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PubMed2026

A novel iPSC model of Bryant-Li-Bhoj neurodevelopmental/neurodegenerative syndrome demonstrates the role of histone H3.3 in chromatin dynamics, neuronal differentiation, and maturation.

BACKGROUND: Bryant-Li-Bhoj neurodevelopmental syndrome (BLBS) is neurogenetic disorder caused by variants in H3-3A and H3-3B, the two genes that encode histone H3.3. Ninety-nine percent of individuals with BLBS show developmental delay/intellectual disability, but the mechanism by which variants in H3.3 result in these phenotypes is not yet understood, limiting the therapeutic interventions available to individuals living with BLBS. METHODS: Here, we investigate how one BLBS-causative variant, H3-3B p.Leu48Arg (L48R), affects neurodevelopment using an induced pluripotent stem cell model differentiated to 2D neural progenitor cells (NPCs), 2D forebrain neurons (FBNs), and 3D dorsal forebrain organoids (DFBOs). We employ a multi-omic approach in the 2D models to quantify the resulting changes in gene expression and chromatin accessibility. We used immunofluorescence (IF) staining to define the identities of cells in the 3D DFBOs and whole-cell patch clamp to investigate the electrophysiological properties of neurons in DFBOs. RESULTS: In the 2D systems, we found dysregulated gene expression and chromatin accessibility affecting neuronal fate, adhesion, neurotransmission, and excitatory/inhibitory balance. Immunofluorescence of DFBOs corroborated altered proportions of radial glia and mature neuronal populations. Patch clamp recordings revealed decreased electrical activity in neurons from L48R DFBOs compared to control DFBOs. CONCLUSIONS: These data provide the first mechanistic insights into the pathogenesis of BLBS from a human-derived model of neurodevelopment, which suggest that H3.3 L48R increases H3-3B expression, resulting in the hyper-deposition of H3.3 into the nucleosome, which underlies changes in gene expression and chromatin accessibility. Functionally, this causes dysregulation of cell adhesion, neurotransmission, and the balance between excitatory and inhibitory signaling. These results are a crucial step towards preclinical development and testing of targeted therapies for this and related disorders.

Histones↗

PubMed2026

Preliminary Exploration on Melatonin-Mediated Protective Effects in Intracranial Aneurysms: Transcriptomic, Proteomic, and Metabolomic Profiling of Cerebral Vascular Tissues Combined with in vivo Animal Experiments.

BACKGROUND: Intracranial aneurysm (IA) is a life-threatening cerebrovascular disease with unclear molecular mechanisms and limited drug treatment. Our previous research has shown that melatonin (MLT) has potential protective effects in IA, but its mechanism remains unclear. The purpose of this study is to explore the pathological mechanism of IA and the therapeutic mechanism of MLT by integrating transcriptomic, proteomic and metabolomic analyses. METHODS: In this study, mouse models of IA were successfully established by combining elastase injection with angiotensin II infusion. C57BL/6 mice were divided into control, IA model, IA model+MLT, and IA model+nimodipine groups. The pathological conditions were evaluated by hematoxylin-eosin (HE) staining, TUNEL staining, and scanning electron microscopy. Transcriptomic (n=3 for each group), proteomic (n=3 for each group), and metabolomic (n=6 for each group) analyses were performed based on cerebral vascular tissue samples. The screening thresholds for differentially expressed genes and differentially expressed proteins were P <0.05 and fold change >1.5 and fold change <0.667. The screening criteria for differential metabolites were variable importance for the projection (VIP)> 1.0, fold change >1.2 and fold change <0.833, and P <0.05. RESULTS: MLT alleviated brain tissue damage, vascular endothelial damage, structural disruption, and apoptosis in IA mice. Transcriptomic, proteomic and metabolomic analyses identified numerous differential molecules. Functional annotation revealed that these molecules may be involved in biological pathways and processes such as immune inflammation, vascular remodeling, extracellular matrix remodeling, neuropeptide activity, oxidative stress and metabolic pathways, thereby regulating the occurrence and development of IA or mediating the therapeutic effects of MLT. Furthermore, transcriptomic and proteomic analyses also suggest that there may be extensive post-transcriptional, translational and post-translational regulatory events in the progression of IA and the therapeutic effects of MLT. Integrated transcriptomic and proteomic analyses suggest that Npy may be a key molecule in regulating IA progression and mediating MLT therapeutic effects, and its potential value is further supported by our immunohistochemical validation results. CONCLUSION: Multi-omics integrative analysis preliminarily revealed that the potential mechanisms of MLT may involve the regulation of inflammatory response, vascular remodeling, extracellular matrix remodeling, neuropeptide activity, oxidative stress, metabolic pathways, and post-transcriptional/translational regulation.

