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PubMed2026

Alternative End Joining Dependency Imposed by miR-21-5p Defines Radiation Resistance and a Targetable Vulnerability in Oral Squamous Cell Carcinoma.

PURPOSE: Clinical control of oral squamous cell carcinoma (OSCC) is constrained by heterogeneous radiosensitivity driven by divergent DNA damage response programs. The architecture and functional contribution of alternative end joining (Alt-EJ), an error-prone DNA double-strand break (DSB) repair pathway frequently upregulated in cancer, to radiation resistance remains poorly defined. METHODS AND MATERIALS: We profiled microRNAs in radioresistant OSCC clones and performed multiomic integration across an institutional OSCC cohort, an external OSCC cohort from the Gene Expression Omnibus, The Cancer Genome Atlas pan-cancer tumors, and cell lines characterized by Sanger Genomics of Drug Sensitivity in Cancer to infer DNA damage response characteristics, genomic scar features, drug sensitivity, and radiation therapy outcomes. DSB repair capacity and pathway usage were validated using functional assays, including Alt-EJ reporters and droplet digital PCR quantification of microhomology-mediated repair events. Core Alt-EJ effectors such as PARP1 and POLQ were perturbed genetically and pharmacologically. Therapeutic efficacy of PARP or POLQ inhibition with or without irradiation was tested in a syngeneic OSCC model, followed by bulk tumor transcriptomics to assess pathway engagement. RESULTS: Upregulation of miR-21-5p was not only selectively detected in radioresistant OSCC, but also modulated radiosensitivity in vitro and in vivo, and was associated with inferior postradiation therapy survival. A calibrated miR-21-5p target-gene signature tracked Alt-EJ activity across patient and mouse tumors and cancer cell lines, correlated with microhomology-mediated indels and broader genomic scarring, and predicted sensitivity to clinically available PARP inhibitors. Functionally, enforced miR-21-5p expression increased Alt-EJ usage and accelerated DSB repair, whereas inhibition or depletion of key Alt-EJ effectors reduced repair efficiency and restored radiosensitivity. In vivo, Alt-EJ targeting with PARP or POLQ inhibitor abrogated miR-21-5p-driven radiation resistance; transcriptomic profiling supported suppression of Alt-EJ programs as the operative mechanism. CONCLUSIONS: These findings establish a mechanistic link between miR-21-5p activity and Alt-EJ dependence, provide a clinically deployable signature to identify Alt-EJ-dependent OSCC, and support rational combinations of Alt-EJ targeting agents with radiation therapy to overcome treatment failure and advance precision radiation oncology.

MicroRNAs↗

PubMed2025

Design of optimized epigenetic regulators for durable gene silencing with application to PCSK9 in nonhuman primates.

Epigenetic editing is a promising strategy for modifying gene expression while avoiding the permanent alterations and potential genotoxicity of genome-editing technologies. Here we designed optimized epigenetic regulators (EpiRegs) by testing combinations of transcription activator-like effector (TALE)-based and catalytically deactivated Cas9 (dCas9)-based epigenetic modification effectors and fusion protein structures. TALE-based EpiReg (EpiReg-T) achieved a final efficiency of 98% in mice, surpassing the initial dCas9-based efficiency of 64%. We demonstrated the approach in macaques by introducing DNA methylation and histone modifications to inhibit proprotein convertase subtilisin/kexin type 9 (PCSK9) expression, thereby lowering low-density lipoprotein cholesterol levels. A single dose of EpiReg-T delivered with lipid nanoparticles achieved efficient (>90%) and long-lasting (343 days) silencing of PCSK9 in the liver. Integrative multiomic analyses revealed minimal off-target effects in EpiReg-T-treated monkeys, mice and human-derived cells. EpiReg can be redirected to other genes by reengineering the DNA-binding domain. Our findings represent a step toward the clinical application of epigenetic editing for the treatment of human diseases.

Animals↗

PubMed2026

In vivo genome-wide CRISPR screens identify FOXR1 as a suppressor of CD8+ T cell antitumor immunity.

