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PubMed2025

Sex as a modifier of genetic risk for type 1 diabetes.

Sex differences influence the pathogenesis of type 1 diabetes (T1D), yet most genetic studies have treated sex as a control covariate rather than a dynamic effect modifier. Sex influences immune cell behaviour, including CD4+ and CD8+ T cell activation, regulatory T cell stability, B cell autoantibody production, dendritic cell priming and monocyte/macrophage inflammation. Underlying mechanisms include hormone-responsive enhancers, X-escape gene dosage and sex-biassed chromatin states, intersecting with T1D-associated variants to produce sex-specific immune phenotypes. These insights help explain regional variation in sex ratios of T1D incidence, such as male predominance in high-risk populations and female excess in low-risk populations. Biological sex shapes T1D risk across multiple layers, including polygenic load; environmental exposures such as vitamin D deficiency and enteroviral infection; and sex-specific hormonal, chromosomal and epigenetic influences. An integrative G × E × S (genetic × environmental × sex-specific) liability-threshold framework is thus supported. Clinical and translational implications include developing sex-specific polygenic risk scores, biomarker panels and interventional strategies targeting pathways such as hormone signalling, vitamin D metabolism and the microbiome. Future multi-omic, longitudinal studies are warranted to test genotype-sex interactions, integrate sex as a core effect modifier and enable precision prevention and treatment of T1D in both males and females.

Humans↗

PubMed2025

Integrative metabolomic and proteomic analysis of diabetic kidney disease progression with younger-onset type 2 diabetes.

AIM: Younger-onset type 2 diabetes (YT2D) confers a disproportionately high risk of diabetic kidney disease (DKD), yet early biomarkers and underlying mechanisms remain poorly defined. We aimed to identify metabolites associated with DKD progression and integrate metabolomic and proteomic data to elucidate pathways involved in a multi-ethnic Asian cohort. MATERIALS AND METHODS: In this prospective study, 787 YT2D patients (diagnosed at ≤ age 40) were followed for a median of 5.7 years. DKD progression was defined as an annual decline in estimated glomerular filtration rate (eGFR) of ≥3 mL/min/1.73 m2 or ≥ 40% reduction in eGFR from baseline. Plasma metabolites were measured by nuclear magnetic resonance spectroscopy. Multivariable regression analysis was performed in a discovery (N = 550) and internal validation cohort (N = 237). Integrative metabolomic-proteomic analysis (N = 428) was performed using sparse partial least squares discriminant analysis (sPLS-DA). RESULTS: Ninety-eight metabolites were differentially expressed between DKD progressors and non-progressors, of which total branched-chain amino acids (BCAAs) (OR = 0.60, 95% CI 0.46-0.79), valine (OR = 0.62, 95% CI 0.48-0.81), and leucine (OR = 0.56, 95% CI 0.43-0.74) associated with DKD progression, independent of metabolic risk factors. Integrative analysis identified three components comprising 23 proteins and 30 metabolites, involved in the citrate cycle and apoptosis, which improved prediction of DKD progression beyond clinical risk factors (AUC 0.69-0.83). CONCLUSION: Lower plasma BCAA levels are independently associated with DKD progression in YT2D. Integrative multi-omics analysis highlights disruptions in metabolic and apoptotic pathways, providing insights into DKD pathophysiology and potential biomarkers for early risk stratification.

Humans↗

PubMed2026

Epigenetics and In Silico Transcriptome Analysis of Pediatric Acute Myeloid Leukemia.

