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PubMed2026

Omics in optic neuropathies: From molecular landscapes to personalized therapeutics.

Optic neuropathies comprise a heterogeneous group of disorders involving transient or permanent injury to retinal ganglion cells (RGCs) and their axons. Clinically, these neurodegenerative conditions manifest as dyschromatopsia, decreased visual acuity, and visual field defects, and in severe cases may ultimately lead to blindness and disability. The marked heterogeneity across disease subtypes, incompletely understood etiologies, and complex pathogenic mechanisms pose substantial challenges to precise diagnosis and effective treatment. Recent advances in omics technologies - including genomics, transcriptomics, proteomics, metabolomics, lipidomics, single-cell and spatial sequencing, and integrative multi-omics approaches - have ushered optic nerve degenerative disease research into an era of high-resolution comprehensive investigation. In this review, we summarize representative applications of omics approaches to elucidate genetic alterations, signaling dysregulation, metabolic reprogramming, and immune responses in optic neuropathies. We further discuss the emerging potential of multi-omics in identifying early diagnostic biomarkers and informing individualized therapeutic strategies. Finally, we provide a forward-looking perspective on the future trajectory of omics technologies and their prospects in both fundamental research and clinical translation, with the overarching aim of accelerating the bench-to-bedside transition in this critical eye disease field.

biomarkers↗
PubMed2026

SCMO: a deep learning model integrating the single-cell resolution TME ecosystem and multi-omics for survival prediction in CRC patients.

BACKGROUND: Colorectal cancer (CRC) remains a leading cause of global cancer mortality, highlighting the need for precise survival prediction to guide clinical decisions. Although tissue-level multi-omics is widely utilized for survival prediction, its limited resolution cannot capture tumor heterogeneity. Single-cell RNA sequencing (scRNA-seq) enables dissection of the tumor microenvironment (TME) at cellular resolution, supporting personalized prognostic assessment. METHODS: We collected 213 CRC scRNA-seq samples and established a CRC-specific TME atlas comprising 339,060 cells. Using this atlas as a reference, we deconvolved bulk RNA-seq data from TCGA-CRC cohort with the EcoTyper algorithm to reconstruct TME features. Clinical, genomic, and transcriptomic data were obtained from the Xena platform; microbial data were sourced from the BIC database. We integrated TME and multi-omics features through a self-normalizing neural network to construct a deep learning model (single-cell resolution TME ecosystem with multi-omics data [SCMO]) for survival prediction. To enhance interpretability, we utilized the Integrated Gradients algorithm and spatial transcriptomic data to analyze multi-omics and TME features. We performed anticancer drug screening with tumor necrosis factor receptor-associated protein 1 (TRAP1), a critical feature according to the Integrated Gradients algorithm, as a potential target. RESULTS: We identified 13 survival-related TME features from the CRC-specific atlas: 12 cell states and one multi-cellular ecosystem. SCMO, which combined TME and multi-omics features, improved survival prediction and outperformed existing methods, achieving a concordance index of 0.762. The SCMO demonstrated robust performance for long-term predictions, achieving areas under the curve (AUCs) of 0.752, 0.772, and 0.869 for 1-, 3-, and 5-year predictions in the training set, with corresponding test set AUCs of 0.639, 0.756, and 0.772. TME features from the SCMO model revealed that ecosystem density increased with CRC malignancy. Multi-omics features included TRAP1 as a potential drug target. Drug screening identified saikosaponin A as a novel TRAP1 inhibitor, and its anticancer activity was validated in vitro. We developed SCMO-Lite, a simplified model incorporating 12 high-attribution-weight multi-omics features, which demonstrated robust risk stratification. CONCLUSIONS: SCMO combines analytical precision with biological interpretability, offering novel insights for oncology survival prediction.

Humans↗
PubMed2026

Bridging genotype, phenotype, and clinical insight: the role of multi-omics in cardiovascular disease.