T cell dysfunction critically limits the efficacy of T cell-based immunotherapies in solid tumors, yet the intrinsic regulators of T cell dysfunction remain incompletely understood. Through an in vivo genome-wide CRISPR screen in tumor-infiltrating CD8+ T cells, we identified Forkhead Box R1 (FOXR1) as a potent transcriptional suppressor of CD8+ T cell effector functions. Genetic ablation of FOXR1 significantly enhanced cytokine production and cytotoxic capacity in both murine and human CD8+ T cells, whereas its overexpression impaired T cell activation and effector molecule expression. Mechanistically, multiomics integration of RNA-seq, CUT&Tag-seq, and ATAC-seq revealed that FOXR1 binds directly to promoter regions of key effector genes, including IL2, GZMB, and PRF1, and represses their expression. Importantly, FOXR1 deletion in human anti-CD19 CAR T cells improved their efficacy against solid tumors, demonstrating that FOXR1 is a checkpoint of T cell effector function and targeting FOXR1 is a promising strategy to enhance CAR T cell efficacy against solid tumors.

Animals↗

PubMed2026

Genomic characterization of aggressiveness in pituitary neuroendocrine tumors.

BACKGROUND: Aggressive evolution of PitNETs is rare; metastatic spread is even more. Defining aggressiveness and malignancy is challenging, subsequently hard to predict, and to understand. The aim was to provide a molecular definition of aggressiveness using genomic approaches. METHODS: PitNETs from 206 patients were included. Associations between 9 clinicopathological features of aggressiveness and PitNETs' omics were explored. Omics included transcriptome, DNA methylation, chromosomal alterations, and mutations. Clonal tumor evolution was monitored in 7 patients. RESULTS: Among the 9 clinicopathological features of aggressiveness, only rapid progression, progression after radiotherapy, Ki67/MIB1 proliferation index ≥10%, temozolomide treatment, metastases, and specific death were associated with specific omics signatures, while tumour maximal diameter ≥40 mm, cavernous, and sphenoid invasion were not. The omic signatures associated with these features of aggressiveness overlapped but remained distinct between corticotroph and mammo-somato-thyrotroph lineages. For each lineage, a common signature of aggressiveness was identified, associating a proliferative transcriptome signature and DNA hypermethylation. Alterations in specific genes were associated with aggressive features, including a novel PitNET gene, LRP1B, and known cancer genes (TP53, CDKN2A), while USP8 and GNAS alterations were not. Integration of gene alterations with methylome and transcriptome signatures isolated a subset of molecularly aggressive PitNETs. Molecular signatures were stable during the course of the disease, despite evolution toward aggressiveness and potential clonal divergence. CONCLUSION: This systematic analysis of clinicopathological features of aggressiveness using an integrated multiomic approach establishes a histomolecular definition of aggressiveness in PitNETs. Prospective cohort studies are needed to validate these molecular signatures and establish their prognostic value.

Humans↗

PubMed2026

MYC-bound enhancer RNAs in cis regulate gene transcription and tumorigenesis.

Emerging evidence suggests that MYC binds RNAs, but its functional consequences remain unclear. Here, we integrate multiomics data and reveal that MYC broadly binds enhancer RNAs (eRNAs), which exhibit high cancer- and tissue-specific expression in cancer cell lines and patient tumors. Moreover, we developed a computational pipeline to identify potential cis-regulatory MYC-eRNA target genes, with most predicted eRNA-target pairs supported by RNA polymerase II-mediated chromatin interaction data. Among these, we functionally characterized MERG1 as an oncogenic eRNA that promotes breast cancer tumorigenesis. Mechanistically, MERG1 interacts with MYC to enhance its occupancy at the GREB1 promoter, driving chromatin remodeling and epigenetic activation. This process specifically amplifies GREB1 expression and promotes tumor progression. Last, nanoparticle-mediated delivery of antisense oligonucleotides targeting MERG1 suppresses MYC-mediated breast cancer growth. These results advance our understanding of the enhancer-driven regulation of gene expression and tumorigenesis and provide insights into the regulatory landscape of MYC in cancer.

