MyoMiner: explore gene co-expression in normal and pathological muscle

International audienceBackground: High-throughput transcriptomics measures mRNA levels for thousands of genes in a biological sample. Most gene expression studies aim to identify genes that are differentially expressed between different biological conditions, such as between healthy and diseased states. However, these data can also be used to identify genes that are co-expressed within a biological condition. Gene co-expression is used in a guilt-by-association approach to prioritize candidate genes that could be involved in disease, and to gain insights into the functions of genes, protein relations, and signaling pathways. Most existing gene co-expression databases are generic, amalgamating data for a given organism regardless of tissue-type.Methods: To study muscle-specific gene co-expression in both normal and pathological states, publicly available gene expression data were acquired for 2376 mouse and 2228 human striated muscle samples, and separated into 142 categories based on species (human or mouse), tissue origin, age, gender, anatomic part, and experimental condition. Co-expression values were calculated for each category to create the MyoMiner database.Results: Within each category, users can select a gene of interest, and the MyoMiner web interface will return all correlated genes. For each co-expressed gene pair, adjusted p-value and confidence intervals are provided as measures of expression correlation strength. A standardized expression-level scatterplot is available for every gene pair r-value. MyoMiner has two extra functions: (a) a network interface for creating a 2-shell correlation network, based either on the most highly correlated genes or from a list of genes provided by the user with the option to include linked genes from the database and (b) a comparison tool from which the users can test whether any two correlation coefficients from different conditions are significantly different.Conclusions: These co-expression analyses will help investigators to delineate the tissue-, cell-, and pathology-specific elements of muscle protein interactions, cell signaling and gene regulation. Changes in co-expression between pathologic and healthy tissue may suggest new disease mechanisms and help define novel therapeutic targets. Thus, MyoMiner is a powerful muscle-specific database for the discovery of genes that are associated with related functions based on their co-expression. MyoMiner is freely available at https://www.sys-myo.com/myominer

Boolean implication networks derived from large scale, whole genome microarray datasets

A method for analysis of microarray data is presented that extracts statistically significant Boolean implication relationships between pairs of genes

Psoriasis drug development and GWAS interpretation through in silico analysis of transcription factor binding sites

BackgroundPsoriasis is a cytokine‐mediated skin disease that can be treated effectively with immunosuppressive biologic agents. These medications, however, are not equally effective in all patients and are poorly suited for treating mild psoriasis. To develop more targeted therapies, interfering with transcription factor (TF) activity is a promising strategy.MethodsMeta‐analysis was used to identify differentially expressed genes (DEGs) in the lesional skin from psoriasis patients (n = 237). We compiled a dictionary of 2935 binding sites representing empirically‐determined binding affinities of TFs and unconventional DNA‐binding proteins (uDBPs). This dictionary was screened to identify “psoriasis response elements” (PREs) overrepresented in sequences upstream of psoriasis DEGs.ResultsPREs are recognized by IRF1, ISGF3, NF‐kappaB and multiple TFs with helix‐turn‐helix (homeo) or other all‐alpha‐helical (high‐mobility group) DNA‐binding domains. We identified a limited set of DEGs that encode proteins interacting with PRE motifs, including TFs (GATA3, EHF, FOXM1, SOX5) and uDBPs (AVEN, RBM8A, GPAM, WISP2). PREs were prominent within enhancer regions near cytokine‐encoding DEGs (IL17A, IL19 and IL1B), suggesting that PREs might be incorporated into complex decoy oligonucleotides (cdODNs). To illustrate this idea, we designed a cdODN to concomitantly target psoriasis‐activated TFs (i.e., FOXM1, ISGF3, IRF1 and NF‐kappaB). Finally, we screened psoriasis‐associated SNPs to identify risk alleles that disrupt or engender PRE motifs. This identified possible sites of allele‐specific TF/uDBP binding and showed that PREs are disproportionately disrupted by psoriasis risk alleles.ConclusionsWe identified new TF/uDBP candidates and developed an approach that (i) connects transcriptome informatics to cdODN drug development and (ii) enhances our ability to interpret GWAS findings. Disruption of PRE motifs by psoriasis risk alleles may contribute to disease susceptibility.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/155494/1/ctm2s4016901500545-sup-0001.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155494/2/ctm2s4016901500545-sup-0018.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155494/3/ctm2s4016901500545-sup-0002.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155494/4/ctm2s4016901500545.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155494/5/ctm2s4016901500545-sup-0009.pd

Functional Analysis of Human Long Non-coding RNAs and Their Associations with Diseases

Within this study, we sought to leverage knowledge from well-characterized protein coding genes to characterize the lesser known long non-coding RNA (lncRNA) genes using computational methods to find functional annotations and disease associations. Functional genome annotation is an essential step to a systems-level view of the human genome. With this knowledge, we can gain a deeper understanding of how humans develop and function, and a better understanding of human disease. LncRNAs are transcripts greater than 200 nucleotides, which do not code for proteins. LncRNAs have been found to regulate development, tissue and cell differentiation, and organ formation. Their dysregulation has been linked to several diseases including autism spectrum disorder (ASD) and cancer. While a great deal of research has been dedicated to protein-coding genes, the relatively recently discovered lncRNA genes have yet to be characterized. LncRNA function is tied closely to when and where they are expressed. Co-expression network analysis offer a means of functional annotation of uncharacterized genes through a guilt by association approach. We have constructed two co-expression networks using known disease-associated protein-coding genes and lncRNA genes. Through clustering of the networks, gene set enrichment analysis, and centrality measures, we found enrichment for disease association and functions as well as identified high-confidence lncRNA disease gene targets. We present a novel approach to the identification of disease state associations by demonstrating genes that are associated with the same disease states share patterns that can be discerned from transcriptomes of healthy tissues. Using a machine learning algorithm, we built a model to classify ASD versus non-ASD genes using their expression profiles from healthy developing human brain tissues. Feature selection during the model-building process also identified critical temporospatial points for the determination of ASD genes. We constructed a webserver tool for the prioritization of genes for ASD association. The webserver tool has a database containing prioritization and co-expression information for nearly every gene in the human genome

