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PubMed2025

Advancing translational exposomics: bridging genome, exposome and personalized medicine.

Understanding the interplay between genetic predisposition and environmental and lifestyle exposures is essential for advancing precision medicine and public health. The exposome, defined as the sum of all environmental exposures an individual encounters throughout their lifetime, complements genomic data by elucidating how external and internal exposure factors influence health outcomes. This treatise highlights the emerging discipline of translational exposomics that integrates exposomics and genomics, offering a comprehensive approach to decipher the complex relationships between environmental and lifestyle exposures, genetic variability, and disease phenotypes. We highlight cutting-edge methodologies, including multi-omics technologies, exposome-wide association studies (EWAS), physiology-based biokinetic modeling, and advanced bioinformatics approaches. These tools enable precise characterization of both the external and the internal exposome, facilitating the identification of biomarkers, exposure-response relationships, and disease prediction and mechanisms. We also consider the importance of addressing socio-economic, demographic, and gender disparities in environmental health research. We emphasize how exposome data can contextualize genomic variation and enhance causal inference, especially in studies of vulnerable populations and complex diseases. By showcasing concrete examples and proposing integrative platforms for translational exposomics, this work underscores the critical need to bridge genomics and exposomics to enable precision prevention, risk stratification, and public health decision-making. This integrative approach offers a new paradigm for understanding health and disease beyond genetics alone.

Humans↗
PubMed2025

Establishing the ELIXIR Microbiome Community.

Microbiome research has grown substantially over the past decade in terms of the range of biomes sampled, identified taxa, and the volume of data derived from the samples. In particular, experimental approaches such as metagenomics, metabarcoding, metatranscriptomics and metaproteomics have provided profound insights into the vast, hitherto unknown, microbial biodiversity. The ELIXIR Marine Metagenomics Community, initiated amongst researchers focusing on marine microbiomes, has concentrated on promoting standards around microbiome-derived sequence analysis, as well as understanding the gaps in methods and reference databases, and identifying solutions to the computational overheads of performing such analyses. Nevertheless, the methods used and the challenges faced are not confined to marine microbiome studies, but are broadly applicable to other biomes. Thus, expanding this Marine Metagenomics Community to a more inclusive ELIXIR Microbiome Community will enable it to encompass a broader range of biomes and link expertise across 'omics technologies. Furthermore, engaging with a large number of researchers will improve the efficiency and sustainability of bioinformatics infrastructure and resources for microbiome research (standards, data, tools, workflows, training), which will enable a deeper understanding of the function and taxonomic composition of the different microbial communities.

Computational Biology↗
PubMed2006

Endocrine disrupting chemicals research program of the U.S. Environmental Protection Agency: summary of a peer-review report.

At the request of the U.S. Environmental Protection Agency (EPA) Office of Research and Development, a subcommittee of the Board of Scientific Counselors Executive Committee conducted an independent and open peer review of the Endocrine Disrupting Chemicals Research Program (EDC Research Program) of the U.S. EPA. The subcommittee was charged with reviewing the design, relevance, progress, scientific leadership, and resources of the program. The subcommittee found that the long-term goals and science questions in the EDC Program are appropriate and represent an understandable and solid framework for setting research priorities, representing a combination of problem-driven and core research. Long-term goal (LTG) 1, dealing with the underlying science surrounding endocrine disruptors, provides a solid scientific foundation for conducting risk assessments and making risk management decisions. LTG 2, dealing with defining the extent of the impact of endocrine-disrupting chemicals (EDCs), has shown greater progress on ecologic effects of EDCs compared with that on human health effects. LTG 3, which involves support of the Endocrine Disruptor Screening and Testing Program of the U.S. EPA, has two mammalian tests already through a validation program and soon available for use. Despite good progress, we recommend that the U.S. EPA a) strengthen their expertise in wildlife toxicology, b) expedite validation of the Endocrine Disruptors Screening and Testing Advisory Committee tests, c) continue dependable funding for the EDC Research Program, d) take a leadership role in the application of "omics" technologies to address many of the science questions critical for evaluating environmental and human health effects of EDCs, and e) continue to sponsor multidisciplinary intramural research and interagency collaborations.

Endocrine System↗
PubMed2025

RBC-GEM: A genome-scale metabolic model for systems biology of the human red blood cell.

