Genome-wide census of ATF4 binding sites and functional profiling of trait-associated genetic variants overlapping ATF4 binding motifs.

Activating Transcription Factor 4 (ATF4) is an important regulator of gene expression in stress responses and developmental processes in many cell types. Here, we catalogued ATF4 binding sites in the human genome and identified overlaps with trait-associated genetic variants. We probed these genetic variants for allelic regulatory activity using a massively parallel reporter assay (MPRA) in HepG2 hepatoma cells exposed to tunicamycin to induce endoplasmic reticulum stress and ATF4 upregulation. The results revealed that in the majority of cases, the MPRA allelic activity of these SNPs was in agreement with the nucleotide preference seen in the ATF4 binding motif from ChIP-Seq. Luciferase and electrophoretic mobility shift assays in additional cellular models further confirmed ATF4-dependent regulatory effects for the SNPs rs532446 (GADD45A intronic; linked to hematological parameters), rs7011846 (LPL upstream; myocardial infarction), rs2718215 (diastolic blood pressure), rs281758 (psychiatric disorders) and rs6491544 (educational attainment). CRISPR-Cas9 disruption and/or deletion of the regulatory elements harboring rs532446 and rs7011846 led to the downregulation of GADD45A and LPL, respectively. Thus, these SNPs could represent examples of GWAS genetic variants that affect gene expression by altering ATF4-mediated transcriptional activation.

g:Profiler-interoperable web service for functional enrichment analysis and gene identifier mapping (2023 update).

g:Profiler is a reliable and up-to-date functional enrichment analysis tool that supports various evidence types, identifier types and organisms. The toolset integrates many databases, including Gene Ontology, KEGG and TRANSFAC, to provide a comprehensive and in-depth analysis of gene lists. It also provides interactive and intuitive user interfaces and supports ordered queries and custom statistical backgrounds, among other settings. g:Profiler provides multiple programmatic interfaces to access its functionality. These can be easily integrated into custom workflows and external tools, making them valuable resources for researchers who want to develop their own solutions. g:Profiler has been available since 2007 and is used to analyse millions of queries. Research reproducibility and transparency are achieved by maintaining working versions of all past database releases since 2015. g:Profiler supports 849 species, including vertebrates, plants, fungi, insects and parasites, and can analyse any organism through user-uploaded custom annotation files. In this update article, we introduce a novel filtering method highlighting Gene Ontology driver terms, accompanied by new graph visualizations providing a broader context for significant Gene Ontology terms. As a leading enrichment analysis and gene list interoperability service, g:Profiler offers a valuable resource for genetics, biology and medical researchers. It is freely accessible at https://biit.cs.ut.ee/gprofiler.

Autoantibody screening of plasma and peritoneal fluid of patients with endometriosis.