Humans↗

PubMed2026

Developmental genetic determinants of the human cerebrospinal fluid-ventricular system.

Primary enlargement of the cerebrospinal fluid (CSF)-filled brain ventricles, known as congenital cerebral ventriculomegaly (CCV), is a hallmark of congenital hydrocephalus. CCV is also enigmatically but frequently associated with autism and other neurodevelopmental disorders. To gain insight into the developmental genetic regulation of the human CSF-ventricular system, we conducted an integrated, multiomic study of about 2700 trio-based exomes from patients with primary CCV. We found that about 25% of cases were associated with rare, damaging de novo variants in mutation-intolerant genes, many of which are linked to other dominant Mendelian disorders. Thirty-five exome-wide significant CCV genes and dozens of other high-confidence CCV genes converged on pathways involved in ATP-dependent Brahma-related gene 1/Brahma-associated factor chromatin remodeling, histone H3 lysine 4 methylation, and phosphoinositide 3-kinase signaling. Knockout of selected CCV genes in mouse models supported that de novo variants in CCV genes caused ventriculomegaly by impairing both CSF dynamics and cortical cytoarchitecture through dysregulation of neuroprogenitor cell growth and maturation in the ventricular and subventricular zones. These findings indicated that genetic and epigenetic programs coordinate the "hand-in-glove" development of the CSF-ventricular system with that of the cerebral cortex and establish a genetic connection between CCV and neurodevelopmental disorders, potentially explaining why some patients with hydrocephalus continue to exhibit CCV and neurodevelopmental disorders despite CSF shunting. We suggest that combined brain imaging and whole-exome sequencing could enable early detection of, and intervention for, autism and other neurodevelopmental disorders.

Humans↗

PubMed2026

Integrated Clinicopathologic and Multiomic Profiling Reveals MEIS1-Rearranged Sarcoma as a Distinct Entity With 2 Prognostic Subgroups.

Sarcomas with MEIS1 fusions represent a rare, recently recognized group of mesenchymal neoplasms with a predilection for genitourinary and gynecologic sites. A subset exhibits skeletal muscle differentiation resembling spindle cell rhabdomyosarcoma. Existing literature is limited to case reports and small series, with scant comprehensive clinicopathologic, molecular, and outcome data. In this study, we analyzed a multi-institutional cohort of 20 MEIS1-rearranged sarcomas using integrated clinicopathologic review, genomic profiling, and DNA methylation analysis. The tumors occurred in 17 females and 3 males (median age, 41 years; range, 6-58 years), arising mainly in the uterus/vagina (n = 12), vulva/perineum (n = 4), bone (n = 2), and kidney (n = 2), with a median size of 9 cm (range, 2.5-20 cm). Histology showed mostly bland spindle cells in fascicles/storiform patterns, alternating cellularity, fibromyxoid stroma, prominent vascularity, and adipose metaplasia (45%). A subset of cases featured high-grade morphology with epithelioid cells and increased mitotic activity. Skeletal muscle markers were variably positive in 9 cases. Fusions involved MEIS1 with NCOA2 (16/20), NCOA1 (3/20), or FOXO1 (1/20). Recurrent additional genomic alterations included CTNNB1 mutations (31.6%) and MDM2 amplification (15%). DNA methylation profiling showed that MEIS1-rearranged sarcomas formed a unifying cluster comprising 2 subgroups, regardless of rhabdomyosarcomatous phenotype, clearly separated from other mesenchymal neoplasms, including various rhabdomyosarcoma subtypes and uterine sarcomas. The 2 DNA methylation (Meth) subgroups correlated with differences in genome-wide copy number variation (CNV) status (Meth-CNV high vs Meth-CNV low), with Meth-CNV high tumors characterized by high mitotic rate, frequent tumor necrosis, recurrent co-occurring CTNNB1 and MDM2 alterations, and recurrent chromosomal arm-level changes. Most importantly, this subgroup exhibited significantly worse overall survival (P = .027) and disease-free survival (median, 5 vs 99 months; P = .017). This study establishes MEIS1-rearranged sarcoma as a distinct entity with generally indolent but potentially aggressive behavior. The 2 methylation/CNV subgroups provide potential utility for prognostic stratification and highlight actionable molecular targets in high-risk cases.