Advancements with cost-effective, high-throughput omics technologies have had a transformative effect on both fundamental and translational research in the medical sciences. These advancements have facilitated a departure from the traditional view of human red blood cells (RBCs) as mere carriers of hemoglobin, devoid of significant biological complexity. Over the past decade, proteomic analyses have identified a growing number of different proteins present within RBCs, enabling systems biology analysis of their physiological functions. Here, we introduce RBC-GEM, one of the most comprehensive, curated genome-scale metabolic reconstructions of a specific human cell type to-date. It was developed through meta-analysis of proteomic data from 29 studies published over the past two decades resulting in an RBC proteome composed of more than 4,600 distinct proteins. Through workflow-guided manual curation, we have compiled the metabolic reactions carried out by this proteome to form a genome-scale metabolic model (GEM) of the RBC. RBC-GEM is hosted on a version-controlled GitHub repository, ensuring adherence to the standardized protocols for metabolic reconstruction quality control and data stewardship principles. RBC-GEM represents a metabolic network is a consisting of 820 genes encoding proteins acting on 1,685 unique metabolites through 2,723 biochemical reactions: a 740% size expansion over its predecessor. We demonstrated the utility of RBC-GEM by creating context-specific proteome-constrained models derived from proteomic data of stored RBCs for 616 blood donors, and classified reactions based on their simulated abundance dependence. This reconstruction as an up-to-date curated GEM can be used for contextualization of data and for the construction of a computational whole-cell models of the human RBC.

Humans↗
PubMed2007

Molecular classification of breast tumors: toward improved diagnostics and treatments.

Recent advances in gene expression profiling and other "omics" technologies have revolutionized cancer research and hold the potential of also revolutionizing clinical practice. These high-throughout approaches have radically changed our ability to study cells and tissues in a more comprehensive way. Combined with advanced bioinformatics and the possibility to simulate biological processes in computers, this field of "systems biology" allows us to study the organism as a whole entity. This chapter describes the molecular classification and characterization of breast tumors into distinct subtypes by using DNA microarrays and discusses the statistical relationships of the subgroups with clinical features of the disease.

BRCA1 Protein↗
PubMed2005

Glial fibrillary acidic protein and related glial proteins as biomarkers of neurotoxicity.

A variety of '-omic' technologies are being increasingly applied in preclinical safety assessments. Such approaches, however, have not been implemented in neurotoxicity safety evaluations. Current regulatory guidelines for assessing neurotoxicity emphasise reliance on traditional histopathological stains and behavioural testing batteries. Although these methods may be sufficient to detect some neurotoxic effects, they lack both the sensitivity and specificity required for broad-scale neurotoxicity screening. The glial reaction to nervous system damage, often termed gliosis, represents a hallmark of all types of nervous system injury. As such, the development and implementation of gliosis biomarkers represents a broadly applicable approach for neurotoxicity safety assessment. Using a panel of known neurotoxic agents, the authors have shown that the astroglial protein, glial fibrillary acidic protein (GFAP), can serve as one such biomarker of neurotoxicity. Qualitative and quantitative analysis of GFAP has shown this biomarker to be a sensitive and specific indicator of the neurotoxic condition. The implementation of GFAP and related glial biomarkers in neurotoxicity screens may serve as the basis for further development of molecular signatures predictive of adverse effects on the nervous system.

Biomarkers↗
PubMed2026

CCNA2 orchestrates the PI3K/AKT signaling axis to propel prostate cancer metastasis.

