Tissue-specific Regulatory Networks Research Articles

Cross-Species Comparative DNA Methylation Reveals Novel Insights into Complex Trait Genetics among Cattle, Sheep, and Goats.

The cross-species characterization of evolutionary changes in the functional genome can facilitate the translation of genetic findings across species and the interpretation of the evolutionary basis underlying complex phenotypes. Yet, this has not been fully explored between cattle, sheep, goats, and other mammals. Here, we systematically characterized the evolutionary dynamics of DNA methylation and gene expression in 3 somatic tissues (i.e. brain, liver, and skeletal muscle) and sperm across 7 mammalian species, including 3 ruminant livestock species (cattle, sheep, and goats), humans, pigs, mice, and dogs, by generating and integrating 160 DNA methylation and transcriptomic data sets. We demonstrate dynamic changes of DNA hypomethylated regions and hypermethylated regions in tissue-type manner across cattle, sheep, and goats. Specifically, based on the phylo-epigenetic model of DNA methylome, we identified a total of 25,074 hypomethylated region extension events specific to cattle, which participated in rewiring tissue-specific regulatory network. Furthermore, by integrating genome-wide association studies of 50 cattle traits, we provided novel insights into the genetic and evolutionary basis of complex phenotypes in cattle. Overall, our study provides a valuable resource for exploring the evolutionary dynamics of the functional genome and highlights the importance of cross-species characterization of multiomics data sets for the evolutionary interpretation of complex phenotypes in cattle livestock.

Integrated metabolite profiling and transcriptome analysis reveals tissue-specific regulation of terpenoid biosynthesis in Artemisia argyi

Artemisia argyi L. is a widely distributed medicinal plant in China. The major bioactive substances of essential oils extracted from leaves are terpenoids. Although many researches have studied the pharmacological effects of the essential oils, the tissue-specific accumulation of terpenoid biosynthesis and the regulatory networks in A. argyi are poorly understood. This study conducted an integrated metabolomic and transcriptomic analysis of roots, stems, and leaves to investigate the tissue-specific regulatory network of terpenoid biosynthesis in A. argyi. We identified 77 unigenes putatively involved in terpenoid backbone biosynthesis. Three rate-determining enzyme genes (DXS, DXR, and HDR) of the methylerythritol phosphate pathway were predominantly expressed in leaves, and strongly co-expressed with eight transcription factors (2 MYBs, 4 WRKYs, and 2 AP2s). An metabolite-transcript correlation analysis revealed 26 putative cytochrome P450s related to terpenoid metabolism in leaves. These results provide a foundation for the future metabolic engineering of useful terpenoids in A. argyi.

A Computational Atlas of Tissue-specific Regulatory Networks

The pattern of how gene co-regulation varies across tissues determines human health. However, inferring tissue-specific regulatory networks and associating them with human phenotypes represent a substantial challenge because multi-tissue projects, including the GTEx, typically contain expression data measured only at one time point from highly heterogeneous donors. Here, we implement an interdisciplinary framework for assembling and programming genomic data from multiple tissues into fully informative gene networks, encapsulated by a complete set of bi-directional, signed, and weighted interactions, from static expression data. This framework can monitor how gene networks change simultaneously across tissues and individuals, infer gene-driven inter-tissue wiring networks, compare and test topological alterations of gene/tissue networks between health states, and predict how regulatory networks evolve across spatiotemporal gradients. Our framework provides a tool to catalogue a comprehensive encyclopedia of mechanistic gene networks that walk medical researchers through tissues in each individual and through individuals for each tissue, facilitating the translation of multi-tissue data into clinical practices.

Gene co-expression analysis of tomato seed maturation reveals tissue-specific regulatory networks and hubs associated with the acquisition of desiccation tolerance and seed vigour

