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PubMed2025

Droplet-Based Single-Cell 3' mRNA Sequencing of Marburg Virus-Infected Samples.

Single-cell technologies are continually evolving with emerging methods that are gradually uncovering the central DNA-RNA-protein dogma. Single-cell RNA sequencing is one arm of a multi-omic approach that achieves an astounding level of granularity to reveal the complexity of virus-host interactions at the transcriptomic level. Cell tropism, virus replication, pathogenesis, and gene expression changes mediated by the virus and the host's immune response to infection are just some areas of study that are gaining better clarity due to the high-resolution analysis afforded by the technology.We describe a single-cell sequencing protocol for Marburg virus infection in vivo using nonhuman primate blood and the 10× Chromium Next GEM single-cell genomics methodology. Working with pathogens of high consequence is logistically complicated, requiring containment in biosafety level (BSL)-4 laboratories and harsh inactivation procedures before samples can safely be removed to lower biosafety conditions. We provide procedural insight into sample isolation and processing conducted in BSL-4 and describe the requirements for safe sample removal without jeopardizing quality for down-stream sequencing and analysis in BSL-2 conditions. Characterization of complicated biological processes mediated by high-containment pathogens, typically restricted to analogous model systems, e.g., minigenome, can be achieved using live virus.

Animals↗

PubMed2027

Systematic Dissection of Key Driver Perturbation Signatures in Single Cells via ECCITE-seq.

CRISPR screens, such as expanded CRISPR-compatible cellular indexing of transcriptomes and epitopes by sequencing (ECCITE-seq), enable the simultaneous measurement of transcriptomes, gRNA identity, and cell-surface protein expression at single-cell resolution to systematically interrogate gene function. This platform provides a powerful and scalable experimental approach for validating disease-associated regulators identified by large-scale association studies and other computational methods, including network-based analyses of multi-omics data. Here, as an example application, we describe an ECCITE-seq framework to characterize the transcriptomic consequences of perturbing multiple neuronal key driver genes associated with Alzheimer's disease (AD) in human-induced pluripotent stem cell (hiPSC)-derived neurons. More broadly, by integrating customized pooled gRNA libraries with different CRISPR effectors across multiple cell types, this approach allows for the assessment of the regulatory impact of candidate genes implicated in development and disease processes.

Humans↗

PubMed2007

Toward whole cell modeling and simulation: comprehensive functional genomics through the constraint-based approach.

The increasing availability of various system-level, or so-called 'omics', datasets, in concert with existing data from the primary research literature, is facilitating the development of genome-scale metabolic models for many organisms. By incorporating the metabolic reaction stoichiometry as well as other physicochemical properties into systemic network reconstructions, these models account for the constraints that restrict an organism's phenotypic behavior. Accordingly, unlike many contemporary modeling strategies, this constraint-based modeling approach does not attempt to predict network behavior exactly; rather, it seeks to clearly distinguish those network states that a system can achieve from those that it cannot. A variety of analytical tools have been designed and developed to probe these models, thus enabling studies that investigate the metabolic capabilities of a number of organisms, that generate and test experimental hypotheses, and that predict accurately metabolic phenotypes and evolutionary outcomes. This chapter introduces the concepts that underlie the constraint-based modeling approach, and describes several of its applications with an emphasis on those potentially relevant to the drug development field. In addition, while this chapter focuses on the primary application of the constraint-based approach to date, namely in modeling metabolic networks, the latter sections of the chapter discuss its relatively recent application to modeling other cellular systems. Finally, the chapter concludes with an assessment of future directions focusing on the efforts that will be required to utilize the constraint-based approach in generating a holistic model of a viable organism.

Animals↗

PubMed2005

Plant cells: secondary metabolite heterogeneity and its manipulation.