BACKGROUND: Prostate cancer (PCa) remains one of the most common malignancies in men, posing a persistent global burden in terms of both public health and socioeconomic costs. Although early detection is essential for improving patient outcomes, existing clinical tools, including prostate-specific antigen (PSA) screening, digital rectal examination, and transrectal ultrasound-guided biopsy, are hampered by suboptimal specificity and positive predictive value, resulting in frequent overdiagnosis and overtreatment of indolent lesions while missing a subset of aggressive tumors at an early stage. In this context, the rapid advancement of high-throughput omics technologies, coupled with sophisticated machine learning (ML) algorithms, provides a powerful computational framework to dissect high-dimensional genomic data, uncover latent gene expression signatures, and identify candidate biomarkers with superior discriminative performance over conventional clinicopathological parameters. Therefore, in this study, we sought to screen for crucial ML-based biomarkers associated with PCa, with a particular focus on systematically assessing the diagnostic and prognostic value of CCNA2. Leveraging large-scale transcriptomic cohorts from public repositories, we employed an ensemble of ML approaches to prioritize candidate genes and subsequently evaluated the diagnostic performance of CCNA2 through receiver operating characteristic curve analysis, as well as its prognostic utility via Kaplan-Meier survival estimation and multivariate Cox proportional hazards modeling. Our findings are anticipated to elucidate the molecular landscape of PCa and offer a promising biomarker candidate for early detection and risk stratification. METHODS: This study integrated single-cell RNA sequencing, bulk transcriptomic data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) repositories, immunofluorescence, and multiple ML algorithms with in vitro functional assays to evaluate CCNA2 expression, clinical relevance, and biological behavior in PCa. RESULTS: CCNA2 was linked to metastasis and poor prognosis. High CCNA2 expression significantly correlated with adverse survival outcomes, and knockdown of CCNA2 suppressed proliferation, migration, and invasion in PCa cell lines. Mechanistically, CCNA2 modulated the PI3K/AKT signaling pathway. An ML-based diagnostic model incorporating CCNA2 demonstrated high predictive accuracy across multiple validation cohorts. CONCLUSIONS: CCNA2 serves as a promising prognostic biomarker and therapeutic target in prostate adenocarcinoma, driving tumor progression potentially via the PI3K/AKT axis.

CCNA2↗
PubMed2026

A Comprehensive Review of Radiomics in Pulmonary Nodule Management: Clinical Applications and Standardization Dilemmas.

Lung cancer is the most common and fatal malignant tumour. Early detection and treatment are likely to reduce mortality, but most pulmonary nodules identified during routine health checks are harmless. Consequently, a clear distinction between benign and malignant nodules is vital to improve early detection and reduce unnecessary interventions. Radiomics, a new omics technology, can be used to extract high-dimensional quantitative features from medical images, providing a profound understanding of tumour pathophysiology. Radiomics has attracted the attention of medical researchers since its formal definition by the Dutch researcher Lambin et al. in 2012. The number of research papers on radiomics has grown tremendously over the past few years. At present, it is used to predict pulmonary nodule malignancy, for noninvasive risk stratification, for integration with genomics to identify genetic mutations associated with lung cancer, and for evaluation of therapeutic responses. With this review, we summarise the literature on radiomics of pulmonary nodules, discuss how it could be used in nodule management, and address the current challenges and future directions for improving precision oncology.

Humans↗
PubMed2006

Metabolomics as a functional genomic tool for understanding lipid dysfunction in diabetes, obesity and related disorders.

With the rise of systems biology, a number of approaches have been developed to globally profile a tier of organization in a cell, tissue or organism. Metabolomics is an approach that attempts to profile all the metabolites in a biological matrix. One of the major challenges of this approach, as with other 'omic' technologies, is that the metabolome is context-dependent and will vary with pathology, developmental stage and environmental factors. Thus, the possibility of globally profiling the metabolome of an organism is a genuine analytical challenge, as by definition this must also take into consideration all relevant factors that influence metabolism. Despite these challenges, the approach has already been applied to understand the metabolism in a range of animal models, and has more recently started to be projected into the clinical situation. In this review, the technologies currently being used in metabolomics will be assessed prior to examining their use to study diseases related to the metabolic syndrome, including Type II diabetes, obesity, cardiovascular disease and fatty liver disease.

Animals↗
PubMed2006

Major advances in fundamental dairy cattle nutrition.

Fundamental nutrition seeks to describe the complex biochemical reactions involved in assimilation and processing of nutrients by various tissues and organs, and to quantify nutrient movement (flux) through those processes. Over the last 25 yr, considerable progress has been made in increasing our understanding of metabolism in dairy cattle. Major advances have been made at all levels of biological organization, including the whole animal, organ systems, tissues, cells, and molecules. At the whole-animal level, progress has been made in delineating metabolism during late pregnancy and the transition to lactation, as well as in whole-body use of energy-yielding substrates and amino acids for growth in young calves. An explosion of research using multicatheterization techniques has led to better quantitative descriptions of nutrient use by tissues of the portal-drained viscera (digestive tract, pancreas, and associated adipose tissues) and liver. Isolated tissue preparations have provided important information on the interrelationships among glucose, fatty acid, and amino acid metabolism in liver, adipose tissue, and mammary gland, as well as the regulation of these pathways during different physiological states. Finally, the last 25 yr has witnessed the birth of "molecular biology" approaches to understanding fundamental nutrition. Although measurements of mRNA abundance for proteins of interest already have provided new insights into regulation of metabolism, the next 25 yr will likely see remarkable advances as these techniques continue to be applied to problems of dairy cattle biology. Integration of the "omics" technologies (functional genomics, proteomics, and metabolomics) with measurements of tissue metabolism obtained by other methods is a particularly exciting prospect for the future. The result should be improved animal health and well being, more efficient dairy production, and better models to predict nutritional requirements and provide rations to meet those requirements.

