Generalized gene co-expression analysis via subspace clustering using low-rank representation

BACKGROUND: Gene Co-expression Network Analysis (GCNA) helps identify gene modules with potential biological functions and has become a popular method in bioinformatics and biomedical research. However, most current GCNA algorithms use correlation to build gene co-expression networks and identify modules with highly correlated genes. There is a need to look beyond correlation and identify gene modules using other similarity measures for finding novel biologically meaningful modules. RESULTS: We propose a new generalized gene co-expression analysis algorithm via subspace clustering that can identify biologically meaningful gene co-expression modules with genes that are not all highly correlated. We use low-rank representation to construct gene co-expression networks and local maximal quasi-clique merger to identify gene co-expression modules. We applied our method on three large microarray datasets and a single-cell RNA sequencing dataset. We demonstrate that our method can identify gene modules with different biological functions than current GCNA methods and find gene modules with prognostic values. CONCLUSIONS: The presented method takes advantage of subspace clustering to generate gene co-expression networks rather than using correlation as the similarity measure between genes. Our generalized GCNA method can provide new insights from gene expression datasets and serve as a complement to current GCNA algorithms

Gene Co-expression Network and Copy Number Variation Analyses Identify Transcription Factors Associated With Multiple Myeloma Progression

Multiple myeloma (MM) has two clinical precursor stages of disease: monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM). However, the mechanism of progression is not well understood. Because gene co-expression network analysis is a well-known method for discovering new gene functions and regulatory relationships, we utilized this framework to conduct differential co-expression analysis to identify interesting transcription factors (TFs) in two publicly available datasets. We then used copy number variation (CNV) data from a third public dataset to validate these TFs. First, we identified co-expressed gene modules in two publicly available datasets each containing three conditions: normal, MGUS, and SMM. These modules were assessed for condition-specific gene expression, and then enrichment analysis was conducted on condition-specific modules to identify their biological function and upstream TFs. TFs were assessed for differential gene expression between normal and MM precursors, then validated with CNV analysis to identify candidate genes. Functional enrichment analysis reaffirmed known functional categories in MM pathology, the main one relating to immune function. Enrichment analysis revealed a handful of differentially expressed TFs between normal and either MGUS or SMM in gene expression and/or CNV. Overall, we identified four genes of interest (MAX, TCF4, ZNF148, and ZNF281) that aid in our understanding of MM initiation and progression

Gene co-expression analyses: an overview from microarray collections in Arabidopsis thaliana

4noBioinformatics web-based resources and databases are precious references for most biological laboratories worldwide. However, the quality and reliability of the information they provide depends on them being used in an appropriate way that takes into account their specific features. Huge collections of gene expression data are currently publicly available, ready to support the understanding of gene and genome functionalities. In this context, tools and resources for gene co-expression analyses have flourished to exploit the ‘guilty by association' principle, which assumes that genes with correlated expression profiles are functionally related. In the case of Arabidopsis thaliana, the reference species in plant biology, the resources available mainly consist of microarray results. After a general overview of such resources, we tested and compared the results they offer for gene co-expression analysis. We also discuss the effect on the results when using different data sets, as well as different data normalization approaches and parameter settings, which often consider different metrics for establishing co-expression. A dedicated example analysis of different gene pools, implemented by including/excluding mutant samples in a reference data set, showed significant variation of gene co-expression occurrence, magnitude and direction. We conclude that, as the heterogeneity of the resources and methods may produce different results for the same query genes, the exploration of more than one of the available resources is strongly recommended. The aim of this article is to show how best to integrate data sources and/or merge outputs to achieve robust analyses and reliable interpretations, thereby making use of diverse data resources an opportunity for added value.openembargoed_20170219Di Salle, Pasquale; Incerti, Guido; Colantuono, Chiara; Chiusano, Maria LuisaDi Salle, Pasquale; Incerti, Guido; Colantuono, Chiara; Chiusano, Maria Luis

