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PubMed2026

Enhancing the fiber degradation efficiency in dairy cattle rumen through engineered bacterial communities.

BACKGROUND: The rumen functions as an anaerobic fermentation chamber, housing microorganisms with cellulolytic and proteolytic capabilities that facilitate feed utilization. Fiber-degrading bacteria possess the capability to enhance the productivity of cellulolytic feed. The application of omics technologies has greatly improved our understanding of the rumen microbiome. Determining microbial composition and functional patterns in the rumen does not equate to a comprehensive exploration of rumen microbial resources and their mechanisms of action. This study seeks to integrate high throughput 16S rRNA data with information on culturomics, cellulolytic activities, nutrition, and synthetic microbial communities (SynCom) engineering. The objective is to evaluate the relationship between rumen microbial activity and fiber utilization efficiency in cattle, ultimately aiming to develop a more powerful intervention strategy for the ruminant industry. RESULTS: The enrichment culture with various carbon sources led to significant alterations in the composition and structure of rumen microbiota, particularly enhancing those associated with carbohydrate metabolism. Employing the culturomics methodology, 896 strains from 78 species (including 8 novel species) were isolated, resulting in a 10.1% isolation rate relative to the rumen bacterial community. Among them, 35 strains demonstrated boosted cellulose-degrading capability on plates, while 25 exhibited the ability to degrade hemicellulose as well. SynComs of these candidates were prepared based on the ratio observed in rumen microbiota exhibiting high cellulolytic performance. SynCom 3 improved the neutral detergent fiber degradation (NDFD) by 20.39% averagely. Additionally, both in vitro and in situ assessments indicated that the optimization of dose/strain in SynCom 3 significantly improved the in vitro NDFD by 20.56% and increased the in situ NDFD by 7.81%, along with the acidic detergent fiber (ADF, + 11.47%). Genomic analysis revealed that the SynCom 3 functioned well in fiber degradation through the synergistic action of key carbohydrate-active enzymes. CONCLUSIONS: This study strengthens rumen microbiome research by integrating omics and SynCom engineering within a microbiota-bacteria-enzymes-genes framework, revealing the significance of enzymatic synergy in carbohydrate metabolism. The findings establish a framework for utilizing low-abundance microbes and engineering functional consortia, which are crucial for improving ruminant feed utilization and biomass conversion. Future research should investigate the transcriptomic profiles and the metabolic cross-feeding mechanisms of fiber-degrading strains in the rumen. Video Abstract.

Animals↗

PubMed2026

Artificial intelligence for translational personalized neoantigen cancer vaccine development.

Personalized neoantigen cancer vaccine is a promising strategy for precision immunotherapy by targeting patient-specific and mutation-derived tumor antigens. Early clinical studies have demonstrated the feasibility, safety, and immunogenicity of these vaccines across multiple solid tumors, with encouraging outcomes particularly when combined with immune checkpoint blockade. However, broader clinical translation remains limited by sequential bottlenecks across the vaccine development pipeline, including false-positive neoantigen selection,  imperfect modeling of antigen processing and HLA presentation, limited prediction of T-cell receptor recognition, and challenges in formulation, delivery, and manufacturing. Artificial intelligence and advanced computational workflows are increasingly integrated into this pipeline to improve candidate prioritization and support more reproducible decision-making. In this review, we summarize clinical progress and key translational barriers in personalized neoantigen vaccination, and discuss how AI-enabled approaches may contribute across four major stages: multi-omics integration for neoantigen discovery, processing-aware HLA presentation prediction, structure-aware and TCR-informed immunogenicity modeling, and data-driven formulation optimization, particularly for lipid nanoparticle-based delivery systems. These approaches are able to help narrow biological and chemical search spaces, improve prioritization, and provide mechanistic insights into antigen presentation and immune recognition rather than replacing experimental validation. This articlefurther addresses future implementation challenges, including dataset diversity, model interpretability, prospective benchmarking, manufacturing traceability, and evolving regulatory frameworks for individualized mRNA cancer immunotherapies. Integrating computational innovation with rigorous immunological validation, scalable manufacturing, and regulatory oversight will be essential for advancing personalized neoantigen vaccines toward broader clinical implementation.