This chapter proposes the concept of rational manipulation of secondary metabolite heterogeneity in plant cell cultures. The heterogeneity of plant secondary metabolites is a very interesting and important issue because these structure-similar natural products have different biological activities. Both taxoids and ginsenosides are two kinds of preeminent examples in the enormous reservoir of pharmacologically valuable heterogeneous molecules in the plant kingdom. They are derived from the five-carbon precursor isopentenyl diphosphate, produced via the mevalonate or the non-mevalonate pathway. The diterpenoid backbone of taxoids is synthesized by taxadiene synthase and the triterpenoid backbone of ginsenosides is synthesized by dammarenediol synthase or beta-amyrin synthase. After various chemical decorations (oxidation, substitution, acylation, glycosylation, benzoylation, and so on) mediated by P450-dependent monooxygenases, glycosyltransferases, acyltransferases, benzoyltransferases, and other enzymes, the terpenoid backbones are converted into heterogeneous taxoids and ginsenosides with different bioactivities. Although detailed information about accumulation and regulation of individual taxoids or ginsenosides in plant cells is still lacking, remarkable progress has recently been made in the structure and bioactivity identification, biosynthetic pathway, manipulation of their heterogeneity by various methodologies including environmental factors, biotransformation, and metabolic engineering in cell/tissue cultures or in plants. Perspectives on a more rational and efficient process to manipulate production of desired plant secondary metabolites by means of metabolic engineering and "omics"-based approaches (e.g., functional genomics) are also discussed.

Biotechnology↗

PubMed2025

Assessment of genomic prediction capabilities of transcriptome data in a barley multi-parent RIL population.

Low-cost and high-throughput RNA sequencing data for barley RILs achieved GP performance comparable to or better than traditional SNP array datasets when combined with parental whole-genome sequencing SNP data. The field of genomic selection (GS) is advancing rapidly on many fronts including the utilization of multi-omics datasets with the goal of increasing prediction ability and becoming an integral part of an increasing number of breeding programs ensuring future food security. In this study, we used RNA sequencing (RNA-Seq) data to perform genomic prediction (GP) on three related barley RIL populations. We investigated the potential of increasing prediction ability by combining genomic and transcriptomic datasets, adding whole-genome sequencing (WGS) SNP data, functional annotation-based filtering, and empirical quality filtering. Our RNA-Seq data were generated cost-efficiently using small-footprint plant cultivation, high-throughput RNA extraction, and Library preparation miniaturization. We also examined sequencing depth reduction as an additional cost-saving measure. We used fivefold cross-validation to evaluate the prediction ability of the gene expression dataset, the RNA-Seq SNP dataset, and the consensus SNP dataset between the RNA-Seq and parental WGS data, resulting in prediction abilities between 0.73 and 0.78. The consensus SNP dataset performed best, with five out of eight traits performing significantly better compared to a 50K SNP array, which served as a benchmark. The advantage of the consensus SNP dataset was most prominent in the inter-population predictions, in which the training and validation sets originated from different RIL sub-populations. We were therefore able to not only show that RNA-Seq data alone are able to predict various complex traits in barley using RILs, but also that the performance can be further increased with WGS data for which the public availability will steadily increase.

Hordeum↗

PubMed2025

Genetic basis and role of exotic accessions in cultivated cotton fiber quality improvement.

Exotic Gossypium accessions still harbor QTL‑validated alleles that, combined with CRISPR pyramiding and genomic selection, can break the entrenched fiber length-strength trade‑off. Cotton's four independent domestications twice in diploids and twice in allotetraploids offer a natural experiment in fiber improvement. Synthesizing three decades of data, we chart how polyploidy, selection and modern breeding have repeatedly reshaped the Gossypium genome. More than 15,000 quantitative trait locus (QTL) and genome wide association mapping studies (GWAS) hits converge on a handful of chromosomal "hotspots"; new MAGIC, NAM, NIL and long-read resources now narrow these peaks to < 200 kb, resolving causal genes such as GhHOX3, GhZF14 and GhMYB7. Multi-omics evidence links auxin, ethylene, gibberellin, brassinosteroid and strigolactone signaling to HDZIP IV, MYB, bHLH/HLH and ERF networks that drive fiber initiation, extreme cell elongation and cellulose deposition. Population genomics shows that ~ 40% of favorable fiber alleles are fixed in elite Gossypium hirsutum, yet wild diploids and landraces still harbor variants that could break the length strength trade-off. We propose a three-step roadmap genomic selection, CRISPR gene pyramiding and accelerated introgression to expand cotton's genetic base and deliver fibers suited to sustainable textile demands.

Gossypium↗

PubMed2006

Recent developments in microarray-based enzyme assays: from functional annotation to substrate/inhibitor fingerprinting.