Humans↗

PubMed2025

Integrating explainable AI with multiomics systems biology and EHR data mining for personalized drug repurposing in Alzheimer's disease.

Alzheimer's disease (AD) is characterized by region- and patient-specific molecular heterogeneity, which hinders therapeutic design. In this study, we introduce PRISM-ML (PRecision-medicine using Interpretable Systems and Multiomics with Machine Learning), an open-source integrated analysis pipeline that combines interpretable machine learning with systems biology and electronic health record (EHR) data mining to elucidate the molecular diversity of AD and predict promising drug repurposing opportunities. First, we integrated and harmonized transcriptomic (bulk RNA-seq) and genomic (genome-wide association study) data from 2105 brain samples, each with matched data from the same individual (1363 AD patients, 742 controls; nine tissues), sourced from three independent studies. Random forest classifiers with SHapley Additive exPlanations (SHAP) identified patient-specific biomarkers; unsupervised clustering resolved 36 molecularly distinct "subtissues" (clusters of samples); and gene-gene co-expression networks prioritized 262 high-centrality bottleneck genes as putative regulators of dysregulated pathways. Next, knowledge graph-based drug repurposing predicted six FDA-approved drugs that simultaneously target multiple bottleneck genes and multiple AD-relevant pathways. Notably, in a large U.S. de-identified insurance-claims database (n = 364733), exposure to promethazine, one of the candidate drugs, was associated with a 57-62 % lower incidence of AD versus an active antihistamine comparator (adjusted hazard ratio 0.38; inverse-probability weighted 0.43; both p < 0.001), providing real-world support for its repurposing potential. In summary, PRISM-ML, as an explainable multi-omics analysis pipeline, is readily transferable to other complex diseases, advancing precision medicine.

Computational Biology↗

PubMed2025

Integration of single cell multiomics data by deep transfer hypergraph neural network.

Multi-omics characterization of individual cells offers remarkable potential for analyzing the dynamics and relationships of gene regulatory states across millions of cells. How to integrate multimodal data is an open problem, existing integration methods struggle with accuracy and modality-specific biological variation retention. In this paper, we present scHyper (scalable, interpretable machine learning for single cell integration), a low-code and data-efficient deep transfer model designed for integrating paired and unpaired single-cell multimodal data. We benchmark scHyper against datasets from different multimodal data. ScHyper learns a low-dimensional representation and aligns the covariance matrices of the measured modalities, achieving high accuracy even with large scale atlas-level datasets with low memory and computational time across different cell lines, shedding light on regulatory relationships between different types of omics. Altogether, we show that scHyper is a versatile and robust tool for cell-type label transfer and integration from multimodal single-cell datasets.

Single-Cell Analysis↗

PubMed2026

AI-Based 3D Heterogeneous Network Model for Functional Prediction of Epigenetics.

Human biology and diseases are the result of constantly evolving processes within an intricately complex molecular network of interactions, such as epigenetic regulation. Epigenetics refers to heritable changes in gene expression that occur without alterations to the underlying DNA sequence. These changes, driven by mechanisms such as DNA methylation, histone modifications, and noncoding RNAs, play critical roles in regulating chromatin structure and gene activity. Epigenetic regulation offers valuable insights into biological systems, and when integrated with sophisticated analyses, it enables us to gain insights into gene regulation and cellular behavior. Here, we describe an artificial intelligence (AI)-based model that is capable of generating 3-dimensional (3D) heterogeneous network by integrating multimodal data for the functional prediction of epigenetic mechanisms, emphasizing its applications in medicine, developmental biology, and personalized therapeutics. Heterogeneous networks in biology are powerful tools for understanding the complex interactions and interdependencies within biological systems. Key advancements in AI and multiomics data integration have propelled this field, offering new insights into disease mechanisms, biomarker discovery, and therapeutic interventions.