INTRODUCTION: It is increasingly evident that the multifactorial nature of cardiovascular disease requires the combination of different omics approaches for improving our mechanistic understanding, identifying novel drug targets, and developing accurate diagnostic, predictive, and prognostic biomarker panels. AREAS COVERED: We review the current state and the potential of multi-omics in cardiovascular disease, with a specific focus on plasma-, spatial-, and single-cell approaches. We discuss lipidomics as a genotype‑to‑phenotype bridge, the utility of remote longitudinal monitoring via microsampling/dried blood spots, and emerging clinical‑trial integrations of multi-omics approaches. We outline critical gaps in standardization and how to overcome these, pre‑analytical challenges and constraints that are often neglected, and data‑integration methods spanning from canonical correlation analysis to modern machine learning approaches. EXPERT OPINION: Multi‑omics can shape cardiovascular care by identifying drug targets in diseased tissue and by yielding small, usable biomarker panels.

Humans↗
PubMed2026

Decoding glioblastoma evolution and heterogeneity through mechanistic modeling: implications for clinical translation.

Glioblastoma (GBM) is one of the most aggressive and lethal primary brain tumors in adults, characterized by dynamic clonal evolution and extensive genomic, cellular, spatial, and microenvironmental heterogeneity. Multi-omics studies have revealed that GBM follows complex evolutionary trajectories involving genetic, epigenetic, transcriptional, and immune-microenvironmental remodeling as tumors grow, adapt to the brain microenvironment, and acquire therapeutic resistance. Increasing evidence suggests that GBM may originate from aberrant neural stem or progenitor cells, including those residing in the subventricular zone, and that glioblastoma stem cells (GSCs) contribute to tumor propagation, heterogeneity, and recurrence. A key conceptual challenge is to reconcile hierarchical cancer stem cell models, in which GSCs are viewed as relatively stable tumor-propagating subpopulations, with dynamic state plasticity models, in which stem-like properties can be reversibly acquired or lost during transitions among proneural-like, mesenchymal-like, invasive, and therapy-tolerant states. Recent advances in single-cell profiling, spatial transcriptomics, lineage tracing, organoid culture, 3D bioprinting, genetically engineered models, and artificial intelligence (AI)-assisted computational modeling have substantially improved the ability to study these processes. However, no currently available model fully recapitulates human GBM heterogeneity, recurrence, treatment history, and tumor-microenvironment interactions. Therefore, model selection should be guided by clearly defined mechanistic questions rather than by reliance on any single platform. This review summarizes current advances in in vitro, ex vivo, in vivo, and computational models for studying GBM evolution and heterogeneity, and discusses how integrated model pipelines may improve preclinical drug testing, treatment-response prediction, and precision neuro-oncology.

Humans↗
PubMed2025

Integrated multi-omics analyses provide new insights into genomic variation landscape and regulatory network candidate genes associated with walnut endocarp.

Persian walnut (Juglans regia) is an economically important nut oil tree; the fruit has a hard endocarp/shell to protect seeds, thus playing a key role in its evolution, and the shell thickness is an important trait for walnut breeding. However, the genomic landscape and the gene regulatory networks associated with walnut shell development remain to be systematically elucidated. Here, we report a high-quality genome assembly of the walnut cultivar 'Xiangling' and construct a graphic structure pan-genome of eight Juglans species to reveal the genetic variations at the genome level. We re-sequence 285 accessions to characterize the genomic variation landscape. Through genome-wide association studies (GWAS), we identified 19 loci associated with more than 268 loci that underwent selection during walnut domestication and improvement. Multi-omics analyses, including transcriptomics, metabolomics, DNA methylation, and spatial transcriptomics across eleven developmental stages, revealed several candidate genes related to secondary cell biosynthesis and lignin accumulation. This integrated multi-omics approach revealed several candidate genes associated with secondary cell biosynthesis and lignin accumulation, such as UGP, MYB308, MYB83, NAC043, NAC073, CCoAOMT1, CCoAOMT7, CHS2, CESA7, LAC7, COBL4, and IRX12. Overexpression of JrUGP and JrMYB308 in Arabidopsis thaliana confirmed their roles in lignin biosynthesis and cell wall thickening. Consequently, our comprehensive multi-omics findings offer novel insights into walnut genetic variation and network regulation of endocarp development and shell thickness, which enable further genome-informed breeding strategies for walnut cultivar improvement.