Animals↗

PubMed2026

Trustworthy Agentic AI in Bioinformatics: From Workflow Automation to Traceable and Validated Biological Inference.

Agentic artificial intelligence is extending bioinformatics beyond conversational assistance by enabling systems to select tools, execute code, revise analytical plans, and interpret biological data. These capabilities may accelerate research, but they also redistribute decisions that determine whether biological conclusions are valid. We conducted a targeted, structured PubMed search in July 2026 and identified 11 peer-reviewed agentic bioinformatics systems for descriptive review based on predefined eligibility criteria for analytical decision-making, tool or code execution, iterative evaluation, or coordinated agent activity. The evidence base covered single-cell transcriptomics, microbial genomics, cancer genomics, and omics applications, together with methodological literature on reproducibility and biological validation. We examined how current systems report delegated authority, provenance, validation, evidence, abstention, and human oversight. Existing platforms implement safeguards such as sandboxed execution, restricted commands, interaction logs, evidence identifiers, automated checks, critic agents, quality scores, and expert assessment. However, published reports rarely provide a connected account linking the original biological question to samples, reference resources, analytical decisions, computational actions, statistical results, supporting evidence, validation outcomes, and final claims. We distinguish inherited bioinformatics errors, errors amplified through autonomous action, and emergent failures arising from memory, retrieval, tool interaction, or agent coordination. We further propose a multidimensional decision-rights profile, consequence-sensitive validation gates, and a claim-to-evidence provenance architecture organized through the Traceable History of Research Evidence, Agent Actions, and Decisions in Bioinformatics (THREAD-Bio) framework. Illustrative cases show that technically successful execution may still support misleading inference. Trustworthy agentic bioinformatics therefore requires claims to remain reconstructible, challengeable, validated, and proportionate to the evidence.

accountable autonomy↗

PubMed2026

SIVA: diagonal integration of spatial multi-omics data via spatially informed variational autoencoders and anchor guidance.

MOTIVATION: Understanding cellular states and regulatory programs requires integrative analysis of multiple omics layers. Although recent spatial sequencing technologies allow molecular profiling of cells within their tissue context, paired spatial multi-omics assays are still limited by technical complexity and cost. This creates a pressing need for diagonal integration methods that enable joint analysis of unpaired spatial omics datasets. RESULTS: We propose SIVA, a deep generative framework based on Spatially-Informed Variational Autoencoders with Anchor Guidance, for diagonal integration of spatial multi-modal data. SIVA employs modality-specific variational autoencoders (VAEs) with a hybrid latent embedding that integrates Gaussian process and standard Gaussian priors, enabling joint modeling of spatially structured variation and dominant underlying data distributions across modalities. To facilitate cross-modal alignment in the absence of one-to-one cell correspondence, SIVA adopts a dual integration strategy combining global distribution alignment via Maximum Mean Discrepancy and local correspondence guidance using mutual nearest neighbor anchors. Extensive experiments across multiple cross-slice integration scenarios demonstrate that SIVA achieves robust and accurate integration of unpaired spatial omics datasets, consistently outperforming existing methods. AVAILABILITY AND IMPLEMENTATION: The source codes are available at https://github.com/PelenJiang/SIVA.

