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PubMed2026

Multi-Omics Analysis Reveals Molecular Networks and Key Pathways Associated with Cysteine- and Methionine-Mediated Biosynthesis of Sulfur-Containing Flavor Metabolites in Lentinula edodes.

Lentinula edodes is renowned for its unique aroma, which is characterized by various volatile sulfur-containing flavor metabolites (SCFMs). Cysteine and methionine could enhance the SCFMs biosynthesis in L. edodes; however, the underlying metabolic pathways remain unclear. To bridge this gap, integrated proteomic and metabolomic analysis were performed to decipher pathways through which cysteine and methionine regulate SCFM biosynthesis. Results showed that exogenous cysteine and methionine supplementation significantly increased the content of lenthionine, the key aroma compound of shiitake mushrooms. Both treatments induced substantial changes in the proteomic and metabolomic profiles. Proteomic analysis revealed that differentially expressed proteins were predominantly enriched in cysteine and methionine metabolism and sulfur metabolism following cysteine treatment, whereas methionine treatment mainly affected proteins associated with tryptophan metabolism and sulfur metabolism. Metabolomic analysis showed that differentially accumulated metabolites were significantly enriched in D-amino acid metabolism and cysteine and methionine metabolism, with glutathione metabolism specifically enriched under cysteine treatment. Integrated omics analysis further uncovered distinct sulfur metabolite-protein regulatory networks under different sulfur nutrition and identified treatment-specific hub proteins. These findings establish a molecular regulatory framework linking SCFM biosynthesis with broader primary metabolic pathways involved in sulfur intermediate generation and regulation, providing new insights into the potential regulatory networks underlying SCFM formation in L. edodes.

Methionine↗

PubMed2026

Multi-omics Mendelian Randomization Prioritizes Neutrophil Extracellular Trap-related Genes Associated with Atrial Fibrillation Risk.

BACKGROUND: Neutrophil extracellular traps (NETs) participate in thrombosis, inflammation, and cardiovascular remodeling, yet whether NET-related genes (NRGs) are associated with atrial fibrillation (AF) risk across multiple molecular layers remains unclear. This study used a multiomics Mendelian randomization framework to prioritize NRGs supported by methylation, expression, and protein quantitative trait loci (QTL) data. METHODS: Genome-wide significant cis instruments (P < 5 × 10-8) were obtained for 90 methylation QTLs (mQTLs), 100 expression QTLs (eQTLs), and 38 protein QTLs (pQTLs) mapped to 137 literature- curated NRG entries. Summary-data-based Mendelian randomization (SMR) coupled with the heterogeneity in dependent instruments (HEIDI) test was applied using whole-blood mQTL data (n = 1,980), eQTLGen blood eQTL data (n = 31,684), and deCODE plasma pQTL data (n = 35,559). AF outcome data were obtained from a meta-analysis including 60,620 cases and 970,216 controls of European ancestry. RESULTS: At the methylation level, 21 CpG-feature associations across 13 genes remained significant after HEIDI filtering and false discovery rate (FDR) correction. Expression-level analysis identified eight significant gene-AF associations, whereas protein-level analysis identified seven significant features representing five unique proteins. Cross-omics integration prioritized C3, MAPK3, and STAT3 as Tier 1 genes, CTSC, LPAR3, and THBD as Tier 2 genes, and fourteen additional genes as Tier 3 candidates. C3 showed risk-increasing protein-level associations together with multiple significant CpG signals, whereas MAPK3 and STAT3 showed directionally protective expression/protein or methylation/protein patterns. DISCUSSION: The cross-omics convergence on C3, MAPK3, and STAT3 is consistent with complement activation, immune-fibrotic signaling, and cytokine-regulatory pathways implicated in AF biology, but the findings should be interpreted as genetic prioritization rather than definitive intervention-ready causality. CpG-level heterogeneity at the C3 locus and the blood/plasma origin of the QTL resources further support a cautious interpretation. Modest colocalization support and the unresolved possibility of pQTL sample overlap further support this cautious, hypothesis-generating interpretation. CONCLUSION: Multi-omics SMR prioritizes C3, MAPK3, and STAT3 as the most consistently supported NET-related genes associated with AF risk. These findings provide a framework for atrialtissue replication and mechanistic validation of NET-related pathways in AF.

