Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “omics technologies”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 235 records · Page 13Linked to original sources
PubMed2026

The Computational Revolution in Natural Product Research: A Data-Driven Roadmap for Next-Generation Drug Development.

Natural products (NPs) have historically provided the foundational scaffolds for drug development, yet traditional bioprospecting faces critical limitations: high rediscovery rates, laborious isolation workflows, and substantial attrition during clinical translation. The emergence of big data technologies is fundamentally transforming this landscape, enabling a shift from serendipity-based discovery toward systematic, data-driven approaches. This review examines how the integration of artificial intelligence (AI), machine learning (ML), and multi-omics datasets is accelerating natural product research across three key domains: (1) genome mining for biosynthetic gene cluster identification using platforms such as antiSMASH, (2) cheminformatics-driven prediction of structure-activity relationships and ADMET properties, and (3) metabolomics-guided dereplication to prioritize novel bioactive scaffolds. We evaluate the convergence of genomics, metabolomics, and computational chemistry in enabling in silico lead optimization and the discovery of cryptic metabolites from previously inaccessible microbial taxa. While challenges in data standardization and scalability persist, the synergy between big data and NP research is accelerating clinical translation. Despite persistent challenges in data standardization, scalability, and equitable benefit-sharing, the convergence of big data and NP research is poised to redefine drug development. These advances position computational NP research as a cornerstone of next-generation drug development.

big data analytics↗
PubMed2026

Molecular Landscape and Advanced Diagnostic Technologies for BRAF Mutations in Cancer: From Quantitative PCR and ddPCR to CRISPR-Based Platforms.

BRAF mutations are key oncogenic alterations across multiple malignancies, including melanoma, thyroid carcinoma, colorectal cancer, non-small cell lung cancer, glioma, and hairy cell leukemia. The most prevalent variant, BRAF-V600E, induces constitutive activation of the MAPK signaling pathway, promoting tumor progression and influencing therapeutic responsiveness. Accurate detection of BRAF alterations is therefore essential for molecular classification, prognostic assessment, treatment selection, and resistance surveillance. This review summarizes the molecular heterogeneity of BRAF mutations and critically evaluates current diagnostic methodologies. Conventional approaches such as allele-specific PCR and Sanger sequencing are compared with advanced quantitative platforms, including high-resolution melting analysis, droplet digital PCR, and next-generation sequencing, with emphasis on analytical sensitivity, mutation coverage, and clinical applicability. Emerging technologies such as CRISPR-based assays, rolling circle amplification systems, and nanoparticle-based biosensors and point-of-care diagnostic platforms are also discussed for their potential to enhance ultra-sensitive detection, particularly in liquid biopsy settings. These emerging tools are highlighted for their potential to enable ultra-sensitive, rapid, and decentralized mutation detection, particularly in liquid biopsy settings. Key challenges, including intratumoral heterogeneity, low allele-frequency variants, FFPE-associated artifacts, and clonal evolution under therapeutic pressure, are examined within a translational framework. In addition, we examine critical barriers to clinical implementation, including standardization, cost, and global accessibility of molecular diagnostics, and outline potential solutions through scalable technologies and decentralized testing strategies. We propose that optimal BRAF testing requires a mutation subclass-informed and clinically integrated strategy combining comprehensive baseline profiling with longitudinal molecular monitoring. Future diagnostic paradigms will likely integrate multi-omics data and artificial intelligence (AI)-assisted interpretation to refine precision oncology implementation. Looking forward, we propose that optimal BRAF testing will require integration of multi-omics profiling with AI-assisted interpretation, enabling automated variant classification, real-time clinical decision support, and improved prediction of therapeutic response and resistance.

Humans↗
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

Impact of microarray technology in nutrition and food research.

Microarrays have become standard tools for gene expression profiling as the mRNA levels of a large number of genes can be measured in a single assay. Many technical aspects concerning microarray production and laboratory usage have been addressed in great detail, but it remains still crucial to establish this technology in new research fields such as human nutrition and food-related areas. The correlation between diet and inter-individual variation in gene expression is an important and relatively unexplored issue in human nutrition. Therefore, nutritionists changed their research field dramatically from epidemiology and physiology towards the "omics" sciences. Nutrigenomics as a field of research is based on the complete knowledge of the human genome and refers to the entire spectrum of human genes that determine the interactions of nutrition with the organism. Nutrigenetics is based on the inter-individual, genetically determined differences in metabolism. Nutrigenomics and nutrigenetics carry the hope that individualized diet can improve human health and prevent nutrition-related diseases. In this article we give an overview of current DNA and protein microarray techniques (including fabrication, experimental procedure and data analysis), we describe their applications to nutrition and food research and point out the limitations, problems and pitfalls of microarray experiments.