Juglans↗
PubMed2026

The mighty microproteins: from versatile cellular regulators to precision medicine therapeutics.

Microproteins, are tiny proteins encoded by small open reading frame (sORF), translation of these non-canonical open reading frames (ncORFs) has been implicated in diverse biological processes and diseases. This review summarizes recent developments in the discovery, biogenesis, and functional characterization of microproteins, and their involvement in various disease, with special focus on their roles in cancer, cardiovascular, metabolic, neurodegenerative and immune-related disorders. We emphasize the regulation of key cellular pathways by microproteins, including mitochondrial homeostasis, apoptosis, metabolic reprogramming, and immune signaling, all of which affect disease initiation and progression. Emerging evidence also supports their potential as disease biomarkers and therapeutic candidates for precision medicine. Finally, the review critically discusses the current challenges including discrepancies in microprotein annotation, the limitations of ribosome profiling and proteogenomic approaches, the gap between computationally predicted and experimentally validated microproteins, and the need for rigorous orthogonal validation by means of CRISPR-based genome editing, ribosome release assays, mutational analysis, high-resolution mass spectrometry, and functional studies. Finally, we review recent development of AI-assisted ORF prediction, single-cell translatomics, spatial proteomics, and integrated multi-omics as emerging technologies reshaping. Microprotein discovery and functional annotation. Finally, we discuss the translational potential of microproteins and highlight the remaining challenges to clinical application, including peptide stability, pharmacokinetics, tissue-specific delivery, immunogenicity, and the need for rigorous preclinical and clinical validation. Together, this review provides an updated and critical overview of the rapidly evolving microprotein field and highlights future research priorities for translating these molecules into clinically useful biomarkers and precision therapeutics.

Microproteins↗
PubMed2026

The domestication-associated WHP10 tandem cluster of amino acid transporter genes enhances whole-plant protein accumulation in maize.

Improving protein accumulation in maize is essential for sustainable agriculture, yet the regulatory mechanisms governing the intermediate "flow" of organic nitrogen remain elusive. Here, we show that the maize stem acts as a regulatory node for nitrogen allocation. By integrating spatial transcriptomics and metabolomics with quantitative genetics, we demonstrate that a transport-oriented stem program orchestrates the high-protein phenotype of the wild maize accession Ames21814. We identified a major locus, Whole-plant High Protein 10 (WHP10), that encodes a tandemly duplicated cluster of amino acid transporter genes. WHP10 exhibits strong vascular-biased expression, driven by promoter divergence that enhances the wild allele's activity. Functional assays and genetic validation support a model in which the WHP10 cluster facilitates the transport of multiple nitrogen-rich amino acids, thereby contributing to vascular-associated amino acid transport and post-uptake organic-nitrogen partitioning. Our findings establish stem flow as a regulatory layer for protein accumulation and identify WHP10 as a high-value target for precision breeding to enhance whole-plant protein accumulation without compromising grain yield.

Zea mays↗
PubMed2026

Perineuronal net degradation in aggressive glioblastomas with KANK1::NTRK2 fusions.