Epigenesis, Genetic↗

PubMed2025

Integrating explainable artificial intelligence with multiomics systems biology and electronic health record data mining for personalized drug repurposing in Alzheimer's disease.

Alzheimer's disease (AD) is characterized by region- and patient-specific molecular heterogeneity, which hinders therapeutic design. In this study, we introduce PRISM-ML (PRecision-medicine using Interpretable Systems and Multiomics with Machine Learning), an open-source integrated analysis pipeline that combines interpretable machine learning with systems biology and electronic health records data mining to elucidate the molecular diversity of AD and predict promising drug repurposing opportunities. First, we integrated and harmonized transcriptomic (bulk RNA-seq) and genomic (genome-wide association study) data from 2105 brain samples, each with matched data from the same individual (1363 AD patients, 742 controls; 9 tissues), sourced from three independent studies. Random forest classifiers with SHapley Additive exPlanations identified patient-specific biomarkers; unsupervised clustering resolved 36 molecularly distinct subtissues (defined as clusters of samples within a brain tissue that share a specific expression pattern); and gene-gene coexpression networks prioritized 262 high-centrality bottleneck genes as putative regulators of dysregulated pathways. Next, knowledge graph-based drug repurposing predicted six Food and Drug Administration (FDA)-approved drugs that simultaneously target multiple bottleneck genes and multiple AD-relevant pathways. Notably, in a large US de-identified insurance-claims database (n = 364 733), exposure to promethazine, one of the candidate drugs, was associated with a 57%-62% lower incidence of AD versus an active antihistamine comparator (adjusted hazard ratio 0.38; inverse-probability weighted 0.43; both P < .001), providing real-world support for its repurposing potential. In summary, PRISM-ML, as an explainable multiomics analysis pipeline, is readily transferable to other complex diseases, advancing precision medicine.

Alzheimer Disease↗

PubMed2026

DIVAS: an R package for identifying shared and individual variations of multiomics data.

MOTIVATION: Multiomics data integration aims to identify biological patterns shared across molecular modalities. Most existing methods detect either jointly shared variation, across all modalities, or individual variation, unique to a single modality, but overlook partially shared variation, shared by only a subset of modalities. This is a critical limitation, because many biological mechanisms manifest in some but not all molecular modalities. RESULTS: We present an open-source R package implementing data integration via analysis of subspaces (DIVAS), a framework for systematically identifying jointly shared, partially shared and individual variations across multiple data types. DIVAS combines angle-based subspace analysis with inference through rotational bootstrap, hierarchically searching all combinations of modalities to decompose multiomics data into interpretable components with scores and loadings. In simulations with a known sharing structure, DIVAS recovered every component across a wide range of noise levels, whereas existing methods did not. Applied to multi-modal COVID-19 data, it reveals partially shared immune and metabolic dysregulation patterns underpinning disease severity that conventional approaches would miss. AVAILABILITY AND IMPLEMENTATION: DIVAS is available at https://github.com/ByronSyun/DIVAS, with documentation and vignettes. The COVID-19 case study vignette is available at https://byronsyun.github.io/DIVAS_COVID19_CaseStudy/.

Multiomics↗

PubMed2026

Unlocking the Circulating Proteome: Toward Clinical Translation.