Confidentiality↗
PubMed2026

De Novo Genome Sequence Assembly of the Algal Endosymbiont Micractinium conductrix Derived From Its Host Paramecium bursaria 186b.

Endosymbiosis is a major driver of evolutionary innovation and underpins the function of diverse ecosystems. The origins and evolution of endosymbiosis are challenging to study experimentally due to the short-lived culturability of many microbial strains derived from endosymbiotic interactions. The facultative endosymbiosis between the ciliate, Paramecium bursaria, and the green alga, Micractinium conductrix (Chlorellaceae, Trebouxiophyceae), is ecologically widespread and has emerged as a powerful lab-tractable model system. This endosymbiosis is founded upon a reciprocal nutrient exchange, but each of the species can be cultured independently enabling quantification of symbiotic fitness effects, new partnerships to be generated in the lab, and co-associations to be subject to experimental evolution. To date, evolve-and-resequence approaches have been limited due to a lack of high-quality genome assemblies enabling gene variants to be identified. Here, we report a near telomere-to-telomere genome assembly for M. conductrix 186b, using a range of sequencing technologies. Comparative analysis shows that this is one of the most complete Chlorellaceae algal genome assemblies available to date. To aid accurate gene calling and annotation, we conducted both RNAseq and Iso-Seq transcriptome sequencing experiments. Collectively, these 'omics datasets will facilitate: (i) comparative genomics studies of endosymbiont evolution, (ii) evolve-and-resequence experiments, (iii) genome-scale metabolic modeling studies, and (iv) identification of targets for genetic modification experiments and biotechnological applications.

Symbiosis↗
PubMed2026

Allelochemical signaling and phytohormone crosstalk in plants: molecular mechanisms and implications for sustainable weed management.

Phytotoxic effects from allelopathy occur due to signaling pathways that induce alterations in hormonal balance within the plants, thereby hindering weed growth. Signaling crosstalk between various hormones and signaling pathways (Ca2⁺, MAPK, ROS) is involved in the molecular response mechanisms found through omics. Utilizing such mechanisms would help develop new environmentally friendly methods for sustainable weed management. Allelopathy serves as an essential component of plant-plant interaction via controlling the secretion of secondary metabolites (allelochemicals), which affect the growth, development, and physiological activity of nearby plants. The latest findings indicate that allelochemicals disturb phytohormone balance and signaling pathways resulting in oxidative stress, metabolism dysfunctions, cellular processes disturbances, and eventually inhibiting the growth of target weed species. Molecular biology progress and omics techniques brought information about the sophisticated regulation processes involved in allelopathic interactions. This review summarizes the information about the molecular mechanism of weed suppression mediated by allelopathy with the emphasis on allelochemical perception, phytohormone signaling, ROS responses, and evidence obtained by the application of transcriptomics, proteomics, metabolomics, and other omics-based studies. In addition, it introduces novel approaches, such as rhizosphere engineering, nanotechnologies, and genome editing, which may improve the effectiveness and reliability of allelopathic weed suppression. Overall, these achievements provide prospects for creating a new generation of weed control technologies that are sustainable, environmentally friendly, and climate-adaptive.

Plant Growth Regulators↗
PubMed2006

Nutrigenomics and nutrigenetics: the 'omics' revolution in nutritional science.

The execution of the Human Genome Project has brought forth a wealth of information about the structure of the genome, which can now be used to study how the interplay between our genes and factors from the environment such as nutrition relate to a state of health or disease. To enable such studies, novel technologies have been designed in particular to monitor the activity of multiple genes simultaneously at the level of the RNA by transcriptomics, or the level of the proteins by proteomics. In addition, genome information has boosted approaches to study the role of genetic variation to explain individual differences in responses to nutrition, underlying in part the susceptibility for nutrition-related disorders. These new areas of science referred to as 'nutrigenomics' and 'nutrigenetics' respectively, will increase our fundamental knowledge of the interaction between life processes and our diet or specific components thereof, which may in time lead to the development of novel functional foods to improve the health status of the general population, and to the personalized diet to prevent the onset of nutrition-related disorders in genetically predisposed individuals.

Genetics↗
PubMed2026

Transcriptomic and metabolomic analyses revealed the action mechanism of nesfatin-1 gene on glucolipid metabolism during early development stage of largemouth bass.

