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PubMed2005

What it takes to get a herbicide's mode of action. Physionomics, a classical approach in a new complexion.

Discovering new herbicides with novel modes of action is a priority assignment in plant protection research. However, for active compounds identified in greenhouse screens, the crucial point is to tread the most efficient path in determining a herbicide's target site, regarding chance of success, time and research costs. Today, in the literature, molecular (functional genomics, transcriptomics), biochemical (proteomics) and analytical (metabolomics) approaches are particularly discussed. So far, less attention has been focused on the comprehensive physiological profiling of the complex plant system as a procedure which enables new herbicides, with an unknown target site for their mode of action, to be screened rapidly. Here, the concept of an array of 'functional' bioassays is presented which has ultimately been developed from the classical tool of mode of action diagnosis by symptoms. These bioassays are designed to differentiate between the distinct responses of the multiple organization units (plant, tissue, meristematic cell, organelle), developmental stages, types of metabolism (phototrophic, heterotrophic) and physiological processes in the plant organism. The response pattern to a herbicide can be viewed as the end result of changes induced in the molecular and biochemical process chain and should be diagnostic of its physiological mode of action. The results can be interpreted directly or a fingerprint database for all known modes of action to be screened for analogy. The term 'physionomics' is proposed for this comprehensive physiological profiling of the plant system, following the parallel terminology of the molecular and biochemical 'omics' technologies. Physionomics procedures provide a first clue to the mode of action of a new herbicide that can direct more time-consuming and costly molecular, biochemical, histochemical or analytical studies to identify a target site more efficiently.

Biological Assay↗
PubMed2025

Automated Machine Learning Tools to Build Regression Models for Schizosaccharomyces pombe Omics Data.

Machine learning is a powerful tool for analyzing biological data and making useful predictions. The surge of biological data from high-throughput omics technologies has raised the need for modeling approaches capable of tackling such amounts of data, which is pivotal to understanding the nature of complex molecular systems. Here, we show how to construct a simple model using automated machine learning (AutoML) to predict protein abundance in Schizosaccharomyces pombe, using data obtained from codon usage bias and quantitative proteomics.

Machine Learning↗
PubMed2006

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

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

Bacterial Physiological Phenomena↗
PubMed2025

Unlocking microbial potential: advances in omics and bioinformatics for aromatic hydrocarbon degradation.

Aromatic hydrocarbons (AHs) are persistent environmental pollutants with high toxicity. Bacterial degradation of AHs provides a sustainable and cost-effective approach for the remediation of sites contaminated with both mono- and polycyclic aromatic hydrocarbons. Aerobic degradation of AHs typically involves oxygenases-mediated hydroxylation followed by aromatic ring cleavage. In contrast, anaerobic degradation relies on diverse activation mechanisms that ultimately converge on the central intermediate benzoyl-CoA. Over the past decades, research on bacterial degradation of AHs has grown steadily, supported by advances in omics and bioinformatics. In this review, we summarize the current knowledge on the pathways, enzymes, and microbial diversity involved in AH degradation, highlighting how omics and bioinformatic approaches are advancing our understanding of this process. However, to improve our knowledge of microbial AHs catabolism, it is crucial to prioritize the characterization of novel enzymes and pathways, especially those mediating anaerobic and hybrid degradation strategies. Addressing this gap requires the development of specialized resources that incorporate a broader taxonomic diversity and an expanded inventory of anaerobic genes and enzymes supported by experimental evidence. Equally important is the integration of multi-omics technologies, artificial intelligence, and ecological modeling into unified analytical pipelines. These efforts will be key to fully unlocking microbial metabolic potential and guiding more effective bioremediation and monitoring strategies for AHs.

Biodegradation, Environmental↗
PubMed2026

Deciphering CD8+ T cell exhaustion in human cancers through single-cell and spatial transcriptomics.