Cancer Vaccines↗

PubMed2026

Genomic and integrative based progression biomarker discovery in adult sepsis: toward clinical stratification and precision medicine.

Sepsis is a life-threatening syndrome characterized by a heterogeneous host response to infection that remains a major cause of mortality worldwide. Current clinical scoring systems capture organ dysfunction but fail to reflect the underlying biological diversity, limiting their utility for patient stratification and targeted therapy. This review provides a comprehensive overview of molecular biomarker approaches used to predict sepsis course and prognosis in adult patients, covering genetic, transcriptomic, proteomic, and integrative strategies up to May 2026. Here, we summarize findings from genetic association studies, along with analyses based on polygenic risk scores to aggregate genetic effects, Mendelian randomization, and rare-variant sequencing approaches. We also review transcriptomic and proteomic strategies for endotyping, and diagnostic and prognostic discrimination. Lastly, we discuss how multi-omics integration is emerging as a promising framework to assist in distinguishing causal therapeutic targets from non-causal biomarkers. We also address the challenges that still constrain clinical translation towards precision medicine.

Biomarker↗

PubMed2026

Enzyme-Metabolite Network Analysis of Endometrial Cancer-Derived Extracellular Vesicles Through Integrated Proteomics and Metabolomics.

Endometrial cancer (EC) is the most common gynecological malignancy in high-income countries. Extracellular vesicles (EVs) are key mediators of intercellular communication and metabolic reprogramming, but their molecular cargo in EC remains poorly characterized. EVs were isolated from four EC cell lines representing Type I and Type II subtypes (AN3CA, ISHIKAWA, HEC1A, and KLE). Untargeted metabolomics was performed by HILIC-LC-MS/MS, proteomics by data-independent acquisition (DIA) mass spectrometry, and multi-omics integration using MetaboAnalyst and OmicsNet. Metabolomic profiling identified 1463 annotated features and revealed significant differences among EC cell lines (PERMANOVA, p = 0.002). Twenty-eight differentially abundant metabolites, including lactic acid, succinic acid, and uric acid, were identified. Proteomic analysis quantified 8513 proteins with subtype-specific expression patterns. Integrated analysis revealed seven significantly enriched pathways, including glycolysis/gluconeogenesis, central carbon metabolism in cancer, and the pentose phosphate pathway. Increased LDHA abundance in metastatic AN3CA-derived EVs was confirmed by Western blot (p = 0.047). EC-derived EVs display subtype- and metastatic-status-specific metabolo-proteomic signatures, with glycolysis, TCA cycle remodeling, and central carbon metabolism as convergent pathway signatures of molecular reprogramming. These findings establish a multi-omics framework for characterizing EV cargo in EC and identify candidate enzyme-metabolite nodes for future biomarker validation in patient-derived specimens.

Female↗

PubMed2026

Integrative proteomics and bioinformatics pipelines for PTM profiling.

Post-translational modifications (PTMs) regulate protein function across all life forms and allow plants to respond rapidly to biotic and abiotic stress. Over 450 PTM types have been described across organisms, of which 23-33 have been experimentally confirmed in plants, including phosphorylation, acetylation, methylation, glycosylation, ubiquitination, and sumoylation. These modifications are highly dynamic and often reversible, and frequently act in combination, or "crosstalk," to fine-tune cellular processes. Advances in high-resolution mass spectrometry and large-scale genome sequencing continue to expand the catalogue of known PTM sites, while machine learning and deep learning approaches increasingly support prediction of PTM site localization and function. Unlike broader surveys of plant PTMs, this review focuses specifically on O-phosphorylation and Lys-N(ε)-acetylation, the two best-characterized and most extensively crosstalking PTMs in plants, and integrates four perspectives: the historical development of proteomic and bioinformatics approaches to these modifications; current mass spectrometry-based workflows and enrichment strategies; the bioinformatics tools and databases available for their analysis; and the technical and species-related challenges, particularly in non-model plants, that currently limit their study. We close by outlining priority directions for future research, including multi-omics integration, AI-based prediction, and the translation of PTM knowledge into crop stress resilience and breeding applications.

