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PubMed2024

Protocol to perform integrative analysis of high-dimensional single-cell multimodal data using an interpretable deep learning technique.

The advent of single-cell multi-omics sequencing technology makes it possible for researchers to leverage multiple modalities for individual cells. Here, we present a protocol to perform integrative analysis of high-dimensional single-cell multimodal data using an interpretable deep learning technique called moETM. We describe steps for data preprocessing, multi-omics integration, inclusion of prior pathway knowledge, and cross-omics imputation. As a demonstration, we used the single-cell multi-omics data collected from bone marrow mononuclear cells (GSE194122) as in our original study. For complete details on the use and execution of this protocol, please refer to Zhou et al.1.

Deep Learning↗

PubMed2026

The role of KIAA1467 in breast cancer: insights from pan-cancer and single-cell sequencing analysis.

BACKGROUND: Improving the response rate of single-agent immune checkpoint blockade (ICB) urgently requires the discovery of new therapeutic targets for combinatorial regimens. Analyses of tumor microenvironment (TME)-associated biomarkers have verified that KIAA1467 drives the formation of an immune-excluded, non-inflamed TME in breast cancer (BRCA). This study systematically explores the expression pattern, prognostic value, immune regulatory function, biological effects, and drug resistance relevance of FAM234B (also known as KIAA1467) in BRCA. METHODS: We performed pan-cancer survival analysis using The Cancer Genome Atlas (TCGA) datasets. Multi-omics bioinformatics analyses were conducted to evaluate KIAA1467 expression across malignancies. Single-cell RNA sequencing (scRNA-seq) data from GSE176078 was utilized to localize KIAA1467 expression at the cellular level. Immunohistochemistry and western blot assays validated KIAA1467 expression in BRCA clinical specimens. Correlation analyses were implemented to assess relationships between KIAA1467 expression, clinicopathological features, immune modulators, tumor-infiltrating immune cells, and p53 mutation status. Functional enrichment analysis uncovered relevant signaling pathways. Bioinformatic half maximal inhibitory concentration (IC50) prediction and in vitro cellular experiments were applied to evaluate associations between KIAA1467 and chemotherapeutic drug sensitivity. RESULTS: TCGA pan-cancer survival analysis demonstrated that elevated KIAA1467 expression significantly predicted shortened overall survival in BRCA and multiple other tumor types. KIAA1467 displayed distinct expression patterns across cancers, with prominent upregulation in BRCA. scRNA-seq confirmed enriched KIAA1467 expression within BRCA cells, and its upregulation in BRCA tissues was further verified by immunohistochemistry and western blot. High KIAA1467 expression was positively correlated with advanced tumor grade and lymphatic metastasis. KIAA1467 showed negative correlations with most immune modulators and core immune checkpoint molecules, as well as tumor-infiltrating immune cells in the TME, implying its potential function in tumor immune evasion. Low KIAA1467 expression was tightly linked to p53 mutations. Enrichment analysis indicated participation of KIAA1467 in epithelial-mesenchymal transition, apoptosis and cell cycle arrest. Furthermore, high KIAA1467 expression corresponded to higher estimated IC50 values of cisplatin, gefitinib, paclitaxel and gemcitabine, consistent with reduced chemosensitivity observed in vitro. CONCLUSIONS: This study reveals the multifaceted oncogenic role of KIAA1467 in BRCA. KIAA1467 participates in remodeling an immunosuppressive TME, correlates with malignant progression and chemoresistance, and may serve as a promising candidate target to optimize ICB-based combination therapy for BRCA. These findings offer new perspectives for the clinical treatment and comprehensive management of BRCA.

KIAA1467↗

PubMed2026

Mitochondrial Function-Related Genes in Sleep Disorders: A Multi-Omics Mendelian Randomization Study.