Autoencoder↗

PubMed2025

IGCN: integrative graph convolution networks for patient level insights and biomarker discovery in multi-omics integration.

MOTIVATION: Developing computational tools for integrative analysis across multiple types of omics data has been of immense importance in cancer molecular biology and precision medicine research. While recent advancements have yielded integrative prediction solutions for multi-omics data, these methods lack a comprehensive and cohesive understanding of the rationale behind their specific predictions. To shed light on personalized medicine and unravel previously unknown characteristics within integrative analysis of multi-omics data, we introduce a novel integrative neural network approach for cancer molecular subtype and biomedical classification applications, named Integrative Graph Convolutional Networks (IGCN). RESULTS: To demonstrate the superiority of IGCN, we compare its performance with other state-of-the-art approaches across different cancer subtype and biomedical classification tasks. Our experimental results show that our proposed model outperforms the state-of-the-art and baseline methods. IGCN identifies which types of omics data receive more emphasis for each patient when predicting a specific class. Additionally, IGCN has the capability to pinpoint significant biomarkers from a range of omics data types. AVAILABILITY AND IMPLEMENTATION: The source code is available at https://github.com/bozdaglab/IGCN.

Humans↗

PubMed2025

PLNMFG: Pseudo-label guided non-negative matrix factorization model with graph constraint for single-cell multi-omics data clustering.

The development of single-cell multi-omics sequencing technologies has enabled the simultaneous analysis of multi-omics data within the same cell. Accurate clustering of these cells is crucial for downstream analyses of complex biological functions. Despite significant advances in multi-omics integration approaches, current methodologies exhibit two major limitations. First, they inadequately incorporate prior biological knowledge from various omic layers. Second, these methods often conduct independent dimensionality reduction on individual omic datasets, thereby failing to capture the intrinsic complementary information and potentially overlooking crucial cross-platform interactions. Motivated by these, this study investigates a non-negative matrix factorization model called PLNMFG, which integrates the unified latent representation learning that retains the features between and within omics and the cluster structure learning that retains the intrinsic structure of the data into one joint framework. Specially, PLNMFG performs adaptive imputation to handle dropout events and uses prior pseudo-labels as constraints during the process of collective non-negative matrix factorization, as a result, a more robust latent representation that preserves the double similarity information is obtained. Graph Laplacian constraint is applied during clustering which further preserves structure characteristic of multi-omics data. In addition, the weight of each omic is adaptively learned based on the omic contribution. A series of experiments on 8 benchmark datasets show that our model performs well in terms of clustering accuracy and computational efficiency.

Single-Cell Analysis↗

PubMed2026

A nonlinear multi-omics data integration and classification model based on pathway self-attention and graph convolutional networks.

The abundance of omics data has significantly advanced the development of multi-omics data integration techniques. Non-linear embedding approaches for data integration have gradually become the mainstream in multi-omics research, as these approaches can substantially improve cancer analysis by enhancing the quality of the embeddings. However, current multi-omics data integration methods are typically confined to omics measurements, neglecting domain-specific prior knowledge encompassing biological pathways. In this study, we proposed a multi-omics integrated classification model, PathTransGCN, based on pathway self-attention and graph convolutional networks (GCN). The model integrated biological pathway information into multi-omics data analysis with the aim of enhancing the accuracy of cancer classification. Multi-omics data for breast cancer (BRCA), non-small cell lung cancer (NSCLC), and low-grade glioma (LGG) were obtained from The Cancer Genome Atlas (TCGA) and UCSC Xena databases. These data included gene mutations, DNA methylation, copy number variations, and gene expression, and were used to assess the model's generalizability across different cancers. First, PathTransGCN employed a pathway self-attention module to learn latent representations of samples across different pathways, thereby obtaining multi-omics integration vectors. Concurrently, a patient similarity network (PSN) was constructed using the similarity network fusion (SNF) approach. Second, the integrated vectors and the PSN were jointly fed into a GCN for end-to-end training, enabling precise classification of cancer subtypes. Through multi-omics data analysis of the BRCA dataset, PathTransGCN outperformed several popular algorithms (such as MoGCN and DeePathNet) in the five-class classification of cancer subtypes, achieving an accuracy rate of 87.6% and an F1 score of 86.4%. Moreover, the model demonstrated robust generalization capabilities across both NSCLC and LGG datasets, while effectively identifying key disease-associated biomarkers at the pathway level. Experimental results demonstrate that PathTransGCN exhibits outstanding performance in integrating omics data and delivering interpretable classification outcomes, presenting significant potential for clinical applications.