Protein Processing, Post-Translational↗

PubMed2026

CMAtlas: a comprehensive DNA methylation atlas for exploring epigenetic alterations in 34 human cancer types.

MOTIVATION: Aberrant DNA methylation is a fundamental epigenetic hallmark of cancer. However, existing resources often lack technological diversity and comprehensive cancer coverage. Furthermore, most platforms fail to achieve deep multi-omics integration and tend to ignore cancer-type-specific methylation features, limiting their utility in precision oncology and drug discovery. RESULTS: We developed Cancer Methylation Atlas (CMAtlas), a comprehensive platform integrating 13 753 samples across 34 cancer types. By applying technology-tailored pipelines to data from various profiling technologies, we identified 830 725 tumor-specific differentially methylated elements (DMEs) and 1 480 098 differentially methylated regions (DMRs), alongside 1 154 256 cancer-type-specific DMEs and 329 154 DMRs. The platform demonstrates high cross-platform consistency and strong concordance between tumor tissues and cell lines, ensuring the robustness of our findings. All DMEs and DMRs are annotated with multi-omics data (RNA expression, somatic mutations, and chromatin accessibility) and clinical relevance (survival associations and cell-free DNA profiling). We further demonstrate the utility of CMAtlas by identifying prognostic aberrant methylation in colorectal cancer driver genes. AVAILABILITY AND IMPLEMENTATION: CMAtlas is freely accessible at {{https://cmatlas.renlab.cn/}}. The platform offers an intuitive web interface supporting gene-centric and cancer-centric queries, alongside customizable analysis modules designed to facilitate user-specific research needs.

Humans↗

PubMed2026

Integrative genomic and transcriptomic analyses identify key regulators of skin pigmentation in Larimichthys crocea.

The yellow body coloration of large yellow croaker (Larimichthys crocea) constitutes a crucial economic trait, yet its underlying genetic regulatory mechanisms remain poorly understood. This study systematically elucidated the molecular basis of body color variation by integrating genome resequencing and skin transcriptome analyses, combined with the contextual analysis of key pigmentation-related genes and phenotypic histological validation. 200 phenotyped individuals (including yellow-selected lines, F1 progeny, and normal control groups, all derived from a well-characterized aquaculture stock) identified 39 significantly associated SNPs (-log₁₀(P) ≥ 6), mapping to multiple candidate genes. These genes were significantly enriched in pathways related to pigment deposition (GO:0033059), melanosome organization (GO:0032438), melanogenesis, and tyrosine metabolism. Cross-developmental stage transcriptome analysis revealed 2395 differentially expressed genes (DEGs). Multi-omics integration identified eight overlapping candidate genes, including tyrp1, slc45a2, oca2, and dgat2, among which tyrp1 was prioritized for in-depth validation based on its core regulatory role in eumelanin synthesis, significant SNP association signal, and consistent downregulation in transcriptomic data. Experimental validation demonstrated that the g.895C > T mutation in exon 2 of tyrp1b was strongly significantly associated with the yellow phenotype: the frequency of mutant genotypes (TT/CT) reached 92.86%in the yellow-selected group, whereas the control group exclusively exhibited the wild-type genotype (CC). qPCR confirmed significantly downregulated tyrp1b expression in the skin of yellow individuals, consistent with the transcriptome trend. Histological and stereomicroscopic observations of skin tissues further validated the physiological basis of the yellow phenotype, revealing a significant reduction in melanophore number and abnormal melanosome morphology in yellow-phenotype individuals, accompanied by increased xanthophore density. These results suggest that tyrp1b mutation is strongly associated with the yellow phenotype. However, the presence of a wild-type CC individual in the yellow group indicates that this mutation is not strictly required for yellow coloration, suggesting that other genetic or environmental factors may also contribute to the phenotype, Additionally, downregulation of the carotenoid metabolism gene bco2 coupled with upregulation of xdh, together with the functional changes of slc45a2 and oca2, may synergistically promote xanthophore pigment deposition, contributing to the yellow phenotype. As melanin synthesis in large yellow croaker relies on the conserved tyrosinase pathway and transporter proteins, mutations in associated genes (tyrp1b, slc45a2, oca2) represent a primary underlying cause for the loss of melanin-based coloration and transition to a yellow phenotype in L. crocea. These findings provide key molecular targets and a theoretical foundation for molecular breeding of body color in this species, and also enrich the understanding of xanthism regulatory mechanisms in teleosts.

