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PubMed2026

Systems genetics approaches model the heritable architecture of polyendocrine metabolic ovarian syndrome.

Polyendocrine metabolic ovarian syndrome (PMOS), formerly known as polycystic ovary syndrome (PCOS), is the most common endocrine disorder in women and is closely associated with complex diseases such as cardiovascular disease and type 2 diabetes. However, the mechanistic links between PMOS and its comorbidities remain poorly understood. Here, we present an integrative systems genetics platform that leverages genetic diversity in both mice and humans to dissect the drivers of PMOS and its associated complications. This framework uncovered conserved genetic and environmental factors underlying PMOS, identified susceptible cell types and organs, and elucidated mechanisms linking PMOS to subsequent pathologies. For instance, we showed that increased ovarian area contributes to both PMOS susceptibility and ovarian cancer progression, while specific ovary-heart signaling circuits modulate cardiac function with aging. We further identified ovarian SF3B1-mediated alternative splicing as a key mechanistic link between PMOS and metabolic traits. Pharmacologic inhibition of SF3B1 in mice reduced circulating testosterone, insulin, and glucose levels as well as fat mass expansion. Transcriptomics analysis of ovaries from mice and experiments using human cell lines localized these effects to exon skipping events in granulosa cells. Together, this study offers a mechanistic framework for modeling the diversity of PMOS pathologies and uncovers SF3B1-mediated splicing as a link between ovary function and systemic metabolism.

Female↗

PubMed2025

Identification of Potential Therapeutic Agents for Type I Interferonopathy Using iPSC-Based Disease Modeling.

PURPOSE: Type I interferonopathy encompasses disorders marked by systemic inflammation and neurological involvement, arising from genetic mutations that result in the upregulation of type I IFN signaling through various mechanisms. Currently, therapeutic options are limited, and no standard therapy exists. This study aims to develop a strategy for identifying new therapeutic targets for type I interferonopathy using induced pluripotent stem cells (iPSCs). METHODS: The IFIH1 R779H variant was introduced into iPSCs through genome editing. RNA sequencing of iPSC-derived dendritic cells (DCs) was performed, and differentially expressed genes (DEGs) were identified. IFN-α secretion, reactive oxygen species (ROS), and mitochondrial oxygen consumption rate (OCR) were analyzed in iPSC-derived DCs. An in silico prediction of compounds binding to the OAS-like domain was conducted. Candidate compounds were evaluated for their ability to inhibit IFN secretion from IFIH1 R779H-mutated iPSC-derived DCs. RESULTS: Transcriptome analysis indicated upregulation of the IFN-related and metabolic pathways. IFIH1 R779H-mutated iPSC-derived DCs exhibited increased OCR and ROS generation, and blocking mitochondrial metabolism significantly reduced excessive IFN-α secretion. Among the DEGs, PML was upregulated, and targeting this gene with arsenic trioxide (ATO), a PML antagonist, suppressed IFN-α secretion from IFIH1 R779H-mutated iPSC-derived DCs. Additionally, bisantrene, phthalylsulfathiazole and ganaplacide were predicted to bind to the RNA binding groove of OAS-like domain of human OASL in silico, effectively inhibiting IFN-α secretion from IFIH1 R779H-mutated DCs. CONCLUSION: Our iPSC-based disease modeling and drug investigation approach provides a robust platform for validating the efficacy and toxicity of candidate therapeutic agents for rare and intractable human diseases such as type I interferonopathy.

Humans↗

PubMed2004

Integrated application of transcriptomics and metabonomics yields new insight into the toxicity due to paracetamol in the mouse.