Humans↗

PubMed2026

hypeR-GEM: connecting metabolite signatures to enzyme-coding genes via genome-scale metabolic models.

MOTIVATION: Enrichment analysis is a cornerstone of "omics" data interpretation, enabling researchers to connect analysis results to biological processes and generate testable hypotheses. Enrichment analysis in metabolomics poses distinct challenges for interpretation and multi-omics integration due to the lack of well-defined and consistent connections to well-curated gene-centered biological knowledge repositories. To address these challenges, we developed hypeR-GEM, a methodology and associated R package that adapts gene set enrichment analysis to metabolomics. hypeR-GEM leverages genome-scale metabolic models (GEMs) to infer reaction-based links between metabolites and enzyme-coding genes, enabling the mapping of metabolite signatures to gene signatures and their subsequent annotation via gene set enrichment analysis. RESULTS: We validated hypeR-GEM using paired metabolomics-proteomics and metabolomics-transcriptomics datasets by assessing whether genes mapped from metabolites significantly overlapped with differentially expressed proteins or transcripts. We further evaluated whether pathways enriched via hypeR-GEM-mapped genes corresponded to those derived from paired proteomic or transcriptomic data. In most datasets analyzed, both the predicted enzyme-coding genes and the associated enriched pathways showed significant concordance with independently derived omics signatures, supporting the utility and robustness of hypeR-GEM. Finally, we applied hypeR-GEM to the analysis of age-associated metabolic signatures from the New England Centenarian Study. The results revealed consistent enrichment of lipid-related pathways, aligning with the well-established role of lipid metabolism in aging, and highlighted additional pathways not captured in the metabolites' annotation, demonstrating hypeR-GEM's practical utility in a real-world use case. AVAILABILITY AND IMPLEMENTATION: The hypeR-GEM R package, documentation, and workflow examples are freely available at https://github.com/montilab/hypeR-GEM and archived at https://doi.org/10.5281/zenodo.20586748.

Metabolomics↗

PubMed2026

Integrative Analysis Uncover the Effects and Multi-Omics Features of Thigh Muscle Fat Infiltration.

The health impacts and underlying biological pathways of thigh muscle fat infiltration (TMFI) remain incompletely understood. In this study, we analyzed TMFI measured by magnetic resonance imaging in 55,120 UK Biobank participants and found that higher TMFI was significantly associated with all-cause mortality as well as with all major system-specific diseases examined (p values ranged from 2.50 × 10-88 to 9.97 × 10-04). TMFI also mediated the effects of lifestyle factors on health-related outcomes, with mediation proportions ranging from 6.7% to 71.7%. A genome-wide association study (GWAS) identified 79 lead single nucleotide polymorphisms (SNPs) linked to TMFI, and the polygenic risk score for TMFI was significantly associated with mortality and all incident diseases across examined organ systems in an independent subset of UK Biobank participants of European ancestry who were not included in the TMFI GWAS (n = 362,286, all p < 0.05). Gene-drug interactions identified multiple drugs that could potentially modulate TMFI. Analysis of single-cell transcriptomic data indicated that myogenic cells were strongly linked to TMFI (p = 7.08 × 10-08). Summary-data-based Mendelian randomization and Transcriptome-Wide Association Study analyses revealed numerous genes whose expression in specific tissues was associated with TMFI. Proteomic and metabolomic profiling uncovered a broad array of circulating biomarkers associated with TMFI, many of which mediated the effects of modifiable factors and genetic risk on TMFI. Overall, our results highlight the biological relevance of TMFI to human health and provide insights into the multi-omics mechanisms underlying TMFI, identifying potential targets for interventions.