Animals↗

PubMed2026

Hormone priming and metabolic engineering of phytohormone crosstalk in rice under combined biotic and abiotic stresses: a multi-omics perspective for climate-resilient crop development.

Rice (Oryza sativa L.) is the caloric backbone for more than half of humanity, yet it remains one of the most vulnerable crops to the simultaneous biotic and abiotic stresses exacerbated by climate change. Phytohormone priming and the complex crosstalk networks governed by transcription factor hubs like WRKY, MYB, and NAC serve as the central adaptive mechanism for stress resilience. This review synthesizes how multi-omics integration, including spatial and single-cell transcriptomics, is resolving the molecular architecture of hormonal priming and epigenetic stress memory. We critically evaluate advanced metabolic engineering and genome-editing strategies such as CRISPR-Cas9, base/prime editing, and synthetic gene circuits that enable precision modifications to decouple stress tolerance from historical yield penalties. Furthermore, we discuss the emerging roles of microbiome-assisted priming via synthetic consortia and the application of artificial intelligence and digital twins (continuously updated computational models of crop physiology) for predictive stress management. By integrating these diverse technological pillars, we propose a systems-level roadmap for developing climate-resilient rice cultivars capable of maintaining yield stability across a volatile combinatorial stress landscape. This synthesis provides a framework for translating mechanistic hormonal insights into field-applicable cultivars to ensure global food security.

CRISPR↗

PubMed2026

Association between the gut microbiome and plasma metabolites linked to vocalization-based temperament in Merino sheep.

BACKGROUND: Temperament, as a determinant of behavioural and emotional responses, has a substantial adaptive value in different environments. This study aims to investigate the association between the gut microbiota and temperament plasticity, and clarify the potential metabolic mechanism that underpins that association by running a multi-omics study in sheep. METHODS: The TrackSheep research cohort was generated using 200 healthy juvenile Merino ewes, and the rumen microbiota, plasma metabolome, and temperament phenotype was measured. RESULTS: Rumen metagenomic analysis identified 25 microbial species and 16 MetaCyc pathways that explained 37.5% and 11.1%, respectively, of the variation in temperament as estimated using the vocal reactivity to stress. Among these, the γ-aminobutyric acid (GABA) shunt and allantoin degradation pathways showed the strongest associations with vocal behaviour. Multi-omic integration linked these microbial pathways to plasma metabolites that are involved in neurotransmission, antioxidant defense, and energy metabolism, including acetyl-L-carnitine (ALCAR) and urocortisone, which partially mediated the effects of microbial pathways on vocalisations. Notably, functional genomic and mediation analyses indicated that the abundance of Cryptobacteroides sp902761655 was associated with the activity of GABA shunt pathway, where GABA co-occurred with succinate production, in turn correlating with reduced inhibitory effects of ALCAR on stress-susceptible temperament. Although plasma metabolite shifts observed immediately after behavioural tests reflected stress exposure, their associations with rumen microbiota highlight microbiome-metabolite interplay that could underly behavioural variation. CONCLUSIONS: Our study provides the first large-scale multi-omics evidence linking the rumen microbiome to a dimension of emotional reactivity in livestock, while underscoring the need for longitudinal and experimental validation to establish causal mechanisms. Video Abstract.