Gene chip array (Affymetrix) data from liver tissue and high resolution 1H NMR spectra from intact liver tissue, tissue extracts and plasma have been analyzed to identify biochemical changes arising from hepatotoxicity in mice dosed with acetaminophen. These data sets have been co-interpreted in terms of common metabolic pathways. The principal metabolic changes comprised a decrease in hepatic glucose and glycogen in intact tissue, coupled with an increase in lipid content, with increases in the levels of glucose, pyruvate, acetate and lactate in plasma, and increases in alanine and lactate in the aqueous tissue extracts. Collectively these data provide evidence for an increased rate of hepatic glycolysis. The metabolic observations were consistent with the altered levels of gene expression relating to lipid and energy metabolism in liver which both preceded and were concurrent with the metabolic perturbations. The results show that these two technology platforms together offer a complementary view into cellular responses to toxic processes, providing new insight into the toxic consequences, even for well-studied therapeutic agents such as acetaminophen.

Acetaminophen↗

PubMed2026

Metabolic-cell-death gene trio predicts survival and cuproptosis sensitivity in colorectal cancer.

BACKGROUND: Metabolic cell death (MCD) modulates colorectal cancer (CRC) progression, yet its prognostic value remains unexplored. We aimed to build an MCD-centred gene signature for outcome prediction and precision therapy. METHODS: Transcriptomes of 1,174 CRC patients were integrated. Weighted gene co-expression network analysis, differential expressions and least absolute shrinkage and selection operator (LASSO) + random survival forest were successively applied to derive a three-gene (CDKN2A/MPC1/AHCY) risk model. Functional, immune-infiltration, drug-sensitivity and genomic analyses were performed, followed by validation in fresh clinical specimens and cell lines. RESULTS: Integrative metabolic-death transcriptomics identified CDKN2A, MPC1 and AHCY as the hub drivers of CRC. Their three-gene signature robustly stratified patients into high- and low-risk subsets [3-year area under the curve (AUC) 0.83-0.85, P<0.001]. High-risk tumors were enriched for extracellular matrix (ECM)-receptor-interaction pathways, displayed abundant myeloid-derived suppressor cell (MDSC) infiltration and were more vulnerable to AZD8186, AZ960 and JAK inhibitors. Guided by these in-silico findings, we functionally confirmed that CDKN2A silencing markedly repressed proliferation, invasion and migration of SW480/HCT116 cells and potentiated cuproptosis via up-regulation of lipoylated DLAT/DLST and CTR1. CONCLUSIONS: We report the first MCD-derived prognostic platform for CRC that simultaneously predicts survival and therapeutic response. Targeting CDKN2A-enhanced cuproptosis represents a promising metabolic-precision strategy for high-risk patients.

Colorectal cancer (CRC)↗

PubMed2005

Proteomics in nutrition research: principles, technologies and applications.

The global profiling of the whole protein complement of the genome expressed in a particular cell or organ, or in plasma or serum, makes it possible to identify biomarkers that respond to alterations in diet or to treatment, and that may have predictive value for the modelling of biological processes. Proteomics has not yet been applied on a large scale in nutritional studies, yet it has advantages over transcriptome profiling techniques in that it directly assesses the entities that carry out the biological functions. The present review summarizes the different approaches in proteomics research, with special emphasis on the current technical 'workhorses': two-dimensional (2D)-PAGE with immobilized pH gradients and protein identification by MS. Using a work-flow approach, we provide information and advice on sample handling and preparation, protein solubilization and pre-fractionation, protein separation by 2D-PAGE, detection and quantification via computer-assisted analysis of gels, and protein identification and characterization techniques by means of MS. Examples from nutritional studies employing proteomics are provided to demonstrate not only the advantages but also the limitations of current proteome analysis platforms.

Electrophoresis, Gel, Two-Dimensional↗

PubMed2005

Avoiding inconsistencies over time and tracking difficulties in Applied Biosystems AB1700/Panther probe-to-gene annotations.