BACKGROUND: Approximately 10% of glioblastomas harbor targetable genomic fusions. NTRK2 participates in a variety of fusion events that drive tumorigenesis. Two previous reports have described KANK1::NTRK2 fusions in adult glioblastoma patients with poor survival. METHODS: We performed a retrospective analysis of glioblastoma patients treated at Dartmouth-Hitchcock Medical Center (DHMC) from 2020 to 2025 to identify cases harboring KANK1::NTRK2 fusions. Clinical presentation, treatment, histopathologic features, and outcomes were reviewed. In addition, we conducted GeoMx whole-transcriptome and high-plex proteomic digital spatial profiling of a KANK1::NTRK2-positive glioblastoma and a comparator tumor from a long-term survivor. Candidate biomarkers were orthogonally validated using immunohistochemistry and/or immunofluorescence. RESULTS: Two patients with KANK1::NTRK2 fusion glioblastoma were identified, both demonstrating rapid progression, therapeutic resistance, and survival of less than 7 months. Proteomic profiling showed increased expression and activation of canonical NTRK2 downstream signaling pathways, particularly MEK1/2 and ERK1/2. This was accompanied by upregulation of extracellular matrix remodeling enzymes, including MMP3, MMP14, and ADAM15, along with reduced expression of extracellular matrix-associated transcripts and perineuronal net components in particular compared to a non-fusion glioblastoma. CONCLUSIONS: These limited, hypothesis-generating findings suggest constitutive NTRK2 signaling may promote coordinated extracellular matrix degradation and remodeling, potentially facilitating rapid and aggressive tumor growth and invasion in a subset of glioblastomas.

NTRK gene fusion↗
PubMed2026

Identification of cryosensitive niches and a targetable FOS/AP‑1 program in the human ovarian cortex by single‑cell and spatial transcriptomics.

BACKGROUND: The ovary is a vital and dynamic reproductive organ. Ovarian tissue cryopreservation (OTC) plays a vital role in preserving female fertility. However, the cellular subtypes most susceptible to cryoinjury and the molecular mechanisms underlying cryopreservation-associated damage remain poorly understood. This study aimed to identify cell populations vulnerable to freezing-thawing and to elucidate the key transcriptomic alterations and signaling pathways associated with ovarian cryoinjury at the single-cell and spatial levels. METHODS: Ovarian cortical tissues from patients undergoing three gender reassignment surgery (GRS) were divided into fresh and vitrification-rapid warming groups. Following collagenase IV digestion, 10x Genomics single-cell RNA-seq was used for dissociated ovarian cell suspensions (27,185 fresh and 25,480 frozen-thawed cells). Eight major cell clusters were identified. Additionally, 110 oocytes (66 fresh, 44 vitrification-rapid warming) were isolated and analyzed using the Smart-seq2 platform. Spatial transcriptomics was performed via BGI Stereo-seq. Molecular validation was performed via β-galactosidase staining, immunofluorescence, and qRT-PCR. RESULTS: Cryopreservation significantly altered the activity of pathways related to focal adhesion, oxidative stress, and apoptosis, particularly in stromal and perivascular cells. The number of FOS-positive perivascular cells was notably increased after vitrification-rapid warming, whereas the number of PTGDS-positive stromal cells decreased. Oocyte analysis revealed that cryopreservation primarily disrupted pathways involved in the cell cycle and meiosis, although the damage was not irreversible, supporting the relative safety of long-term cryostorage. Spatial transcriptomics and functional validation further confirmed the rapid and robust activation of the FOS/AP-1 pathway after vitrification-rapid warming, particularly in perivascular and granulosa cells. Treatment with T-5224 (a FOS/AP-1 inhibitor) significantly rescued the morphology and function of cultured frozen-thawed ovaries. CONCLUSIONS: Stromal and perivascular cells are the main cell types that are sensitive to ovarian cryopreservation. The FOS/AP-1 pathway is markedly activated after, suggesting the exacerbation of metabolic impairment. In oocytes within the ovarian cortex, the cell cycle and meiosis-related physiological processes were the primary processes affected.

Female↗
PubMed2026

Multi‑omics approaches to decipher the molecular mechanisms of exercise‑mediated bone protection: From mechanistic insights to personalized exercise prescription (Review).

