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PubMed2026

Cross-tissue multi-omics integration highlights BPHL and mitochondrial targets in Alzheimer's disease.

BACKGROUND: Mitochondrial dysfunction is a hallmark of Alzheimer's disease (AD), yet specific molecular targets remain to be fully characterized. METHODS: A summary-data-based Mendelian randomization (SMR) framework integrated AD genome-wide association study (GWAS) statistics (39,918 cases) with blood DNA methylation quantitative trait loci (mQTL), gene expression (eQTL), and protein (pQTL) data for 1136 mitochondria-related genes. Associations were assessed using Bayesian colocalization and HEIDI testing. Tissue relevance was evaluated in four brain regions (hippocampus, amygdala, cortex, frontal cortex) using GTEx and external transcriptomic datasets. RESULTS: Screening identified eight candidates supported across blood mQTL and eQTL layers. Stepwise central nervous system (CNS) evaluation singled out biphenyl hydrolase-like (BPHL) as the consistent candidate. Higher genetically predicted BPHL expression was associated with reduced AD risk across the hippocampus (OR=0.920, 95% CI 0.873-0.970), amygdala (OR=0.925, 95%CI 0.880-0.973), cortex (OR=0.943, 95% CI 0.908-0.978), and frontal cortex (OR=0.938, 95%CI 0.901-0.976). These findings aligned with protein-protein interactions connecting BPHL to respiratory complexes and lower BPHL expression in independent AD brains. Functional enrichment converged on oxidative phosphorylation pathways. CONCLUSIONS: By integrating multi-omics data with tissue-specific validation, this study nominates BPHL as a consistent protective candidate in the brain. These findings provide genetic support for mitochondrial molecular perturbations in AD, offering insights for future validation.

Alzheimer Disease↗

PubMed2026

Genomic and proteogenomic insights into Spontaneous Coronary Artery Dissection (SCAD): A systematic review of emerging multi-omic evidence.

BACKGROUND: Spontaneous coronary artery dissection (SCAD) is a major cause of myocardial infarction in young women without traditional cardiovascular risk factors (Hayes et al., 2018; Adlam et al., 2018 [1, 2]). Despite growing awareness, its biological underpinnings remain incompletely understood, and clinical management is largely based on observational evidence rather than mechanistic insight (Saw et al., 2014; Lettieri et al., 2015; Steg et al., 2024 [3-5]). OBJECTIVES: To systematically integrate genomic, epitranscriptomic, proteomic, and metabolomic data in order to characterize the multi-omic architecture of SCAD and identify potential biomarkers and therapeutic targets. METHODS: A systematic review was conducted in accordance with the PRISMA 2020 statement (Arbelo et al., 2023 [6]). PubMed/MEDLINE was searched for original studies investigating genomic and multi-omic features of SCAD. Data were extracted on study design, patient characteristics, identified variants, circulating biomarkers, and implicated biological pathways. Functional enrichment analysis was performed using the DAVID bioinformatics resource (Page et al., 2021 [7]). RESULTS: A total of 16 studies were included. Genome-wide association studies consistently identified susceptibility loci related to arterial structure and extracellular matrix integrity, including ADAMTSL4, PHACTR1/EDN1, LRP1, and FBN1 (Huang et al., 2009; Saw et al., 2020; Turley et al., 2020 [8-10]). Rare variant analyses further supported the role of genes involved in extracellular matrix remodeling and vascular smooth muscle cell function, including COL3A1, COL4A1/2, SMAD3, and TLN1 (Adlam et al., 2023; Turley et al., 2021, 2019; Carss et al., 2020; Zekavat et al., 2022; Wang et al., 2022 [11-16]), while ancestry-specific signals such as TSR1 variants were observed in distinct populations (Turley et al., 2023 [17]). Proteogenomic approaches linked genetic susceptibility loci to circulating proteins involved in matrix remodeling and inflammation, including cathepsin B and ECM1 (Maioli et al., 2010 [18]). Epitranscriptomic analyses identified differential microRNA expression profiles associated with vascular injury and repair pathways (Sun et al., 2019 [19]). CONCLUSIONS: SCAD is characterized by a complex, multi-layered biological architecture involving genetic susceptibility, extracellular matrix dysregulation, and vascular signaling pathways. Integration of multi-omic data provides novel insights into disease mechanisms and highlights potential biomarkers and targets for precision medicine approaches in SCAD.

