A data mining method to predict transcriptional regulatory sites based on differentially expressed genes in human genome

[[abstract]]Very large-scale gene expression analysis, i.e., UniGene and dbEST, is provided to find those genes with significantly differential expression in specific tissues. The differentially expressed genes in a specific tissue are potentially regulated concurrently by a combination of transcription factors. This study attempts to mine putative binding sites on how combinations of the known regulatory sites homologs and over-represented repetitive elements are distributed in the promoter regions of considered groups of differentially expressed genes. We propose a data mining approach to statistically discover the significantly tissue-specific combinations of known site homologs and over-represented repetitive sequences, which are distributed in the promoter regions of differentially gene groups. The association rules mined would facilitate to predict putative regulatory elements and identify genes potentially co-regulated by the putative regulatory elements

MicroRNA and transcription factor co-regulatory networks and subtype classification of seminoma and non-seminoma in testicular germ cell tumors

Recent studies have revealed that feed-forward loops (FFLs) as regulatory motifs have synergistic roles in cellular systems and their disruption may cause diseases including cancer. FFLs may include two regulators such as transcription factors (TFs) and microRNAs (miRNAs). In this study, we extensively investigated TF and miRNA regulation pairs, their FFLs, and TF-miRNA mediated regulatory networks in two major types of testicular germ cell tumors (TGCT): seminoma (SE) and non-seminoma (NSE). Specifically, we identified differentially expressed mRNA genes and miRNAs in 103 tumors using the transcriptomic data from The Cancer Genome Atlas. Next, we determined significantly correlated TF-gene/miRNA and miRNA-gene/TF pairs with regulation direction. Subsequently, we determined 288 and 664 dysregulated TF-miRNA-gene FFLs in SE and NSE, respectively. By constructing dysregulated FFL networks, we found that many hub nodes (12 out of 30 for SE and 8 out of 32 for NSE) in the top ranked FFLs could predict subtype-classification (Random Forest classifier, average accuracy ≥90%). These hub molecules were validated by an independent dataset. Our network analysis pinpointed several SE-specific dysregulated miRNAs (miR-200c-3p, miR-25-3p, and miR-302a-3p) and genes (EPHA2, JUN, KLF4, PLXDC2, RND3, SPI1, and TIMP3) and NSE-specific dysregulated miRNAs (miR-367-3p, miR-519d-3p, and miR-96-5p) and genes (NR2F1 and NR2F2). This study is the first systematic investigation of TF and miRNA regulation and their co-regulation in two major TGCT subtypes

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

Machine Learning Models for Deciphering Regulatory Mechanisms and Morphological Variations in Cancer

The exponential growth of multi-omics biological datasets is resulting in an emerging paradigm shift in fundamental biological research. In recent years, imaging and transcriptomics datasets are increasingly incorporated into biological studies, pushing biology further into the domain of data-intensive-sciences. New approaches and tools from statistics, computer science, and data engineering are profoundly influencing biological research. Harnessing this ever-growing deluge of multi-omics biological data requires the development of novel and creative computational approaches. In parallel, fundamental research in data sciences and Artificial Intelligence (AI) has advanced tremendously, allowing the scientific community to generate a massive amount of knowledge from data. Advances in Deep Learning (DL), in particular, are transforming many branches of engineering, science, and technology. Several of these methodologies have already been adapted for harnessing biological datasets; however, there is still a need to further adapt and tailor these techniques to new and emerging technologies. In this dissertation, we present computational algorithms and tools that we have developed to study gene-regulation and cellular morphology in cancer. The models and platforms that we have developed are general and widely applicable to several problems relating to dysregulation of gene expression in diseases. Our pipelines and software packages are disseminated in public repositories for larger scientific community use. This dissertation is organized in three main projects. In the first project, we present Causal Inference Engine (CIE), an integrated platform for the identification and interpretation of active regulators of transcriptional response. The platform offers visualization tools and pathway enrichment analysis to map predicted regulators to Reactome pathways. We provide a parallelized R-package for fast and flexible directional enrichment analysis to run the inference on custom regulatory networks. Next, we designed and developed MODEX, a fully automated text-mining system to extract and annotate causal regulatory interaction between Transcription Factors (TFs) and genes from the biomedical literature. MODEX uses putative TF-gene interactions derived from high-throughput ChIP-Seq or other experiments and seeks to collect evidence and meta-data in the biomedical literature to validate and annotate the interactions. MODEX is a complementary platform to CIE that provides auxiliary information on CIE inferred interactions by mining the literature. In the second project, we present a Convolutional Neural Network (CNN) classifier to perform a pan-cancer analysis of tumor morphology, and predict mutations in key genes. The main challenges were to determine morphological features underlying a genetic status and assess whether these features were common in other cancer types. We trained an Inception-v3 based model to predict TP53 mutation in five cancer types with the highest rate of TP53 mutations. We also performed a cross-classification analysis to assess shared morphological features across multiple cancer types. Further, we applied a similar methodology to classify HER2 status in breast cancer and predict response to treatment in HER2 positive samples. For this study, our training slides were manually annotated by expert pathologists to highlight Regions of Interest (ROIs) associated with HER2+/- tumor microenvironment. Our results indicated that there are strong morphological features associated with each tumor type. Moreover, our predictions highly agree with manual annotations in the test set, indicating the feasibility of our approach in devising an image-based diagnostic tool for HER2 status and treatment response prediction. We have validated our model using samples from an independent cohort, which demonstrates the generalizability of our approach. Finally, in the third project, we present an approach to use spatial transcriptomics data to predict spatially-resolved active gene regulatory mechanisms in tissues. Using spatial transcriptomics, we identified tissue regions with differentially expressed genes and applied our CIE methodology to predict active TFs that can potentially regulate the marker genes in the region. This project bridged the gap between inference of active regulators using molecular data and morphological studies using images. The results demonstrate a significant local pattern in TF activity across the tissue, indicating differential spatial-regulation in tissues. The results suggest that the integrative analysis of spatial transcriptomics data with CIE can capture discriminant features and identify localized TF-target links in the tissue