Exhausted CD8+ T cells (Tex) within the tumor microenvironment (TME) represents a critical barrier limiting anti-tumor immune responses. Tex cells are characterized by upregulated inhibitory immune checkpoint receptors, reduced cytotoxicity, and functional heterogeneity. Their genomic features and regulatory networks remain poorly defined, and only a minority of patients respond to immune checkpoint blockade (ICB) therapy. Single-cell RNA sequencing (scRNA-seq), through high-resolution transcriptomic profiling, has revealed diverse Tex subpopulations, identified subpopulation-specific marker genes and regulatory pathways. Spatial transcriptomics has further mapped the spatial distribution of Tex and their interaction networks with immune cells, tumor cells, and stromal cells, elucidating the impact of spatial heterogeneity on Tex functionality. Current studies indicate that the exhausted state of Tex is dynamic and modifiable, with functional differences among subpopulations closely associated with tumor progression and therapeutic response. However, the genomic characteristics, epigenetic regulation, and spatial interaction mechanisms of Tex require further exploration. This review summarizes recent advances in high-resolution omics technologies for precisely dissecting Tex heterogeneity, functional features, and interactions with other cells. It emphasizes the central value of optimizing Tex-targeted tumor immunotherapy strategies, providing theoretical foundations and directional guidance for developing more effective anti-tumor immunotherapies.

Humans↗
PubMed2026

Mirror worlds: The shared regulatory architecture of cell fate in development and cancer.

Lineage plasticity has emerged as a central mechanism through which cancer cells adapt to therapeutic pressure, evade immune surveillance, and acquire aggressive phenotypes. Although recognized across tumor types, the regulatory principles governing how cancer cells reprogram cellular identity remain incompletely understood. In this review, we propose that lineage plasticity in cancer reflects the redeployment of regulatory frameworks established during normal development. Rather than representing a stochastic byproduct of genomic instability, cancer plasticity frequently unfolds within gene regulatory architectures that also govern cell fate specification, lineage commitment, and controlled state transitions during embryogenesis and tissue homeostasis. Developmental transcription factors, including members of the SOX family, FOXA1, ASCL1, NKX2-1, and epithelial-mesenchymal transition regulators, function as lineage gatekeepers during development but are repurposed in cancer to destabilize lineage commitment and enable phenotypic switching. Similarly, epigenetic regulators that guide developmental trajectories, including chromatin remodeling complexes, Polycomb group proteins, and DNA methylation machinery, are frequently dysregulated or redistributed in tumors, altering the repression of lineage-stabilizing and alternative lineage programs and thereby weakening epigenetic barriers to lineage transitions. Together, these observations support a model in which development and cancer operate as mirror regulatory systems: one establishing and stabilizing cellular identity, the other exploiting the same regulatory architecture to permit adaptive reprogramming under selective pressure. We further discuss how emerging single-cell and spatial multi-omics technologies, integrated with artificial intelligence-based modeling, enable mapping of cell state landscapes and transitional trajectories, transforming lineage plasticity from a descriptive phenomenon into a measurable and predictable property of tumor evolution.

Humans↗
PubMed2006

Predictive value of in vitro safety studies.

The predictive value of in vitro safety studies is discussed for three important areas of pharmaceutical safety evaluations. In genetic toxicology, currently assays are sensitive for the prediction of cancer, but their overall predictive value is strongly diminished because of their low specificity. In the area of safety pharmacology blockage of hERG channel in vitro has recently been introduced to predict cardiac repolarization delay (QT interval prolongation) in patients. There is a plethora of in vitro methods to predict and characterize liver toxicity. However, little data is available that demonstrate a reliable prediction for hepatotoxicity in vivo over a wide range of chemical structures. In all three areas, further improvements are needed. 'Omics' technologies and new cell lines derived from stem cells are expected to strongly contribute to establish new and more predictive in vitro assays.

Animals↗
PubMed2006

The application of systems biology to drug discovery.