Recent advances in proteomics have provided impetus towards the development of robust technologies for high-throughput studies of enzymes. The term "catalomics" defines an emerging '-omics' field in which high-throughput studies of enzymes are carried out by using advanced chemical proteomics approaches. Of the various available methods, microarrays have emerged as a powerful and versatile platform to accelerate not only the functional annotation but also the substrate and inhibitor specificity (e.g. substrate and inhibitor fingerprinting, respectively) of enzymes. Herein, we review recent developments in the fabrication of various types of microarray technologies (protein-, peptide- and small-molecule-based microarrays) and their applications in high-throughput characterizations of enzymes.

Animals↗

PubMed2006

Exploiting new systems-based strategies to elucidate plant-bacterial interactions in the rhizosphere.

The rhizosphere is the site of intense interactions between plant, bacterial, and fungal partners. In plant-bacterial interactions, signal molecules exuded by the plant affect both primary initiation and subsequent behavior of the bacteria in complex beneficial associations such as biocontrol. However, despite this general acceptance that plant-root exudates have an effect on the resident bacterial populations, very little is still known about the influence of these signals on bacterial gene expression and the roles of genes found to have altered expression in plant-microbial interactions. Analysis of the rhizospheric communities incorporating both established techniques, and recently developed "omic technologies" can now facilitate investigations into the molecular basis underpinning the establishment of beneficial plant-microbial interactomes in the rhizosphere. The understanding of these signaling processes, and the functions they regulate, is fundamental to understanding the basis of beneficial microbial-plant interactions, to overcoming existing limitations, and to designing improved strategies for the development of novel Pseudomonas biocontrol strains.

Bacterial Physiological Phenomena↗

PubMed2006

The identification of human tumour antigens: Current status and future developments.

The biggest challenge facing us today in cancer control and prevention is the identification of novel biomarkers for detection and improved therapeutic interventions to reduce mortality and morbidity rates. Biomarkers are important indicators to inform us of the physiological state of the cell at a specific time. It is now clear that malignant transformation occurs by changes in cellular DNA and protein expression with subsequent clonal proliferation of the altered cells. The affected genes and their expressed protein products or biomarkers are those involved in the normal growth and maintenance of the cancerous cells. These biomarkers could prove pivotal for the identification of early cancer and people at risk of developing cancer. Altered proteins or changes in gene expression in malignant cells may lead to the expression of tumour antigens recognised by host immune system. In this review we discuss current research into the molecular technologies making possible the global genomic-wide analysis of changes in DNA (genotyping), RNA expression (transcriptomics) and protein expression (proteomics) that have accelerated the rate of new biomarker/tumour antigen discovery. To gain a comprehensive understanding of the physiology and pathophysiology of cancer an approach that harmoniously integrates the various 'omic' platforms are key to unraveling the complexity 'needle-in-a-haystack' quality of biomarker/tumour antigen discovery.

Antigens, Neoplasm↗

PubMed2026

Metabolic convergence of diabetes and prostate cancer: from dysglycemia to tumor microenvironment reprogramming.

The relationship between diabetes mellitus and prostate cancer (PC) represents one of the most intriguing paradoxes in cancer epidemiology, with diabetic individuals exhibiting a reduced incidence of PC yet poorer prognosis following diagnosis. This apparent contradiction underscores the need for an integrated understanding of how systemic metabolic dysfunction influences prostate carcinogenesis and disease progression. The present review critically synthesizes contemporary epidemiological, mechanistic, and translational evidence to establish metabolic convergence as a unifying framework linking diabetes-associated metabolic abnormalities with PC biology. Current evidence indicates that chronic dysglycemia, hyperinsulinemia, insulin resistance, and endocrine perturbations orchestrate interconnected intracellular signaling networks involving PI3K-AKT-mTOR, AMPK, AGE-RAGE signaling, oxidative stress, mitochondrial dysfunction, and epigenetic reprogramming, collectively driving metabolic adaptation and tumor evolution. Beyond tumor-intrinsic mechanisms, diabetes profoundly remodels the prostate tumor microenvironment through alterations in stromal metabolism, cancer-associated fibroblast activation, adipocyte-tumor crosstalk, extracellular matrix (ECM) remodeling, hypoxic adaptation, and vascular dysfunction, while simultaneously promoting immunometabolic reprogramming characterized by macrophage polarization, T-cell dysfunction, immune checkpoint activation, and immune evasion. The review further examines the bidirectional interactions between antidiabetic therapies and PC treatment, critically evaluating the translational potential of metformin and emerging glucose-lowering agents within the context of precision metabolic therapeutics. Finally, future directions encompassing biomarker-guided patient stratification, longitudinal metabolic profiling, multi-omics integration, artificial intelligence, and clinically relevant mechanistic validation are discussed as essential components of next-generation precision oncology. Collectively, this review reframes diabetes as an active metabolic determinant of PC rather than a coincidental comorbidity and highlights metabolism-centered precision strategies as promising avenues for improving risk stratification, therapeutic decision-making, and clinical outcomes in diabetes-associated PC.