The global burden of bone metabolic disorders necessitates a shift from generic exercise recommendations toward personalized prescription strategies. Exercise confers skeletal protection through mechanotransduction, yet the underlying molecular networks remain incompletely understood. Multi‑omics technologies, including transcriptomics, proteomics, metabolomics and single‑cell spatial approaches, have revolutionized the capacity to decode exercise‑mediated bone adaptation at the systems level. The present review synthesizes current single‑omics landscapes and integrative multi‑omics analyses that elucidate the core regulatory networks, mechanobiological coupling mechanisms and multiorgan crosstalk that are implicated in the bone response to mechanical loading. Translational applications across clinical scenarios such as osteoporosis, osteoarthritis and disuse bone loss are evaluated, and the technical, analytical and translational challenges limiting clinical implementation are addressed. Finally, the present review provides a framework for translating multi‑omics molecular signatures into personalized exercise prescriptions for optimized skeletal health.

Humans↗
PubMed2026

A single-cell study of transcription and RNA splicing in MDD and ALC.

Major depressive disorder (MDD) and problematic alcohol use (ALC) commonly co-occur, yet the extent, genomic distribution, and biological context of their shared genetic architecture remain incompletely understood. Here, we integrated genome-wide and local genetic architecture analyses with tissue, spatial, single-cell, and multi-omics analyses to characterize the shared genetic basis of MDD and ALC. Across methods, the two phenotypes showed a consistent positive genetic correlation (rg = 0.380-0.582). MiXeR estimated that they shared approximately 5479 variants with non-zero additive genetic effects, with the shared component accounting for a larger proportion of the polygenic architecture of ALC than of MDD. Local analyses further indicated that shared genetic covariance was concentrated in a limited number of genomic segments. At the tissue and cellular levels, genetic signals were primarily associated with central nervous system tissues and neuronal lineages, with additional support for oligodendrocyte-related populations; the two phenotypes also differed in the distribution and within-cell-type heterogeneity of disease-relevance scores. Multi-omics integration prioritized MED19 and ACO2 as candidate genes and highlighted processes related to mitochondrial energy metabolism and synaptic function. These findings refine the genomic, tissue, and cellular context of the shared genetic architecture of MDD and ALC and provide prioritized genomic regions, cell types, and candidate genes for validation in independent populations and functional studies.

Major Depressive Disorder↗
PubMed2026

AI proteomics: from protein identification to virtual cells.

Artificial intelligence (AI) is transforming scientific research, including proteomics. In this Perspective, we highlight key mass spectrometry (MS)-based proteomics areas where AI is driving innovation, ranging from protein identification to building AI virtual cells. These include improving peptide and protein identification and quantification; characterizing protein-protein interactions and protein complexes; advancing spatial and perturbation proteomics; integrating multi-omics data; and, ultimately, enabling AI virtual cells. Finally, we call for global collaboration among data producers, data consumers and other stakeholders to establish an AI-friendly ecosystem for MS-based proteomics, laying the foundation for transformative advancements in proteomics driven by AI.

Proteomics↗
PubMed2026

Convergent methodologies in prosthetic joint infection research: integrating transdisciplinary approaches to understand and prevent biofilm-driven failure of orthopaedic prostheses.

Prosthetic joint infections (PJIs) remain among the most devastating complications of arthroplasty, imposing substantial clinical, economic and patient burdens. Although culture-based diagnostics underpin current clinical practice, PJIs are biofilm-driven infections shaped by taxonomic diversity, spatial organization, host responses and surface interactions, meaning conventional approaches provide only a partial and often decontextualized view of the infection process. We examine how convergent methodologies can transform PJI research by integrating approaches that have traditionally been studied in isolation, including sequencing, transcriptomics, metabolomics, advanced imaging and culture-based characterization. We discuss how whole-genome sequencing, shotgun metagenomics, transcriptomic and metabolomic approaches resolve pathogen identity, functional activity and adaptive persistence and how cross-scale imaging and spatial biology techniques reveal where microbes colonize, interact and survive across implant surfaces. We highlight emerging opportunities to unify these datasets into coherent frameworks that capture both the molecular and physical dimensions of PJIs. Integrating these complementary approaches will enable a multi-layered understanding of PJIs that link composition, function and spatial organization. Ultimately, this provides a foundation for predictive diagnostics, precision antimicrobial strategies and improved implant design and supports a shift towards more effective, mechanism-informed management of implant-associated infection.