STUDY QUESTION: Are there specific autoantibody profiles in patients with endometriosis that are different from those in controls? SUMMARY ANSWER: This study did not reveal a significantly higher prevalence of autoantibodies in the studied groups of patients. WHAT IS KNOWN ALREADY: Various inflammatory factors are postulated to be involved in the pathomechanisms of endometriosis, and a potential link exists with autoimmune diseases, which may also play an important role. As the diagnosis of endometriosis remains invasive, it can only be confirmed using laparoscopy with histopathological examination of tissues. Numerous studies have focused on identifying useful biomarkers to confirm the disease, but without unequivocal effects. Autoantibodies are promising molecules that serve as potential prognostic factors. STUDY DESIGN, SIZE, DURATION: A multicentre, cross-sectional study was conducted over 18 months (between 2018 and 2019), at eight Departments of Obstetrics and Gynaecology in several cities across Poland on 137 patients undergoing laparoscopic examination for the diagnosis of endometriosis. PARTICIPANTS/MATERIALS, SETTINGS, METHODS: During laparoscopy, we obtained plasma samples from 137 patients and peritoneal fluid (PF) samples from 98 patients. Patients with autoimmune diseases were excluded from the study. Autoantibody profiling was performed using HuProt v3.1 human proteome microarrays. MAIN RESULTS AND THE ROLE OF CHANCE: We observed no significant differences in the expression of autoantibodies in the plasma or PF between the endometriosis and control groups. The study revealed that in the PF of women with Stage II endometriosis, compared with other stages, there were significantly higher reactivity signals for ANAPC15 and GABPB1 (adj. P < 0.016 and adj. P < 0.026, respectively; logFC > 1 in both cases). Comparison of the luteal and follicular phases in endometriosis patients revealed that levels of NEIL1 (adj. P < 0.029), MAGEB4 (adj. P < 0.029), and TNIP2 (adj. P < 0.042) autoantibody signals were significantly higher in the luteal phase than in the follicular phase in PF samples of patients with endometriosis. No differences were observed between the two phases of the cycle in plasma or between women with endometriosis and controls. Clustering of PF and plasma samples did not reveal unique autoantibody profiles for endometriosis; however, comparison of PF and plasma in the same patient showed a high degree of concordance. LIMITATIONS, REASONS FOR CAUTION: Although this study was performed using the highest-throughput protein array available, it does not cover the entire human proteome and cannot be used to study potentially promising post-translational modifications. Autoantibody levels depend on numerous factors, such as infections; therefore the autoantibody tests should be repeated for more objective results. WIDER IMPLICATIONS OF THE FINDINGS: Although endometriosis has been linked to different autoimmune diseases, it is unlikely that autoimmune responses mediated by specific autoantibodies play a pivotal role in the pathogenesis of this inflammatory disease. Our study shows that in searching for biomarkers of endometriosis, it may be more efficient to use higher-throughput proteomic microarrays, which may allow the detection of potentially new biomarkers. Only research on such a scale, and possibly with different technologies, can help discover biomarkers that will change the method of endometriosis diagnosis. STUDY FUNDING/COMPETING INTEREST(S): This study was funded by a grant from the Polish Ministry of Health (grant no. 6/6/4/1/NPZ/2017/1210/1352). It was also funded by the Estonian Research Council (grant PRG1076) and the Horizon 2020 Innovation Grant (ERIN; grant no. EU952516), Enterprise Estonia (grant no. EU48695), and MSCA-RISE-2020 project TRENDO (grant no. 101008193). The authors declare that there is no conflict of interest. TRIAL REGISTRATION NUMBER: N/A.

PAWER: protein array web exploreR.

BACKGROUND: Protein microarray is a well-established approach for characterizing activity levels of thousands of proteins in a parallel manner. Analysis of protein microarray data is complex and time-consuming, while existing solutions are either outdated or challenging to use without programming skills. The typical data analysis pipeline consists of a data preprocessing step, followed by differential expression analysis, which is then put into context via functional enrichment. Normally, biologists would need to assemble their own workflow by combining a set of unrelated tools to analyze experimental data. Provided that most of these tools are developed independently by various bioinformatics groups, making them work together could be a real challenge. RESULTS: Here we present PAWER, the online web tool dedicated solely to protein microarray analysis. PAWER enables biologists to carry out all the necessary analysis steps in one go. PAWER provides access to state-of-the-art computational methods through the user-friendly interface, resulting in publication-ready illustrations. We also provide an R package for more advanced use cases, such as bespoke analysis workflows. CONCLUSIONS: PAWER is freely available at https://biit.cs.ut.ee/pawer .

Healthspan pathway maps in C. elegans and humans highlight transcription, proliferation/biosynthesis and lipids.

The molecular basis of aging and of aging-associated diseases is being unraveled at an increasing pace. An extended healthspan, and not merely an extension of lifespan, has become the aim of medical practice. Here, we define health based on the absence of diseases and dysfunctions. Based on an extensive review of the literature, in particular for humans and C. elegans, we compile a list of features of health and of the genes associated with them. These genes may or may not be associated with survival/lifespan. In turn, survival/lifespan genes that are not known to be directly associated with health are not considered. Clusters of these genes based on molecular interaction data give rise to maps of healthspan pathways for humans and for C. elegans. Overlaying healthspan-related gene expression data onto the healthspan pathway maps, we observe the downregulation of (pro-inflammatory) Notch signaling in humans and of proliferation in C. elegans. We identify transcription, proliferation/biosynthesis and lipids as a common theme on the annotation level, and proliferation-related kinases on the gene/protein level. Our literature-based data corpus, including visualization, should be seen as a pilot investigation of the molecular underpinnings of health in two different species. Web address: http://pathways.h2020awe.eu.

HENA, heterogeneous network-based data set for Alzheimer's disease.