Animals↗

PubMed2026

Integrated Metabolomic and Transcriptomic Analysis Reveals Tissue-Specific Secondary Metabolic Differentiation and Indole Alkaloid Accumulation in Evodia rutaecarpa.

Evodia rutaecarpa is a valuable medicinal plant, yet its non-medicinal tissues remain largely underexplored. Here, we integrated ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS)-based widely targeted metabolomics and RNA sequencing (RNA-seq) transcriptomics to systematically profile the metabolic and transcriptional landscapes of roots, stems, leaves, and flowers of Evodia rutaecarpa (Juss.) Benth. Our aim was to characterize tissue-specific metabolic differentiation and its underlying transcriptional regulatory mechanisms. Metabolomic analysis, employing principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) with robust model parameters (R2Y > 0.9, Q2 > 0.5), identified 3090 differential metabolite features (variable importance in projection, VIP > 1.0; p < 0.05) across the four tissues, which exhibited distinct tissue-specific clustering patterns. Integrated Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and weighted gene co-expression network analysis (WGCNA) revealed that roots specifically accumulated quinolone alkaloids and flavonoid glycosides, accompanied by the coordinated upregulation of genes involved in flavonoid and phenylpropanoid biosynthetic pathways. In contrast, stems, leaves, and flowers were enriched in indole alkaloids (evodiamine and rutaecarpine) and volatile oil precursors, with concurrent upregulation of genes involved in tryptophan metabolism and indole alkaloid biosynthesis (e.g., tryptophan decarboxylase, TDC; s N-methyltransferase, NMT). Notably, leaves and flowers displayed particularly high accumulation levels of these bioactive alkaloids, suggesting their potential as alternative sources for industrial and pharmaceutical applications. WGCNA further identified multiple transcription factors and structural gene modules tightly correlated with evodiamine accumulation, offering promising candidate regulators for future biosynthetic pathway engineering. Collectively, this multi-omics integration study systematically elucidates the tissue-partitioned secondary metabolism of Evodia rutaecarpa (Juss.) Benth. and provides a solid scientific foundation for full-plant resource utilization, targeted development of non-medicinal tissues, and future metabolic engineering of indole alkaloid production.

Evodia rutaecarpa↗

PubMed2026

Multi-omics analysis reveals coordinated epigenetic dysregulation in atrazine-induced dopaminergic neurotoxicity.

Atrazine (ATR), a widely used triazine herbicide, has been linked to neurotoxicity, yet the epigenetic mechanisms underlying its dopaminergic effects remain unclear. This study investigated whether coordinated miRNA dysregulation and DNA methylation alterations contribute to ATR-induced Parkinson's disease (PD)-like neurotoxicity. Male Sprague-Dawley rats were administered ATR (50 mg/kg/day) for 90 days, resulting in motor and cognitive deficits with dopaminergic dysfunction, including increased α-synuclein and reduced tyrosine hydroxylase expression. Small RNA sequencing identified 72 differentially expressed miRNAs in the substantia nigra, enriched in PI3K-Akt, MAPK, and Ras signaling pathways. In a cohort of six PD patients and six matched controls, genome-wide DNA methylation profiling revealed 4694 differentially methylated positions, predominantly hypomethylated, with overlapping enrichment in neuronal signaling pathways. Weighted gene co-expression network analysis identified a PD-associated module strongly correlated with disease status (r = -0.95, P < 0.001). Multi-omics integration identified CASP3 as a central hub gene. External validation supported CASP3 relevance in PD (AUC = 0.833), and molecular docking suggested potential ATR-CASP3 interaction. Further analysis predicted upregulated miR-3552 as a potential upstream regulator of CASP3. These findings indicate that ATR-induced neurotoxicity may be mediated through the miR-3552/CASP3 signaling axis, ultimately regulating apoptosis and contributing to neurodegeneration.