BackgroundDuring maturation seeds acquire several physiological traits to enable them to survive drying and disseminate the species. Few studies have addressed the regulatory networks controlling acquisition of these traits at the tissue level particularly in endospermic seeds such as tomato, which matures in a fully hydrated environment and does not undergo maturation drying. Using temporal RNA-seq analyses of the different seed tissues during maturation, gene network and trait-based correlations were used to explore the transcriptome signatures associated with desiccation tolerance, longevity, germination under water stress and dormancy.ResultsDuring maturation, 15,173 differentially expressed genes were detected, forming a gene network representing 21 expression modules, with 3 being specific to seed coat and embryo and 5 to the endosperm. A gene-trait significance measure identified a common gene module between endosperm and embryo associated with desiccation tolerance and conserved with non-endospermic seeds. In addition to genes involved in protection such LEA and HSP and ABA response, the module included antioxidant and repair genes. Dormancy was released concomitantly with the increase in longevity throughout fruit ripening until 14 days after the red fruit stage. This was paralleled by an increase in SlDOG1–2 and PROCERA transcripts. The progressive increase in seed vigour was captured by three gene modules, one in common between embryo and endosperm and two tissue-specific. The common module was enriched with genes associated with mRNA processing in chloroplast and mitochondria (including penta- and tetratricopeptide repeat-containing proteins) and post-transcriptional regulation, as well several flowering genes. The embryo-specific module contained homologues of ABI4 and CHOTTO1 as hub genes associated with seed vigour, whereas the endosperm-specific module revealed a diverse set of processes that were related to genome stability, defence against pathogens and ABA/GA response genes.ConclusionThe spatio-temporal co-expression atlas of tomato seed maturation will serve as a valuable resource for the in-depth understanding of the dynamics of gene expression associated with the acquisition of seed vigour at the tissue level.

ZokorDB: tissue specific regulatory network annotation for non‐coding elements of plateau zokor

BackgroundPlateau zokor inhabits in sealed burrows from 2,000 to 4,200 meters at Qinghai‐Tibet Plateau. This extreme living environment makes it a great model to study animal adaptation to hypoxia, low temperature, and high carbon dioxide concentration.MethodsWe provide an integrated resource, ZokorDB, for tissue specific regulatory network annotation for zokor. ZokorDB is based on a high‐quality draft genome of a plateau zokor at 3,300 m and its transcriptional profiles in brain, heart, liver, kidney, and lung. The conserved non‐coding elements of zokor are annotated by their nearest genes and upstream transcriptional factor motif binding sites.ResultsZokorDB provides a general draft gene regulatory network (GRN), i.e., potential transcription factor (TF) binds to non‐coding regulatory elements and regulates the expression of target genes (TG). Furthermore, we refined the GRN by incorporating matched RNA‐seq and DNase‐seq data from mouse ENCODE project and reconstructed five tissue‐specific regulatory networks.ConclusionsA web‐based, open‐access database is developed for easily searching, visualizing, and downloading the annotation and data. The pipeline of non‐coding region annotation for zokor will be useful for other non‐model species. ZokorDB is free available at the website (bigd.big.ac.cn/zokordb/).

Identification of novel prostate cancer drivers using RegNetDriver: a framework for integration of genetic and epigenetic alterations with tissue-specific regulatory network

We report a novel computational method, RegNetDriver, to identify tumorigenic drivers using the combined effects of coding and non-coding single nucleotide variants, structural variants, and DNA methylation changes in the DNase I hypersensitivity based regulatory network. Integration of multi-omics data from 521 prostate tumor samples indicated a stronger regulatory impact of structural variants, as they affect more transcription factor hubs in the tissue-specific network. Moreover, crosstalk between transcription factor hub expression modulated by structural variants and methylation levels likely leads to the differential expression of target genes. We report known prostate tumor regulatory drivers and nominate novel transcription factors (ERF, CREB3L1, and POU2F2), which are supported by functional validation.

Understanding tissue-specificity with human tissue-specific regulatory networks

Tissue-specificity is important for the function of human body. However, it is still not clear how the functional diversity of different tissues is achieved. Here we construct gene regulatory networks in 13 human tissues by integrating large-scale transcription factor (TF)-gene regulations with gene and protein expression data. By comparing these regulatory networks, we find many tissue-specific regulations that are important for tissue identity. In particular, the tissue-specific TFs are found to regulate more genes than those expressed in multiple tissues, and the processes regulated by these tissue-specific TFs are closely related to tissue functions. Moreover, the regulations that are present in certain tissue are found to be enriched in the tissue associated disease genes, and these networks provide the molecular context of disease genes. Therefore, recognizing tissuespecific regulatory networks can help better understand the molecular mechanisms underlying diseases and identify new disease genes.

MicroTSS: accurate microRNA transcription start site identification reveals a significant number of divergent pri-miRNAs.