Prosthesis-Related Infections↗
PubMed2026

Machine learning and multi-omics clustering to map cellular rewiring and immune evasion in ccRCC.

Immune checkpoint blockade (ICB) efficacy in clear cell renal cell carcinoma (ccRCC) is limited by tumor microenvironment (TME) heterogeneity. Because traditional bulk-derived models lack spatial resolution, we developed an integrated framework connecting macroscopic survival risks to microscopic TME structures. We applied ten algorithms to establish multi-omics subtypes and evaluated 101 machine-learning combinations across three independent cohorts to generate a Consensus Machine Learning-driven Signature (CMLS). The signature's spatial and cellular origins were decoded using spatial transcriptomics (ST) and a 140,000-cell scRNA-seq atlas. Expression of key genes was experimentally validated via RT-qPCR in 17 paired ccRCC clinical tissues. We identified two molecular subtypes with distinct clinical and epigenetic profiles. SuperPC optimization yielded a 24-gene CMLS serving as an independent prognostic factor. scRNA-seq and ST deconvolution revealed these signals predominantly originate from cancer-associated fibroblasts (CAFs) and malignant epithelial cells, which collaborate to drive spatial immune exclusion. RT-qPCR confirmed significant overexpression of five core CMLS genes in ccRCC versus adjacent normal tissues. Low CMLS scores correlated with enhanced ICB responsiveness, whereas high-CMLS tumors demonstrated specific vulnerability to dasatinib and dabrafenib. The CMLS translates spatial immune-exclusion dynamics into a quantifiable metric, outperforming tumor mutational burden in predicting ICB benefits, providing a robust tool for patient stratification in ccRCC.

Humans↗
PubMed2026

Multiomics approaches to cardiovascular disease: technological innovations and clinical translation.

Cardiovascular diseases (CVDs) remain the leading cause of global morbidity and mortality, reflecting a persistent gap between clinical phenotyping and the molecular mechanisms that govern disease initiation, progression, and interindividual variability. Recent advances in emerging technologies have fundamentally reshaped cardiovascular physiology by enabling high-resolution, cross-layer profiling of the heart and vasculature across genomic, epigenomic, transcriptomic, proteomic, metabolomic, lipidomic, glycomic, and fluxomic layers, increasingly at single-cell and spatial resolution. These approaches reveal CVD as a coordinated, multilayered process driven by dynamic interactions among cell types, regulatory programs, and metabolic states, rather than isolated gene-level defects. In this review, we synthesize how emerging multiomic, computational, and functional genomic technologies are redefining the study of cardiovascular disease across molecular, cellular, and tissue levels. We highlight recent innovations in single-cell and spatial atlases, long-read sequencing, proteomics and metabolomics, integrative data modeling, and functional omics approaches, including genome-scale perturbation screens and single-cell perturbation frameworks. These platforms enable mechanistic dissection of regulatory circuits, distinguish primary disease drivers from secondary adaptations, and directly assess therapeutic reversibility, advancing the field beyond associative biomarker discovery toward mechanism-guided target prioritization. We further discuss key methodological and translational challenges accompanying high-dimensional cardiovascular data, including preanalytical variability, control selection, temporal misalignment across molecular layers, population diversity, and reference bias. By integrating technological innovation with computational rigor and functional validation, this review frames emerging omics-enabled strategies as a unified, physiologically grounded framework for translating molecular insight into clinically meaningful cardiovascular phenotypes and advancing precision cardiovascular medicine.

Humans↗
PubMed2025

Multi-omics technologies: Novel tools and methods for assessing nerve injury and regeneration.