Blood-based proteomics is approaching a translational inflection point. Driven by advances in measurement technologies, rapid expansion of analytical capabilities, and growing adoption across research and medical communities, there is increasing demand for clinically actionable biomarkers. As the field transitions away from purely large-scale discovery-oriented studies toward more informed, targeted, application-driven analyses, the generation of proteomic data is no longer the bottleneck. Instead, the central challenge is to translate these measurements into robust, reproducible, and clinically meaningful insights. In this Review, we assess recent technological and methodological developments, evaluate persistent preanalytical and interpretative limitations, and outline the key steps required for clinical translation. We focus on three deeply interconnected dimensions: the capabilities and constraints of current measurement platforms, the role of computational and machine learning approaches in extracting biological and clinical signals, and the emergence of large-scale population studies that create new opportunities for validation and generalization. Finally, we discuss a forward-looking vision in which proteomics plays a central role in dynamic, multilayered omics frameworks, where integration with genomics, temporal profiling, and imaging can deepen our understanding of health, disease, and therapeutic response.

Humans↗

PubMed2025

Integration of multi-source gene interaction networks and omics data with graph attention networks to identify novel disease genes.

MOTIVATION: The pathogenesis of diseases is closely associated with genes, and the discovery of disease genes holds significant importance for understanding disease mechanisms and designing targeted therapeutics. However, biological validation of all genes for diseases is expensive and challenging. RESULTS: In this study, we propose DGP-AMIO, a computational method based on graph attention networks, to rank all unknown genes and identify potential novel disease genes by integrating multi-omics and gene interaction networks from multiple data sources. DGP-AMIO outperforms other methods significantly on 20 disease datasets, with an average AUROC and AUPR exceeding 0.9. The superior performance of DGP-AMIO is attributed to the integration of multiomics and gene interaction networks from multiple databases, as well as triGAT, a proposed GAT-based method that enables precise identification of disease genes in directed gene networks. Enrichment analysis conducted on the top 100 genes predicted by DGP-AMIO and literature research revealed that a majority of enriched GO terms, KEGG pathways and top genes were associated with diseases supported by relevant studies. We believe that our method can serve as an effective tool for identifying disease genes and guiding subsequent experimental validation efforts. AVAILABILITY AND IMPLEMENTATION: DGP-AMIO is publicly available at https://github.com/yangkaiyuan1027/DGP-AMIO.

Gene Regulatory Networks↗

PubMed2025

Mechanistic roles of GmSWEET10a/b and GmSUT1 in the oil-protein balance in soybean mature seeds at transcriptional and metabolic levels.

Previous investigations indicated that the soybean (Glycine max) SUGARS WILL EVENTUALLY BE EXPORTED TRANSPORTER10a/b (GmSWEET10a/b) genes promote oil accumulation, while inhibiting protein accumulation in seeds. To clarify the mechanisms modulated by GmSWEET10a/b in mediating the oil and protein accumulations in soybean seeds, an integrated comparative multiomics was conducted using the double gmsweet10a,b mutant and wild-type (WT) embryos. Spatial metabolomic analysis revealed that gmsweet10a,b embryos were surrounded by a sugar-reduced seed coat and experienced a sugar-starvation state in embryonic tissues in vivo. The decreased sugar content in the gmsweet10a,b embryos reduced the availability of carbon skeletons required for oil synthesis and was associated with decreased expression levels of genes involved in sucrose metabolism, fatty acid biosynthesis, and triacylglycerol assembly. Meanwhile, the expression of genes encoding storage protein was induced in gmsweet10a,b embryos, when compared with WT. These changes resulted in decreased oil content and increased protein content in gmsweet10a,b embryos versus WT. In vitro sugar-starvation assay also supported the suppression of fatty acid biosynthesis and the enhanced storage protein accumulation in developmental embryo under sugar-starved conditions. Furthermore, the knockout of SUCROSE TRANSPORTER 1 (GmSUT1), which was upregulated in gmsweet10a,b embryos, significantly decreased the sugar level, resulting in lower oil content but higher protein content in gmsut1 embryos than WT ones. Our findings provided a mechanistic understanding of the modulation of sugar transport between seed coat to embryo by both GmSWEET10a/b and GmSUT1, which plays a pivotal role in balancing oil and protein accumulations in soybean mature seeds.

Seeds↗

PubMed2026

Network methods for diagonal integration of unpaired single-cell multiomics data: a review.