Humans↗

PubMed2005

Large-scale in vivo flux analysis shows rigidity and suboptimal performance of Bacillus subtilis metabolism.

Qualitative theoretical approaches such as graph theory and stoichiometric analyses are beginning to uncover the architecture and systemic functions of complex metabolic reaction networks. At present, however, only a few, largely unproven quantitative concepts propose functional design principles of the global flux distribution. As operational units of function, molecular fluxes determine the systemic cell phenotype by linking genes, proteins and metabolites to higher-level biological functions. In sharp contrast to other 'omics' analyses, 'fluxome' analysis remained tedious. By large-scale quantification of in vivo flux responses, we identified a robust flux distribution in 137 null mutants of Bacillus subtilis. On its preferred substrate, B. subtilis has suboptimal metabolism because regulators of developmental programs maintain a 'standby' mode that invests substantial resources in anticipation of changing environmental conditions at the expense of optimal growth. Network rigidity and robustness are probably universal functional design principles, whereas the standby mode may be more specific.

Acetyl Coenzyme A↗

PubMed2026

Pan-cancer multi-omics machine learning defines a lactylation-associated immune-excluded tumor state with proteomic and experimental corroboration.

BACKGROUND: Histone lactylation links lactate metabolism to chromatin regulation, but whether lactylation-program-associated transcriptional patterns delineate recurrent pan-cancer tumor states remains unclear. METHODS: We integrated mRNA, lncRNA, and miRNA profiles from 9712 TCGA tumors across 33 cancer types with GTEx references, six GEO cohorts, IMvigor210, and an institutional clear-cell renal cell carcinoma (ccRCC) cohort used for exploratory DIA-NN proteomic corroboration. Random-effects co-expression meta-analysis, multi-omics consensus clustering, regulon inference, immune deconvolution, TIDE, oncoPredict, and SHAP-based machine learning were applied. hsa-miR-431-5p was functionally evaluated as a proof-of-concept CS2-associated miRNA in bladder cancer models. RESULTS: LacCoEx-Atlas comprised 398,491 lactylation-related co-expression pairs across 24,667 RNA features under a random-effects framework (median I² = 88.6%). Consensus clustering identified two subtypes: CS2 showed glycolytic-mesenchymal-immune-excluded features, M2 macrophage enrichment, CD8⁺ T-cell depletion, elevated HDAC4/NSD3/KDM6B activity, and worse survival, whereas CS1 showed oxidative, sirtuin-active programs. CS2 had fewer predicted ICI responders (18.3% vs. 52.0%) and a lower observed ORR in IMvigor210 (15.3% vs. 24.0%). oncoPredict identified NU7441 as a hypothesis-generating CS2-associated sensitivity signal (Hedges' g = 1.17). DIA-NN proteomics in 50 ccRCC specimens provided exploratory support for CS2-associated hypoxia, ECM degradation, and metastasis programs. The 10-feature mRNA LARItools model achieved an apparent AUC of 0.9413, while a separate multi-omics model achieved 0.971; neither was independently validated. LARItools reproduced prognostic separation across six GEO cohorts. miR-431-5p promoted malignant phenotypes and EMT in bladder cancer cells, with concordant CMU4h expression findings. CONCLUSIONS: Lactylation-program-associated transcriptional patterns delineate a recurrent immune-excluded pan-cancer tumor state associated with adverse prognosis, reduced predicted immunotherapy responsiveness, exploratory single-cancer protein-level support, and testable DNA damage response-targeting hypotheses. LacCoEx-Atlas and LARItools provide open resources for lactylation-program-associated tumor-state stratification and future translational research.