BACKGROUND: Significant inconsistencies between probe-to-gene annotations between different releases of probe set identifiers by commercial microarray platform solutions have been reported. Such inconsistencies lead to misleading or ambiguous interpretation of published gene expression results. RESULTS: We report here similar inconsistencies in the probe-to-gene annotation of Applied Biosystems AB1700 data, demonstrating that this is not an isolated concern. Moreover, the online information source PANTHER does not provide information required to track such inconsistencies, hence, even correctly annotated datasets, when resubmitted after PANTHER was updated to a new probe-to-gene annotation release, will generate differing results without any feedback on the origin of the change. CONCLUSION: The importance of unequivocal annotation of microarray experiments can not be underestimated. Inconsistencies greatly diminish the usefulness of the technology. Novel methods in the analysis of transcriptome profiles often rely on large disparate datasets stemming from multiple sources. The predictive and analytic power of such approaches rapidly diminishes if only least-common subsets can be used for analysis. We present here the information that needs to be provided together with the raw AB1700 data, and the information required together with the biologic interpretation of such data to avoid inconsistencies and tracking difficulties.

Algorithms↗

PubMed2005

T-STAG: resource and web-interface for tissue-specific transcripts and genes.

T-STAG (tissue-specific transcripts and genes) is a resource and web-interface, designated to analyze tissue/tumor-specific expression patterns in human and mouse transcriptomes. It integrates our refined prediction of specific expression patterns both in genes as well as in individual isoforms with man-mouse orthology data. In combination with the features for combining/contrasting the genes expressed in different tissues, T-STAG implicates important biological applications, such as the detection of differentially expressed genes in tumors, the retrieval of orthologs with significant expression in the same tissue etc. Additionally, our refined categorization of expressed sequence tags (ESTs) according to the normalization of cDNA libraries allows searching for putative low-abundant transcripts. The results are tightly linked to our visualization tools, GeneNest (expression patterns of genes) and SpliceNest (gene structure and alternative splicing). The user-friendly interface of T-STAG offers a platform for comprehensive analysis of tissue and/or tumor-specific expression patterns revealed by the EST data. T-STAG is freely accessible at http://tstag.molgen.mpg.de.

Animals↗

PubMed2000

Conceptual modelling of genomic information.

MOTIVATION: Genome sequencing projects are making available complete records of the genetic make-up of organisms. These core data sets are themselves complex, and present challenges to those who seek to store, analyse and present the information. However, in addition to the sequence data, high throughput experiments are making available distinctive new data sets on protein interactions, the phenotypic consequences of gene deletions, and on the transcriptome, proteome, and metabolome. The effective description and management of such data is of considerable importance to bioinformatics in the post-genomic era. The provision of clear and intuitive models of complex information is surprisingly challenging, and this paper presents conceptual models for a range of important emerging information resources in bioinformatics. It is hoped that these can be of benefit to bioinformaticians as they attempt to integrate genetic and phenotypic data with that from genomic sequences, in order to both assign gene functions and elucidate the different pathways of gene action and interaction. RESULTS: This paper presents a collection of conceptual (i.e. implementation-independent) data models for genomic data. These conceptual models are amenable to (more or less direct) implementation on different computing platforms.

Computational Biology↗

PubMed2004

Profiling gene expression during the differentiation and development of the murine embryonic gonad.

The application of microarray technology to the study of mammalian organogenesis can provide greater insights into the steps necessary to elicit a functionally competent tissue. To this end, a temporal profile of gene expression was generated with the purpose of identifying changes in gene expression occurring within the developing male and female embryonic gonad. Gonad tissue was collected from mouse embryos at 11.5, 12.5, 14.5, 16.5, and 18.5 days postcoitum (dpc) and relative steady-state levels of mRNA were determined using the Affymetrix MGU74v2 microarray platform. Statistical analysis produced 3693 transcripts exhibiting differential expression during male and/or female gonad development. At 11.5 dpc, the gonad is morphologically indifferent, but at 12.5 dpc, transitions to a male or female phenotype are discernible by the appearance of testicular cords. A number of genes are expressed during this period and many share similar expression profiles in both sexes. As expected, the expression of two well-known sex determination genes, specifically Sry and Sox9, is unique to the testis. Beyond 12.5 dpc, differential gene expression becomes increasingly evident as the male and female tissue morphologically and physiologically diverges. This is evident by two unique waves of transcriptional activity occurring after 14.5 dpc in the male and female. With this study, a large number of transcripts comprising the murine transcriptome can be examined throughout male and female embryonic gonad development and allow for a more complete description of gonad differentiation and development.