Mitochondrial dysfunction is linked to sleep disorders in previous report, but the potential roles of specific genes remain unclear. This study aimed to dissect different subtype-specific genetic associations and their underlying mechanisms. A multi-omics Summary-data-based Mendelian Randomization (SMR) approach was performed to identify potential causal links between mitochondrial function-related genes and sleep disorders. We integrated GWAS data from FinnGen database (the discovery set), independent GWAS datasets (covering different sleep-disorder subtypes and used for validation), and cis-QTLs (including mQTLs, eQTLs, and pQTLs) to perform systematic exploration. Specially, we performed targeted validation of tissue-specific effects, leveraging gene expression data from disease-relevant brain regions within the GTEx database. Our SMR analysis identified mitochondrial function-related genes potentially modulating sleep disorders across biological layers, initially identifying 102 genes at the methylation level, 48 at the gene expression level, and 6 at the protein abundance level. Integrative analysis subsequently prioritized DCXR and ACADVL and revealed their distinct, subtype-specific associations. DCXR exhibited a protective role in sleep apnea while ACADVL showed a paradoxical risk conferring role in daytime sleepiness. In addition, the analysis identified an epigenetic regulatory mechanism for DCXR in which its expression and protein levels are modulated by DNA methylation. Finally, validation in brain-hypothalamus tissue confirmed DCXR as a significant potential protective factor (OR = 0.929, 95% CI: 0.887-0.973, P_HEIDI = 0.999, FDR = 0.2449). Our findings implicate key mitochondrial genes, particularly DCXR and ACADVL, in the pathophysiology of specific sleep disorder subtypes, highlighting potential avenues for precision medicine. Clinical trial number: Not applicable.

Humans↗

PubMedDate not supplied

The role of mass spectrometry in plant systems biology.

Large-scale analyses of proteins and metabolites are intimately bound to advancements in MS technologies. The aim of these non-targeted "omic" technologies is to extend our understanding beyond the analysis of only parts of the system. Here, metabolomics and proteomics emerged in parallel with the development of novel mass analyzers and hyphenated techniques such as gas chromatography coupled to time-of-flight mass spectrometry (GC-TOF-MS) and multidimensional liquid chromatography coupled to mass spectrometry (LC-MS). The analysis of (i) proteins (ii) phosphoproteins, and (iii) metabolites is discussed in the context of plant physiology and environment and with a focus on novel method developments. Recently published studies measuring dynamic (quantitative) behavior at these levels are summarized; for these works, the completely sequenced plants Arabidopsis thaliana and Oryza sativa (rice) have been the primary models of choice. Particular emphasis is given to key physiological processes such as metabolism, development, stress, and defense. Moreover, attempts to combine spatial, tissue-specific resolution with systematic profiling are described. Finally, we summarize the initial steps to characterize the molecular plant phenotype as a corollary of environment and genotype.

Mass Spectrometry↗

PubMed2026

Integrative genomic and transcriptomic analyses identify key regulators of skin pigmentation in Larimichthys crocea.

The yellow body coloration of large yellow croaker (Larimichthys crocea) constitutes a crucial economic trait, yet its underlying genetic regulatory mechanisms remain poorly understood. This study systematically elucidated the molecular basis of body color variation by integrating genome resequencing and skin transcriptome analyses, combined with the contextual analysis of key pigmentation-related genes and phenotypic histological validation. 200 phenotyped individuals (including yellow-selected lines, F1 progeny, and normal control groups, all derived from a well-characterized aquaculture stock) identified 39 significantly associated SNPs (-log₁₀(P) ≥ 6), mapping to multiple candidate genes. These genes were significantly enriched in pathways related to pigment deposition (GO:0033059), melanosome organization (GO:0032438), melanogenesis, and tyrosine metabolism. Cross-developmental stage transcriptome analysis revealed 2395 differentially expressed genes (DEGs). Multi-omics integration identified eight overlapping candidate genes, including tyrp1, slc45a2, oca2, and dgat2, among which tyrp1 was prioritized for in-depth validation based on its core regulatory role in eumelanin synthesis, significant SNP association signal, and consistent downregulation in transcriptomic data. Experimental validation demonstrated that the g.895C > T mutation in exon 2 of tyrp1b was strongly significantly associated with the yellow phenotype: the frequency of mutant genotypes (TT/CT) reached 92.86%in the yellow-selected group, whereas the control group exclusively exhibited the wild-type genotype (CC). qPCR confirmed significantly downregulated tyrp1b expression in the skin of yellow individuals, consistent with the transcriptome trend. Histological and stereomicroscopic observations of skin tissues further validated the physiological basis of the yellow phenotype, revealing a significant reduction in melanophore number and abnormal melanosome morphology in yellow-phenotype individuals, accompanied by increased xanthophore density. These results suggest that tyrp1b mutation is strongly associated with the yellow phenotype. However, the presence of a wild-type CC individual in the yellow group indicates that this mutation is not strictly required for yellow coloration, suggesting that other genetic or environmental factors may also contribute to the phenotype, Additionally, downregulation of the carotenoid metabolism gene bco2 coupled with upregulation of xdh, together with the functional changes of slc45a2 and oca2, may synergistically promote xanthophore pigment deposition, contributing to the yellow phenotype. As melanin synthesis in large yellow croaker relies on the conserved tyrosinase pathway and transporter proteins, mutations in associated genes (tyrp1b, slc45a2, oca2) represent a primary underlying cause for the loss of melanin-based coloration and transition to a yellow phenotype in L. crocea. These findings provide key molecular targets and a theoretical foundation for molecular breeding of body color in this species, and also enrich the understanding of xanthism regulatory mechanisms in teleosts.