Humans↗

PubMed2026

Beyond ion channel dysfunction: Integration of the transcriptome and proteome from patient-specific re-engineered cardiac cells, and population-level QT genome-wide association study reveals broad cellular dysfunction.

BACKGROUND: Congenital long QT syndrome (LQTS) is a cardiac channelopathy with increased risk of cardiac-triggered syncope/seizures, sudden cardiac arrest, and sudden cardiac death. OBJECTIVE: This study aimed to describe the transcriptomic and proteomic profiles in patient-derived inducible pluripotent stem cell-derived cardiomyocyte (iPSC-CM) models of the 3 canonical genotypes of congenital LQTS: LQT1, LQT2, and LQT3 and integrate these omics-level findings with each other and with population/clinical level QT-genome-wide association study (GWAS) data. METHODS: LQT1, LQT2, LQT3 and respective isogenic control iPSC-CMs were cultured, and RNA and protein samples were collected. RNA sequencing and mass spectrometry-enabled proteomic analysis was performed. PrediXcan analysis was performed using QT GWAS summary statistics and transcriptome expression data. Differential gene and protein expression and ingenuity pathway analysis (IPA) was performed comparing each LQT genotype with its respective isogenic control. RESULTS: 1645 differentially expressed genes (DEGs) were identified; 13 were altered in all 3 LQTS genotypes. IPA analysis of DEGs revealed 301 altered pathways; 47 were altered in all LQTS genotypes. Proteomic analysis identified 2561 differentially expressed proteins (DEPs); 30 were altered in all 3 genotypes. IPA analysis of DEPs identified 646 altered pathways. 306 genes/proteins were identified as significantly altered in both the transcriptome and proteome; pathway analysis of these 301 genes identified 201 altered pathways. 7 pathways were altered in all 3 LQTS genotypes in both the transcriptome and proteome. Integration of the population-level PrediXcan results and the cardiomyocyte-derived omics results identified multiple shared pathways. CONCLUSION: Multi-omics analysis of LQTS and integration of omics results with QT GWAS data reveals that primary LQTS-causative ion channel defects precipitate secondary alterations in a wide range of cellular pathways. Our findings suggest more broad molecular level changes throughout the cell. This study lays the foundation for further exploration of broad cellular changes resulting from ion channel disturbances and how they contribute to disease mechanism.

Humans↗

PubMed2025

Write and Read: Harnessing Synthetic DNA Modifications for Nanopore Sequencing.

An exciting feature of nanopore sequencing is its ability to record multi-omic information on the same sequenced DNA molecule. Well-trained models allow the detection of nucleotide-specific molecular signatures through changes in ionic current as DNA molecules translocate through the nanopore. Thus, naturally occurring DNA modifications, such as DNA methylation and hydroxymethylation, may be recorded simultaneously with the genetic sequence. Additional genomic information, such as chromatin state or the locations of bound transcription factors, may also be recorded if their locations are chemically encoded into the DNA. Here, we present a versatile "write-and-read" framework, where chemo-enzymatic DNA labeling with unnatural synthetic tags results in predictable electrical fingerprints in nanopore sequencing. As a proof-of-concept, we explore a DNA glucosylation approach that selectively modifies 5-hydroxymethylcytosine (5hmC) with glucose or glucose-azide adducts. We demonstrate that these modifications generate distinct and reproducible electrical shifts, enabling the direct detection of chemically altered nucleotides. We further demonstrate that enzymatic alkylation, such as the enzymatic transfer of azide residues to the N6 position of adenines, also produces characteristic nanopore signal shifts relative to the native adenine and 6-methyladenine. Beyond direct nucleotide detection, this approach introduces new possibilities for bio-orthogonal DNA labeling, enabling an extended alphabet of sequence-specific detectable moieties. The future use of programmable chemical modifications for simultaneous analysis of multiple omics features on individual molecules opens new avenues for genetic research and discovery.