Animals↗

PubMed2006

Reliable and controllable antibody fragment selections from Camelid non-immune libraries for target validation.

With the completion of the sequence of the human genome, emphasis is now switching to the human proteome. However, the number of proteins is not only larger than mRNAs in the transcriptome, proteins need often to be in complex with other proteins to be functional. A favourable option to study proteins in their natural context is with a combination of biochemical and microscopic techniques using specific antibodies. Therefore, we designed a fast, reliable and controllable selection and screening of single-domain antibody fragments (VHH) from a Camelid non-immune library. We isolated VHH for four muscle disease related proteins; emerin, actin, tropomyosin-1, and nuclear poly(A)-binding protein. Important features of antibodies for target validation studies are recognition of the antigen in natural conformations and biologically relevant complexes. We show that selected antibody fragments are functional in various immunological techniques and prove useful in diagnostic applications. Our selection strategy is amenable to automation and to the establishment of proteomics platforms. It opens the way to quickly and cost-effectively obtain multiple antibody fragments for many antigens that can detect changes in their localization, level, and modification as well as subtle changes in supramolecular structures, which often associate with disease.

Actins↗

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↗

PubMed2025

Multi-Omics Analyses Reveal the Red and Far-Red Light Combination Enhancing Heterologous Protein and Metabolite Production in Nicotiana benthamiana.

Transient expression of exogenous protein in Nicotiana benthamiana leaves via agroinfiltration offers a rapid and efficient platform for functional gene discovery and heterologous production of valuable eukaryotic proteins and metabolites. Though light quality is an important factor for plant photomorphogenesis, its impact on the efficiency of transient expression remains unexplored. In this study, we examined the influence of five representative light qualities with varying wavelength mix on the N. benthamiana growth and recombinant green fluorescent protein (GFP) production. Plants with red and far-red light treatment (LED-red) showed the highest GFP expression, 57.4% higher than white light. Further study showed that a higher dosage of post-infiltration Agrobacterium and the resulting increase in the number of transcripts contribute to the expression rate enhancement. Moreover, as for exogenous metabolites, a 76.5% increase of accumulated taxadiene was also observed in LED-red group. Integrated transcriptomic, proteomic and metabolomic revealed that LED-red plants reduced the resistance pathways before infiltration, inducing a higher dosage of post-agroinfiltration Agrobacterium. Our results suggest that N. benthamiana grown under LED-red creates a more favorable environment for Agrobacterium growth, enhancing heterologous protein and metabolite production. This study highlights the potential utilization of light quality as an implementable tool in plant synthetic biology.

Nicotiana↗

PubMed2005

Integrating genomics and transcriptomics with geo-ethnicity and the environment for the resolution of complex cardiovascular diseases.

One of the crucial steps on the way to individualized medicine to treat cardiovascular disease (CVD) is to better understand the identities, roles, extent and at least the major patterns of interaction between influential genomic and environmental factors. It is clear that such a bold goal can hardly be achieved without a major upgrade of our conceptualization of the phenomena studied, taking advantage of recent developments of novel technological and computational tools. Firstly, the search for the genomic components of the most common multifactorial CVDs is no longer restricted to protein-coding genes; truly genome-wide investigations should replace them in both humans and animal models. Secondly, the 'environment' has also undergone semantic expansion, incorporating such remote constituents as developmental plasticity and epigenetics on one side, and socioeconomic status on the other. To elucidate and analyze the resulting complex picture, appropriate statistical models and approaches need to be designed to tackle issues such as population stratification and admixture, multiple testing, and multidimensionality reduction in models involving multiple genes and environmental factors. Eventually, an integrated platform bringing together all of the above will probably be necessary to secure relevant information specific to a particular combination of conditions and settings (age, geo-ethnicity and exposure), which may perhaps become visible only after a step back, through systems (network) biology.