Animals↗

PubMed2026

An integrated proteomics and transcriptomics analysis highlights concordance between protein turnover and carbohydrate transport and metabolism as key functional categories during the growth of Trichophyton rubrum.

Dermatophytes are a class of keratinophilic skin fungi that invade host skin, hair, and nails to acquire nutrients. An integrated multi-omics approach utilizing liquid chromatography-tandem mass spectrometry and RNA-seq after growth in a protein-rich soy medium was employed to capture the major subset of secreted protein families of Trichophyton rubrum. The secretome consisted mainly of proteases and cell wall-degrading enzymes, with subtilisins (Sub6 and Sub7), metallopeptidase (LAP2), and chitinase having the most abundant peptides. Transcriptional profiling indicated fungal adaptation in protein-rich media to process the protein nutrients through modulation of metabolism and general cellular function pathways. Correlation analysis between proteomics and transcriptomics data using functional KOG categories shows high concordance of KOG categories O (posttranslational modification, protein turnover, and chaperones), P (inorganic ion transport and metabolism), and G (carbohydrate transport and metabolism), as per cosine similarity analysis.IMPORTANCEDermatophytes are keratinophilic skin fungal pathogens that invade host skin, hair, and nails to acquire nutrients. There is an epidemic-like increase in infections, as well as an increase in antimicrobial resistance among dermatophytes, as witnessed over the last decade. There is hence a need to understand the key pathways and virulence factors required during growth and infection. We present an integrated multi-omics analysis (proteomics and transcriptomics data) using a vector-based similarity approach to show high concordance of KOG functional categories belonging to posttranslational modification, protein turnover, carbohydrate transport, and metabolism.

Proteomics↗

PubMed2026

Bridging the airway microbiome and targeted therapy in bronchiectasis: multi-omics insights, endotypes and emerging therapies.

Bronchiectasis is a heterogeneous chronic airway disease primarily driven by persistent infection, microbial dysbiosis and dysregulated host immunity. While culture-based microbiology has historically informed clinical management, advances in high-throughput sequencing and multi-omic technologies have transformed our understanding of the airway ecosystem, revealing that disease activity is shaped not only by individual pathogens, but by complex and dynamic host-microbe interactions. Despite the breadth of descriptive microbiome data, translation into clinically actionable diagnostics or therapies has been limited. Importantly, cross-sectional correlations between microbiota and inflammation do not establish cause and effect, underscoring the need to embed host-microbiome profiling within both longitudinal and interventional therapeutic trials. In this review, we critically appraise current microbial and host multi-omics research in bronchiectasis, integrating microbiome studies with host inflammatory, proteomic and immunophenotyping data. We highlight themes emerging across cohorts, including low microbial diversity, pathogen dominance, loss of commensal networks and neutrophil-driven inflammation, and discuss how these features align with biological endotypes associated with exacerbations and treatment response. Drawing on lessons from host-directed therapeutic successes, we examine translational roadblocks limiting microbiome-guided care. We further review emerging microbiome-modulating strategies such as pathogen-specific biologics, bacteriophage therapy, live biotherapeutic products, biofilm-targeting adjuncts and precision antibiotic stewardship. Finally, we propose a roadmap toward microbiome-informed precision medicine through harmonised methodologies, integration of host and microbial biomarkers into clinical trials, and embedding multi-omics pipelines within large international registries. Collectively, these advances have the potential to shift bronchiectasis research and clinical management towards rationally designed, precision medicine-driven therapeutic strategies.

Humans↗

PubMed2026

Leveraging single-cell and spatial omics for brain tumour insights to improve therapeutic strategies.