Recent advances in the 'omics' technologies, scientific computing and mathematical modeling of biological processes have started to fundamentally impact the way we approach drug discovery. Recent years have witnessed the development of genome-scale functional screens, large collections of reagents, protein microarrays, databases and algorithms for data and text mining. Taken together, they enable the unprecedented descriptions of complex biological systems, which are testable by mathematical modeling and simulation. While the methods and tools are advancing, it is their iterative and combinatorial application that defines the systems biology approach.

Animals↗
PubMed2004

Unintended effects and their detection in genetically modified crops.

The commercialisation of GM crops in Europe is practically non-existent at the present time. The European Commission has instigated changes to the regulatory process to address the concerns of consumers and member states and to pave the way for removing the current moratorium. With regard to the safety of GM crops and products, the current risk assessment process pays particular attention to potential adverse effects on human and animal health and the environment. This document deals with the concept of unintended effects in GM crops and products, i.e. effects that go beyond that of the original modification and that might impact primarily on health. The document first deals with the potential for unintended effects caused by the processes of transgene insertion (DNA rearrangements) and makes comparisons with genetic recombination events and DNA rearrangements in traditional breeding. The document then focuses on the potential value of evolving "profiling" or "omics" technologies as non-targeted, unbiased approaches, to detect unintended effects. These technologies include metabolomics (parallel analysis of a range of primary and secondary metabolites), proteomics (analysis of polypeptide complement) and transcriptomics (parallel analysis of gene expression). The technologies are described, together with their current limitations. Importantly, the significance of unintended effects on consumer health are discussed and conclusions and recommendations presented on the various approaches outlined.

Animals↗
PubMed2026

Multi-omics insights into aroma formation in congou black tea during fermentation.

This study used multi-omics technologies to analyze aroma formation during Congou black tea fermentation. Volatile compounds were analyzed by headspace solid phase microextraction coupled with gas chromatography mass spectrometry using two columns of different polarity. Fermentation increased total volatile normalized peak area fivefold, with alcohols, aldehydes, and acids increasing over sevenfold. 29 differential metabolites were screened, including amino acid derived phenylacetaldehyde, phenylethanol, and 2-methylbutanal; fatty acid derived (E,E)-2,4-heptadienal, hexanal, and 1-hexanol; and isoprenoid derived linalool, geraniol, and beta ionone. Transcriptomic, proteomic, and enzyme analyses revealed that biosynthesis contributed to early accumulation of amino acid and isoprenoid derived aromas, whereas ortho quinone mediated Strecker degradation and free radical induced fatty acid auto oxidation dominated generation of amino and fatty acid derived aromas during middle and late fermentation. In conclusion, aroma formation during fermentation results from biosynthesis and non-enzymatic oxidation, with the latter possibly dominating amino and fatty acid derived aromas.

Fermentation↗
PubMed2005

Prospects for personalized cardiovascular medicine: the impact of genomics.

Sequencing of the human genome has ushered in prospects for individualizing cardiovascular health care. There is growing evidence that the practice of cardiovascular medicine might soon have a new toolbox to predict and treat disease more effectively. The Human Genome Project has spawned several important "omic" technologies that allow "whole genome" interrogation of sequence variation (re-sequencing, genotyping, comparative genome hybridization), transcription (expression profiling, tissue arrays), proteins (gas or liquid chromatography and tandem mass spectroscopy [MS]), and metabolites (MS or nuclear magnetic resonance profiling); deoxyribonucleic acid, ribonucleic acid, protein, and metabolic approaches all provide more exacting detail of cardiovascular disease mechanisms and, in some cases, are redefining its taxonomy. Pharmacogenomic approaches are emerging across broad classes of cardiovascular therapeutics to assist practitioners in making more precise decisions about which drugs to give to which patients to optimize the benefit-to-risk ratio. Molecular imaging is developing chemical and biological probes that can sense molecular pathway mechanisms that will allow us to monitor health and disease. Together, these tools will enable a paradigm shift from genetic medicine--on the basis of the study of individual inherited characteristics, most often single genes--to genomic medicine, which by its nature is comprehensive and focuses on the functions and interactions of multiple genes and gene products, among themselves and with their environment. The information gained from such analyses, in combination with clinical data, is now allowing us to assess individual risks and guide clinical management and decision-making, all of which form the basis for cardiovascular genomic medicine.