Atrial fibrillation↗

PubMed2025

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

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

Metabolomics↗

PubMed2026

The current and future perspective of ChickenGTEx project and its applications in precision breeding.

The Chicken Genotype-Tissue Expression (ChickenGTEx) project was established to systematically characterize the regulatory landscape of the chicken genome and to accelerate the translation of functional genomics into precision breeding. By integrating whole-genome sequencing with multi-tissue transcriptomic profiling, ChickenGTEx provides a comprehensive atlas of gene expression regulation across diverse tissues and physiological systems. Current findings demonstrate that complex production traits are governed by coordinated regulatory networks rather than isolated loci, with substantial contributions from tissue-specific gene expression, structural variation, and genotype-by-sex interactions. Sex-dependent regulatory effects further refine the genetic architecture of metabolic, immune, and reproductive traits, highlighting the importance of incorporating sex as a biological variable in genomic analyses. Application of integrative omics frameworks within elite layer populations has revealed multilayer regulatory mechanisms underlying extended laying performance, feed efficiency, metabolic health, and eggshell quality. By partitioning phenotypic variance into genetic, regulatory, and host-microbiome components, these approaches move beyond association-based mapping toward causal inference and biological interpretation. Importantly, validated regulatory loci identified through ChickenGTEx and related analyses provide actionable markers for genomic selection and rational targets for precision genome modification. Looking forward, continued expansion of regulatory atlases, incorporation of single-cell and longitudinal data in diverse environmental conditions, and integration of functional annotation into breeding pipelines will further enhance prediction accuracy and sustainable genetic improvement. The ChickenGTEx project thus represents a foundational platform bridging functional genomics and practical poultry breeding.

Animals↗

PubMed2025

Integrating multi-omics approaches in acute myeloid leukemia (AML): Advancements and clinical implications.

Acute myeloid leukemia (AML) is a highly heterogeneous and aggressive hematologic malignancy characterized by clonal proliferation of myeloid precursors. Despite significant advancements in genomic profiling and targeted therapies, patient outcomes remain suboptimal due to disease complexity, resistance mechanisms, and high relapse rates. The integration of multi-omics approaches-spanning genomics, epigenomics, transcriptomics, proteomics, and metabolomics-has revolutionized AML research, offering a comprehensive understanding of leukemogenesis, tumor heterogeneity, and therapeutic vulnerabilities. Recent studies leveraging high-throughput sequencing, mass spectrometry, and advanced computational tools have uncovered novel biomarkers, clonal evolution dynamics, and microenvironmental interactions that drive AML progression and resistance. For instance, single-cell multi-omics has revealed chemotherapy-resistant leukemic stem cell populations, while proteogenomic analyses have identified actionable targets such as MCL1 and metabolic dependencies like OXPHOS. Clinically, integrated omics platforms are refining risk stratification, minimal residual disease (MRD) monitoring, and personalized therapy selection. However, challenges such as data integration complexity, cost barriers, and ethical considerations remain. This review highlights the transformative potential of multi-omics in AML, emphasizing recent advancements in technology, biomarker discovery, and therapeutic innovation. By bridging the gap between molecular insights and clinical practice, multi-omics integration promises to redefine AML management, paving the way for precision oncology and improved patient outcomes.

Humans↗

PubMed2026

Unveiling the Molecular Secrets of Seaweeds: A Comprehensive Review of Bioinformatics Applications in Algal Research.

Recent advances in high-throughput sequencing, bioinformatics, and multi-omics technologies have transformed seaweed research by overcoming long-standing challenges associated with complex genomes, diverse life cycles, and limited genomic resources. This review provides a comprehensive overview of bioinformatics approaches used to investigate seaweed genomics, transcriptomics, proteomics, metabolomics, microbiomes, and functional genomics, with emphasis on the computational tools and databases that support these analyses. Applications of bioinformatics in phylogenetics, drug discovery, microbiome characterization, and the development of biofuels, nutraceuticals, pharmaceuticals, and sustainable agriculture are also discussed. Particular attention is given to emerging strategies involving multi-omics integration, genome editing, artificial intelligence, machine learning, and synthetic biology that are reshaping seaweed research. The review further examines current challenges, including incomplete genomic resources, data standardization, and the need for experimental validation of computational predictions. Collectively, these advances highlight the growing role of bioinformatics in enabling systems-level understanding of seaweed biology and accelerating their translation into sustainable biotechnological and marine bioeconomy applications.

macroalgal genomics↗

PubMed2026

Integrated multi-omics identification of m6A-SNP-related diagnostic biomarkers in amyotrophic lateral sclerosis.