Nesfatin-1 has biological roles including the suppression of food intake and the regulation of glucose and lipid metabolism. However, the information available regarding nesfatin-1 in the glycolipid metabolism in the early development stage of fish is still limited. In order to investigate the role of the nesfatin-1 gene in the early development stage of the largemouth bass (Micropterus salmoides), the nesfatin-1 gene was knocked down using siRNA interference technology. Then, we evaluated its mRNA expression levels, transcriptomes and metabolomes. The mRNA expression levels of nesfatin-1 gene were appreciably decreased at 48 h, 72 h and 96 h after injection of nesfatin-1 siRNA in the early development stage. The omics results revealed that knockdown of the nesfatin-1 gene induced 1833 differentially expressed genes (DEGs) and 2370 differentially expressed metabolites (DEMs). Bioinformatic analysis enriched the most affected molecular pathways (sphingolipid metabolism, fatty acid elongation, amino sugar and nucleotide sugar metabolism and biosynthesis of unsaturated fatty acids) and metabolic pathways (biosynthesis of unsaturated fatty acids, sphingolipid metabolism and amino sugar and nucleotide sugar metabolism) in early development stage of largemouth bass. In amino sugar and nucleotide sugar metabolism, increased expression levels of genes such as chic, chs1, and gck genes, alongside decreased expression levels of the chia.1 gene, resulted in significantly elevated concentrations of N-Acetyl-D-glucosamine, β-d-fructose 6-phosphate, β-d-Fructose, D-mannose 6-phosphate, d-glucose, d-glucose 1-phosphate, UDP-glucose, and UDP-glucuronate, whilst the concentration of UDP-N-acetyl-α-D-glucosamine was markedly reduced. Therefore, the nesfatin-1 gene may influence the early development stage of largemouth bass by affecting signaling pathways associated with glycolipid metabolism. Our findings further expand the understanding of molecular mechanisms of the nesfatin-1 gene, and provide further theoretical support for the initial breeding and feed adaptation of largemouth bass.

Animals↗
PubMedDate not supplied

Metabonomics technologies and their applications in physiological monitoring, drug safety assessment and disease diagnosis.

In this review, metabonomics, a combination of data-rich analytical chemical measurements and chemometrics for profiling metabolism in complex systems, is described and its applications are reviewed. Metabonomics is typically carried out using biofluids or tissue samples. The relevance of the technique is reviewed in relation to other '-omics', and it is shown how the methods can be applied to physiological evaluation, drug safety assessment, characterization of genetically modified animal models of disease, diagnosis of human disease, and drug therapy monitoring. The different types of analytical data, mainly from nuclear magnetic resonance spectroscopy and mass spectrometry, are summarized. The outputs from a metabonomics study allow sample classification, for example according to phenotype, drug safety or disease diagnosis, and interpretation of the reasons for classification yields information on combination biomarkers of effect. Transcriptomic and metabonomic data is currently being further integrated into a holistic understanding of systems biology. An assessment of the possible future role and impact of metabonomics is presented.

Age Factors↗
PubMed2005

Systems biology for cancer.

PURPOSE OF REVIEW: Significant insight can be gained into complex biologic mechanisms of cancer via a combined computational and experimental systems biology approach. This review highlights some of the major systems biology efforts that were applied to cancer in the past year. RECENT FINDINGS: Two main approaches to computational systems biology are discussed: mechanistic dynamical simulations and inferential data mining. Significant developments have occurred in both areas. For example, mechanistic simulations of the EGFR pathway are promoting understanding of cancer, and Bayesian inference approaches allow for the reconstruction of regulatory networks. In addition, the article reports on advancements in experimental systems biology for determining protein-protein interactions and quantifying protein expression to generate the necessary data for computational modeling and inferential data mining. Emerging approaches will further improve the ability to bridge the gap between in vitro systems and in vivo human biology. Technologies paving the way include in vitro models that better reflect in vivo tumors, microfabricated devices of human physiology, and improved animal models. SUMMARY: An important challenge facing the field is how better to translate in vitro discoveries to the clinic. Computational systems biology approaches that use omic data to predict biology along with novel experimental systems that better represent human in vivo biology will prove useful in bridging this gap. Although still early, the potential application of systems biology and the future evolution of the field will significantly affect understanding of cancer disease mechanisms and the ability to devise effective therapeutics.

Computational Biology↗
PubMed2005

Liver tumors in wild flatfish: a histopathological, proteomic, and metabolomic study.