MOTIVATION: Advances in single-cell sequencing have enabled multiomics profiling at unprecedented resolution; however, mass spectrometry-based single-cell proteomics (scMS) remains inherently destructive, precluding simultaneous transcriptomic capture. Unlike antibody-based methods such as CITE-seq, which permit paired profiling but are restricted to targeted protein panels, scMS provides unbiased, genome-scale coverage of the intracellular proteome yet necessitates post hoc integration of unpaired datasets. This diagonal integration challenge, where transcriptomes and proteomes are measured in separate cells lacking shared anchors, remains underserved by existing reviews, which focus predominantly on vertical integration strategies enabled by non-destructive assays. RESULTS: We survey the complete computational pipeline for constructing mechanistic proteogenomic networks from unpaired single-cell data, covering: (i) unimodal network inference such as knowledge-based approaches, probabilistic graphical models, temporal directionality inference, and generative and foundation model strategies that establish the transcriptomic scaffold; (ii) cross-modal integration architectures such as network propagation, graph neural networks (scMRDR, scmFormer, scCotag), and consensus frameworks designed explicitly for the unpaired proteomics setting; and (iii) benchmarking paradigms spanning network reconstruction (BEELINE, GRETA, CausalBench) and multi-task integration evaluation (scMultiBench, SCMMIB), with guidance on metric selection under network sparsity and class imbalance. We identify three principal axes of future development: generative proteomic translation from transcriptomic precursors, inductive prior embedding in next-generation architectures, and perturbation-based causal benchmarking. AVAILABILITY AND IMPLEMENTATION: This is a review article; no novel software is distributed. A curated benchmark resource table, methods starter guide, and per-method bottleneck annotations are provided in the Supplementary Material.

Multiomics↗

PubMed2026

Multi-omic biomarkers in cardiovascular disease: Discovery to clinical translation.

Cardiovascular disease (CVD) remains the leading cause of mortality worldwide, necessitating improved risk stratification and early detection strategies. Multiomics approaches that integrate genomics, transcriptomics, proteomics, metabolomics, and epigenomics offer unprecedented opportunities for biomarker discovery and precision medicine in cardiovascular care. This narrative review examines the current landscape of multiomics biomarkers for CVD, tracing their evolution from discovery to clinical translation. We synthesize evidence from recent studies evaluating the clinical utility of integrated omics approaches across diverse cardiovascular conditions, including atherosclerotic cardiovascular disease, heart failure, and atrial fibrillation. High-throughput proteomics has identified novel protein signatures that enhance cardiovascular risk prediction beyond traditional risk factors. Metabolomics has revealed pathway-specific biomarkers, including trimethylamine N-oxide and lipid species, associated with atherogenesis. Polygenic risk scores derived from genomic data demonstrate incremental value when combined with clinical risk scores. Multiomics biomarkers represent a transformative approach to cardiovascular risk assessment and disease management.

Humans↗

PubMed2024

Multiome Perturb-seq unlocks scalable discovery of integrated perturbation effects on the transcriptome and epigenome.

Single-cell CRISPR screens link genetic perturbations to transcriptional states, but high-throughput methods connecting these induced changes to their regulatory foundations are limited. Here, we introduce Multiome Perturb-seq, extending single-cell CRISPR screens to simultaneously measure perturbation-induced changes in gene expression and chromatin accessibility. We apply Multiome Perturb-seq in a CRISPRi screen of 13 chromatin remodelers in human RPE-1 cells, achieving efficient assignment of sgRNA identities to single nuclei via an improved method for capturing barcode transcripts from nuclear RNA. We organize expression and accessibility measurements into coherent programs describing the integrated effects of perturbations on cell state, finding that ARID1A and SUZ12 knockdowns induce programs enriched for developmental features. Modeling of perturbation-induced heterogeneity connects accessibility changes to changes in gene expression, highlighting the value of multimodal profiling. Overall, our method provides a scalable and simply implemented system to dissect the regulatory logic underpinning cell state. A record of this paper's transparent peer review process is included in the supplemental information.

Humans↗