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PubMed2025

Admixture-mapping analysis reveals genetic determinants of the human plasma proteome.

Protein profiling and genetic findings can be integrated to define the genetic architecture of the circulating proteome in chronic diseases. Most self-identified African American (AA) individuals have both African and European genetic ancestry. Admixture mapping can detect genomic association regions in which causal variants exist with substantial differences in allele frequency or effect sizes between genetic ancestries. We performed admixture mapping of the circulating proteome in 1,989 participants from the Jackson Heart Study (JHS), investigating the relation of local African ancestry within genomic regions with levels of circulating proteins. We conditioned protein-local ancestry association models on variants previously found to be associated with those proteins in genome-wide association studies (GWASs). We replicated findings in 196 AA participants from the Multi-Ethnic Study of Atherosclerosis (MESA). 62 proteins were associated with local African ancestry. 21 of 62 remained statistically significant after conditioning on protein-associated variants observed in previous GWASs. 48 of 54 available protein-local ancestry associations were replicated in the MESA. Proteins associated with local African ancestry included chemokines, factors associated with vascular biology and inflammation, and other biologically interesting proteins. Admixture associations unexplained by previously reported protein-associated variants in conditional analysis suggest the existence of causal variants missed by standard GWAS techniques.

Aged↗

PubMed2026

Proteogenomic features define subtypes of mantle cell lymphoma.

Mantle cell lymphoma (MCL) is a biologically heterogeneous B-cell malignancy. Although genomics and transcriptomics have delineated parts of the MCL disease spectrum, proteomics remains largely unexplored. Here, we conducted a comprehensive proteogenomic analysis integrating genomics, transcriptomics, and proteomics on peripheral blood samples from 27 patients with MCL and 4 healthy donors to investigate the translational and posttranslational dimensions of MCL. Our study identified 1296 downregulated and 468 upregulated proteins in MCL cells. The splicing pathways were significantly upregulated at both the mRNA and protein levels, suggesting a critical role for aberrant RNA splicing in MCL pathogenesis. Integration of proteomic data with genetic aberrations revealed immunoglobulin heavy chain variable mutational status and CCND1 mutation are associated with distinctive transcriptomic and proteomic profiles, which correspond to significant differences in clinical outcomes. A multiomics molecular stratification model incorporating proteomic data showed superior predictive power for patient survival compared with single-omics models (concordance index, 0.83 vs 0.74). This study provides, to our knowledge, the first comprehensive proteogenomic profile of MCL, offering novel insights into its molecular mechanisms and clinical behavior. The identification of molecular subtypes and prognostic protein signatures underscores the potential of proteomics to guide precision medicine strategies for MCL.

Humans↗

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↗

PubMed2026

Proteomics at scale: Bottlenecks and opportunities for early-career researchers in a fast developing field.

The field of proteomics has rapidly evolved over the last five years enabled by rapid advances in instrumentation and computation. At the same time, the proteomics community is also growing. This is reflected by the increasing participation in international conferences such as those organized by the European Proteomics Association and the Human Proteome Organization. These events provide early-career researchers with unique opportunities to exchange ideas, develop collaborations, and build networks that support professional development. One such network is the Young Proteomics Investigators Club, a European initiative supported by European Proteomics Association and led by early-career researchers. In this Community-Driven project, we investigate recent trends in proteomics by screening conference abstracts and evaluating the session attendance at Human Proteome Organization Congresses and European Proteomics Association conferences. Based on these analyses, we identified five areas that, from our perspective, are shaping the current trends in proteomics: clinical proteomics, proteomics of post-translational modifications, single-cell proteomics, systems biology and multi-omics, and computational proteomics. For each area, we highlight both unique challenges and identify a common theme: a shift from exploratory studies with manageable sample numbers towards large screenings and cohorts and the generation of big data, which often comes with the lack of computational support, organizational networks, and infrastructure. In this light, we describe the unique challenges and opportunities faced by early-career researchers. We point to actionable directions for enabling reproducible and transparent proteomics as well as community-driven projects and initiatives, which are often providing training and support. SIGNIFICANCE: In this perspective, the Young Proteomics Investigators Club (YPIC) discusses advances in analytical developments and computational approaches in proteomics research. Based on empirical analysis of recent European Proteomics Association conference and Human Proteome Organization congresses contributions, we identify clinical, single-cell, post-translational and systems-level proteomics as the research areas that have gained most momentum in the last three to five years. What makes this work distinctive is that it is written by and for early-career researchers, thereby uniquely identifying where momentum, challenges, and unmet needs converge for the newest generation of proteomics researchers. Rather than cataloguing advances, we examine the widening gap between what modern proteomics can generate and what individual researchers can realistically process, validate, and interpret. We describe specific structural barriers including access to high performance computing, limited formal training in scalable data analysis, the need for unified benchmarking standards and navigating clinical collaboration frameworks. We then highlight opportunities for the field, such as community-curated benchmarks, interdisciplinary mentorship models, and shared computational infrastructure. By making these challenges explicit from an early-career researchers standpoint, we aim to inform how training, funding, and community initiatives can be shaped to support the next generation of proteomics researchers.