Pediatric acute myeloid leukemia (AML) is a heterogeneous hematologic malignancy that accounts for about 15%-20% of childhood leukemias. Despite therapeutic advances, relapses remain common, and survival for high-risk patients is below 60%. Unlike adult AML, pediatric AML displays distinct genetic mutations, including FLT3-ITD, NPM1, KMT2A rearrangements, and core-binding factors (CBF) fusions, as well as extensive epigenetic dysregulation. Aberrant DNA methylation, histone modifications, and altered non-coding RNA expressions disrupt hematopoietic differentiation and activate oncogenic transcriptional networks. Recent advances in silico transcriptomic analysis have transformed the study of pediatric AML by integrating gene expression and epigenetic data to identify molecular drivers and regulatory networks. Computational RNA-seq pipelines and pathway analyses have highlighted key epigenetic regulators, including DNMT3A, TET2, and HDACs, as potential therapeutic targets. Multi-omics approaches combining transcriptomic, methylomic, and chromatin accessibility data are increasingly used to define biomarkers for diagnosis, prognosis, and therapeutic response. This review provides a comprehensive overview of the molecular and epigenetic landscape of pediatric AML, emphasizing the power of in silico transcriptome analysis to uncover disease mechanisms, refine patient stratification, and guide the development of precision-based epigenetic therapies aimed at improving long-term outcomes in children with AML.

Humans↗

PubMed2025

In vivo and in silico models of Drosophila for Parkinson's disease.

The fruit fly Drosophila melanogaster has emerged as an important model organism to shed light on neurodegeneration. Parkinson's disease (PD) is the second most prevalent neurodegenerative disorder, the cause of which is still mostly unclear. The long-term use of available PD drugs may have major side effects, and they only target the symptoms without providing any effective cure for the disease. Therefore, in vivo and in silico approaches are extensively used to model PD-like phenotypes in Drosophila and investigate cellular alterations underlying PD pathogenesis. In vivo models are particularly crucial to provide insight into the PD-related molecular processes. It has been a preferred approach to investigate these models by collecting omics datasets, which can be further analysed using in silico modeling such as genome-scale metabolic models and artificial intelligence applications. This review aims to summarise in vivo and in silico modeling studies in the literature to illustrate the potential of the Drosophila in the characterisation of PD-related biological mechanisms towards providing early biomarkers and novel treatment options for PD.

Humans↗

PubMed2026

Tumor Mutational Concordance and Recurrence Timing in Hepatocellular Carcinoma.

INTRODUCTION: In hepatocellular carcinoma (HCC), intrahepatic recurrence includes true recurrence from clonal relapse and multicentric recurrence from de novo tumorigenesis. Recurrence timing is used to distinguish these types; however, its accuracy remains unclear. This study aimed to classify recurrent tumors based on somatic mutational concordance and assess the validity of recurrence timing. METHODS: Whole-exome sequencing was performed on paired primary and recurrent HCC tumors from 49 patients enrolled in a prospective institutional omics project. Tumors with ≥ 10 shared somatic mutations were classified as true recurrence. Clinicopathological features, recurrence timing, driver mutation patterns, and survival outcomes were compared between recurrence types. Mutational concordance was quantified using shared variant counts and the Jaccard similarity index. RESULTS: Of the 49 patients, 22 (44.9%) showed true recurrence and 27 (55.1%) had multicentric recurrence. Multicentric recurrence tumors harbored no shared variants or only a single shared variant with the primary tumor. True recurrence was associated with significantly higher concordance in histological differentiation and Edmondson-Steiner grading and greater retention of CTNNB1, TP53, ARID1A, and KEAP1 mutations. The number of shared variants (median: 115 vs. 0, and p < 0.001) and the Jaccard index (median: 0.44 vs. 0.00 and p < 0.001) were significantly higher in the true recurrence group. Recurrence timing was inconsistently correlated with mutational concordance, although a 3-year cutoff yielded significant separation. CONCLUSION: Recurrence timing alone insufficiently reflects clonal relationships. Genomic profiling offers a reliable framework for distinguishing between recurrence types and guiding HCC management.

clonal relapse↗

PubMed2026

AI In Leukemia Diagnostics: Complementing the Pathologist's Role.