Animals↗

PubMed2026

Pan-cancer analysis identifies GPRIN1 as a prognostic biomarker and promoter of cell proliferation in pancreatic cancer.

BACKGROUND: G protein-regulated inducer of neurite outgrowth 1 (GPRIN1), an emerging modulator of GPCR signaling, has been implicated in oncogenesis. However, its comprehensive role across human cancers, particularly in reshaping the tumor microenvironment (TME), remains poorly characterized. We aimed to elucidate the pan-cancer significance of GPRIN1 in TME modulation and its therapeutic implications. METHODS: We analyzed multi-omics data from TCGA and other public databases, performing a systematic analysis of GPRIN1 regarding expression, prognosis, immune infiltration, and genomic instability across 33 cancer types. To validate these bioinformatic findings, we performed lentiviral shRNA-mediated knockdown in pancreatic (PANC-1) and hepatic (HepG2) cancer cells to assess proliferation and migration. Crucially, the clinical relevance of GPRIN1 was further validated in an independent cohort of pancreatic cancer patients (N = 17) using immunohistochemistry (IHC). RESULTS: The analysis identified a lineage-dependent expression pattern. Epithelial tumors exhibited upregulation, whereas glioblastoma samples displayed downregulation. GPRIN1 expression consistently correlated with immune subtypes and CD8+ T cell abundance. In vitro assays demonstrated that GPRIN1 depletion significantly inhibited cell proliferation and migration (P < 0.0001). In the clinical validation cohort, multivariate Cox regression analysis identified high GPRIN1 protein levels as an independent predictor of post-operative recurrence. These patients also showed a trend toward extended overall survival. CONCLUSIONS: These findings define GPRIN1 as a context-dependent regulator of the TME. By integrating computational and experimental data, this study supports GPRIN1 as a potential biomarker for risk assessment in pancreatic cancer.

Humans↗

PubMed2026

AI-integrated digital breeding for crop improvement.

Crop breeding increasingly depends on the effective integration and interpretation of large, heterogeneous datasets spanning genomic, phenotypic, multi-omics, and environmental layers. Conventional breeding approaches are often insufficient to capture the complex relationships among these data or to support timely selection decisions. Digital breeding can help address this limitation by complementing field experimentation, mixed models, and genomic prediction with the integration of biological data and computational prediction throughout the breeding process. In particular, the rapid advancement of artificial intelligence (AI) has improved the analysis of high-dimensional datasets and broadened its application to trait prediction, selection, and breeding design. Here, we review recent developments in AI-enabled digital breeding, encompassing genomic, phenomic, and multi-omics data generation and analysis, predictive modeling, explainable and generative AI, and data-driven breeding decision support. We further discuss emerging AI applications, their current contributions to crop research and breeding, and the major considerations affecting their reliable and practical implementation. Collectively, this review provides a structured understanding of the roles of AI across the digital breeding process and offers guidance for future methodological development and practical application in crop improvement.

artificial intelligence↗

PubMed2005

Metabolomics reviewed: a new "omics" platform technology for systems biology and implications for natural products research.

Metabolomics is the study of global metabolite profiles in a system (cell, tissue, or organism) under a given set of conditions. The analysis of the metabolome is particularly challenging due to the diverse chemical nature of metabolites. Metabolites are the result of the interaction of the system's genome with its environment and are not merely the end product of gene expression but also form part of the regulatory system in an integrated manner. Metabolomics has its roots in early metabolite profiling studies but is now a rapidly expanding area of scientific research in its own right. Metabolomics (or metabonomics) has been labeled one of the new "omics", joining genomics, transcriptomics, and proteomics as a science employed toward the understanding of global systems biology. Metabolomics is fast becoming one of the platform sciences of the "omics", with the majority of the papers in this field having been published only in the last two years. In this review metabolomic methodologies are discussed briefly followed by a more detailed review of the use of metabolomics in integrated applications where metabolomics information has been combined with other "omic" data sets (proteomics, transcriptomics) to enable greater understanding of a biological system. The potential of metabolomics for natural product drug discovery and functional food analysis, primarily as incorporated into broader "omic" data sets, is discussed.