Absorption↗
PubMed2005

The use of histologically defined specific biomarkers in drug development with special reference to the glutathione S-transferases.

Failure of drug candidates late in development is very expensive. This can be reduced by using more specific biomarkers of effect and toxicity during the preclinical and development testing. However, traditional toxicity tests have not been developed to study toxicology and so may lack sufficient sensitivity and specificity hence the search for new biomarkers using the many "-omics" technologies. Important aspects of useful biomarkers are that their origin is known and localised so that one knows what is being observed. Furthermore, biomarkers with a localised origin are less likely to be subject to background variation and have a wider dynamic range. This paper will discuss how using biomarkers with a known cellular origin, toxic effects can be found earlier and at lower doses of compound.

Adaptation, Biological↗
PubMed2026

Cellular Recovery and Therapeutic Rechallenge After Cancer Therapy-Induced Kidney Injury: Mechanistic Insights and Clinical Implications.

Cancer therapy-related acute kidney injury has become an increasingly common challenge as modern treatments prolong survival and increase exposure to potentially nephrotoxic therapies. Decisions regarding therapeutic rechallenge have relied on normalizing serum creatinine and recovering estimated glomerular filtration rate, despite growing evidence that biochemical recovery does not necessarily indicate restoration of kidney integrity or resilience. In this review, we propose biological kidney recovery as a conceptual framework that integrates mechanisms of kidney injury and repair (adaptive and maladaptive) with emerging biomarkers and therapeutic rechallenge. We first summarize the distinct mechanisms of kidney injury induced by platinum-based chemotherapy, immune checkpoint inhibitors, and vascular endothelial growth factor pathway inhibitors, highlighting how these differences influence subsequent repair. We then discuss the cellular and metabolic processes underlying adaptive repair, the transition to maladaptive remodeling, and current approaches for assessing biological recovery through pathology, biomarkers, and multi-omics technologies. Finally, we present a practical framework for individualized therapeutic rechallenge based on an integrated assessment of kidney-, tumor-, and patient-related factors and outline future directions for precision onco-nephrology. By shifting the focus from filtration alone to biological recovery, this framework enables more informed therapeutic rechallenge aimed at preserving both oncologic efficacy and long-term kidney health.

Humans↗
PubMed2005

Integrating data from biological experiments into metabolic networks with the DBE information system.

Modern 'omics'-technologies result in huge amounts of data about life processes. For analysis and data mining purposes this data has to be considered in the context of the underlying biological networks. This work presents an approach for integrating data from biological experiments into metabolic networks by mapping the data onto network elements and visualising the data enriched networks automatically. This methodology is implemented in DBE, an information system that supports the analysis and visualisation of experimental data in the context of metabolic networks. It consists of five parts: (1) the DBE-Database for consistent data storage, (2) the Excel-Importer application for the data import, (3) the DBE-Website as the interface for the system, (4) the DBE-Pictures application for the up- and download of binary (e. g. image) files, and (5) DBE-Gravisto, a network analysis and graph visualisation system. The usability of this approach is demonstrated in two examples.

Computational Biology↗
PubMed2005

Selected topics in probiotics and prebiotics: meeting report for the 2004 international scientific association for probiotics and prebiotics.

On August 29-31, 2004, 84 academic and industry scientists from 16 countries gathered in Copper Mountain, Colorado USA to discuss certain issues at the forefront of the science of probiotics and prebiotics. The format for this invitation only meeting included six featured lectures: engineering human vaginal lactobacilli to express HIV-inhibitory molecules (Peter Lee, Stanford University), programming the gut for health (Thaddeus Stappenbeck, Washington University School of Medicine), immune modulation by intestinal helminthes (Joel Weinstock, University of Iowa Hospitals and Clinics), hygiene as a cause of autoimmune disorders (G. A. Rook, University College London), prebiotics and bone health (Connie Weaver, Purdue University) and prebiotics and colorectal cancer risk (Ian Rowland, Northern Ireland Centre for Food and Health). In addition, all participants were included in one of eight discussion groups on the topics of engineered probiotics, host-commensal bacteria communication, 'omics' technologies, hygiene and immune regulation, biomarkers for healthy people, prebiotic and probiotic applications to companion animals, development of a probiotic dossier, and physiological relevance of prebiotic activity. Brief conclusions from these discussion groups are summarized in this paper.