Artificial intelligence (AI) is reshaping every stage of leukemia diagnostics, from digital morphology and multiparameter flow cytometry to next-generation sequencing, multi-omics analysis, and emerging computational frontiers such as quantum-inspired feature selection. This review outlines how contemporary AI tools can automate labor-intensive quantitation, flag diagnostically salient patterns, and standardize interpretation, while the pathologist or hematologist retains authority over validation, context-specific integration, and clinical decision-making. We present an illustrative "human-in-the-loop" workflow that embeds AI modules within current laboratory information systems, emphasizing points where expert oversight mitigates algorithmic bias and resolves discordant findings. We further map the validator-integrator role across morphology, flow cytometry, and genomic/multi-omic interpretation and provide practical training competencies and use cases for AI-assisted hematopathology. Beyond technical deployment, the article addresses the educational transformation required for sustainable adoption. Drawing on international competency frameworks, including the Digital Health Competencies in Medical Education Framework and recently proposed AI-specific Entrustable Professional Activities, we map core skills that future hematopathologists must master: data-science literacy, critical appraisal of AI outputs, and ethical governance. We highlight evaluated training models such as the Pathology Informatics Essentials for Residents curriculum, Stanford Artificial Intelligence in Machine and Imaging workshops, and College of American Pathologists bootcamps and propose integration strategies adaptable across resource settings. By pairing rigorous validation with targeted education, AI can elevate rather than eclipse the diagnostic role of the leukemia specialist, enabling more timely, reproducible, and personalized patient care.

Humans↗

PubMed2006

Protein biomarkers associated with acute renal failure and chronic kidney disease.

Acute renal failure (ARF) as well as chronic kidney disease (CKD) are currently categorized according to serum creatinine concentrations. Serum creatinine, however, has shortcomings because of its low predictive values. The need for novel markers for the early diagnosis and prognosis of renal diseases is imminent, particularly for markers reflecting intrinsic organ injury in stages when glomerular filtration is not impaired. This review summarizes protein markers discussed in the context of ARF as well as CKD, and provides an overview on currently available discovery results following 'omics' techniques. The identified set of candidate marker proteins is discussed in their cellular and functional context. The systematic review of proteomics and genomics studies revealed 56 genes to be associated with acute or chronic kidney disease. Context analysis, i.e. correlation of biological processes and molecular functions of reported kidney markers, revealed that 15 genes on the candidate list were assigned to the most significant ontology groups: immunity and defence. Other significantly enriched groups were cell communication (14 genes), signal transduction (22 genes) and apoptosis (seven genes). Among 24 candidate protein markers, nine proteins were also identified by gene expression studies. Next generation candidate marker proteins with improved diagnostic and prognostic values for kidney diseases will be derived from whole genome scans and protemics approaches. Prospective validation still remains elusive for all proposed candidates.

Acute Kidney Injury↗

PubMed2005

From clones of cells to cloned genes and the proteinpaedia.

This brief description follows the association of the author with Ivan Lefkovits from 1971 until this volume. Sketches of our mutual interests are included. Time periods in California, Basel and Texas are described. Decisions about preparing new tools for clonal analysis are elucidated, and experimental approaches leading to the x-omic revolution are described.

B-Lymphocytes↗

PubMed2006

A trial of phenome analysis using 4000 Ds-insertional mutants in gene-coding regions of Arabidopsis.

Mutant lines covering all Arabidopsis genes allow us to pursue systematic functional genomics. A comprehensive phenotype description, called a phenome, is highly sought after in the profiling of -omics data. We selected 4000 transposon-insertional lines with transposon insertions in their gene-coding regions, and systematically observed the visible phenotype of each line. For the first 3 weeks after germination, plants were grown on agar plates and the juvenile phenotypes were recorded. Then the plants were transferred to soil and their phenotypes were recorded at each growth stage. About 140 lines showed clear and reproducible visible phenotypes, including novel phenotypic mutants as well as previously reported ones. All descriptions of the mutants showing visible phenotypes were classified into eight primary categories (seedling, leaves, flowering and growth, stems, branching, flowers, siliques and seed yield) and 43 secondary categories of morphological phenotypes. Phenotypic images have been entered into a searchable database (http://rarge.gsc.riken.jp/phenome/). One example investigated through the use of plural alleles was a mutant of a novel gene related to glycerolipid biosynthesis, with a unique visible phenotype of sepal opening. Our results suggest that we can find more novel visible phenotypes and their corresponding genes, and that phenotypic mutants of gene knockouts are not exhausted yet. This study provides basic data on large-scale phenotyping of gene knockout lines in plants, and will contribute to the completion of an international effort to develop a phenome database of all the functional genes in Arabidopsis.