Alzheimer's disease and other types of dementia are the top cause for disabilities in later life and various types of experiments have been performed to understand the underlying mechanisms of the disease with the aim of coming up with potential drug targets. These experiments have been carried out by scientists working in different domains such as proteomics, molecular biology, clinical diagnostics and genomics. The results of such experiments are stored in the databases designed for collecting data of similar types. However, in order to get a systematic view of the disease from these independent but complementary data sets, it is necessary to combine them. In this study we describe a heterogeneous network-based data set for Alzheimer's disease (HENA). Additionally, we demonstrate the application of state-of-the-art graph convolutional networks, i.e. deep learning methods for the analysis of such large heterogeneous biological data sets. We expect HENA to allow scientists to explore and analyze their own results in the broader context of Alzheimer's disease research.

g:Profiler: a web server for functional enrichment analysis and conversions of gene lists (2019 update).

Biological data analysis often deals with lists of genes arising from various studies. The g:Profiler toolset is widely used for finding biological categories enriched in gene lists, conversions between gene identifiers and mappings to their orthologs. The mission of g:Profiler is to provide a reliable service based on up-to-date high quality data in a convenient manner across many evidence types, identifier spaces and organisms. g:Profiler relies on Ensembl as a primary data source and follows their quarterly release cycle while updating the other data sources simultaneously. The current update provides a better user experience due to a modern responsive web interface, standardised API and libraries. The results are delivered through an interactive and configurable web design. Results can be downloaded as publication ready visualisations or delimited text files. In the current update we have extended the support to 467 species and strains, including vertebrates, plants, fungi, insects and parasites. By supporting user uploaded custom GMT files, g:Profiler is now capable of analysing data from any organism. All past releases are maintained for reproducibility and transparency. The 2019 update introduces an extensive technical rewrite making the services faster and more flexible. g:Profiler is freely available at https://biit.cs.ut.ee/gprofiler.

funcExplorer: a tool for fast data-driven functional characterisation of high-throughput expression data.

BACKGROUND: A widely applied approach to extract knowledge from high-throughput genomic data is clustering of gene expression profiles followed by functional enrichment analysis. This type of analysis, when done manually, is highly subjective and has limited reproducibility. Moreover, this pipeline can be very time-consuming and resource-demanding as enrichment analysis is done for tens to hundreds of clusters at a time. Thus, the task often needs programming skills to form a pipeline of different software tools or R packages to enable an automated approach. Furthermore, visualising the results can be challenging. RESULTS: We developed a web tool, funcExplorer, which automatically combines hierarchical clustering and enrichment analysis to detect functionally related gene clusters. The functional characterisation is achieved using structured knowledge from data sources such as Gene Ontology, KEGG and Reactome pathways, Human Protein Atlas, and Human Phenotype Ontology. funcExplorer includes various measures for finding biologically meaningful clusters, provides a modern graphical user interface, and has wide-ranging data export and sharing options as well as software transparency by open-source code. The results are presented in a visually compact and interactive format, enabling users to explore the biological essence of the data. We compared our results with previously published gene clusters to demonstrate that funcExplorer can perform the data characterisation equally well, but without requiring labour-intensive manual interference. CONCLUSIONS: The open-source web tool funcExplorer enables scientists with high-throughput genomic data to obtain a preliminary interactive overview of the expression patterns, gene names, and shared functionalities in their dataset in a visually pleasing format. funcExplorer is publicly available at https://biit.cs.ut.ee/funcexplorer.

Prostaglandin D2 Receptor DP1 Antibodies Predict Vaccine-induced and Spontaneous Narcolepsy Type 1: Large-scale Study of Antibody Profiling.

BACKGROUND: Neuropathological findings support an autoimmune etiology as an underlying factor for loss of orexin-producing neurons in spontaneous narcolepsy type 1 (narcolepsy with cataplexy; sNT1) as well as in Pandemrix influenza vaccine-induced narcolepsy type 1 (Pdmx-NT1). The precise molecular target or antigens for the immune response have, however, remained elusive. METHODS: Here we have performed a comprehensive antigenic repertoire analysis of sera using the next-generation phage display method - mimotope variation analysis (MVA). Samples from 64 children and adolescents were analyzed: 10 with Pdmx-NT1, 6 with sNT1, 16 Pandemrix-vaccinated, 16 H1N1 infected, and 16 unvaccinated healthy individuals. The diagnosis of NT1 was defined by the American Academy of Sleep Medicine international criteria of sleep disorders v3. FINDINGS: Our data showed that although the immunoprofiles toward vaccination were generally similar in study groups, there were also striking differences in immunoprofiles between sNT1 and Pdmx-NT1 groups as compared with controls. Prominent immune response was observed to a peptide epitope derived from prostaglandin D2 receptor (DP1), as well as peptides homologous to B cell lymphoma 6 protein. Further validation confirmed that these can act as true antigenic targets in discriminating NT1 diseased along with a novel epitope of hemagglutinin of H1N1 to delineate exposure to H1N1. INTERPRETATION: We propose that DP1 is a novel molecular target of autoimmune response and presents a potential diagnostic biomarker for NT1. DP1 is involved in the regulation of non-rapid eye movement (NREM) sleep and thus alterations in its functions could contribute to the disturbed sleep regulation in NT1 that warrants further studies. Together our results also show that MVA is a helpful method for finding novel peptide antigens to classify human autoimmune diseases, possibly facilitating the design of better therapies.