Single-cell and spatial omics (SPOs) technologies have advanced how healthcare physicians characterise brain tumours by enabling detailed understanding of their cellular architecture, functional states, and microenvironmental dynamics. These approaches provide high-resolution detection of tumour heterogeneity and allow precise analysis of the brain tumour microenvironment. Their application has also led to the discovery of novel biomarkers used for early brain tumour detection, prognosis, and improved tumour stratification. Furthermore, integrative multi-omic analyses have revealed new therapeutic targets, clarified mechanisms of drug resistance, and uncovered molecular pathways underpinning treatment failure. By bridging cellular-level insights with spatial context, SPOs hold significant promise for advancing personalised diagnostics, predicting therapeutic response, and guiding the development of targeted interventions for brain tumours. Despite these advances, several limitations constrain the full translational potential of SPOs, including high experimental costs, substantial computational demands, lack of standardised protocols, and challenges in data integration and reproducibility. Addressing these barriers through scalable bioinformatic pipelines, consensus experimental frameworks, and cost-effective platforms remains critical for broadening accessibility and enabling clinical adoption.

Brain Neoplasms↗

PubMed2026

Integrated salivary proteomic and metabolomic analyses reveal molecular characterization and novel biomarker panels of chronic obstructive pulmonary disease.

Chronic obstructive pulmonary disease (COPD) is a respiratory disorder characterized by chronic inflammation, oxidative stress, and metabolic dysregulation. The lack of convenient and easily-accessible non-invasive diagnostic approaches remains a major clinical challenge. This study applied an integrated saliva-based proteomic and untargeted metabolomic strategy to identify potential biomarkers for COPD classification. Comprehensive multi-omics analyses identified 225 differentially abundant proteins and 60 differentially abundant metabolites between patients with COPD and healthy controls, including 24 biologically relevant endogenous metabolites. Functional enrichment analyses revealed pronounced dysregulation of mitochondrial energy metabolism, redox homeostasis, lipid remodeling, and inflammatory-related pathways in COPD. By integrating salivary proteomic and metabolomic biomarkers, a stepwise feature selection combined with LASSO logistic regression was used to construct diagnostic models, yielding an optimized biomarker panel consisting of 11 proteins and 2 endogenous metabolites. This integrated model achieved excellent diagnostic performance, with an area under the ROC curve of 0.96. Collectively, these findings demonstrate that integrated salivary proteomic and metabolomic profiling provides a robust, non-invasive approach for COPD classification and offers a promising foundation for the development of biosensor-based diagnostic platforms and early disease detection. SIGNIFICANCE: Chronic obstructive pulmonary disease (COPD) remains a major global health burden. Current diagnostic approaches rely largely on spirometry and clinical assessment, which are limited in sensitivity for early-stage disease and unsuitable for large-scale screening. This study employs an integrated saliva-based proteomic and metabolomic strategy to identify non-invasive biomarkers for COPD classification. Our findings reveal coordinated dysregulation of mitochondrial energy metabolism, redox homeostasis, and lipid remodeling in COPD, highlighting the interconnected roles of metabolic reprogramming, oxidative stress, and inflammation in disease pathophysiology. Notably, a robust diagnostic panel comprising 11 proteins and 2 endogenous metabolites was established, achieving excellent classification performance (AUC of 0.96). To our knowledge, the integrated application of salivary proteomics and metabolomics for COPD diagnosis remains largely unexplored, underscoring the significance and translational potential of our findings.

Humans↗

PubMed2026

Genomic profiling as an option for ovarian cancer diagnostics.