Cardiology↗
PubMed2006

The dynamics of the LPS triggered inflammatory response of murine microglia under different culture and in vivo conditions.

Overall, the inflammatory potential of lipopolysaccharide (LPS) in vitro and in vivo was investigated using different omics technologies. We investigated the hippocampal response to intracerebroventricular (i.c.v) LPS in vivo, at both the transcriptional and protein level. Here, a time course analysis of interleukin-6 (IL-6) and monocyte chemotactic protein-1 (MCP-1) showed a sharp peak at 4 h and a return to baseline at 16 h. The expression of inflammatory mediators was not temporally correlated with expression of the microglia marker F4/80, which did not peak until 2 days after LPS injection. Of 480 inflammation-related genes present on a microarray, 29 transcripts were robustly up-regulated and 90% of them were also detected in LPS stimulated primary microglia (PM) cultures. Further in vitro to in vivo comparison showed that the counter regulation response observed in vivo was less evident in vitro, as transcript levels in PM decreased relatively little over 16 h. This apparent deficiency of homeostatic control of the innate immune response in cultures may also explain why a group of genes comprising tnf receptor associated factor-1, endothelin-1 and schlafen-1 were regulated strongly in vitro, but not in vivo. When the overall LPS-induced transcriptional response of PM was examined on a large Affymetrix chip, chemokines and cytokines constituted the most strongly regulated and largest groups. Interesting new microglia markers included interferon-induced protein with tetratricopeptide repeat (ifit), immune responsive gene-1 (irg-1) and thymidylate kinase family LPS-inducible member (tyki). The regulation of the former two was confirmed on the protein level in a proteomics study. Furthermore, conspicuous regulation of several gene clusters was identified, for instance that of genes pertaining to the extra-cellular matrix and enzymatic regulation thereof. Although most inflammatory genes induced in vitro were transferable to our in vivo model, the observed discrepancy for some genes potentially represents regulatory factors present in the central nervous system (CNS) but not in vitro.

Animals↗
PubMed2026

Osteoarthritis Year in Review 2026: Genetics, genomics and epigenetics.

OBJECTIVE: The purpose of this narrative review is to highlight advances made over the past 12 months in the field of osteoarthritis (OA) genetics, genomics and epigenomics, with a particular focus on the interpretation of OA risk loci through functional genomic and regulatory approaches. DESIGN: PubMed and Europe PMC were searched to identify studies relevant to OA genetics, genomics and epigenomics published between 1st March 2025 and 30th April 2026. Searches used combinations of terms relating to genetics, genomics, epigenomics, functional genomics, molecular quantitative trait loci, chromatin accessibility and enhancer biology. Studies were limited to human subjects and English-language publications, with additional articles identified through citation screening and expert knowledge of the field. RESULTS: Over the past year, the field has continued to transition from large-scale locus discovery towards biological interpretation of OA genetic risk. Major advances included the largest OA genome-wide association study to date, further development of polygenic risk score approaches, and increasing integration of molecular quantitative trait loci, chromatin accessibility, and enhancer biology datasets to prioritise effector genes and elucidate regulatory mechanisms. Several studies highlighted the highly context-dependent nature of OA genetic risk mechanisms, demonstrating that distinct tissues, cell types, and regulatory layers can identify different candidate effector genes at the same locus. Additional developments included increasing application of singlecell and multi-omic technologies to study OA-relevant tissues. CONCLUSION: Recent advances in OA genetics have shifted the field from locus discovery towards mechanistic interpretation. Emerging evidence demonstrates that the biological consequences of genetic variation are highly dependent upon tissue, cell state and disease context, with different functional genomic approaches often prioritising distinct candidate genes and regulatory mechanisms at the same susceptibility locus. Together, these findings suggest that OA risk loci should increasingly be viewed as dynamic regulatory systems rather than simple variant-to-gene relationships, providing a framework for future studies aimed at resolving causal mechanisms, defining disease endotypes, and identifying therapeutic targets.