Meta-signature of human endometrial receptivity: a meta-analysis and validation study of transcriptomic biomarkers.

Previous transcriptome studies of the human endometrium have revealed hundreds of simultaneously up- and down-regulated genes that are involved in endometrial receptivity. However, the overlap between the studies is relatively small, and we are still searching for potential diagnostic biomarkers. Here we perform a meta-analysis of endometrial-receptivity associated genes on 164 endometrial samples (76 from 'pre-receptive' and 88 from mid-secretory, 'receptive' phase endometria) using a robust rank aggregation (RRA) method, followed by enrichment analysis, and regulatory microRNA prediction. We identify a meta-signature of endometrial receptivity involving 57 mRNA genes as putative receptivity markers, where 39 of these we confirm experimentally using RNA-sequencing method in two separate datasets. The meta-signature genes highlight the importance of immune responses, the complement cascade pathway and the involvement of exosomes in mid-secretory endometrial functions. Bioinformatic prediction identifies 348 microRNAs that could regulate 30 endometrial-receptivity associated genes, and we confirm experimentally the decreased expression of 19 microRNAs with 11 corresponding up-regulated meta-signature genes in our validation experiments. The 57 identified meta-signature genes and involved pathways, together with their regulatory microRNAs could serve as promising and sought-after biomarkers of endometrial receptivity, fertility and infertility.

Autoantibody Repertoire in APECED Patients Targets Two Distinct Subgroups of Proteins.

High titer autoantibodies produced by B lymphocytes are clinically important features of many common autoimmune diseases. APECED patients with deficient autoimmune regulator (AIRE) gene collectively display a broad repertoire of high titer autoantibodies, including some which are pathognomonic for major autoimmune diseases. AIRE deficiency severely reduces thymic expression of gene-products ordinarily restricted to discrete peripheral tissues, and developing T cells reactive to those gene-products are not inactivated during their development. However, the extent of the autoantibody repertoire in APECED and its relation to thymic expression of self-antigens are unclear. We here undertook a broad protein array approach to assess autoantibody repertoire in APECED patients. Our results show that in addition to shared autoantigen reactivities, APECED patients display high inter-individual variation in their autoantigen profiles, which collectively are enriched in evolutionarily conserved, cytosolic and nuclear phosphoproteins. The APECED autoantigens have two major origins; proteins expressed in thymic medullary epithelial cells and proteins expressed in lymphoid cells. These findings support the hypothesis that specific protein properties strongly contribute to the etiology of B cell autoimmunity.

g:Profiler-a web server for functional interpretation of gene lists (2016 update).

Functional enrichment analysis is a key step in interpreting gene lists discovered in diverse high-throughput experiments. g:Profiler studies flat and ranked gene lists and finds statistically significant Gene Ontology terms, pathways and other gene function related terms. Translation of hundreds of gene identifiers is another core feature of g:Profiler. Since its first publication in 2007, our web server has become a popular tool of choice among basic and translational researchers. Timeliness is a major advantage of g:Profiler as genome and pathway information is synchronized with the Ensembl database in quarterly updates. g:Profiler supports 213 species including mammals and other vertebrates, plants, insects and fungi. The 2016 update of g:Profiler introduces several novel features. We have added further functional datasets to interpret gene lists, including transcription factor binding site predictions, Mendelian disease annotations, information about protein expression and complexes and gene mappings of human genetic polymorphisms. Besides the interactive web interface, g:Profiler can be accessed in computational pipelines using our R package, Python interface and BioJS component. g:Profiler is freely available at http://biit.cs.ut.ee/gprofiler/.

TRIB3 enhances cell viability during glucose deprivation in HEK293-derived cells by upregulating IGFBP2, a novel nutrient deficiency survival factor.