INTRODUCTION: Ovarian cancer (OC) is a highly heterogeneous and lethal gynecological malignancy. Precision oncology has shifted the management paradigm to comprehensive molecular profiling. Genomic-based diagnostics are now a clinical necessity for accurate prognostic stratification and the rational selection of targeted therapeutics, such as PARP and immune checkpoint inhibitors. AREAS COVERED: This review evaluates current literature regarding the distinct genomic landscapes defining OC histotypes to underlined the role of molecular profiling in the diagnostic field of OC. We discuss the practical implementation, technical aspect, and clinical validity of the main molecular diagnostic platforms, focusing on tissue-based Comprehensive Genomic Profiling (CGP) and Homologous Recombination Deficiency (HRD). Furthermore, we explore emerging translational data on liquid biopsy (LBx) applications. EXPERT OPINION: While current tissue-based methodologies provide critical baseline data, the OC diagnostic paradigm must pivot from static testing to proactive and longitudinal tracking. Integrating advanced LBx approaches enables a real-time monitoring of dynamic parameters as minimal residual disease (MRD) and acquired resistance. Integrating these dynamic blood-based assays with multi-omic profiling and artificial intelligence (AI)-driven tools allows a full understanding of the complex tumor behavior.

Humans↗

PubMed2025

Unlocking microbial potential: advances in omics and bioinformatics for aromatic hydrocarbon degradation.

Aromatic hydrocarbons (AHs) are persistent environmental pollutants with high toxicity. Bacterial degradation of AHs provides a sustainable and cost-effective approach for the remediation of sites contaminated with both mono- and polycyclic aromatic hydrocarbons. Aerobic degradation of AHs typically involves oxygenases-mediated hydroxylation followed by aromatic ring cleavage. In contrast, anaerobic degradation relies on diverse activation mechanisms that ultimately converge on the central intermediate benzoyl-CoA. Over the past decades, research on bacterial degradation of AHs has grown steadily, supported by advances in omics and bioinformatics. In this review, we summarize the current knowledge on the pathways, enzymes, and microbial diversity involved in AH degradation, highlighting how omics and bioinformatic approaches are advancing our understanding of this process. However, to improve our knowledge of microbial AHs catabolism, it is crucial to prioritize the characterization of novel enzymes and pathways, especially those mediating anaerobic and hybrid degradation strategies. Addressing this gap requires the development of specialized resources that incorporate a broader taxonomic diversity and an expanded inventory of anaerobic genes and enzymes supported by experimental evidence. Equally important is the integration of multi-omics technologies, artificial intelligence, and ecological modeling into unified analytical pipelines. These efforts will be key to fully unlocking microbial metabolic potential and guiding more effective bioremediation and monitoring strategies for AHs.

Biodegradation, Environmental↗

PubMed2024

Integrated Multi-Omics Analyses Reveal Lipid Metabolic Signature in Osteoarthritis.

Osteoarthritis (OA) is the most common degenerative joint disease and the second leading cause of disability worldwide. Single-omics analyses are far from elucidating the complex mechanisms of lipid metabolic dysfunction in OA. This study identified a shared lipid metabolic signature of OA by integrating metabolomics, single-cell and bulk RNA-seq, as well as metagenomics. Compared to the normal counterparts, cartilagesin OA patients exhibited significant depletion of homeostatic chondrocytes (HomCs) (P = 0.03) and showed lipid metabolic disorders in linoleic acid metabolism and glycerophospholipid metabolism which was consistent with our findings obtained from plasma metabolomics. Through high-dimensional weighted gene co-expression network analysis (hdWGCNA), weidentified PLA2G2A as a hub gene associated with lipid metabolic disorders in HomCs. And an OA-associated subtype of HomCs, namely HomC1 (marked by PLA2G2A, MT-CO1, MT-CO2, and MT-CO3) was identified, which also exhibited abnormal activation of lipid metabolic pathways. This suggests the involvement of HomC1 in OA progression through the shared lipid metabolism aberrancies, which were further validated via bulk RNA-Seq analysis. Metagenomic profiling identified specific gut microbial species significantly associated with the key lipid metabolism disorders, including Bacteroides uniformis (P < 0.001, R = -0.52), Klebsiella pneumonia (P = 0.003, R = 0.42), Intestinibacter_bartlettii (P = 0.009, R = 0.38), and Streptococcus anginosus (P = 0.009, R = 0.38). By integrating the multi-omics features, a random forest diagnostic model with outstanding performance was developed (AUC = 0.97). In summary, this study deciphered the crucial role of a integrated lipid metabolic signature in OA pathogenesis, and established a regulatory axis of gut microbiota-metabolites-cell-gene, providing new insights into the gut-joint axis and precision therapy for OA.