Animals↗

PubMed2004

Intregrated analysis of the human cardiac transcriptome, proteome and phosphoproteome.

Altered expression of different classes of genes has been shown to differentiate between failing and nonfailing human hearts. However, characterization of proteins and the post-translational modifications that regulate their functions is required for understanding both the physiology of cardiac muscle and the mechanisms leading to pathological states associated with cardiac diseases. We present in this paper, an analysis of the human cardiac transcriptome, proteome and phosphoproteome. Data from two sources (i) experiments performed in our laboratory and (ii) bioinformatics searches of public databases (SWISS-PROT, NCBI, Cardiac Gene Expression Knowledge Base, Gene Ontology Consortium and Affymetrix) are reported in a relational database that allows user-designed specific queries. Microarray experiments were performed with Affymetrix Hu95Av2. Cardiac proteins were digested with trypsin. An 11 step cation exchange procedure produced fractions for analysis in separate reversed phase high-performance liquid chromatography-tandem mass spectrometry (MS/MS) experiments. Immobilized metal affinity chromatography was used to select the phosphopeptides from the same tryptic peptide mixture. They were then further investigated by MS/MS. Gel-free approaches were used to detect 267 proteins and 47 phosphopeptides. Our human cardiac database contains 447 entries. We propose the use of this platform, built with data derived from nonfailing hearts, as a template for initiating the effort to characterize the human cardiac proteome and its associated post-translational modifications.

Chromatography, Affinity↗

PubMed2003

Genome-scale functional profiling of the mammalian AP-1 signaling pathway.

Large-scale functional genomics approaches are fundamental to the characterization of mammalian transcriptomes annotated by genome sequencing projects. Although current high-throughput strategies systematically survey either transcriptional or biochemical networks, analogous genome-scale investigations that analyze gene function in mammalian cells have yet to be fully realized. Through transient overexpression analysis, we describe the parallel interrogation of approximately 20,000 sequence annotated genes in cancer-related signaling pathways. For experimental validation of these genome data, we apply an integrative strategy to characterize previously unreported effectors of activator protein-1 (AP-1) mediated growth and mitogenic response pathways. These studies identify the ADP-ribosylation factor GTPase-activating protein Centaurin alpha1 and a Tudor domain-containing hypothetical protein as putative AP-1 regulatory oncogenes. These results provide insight into the composition of the AP-1 signaling machinery and validate this approach as a tractable platform for genome-wide functional analysis.

Animals↗

PubMed2006

Conifer defence against insects: microarray gene expression profiling of Sitka spruce (Picea sitchensis) induced by mechanical wounding or feeding by spruce budworms (Choristoneura occidentalis) or white pine weevils (Pissodes strobi) reveals large-scale changes of the host transcriptome.