Animals↗

PubMed2026

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

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

Metabolomics↗

PubMed2026

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↗

PubMed2025

Genomic analysis of Dasiphora on the Qinghai-Tibet Plateau provides insights into genetic divergence and flower color variation.

The Qinghai-Tibet Plateau (QTP) harbors diverse alpine flora, including the ecologically significant shrubs Dasiphora fruticosa and D. glabra, for which taxonomic uncertainties remain and adaptive mechanisms are still poorly understood. Based on high-quality genome assembly, population resequencing, and multi-omics integration, we elucidated their evolutionary divergence and flower color genetics. Chromosome-level haplotype-resolved genomes were assembled: autotetraploid D. fruticosa (929.99 Mb) and diploid D. glabra (450.89 Mb). Phylogenetic analysis showed that the tetraploid D. fruticosa and D. glabra in this study clustered together, while the diploid D. fruticosa sequenced by previous research formed a distinct lineage clustered outside. Consistently, population structure analysis of 55 samples revealed three major clades, with D. fruticosa further subdivided into two divergent branches. Additionally, hybridization events detected by Admixture, coupled with ploidy complexity identified via flow cytometry highlight the intricate genetic relationships within this genus. Adaptive gene families expanded in antioxidant (flavonoid synthesis) and secondary metabolism pathways, adapting to ultraviolet radiation and cold stress. Natural selection analysis identified 193 candidate genes (e.g., TFB5 in the DNA repair pathway), predominantly localized to chromosome 5, which are potential candidates for high-altitude adaptation. Transcriptome and metabolome analyses showed D. fruticosa's yellow petals derive from flavonol (quercetin) accumulation, while D. glabra's white petals result from proanthocyanidin biosynthesis via high LAR/ANR expression. This study provides insights into the taxonomic revision and adaptive genetic divergence of alpine plants, and offers a foundation for horticultural improvement of Dasiphora.

Flowers↗

PubMed2026

Predictive biomarkers in cancer immunotherapy for genitourinary malignancies.

Immunotherapy has transformed the management of genitourinary cancers, offering durable responses in selected patient groups. However, the clinical benefit of immune checkpoint inhibitors varies significantly across renal cell carcinoma, urothelial carcinoma, and prostate cancer, underscoring the need for reliable predictive biomarkers. This review summarizes current knowledge on established and emerging biomarkers, including PD L1 expression, tumor mutational burden, molecular subtypes, genomic alterations, tumor microenvironment characteristics, circulating biomarkers, microbiome influences, and multi omic integrative approaches. We discuss their potential clinical relevance, limitations, and applicability across different tumor types. Future directions emphasize the development of composite biomarkers, standardization of testing platforms, real time monitoring strategies, and the integration of advanced technologies such as artificial intelligence and spatial profiling. Understanding and validating these biomarkers will be essential for optimizing personalized immunotherapy in genitourinary cancers.

Circulating tumor DNA↗

PubMed2005

New paradigms in cardiovascular medicine: emerging technologies and practices: perioperative genomics.

Considerable progress has been made in understanding the pathophysiology of perioperative stress responses and their impact on the cardiovascular system; however, researchers are just beginning to unravel genetic and molecular determinants that predispose to increased risk for postoperative cardiovascular adverse events. A new field, coined perioperative genomics, aims to apply functional genomic approaches to uncover the biological reasons why similar patients can have dramatically different clinical outcomes after surgery. For the perioperative physician, such findings may soon translate into prospective risk assessment incorporating genomic profiling of markers important in inflammatory, thrombotic, vascular, and neurologic responses to perioperative stress, with implications ranging from individualized additional pre-operative testing and physiological optimization, to perioperative decision-making, choice of monitoring strategies, and critical care resource utilization. We review current knowledge regarding genomic technologies in perioperative cardiovascular disease characterization and outcome prediction, as well as discuss future trends/challenges for translating integrated "omic" information into daily clinical management of the surgical patient.