Humans↗

PubMed2026

moiraine: an R package to construct reproducible pipelines for the application and comparison of multi-omics integration methods.

MOTIVATION: In the past decades, many statistical methods for integrating multi-omics data have been developed. They have been implemented into software tools, which differ widely in their programming choices, such as the format required for data input, or the format of the generated integration results. This lack of standards renders cumbersome and time-intensive the application and comparison of different integration tools to the same multi-omics dataset. RESULTS: We have developed the moiraine R package for constructing reproducible multi-omics integration pipelines, which enables users to apply one or more statistical methods for multi-omics integration to their own multi-omics dataset. moiraine facilitates the preprocessing of the omics datasets and automates their formatting for the integration step. It simplifies the interpretation and evaluation of the integration results through the construction of visualizations in which metadata about samples and features can easily be included. Crucially, it enables the comparison of results obtained with different integration tools, allowing users to assess the robustness of their results. AVAILABILITY AND IMPLEMENTATION: The moiraine R package is publicly available at https://github.com/Plant-Food-Research-Open/moiraine; an archival snapshot of the package is available on Zenodo at https://doi.org/10.5281/zenodo.17172718. A detailed tutorial is available at https://plant-food-research-open.github.io/moiraine-manual/.

Software↗

PubMed2025

MEANtools integrates multi-omics data to identify metabolites and predict biosynthetic pathways.

During evolution, plants have developed the ability to produce a vast array of specialized metabolites, which play crucial roles in helping plants adapt to different environmental niches. However, their biosynthetic pathways remain largely elusive. In the past decades, increasing numbers of plant biosynthetic pathways have been elucidated based on approaches utilizing genomics, transcriptomics, and metabolomics. These efforts, however, are limited by the fact that they typically adopt a target-based approach, requiring prior knowledge. Here, we present MEANtools, a systematic and unsupervised computational integrative omics workflow to predict candidate metabolic pathways de novo by leveraging knowledge of general reaction rules and metabolic structures stored in public databases. In our approach, possible connections between metabolites and transcripts that show correlated abundance across samples are identified using reaction rules linked to the transcript-encoded enzyme families. MEANtools thus assesses whether these reactions can connect transcript-correlated mass features within a candidate metabolic pathway. We validate MEANtools using a paired transcriptomic-metabolomic dataset recently generated to reconstruct the falcarindiol biosynthetic pathway in tomato. MEANtools correctly anticipated five out of seven steps of the characterized pathway and also identified other candidate pathways involved in specialized metabolism, which demonstrates its potential for hypothesis generation. Altogether, MEANtools represents a significant advancement to integrate multi-omics data for the elucidation of biochemical pathways in plants and beyond.

Metabolomics↗

PubMed2026

Recent Advances in Multi-Omics of Systemic Lupus Erythematosus.

This comprehensive narrative review examines recent advances in multi-omics research for Systemic Lupus Erythematosus (SLE), emphasizing integrated approaches over single-omics studies. The review critically evaluates technological advancements, methodological innovations, and clinical applications while identifying current limitations and future research directions. We conducted a comprehensive narrative review following SANRA guidelines, searching PubMed, Web of Science, Scopus, and Embase, covering publications from January 2018 to June 2025. The review focuses on studies integrating two or more omics layers in SLE research, with emphasis on computational methods, biomarker validation, and clinical applications. Multi-omics integration has revealed critical insights into SLE pathogenesis, including immune cell heterogeneity, gene-environment interactions, and metabolic dysregulation. However, significant challenges remain in data integration methodologies, small sample sizes, and biomarker reproducibility. Current computational approaches include early integration (concatenation), intermediate integration (joint dimensionality reduction), and late integration (ensemble methods). While multi-omics approaches offer unprecedented insights into SLE complexity, standardized integration protocols and robust validation frameworks are urgently needed. Small sample sizes and heterogeneity issues limit reproducibility, particularly affecting biomarker discovery and clinical translation. Multi-omics integration represents a paradigm shift toward precision medicine in SLE, but realizing this potential requires addressing current methodological limitations, standardizing validation processes, and developing robust computational frameworks for reliable clinical applications.