Arabidopsis↗

PubMed2005

Towards discovery-driven translational research in breast cancer.

Discovery-driven translational research in breast cancer is moving steadily from the study of cell lines to the analysis of clinically relevant samples that, together with the ever increasing number of novel and powerful technologies available within genomics, proteomics and functional genomics, promise to have a major impact on the way breast cancer will be diagnosed, treated and monitored in the future. Here we present a brief report on long-term ongoing strategies at the Danish Centre for Translational Breast Cancer Research to search for markers for early detection and targets for therapeutic intervention, to identify signalling pathways affected in individual tumours, as well as to integrate multiplatform 'omic' data sets collected from tissue samples obtained from individual patients. The ultimate goal of this initiative is to coalesce knowledge-based complementary procedures into a systems biology approach to fight breast cancer.

Biomarkers, Tumor↗

PubMed2004

The application of two-dimensional polyacrylamide gel electrophoresis and downstream analyses to a mixed community of prokaryotic microorganisms.

Summary In the post-genomic era, the focus of numerous researchers has moved to studying the functional products of gene expression. In microbiology, these "omic" approaches have largely been limited to pure cultures of microorganisms. Consequently, they do not provide information on gene expression in a complex mixture of microorganisms as found in the environment. Our method enabled the successful extraction and purification of the entire proteome from a laboratory-scale activated sludge system optimized for enhanced biological phosphorus removal, its separation by two-dimensional polyacrylamide gel electrophoresis and the mapping of this metaproteome. Highly expressed protein spots were excised and identified using quadrupole time-of-flight mass spectrometry with de novo peptide sequencing. The proteins isolated were putatively identified as an outer membrane protein (porin), an acetyl coenzyme A acetyltransferase and a protein component of an ABC-type branched-chain amino acid transport system. These proteins possibly stem from the dominant and uncultured Rhodocyclus-type polyphosphate-accumulating organism in the activated sludge. We propose the term "metaproteomics" for the large-scale characterization of the entire protein complement of environmental microbiota at a given point in time.

Acetyl-CoA C-Acyltransferase↗

PubMed2006

Molecular-based strategies to exploit Pseudomonas biocontrol strains for environmental biotechnology applications.

Exploitation of beneficial plant-microbe interactions in the rhizosphere can result in the promotion of plant health and have significant implications for low input sustainable agriculture applications such as biocontrol. Bacteria such as Bacillus and Pseudomonas, and fungi such as Trichoderma, have been developed as commercial biocontrol products. Registration of microbial inocualants as biocontrol agents in either the European Union or the United States requires production of extensive dossiers covering efficacy, safety and risk assessment. Despite the fact that a number of Pseudomonas biocontrol products have been marketed there are still some limitations hampering the development of this technology for widespread use in agriculture. Although many strains show good performance in specific trials, this is often not translated into consistent, effective biocontrol in diverse field situations. Advances in 'Omics' technology and the publication of complete genome sequences of a number of plant-associative bacterial strains, has facilitated investigations into the molecular basis underpinning the establishment of beneficial plant-microbe interactions in the rhizosphere. The understanding of these molecular signalling processes and the functions they regulate is fundamental to promoting beneficial microbe-plant interactions, to overcome existing limitations and to designing improved strategies for the development of novel Pseudmonas biocontrol inoculant consortia.

Biotechnology↗

PubMed2006

Application of genomics in preclinical drug safety evaluation.