Activity of microRNAs and transcription factors in Gene Regulatory Networks

In biological research, diverse high-throughput techniques enable the investigation of whole systems at the molecular level. The development of new methods and algorithms is necessary to analyze and interpret measurements of gene and protein expression and of interactions between genes and proteins. One of the challenges is the integrated analysis of gene expression and the associated regulation mechanisms. The two most important types of regulators, transcription factors (TFs) and microRNAs (miRNAs), often cooperate in complex networks at the transcriptional and post-transcriptional level and, thus, enable a combinatorial and highly complex regulation of cellular processes. For instance, TFs activate and inhibit the expression of other genes including other TFs whereas miRNAs can post-transcriptionally induce the degradation of transcribed RNA and impair the translation of mRNA into proteins. The identification of gene regulatory networks (GRNs) is mandatory in order to understand the underlying control mechanisms. The expression of regulators is itself regulated, i.e. activating or inhibiting regulators in varying conditions and perturbations. Thus, measurements of gene expression following targeted perturbations (knockouts or overexpressions) of these regulators are of particular importance. The prediction of the activity states of the regulators and the prediction of the target genes are first important steps towards the construction of GRNs. This thesis deals with these first bioinformatics steps to construct GRNs. Targets of TFs and miRNAs are determined as comprehensively and accurately as possible. The activity state of regulators is predicted for specific high-throughput data and specific contexts using appropriate statistical approaches. Moreover, (parts of) GRNs are inferred, which lead to explanations of given measurements. The thesis describes new approaches for these tasks together with accompanying evaluations and validations. This immediately defines the three main goals of the current thesis: 1. The development of a comprehensive database of regulator-target relation. Regulators and targets are retrieved from public repositories, extracted from the literature via text mining and collected into the miRSel database. In addition, relations can be predicted using various published methods. In order to determine the activity states of regulators (see 2.) and to infer GRNs (3.) comprehensive and accurate regulator-target relations are required. It could be shown that text mining enables the reliable extraction of miRNA, gene, and protein names as well as their relations from scientific free texts. Overall, the miRSel contains about three times more relations for the model organisms human, mouse, and rat as compared to state-of-the-art databases (e.g. TarBase, one of the currently most used resources for miRNA-target relations). 2. The prediction of activity states of regulators based on improved target sets. In order to investigate mechanisms of gene regulation, the experimental contexts have to be determined in which the respective regulators become active. A regulator is predicted as active based on appropriate statistical tests applied to the expression values of its set of target genes. For this task various gene set enrichment (GSE) methods have been proposed. Unfortunately, before an actual experiment it is unknown which genes are affected. The missing standard-of-truth so far has prevented the systematic assessment and evaluation of GSE tests. In contrast, the trigger of gene expression changes is of course known for experiments where a particular regulator has been directly perturbed (i.e. by knockout, transfection, or overexpression). Based on such datasets, we have systematically evaluated 12 current GSE tests. In our analysis ANOVA and the Wilcoxon test performed best. 3. The prediction of regulation cascades. Using gene expression measurements and given regulator-target relations (e.g. from the miRSel database) GRNs are derived. GSE tests are applied to determine TFs and miRNAs that change their activity as cellular response to an overexpressed miRNA. Gene regulatory networks can constructed iteratively. Our models show how miRNAs trigger gene expression changes: either directly or indirectly via cascades of miRNA-TF, miRNA-kinase-TF as well as TF-TF relations. In this thesis we focus on measurements which have been obtained after overexpression of miRNAs. Surprisingly, a number of cancer relevant miRNAs influence a common core of TFs which are involved in processes such as proliferation and apoptosis