BACKGROUND: Amyotrophic lateral sclerosis (ALS) lacks reliable and minimally invasive biomarkers for early diagnosis. m6A-associated single-nucleotide polymorphisms (m6A-SNPs) may influence RNA methylation and gene expression, offering opportunities to identify clinically relevant diagnostic markers. METHODS: We integrated eQTLGen cis-eQTL data, RMVar m6A-SNP annotations, and ALS transcriptomic datasets to identify m6A-SNP-related genes. Random Forest and LASSO regression were combined to screen robust diagnostic markers. A nomogram was constructed and validated using independent cohorts. Immune infiltration, predicted m6A modification sites, and potential RBP-SNP interactions were assessed. Peripheral blood samples from ALS patients were used for exploratory validation of gene expression and global m6A levels. RESULTS: We identified 109 ALS-associated m6A-SNP-related genes with cis-eQTL signals and narrowed these to seven candidate diagnostic markers (TMED5, OXR1, BRI3, FEM1C, SUZ12, EIF2AK4, and TJAP1). The seven-gene model outperformed the individual markers in the training cohort and retained moderate discrimination in the independent validation cohort. ALS samples showed differences in inferred immune-cell composition, including monocytes, neutrophils, and T-cell subsets. The selected SNP loci were located near predicted m6A sites and annotated RBP-binding regions. Exploratory clinical validation showed significant upregulation of FEM1C and SUZ12 at both mRNA and protein levels, accompanied by reduced global m6A modification. CONCLUSIONS: Through multi-omics integration and exploratory clinical validation, this study identifies m6A-SNP-related candidate markers associated with ALS. The findings support further evaluation of m6A-related signatures for ALS discrimination and molecular characterization, while larger independent cohorts and additional calibration are required before clinical application.

Humans↗

PubMed2025

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

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

Single-Cell Analysis↗

PubMed2026

A 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

Decoding cancer with artificial intelligence: Transforming research, diagnosis, and therapy with future insights.

Cancer remains one of the leading global health burdens, with increasing complexity in genomic, imaging, and clinical datasets presenting significant challenges for effective management. Artificial intelligence (AI) has emerged as a powerful tool to address these challenges by enabling pattern recognition, knowledge integration, and data-driven decision-making. This review highlights recent advances in the application of AI across cancer research, diagnosis, and therapy. In research, AI accelerates drug discovery and repurposing, enhances genomic data interpretation, and facilitates biomarker identification through multi-omics integration. In diagnosis, AI has demonstrated high technical performance in radiology for lesion detection and image segmentation, in pathology for tumour grading and molecular prediction, and in liquid biopsy for non-invasive biomarker analysis. In therapy, AI supports precision medicine by predicting treatment responses, monitoring disease progression, and optimizing clinical trial design. Despite these advances, barriers such as data heterogeneity, algorithmic bias, interpretability, and regulatory challenges remain. Future directions, including explainable AI, federated learning, multimodal modelling, and digital twins, hold promise for translating AI-driven innovations into routine oncology practice. Significance Statement This review provides a timely synthesis of recent (2020-2025) advances in artificial intelligence across cancer research, diagnosis, and therapy, highlighting applications in drug discovery, genomics, multi-omics biomarker identification, and clinical decision-making. By integrating technological progress with translational and clinical relevance, this work serves as a valuable resource for bridging AI innovation with precision oncology practice. As a narrative review, the literature was identified through targeted PubMed, Scopus, and Google Scholar searches, combining terms for artificial intelligence, machine learning, and deep learning with cancer-related keywords, with priority given to peer-reviewed studies published between 2020 and 2025, seminal earlier works, and official regulatory or guideline documents. Within each domain, representative studies were selected to illustrate methodological diversity, clinical context, and current translational readiness rather than to provide exhaustive coverage of an extremely rapidly evolving field.

Artificial intelligence↗

PubMed2026

Charting host structural variations in cervical cancer by long-read sequencing pinpoints a functional deletion in PIAS1.