Humans↗

PubMed2026

Immune cell-specific genetic architecture of Alzheimer's disease revealed by multi-omics analysis for therapeutic target discovery and prioritization.

Alzheimer's disease (AD) is a multifactorial neurodegenerative condition in which accumulating genetic and molecular evidence implicates dysregulation of peripheral immune processes in disease pathogenesis. Nevertheless, the contribution of distinct peripheral immune cell subsets and associated gene regulatory landscapes to AD risk remains incompletely defined. To address this gap, we integrated single-cell expression quantitative trait loci (sc‑eQTL) data from the OneK1K cohort with AD GWAS summary statistics. We systematically interrogated immune cell-specific genes for their contributions to AD risk by integrating genetic causal inference with Bayesian colocalization analyses, and identified 24 eGenes that passed both the MR significance threshold (P < 0.05) and the criterion for strong shared genetic signals (PP.H4 > 0.8). Notable candidates included GATS, HLA-DOB, HLA-DQA1, PM20D1, and others, with each gene demonstrating a cell-type-specific association restricted to its corresponding immune cell type, such as monocytes, CD8 + T cells, or B cells. Independent peripheral blood single-cell transcriptomic data further supported disease-associated shifts in cell-type-specific expression patterns in AD. Phenome-wide association studies (PheWAS) indicated limited associations with off-target traits, indicating a favorable safety profile for therapeutic intervention, with the exceptions of B4GALNT3, PM20D1, and CNN2. Integration of immune gene targets with pharmacological databases yielded three candidate compound, including NSC321521 (targeting HLA-DQA1), phenoxybenzamine (targeting GSTP1), and rimexolone (targeting BIN1). Among these compounds, Predicted blood-brain barrier permeability was observed only for phenoxybenzamine and rimexolone, with docking studies indicating stable interactions, such as those between NSC321521 and HLA-DQA1, phenoxybenzamine and GSTP1, and rimexolone and BIN1. This integrative approach highlights key immune‑cell‑specific genes involved in AD and proposes repurposable drugs with central nervous system potential, paving the way for more targeted immunomodulatory strategies in AD.

Humans↗

PubMed2026

Multi-omic analyses of the same sample using metabolomics, lipidomics, proteomics, phosphoproteomics, and glycoproteomics.

Mass spectrometry (MS)-based multi-omics offers powerful tools to comprehensively characterize proteins, post-translational modifications, metabolites, and lipids. However, these measurements are typically performed using separate sample preparation workflows and modality-specific liquid chromatography mass spectrometry (LC-MS) platforms, limiting integration and constraining applications to small amounts of sample materials, especially scarce clinical specimens. Here, we describe a unified nano-LC-MS framework that enables metabolomic, lipidomic, proteomic, phosphoproteomic, and glycoproteomic analyses from the same starting material using a single nano-LC-MS platform, with only the chromatographic conditions, acquisition methods, and enrichment procedures tailored to each omics. This integrated strategy reduces workflow complexity and sample consumption while improves analytical continuity across molecular layers. By enabling deep multi-omics characterization from the same sample, this platform provides a practical foundation for comprehensive analysis of precious clinical samples.

Proteomics↗

PubMed2025

Multi-omics reveals an ecdysone-activated Eip75B-FABP signaling axis coordinating nutrient metabolism for development in Hermetia illucens.