Biological Products↗

PubMed2006

Transforming omics data into context: bioinformatics on genomics and proteomics raw data.

Differential gene expression analysis and proteomics have exerted significant impact on the elucidation of concerted cellular processes, as simultaneous measurement of hundreds to thousands of individual objects on the level of RNA and protein ensembles became technically feasible. The availability of such data sets has promised a profound understanding of phenomena on an aggregate level, expressed as the phenotypic response (observables) of cells, e.g., in the presence of drugs, or characterization of cells and tissue displaying distinct patho-physiological states. However, the step of transforming these data into context, i.e., linking distinct expression or abundance patterns with phenotypic observables - and furthermore enabling a sound biological interpretation on the level of reaction networks and concerted pathways, is still a major shortcoming. This finding is certainly based on the enormous complexity embedded in cellular reaction networks, but a variety of computational approaches have been developed over the last few years to overcome these issues. This review provides an overview on computational procedures for analysis of genomic and proteomic data introducing a sequential analysis workflow: Explorative statistics for deriving a first, from the purely statistical viewpoint, relevant candidate gene/protein list, followed by co-regulation and network analysis to biologically expand this core list toward functional networks and pathways. The review on these procedures is complemented by example applications tailored at identification of disease-associated proteins. Optimization of computational procedures involved, in conjunction with the continuous increase in additional biological data, clearly has the potential of boosting our understanding of processes on a cell-wide level.

Animals↗

PubMed2026

Multi-omics Mendelian Randomization Prioritizes Neutrophil Extracellular Trap-related Genes Associated with Atrial Fibrillation Risk.

BACKGROUND: Neutrophil extracellular traps (NETs) participate in thrombosis, inflammation, and cardiovascular remodeling, yet whether NET-related genes (NRGs) are associated with atrial fibrillation (AF) risk across multiple molecular layers remains unclear. This study used a multiomics Mendelian randomization framework to prioritize NRGs supported by methylation, expression, and protein quantitative trait loci (QTL) data. METHODS: Genome-wide significant cis instruments (P < 5 × 10-8) were obtained for 90 methylation QTLs (mQTLs), 100 expression QTLs (eQTLs), and 38 protein QTLs (pQTLs) mapped to 137 literature- curated NRG entries. Summary-data-based Mendelian randomization (SMR) coupled with the heterogeneity in dependent instruments (HEIDI) test was applied using whole-blood mQTL data (n = 1,980), eQTLGen blood eQTL data (n = 31,684), and deCODE plasma pQTL data (n = 35,559). AF outcome data were obtained from a meta-analysis including 60,620 cases and 970,216 controls of European ancestry. RESULTS: At the methylation level, 21 CpG-feature associations across 13 genes remained significant after HEIDI filtering and false discovery rate (FDR) correction. Expression-level analysis identified eight significant gene-AF associations, whereas protein-level analysis identified seven significant features representing five unique proteins. Cross-omics integration prioritized C3, MAPK3, and STAT3 as Tier 1 genes, CTSC, LPAR3, and THBD as Tier 2 genes, and fourteen additional genes as Tier 3 candidates. C3 showed risk-increasing protein-level associations together with multiple significant CpG signals, whereas MAPK3 and STAT3 showed directionally protective expression/protein or methylation/protein patterns. DISCUSSION: The cross-omics convergence on C3, MAPK3, and STAT3 is consistent with complement activation, immune-fibrotic signaling, and cytokine-regulatory pathways implicated in AF biology, but the findings should be interpreted as genetic prioritization rather than definitive intervention-ready causality. CpG-level heterogeneity at the C3 locus and the blood/plasma origin of the QTL resources further support a cautious interpretation. Modest colocalization support and the unresolved possibility of pQTL sample overlap further support this cautious, hypothesis-generating interpretation. CONCLUSION: Multi-omics SMR prioritizes C3, MAPK3, and STAT3 as the most consistently supported NET-related genes associated with AF risk. These findings provide a framework for atrialtissue replication and mechanistic validation of NET-related pathways in AF.