Host structural variations (SVs) are critical in cancer development but their landscape and interaction with HPV integration in cervical carcinogenesis remain unclear. In this study, we performed Nanopore long-read sequencing on five HPV-positive cervical cancer tissues and two cell lines to profile host SVs. We identified thousands of SVs and statistically demonstrated their significant enrichment in genomic windows ±25 to ±50 kb from HPV integration sites. Cross-sample analysis revealed 60 shared SVs, including a recurrent deletion within the PIAS1 gene. Multi-omics integration (Hi-C, H3K27ac ChIP-seq, and TCGA data) showed that this deletion is associated with reduced PIAS1 expression, disruption of local topologically associating domains, advanced pathological tumor stage, and poorer overall survival. Functional assays confirmed that PIAS1 deficiency inhibits cervical cancer cell proliferation and migration. Our findings identify a PIAS1 deletion as a candidate driver event, and underscore the pivotal role of host genomic instability in HPV-associated oncogenesis.

Cervical cancer↗

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↗

PubMed2005

A systems biology approach to genetic studies of complex diseases.

Revealing mechanisms underlying complex diseases poses great challenges to biologists. The traditional linkage and linkage disequilibrium analysis that have been successful in the identification of genes responsible for Mendelian traits, however, have not led to similar success in discovering genes influencing the development of complex diseases. Emerging functional genomic and proteomic ('omic') resources and technologies provide great opportunities to develop new methods for systematic identification of genes underlying complex diseases. In this report, we propose a systems biology approach, which integrates omic data, to find genes responsible for complex diseases. This approach consists of five steps: (1) generate a set of candidate genes using gene-gene interaction data sets; (2) reconstruct a genetic network with the set of candidate genes from gene expression data; (3) identify differentially regulated genes between normal and abnormal samples in the network; (4) validate regulatory relationship between the genes in the network by perturbing the network using RNAi and monitoring the response using RT-PCR; and (5) genotype the differentially regulated genes and test their association with the diseases by direct association studies. To prove the concept in principle, the proposed approach is applied to genetic studies of the autoimmune disease scleroderma or systemic sclerosis.

Genomics↗

PubMed2026

Transcriptomic and Metabolomic Profiling Identifies a Core Gene-Metabolite Axis Driving African Swine Fever Virus Replication in the Soft Tick Ornithodoros lahorensis.

African swine fever virus (ASFV) causes an incurable swine disease with nearly 100% mortality, posing a catastrophic threat to global pig production. The soft tick Ornithodoros lahorensis acts as a critical biological vector that sustains persistent ASFV replication and mediates long-distance viral transmission, yet the molecular mechanisms governing ASFV-tick interplay remain poorly understood. Here, we integrated transcriptomics and metabolomics to systematically dissect molecular changes in O. lahorensis across three infection stages: Uninfected control, early infection (7 days post-infection, dpi), and late persistent infection (21 dpi). Multi-omics integration revealed that ASFV extensively remodels tick host metabolism, predominantly activating purine/pyrimidine metabolism, lipid biosynthesis, and energy metabolism. We further characterized a conserved regulatory module consisting of 12 core genes and 8 signature metabolites that collectively support ASFV genome replication and virion assembly. Three hub metabolic genes (TK1, ATP5F1B, and IMPDH) were selected for functional validation via siRNA silencing in ticks; individual gene silencing suppressed ASFV loads by 89.2%, 91.5%, and 87.8%, respectively (p < 0.001***). This work represents the first comprehensive multi-omics investigation of ASFV infection in O. lahorensis. We identified tick-specific molecular targets to block vector-mediated ASFV spread and established a standardized multi-omics analytical pipeline for tick-virus interaction research. Our findings elucidate the mechanistic basis of long-term ASFV persistence in soft ticks and deliver novel actionable clues for developing vector-targeted ASF intervention strategies.

Animals↗

PubMed2026

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

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

Animals↗

PubMed2025

Multi-Omics insights into OsZFP252-OsGA20ox5 mediated drought tolerance in rice through stomatal and vascular regulation.