Glucose deprivation occurs in several human diseases, including infarctions and solid tumors, and leads to cell death. In this article, we investigate the role of the pseudokinase Tribbles homolog 3 (TRIB3) in the cellular stress response to glucose starvation using cell lines derived from HEK293, which is highly glycolytic under standard conditions. Our results show that TRIB3 mRNA and protein levels are strongly upregulated in glucose-deprived cells via the induction of activating transcription factor 4 (ATF4) by the endoplasmic reticulum (ER) stress sensor kinase PERK. Cell survival in glucose-deficient conditions is enhanced by TRIB3 overexpression and reduced by TRIB3 knockdown. Genome-wide gene expression profiling uncovered approximately 40 glucose deprivation-responsive genes that are affected by TRIB3, including several genes involved in signaling processes and metabolism. Based on transcription factor motif analysis, the majority of TRIB3-downregulated genes are target genes of ATF4, which TRIB3 is known to inhibit. The gene most substantially upregulated by TRIB3 is insulin-like growth factor binding protein 2 (IGFBP2). IGFBP2 mRNA and protein levels are downregulated in cells subjected to glucose deprivation, and reduced IGFBP2 expression aggravates cell death during glucose deficiency, while overexpression of IGFBP2 prolongs cell survival. Moreover, IGFBP2 silencing abrogates the pro-survival effect of TRIB3. Since TRIB3 augments IGFBP2 expression in glucose-starved cells, the data indicate that IGFBP2 contributes to the attenuation of cell death by TRIB3. These results implicate TRIB3 and IGFBP2, both of which are known to be overexpressed in several types of cancers, as pro-survival modulators of cell viability in nutrient-deficient microenvironments.

FUN-L: gene prioritization for RNAi screens.

MOTIVATION: Most biological processes remain only partially characterized with many components still to be identified. Given that a whole genome can usually not be tested in a functional assay, identifying the genes most likely to be of interest is of critical importance to avoid wasting resources. RESULTS: Given a set of known functionally related genes and using a state-of-the-art approach to data integration and mining, our Functional Lists (FUN-L) method provides a ranked list of candidate genes for testing. Validation of predictions from FUN-L with independent RNAi screens confirms that FUN-L-produced lists are enriched in genes with the expected phenotypes. In this article, we describe a website front end to FUN-L. AVAILABILITY AND IMPLEMENTATION: The website is freely available to use at http://funl.org

Integration of biological data by kernels on graph nodes allows prediction of new genes involved in mitotic chromosome condensation.

The advent of genome-wide RNA interference (RNAi)-based screens puts us in the position to identify genes for all functions human cells carry out. However, for many functions, assay complexity and cost make genome-scale knockdown experiments impossible. Methods to predict genes required for cell functions are therefore needed to focus RNAi screens from the whole genome on the most likely candidates. Although different bioinformatics tools for gene function prediction exist, they lack experimental validation and are therefore rarely used by experimentalists. To address this, we developed an effective computational gene selection strategy that represents public data about genes as graphs and then analyzes these graphs using kernels on graph nodes to predict functional relationships. To demonstrate its performance, we predicted human genes required for a poorly understood cellular function-mitotic chromosome condensation-and experimentally validated the top 100 candidates with a focused RNAi screen by automated microscopy. Quantitative analysis of the images demonstrated that the candidates were indeed strongly enriched in condensation genes, including the discovery of several new factors. By combining bioinformatics prediction with experimental validation, our study shows that kernels on graph nodes are powerful tools to integrate public biological data and predict genes involved in cellular functions of interest.

Robust rank aggregation for gene list integration and meta-analysis.

MOTIVATION: The continued progress in developing technological platforms, availability of many published experimental datasets, as well as different statistical methods to analyze those data have allowed approaching the same research question using various methods simultaneously. To get the best out of all these alternatives, we need to integrate their results in an unbiased manner. Prioritized gene lists are a common result presentation method in genomic data analysis applications. Thus, the rank aggregation methods can become a useful and general solution for the integration task. RESULTS: Standard rank aggregation methods are often ill-suited for biological settings where the gene lists are inherently noisy. As a remedy, we propose a novel robust rank aggregation (RRA) method. Our method detects genes that are ranked consistently better than expected under null hypothesis of uncorrelated inputs and assigns a significance score for each gene. The underlying probabilistic model makes the algorithm parameter free and robust to outliers, noise and errors. Significance scores also provide a rigorous way to keep only the statistically relevant genes in the final list. These properties make our approach robust and compelling for many settings. AVAILABILITY: All the methods are implemented as a GNU R package RobustRankAggreg, freely available at the Comprehensive R Archive Network http://cran.r-project.org/.