Understanding the response of biological systems to xenobiotics is fundamental to the evaluation of drug safety. Toxicologists have traditionally gathered pathological, morphological, chemical and biochemical information from in vivo studies of preclinical species in order to assess drug safety and to determine how new drugs can be safely administered to the human patient population. In recent years the emerging "-omics" technologies have been developed and integrated into preclinical studies in order to better assess drug safety by gaining information on the cellular and molecular events underlying adverse drug reactions. Genomics approaches in particular have become readily available and are being applied in several stages of drug development. The burgeoning literature on what has become known as "toxicogenomics" has for the most part highlighted successful applications of gene expression profiling in predictive toxicology, enabling decisions to be made on the developability of a compound early in the drug development process. It is also becoming apparent that toxicogenomic approaches are good starting points to develop experiments designed to gain a mechanistic insight into drug toxicities within and across species. Gene expression arrays permit the measurement of responses of essentially all the genes in the entire genome to be monitored, and knowledge of the function of the genes affected can identify the potential mechanisms to then be confirmed using conventional biochemical, toxicological and pathological approaches. As toxicologists put these technologies into practice they build up a knowledge base to better characterize toxicities at the molecular level and to make the search for much needed, novel biomarkers of toxicity more achievable.

Drug Evaluation, Preclinical↗

PubMed2024

Principles of bacterial genome organization, a conformational point of view.

Bacterial chromosomes are large molecules that need to be highly compacted to fit inside the cells. Chromosome compaction must facilitate and maintain key biological processes such as gene expression and DNA transactions (replication, recombination, repair, and segregation). Chromosome and chromatin 3D-organization in bacteria has been a puzzle for decades. Chromosome conformation capture coupled to deep sequencing (Hi-C) in combination with other "omics" approaches has allowed dissection of the structural layers that shape bacterial chromosome organization, from DNA topology to global chromosome architecture. Here we review the latest findings using Hi-C and discuss the main features of bacterial genome folding.

Genome, Bacterial↗

PubMed2026

Rhizosphere Dialogue: Microorganisms Mediated by Root Exudates Alleviate Drought Stress in Grasses.

Drought stress threatens the ecological functions and economic value of grasses, posing a major challenge to their sustainable production. Plants co-evolve with rhizosphere microbial communities, sometimes described as the plant's second genome, that can contribute to drought adaptation. Drought alters root architecture, hormonal and redox regulation and belowground carbon allocation, thereby modifying the quantity and composition of root exudation and reshaping the rhizosphere environment. This review uses the rhizosphere dialogue as an integrative framework to link these plant responses with microbial recruitment and subsequent feedback to the host. We summarise three linked stages of this dialogue: drought-induced changes in root exudation; microbial recruitment and colonisation through chemotaxis, attachment, biofilm formation, and root colonisation; and microbiome-mediated feedback that improves plant water relations, hormonal and redox homoeostasis, nutrient acquisition, and root function. We highlight microbial extracellular polymeric substances, 1-aminocyclopropane-1-carboxylate deaminase, and microbial volatile organic compounds as key mediators of drought alleviation. We then discuss how this framework may inform rational synthetic microbial community (SynCom) design, microbiome-informed breeding, artificial intelligence and machine-learning assisted strain prioritisation, rhizosphere legacy effects, and real-time monitoring. Future work should distinguish active exudate-mediated recruitment from drought-driven environmental filtering and integrate multi-omics, plant genetics, functional validation, and multi-location field trials to determine whether rhizosphere dialogue can become a predictive framework for climate-resilient grass production.

drought stress↗

PubMed2025

Through the lens of bioenergy crops: advances, bottlenecks, and promises of plant engineering.

Advances in engineering of bioenergy crops were driven over the past years by adapting technological breakthroughs and accelerating conventional applications but also exposed intriguing challenges. New tools revealed rich interconnectivity in the exponentially growing and dynamic 'big' omics data' of metabolomes, transcriptomes, and genomes at previously inaccessible magnitude (global, cross-species, meta-) and resolution (single cell). Insights enabled fresh hypotheses and stimulated disciplines such as functional genomics with discovery of broad regulatory networks and their determinants, that is, DNA parts, including promoters, regulatory elements, and transcription factors. Their rational design, assembly into increasingly complex blueprints, and installation into diverse chassis is an existing frontier that may benefit from emerging technologies to address bottlenecks. Interweaving nature-inspired to fully synthetic parts has already allowed building of fine-tuned regulatory circuits, or new-to-nature metabolic routes insulated from the biological context of the chassis species. Similarly, developments and the evolving need for unifying principles in plant transformation and species-agnostic technologies highlight future opportunities for engineering the next generation of bioenergy plants.