Humans↗

PubMed2004

The future of motor neuron disease: the challenge is in the genes.

Adult-onset motor neuron disease (MND) includes sporadic and familial forms of amyotrophic lateral sclerosis (ALS), lower motor neuron disease including progressive and segmental spinal muscular atrophy (LMND) and primary lateral sclerosis (PLS). ALS/MND can be considered to be a spectrum of neurodegenerative diseases characterised by a preferential degeneration of upper and/or lower motor neurons. ALS and LMND have a complex multifactorial aetiology and a large clinical variability. This combination warrants an increasing genomics approach in future research. Genomics is the structural and functional study of genomes--i. e. the complete set of chromosomes and the genes they contain. Several methods may help to understand gene functions, and every method has led to its own "omics". The study of the complex relationship between on the one hand genomics data, transcriptomics data, proteomics data, and interactomics data and on the other hand the phenotype, is called "phenomics". In phenomics, the extensive and detailed phenotyping by the clinician is a prerequisite for meaningful associations. As a consequence, in ALS/MND clinicians have the task to agree about different clinical subtypes in order to make these associations and hence to gain further insight into the complex pathogenesis and identification of diagnostic markers in ALS/MND. Also, several new approaches in the treatment of ALS/MND are here discussed, including the viral delivery of protective compounds, RNA-interference, and stem cell therapy. Further, we argue that a future challenge is to allow for patients to have early access to multidisciplinary centres with specialist knowledge of ALS/MND. These centres can apply specific models of care for people with ALS/MND, but must be designed in a patient-centred format. Ultimately, these models should be assessed according to their outcomes.

Forecasting↗

PubMed2026

Holistic approaches for improvement of maize resistance against lodging stress: current status and future perspective.

Lodging is a major constraint in maize production, causing significant yield losses, reduced grain quality, and harvesting inefficiencies, thereby posing a serious challenge to global food security and climate-resilient agriculture. This review synthesizes current knowledge on the genetic, physiological, and agronomic determinants of maize lodging resistance and evaluates holistic strategies for improving tolerance to lodging stress. Recent advances in quantitative trait locus (QTL) mapping, genome-wide association studies (GWAS), functional gene characterization, genome editing, high-throughput phenotyping, and precision agronomy have provided powerful tools to enhance stalk biomechanics, root anchorage, and adaptive plant architecture. Integrating genomic discovery with advanced phenomics and optimized agronomic management offers a scalable framework for accelerating the development of high-yielding, lodging-resilient maize cultivars. However, critical gaps remain in understanding the genetic coordination between stalk strength and root system architecture, integrating multi-omics approaches to unravel regulatory networks, validating genome-editing interventions across diverse agro-ecologies, and developing environment-responsive predictive breeding models and cost-effective phenotyping tools, particularly for stress-prone regions. Addressing these challenges through coordinated multi-environment trials and integrative molecular-agronomic strategies will facilitate the translation of genomic discoveries into climate-resilient, high-performing maize cultivars. By consolidating molecular insights with applied breeding and management practices, this review provides a comprehensive framework that guides researchers in designing genome-informed and field-validated approaches to improve maize resistance to lodging stress and support sustainable crop production systems.

Zea mays↗

PubMed2026

Scalable production of pectinases from Bacillus licheniformis SMIA-2 using agro-Industrial by-products with genomic insights.

UNLABELLED: The study re-analyzed the draft genome of Bacillus licheniformis SMIA-2 and generated a reference-guided pseudo-scaffold. Cross-validated genome annotation identified five candidate loci associated with pectin degradation, including putative pectate lyases, polygalacturonase, and downstream uronate-catabolic genes. Submerged fermentation with passion fruit peel flour and corn steep liquor yielded crude enzymatic extracts, which were spray-dried at 110 °C using maltodextrin and microcrystalline cellulose as stabilizers. The dried formulation retained pectinase activity for 180 days at 5 °C and showed additional cellulase, amylase, xylanase, and protease activities. Pectinase displayed optimal activity at pH 8.5 and 70 °C, with stability between pH 8.0-8.5 and 65-70 °C. Despite not using a reference strain and the absence of some omics analyses, with genomic and industrial claims presented as evidence of biotechnological potential rather than definitive functional validation of individual genes, these results support a sustainable, scalable, and alkaline-tolerant enzyme platform based on agro-industrial residues. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at https://doi.org/10.1007/s10068-026-02252-3.