INTRODUCTION: Efficient nutrient storage is essential for insect development and energy homeostasis; however, the mechanisms coordinating nutrient allocation during ontogeny are not well understood. Elucidating these systems may yield valuable insights to insect metabolic adaptation. OBJECTIVES: This study aimed to identify regulatory modules governing nutrient metabolism in insects, focusing on hormonal and metabolic interplay. METHODS: Multi-omics profiling (proteomics, phosphoproteomics, and transcriptomics) was conducted throughout the life cycle, from egg to adult, to identify metabolic regulators. RNAi was utilized for gene knockdown, followed by qRT-PCR and mitochondrial DNA quantification to evaluate knockdown efficiency and its metabolic implications. Assessments of nutrient metabolism were performed using assays for triglycerides, crude protein, and fatty acid synthase. EMSA and BODIPY staining examined transcriptional regulation and lipid droplet dynamics. RESULTS: Utilizing an integrative multi-omics approach, this study elucidates the temporal metabolic regulators in insects. A conserved regulatory module was identified in which the PPAR homolog, ecdysone-induced protein 75B (Eip75B), functions as a transcriptional activator of fatty acid binding protein (FABP), sustaining lipid metabolic homeostasis during the larval stage. PPARγ modulators (rosiglitazone and GW9662) alter lipid accumulation, along with the expression of Eip75B and FABP, which was measured by qRT-PCR. Furthermore, the deficiency of FABP may reprogram metabolic pathways by inhibiting lipid storage and promoting mitochondrial β-oxidation, as supported by increased mitochondrial DNA copy number, as well as enhancing protein synthesis. This metabolic change could be modulated by ecdysone signaling, as hormonal supplementation effectively rescued the lipid loss phenotype. Our results establish the ecdysone-Eip75B-FABP signaling axis as a central regulatory module that integrates hormonal and nutrient-sensing signals to control insect nutritional metabolism. CONCLUSION: The ecdysone-Eip75B-FABP axis integrates hormonal and nutrient signals to regulate metabolic plasticity, underscoring a universal strategy for developmental energy allocation. The data also offer potential implications for research on metabolic disorders and bioenergy applications.

Animals↗

PubMed2026

Admission Gut and Plasma-Derived Signatures Associated With Severity and 90-day Outcome in Hepatitis A-Related and Drug-Induced Acute Liver Failure.

Acute liver failure (ALF) due to hepatitis A (ALF-A) has high mortality, but admission-day markers associated with disease severity and outcome are unknown. We aimed to define gut microbiome and plasma multi-omics signatures associated with severity and 90-day outcomes in ALF-A, compared to drug-induced ALF (ALF-D) as non-viral ALF group. ALF patients (aged 26 ± 9 years; 50.7% male, 49.3% female) with IgM HAV positive (ALF-A, n = 33), ALF-D (n = 38) were recruited along with acute viral hepatitis (AVH-A, n = 27) and healthy subjects (HC, n = 20). Stool bacteria profiling was done at D0; plasma cytokines, metabolites and barrier markers were analysed at D0, 3, 5, 7. Correlations with clinical severity parameters were assessed. Associations with 90-day mortality were assessed using severity-adjusted association analyses. ALF-A patients were more severe at the time of admission than ALF-D. In ALF-A, 90-day mortality was 33% versus 18.4% with increased severity scores like KCH 21% versus 10.5%, SOFA score 7.97 ± 2.89 versus 5.87 ± 2.32, SIRS 60.6% versus 18.4%, mechanical ventilation 39% versus 21%. At D0, ALF-A was enriched for lactate/ammonia/bile salt hydrolase/histamine-producing pathobionts like Enterococcus, Ruminococcus gnavus, Flavonifractor, Thomasclavelia correlating positively with severity (|r| > 0.4, p < 0.05) showing higher baseline abundance in ALF-A non-survivors (NS). At D0 in ALF-A, histamine accumulation, reduced tryptophan, butyrate metabolism inversely correlated with severity and worsened in ALF-A_NS by D7. Pro-inflammatory cytokines, IFABP2 were elevated at Day 0 in ALF-A, correlated positively with severity, and remained increased through Day 7 in ALF-A_NS. In contrast, ALF-D_NS retained commensals, showed increased fatty acid, bile acid biosynthesis with low levels of pro-inflammatory cytokines. In ALF-A, gut and plasma integrated multi-omics features were associated with disease severity, 90-day outcomes and identified candidate biomarkers for future validation.

Humans↗

PubMed2026

MetaServe: a lightweight, metadata-aware governance and delivery layer for pre-publication research omics data.