Proteomics↗

PubMed2026

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

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

Evodia rutaecarpa↗

PubMed2025

Consumption of traditional Sardinian fermented milk promotes changes in the rat gut microbiota composition and functions.

BACKGROUND: Fermented milk products are part of the staple diet for many Mediterranean populations. Most of these traditional foods are enriched with lactobacilli and other lactic acid bacteria, as well as with metabolites resulting from lactose fermentation. Currently, there is very little scientific knowledge on how dietary supplementation with fermented milk affects the composition of the gut microbiota and its metabolic activities. RESULTS: We integrated 16 S rRNA gene-based taxonomic profiling with metaproteomics-based functional analysis to investigate gut microbiota changes in rats exposed to an 8-week dietary supplementation with casu axedu, a traditional fermented milk produced within rural communities in Sardinia (Italy). Several microbial taxa showed a significantly increased abundance at the end of the dietary treatment, including Phascolarctobacterium, Prevotella, Blautia glucerasea, and Lactococcus lactis, while Bacteroides dorei and Helicobacter rodentium were decreased compared to the control rats. Metaproteomic analysis highlighted a striking reshaping of the Prevotella proteome in agreement with its blooming in casu axedu-fed animals, suggesting an increase of the glycolytic activity through the Embden-Meyerhof-Parnas pathway over the Entner-Doudoroff pathway. Moreover, an increased production of enzymes involved in succinate biosynthesis was observed, which in turn significantly boosted the abundance of Phascolarctobacterium and its production of propionate. Fermented milk consumption also promoted microbial synthesis of branched chain essential amino acids L-valine and L-leucine. Finally, metaproteomic data indicated a reduction of bacterial virulence factors and host inflammatory markers, suggesting that the consumption of casu axedu can have beneficial effects on the gut mucosa health. CONCLUSIONS: Our integrated multi-omics approach reveals that dietary supplementation with the traditional Sardinian fermented milk, casu axedu, induces significant shifts in the rat gut microbiota composition and function, characterized by the enrichment of beneficial taxa and metabolic pathways associated with improved gut health and reduced inflammation.

Animals↗

PubMed2003

MatchMiner: a tool for batch navigation among gene and gene product identifiers.

MatchMiner is a freely available program package for batch navigation among gene and gene product identifier types commonly encountered in microarray studies and other forms of 'omic' research. The user inputs a list of gene identifiers and then uses the Merge function to find the overlap with a second list of identifiers of either the same or a different type or uses the LookUp function to find corresponding identifiers.

Algorithms↗

PubMed2026

Decoding protein signatures and protein interactions in oral potentially malignant disorders: a systematic review and network analysis.