Animals↗

PubMed2026

Single-organ proteomics in Drosophila melanogaster larva.

The combination of genetic accessibility, organ complexity, evolutionary conservation, and cost-efficiency makes Drosophila melanogaster (Dm) a well-known model system for biomedical and fundamental biological research. Proteomic analysis of single organs enables the identification and quantification of proteins expressed in specific organs. This will help to uncover specific biological functions and unique protein profiles that are not detectable in whole-organism analyses. In this study we have isolated single organs form Dm larvae, and we have performed a deep proteomics mapping by following a minimal manipulation preparation procedure. The combined dataset across all organs comprised 9132 identified proteins. As anticipated, principal component analysis (PCA) revealed clear separation between the proteomes of most organs, confirming distinct protein profiles. These findings demonstrate the applicability of the sample preparation strategy for high-resolution proteomic characterization of individual organs in Drosophila. Given the extensive genetic tools available for this model organism, our approach has the potential to open new avenues for proteomic studies in Drosophila melanogaster and any other biological systems where the sample amount is limiting. SIGNIFICANCE STATEMENT: Drosophila melanogaster is a well-known model system for biomedical and fundamental biological research that serves as a valuable in vivo model organism due to its high degree of evolutionary conservation with higher vertebrates, tractable genetics, and logistical efficiency. However, the proteome of Drosophila at single organ level has been elusive to date, due to several factors like low sensitivity of previous generation mass spectrometers and sample preparation procedures, difficult isolation of some organs. In this study we have applied a compilation of advanced methods including minimal sample manipulation together with simple, straightforward and efficient protein extraction and digestion methods. Obtained peptides were minimally handled to be analyzed by applying specific and sensitive nLC methods coupled on-line to state-of-the-art MS/MS system. Altogether, the applied strategy allowed us to get the first single organ study to date for this animal. These datasets represent a significative resource for future genomic, transcriptomic and proteomic studies in Drosophila, as multi-omic integration requires deep proteomics to translate data into functional biochemistry, and serves as a critical bridge and an indispensable standalone resource across the genomic, transcriptomic, and proteomic landscapes.

Animals↗

PubMed2026

The chemical landscape of plant surface metabolites: Acylsugars as models of ecological function and structural diversity.

Plants produce a multifunctional assortment of specialized metabolites that play important roles in defense, environmental adaptation, and ecological interactions. Among these compounds, acylsugars, nonvolatile metabolites produced primarily in glandular trichomes of Solanaceae species, have emerged as informative model systems for understanding plant surface chemistry. Differences in acyl chain length, branching pattern, saturation, and attachment position generate extensive chemical diversity that influences herbivore deterrence, pathogen resistance, and the physicochemical properties of leaf surfaces. Recent advances in analytical chemistry, particularly liquid chromatography-ion mobility-tandem mass spectrometry (LC-IM-MS/MS), have greatly improved the ability to separate structurally related acylsugar isomers and characterize metabolite complexity at high resolution. When integrated with genomics, transcriptomics, and emerging spatial metabolomics approaches, these analytical tools provide new insights into acylsugar biosynthesis, pathway regulation, evolutionary diversification, and ecological function across plant species. This review positions acylsugars, particularly those of Solanum species, as model systems for understanding how structural diversity, spatial localization, and specialized metabolism shape ecological and physiological function at plant surfaces. We examine acylsugar structural diversity, biosynthetic pathways, ecological and physiological functions, and interactions with environmental and atmospheric processes. Major challenges, including extensive isomeric complexity, incomplete pathway characterization, and difficulties linking chemical structure to biological function, are discussed alongside emerging opportunities in integrative omics, crop improvement, sustainable pest management, and environmental monitoring. Overall, acylsugars provide a powerful model for linking molecular structure, spatial localization, and ecological function, offering broader insight into how specialized metabolism shapes plant adaptation, defense, and environmental interactions.

Acylsugars↗