Agro-industrial residues↗

PubMed2026

Integrated transcriptomic and metabolomic analysis reveals candidate regulatory networks associated with starch accumulation in tetraploid potato.

Potato (Solanum tuberosum L.) tuber starch is a major determinant of crop quality and industrial value, yet the regulatory mechanisms underlying starch accumulation in autotetraploid cultivars remain poorly resolved. Here, we performed integrated transcriptomic and metabolomic analyses using a segregating tetraploid population derived from parents with contrasting starch content. Extreme phenotypes were selected to systematically dissect the molecular basis of starch accumulation. Transcriptome profiling revealed extensive transcriptional reprogramming between high- and low-starch genotypes, with differentially expressed genes significantly enriched in carbohydrate metabolism, particularly the starch and sucrose metabolism pathway. Notably, multiple transcription factor families, including AP2/ERF, MYB, and bHLH, were prominently represented, suggesting coordinated regulatory control. Metabolomic analysis identified substantial metabolic divergence, with differentially accumulated metabolites predominantly enriched in starch and sucrose metabolism as well as secondary metabolic pathways. Most metabolites exhibited negative associations with starch content, indicating competitive carbon allocation between primary and secondary metabolism. Integrative multi-omics analysis further resolved a core regulatory module comprising key structural genes and transcription factors tightly associated with starch-related metabolites. In particular, genes involved in sucrose cleavage and ADP-glucose metabolism, together with trehalose-6-phosphate synthase (TPS) and UDP-glucose-associated pathways, emerged as critical nodes linking carbon flux to starch biosynthesis. Correlation network analysis suggested that AP2/ERF-, MYB-, and bHLH-type transcription factors modulate these pathways by coordinating structural gene expression and metabolic flux distribution. Collectively, our study establishes a transcriptional-metabolic framework for starch accumulation in tetraploid potato, highlighting the central role of carbon allocation and signaling intermediates in shaping starch content, and providing candidate targets for molecular breeding and genome editing.

Solanum tuberosum↗

PubMed2026

Pan-cancer analysis identifies KANSL2 as a cell-cycle-associated regulator of tumor progression and immunity in liver hepatocellular carcinoma.

KANSL2, a core component of the NSL histone acetyltransferase complex, has been implicated in tumorigenesis. However, its pan-cancer relevance and functional role in liver hepatocellular carcinoma (LIHC) remain unclear. Multi-omics data from TCGA, GEO, and HPA were integrated to systematically evaluate KANSL2 expression, clinical significance, genomic alterations, and immune associations across cancers. Functional enrichment, immune infiltration analyses, and single-cell transcriptomics were performed. In vitro assays were conducted to validate the biological effects of KANSL2 in LIHC cells. KANSL2 is broadly upregulated across cancers and exhibits strong diagnostic performance. Elevated KANSL2 expression correlates with unfavorable prognosis, particularly in LIHC. Mechanistically, KANSL2 and its co-expressed genes are enriched in cell-cycle progression. KANSL2 expression is also closely associated with immune infiltration and immunoregulatory signaling within the tumor microenvironment, with single-cell data indicating preferential expression in proliferative T-cell subsets. Functional experiments demonstrate that KANSL2 silencing suppresses proliferation, migration, and invasion, and induces G2/M phase arrest in LIHC cells. Notably, its effects on apoptosis are limited, suggesting that KANSL2 primarily drives tumor progression through cell-cycle-dependent mechanisms. This study identifies KANSL2 as a key regulator of tumor progression and immune remodeling in LIHC. By promoting malignancy predominantly via cell-cycle control, KANSL2 represents a promising biomarker for diagnosis and prognosis, and a potential therapeutic target.

Carcinoma, Hepatocellular↗

PubMed2025

Exposure to zinc oxide nanoparticles inhibits preimplantation embryonic development by disrupting zygotic genome activation.