Fish play host to viral, bacterial, and parasitic diseases in addition to non-infectious conditions such as cancer. The National Marine Monitoring Programme (NMMP) provides information to the U.K. Government on the health status of marine fish stocks. An aspect of this work relates to the presence of tumors and other pathologies in the liver of the offshore sentinel flatfish species, dab (Limanda limanda). Using internationally agreed quality assurance criteria, tumors and pre-tumors are diagnosed using histopathology. The current study has expanded upon this work by integrating these traditional diagnostic approaches with ones utilizing modern technologies for analysis of proteomic and metabolomic profiles of selected lesions. We have applied SELDI and FT-ICR technologies (for proteomic and metabolomic analyses, respectively) to tumor and non-tumor samples resected from the liver of dab. This combined approach has demonstrated how these technologies are able to identify protein and metabolite profiles that are specific to liver tumors. Using histopathology to classify "analysis groups" is key to the success of such an approach since it allows for elimination of spurious samples (e.g., those containing parasite infections) that may confuse interpretation of "omic" data. As such, the pathology laboratory plays a central role in collating information relating to particular specimens and in establishing sampling groups relative to specific diagnostic questions. In this study, we present pilot data, which illustrates that proteomics and metabolomics can be used to discriminate fish liver tumors and suggest future directions for work of this type.

Adenoma, Liver Cell↗
PubMed2025

Molecular profiling of exhaled breath condensate in respiratory diseases.

BACKGROUND: Respiratory disorders, , continue to pose a major global health burden. Their complexity and heterogeneity challenge accurate diagnosis, effective monitoring, and therapeutic decision-making. Exhaled breath condensate (EBC) provides a reliable, non-invasive means of sampling the molecular environment of the airways. AIM: This review presents the state-of-the-art in EBC-based omics approaches-particularly metabolomics and proteomics-to characterize molecular signatures associated with chronic respiratory (e.g. asthma, chronic obstructive pulmonary disease, and rhinitis) and infectious diseases (e.g. COVID-19). RESULTS: We critically examine findings from studies applying nuclear magnetic resonance (NMR), mass spectrometry (MS), and sensor-based technologies to analyze EBC across various respiratory conditions. NMR, valued for its reproducibility and minimal sample preparation, consistently discriminates among disease phenotypes, identifies distinct metabotypes, and monitors treatment response over time. MS-based approaches afford enhanced sensitivity and specificity, enabling detailed profiling of inflammatory mediators, such as lipid-derived eicosanoids and amino acid derivatives. Proteomic studies reveal protein-level alterations associated with inflammation and tissue remodeling. In COVID-19 and long COVID, metabolomic and volatile compound profiling distinguishes affected individuals from healthy controls suggesting clinical potential. However, inconsistent sample processing and lack of analytical standardization remain limiting factors. CONCLUSIONS: EBC profiling shows clear promise for improving diagnosis, monitoring, and stratification in respiratory medicine. Yet, translation into clinical practice is hindered by limited standardization and validation. Broader, longitudinal studies will be essential to establish robust molecular signatures across disease states. This review underscores the timely need to implement breathomics investigations to gain mechanistic insight into the underlying biology of respiratory diseases.

Humans↗
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↗
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

Genetic architecture of endometriosis: risk factors, comorbidities and clinical implications.