5-hydroxymethylcytosine (5hmC)↗

PubMed2026

Multi-omics dynamic profiling reveals predictive biomarkers for first-line immunochemotherapy in extensive-stage small-cell lung cancer.

BACKGROUND: Extensive-stage small-cell lung cancer (ES-SCLC) is associated with a poor prognosis. Although first-line immunochemotherapy improves clinical outcomes, robust prognostic biomarkers for this treatment modality remain unavailable. The aim of this study was to identify non-invasive, easily accessible, and dynamically monitored biomarkers of ES-SCLC by machine learning integrating serum metabolomics, lipidomics, and proteomics at multiple time points. METHODS: A total of 816 serum samples were collected from ES-SCLC patients receiving first-line immunotherapy combined with chemotherapy or first-line chemotherapy for metabolomics, lipidomics, and proteomics analysis. The immunochemotherapy cohort was randomly divided into training and validation subsets at a 6:4 ratio. Biomarkers were identified using machine learning algorithms, and their prognostic significance was evaluated through receiver operating characteristic (ROC) analysis, Kaplan–Meier survival analysis, and multivariate Cox regression. Potential metabolic pathways and mechanisms were further explored via integrated multi-omic analysis. RESULTS: The immunochemotherapy exhibited a prolonged median progression-free survival (PFS) and higher objective response rate (ORR) compared to the chemotherapy group. A total of 5 serum metabolites (uric acid, L-aspartate-semialdehyde, dimethisterone, xanthine, L-cysteine), 6 lipids (Cer d18:1/26:0, Cer d18:2/25:0, SM d18:1/20:1, SM d17:1/25:1, DG O-18:1_16:0, PS 18:0_24:0), and 3 proteins (ACIN1, ACSL4, PHGDH) were identified and constructed into independent prognostic models. Among patients receiving immunochemotherapy, those categorized as low-risk based on the model demonstrated significantly longer PFS compared with those in the high-risk group. These prognostic signatures also retained predictive value in patients who underwent second-line treatment with anlotinib plus immunochemotherapy. Integrated analysis revealed that glycine, serine, and threonine metabolism was the commonly enriched pathway across all three omics layers. Notably, PHGDH (protein), L-aspartate-semialdehyde and L-cysteine (metabolites), and PS (18:0_24:0) (lipid), key elements in this pathway, were all incorporated in the predictive model. In addition, models of the composition of these substances after one cycle of treatment can still predict the prognosis of patients. CONCLUSION: In this study, we constructed and validated a set of non-invasive, dynamically monitorable prognostic models (containing 5 metabolites, 6 lipids, and 3 proteins) using machine learning by integrating multiple time point data from the serum metabolome, lipid panel, and proteome to accurately distinguish the prognostic risk of patients with ES-SCLC receiving immunochemotherapy. PFS was significantly prolonged in patients in the low-risk group, and this model remains predictive in the subsequent second-line treatment with anlotinib in combination with immunochemotherapy. Glycine-serine-threonine metabolic pathway may be the key mechanism, of which PHGDH, L-aspartate semialdehyde, L-cysteine and PS (18:0_24:0) are the core predictors. This study provides the first multi-omics dynamic prognostic tool for ES-SCLC immunochemotherapy and reveals potential therapeutic targets.

Humans↗

PubMed2026

Muscular fiber properties and multi-omics investigation of larval and adult locomotor muscle in Microhyla fissipes.