Eukaryotic transcriptional regulation : from data mining to transcriptional profiling

textSurvival of cells and organisms requires that each of thousands of genes is expressed at the correct time in development, in the correct tissue, and under the correct conditions. Transcription is the primary point of gene regulation. Genes are activated and repressed by transcription factors, which are proteins that become active through signaling, bind, sometimes cooperatively, to regulatory regions of DNA, and interact with other proteins such as chromatin remodelers. Yeast has nearly six thousand genes, several hundred of which are transcription factors; transcription factors comprise around 2000 of the 22,000 genes in the human genome. When and how these transcription factors are activated, as well as which subsets of genes they regulate, is a current, active area of research essential to understanding the transcriptional regulatory programs of organisms. We approached this problem in two divergent ways: first, an in silico study of human transcription factor combinations, and second, an experimental study of the transcriptional response of yeast mutants deficient in DNA repair. First, in order to better understand the combinatorial nature of transcription factor binding, we developed a data mining approach to assess whether transcription factors whose binding motifs were frequently proximal in the human genome were more likely to interact. We found many instances in the literature in which over-represented transcription factor pairs co-regulated the same gene, so we used co-citation to assess the utility of this method on a larger scale. We determined that over-represented pairs were more likely to be co-cited than would be expected by chance. Because proper repair of DNA is an essential and highly-conserved process in all eukaryotes, we next used cDNA microarrays to measure differentially expressed genes in eighteen yeast deletion strains with sensitivity to the DNA cross-linking agent methyl methane sulfonate (MMS); many of these mutants were transcription factors or DNA-binding proteins. Combining this data with tools such as chromatin immunoprecipitation, gene ontology analysis, expression profile similarity, and motif analysis allowed us to propose a model for the roles of Iki3 and of YML081W, a poorly-characterized gene, in DNA repair.Institute for Cellular and Molecular Biolog

INTEGRATIVE ANALYSIS OF OMICS DATA IN ADULT GLIOMA AND OTHER TCGA CANCERS TO GUIDE PRECISION MEDICINE

Transcriptomic profiling and gene expression signatures have been widely applied as effective approaches for enhancing the molecular classification, diagnosis, prognosis or prediction of therapeutic response towards personalized therapy for cancer patients. Thanks to modern genome-wide profiling technology, scientists are able to build engines leveraging massive genomic variations and integrating with clinical data to identify “at risk” individuals for the sake of prevention, diagnosis and therapeutic interventions. In my graduate work for my Ph.D. thesis, I have investigated genomic sequencing data mining to comprehensively characterise molecular classifications and aberrant genomic events associated with clinical prognosis and treatment response, through applying high-dimensional omics genomic data to promote the understanding of gene signatures and somatic molecular alterations contributing to cancer progression and clinical outcomes. Following this motivation, my dissertation has been focused on the following three topics in translational genomics. 1) Characterization of transcriptomic plasticity and its association with the tumor microenvironment in glioblastoma (GBM). I have integrated transcriptomic, genomic, protein and clinical data to increase the accuracy of GBM classification, and identify the association between the GBM mesenchymal subtype and reduced tumorpurity, accompanied with increased presence of tumor-associated microglia. Then I have tackled the sole source of microglial as intrinsic tumor bulk but not their corresponding neurosphere cells through both transcriptional and protein level analysis using a panel of sphere-forming glioma cultures and their parent GBM samples.FurthermoreI have demonstrated my hypothesis through longitudinal analysis of paired primary and recurrent GBM samples that the phenotypic alterations of GBM subtypes are not due to intrinsic proneural-to-mesenchymal transition in tumor cells, rather it is intertwined with increased level of microglia upon disease recurrence. Collectively I have elucidated the critical role of tumor microenvironment (Microglia and macrophages from central nervous system) contributing to the intra-tumor heterogeneity and accurate classification of GBM patients based on transcriptomic profiling, which will not only significantly impact on clinical perspective but also pave the way for preclinical cancer research. 2) Identification of prognostic gene signatures that stratify adult diffuse glioma patientsharboring1p/19q co-deletions. I have compared multiple statistical methods and derived a gene signature significantly associated with survival by applying a machine learning algorithm. Then I have identified inflammatory response and acetylation activity that associated with malignant progression of 1p/19q co-deleted glioma. In addition, I showed this signature translates to other types of adult diffuse glioma, suggesting its universality in the pathobiology of other subset gliomas. My efforts on integrative data analysis of this highly curated data set usingoptimizedstatistical models will reflect the pending update to WHO classification system oftumorsin the central nervous system (CNS). 3) Comprehensive characterization of somatic fusion transcripts in Pan-Cancers. I have identified a panel of novel fusion transcripts across all of TCGA cancer types through transcriptomic profiling. Then I have predicted fusion proteins with kinase activity and hub function of pathway network based on the annotation of genetically mobile domains and functional domain architectures. I have evaluated a panel of in -frame gene fusions as potential driver mutations based on network fusion centrality hypothesis. I have also characterised the emerging complexity of genetic architecture in fusion transcripts through integrating genomic structure and somatic variants and delineating the distinct genomic patterns of fusion events across different cancer types. Overall my exploration of the pathogenetic impact and clinical relevance of candidate gene fusions have provided fundamental insights into the management of a subset of cancer patients by predicting the oncogenic signalling and specific drug targets encoded by these fusion genes. Taken together, the translational genomic research I have conducted during my Ph.D. study will shed new light on precision medicine and contribute to the cancer research community. The novel classification concept, gene signature and fusion transcripts I have identified will address several hotly debated issues in translational genomics, such as complex interactions between tumor bulks and their adjacent microenvironments, prognostic markers for clinical diagnostics and personalized therapy, distinct patterns of genomic structure alterations and oncogenic events in different cancer types, therefore facilitating our understanding of genomic alterations and moving us towards the development of precision medicine