Non-parametric algorithms for evaluating gene expression in cancer using DNA microarray technology

Microarray technology has transformed the field of cancer biology by enabling the simultaneous evaluation of tens of thousands mRNA expression levels in a single experiment. This technology has been applied to medical science in order to find gene expression markers that cluster diseased and normal tissues, genes affected by treatments, and gene network interactions. All methods of microarray data analysis can be summarized as a study of differential gene expression. This study addresses three questions, 1) the roles of selectively expressed genes for the classification of cancer, 2) issues of accounting for both experimental and biological noise, and 3) issues of comparing data derived from different research groups using the Affymetrix GeneChipTM platform. A key finding of this study is that selectively expressed genes are very powerful when used for disease classification. A model was designed to reduce noise and eliminate false positives from true results. With this approach, data from different research groups can be integrated to increase information and enable a better understanding of cancer

Developing RNA diagnostics for studying healthy human ageing

Developing strategies to cope with increase in the ageing population and age-related chronic diseases is one of the societies biggest challenges. The characteristics of the ageing process shows significant inter-individual variation. Building genomic signatures that could account for variation in health outcomes with age may facilitate early prognosis of individual age-correlated diseases (e.g. cancer, coronary artery diseases and dementia) and help in developing better targeted treatments provided years in advance of acquiring disabling symptoms for these diseases. The aim of this thesis was to explore methods for diagnosing molecular features of human ageing. In particular, we utilise multi-platform transcriptomics, independent clinical data and classification methods to evaluate which human tissues demonstrate a reproducible molecular signature for age and which clinical phenotypes correlated with these new RNA biomarkers. [Continues.

Whole genome survey of coding SNPs reveals a reproducible pathway determinant of Parkinson disease

It is quickly becoming apparent that situating human variation in a pathway context is crucial to understanding its phenotypic significance. Toward this end, we have developed a general method for finding pathways associated with traits that control for pathway size. We have applied this method to a new whole genome survey of coding SNP variation in 187 patients afflicted with Parkinson disease (PD) and 187 controls. We show that our dataset provides an independent replication of the axon guidance association recently reported by Lesnick et al. [PLoS Genet 2007;3:e98], and also indicates that variation in the ubiquitin-mediated proteolysis and T-cell receptor signaling pathways may predict PD susceptibility. Given this result, it is reasonable to hypothesize that pathway associations are more replicable than individual SNP associations in whole genome association studies. However, this hypothesis is complicated by a detailed comparison of our dataset to the second recent PD association study by Fung et al. [Lancet Neurol 2006;5:911–916]. Surprisingly, we find that the axon guidance pathway does not rank at the very top of the Fung dataset after controlling for pathway size. More generally, in comparing the studies, we find that SNP frequencies replicate well despite technologically different assays, but that both SNP and pathway associations are globally uncorrelated across studies. We thus have a situation in which an association between axon guidance pathway variation and PD has been found in 2 out of 3 studies. We conclude by relating this seeming inconsistency to the molecular heterogeneity of PD, and suggest future analyses that may resolve such discrepancies

Coordinate and Region-Specific Roles for Fibroblast Growth Factors 2 and 9 as Molecular Organizers in Major Depression and Animal Models of Affective Disorders.

The neurotrophic hypothesis posits that changes in the expression and function of growth factors in the brain underlie the pathophysiology of Major Depressive Disorder (MDD). Previous work implicated the fibroblast growth factor (FGF) system, identifying FGF2 as an endogenous anxiolytic and antidepressant molecule whose expression is downregulated in the depressed brain. Notably, FGF9 showed a diagnosis-specific pattern of expression that was opposite to FGF2. Therefore, we investigated the hypotheses that FGF2 and FGF9 were critical to the regulation of affect and that their expression becomes disrupted in MDD. Because the literature supporting the role of FGF9 in affect regulation was small, we performed exploratory analyses and demonstrated that FGF9 expression is consistently upregulated in the hippocampus (but not the anterior cingulate cortex or dorsolateral prefrontal cortex) of individuals diagnosed with MDD. We also showed that reducing endogenous expression of FGF9 in the dentate gyrus is sufficient to reduce anxiety-like behavior, and hippocampal FGF9 levels differ in an animal model of affective dysregulation. Because they showed opposite effects in MDD and animal models, we hypothesized that FGF2 and FGF9 might act as physiological antagonists to mediate affect. We examined more complex questions regarding FGF2. We used animal models to demonstrate that altered hippocampal FGF2 expression predisposes individuals for affective dysregulation. Because we hypothesized that relative levels of FGF2 and FGF9 might be important to MDD pathophysiology, we examined diagnosis-specific relationships in expression between FGF2, FGF9, and FGF receptors, and we found regional patterns of alteration with MDD. In the anterior cingulate cortex, correlations between FGF family members were lost in MDD, while in the hippocampus, new relationships emerged. These changes were related to alterations in correlated gene expression of transcripts related to fundamental biology and circuit function, supporting the hypothesis that FGF2 and FGF9 may influence affect by acting as molecular organizers whose effects become dysregulated during MDD. Future studies will examine the role of FGF2 and FGF9 in MDD, with a particular emphasis on understanding how neural circuitry is altered at the cellular level.PhDNeuroscienceUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133376/1/eaurbach_1.pd