BACKGROUND: Institutional research teams and core facilities routinely manage pre-publication omics datasets that span heterogeneous file types, nested project structures, and multiple downstream uses. Public repositories mainly support post-publication dissemination, while workflow systems and enterprise data platforms do not directly provide a lightweight governance and delivery layer for internal research assets. RESULTS: We present MetaServe, an open-source governance and delivery layer for pre-publication research assets in institutional multi-omics settings. MetaServe registers and delivers heterogeneous assets, including sequencing files, processed matrices, imaging data, analysis-ready objects, tabular files, and documents, without requiring repository-grade standardization. Its metadata-aware design combines file-type recognition, partial automatic extraction for selected formats, manually supplied project and biological annotations, and indexed faceted retrieval. MetaServe supports authenticated web download, viewer-oriented handoff for compatible services such as cellxgene, and path-manifest export for downstream workflows under shared-storage assumptions. The current implementation combines role-based controls, explicit file-level sharing, path-constrained delivery, and operational traceability to support controlled institutional access. MetaServe has been deployed at the Chinese Institutes for Medical Research (CIMR) as part of an institutional multi-omics data-management system. CONCLUSIONS: MetaServe provides a practical layer between institutional storage and downstream analytical platforms for pre-publication research data. Its contribution is the integration of lightweight metadata-aware registration, permission-aware retrieval, and controlled delivery for heterogeneous institutional omics assets. Rather than replacing workflow engines, public repositories, or enterprise-scale research data platforms, MetaServe offers a deployable governance layer for core facilities and collaborative teams that need structured discovery and traceable delivery before public deposition or manuscript release.

Metadata↗

PubMed2026

Trimethylamine-producing microbe Bacillus megaterium KCTC 3007 promotes antitumor immunity in endometrial cancer via type I interferon response pathways.

BACKGROUND: Endometrial cancer (ECa) is one of the most common gynecologic malignancies, with limited therapeutic responses in metastatic or recurrent cases. The bacterial microbiota has emerged as a key modulator of carcinogenesis and antitumor immunity. However, the role of endometrial microbiota in ECa pathogenesis and prognosis remains poorly understood. METHODS: We performed comprehensive multi-omics analysis integrating metatranscriptomics, transcriptomics, and targeted metabolomics from 60 ECa and 18 benign patients. RNA sequencing enabled simultaneous profiling of active tissue-resident microbiota and host gene expression. Serum metabolomics was conducted on all patients. Identified microbial-metabolite associations were validated through in vitro co-culture experiments using peripheral blood mononuclear cells (PBMCs), cancer cell lines, RNA sequencing, and live cell imaging. RESULTS: ECa patients exhibited significantly altered microbial diversity and composition compared to benign controls. Through integrated multi-omics analysis, we identified Bacillus megaterium (BM) KCTC 3007 as a beneficial microbe associated with prolonged recurrence-free survival. In an exploratory analysis of ECa subtypes, Cupriavidus taiwanensis and Marinomonas primoryensis showed potential links to poor prognosis, although these observations warrant caution due to the limited size of certain subgroups. Tissue BM abundance positively correlated with serum trimethylamine N-oxide (TMAO) levels, particularly in postmenopausal women. In vitro experiments demonstrated that BM KCTC 3007 enhanced antitumor immunity by promoting interleukin and type I interferon expression, expanding CD8 + T cell populations, and increasing immune cell-tumor cell interactions. RNA sequencing revealed activation of interferon alpha response and immune cell proliferation pathways, with IFNAR1 identified as a key upstream regulator. TMAO treatment recapitulated these immune-activating effects, enhancing CD8 + T cell responses and preferentially inducing pyroptotic cancer cell death. CONCLUSIONS: We provide the first evidence that tissue-resident BM KCTC 3007 promotes antitumor immunity in ECa through TMAO production and subsequent type I interferon-mediated immune activation. This integrated multi-omics approach establishes a complete microbe-metabolite-host mechanistic pathway and highlights the therapeutic potential of TMAO-producing probiotic strains for ECa treatment. Video Abstract.

Female↗