BACKGROUND: Proteomic profiling offers thorough insights into protein structure and function, as well as it acts as an essential approach for analyzing molecular changes at the tissue level. However, because of the proteome's diversity and dynamic nature, biomarker discovery remains challenging. By combining proteomics with bioinformatics, the level of understanding in relation to molecular interactions and disease processes can be improved. Through an integrative approach, few limitations can be addressed, thereby promoting proteomic profiling for the discovery of new therapeutic targets and novel biomarkers for a variety of disorders. AIM: To identify differentially expressed protein markers and their key molecular pathways associated with Oral Potentially Malignant Disorders. METHODS: Systematic Review was conducted following the PRISMA guidelines and the protocol registered in the International Prospective Register of Systematic Reviews (PROSPERO) with the registration ID number CRD42024557545. A comprehensive literature review was performed using electronic databases, yielding 12,797, studies from which 15 eligible articles were selected. The Newcastle-Ottawa Scale was used to assess the risk of bias. Vote counting was performed to identify proteins reported in more than one study. A bipartite network was constructed using Cytoscape to identify shared and disease-specific protein markers. Lesion-wise protein-protein interaction networks were generated using STRING and analysed in Cytoscape to identify highly interconnected hub proteins, and pathway enrichment analysis for these hubs was performed using Reactome. RESULTS: A total of fifteen studies (Leukoplakia (LK) - n = 1, Proliferative Verrucous Leukoplakia (PVL) - n = 2, Oral Submucous Fibrosis (OSMF) - n = 7, and Oral Lichen Planus (OLP) - n = 5) were included. The Newcastle-Ottawa Scale was used to evaluate methodological quality and the quality of studies included in this systematic review was high for 4 articles and moderate in the remaining 11. The most commonly employed technique was mass spectrometry. A total of 318 candidate proteins (LK - 14, PVL - 82, OSMF - 172, and OLP - 50) were identified across the oral potentially malignant disorders. Key markers identified through vote counting included ERO1A, NUCB1, RHOA, and IL36A for PVL; LUM, KRT1, KRT9, ALB, and VIM for OSMF; and ALB, LYZ, HP, HBB, and AMY1A for OLP. The bipartite network showed that OSMF and OLP shared the highest number of proteins, indicating the strongest overlap among lesions. Network analysis further highlighted distinct hub proteins for each lesion: for LK- AMY1A, AMY1B and APOA1; for PVL- CFL1, RHOA and CDC42; for OSMF- HSP90AA1, ENO1 and SERPINA1; and for OLP- HP, B2M, and ORM1. Lesion-specific pathway enrichment revealed that LK was associated with epithelial differentiation, PVL with oncogenic signaling, OSMF with stress-driven fibrosis, and OLP with immune-mediated inflammation. CONCLUSIONS: Proteomic expression offers insights into disease pathogenesis by identifying important molecular changes across OPMDs. However, the majority of biomarkers are still in the exploratory stage due to the considerable variation in lesion types, sample sources, proteomic techniques, and reporting systems. In order to create reliable and clinically applicable biomarkers, future studies should concentrate on combining multi-omics techniques with large-scale, standardized cohorts.

Humans↗

PubMed2025

Deep learning-based cell-specific gene regulatory networks inferred from single-cell multiome data.

Gene regulatory networks (GRNs) provide a global representation of how genetic/genomic information is transferred in living systems and are a key component in understanding genome regulation. Single-cell multiome data provide unprecedented opportunities to reconstruct GRNs at fine-grained resolution. However, the inference of GRNs is hindered by insufficient single omic profiles due to the characteristic high loss rate of single-cell sequencing data. In this study, we developed scMultiomeGRN, a deep learning framework to infer transcription factor (TF) regulatory networks via unique integration of single-cell genomic (single-cell RNA sequencing) and epigenomic (single-cell ATAC sequencing) data. We create scMultiomeGRN to elucidate these networks by conceptualizing TF network graph structures. Specifically, we build modality-specific neighbor aggregators and cross-modal attention modules to learn latent representations of TFs from single-cell multi-omics. We demonstrate that scMultiomeGRN outperforms state-of-the-art models on multiple benchmark datasets involved in diseases and health. Via scMultiomeGRN, we identified Alzheimer's disease-relevant regulatory network of SPI1 and RUNX1 for microglia. In summary, scMultiomeGRN offers a deep learning framework to identify cell type-specific gene regulatory network from single-cell multiome data.

Deep Learning↗

PubMed2026

Differentiating hemorrhagic shock and organophosphate poisoning through integrated skin microbiome-metabolome signatures.

