG integrates taxonomic, functional, and pathogenesis-related information on the human microbiome sourced from various databases, and further connects these with existing biomedical KGs to expand the biological network. Through various case studies involving the human microbiome, we demonstrate its utility in enabling hypothesis generation regarding the relationships between microbes and diseases, generating sample-specific graph embeddings, and providing robust pathogen prediction. CODE AVAILABILITY: The source code and technical details for constructing the MetagenomicKG and reproducing all analyses are available on GitHub at https://github.com/KoslickiLab/MetagenomicKG. The data used in this manuscript, including the pre-built files and use case input data, are archived on Zenodo with DOI: 10.5281/zenodo.17546861.

Metagenomics↗

PubMed2026

The current and future perspective of ChickenGTEx project and its applications in precision breeding.

The Chicken Genotype-Tissue Expression (ChickenGTEx) project was established to systematically characterize the regulatory landscape of the chicken genome and to accelerate the translation of functional genomics into precision breeding. By integrating whole-genome sequencing with multi-tissue transcriptomic profiling, ChickenGTEx provides a comprehensive atlas of gene expression regulation across diverse tissues and physiological systems. Current findings demonstrate that complex production traits are governed by coordinated regulatory networks rather than isolated loci, with substantial contributions from tissue-specific gene expression, structural variation, and genotype-by-sex interactions. Sex-dependent regulatory effects further refine the genetic architecture of metabolic, immune, and reproductive traits, highlighting the importance of incorporating sex as a biological variable in genomic analyses. Application of integrative omics frameworks within elite layer populations has revealed multilayer regulatory mechanisms underlying extended laying performance, feed efficiency, metabolic health, and eggshell quality. By partitioning phenotypic variance into genetic, regulatory, and host-microbiome components, these approaches move beyond association-based mapping toward causal inference and biological interpretation. Importantly, validated regulatory loci identified through ChickenGTEx and related analyses provide actionable markers for genomic selection and rational targets for precision genome modification. Looking forward, continued expansion of regulatory atlases, incorporation of single-cell and longitudinal data in diverse environmental conditions, and integration of functional annotation into breeding pipelines will further enhance prediction accuracy and sustainable genetic improvement. The ChickenGTEx project thus represents a foundational platform bridging functional genomics and practical poultry breeding.

Animals↗

PubMed2026

Comprehensive evaluation of new sequencer T20 and well-established T7 with 507 human samples.

The DNBSEQ-T20×2 (T20) sequencer, developed by MGI Tech, enables cost-effective human whole-genome sequencing (WGS) at 30× coverage for less than $100 per genome. Here, we evaluate the sequencing performance and data quality of the T20 platform by benchmarking it against the established DNBSEQ-T7 (T7) sequencer using 507 samples derived from blood (N = 75), stool (N = 242), and saliva (N = 190). The T20 exhibited lower sequencing quality metrics compared with the T7, with Q20 scores of 95.76%-95.83% and Q30 scores of 87.25%-87.40%, compared with 97.81%-97.93% and 93.26%-93.60%, respectively, for T7 data. Quality differences were more evident toward the end of reads, and PCR-free libraries sequenced on the T20 showed similar reductions in quality scores. The median empirical base error rate estimated from 102 ZymoBIOMICS samples was 0.33%. The T20 demonstrated comparable coverage uniformity to the T7 and showed high concordance in microbiome composition analysis, with a median Bray-Curtis dissimilarity of 0.02. Variant calling performance was highly consistent between the two platforms. Among variants with non-missing genotype calls on both platforms, 94.92% of SNPs and 87.20% of InDels showed concordant genotypes between T20 and T7. Overall, the T20 delivers reliable sequencing accuracy and reproducibility for large-scale genomic and microbiome studies, providing a cost-effective alternative for high-throughput sequencing applications.

Metagenomics↗

PubMed2025

Metabolism and gene expression models for the microbiome reveal how diet and metabolic dysbiosis impact disease.

The gut microbiome plays a critical role in human health, spurring extensive research using multi-omic technologies. Although these tools offer valuable insights, they often fall short in capturing the complexity of microbial interactions that associate with disease onset, progression, and treatment. Thus, integration of multi-omics datasets with metabolic models is needed to predict associations between microbial activity and disease. Here, we automated the reconstruction of 495 metabolic and gene expression models (ME-models), overcoming the main limitation preventing the wide use of this approach. We integrated them with multi-omics data from patients with inflammatory bowel disease (IBD), identifying taxa associated with variations in amino acids, short-chain fatty acids, and pH in the gut of IBD patients. In general, this approach provides testable hypotheses of the metabolic activity of the gut microbiota, and the automated pipeline opens the opportunity to study microbial interactions in other biologically relevant settings using ME-models.

Humans↗