Comprehensive transcriptome analysis of mouse embryonic stem cell adipogenesis unravels new processes of adipocyte development.

BACKGROUND: The current epidemic of obesity has caused a surge of interest in the study of adipose tissue formation. While major progress has been made in defining the molecular networks that control adipocyte terminal differentiation, the early steps of adipocyte development and the embryonic origin of this lineage remain largely unknown. RESULTS: Here we performed genome-wide analysis of gene expression during adipogenesis of mouse embryonic stem cells (ESCs). We then pursued comprehensive bioinformatic analyses, including de novo functional annotation and curation of the generated data within the context of biological pathways, to uncover novel biological functions associated with the early steps of adipocyte development. By combining in-depth gene regulation studies and in silico analysis of transcription factor binding site enrichment, we also provide insights into the transcriptional networks that might govern these early steps. CONCLUSIONS: This study supports several biological findings: firstly, adipocyte development in mouse ESCs is coupled to blood vessel morphogenesis and neural development, just as it is during mouse development. Secondly, the early steps of adipocyte formation involve major changes in signaling and transcriptional networks. A large proportion of the transcription factors that we uncovered in mouse ESCs are also expressed in the mouse embryonic mesenchyme and in adipose tissues, demonstrating the power of our approach to probe for genes associated with early developmental processes on a genome-wide scale. Finally, we reveal a plethora of novel candidate genes for adipocyte development and present a unique resource that can be further explored in functional assays.

Mining for coexpression across hundreds of datasets using novel rank aggregation and visualization methods.

We present a web resource MEM (Multi-Experiment Matrix) for gene expression similarity searches across many datasets. MEM features large collections of microarray datasets and utilizes rank aggregation to merge information from different datasets into a single global ordering with simultaneous statistical significance estimation. Unique features of MEM include automatic detection, characterization and visualization of datasets that includes the strongest coexpression patterns. MEM is freely available at http://biit.cs.ut.ee/mem/.

The FunGenES database: a genomics resource for mouse embryonic stem cell differentiation.

Embryonic stem (ES) cells have high self-renewal capacity and the potential to differentiate into a large variety of cell types. To investigate gene networks operating in pluripotent ES cells and their derivatives, the "Functional Genomics in Embryonic Stem Cells" consortium (FunGenES) has analyzed the transcriptome of mouse ES cells in eleven diverse settings representing sixty-seven experimental conditions. To better illustrate gene expression profiles in mouse ES cells, we have organized the results in an interactive database with a number of features and tools. Specifically, we have generated clusters of transcripts that behave the same way under the entire spectrum of the sixty-seven experimental conditions; we have assembled genes in groups according to their time of expression during successive days of ES cell differentiation; we have included expression profiles of specific gene classes such as transcription regulatory factors and Expressed Sequence Tags; transcripts have been arranged in "Expression Waves" and juxtaposed to genes with opposite or complementary expression patterns; we have designed search engines to display the expression profile of any transcript during ES cell differentiation; gene expression data have been organized in animated graphs of KEGG signaling and metabolic pathways; and finally, we have incorporated advanced functional annotations for individual genes or gene clusters of interest and links to microarray and genomic resources. The FunGenES database provides a comprehensive resource for studies into the biology of ES cells.

Ranking genes by their co-expression to subsets of pathway members.

Cellular processes are often carried out by intricate systems of interacting genes and proteins. Some of these systems are rather well studied and described in pathway databases, while the roles and functions of the majority of genes are poorly understood. A large compendium of public microarray data is available that covers a variety of conditions, samples, and tissues and provides a rich source for genome-scale information. We focus our study on the analysis of 35 curated biological pathways in the context of gene co-expression over a large variety of biological conditions. By defining a global co-expression similarity rank for each gene and pathway, we perform exhaustive leave-one-out computations to describe existing pathway memberships using other members of the corresponding pathway as reference. We demonstrate that while successful in recovering biological base processes such as metabolism and translation, the global correlation measure fails to detect gene memberships in signaling pathways where co-expression is less evident. Our results also show that pathway membership detection is more effective when using only a subset of corresponding pathway members as reference, supporting the existence of more tightly co-expressed subsets of genes within pathways. Our study assesses the predictive power of global gene expression correlation measures in reconstructing biological systems of various functions and specificity. The developed computational network has immediate applications in detecting dubious pathway members and predicting novel member candidates.