Accurate determination of cause of death and estimation of postmortem interval (PMI) are critical yet challenging tasks in forensic science, particularly in cases with rapid demise and absence of obvious morphological abnormalities. We employed an integrative multi-omics approach to characterize postmortem microbial succession and metabolic alterations on facial skin in mouse models of hemorrhagic shock (HS) and organophosphorus poisoning (OP) across three decomposition stages: bloating (2 days), active decay (8 days), and advanced decay (16 days). Metagenomic profiling revealed significantly reduced α-diversity in HS compared with OP throughout all stages (p < 0.001), accompanied by stage-dependent compositional shifts, including early enrichment of Firmicutes in HS and Proteobacteria in OP. A total of 237 differential taxa were identified, with Providencia and Morganella predominating in OP, whereas Staphylococcus and Corynebacterium dominated bloating stage of HS. Untargeted metabolomics uncovered distinct cause-of-death-linked metabolites, notably elevated 2'-deoxycytidine-5'-diphosphate in early OP and persistent cholic acid/cholate accumulation in HS at later PMI. Functional analysis highlighted histidine and phosphate/phosphonate metabolism as key discriminatory pathways, exhibiting stage-specific oscillations and strong correlations with characteristic taxa. These findings demonstrate that skin-based metagenomic-metabolomic integration provides robust, mechanistically informed biomarkers for both PMI estimation and cause-of-death differentiation, offering a minimally invasive and temporally dynamic tool for forensic investigations.

Animals↗

PubMed2006

Capillary electrophoresis at the omics level: towards systems biology.

Emerging systems biology aims at integrating the enormous amount of existing omics data in order to better understand their functional relationships at a whole systems level. These huge datasets can be obtained through advances in high-throughput, sensitive, precise, and accurate analytical instrumentation and technological innovation. Separation sciences play an important role in revealing biological processes at various omic levels. From the perspective of systems biology, CE is a strong candidate for high-throughput, sensitive data generation which is capable of tackling the challenges in acquiring qualitative and quantitative knowledge through a system-level study. This review focuses on the applicability of CE to systems-based analytical data at the genomic, transcriptomic, proteomic, and metabolomic levels.

Animals↗

PubMed2026

Developmental and cellular vulnerabilities underlie genetic architecture of schizophrenia.

Schizophrenia (SZ) is a highly heritable neuropsychiatric condition with complex polygenic architecture. Elucidating the cellular and developmental substrates vulnerable to the genetic risk is essential for understanding the underlying neurobiological mechanisms. Here, we integrated genome-wide association study (GWAS) and whole-exome sequencing (WES) data with a developmental multi-omics atlas of the human cortex (including 5 cortical regions), comprising about 3 million single-nucleus RNA sequencing (snRNA-Seq) and single-nucleus assay for transposase-accessible chromatin using sequencing (snATAC-Seq) profiles across 8 neurodevelopmental processes, to map cell-type-specific enrichment of SZ genetic risk. Our enrichment analyses revealed that both common and rare genetic liabilities converged on broad excitatory and inhibitory neuronal classes. Across different statistical frameworks, we identified genetic enrichment within intratelencephalic (IT) projection neurons and layer 6b excitatory neurons (Ex-L6b) networks across multiple cortical regions. Stage-resolved developmental mapping in the frontal cortex showed that genetic liabilities, particularly the rare variants, are predominantly concentrated within early developmental processes, namely neurogenesis and neuronal migration. Differential expression analysis in postmortem frontal cortex snRNA-Seq datasets cross-validated the cellular substrates of the genetic liabilities. Collectively, our findings establish a high-resolution cellular and temporal framework of SZ susceptibility, implicating mature associative IT microcircuits, deep-layer thalamocortical-regulating networks, and early developmental specification windows as primary points of genetic convergence in SZ.

Journal Article↗

PubMed2026

Scalable, generalizable and uncertainty-aware integration of spatial multiomics across diverse modalities and platforms with SCIGMA.

Recent advances in spatial omics technologies have enabled simultaneous profiling of transcriptomic, proteomic, epigenomic, metabolomic and imaging data at high spatial resolution, offering unprecedented opportunities to dissect tissue complexity. However, integrating these diverse and large-scale spatial multimodal datasets remains a major computational challenge. We present SCIGMA, a scalable and generalizable deep learning framework for spatial multiomics integration. SCIGMA introduces an uncertainty-aware contrastive learning objective and multiview graph neural networks to preserve modality-specific signals while learning biologically meaningful joint representations. Unlike previous methods, SCIGMA provides spatially resolved uncertainty estimates, interpretably identifying regions of biological or technical heterogeneity. SCIGMA supports integration of up to five modalities, and its modular framework is extensible to future technologies with even more modalities. It also scales to more than 1 million spatial locations, enabling analysis of high-resolution datasets such as Visium HD and Xenium Prime. We evaluated SCIGMA across 19 datasets spanning 8 modalities, 10 tissues and 9 platforms. On benchmarkable datasets, SCIGMA outperformed other methods in spatial domain detection, modality preservation, feature reconstruction and reproducibility. SCIGMA identifies biologically meaningful structures, refined spatial domains and modality-specific regulatory programs, providing a robust, flexible and future-ready solution for scalable spatial multimodal integration.