Conifers are resistant to attack from a large number of potential herbivores or pathogens. Previous molecular and biochemical characterization of selected conifer defence systems support a model of multigenic, constitutive and induced defences that act on invading insects via physical, chemical, biochemical or ecological (multitrophic) mechanisms. However, the genomic foundation of the complex defence and resistance mechanisms of conifers is largely unknown. As part of a genomics strategy to characterize inducible defences and possible resistance mechanisms of conifers against insect herbivory, we developed a cDNA microarray building upon a new spruce (Picea spp.) expressed sequence tag resource. This first-generation spruce cDNA microarray contains 9720 cDNA elements representing c. 5500 unique genes. We used this array to monitor gene expression in Sitka spruce (Picea sitchensis) bark in response to herbivory by white pine weevils (Pissodes strobi, Curculionidae) or wounding, and in young shoot tips in response to western spruce budworm (Choristoneura occidentalis, Lepidopterae) feeding. Weevils are stem-boring insects that feed on phloem, while budworms are foliage feeding larvae that consume needles and young shoot tips. Both insect species and wounding treatment caused substantial changes of the host plant transcriptome detected in each case by differential gene expression of several thousand array elements at 1 or 2 d after the onset of treatment. Overall, there was considerable overlap among differentially expressed gene sets from these three stress treatments. Functional classification of the induced transcripts revealed genes with roles in general plant defence, octadecanoid and ethylene signalling, transport, secondary metabolism, and transcriptional regulation. Several genes involved in primary metabolic processes such as photosynthesis were down-regulated upon insect feeding or wounding, fitting with the concept of dynamic resource allocation in plant defence. Refined expression analysis using gene-specific primers and real-time PCR for selected transcripts was in agreement with microarray results for most genes tested. This study provides the first large-scale survey of insect-induced defence transcripts in a gymnosperm and provides a platform for functional investigation of plant-insect interactions in spruce. Induction of spruce genes of octadecanoid and ethylene signalling, terpenoid biosynthesis, and phenolic secondary metabolism are discussed in more detail.

Animals↗

PubMed2006

An atlas of gene expression from seed to seed through barley development.

Assaying relative and absolute levels of gene expression in a diverse series of tissues is a central step in the process of characterizing gene function and a necessary component of almost all publications describing individual genes or gene family members. However, throughout the literature, such studies lack consistency in genotype, tissues analyzed, and growth conditions applied, and, as a result, the body of information that is currently assembled is fragmented and difficult to compare between different studies. The development of a comprehensive platform for assaying gene expression that is available to the entire research community provides a major opportunity to assess whole biological systems in a single experiment. It also integrates detailed knowledge and information on individual genes into a unified framework that provides both context and resource to explore their contributions in a broader biological system. We have established a data set that describes the expression of 21,439 barley genes in 15 tissues sampled throughout the development of the barley cv. Morex grown under highly controlled conditions. Rather than attempting to address a specific biological question, our experiment was designed to provide a reference gene expression data set for barley researchers; a gene expression atlas and a comparative data set for those investigating genes or regulatory networks in other plant species. In this paper we describe the tissues sampled and their transcriptomes, and provide summary information on genes that are either specifically expressed in certain tissues or show correlated expression patterns across all 15 tissue samples. Using specific examples and an online tutorial, we describe how the data set can be interrogated for patterns and levels of barley gene expression and how the resulting information can be used to generate and/or test specific biological hypotheses.

Databases, Genetic↗

PubMedDate not supplied

Application of proteomic strategies to the identification of urinary biomarkers for prostate cancer: a review.

In the post-genomic era, genes and proteins are now studied on a more comprehensive scale. Studying disease processes at only the genetic or transcriptomic level will give an incomplete amount of information. A proteomic approach potentially allows for a more global overview of how disease processes affect the proteins present in cells, tissues and organisms. The challenge arises in determining which proteins are affected in specific diseases and establishing which of these changes are unique to a particular disease. Existing and emerging proteomic technologies allow for high throughput analysis of proteins in a variety of sample types. Prostate cancer is a significant male health problem in the Western world. It is widely accepted that more specific prognostic and diagnostic markers of prostate cancer are urgently required. The present paper suggests that urine may be an attractive biofluid in which to pursue the identification of novel biomarkers of prostate cancer. This review introduces some proteomic techniques including mass spectrometry and the newer, quantitative proteomic strategies. It focuses on the potential application of these platforms to novel urinary biomarker identification in prostate malignancy. It also includes a synopsis of the current literature on urinary proteomics.

Biomarkers, Tumor↗