Genetics↗
PubMed2006

Confirmation of the expression of a large set of conserved hypothetical proteins in Shewanella oneidensis MR-1.

High-throughput "omic" technologies have allowed for a relatively rapid, yet comprehensive analysis of the global expression patterns within an organism in response to perturbations. In the current study, 9503 different tryptic peptides were identified with high confidence from capillary liquid chromatography-mass spectrometry analysis of 26 chemostat cultures of Shewanella oneidensis MR-1 under various conditions. Using at least one distinctive and a total of two total peptide identifications per protein, we detected the expression of 758 conserved hypothetical proteins. This included 359 such proteins previously described [Kolker, E., Picone, A.F., Galperin, M.Y., Romine, M.F., Higdon, R., Makarova, K.S., Kolker, N., Anderson, G.A., Qiu, X., Auberry, K.J., Babnigg, G., Beliaev, A.S., Edlefsen, P., Elias, D.A., Gorby, Y.A., Holzman, T., Klappenbach, J.A., Konstantinidis, K.T., Land, M.L., Lipton, M.S., McCue, L.A., Monroe, M., Pasa-Tolic, L., Pinchuk, G., Purvine, S., Serres, M.H., Tsapin, S., Zakrajsek, B.A., Zhu, W., Zhou, J., Larimer, F.W., Lawrence, C.E., Riley, M., Collart, F.R., Yates, J.R., III, Smith, R.D., Giometti, C.S., Nealson, K.H., Fredrickson, J.K., Tiedje, J.M., 2005. Global profiling of Shewanella oneidensis MR-1: expression of hypothetical genes and improved functional annotations. Proc Natl Acad Sci U S A 102, 2099-2104] with an additional 399 reported herein for the first time. The latter 399 proteins ranged from 5.3 to 208.3 kDa, with 44 being of 100 amino acid residues or less. Using a combination of information including peptide detection in cells grown under specific culture conditions and predictive algorithms such as PSORT and PSORT-B, possible/plausible functions are proposed for some conserved hypothetical proteins. Such proteins were found not only to be expressed, but 19 were only expressed under certain culturing conditions, thereby providing insight into potential functions. These findings also impact the genomic annotation for S. oneidensis MR-1 by confirming that these genes code for expressed proteins. Our results indicate that 399 proteins can now be upgraded from "conserved hypothetical protein" to "expressed protein in Shewanella," 19 of which appeared to be expressed under specific culture conditions.

Bacterial Proteins↗
PubMed2025

Using Large Genomic Biobanks to Generate Insights into Genetic Kidney Disease.

Chronic kidney disease (CKD) affects approximately 9% of the global population, leading to increased risks of end-stage kidney disease (ESKD), cardiovascular disease (CVD), and mortality. Patients with CKD are a huge burden on health care resources globally. CKD is a complex condition influenced by a combination of genetic, environmental, and traditional risk factors. Family studies have suggested heritability rates for CKD ranging from 30% to 75%, and large genomic biobank studies have proven essential in identifying genes with substantial effects on CKD risk and in capturing cumulative genetic risk through polygenic risk scores. These biobanks are crucial for discovering new genes associated with kidney health and disease, and their growing size enhances the power to detect novel genetic associations. Integrating multi-omics technologies such as transcriptomics, metabolomics, and proteomics further enriches our understanding of CKD, while advanced computational tools continue to expand our insights into genetic data. Polygenic risk scores, derived from hundreds of genetic variants with small effect sizes, can help identify individuals at high risk of CKD. Genomic biobanks offer valuable opportunities for early identification and personalized treatment of monogenic kidney disorders, such as autosomal dominant polycystic kidney disease and Alport syndrome. These biobanks help fill knowledge gaps, particularly in individuals with milder or asymptomatic presentations who are often underrepresented in traditional studies. Expanding genomic biobank efforts globally, especially in diverse populations, is vital to enhancing our understanding of the genetic underpinnings of kidney disease. This review highlights the significant contributions of genomic biobanks to advancing our comprehension of the genetics of CKD.