Animals↗
PubMedDate not supplied

Guidance values for the biomonitoring of occupational exposure. State of the art.

Biomonitoring was developed for the assessment of the health risks from exposure to chemicals at work, and the approaches and concepts of biomonitoring are derived from such exposures. At present, biomonitoring is increasingly used also to assess exposure from the environment. Biomonitoring and assessment of external exposure are complementing activities, where the exposure assessments are much more widely applied, especially when the number of chemicals concerned is considered; environmental analysis also offers the distinct advantage of speciation analysis--which is very poorly developed for biomonitoring. Biomonitoring on the other hand provides information on exposure from all sources, and via all absorption routes, and considers also accumulation of the chemical in the body. Bio monitoring using exposure biomarkers thus consider interindividual differences in the absorption, while use of effec biomarkers ideally also considers interindividual differences in sensitivity. Few effect biomarkers, however, have been validated. The major challenges of biomonitoring are the development of monitoring methods, which are inexpensive enough to be applied at a frequency that makes possible meaningful biomonitoring of chemicals with a short half-time; development of exposure biomarker guidance values specific to individual species of different metals; ex pansion of the repertoire of validated effect biomarkers; and validation and application to effect monitoring of the omic technologies. Another major challenge is a reconsideration of the basis of biomonitoring action limits to reflec the change in the work place: Biomonitoring should be adapted to assist in the generation of a healthy workplace which is capable of attracting workers, and assist them to perform their work effectively--rather than just to guarantee absence of serious health effects.

Air Pollutants, Occupational↗
PubMed2026

Unraveling lung cancer complexity: Spatial omics in tumor microenvironment characterization and precision medicine.

Heterogeneous tumor microenvironment (TME) in lung cancer plays a crucial role in disease progression and resistance to therapy. Despite advances in single-cell and bulk omics profiling, these methods often overlook spatial context, which is vital for understanding cell-cell interactions and regional heterogeneity. In recent years, spatial omics technologies-including spatial genomics, transcriptomics, proteomics, and metabolomics-have revolutionized the ability to map molecular landscapes while maintaining tissue architecture. These advancements have become essential components of next-generation lung cancer management. By providing unprecedented resolution in characterizing the lung cancer TME, spatial omics could reveal prognostic and predictive biomarkers and identify new therapeutic vulnerabilities. This review will provide the first critical evaluation of spatial multi-omics approaches for lung cancer prognosis. It will also assess various integration strategies for multi-omics data to explore the clinical translational potential of these tools for therapy selection and patient stratification. Therefore, a deeper understanding of spatial omics technologies and their application in lung cancer can significantly improve precision diagnostics and therapeutic decision-making.

Lung cancer↗
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

Exploring the Translation of Organ-on-a-Chip Technology for Human-Relevant Diagnostic Biomarkers.

Microphysiological systems (MPSs) are gaining traction as a viable alternative model for toxicity studies. Further characterization is necessary to explore the full translational potential of MPSs to human physiology, along with the utility of these platforms to serve as a diagnostic tool. Multiomics analyses have emerged as a key means for identifying host biomarkers associated with chemical and drug exposure. Correlations between published human omics and MPS technology omics data will inform the potential of organ chips to accurately represent human responses and provide an alternative approach for improved biomarker discovery for toxicity assessment and exposure identification. To interrogate these potential overlaps, TissUse Chip3 multiorgan chips (MOCs) seeded with kidney organoids, liver organoids, and respiratory tract tissue were exposed to low, therapeutic, and toxic doses of acetaminophen (n = 4 for each condition) for 24 h and subjected to proteomic and metabolomic analysis. The data from our organ chips are largely consistent with biomarkers and dysregulations identified in published human omics data, in vitro and in vivo data, to include the identification of several known acetaminophen metabolites and biotransformation products. These data suggest that organ chips may be a suitable surrogate for human biomarker identification and drug or hazardous chemical exposure diagnosis.

Humans↗