A large fraction of microRNAs (miRNAs) are derived from intergenic non-coding loci and the identification of their promoters remains 'elusive'. Here, we present microTSS, a machine-learning algorithm that provides highly accurate, single-nucleotide resolution predictions for intergenic miRNA transcription start sites (TSSs). MicroTSS integrates high-resolution RNA-sequencing data with active transcription marks derived from chromatin immunoprecipitation and DNase-sequencing to enable the characterization of tissue-specific promoters. MicroTSS is validated with a specifically designed Drosha-null/conditional-null mouse model, generated using the conditional by inversion (COIN) methodology. Analyses of global run-on sequencing data revealed numerous pri-miRNAs in human and mouse either originating from divergent transcription at promoters of active genes or partially overlapping with annotated long non-coding RNAs. MicroTSS is readily applicable to any cell or tissue samples and constitutes the missing part towards integrating the regulation of miRNA transcription into the modelling of tissue-specific regulatory networks.

Porcine tissue-specific regulatory networks derived from meta-analysis of the transcriptome.

The processes that drive tissue identity and differentiation remain unclear for most tissue types. So are the gene networks and transcription factors (TF) responsible for the differential structure and function of each particular tissue, and this is particularly true for non model species with incomplete genomic resources. To better understand the regulation of genes responsible for tissue identity in pigs, we have inferred regulatory networks from a meta-analysis of 20 gene expression studies spanning 480 Porcine Affymetrix chips for 134 experimental conditions on 27 distinct tissues. We developed a mixed-model normalization approach with a covariance structure that accommodated the disparity in the origin of the individual studies, and obtained the normalized expression of 12,320 genes across the 27 tissues. Using this resource, we constructed a network, based on the co-expression patterns of 1,072 TF and 1,232 tissue specific genes. The resulting network is consistent with the known biology of tissue development. Within the network, genes clustered by tissue and tissues clustered by site of embryonic origin. These clusters were significantly enriched for genes annotated in key relevant biological processes and confirm gene functions and interactions from the literature. We implemented a Regulatory Impact Factor (RIF) metric to identify the key regulators in skeletal muscle and tissues from the central nervous systems. The normalization of the meta-analysis, the inference of the gene co-expression network and the RIF metric, operated synergistically towards a successful search for tissue-specific regulators. Novel among these findings are evidence suggesting a novel key role of ERCC3 as a muscle regulator. Together, our results recapitulate the known biology behind tissue specificity and provide new valuable insights in a less studied but valuable model species.

Intronic Cis-Regulatory Modules Mediate Tissue-Specific and Microbial Control of angptl4/fiaf Transcription

The intestinal microbiota enhances dietary energy harvest leading to increased fat storage in adipose tissues. This effect is caused in part by the microbial suppression of intestinal epithelial expression of a circulating inhibitor of lipoprotein lipase called Angiopoietin-like 4 (Angptl4/Fiaf). To define the cis-regulatory mechanisms underlying intestine-specific and microbial control of Angptl4 transcription, we utilized the zebrafish system in which host regulatory DNA can be rapidly analyzed in a live, transparent, and gnotobiotic vertebrate. We found that zebrafish angptl4 is transcribed in multiple tissues including the liver, pancreatic islet, and intestinal epithelium, which is similar to its mammalian homologs. Zebrafish angptl4 is also specifically suppressed in the intestinal epithelium upon colonization with a microbiota. In vivo transgenic reporter assays identified discrete tissue-specific regulatory modules within angptl4 intron 3 sufficient to drive expression in the liver, pancreatic islet β-cells, or intestinal enterocytes. Comparative sequence analyses and heterologous functional assays of angptl4 intron 3 sequences from 12 teleost fish species revealed differential evolution of the islet and intestinal regulatory modules. High-resolution functional mapping and site-directed mutagenesis defined the minimal set of regulatory sequences required for intestinal activity. Strikingly, the microbiota suppressed the transcriptional activity of the intestine-specific regulatory module similar to the endogenous angptl4 gene. These results suggest that the microbiota might regulate host intestinal Angptl4 protein expression and peripheral fat storage by suppressing the activity of an intestine-specific transcriptional enhancer. This study provides a useful paradigm for understanding how microbial signals interact with tissue-specific regulatory networks to control the activity and evolution of host gene transcription.

Visual analysis of transcriptome data in the context of anatomical structures and biological networks.