Atrial fibrillation↗

PubMed2026

Hepatic metabolic adaptation to endurance exercise: temporal and sex differences by multiomics integration and validation.

BACKGROUND: Although endurance exercise benefits liver health, sex-specific adaptive trajectories remain unclear. This study mapped dynamic liver adaptation in males and females during prolonged training and identified underlying molecular programs. METHODS: Using publicly available time-resolved liver multi-omics data generated by the Molecular Transducers of Physical Activity Consortium (MoTrPAC), we established a computational pipeline for differential analysis of transcriptomic, proteomic, phosphoproteomic, and metabolomic data with FDR correction, followed by FGSEA pathway enrichment. Kinase activities were inferred through ortholog mapping and PhosphoSitePlus. Cross-omics co-expression networks were constructed using WGCNA and topological overlap to link omics features with physiological phenotypes. For experimental validation, liver tissues were collected from endurance-trained Sprague-Dawley rats, and key nodes were confirmed by Western blotting, qRT-PCR, and immunofluorescence/immunohistochemical staining. Public scRNA-seq data were further integrated to map multi-omics signals to single-cell resolution and assess functional changes in specific cell types. RESULTS: The hepatic response to exercise stress was stage-specific, shifting from early transcriptional activation to later proteomic and metabolic remodeling. Multi-omics integration revealed distinct sex-associated adaptive trajectories: males were more strongly associated with energy metabolism, redox-related programs, and amino acid/organic acid catabolism, whereas females showed prominent membrane lipid remodeling, proteostasis -related programs, and mitochondrial/ribosomal translational features. Single-cell analysis showed that tissue remodeling occurred without major lineage turnover, instead involving altered communication among pre-existing cell communities. Validation of PPP1R3G identified a protein-dominant exercise-responsive marker, supporting the contribution of post-transcriptional or protein-level regulation. CONCLUSIONS: Hepatic adaptation to endurance stress follows a cross-omics evolutionary pattern with sex-specific reprogramming of energy supply and homeostatic maintenance. This time-resolved framework clarifies how exercise improves liver function and supports sex-oriented metabolic interventions and therapeutic target discovery.

Animals↗

PubMed2004

Toward large-scale modeling of the microbial cell for computer simulation.

In the post-genomic era, the large-scale, systematic, and functional analysis of all cellular components using transcriptomics, proteomics, and metabolomics, together with bioinformatics for the analysis of the massive amount of data generated by these "omics" methods are the focus of intensive research activities. As a consequence of these developments, systems biology, whose goal is to comprehend the organism as a complex system arising from interactions between its multiple elements, becomes a more tangible objective. Mathematical modeling of microorganisms and subsequent computer simulations are effective tools for systems biology, which will lead to a better understanding of the microbial cell and will have immense ramifications for biological, medical, environmental sciences, and the pharmaceutical industry. In this review, we describe various types of mathematical models (structured, unstructured, static, dynamic, etc.), of microorganisms that have been in use for a while, and others that are emerging. Several biochemical/cellular simulation platforms to manipulate such models are summarized and the E-Cell system developed in our laboratory is introduced. Finally, our strategy for building a "whole cell metabolism model", including the experimental approach, is presented.

Biotechnology↗

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↗

PubMed2003

In silico prediction of the structure of membrane proteins: is it feasible?

In the 'omic' era, hundreds of genomes are available for protein sequence analysis, and some 30 per cent of all sequences are of membrane proteins. Unlike globular proteins, a 3D model for membrane proteins can hardly be computed starting from the sequence. Why is this so? What can we really compute and with what reliability? These and other matters are outlined.

Computational Biology↗

PubMed2026

SCMO: a deep learning model integrating the single-cell resolution TME ecosystem and multi-omics for survival prediction in CRC patients.