Whole transcriptome approach to evaluate the effect of aluminium hydroxide in ovine encephalon

Aluminium hydroxide adjuvants are crucial for livestock and human vaccines. Few studies have analysed their effect on the central nervous system in vivo. In this work, lambs received three different treatments of parallel subcutaneous inoculations during 16 months with aluminium-containing commercial vaccines, an equivalent dose of aluminium hydroxide or mock injections. Brain samples were sequenced by RNA-seq and miRNA-seq for the expression analysis of mRNAs, long non-coding RNAs and microRNAs and three expression comparisons were made. Although few differentially expressed genes were identified, some dysregulated genes by aluminium hydroxide alone were linked to neurological functions, the lncRNA TUNA among them, or were enriched in mitochondrial energy metabolism related functions. In the same way, the miRNA expression was mainly disrupted by the adjuvant alone treatment. Some differentially expressed miRNAs had been previously linked to neurological diseases, oxidative stress and apoptosis. In brief, in this study aluminium hydroxide alone altered the transcriptome of the encephalon to a higher degree than commercial vaccines that present a milder effect. The expression changes in the animals inoculated with aluminium hydroxide suggest mitochondrial disfunction. Further research is needed to elucidate to which extent these changes could have pathological consequences

Whole Transcriptome Approach to Evaluate the Effect of Aluminium Hydroxide in Ovine Encephalon

Aluminium hydroxide adjuvants are crucial for livestock and human vaccines. Few studies have analysed their effect on the central nervous system in vivo. In this work, lambs received three different treatments of parallel subcutaneous inoculations during 16 months with aluminium-containing commercial vaccines, an equivalent dose of aluminium hydroxide or mock injections. Brain samples were sequenced by RNA-seq and miRNA-seq for the expression analysis of mRNAs, long non-coding RNAs and microRNAs and three expression comparisons were made. Although few differentially expressed genes were identified, some dysregulated genes by aluminium hydroxide alone were linked to neurological functions, the lncRNA TUNA among them, or were enriched in mitochondrial energy metabolism related functions. In the same way, the miRNA expression was mainly disrupted by the adjuvant alone treatment. Some differentially expressed miRNAs had been previously linked to neurological diseases, oxidative stress and apoptosis. In brief, in this study aluminium hydroxide alone altered the transcriptome of the encephalon to a higher degree than commercial vaccines that present a milder effect. The expression changes in the animals inoculated with aluminium hydroxide suggest mitochondrial disfunction. Further research is needed to elucidate to which extent these changes could have pathological consequences.This work was supported by the Spanish Ministry of Economy grant [MINECO project AGL2013-49137-C3 to BMJ, LL and DA]; University of the Basque Country (UPV/EHU) predoctoral fellowships [PIF15/361 to EV-M and PIF17/306 to MB-A]; and University of the Basque Country (UPV/EHU) postdoctoral fellowship [ESPDOC16/43 to NA]. Thanks to M. Ortega for technical help