Humans↗
PubMed2004

Interaction networks: coordinating responses to xenobiotic exposure.

In the last decade the increased usage of '-omic' technologies, plus the sequencing of over 800 complete genomes has led to a vast increase in the amount of information available to the researcher for examining cellular responses to xenobiotics. Much effort has been put into the identification and analysis of expression profiles associated with pathobiological conditions and/or xenobiotic exposure. These profiles are commonly used in two applications. Firstly, comparative profile experiments are used to classify pathobiological states and for the screening of novel chemical entities to predict their action(s) on the body. Secondly, mechanistic investigations will gain information on the molecular mechanisms underlying toxic responses/pathobiological states. During the course of such analysis it has become increasingly clear that a series of highly refined interaction networks exist within the body, regulating both the sensitivity and selectivity of the body's response to pathobiological states/xenobiotic exposure. These interaction networks exist at several levels: Firstly, within individual cells, the interaction between factors that transmit xenobiotics signals will determine the overall cellular response. Secondly, intraorgan communication occurs between the different cell types/sub-types which makes up an organ, coordinating the overall organ response. Finally, interorgan interactions provide axes of response through the body.

Animals↗
PubMed2026

Targeting DNA Methylation: New Paradigms and the Advent of Gene-Selective Tools.

DNA methylation can function as a toxic alkylation reaction exploited by chemotherapeutic agents to induce cancer cell death. However, finely tuned DNA methylation plays a fundamental role in cellular physiology, particularly in the epigenetic regulation of gene expression. Once thought to act solely as a repressor of gene transcription, its functional role has since been elucidated as genomic locus-specific and deeply connected with other epigenetic factors. Following the clinical approval of DNA methyltransferase inhibitors, such as Azacitidine and Decitabine, for the treatment of hematological malignancies, considerable efforts have been devoted to developing pharmacological tools that modulate epigenetic DNA methylation. However, the lack of gene selectivity in these agents limits their therapeutic efficacy and increases off-target toxicity. Moreover, the non-gene-selective nature of current DNA methylation-targeting molecules fails to meet the standards required to discern the nuanced roles of DNA methylation across diverse pathophysiological contexts and genomic loci, particularly in an era where next-generation sequencing and omics technologies enable high-resolution epigenetic analyses. In this review, we examine the mechanisms and roles of DNA methylation in epigenetic regulation, evaluate the current landscape of DNA methylation modulators, from traditional DNMT inhibitors to cutting-edge CRISPR-dCas9 fusion systems and protein-protein interaction disruptors, and discuss their clinical relevance. Finally, we emphasize the need for precise, locus-specific tools to advance both cancer research and therapeutic strategies.

Humans↗
PubMed2006

Statistically integrated metabonomic-proteomic studies on a human prostate cancer xenograft model in mice.

A novel statistically integrated proteometabonomic method has been developed and applied to a human tumor xenograft mouse model of prostate cancer. Parallel 2D-DIGE proteomic and 1H NMR metabolic profile data were collected on blood plasma from mice implanted with a prostate cancer (PC-3) xenograft and from matched control animals. To interpret the xenograft-induced differences in plasma profiles, multivariate statistical algorithms including orthogonal projection to latent structure (OPLS) were applied to generate models characterizing the disease profile. Two approaches to integrating metabonomic data matrices are presented based on OPLS algorithms to provide a framework for generating models relating to the specific and common sources of variation in the metabolite concentrations and protein abundances that can be directly related to the disease model. Multiple correlations between metabolites and proteins were found, including associations between serotransferrin precursor and both tyrosine and 3-D-hydroxybutyrate. Additionally, a correlation between decreased concentration of tyrosine and increased presence of gelsolin was also observed. This approach can provide enhanced recovery of combination candidate biomarkers across multi-omic platforms, thus, enhancing understanding of in vivo model systems studied by multiple omic technologies.

Animals↗