BACKGROUND: Colorectal cancer (CRC) remains a leading cause of global cancer mortality, highlighting the need for precise survival prediction to guide clinical decisions. Although tissue-level multi-omics is widely utilized for survival prediction, its limited resolution cannot capture tumor heterogeneity. Single-cell RNA sequencing (scRNA-seq) enables dissection of the tumor microenvironment (TME) at cellular resolution, supporting personalized prognostic assessment. METHODS: We collected 213 CRC scRNA-seq samples and established a CRC-specific TME atlas comprising 339,060 cells. Using this atlas as a reference, we deconvolved bulk RNA-seq data from TCGA-CRC cohort with the EcoTyper algorithm to reconstruct TME features. Clinical, genomic, and transcriptomic data were obtained from the Xena platform; microbial data were sourced from the BIC database. We integrated TME and multi-omics features through a self-normalizing neural network to construct a deep learning model (single-cell resolution TME ecosystem with multi-omics data [SCMO]) for survival prediction. To enhance interpretability, we utilized the Integrated Gradients algorithm and spatial transcriptomic data to analyze multi-omics and TME features. We performed anticancer drug screening with tumor necrosis factor receptor-associated protein 1 (TRAP1), a critical feature according to the Integrated Gradients algorithm, as a potential target. RESULTS: We identified 13 survival-related TME features from the CRC-specific atlas: 12 cell states and one multi-cellular ecosystem. SCMO, which combined TME and multi-omics features, improved survival prediction and outperformed existing methods, achieving a concordance index of 0.762. The SCMO demonstrated robust performance for long-term predictions, achieving areas under the curve (AUCs) of 0.752, 0.772, and 0.869 for 1-, 3-, and 5-year predictions in the training set, with corresponding test set AUCs of 0.639, 0.756, and 0.772. TME features from the SCMO model revealed that ecosystem density increased with CRC malignancy. Multi-omics features included TRAP1 as a potential drug target. Drug screening identified saikosaponin A as a novel TRAP1 inhibitor, and its anticancer activity was validated in vitro. We developed SCMO-Lite, a simplified model incorporating 12 high-attribution-weight multi-omics features, which demonstrated robust risk stratification. CONCLUSIONS: SCMO combines analytical precision with biological interpretability, offering novel insights for oncology survival prediction.

Humans↗

PubMed2006

The potential of ecotoxicoproteomics in environmental monitoring: biomarker profiling in mussel plasma using ProteinChip array technology.

New global technologies, allowing simultaneous analysis of thousands of genes, proteins, and metabolites (so-called "omics" technologies), are being adopted rapidly by industry, academia, and regulatory agencies. This study evaluated the potential of proteomics in ecotoxicological research (i.e., ecotoxicoproteomics). Filter-feeding mussels (Mytilus edulis) were exposed continuously for 3 wk to oil, or oil spiked with alkylphenols and extra polycyclic aromatic hydrocarbons. The influence of chronic exposure on mussel plasma protein expression was investigated utilizing ProteinChip array technology in combination with surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI TOF MS). Results indicated that exposure to spiked oil had a more significant effect on protein expression in mussels than oil alone. In total, 83 mass peaks (intact or modified proteins/peptides) were significantly altered by spiked oil, while 49 were altered by oil. In exposed organisms, the majority of peaks were upregulated compared to controls (i.e., 69% in oil and 71% in spiked oil). Some peaks (32 in total) were affected by both treatments; however, the degree of response was higher in the spiked oil group for 25 of the 32 commonly affected features. Additionally, certain peaks revealed exposure- or gender-specific responses. Multivariate analysis with regression tree-based methods detected protein patterns associated with exposure that correctly classified masked samples with 90-95% accuracy. Similarly, 92% of females and 85% of males were correctly classified (independent of exposure). Results indicate that proteomics have the potential to make a valuable contribution to environmental monitoring and risk assessment.

Animals↗

PubMed2004

Metabolic profiles to define the genome: can we hear the phenotypes?

There is an increased reliance on genetically modified organisms as a functional genomic tool to elucidate the role of genes and their protein products. Despite this, many models do not express the expected phenotype thought to be associated with the gene or protein. There is thus an increased need to further define the phenotype resultant from a genetic modification to understand how the transcriptional or proteomic network may conspire to alter the expected phenotype. This is best typified by the description of the silent phenotype in genetic manipulations of yeast. High-resolution proton nuclear magnetic resonance ((1)H NMR) spectroscopy provides an ideal mechanism for the profiling of metabolites within biofluids, tissue extracts or, with recent advances, intact tissues. These metabolic datasets can be readily mined using a range of pattern recognition techniques, including hierarchical cluster analysis, principal components analysis, partial least squares and neural networks, with the combined approach being termed metabolomics. This review describes the application of NMR-based metabolomics or metabonomics to genetic and chemical interventions in a number of different species, demonstrating the versatility of such an approach, as well as suggesting how it may be integrated with other "omic" technologies.

Animals↗

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↗