The complexity and temporal as well as spatial resolution of transcriptome datasets is constantly increasing due to extensive technological developments. Here we present methods for advanced visualization and intuitive exploration of transcriptomics data as necessary prerequisites in order to facilitate the gain of biological knowledge. Color-coding of structural images based on the expression level enables a fast visual data analysis in the background of the examined biological system. The network-based exploration of these visualizations allows for comparative analysis of genes with specific transcript patterns and supports the extraction of functional relationships even from large datasets. In order to illustrate the presented methods, the tool HIVE was applied for visualization and exploration of database-retrieved expression data for master regulators of Arabidopsis thaliana flower and seed development in the context of corresponding tissue-specific regulatory networks.

Stepwise assembly of the Nova -regulated alternative splicing network in the vertebrate brain

Novel organismal structures in metazoans are often undergirded by complex gene regulatory networks; as such, understanding the emergence of new structures through evolution requires reconstructing the series of evolutionary steps leading to these underlying networks. Here, we reconstruct the step-by-step assembly of the vertebrate splicing network regulated by Nova, a splicing factor that modulates alternative splicing in the vertebrate central nervous system by binding to clusters of YCAY motifs on pre-RNA transcripts. Transfection of human HEK293T cells with Nova orthologs indicated vertebrate-like splicing regulatory activity in bilaterian invertebrates, thus Nova acquired the ability to bind YCAY clusters and perform vertebrate-like splicing modulation at least before the last common ancestor of bilaterians. In situ hybridization studies in several species showed that Nova expression became restricted to CNS later on, during chordate evolution. Finally, comparative genomics studies revealed a diverse history for Nova-regulated exons, with target exons arising through both de novo exon creation and acquisition of YCAY motifs by preexisting exons throughout chordate and vertebrate history. In addition, we find that tissue-specific Nova expression patterns emerged independently in other lineages, suggesting independent assembly of tissue-specific regulatory networks.

Tissue-specific regulatory network extractor (TS-REX): a database and software resource for the tissue and cell type-specific investigation of transcription factor-gene networks

The prediction of transcription factor binding sites in genomic sequences is in principle very useful to identify upstream regulatory factors. However, when applying this concept to genomes of multicellular organisms such as mammals, one has to deal with a large number of false positive predictions since many transcription factor genes are only expressed in specific tissues or cell types. We developed TS-REX, a database/software system that supports the analysis of tissue and cell type-specific transcription factor-gene networks based on expressed sequence tag abundance of transcription factor-encoding genes in UniGene EST libraries. The use of expression levels of transcription factor-encoding genes according to hierarchical anatomical classifications covering different tissues and cell types makes it possible to filter out irrelevant binding site predictions and to identify candidates of potential functional importance for further experimental testing. TS-REX covers ESTs from H. sapiens and M. musculus, and allows the characterization of both presence and specificity of transcription factors in user-specified tissues or cell types. The software allows users to interactively visualize transcription factor-gene networks, as well as to export data for further processing. TS-REX was applied to predict regulators of Polycomb group genes in six human tumor tissues and in human embryonic stem cells.

Tissue‐specific regulatory elements in mammalian promoters

Transcription factor-binding sites and the cis-regulatory modules they compose are central determinants of gene expression. We previously showed that binding site motifs and modules in proximal promoters can be used to predict a significant portion of mammalian tissue-specific transcription. Here, we report on a systematic analysis of promoters controlling tissue-specific expression in heart, kidney, liver, pancreas, skeletal muscle, testis and CD4 T cells, for both human and mouse. We integrated multiple sources of expression data to compile sets of transcripts with strong evidence for tissue-specific regulation. The analysis of the promoters corresponding to these sets produced a catalog of predicted tissue-specific motifs and modules, and cis-regulatory elements. Predicted regulatory interactions are supported by statistical evidence, and provide a foundation for targeted experiments that will improve our understanding of tissue-specific regulatory networks. In a broader context, methods used to construct the catalog provide a model for the analysis of genomic regions that regulate differentially expressed genes.

Embryonic growth factors

The role of growth factors in development is under analysis on three main fronts: examination of patterns of growth factor expression in embryogenesis, studies of biological activity in vitro, and mutational analysis in vivo. Recent findings indicate that growth factors control developmental decisions, are strictly controlled in their delivery to responding cells, and act in conjunction to create tissue-specific regulatory networks.