The potential adverse effects of zinc oxide nanoparticles (ZnONPs) on human reproductive health may arise from their increasing industrial and commercial applications. However, their effects on preimplantation embryonic development and the related molecular mechanisms are still not well understood. Here, we demonstrate that ZnONPs exposure exhibit toxicity to a critical developmental period in mice. We observed that sustained exposure to ZnONPs in vitro resulted in embryonic development arrest at the 2-cell stage. To identify the susceptible stage, we controlled experiments to treat embryos with ZnONPs in the different processes of early embryonic development and determined that ZnONPs mainly to affect 2-cell stage embryos. According to the RNA-seq and EU (5-ethynyl uridine) analysis, the transcriptional activity of minor ZGA genes increased in the late 2-cell embryos following ZnONPs exposure. Subsequently, we employed multi-omics assays, including CUT&Tag and ATAC-seq. We found that ZnONPs exposure led to increased enrichment of H3K27ac (Histone H3 acetylated lysine 27) in late 2-cell embryos and enhanced chromatin accessibility, which led to abnormal upregulation of minor zygotic genome activation (ZGA) genes. In addition, the direct occupancy of ZnONPs at H3K27ac modification sites was verified through pulldown and immunoprecipitation. In conclusion, our findings demonstrate that ZnONPs exposure disrupting minor ZGA by interfering with H3K27ac erasure on the embryonic genome and ultimately impairing the developmental potential of embryos.

Animals↗

PubMed2026

Integrative Transcriptomic and Proteomic Profiling Identifies S100P as a Potential Functional Biomarker for Sessile Serrated Lesions.

BACKGROUND: Sessile serrated lesions (SSLs) account for 15% of colorectal cancers (CRCs) but detection remains difficult due to flat morphology, mucinous features, and subtle histology. AIMS: This study aimed to identify novel and functionally relevant biomarkers of SSLs using transcriptomic screening and multi-omics validation. METHODS: Paired SSL and normal mucosa specimens (n = 6) underwent RNA sequencing. Differentially expressed genes (DEGs) were filtered for membrane or secretory proteins and validated across TCGA and adenoma transcriptomes. Functional significance was assessed using CRISPR dependency profiling, proteotranscriptomic concordance, pharmacogenomic sensitivity, and connectivity map analysis. RESULTS: We identified 216 upregulated genes in SSLs, including 68 encoding secretory/membrane proteins that better discriminated SSLs from controls and were enriched for adhesion and neuronal signaling while suppressing TNFα-NFκB inflammatory pathways. Cross-cohort comparison revealed five overlapping candidates between SSLs and TCGA CMS1 tumors. Among them, S100P emerged as the primary biomarker candidate, showing consistent upregulation in SSLs and CMS1 tumors while remaining low in normal mucosa and conventional adenomas. TFF1 also showed RNA-level upregulation but appeared more context-dependent. S100P demonstrated strong RNA-protein concordance in CRC cell-line profiling, supporting its detectability as a biomarker candidate. Pharmacogenomic profiling of LS411N cells revealed marked sensitivity to SN-38 and fluoropyrimidines, consistent with serrated CRC vulnerabilities. Connectivity map analysis identified perturbations, including MAPK1 and histone acetyltransferase suppression, that may reverse parts of the SSL transcriptional program. CONCLUSION: These findings prioritize S100P as a promising biomarker candidate for SSLs that warrants further validation in larger cohorts and clinically applicable platforms.

Humans↗

PubMed2026

Circulating Biomarkers Related to Mitral Valve Prolapse: Current Evidence and Mechanistic Perspective.

PURPOSE OF REVIEW: Although imaging remains central to diagnosis and risk stratification, circulating biomarkers provide complementary information reflecting myocardial stress, fibrosis, extracellular matrix remodeling, inflammation, and metabolic dysregulation. The purpose of this review is to critically evaluate current and emerging circulating biomarkers in MVP and to assess whether these biomarkers may improve individualized management of this disease. RECENT FINDINGS: Natriuretic peptides are the most validated biomarkers in MVP, consistently predicting adverse outcomes and providing complementary prognostic information that may help inform the timing of surgical intervention, particularly in asymptomatic patients with significant mitral regurgitation. In contrast, evidence supporting fibrosis and inflammation mediators, proteomics, metabolomics, and circulating microRNAs remains emerging. In arrhythmic MVP, no circulating biomarker is currently recommended for routine risk stratification. Future research should prioritize phenotype-specific MVP registries, biomarker-CMR integration, multi-omics profiling, and biomarker-guided interventional trials.

Humans↗