BACKGROUND: In 1999, Dr Susan Treloar and colleagues conducted a landmark twin study in Australia and reported their estimate of 51% for the heritability of endometriosis. This important result led several groups to begin mapping genetic factors contributing to increased endometriosis risk. Despite early challenges, advances in genome-wide association studies (GWAS) have identified multiple genetic risk factors and some target genes implicated in follow-up studies on genetic regulation of transcription. Access to large publicly available genetic datasets and analysis with endometriosis GWAS results is also providing new opportunities to answer important questions about comorbid conditions associated with endometriosis and their implications for clinical practice. OBJECTIVE AND RATIONALE: The objective of the review is to summarize the last 25 years of genetic studies in endometriosis, outline contributions to our understanding of the disease, and suggest future directions to accelerate biological insights from genetic studies to improve clinical outcomes. SEARCH METHODS: A comprehensive review of scientific literature on the genetics of endometriosis was conducted through searches in PubMed and Google Scholar up to June 2026. Search terms included "endometriosis AND (genetics OR GWAS OR genetic risk factors)", For studies addressing the functional characterization of genetic risk loci, additional searches employed the terms "endometriosis AND (genotype-phenotype associations OR colocalization OR eQTL OR mQTL OR multi omics methods)". To identify studies examining shared genetic risk between endometriosis and comorbid conditions, the search strategy included "endometriosis AND (genetic correlation OR colocalization OR Mendelian randomisation)". Publications reporting discoveries related to genetic risk factors for endometriosis and studies interpreting their biological and clinical significance were critically evaluated, and 144 publications were discussed in the review. OUTCOMES: Discovery of genetic risk factors started slowly and has accelerated in recent years with developments in technology and international collaborations to combine data and increase statistical power. GWAS have mapped 80 genetic risk factors that implicate gene regulation of hormonal targets, development of the reproductive tract, regulation of cell proliferation, and regulation of epithelial cell differentiation. In common with most other complex diseases, effects of individual common genetic risk factors are small. However, several examples demonstrate that small effect sizes are not a good predictor for the impact of drugs developed against genetically validated targets. Genetic risk factors implicate five genes regulating gonadotrophin release and oestrogen action, the major target pathway of current drugs for treatment of endometriosis demonstrating proof-of-principal for biologically meaningful results. Genetic correlation and Mendelian Randomization studies highlight important causal relationships between endometriosis and comorbid conditions including a possible role for testosterone during development and shared genetic risk factors for gynaecological, gastrointestinal, pain, psychiatric, and inflammatory conditions. Understanding causal relationships between endometriosis and related conditions will aid clinical management and more personalized treatments. WIDER IMPLICATIONS: Genetic studies provide novel insights into endometriosis pathogenesis and associations with related comorbid conditions. Genetic factors modifying gene regulation and disease risk likely act in specific cell types, and access to datasets from genetically informed cell-based models, single-cell and spatial omics data are needed to accelerate progress. Future studies should address critical questions of heterogeneity and disease subtypes, expand the search for genetic risk factors to non-European populations, evaluate the role of rare and structural variants, and better integrate data from functional, genomics, genetics, and clinical studies to reduce diagnostic delay, develop novel treatment strategies, and translate discoveries into personalized management strategies for affected individuals. REGISTRATION NUMBER: N/A.

comorbid conditions↗
PubMed2006

Proteome chips for whole-organism assays.

Over the past 5 years, protein-chip technology has emerged as a useful tool for the study of many kinds of protein interactions and biochemical activities. The construction of Saccharomyces cerevisiae whole-proteome arrays has enabled further studies of such interactions in a proteome-wide context. Here, we explore some of the recent advances that have been made at the '-omic' level using protein microarrays.

Animals↗
PubMed2026

Single-cell mapping of regulatory DNA-protein interactions.

Gene expression is controlled by transcription factors (TFs), whose genome binding is shaped by chromatin accessibility and histone modifications, yet mapping these interactions, particularly those with weak affinity or a transient nature, in single cells remains technically challenging. To address this gap, we developed docking and deamination followed by sequencing (D&D-seq), a single-cell immuno-tethering technology for profiling DNA-protein interactions. D&D-seq couples an antibody-binding nanobody to a cytosine base editor, a combination that enables detection of weak or transient factor binding through targeted cytosine-to-uracil editing at protein-bound genomic sites. This approach is compatible with standard single-cell multi-omic workflows and therefore allows integrated analyses of gene regulation. Using assay for transposase-accessible chromatin using sequencing (ATAC-seq) and single-cell ATAC-seq (scATAC-seq), we assessed chromatin accessibility as a functional readout of TF activity, and by coupling D&D-seq with whole-genome sequencing, we captured CTCF binding in both active and inactive chromatin compartments.

Animals↗
PubMed2026

Proteomics in environmental pollution research: Advances, challenges, and future directions.

Environmental proteomics has emerged as a powerful approach for elucidating the molecular mechanisms underlying pollutant-induced biological effects. Although this field has developed rapidly, the systematic review of recent proteomics applications in environmental pollution research remains limited. This review explored the emerging roles of toxicoproteomics in biomarker discovery and mechanistic elucidation, as well as ecotoxicoproteomics in ecological risk assessment and bioremediation strategies. Here, we review the field, highlighting recent trends such as the integration of proteomics with genomics, transcriptomics, and metabolomics to provide a comprehensive view of biological responses to environmental stressors. We further discuss the growing application of artificial intelligence in improving proteomics data interpretation and accelerating biomarker discovery. In addition, recent technological advances in environmental proteomics are highlighted, including next-generation tissue microarray proteomics, nanoscale proteomics, single-cell proteomics, and spatial proteomics. Despite its potential, proteomics faces challenges, such as high operational costs, computational complexity in analysis, and technical limitations in low-abundance protein detection. We propose that the convergence of proteomics with artificial intelligence and multi-omics approaches offers promising solutions to these challenges, enhancing the practical application of proteomics in environmental monitoring and risk assessment.

Proteomics↗