Recently, with the rapid advancement of multi-omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, new tools and approaches have been introduced for studying nerve injury and regeneration. This review highlights the application and progress of multi-omics in uncovering the mechanisms of nerve injury, guiding the development of regenerative strategies, and promoting clinical translation. By integrating multi-omics datasets, researchers can comprehensively track dynamic molecular changes following nerve injury, including abnormal gene expression, disrupted protein signaling, altered metabolic programs, and shifts in the immune microenvironment. Single-cell multi-omics technologies resolve cellular heterogeneity, revealing the distinct functions of neurons, glial cells, and immune cell subpopulations during the injury response. Spatially resolved transcriptomics maintain the spatial context of lesion and regeneration sites, enabling precise localization for targeted interventions. Multi-omics technologies not only identify key molecular players involved in nerve regeneration but also create opportunities for personalized medicine. Nonetheless, integrating multi-omics data poses technical challenges, including high dimensionality, batch effects, and algorithmic constraints, while ethical concerns related to stem cell therapy and gene editing require stringent oversight. To transition from structural reconstruction to functional remodeling, future research should emphasize artificial intelligence-driven data integration, organ-on-a-chip modeling, and cross-disciplinary collaboration to overcome existing technical barriers and accelerate the clinical application of neuroregenerative therapies.

artificial intelligence↗
PubMed2006

What do -omics mean for the science and policy of the nutritional sciences?

The development of systems biology is revolutionizing the way we are studying and learning about human health. It is a way of thinking and a systematic attempt to integrate information from several fields of study (physical, biological, chemical, engineering, etc) to develop a more kinetic and real-time understanding of complex biological processes. It uses mathematical modeling tools to chart dynamic interactions between the components of a biological system, eg, genes, transcripts, proteins, metabolites, and cells, to simulate and analyze networks and pathways and the spatial and temporal relations that exist in biological systems. The term -omics represents the rigorous study of various collections of molecules, biological processes, or physiologic functions and structures as systems, represented most prominently by genomics. In the field of nutrition, had a systems approach been applied to evaluating the effect of vitamin A status on mortality rates in young children in developing countries, it might not have taken 20 y to go from the initial epidemiologic observations to global vitamin A supplementation programs. Better understanding of the functional biology of retinoids on different tissues that mediate host resistance to infection, and their synergistic interactions in biological, metabolic, and functional terms, could have provided a plausible mechanism for the observed effect on mortality. There are 3 policy take-home messages: 1) When controversies exist, invest in the science needed to sort them out. 2) Increase the amounts of money available for health research and interventions relevant to developing countries. 3) Ensure that policymakers understand the issues and why they are important and understand the science and its relevance.

Animals↗
PubMed2026

Deciphering the Genetic Underpinnings of Liver Cirrhosis-Heart Failure Comorbidity Through Multi-Omics: CRIM1 as a Key Endothelial Mediator.

The co-occurrence of liver cirrhosis (LC) and heart failure (HF) poses considerable clinical challenges, yet the cellular and molecular determinants of this comorbidity remain poorly characterized. To address this, we developed an integrative multi-omics pipeline encompassing GWAS meta-analysis, gsMap-based spatial transcriptomic projection, GeneEnrich functional annotation, single-cell atlas construction, seismicGWAS and ECLIPSER cell-type scoring, eCAVIAR and fastenloc colocalization, hdWGCNA network inference, scTenifoldKnk in silico gene perturbation, and GCTA-COJO fine-mapping. Quality-controlled meta-analysis yielded 12,347,758 and 9,256,862 variant-level associations for LC and HF, respectively. Spatial projection confirmed preferential enrichment of disease signals within embryonic hepatic and cardiac compartments. Pathway analyses disclosed that LC-linked loci were concentrated in lipid metabolic programs, whereas HF-linked loci implicated mitochondrial bioenergetics and lysosomal degradation. At the cellular level, endothelial cells emerged as the dominant HF-associated population. Convergent evidence from five orthogonal algorithms pinpointed CRIM1 as the sole robustly supported shared gene, selectively enriched in HF endothelial cells; virtual perturbation further identified LCP1 and PTPRC as downstream regulatory nodes. Fine-mapping of the chromosome 2 locus harboring rs12476437 revealed multiple statistically independent signals in the vicinity of CRIM1. Collectively, these findings computationally prioritize the endothelial-CRIM1 axis as a previously unappreciated candidate mechanistic bridge between LC and HF requiring experimental validation.

Humans↗