Multiomics↗

PubMed2005

KEGG-based pathway visualization tool for complex omics data.

Pathway-level visualization of omics data provides an essential means for systems biology, to capture the systematic properties of the inner activities of cells. Here we describe a web-based resource consisting of a web-application for the visualization of complex omics data onto KEGG pathways to overview all entities in the context of cellular pathways, and databases created with the software to visualize a series of microarray data. The web-application accepts transcriptome, proteome, metabolome, or the combination of these data as input, and because of this scalability it is advantageous for the visualization of cell simulation results. The web server can be accessed at http://www.g-language.org/data/marray/.

Computer Graphics↗

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↗

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

Integrative multi-omics identifies DOC2A as a novel pharmacological target for bipolar disorder.

BACKGROUND: Current bipolar disorder (BD) therapies suffer from limited efficacy and adverse effects, necessitating mechanistically grounded targets. METHODS: We integrated BD genome-wide association study data (158,036 cases; 2,796,499 controls) with brain proteomics (ROSMAP and Banner dorsolateral prefrontal cortex, n = 376 and 152) to perform proteome-wide association studies (PWAS). Bayesian colocalization and summary-data-based Mendelian randomization (SMR) prioritized causal genes. Cell-type-specific transcriptomics validated dysregulation in iPSC-derived neurons, astrocytes, and postmortem hippocampus/prefrontal cortex. Weighted gene co-expression networks (WGCNAs), functional enrichment, and molecular docking assessed functional pathways and druggability. RESULTS: PWAS identified eight BD-associated genes (false discovery rate < 0.05), with DOC2A emerging as the top candidate. Colocalization (H4 > 0.8) and SMR supported a causal association of DOC2A with BD, with no pleiotropy (heterogeneity in dependent instruments P > 0.01); DOC2A expression decreased in BD across neurons (P = 4.26 × 10-2), astrocytes (P = 2.09 × 10-2), hippocampus (P = 9.80 × 10-3, t = -2.738), and prefrontal cortex (P = 1.44 × 10-2, t = -2.580); WGCNA positioned DOC2A as a key regulator (module membership/gene significance P < 0.05) of co-expression networks enriched for BD-associated processes including neurotransmitter secretion and postsynaptic actin cytoskeleton organization (P < 0.05); molecular docking revealed favorable-affinity binding (ΔG < -4 kcal/mol) between DOC2A and BD-related drugs and neuroprotective compounds. CONCLUSIONS: Our convergent multi-omics framework highlights DOC2A dysregulation as a key contributor to synaptic dysfunction in BD and nominates it as a promising therapeutic target. The demonstrated interaction with existing neuroactive compounds provides immediate translational avenues.

Bipolar Disorder↗

PubMed2001

Searching for pharmacogenomic markers: the synergy between omic and hypothesis-driven research.

With 35,000 genes and hundreds of thousands of protein states to identify, correlate, and understand, it no longer suffices to rely on studies of one gene, gene product, or process at a time. We have entered the "omic" era in biology. But large-scale omic studies of cellular molecules in aggregate rarely can answer interesting questions without the assistance of information from traditional hypothesis-driven research. The two types of science are synergistic. A case in point is the set of pharmacogenomic studies that we and our collaborators have done with the 60 human cancer cell lines of the National Cancer Institute's drug discovery program. Those cells (the NCI-60) have been characterized pharmacologically with respect to their sensitivity to >70,000 chemical compounds. We are further characterizing them at the DNA, RNA, protein, and functional levels. Our major aim is to identify pharmacogenomic markers that can aid in drug discovery and design, as well as in individualization of cancer therapy. The bioinformatic and chemoinformatic challenges of this study have demanded novel methods for analysis and visualization of high-dimensional data. Included are the color-coded "clustered image map" and also the MedMiner program package, which captures and organizes the biomedical literature on gene-gene and gene-drug relationships. Microarray transcript expression studies of the 60 cell lines reveal, for example, a gene-drug correlation with potential clinical implications--that between the asparagine synthetase gene and the enzyme-drug L-asparaginase in ovarian cancer cells.

Biomarkers, Tumor↗