Rice growth is highly dependent on water availability, and drought stress significantly impacts its entire life cycle. However, previous studies lack systematic investigations into drought-responsive candidate genes across the full life cycle of rice. This study integrates transcriptomic and phenotypic data from two rice lines, IR64 (drought-sensitive) and DK151 (drought-tolerant), under varied environmental conditions at distinct growth stages. Using k-means clustering, 13 369 genes were categorized into 17 distinct expression patterns, revealing drought-responsive genes specifically upregulated or downregulated under drought stress. Weighted co-expression network analysis (WGCNA) further identified four gene modules strongly correlated with drought-related phenotypes, co-localizing 2859 drought-responsive genes through both approaches. Proteomics and metabolomics were supplemented at the booting stage, where phenotypic and transcriptomic differences under drought were most pronounced. Integrated omics results demonstrate gibberellin (GA) and abscisic acid (ABA) pathways play a key role during drought tolerance in rice, and 79 high-confidence drought-resistant candidate genes were prioritized from the 2859 drought-responsive genes. Among these, Gibberellin 20-oxidase 5 (OsGA20ox5) was identified as a key negative regulator of drought tolerance. Furthermore, the transcription factor zinc finger protein 252 (OsZFP252) directly binds to the OsGA20ox5 promoter, repressing its expression and enhancing ABA biosynthesis, thereby improving drought tolerance by increasing stomatal closure and expanding vascular bundle water transport capacity. Notably, the drought-tolerant haplotype 2-4 (Hap2-4) of OsGA20ox5 provides valuable insights for drought-resistant breeding.

Oryza↗

PubMed2026

Plasma proteins are integral to cross-tissue gene regulatory networks implicated in cardiometabolic disorders and coronary artery disease.

The plasma proteome has demonstrated promise for identifying diagnostic markers for cardiometabolic disorders (CMDs) and coronary artery disease (CAD). However, identifying the organ of origin for these biomarkers is critical for establishing biological relevance. We performed a multi-omic integrative analysis across multiple tissues from the STARNET study by profiling 974 plasma proteins in 532 CAD patients, integrating RNA sequencing (RNA-seq) data from the arterial wall, major metabolic organs, and blood. We identified 144 cis-protein quantitative trait loci in plasma, colocalizing with tissue cis-expression quantitative trait loci. Additionally, by mapping tissue mRNA "seed genes," we traced 262 plasma proteins to their source organs, primarily the liver. Crucially, we found that 851 plasma proteins are associated with the activity of cross-tissue gene regulatory networks (GRNs), including GRNs implicated in CMD and CAD development. Our findings demonstrate that plasma proteins are integral components of GRNs, with potential for developing reliable diagnostics and precise therapeutic targets. A record of this paper's transparent peer review process is included in the supplemental information.

cardiometabolic disorders↗

PubMed2025

Unveiling the mechanism of micro-and-nano plastic phytotoxicity on terrestrial plants: A comprehensive review of omics approaches.

Micro-and-nano plastics (MNPs) are pervasive in terrestrial ecosystems and represent an increasing threat to plant health; however, the mechanisms underlying their phytotoxicity remain inadequately understood. MNPs can infiltrate plants through roots or leaves, causing a range of toxic effects, including inhibiting water and nutrient uptake, reducing seed germination rates, and impeding photosynthesis, resulting in oxidative damage within the plant system. The effects of MNPs are complex and influenced by various factors including size, shape, functional groups, and concentration. Recent advancements in omics technologies such as proteomics, metabolomics, transcriptomics, and microbiomics, coupled with emerging technologies like 4D omics, phenomics, spatial transcriptomics, and single-cell omics, offer unprecedented insight into the physiological, molecular, and cellular responses of terrestrial plants to MNPs exposure. This literature review synthesizes current findings regarding MNPs-induced phytotoxicity, emphasizing alterations in gene expression, protein synthesis, metabolic pathways, and physiological disruptions as revealed through omics analyses. We summarize how MNPs interact with plant cellular structures, disrupt metabolic processes, and induce oxidative stress, ultimately affecting plant growth and productivity. Furthermore, we have identified critical knowledge gaps and proposed future research directions, highlighting the necessity for integrative omics studies to elucidate the complex pathways of MNPs toxicity in terrestrial plants. In conclusion, this review underscores the potential of omics approaches to elucidate the mechanisms of MNPs-phytotoxicity and to develop strategies for mitigating the environmental impact of MNPs on plant health.

Plants↗