Crops, Agricultural↗

PubMed2025

Genomic context-dependent roles of 5-hydroxymethylcytosine in regulating gene expression during rice drought response.

DNA methylation (5-methylcytosine, 5mC) is a key epigenetic regulator of genome stability and stress adaptation in plants. However, the functional role of its oxidative derivative, 5-hydroxymethylcytosine (5hmC), remains poorly understood in plant systems, largely due to its low abundance and unresolved enzymatic origins. Here, we integrated ACE-seq (APOBEC-coupled epigenetic sequencing) with an optimized Tn5mC-seq (transposase-based library preparation in the context of whole-genome bisulfite sequencing, WGBS) approach to generate the first single-base resolution map of 5hmC in rice (Oryza sativa), unveiling its stress-responsive dynamics and regulatory interplay with 5mC during drought adaptation. Genome-wide profiling revealed a basal 5hmC level of ~0.03 (defined as the ratio of C/(C + T) at each site), with drought triggering a pronounced reduction in 5hmC abundance and locus number, followed by incomplete recovery post-rehydration. Unlike 5mC, which accumulates in heterochromatin, 5hmC preferentially localized to euchromatic regions, including promoters, exons, and intergenic elements, and exhibited enrichment at ABA-responsive transcription factors (e.g., OsATAF1, bZIP50). Strikingly, drought induced an antagonistic relationship between 5hmC and 5mC, with the latter increasing globally to reinforce transposon silencing. Multi-omics analyses demonstrated that 5hmC depletion in promoters correlated with transcriptional downregulation, while its accumulation in gene bodies (notably 5'-UTRs) suppressed stress-responsive genes. These findings highlight 5hmC's bifunctional regulatory capacity, contingent on genomic context, and its role in balancing transcriptional plasticity with genome stability during stress. Our work establishes 5hmC as a dynamic epigenetic mark in plant environmental adaptation and provides a foundation for leveraging DNA hydroxymethylation in crop resilience engineering.

Oryza↗

PubMed2025

Genetic effects on chromatin accessibility reveal the molecular mechanisms of complex traits in maize.

Cis-regulatory elements (CREs) are critical for modulating gene expression and phenotypic diversity in maize. While genome-wide association study (GWAS) hits and expression quantitative trait loci (eQTLs) are often enriched in CREs, their molecular mechanisms remain poorly understood. Characterizing CREs within accessible chromatin regions (ACRs) offers a powerful approach to link noncoding variants to chromatin structure alterations and phenotypic variation. Here, we generated ATAC-seq profiles from seedling leaves of 214 maize inbred lines, identifying 82 174 consensus ACRs. Notably, 39.55% of these ACRs exhibited significant population-wide chromatin accessibility variation. By mapping chromatin accessibility quantitative trait loci (caQTLs), we discovered 27 004 loci, including 1398 predicted to disrupt transcription factor (TF)-binding sites. Integration with multi-omics data revealed 7405 caACR-target gene pairs and linked 56 caACRs to GWAS signals for 51 agronomic traits, with significant enrichment in flowering-related pathways. Functional candidates such as ZmZIM30 - putatively regulated by caACRs - emerged as key regulators of flowering time. At the fad7 locus associated with linolenic acid content, allelic variants overlapping a caQTL showed differential chromatin accessibility. Our study provides a high-resolution cis-elements of maize leaves, deciphers the genetic basis of chromatin accessibility variation, and bridges noncoding caQTLs to molecular mechanisms underlying GWAS hits.

Zea mays↗