During metamorphosis, Microhyla fissipes undergoes a critical transition from an aquatic to a terrestrial lifestyle, accompanied by significant remodeling of skeletal muscle. Notably, larval tail muscle degenerates, while adult hindlimb muscle develops. However, the molecular mechanisms that orchestrate these muscle type-specific adaptations to the changing environment remain unclear. In this study, histological observation, transcriptomics, and metabolomics were integrated to compare locomotor muscles from two stages: larval muscle from tail versus adult muscle from hindlimb. Our results revealed that adult muscle fibers exhibited reduced diameter and shorter sarcomere length compared to those of tadpoles. Transcriptomic analysis identified 4103 differentially expressed genes (DEGs), including 2182 up-regulated and 1921 down-regulated genes. Up-regulated genes were mainly involved in energy metabolism and cellular homeostasis pathways, including PPAR signaling and oxidative phosphorylation, whereas down-regulated genes were associated with carbohydrate metabolism and cell proliferation pathways, such as glycolysis/gluconeogenesis and PI3K-Akt signaling. Metabolic profiling indicated a metabolic shift from anaerobic to aerobic energy production, with 57 differential metabolites identified, mainly involved in protein metabolism and insulin-related pathways. Integrated multi-omics analysis further highlighted the AMPK and FoxO signaling pathways play key roles in this process. In conclusion, our findings demonstrate that the metabolic and structural differences between larval and adult skeletal muscles are mediated by AMPK- and FoxO-dependent signaling pathways, providing novel insights into the molecular mechanisms underlying adaptive development and locomotor transition in anuran amphibians.

Animals↗

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↗

PubMed2026

Integrated multi-omics profiling of amniotic fluid identifies predictive biomarkers for fetal growth restriction trajectories.

BACKGROUND: Fetal growth restriction (FGR) is a complex condition with highly heterogeneous clinical outcomes, making prenatal distinction between transient and persistent growth failure challenging. This study aims to identify amniotic fluid (AF) biomarkers capable of differentiating distinct FGR trajectories and characterizing persistent growth failure mechanisms. METHODS: Integrated proteomic and metabolomic profiling was performed on AF samples from transient FGR (n = 11), persistent FGR (n = 9), and healthy controls (n = 13). Diagnostic and prognostic models were developed using multivariate analysis. Selected protein candidates were validated via ELISA in an independent cohort (n = 69). RESULTS: Multi-omics analysis revealed distinct molecular signatures for FGR stratification. A two-protein diagnostic panel (PDGFA and phospho-STAT5A) achieved an AUC of 1.000 in the discovery stage and 0.780 in the external validation cohort. For prognostic assessment, a molecular signature including IREB2, HLA-C, and PLXNB2 accurately predicted persistent growth failure from transient recovery (AUC = 0.966). Cross-platform integration highlighted the mass spectrometry-derived WASHC2C as a central hub protein with a significant progressive increase across the control, transient, and persistent groups (p < 0.001). CONCLUSIONS: This study establishes a multi-omics framework for prenatal FGR stratification. Our findings identify distinct molecular signatures reflecting the intrauterine environment and provide high-performance molecular tools for predicting divergent fetal growth trajectories to guide personalized clinical decision-making.

Humans↗

PubMed2025

LimROTS: a hybrid method integrating empirical Bayes and reproducibility-optimized statistics for robust differential expression analysis.

MOTIVATION: Differential expression analysis plays a vital role in omics research enabling precise identification of features that associate with different phenotypes. This process is critical for uncovering biological differences between conditions, such as disease versus healthy states. In proteomics, several statistical methods have been used, ranging from simple t-tests to more advanced methods like DEqMS, limma and ROTS. However, a flexible method for reproducibility-optimized statistics tailored for clinical omics data has been lacking. RESULTS: In this study, we developed LimROTS, a hybrid method that integrates a linear regression model and the empirical Bayes approach with reproducibility optimized statistics, to create a novel moderated ranking statistic, for robust and flexible analysis of proteomics data. We validated its performance using twenty-one proteomics gold standard spike-in datasets with different protein mixtures, MS instruments, and techniques for benchmarking. This hybrid approach improves accuracy and reproducibility of complex proteomics data, making LimROTS a powerful tool for high-dimensional omics data analysis. AVAILABILITY AND IMPLEMENTATION: LimROTS has been implemented as an R/Bioconductor package, available at https://doi.org/doi:10.18129/B9.bioc.LimROTS. Additionally, the code used in this study is available in GitHub repository https://github.com/AliYoussef96/LimROTSmanuscript.

Bayes Theorem↗