A Validated Regulatory Network for Th17 Cell Specification.

BackgroundThe progress of the cell cycle of yeast involves the regulatory relationships between genes and the interactions proteins. However, it is still obscure which type of protein plays a decisive role in regulation and how to identify the vital nodes in the regulatory network. To elucidate the sensitive node or gene in the progression of yeast, here, we select 8 crucial regulatory factors from the yeast cell cycle to decipher a specific network and propose a simple mixed K2 algorithm to identify effectively the sensitive nodes and genes in the evolution of yeast.ResultsConsidering the multivariate of cell cycle data, we first utilize the K2 algorithm limited to the stationary interval for the time series segmentation to measure the scores for refining the specific network. After that, we employ the network entropy to effectively screen the obtained specific network, and simulate the gene expression data by a normal distribution approximation and the screened specific network by the partial least squares method. We can conclude that the robustness of the specific network screened by network entropy is better than that of the specific network with the determined relationship by comparing the obtained specific network with the determined relationship. Finally, we can determine that the node CDH1 has the highest score in the specific network through a sensitivity score calculated by network entropy implying the gene CDH1 is the most sensitive regulatory factor.ConclusionsIt is clearly of great potential value to reconstruct and visualize gene regulatory networks according to gene databases for life activities. Here, we present an available algorithm to achieve the network reconstruction by measuring the network entropy and identifying the vital nodes in the specific nodes. The results indicate that inhibiting or enhancing the expression of CDH1 can maximize the inhibition or enhancement of the yeast cell cycle. Although our algorithm is simple, it is also the first step in deciphering the profound mystery of gene regulation.

Changes in transcriptional regulatory networks can significantly alter cell fate. To gain insight into transcriptional dynamics, several studies have profiled bulk multi-omic data sets with parallel transcriptomic and epigenomic measurements at different stages of a developmental process. However, integrating these data to infer cell type–specific regulatory networks is a major challenge. We present dynamic regulatory module networks (DRMNs), a novel approach to infer cell type–specific cis-regulatory networks and their dynamics. DRMN integrates expression, chromatin state, and accessibility to predict cis-regulators of context-specific expression, where context can be cell type, developmental stage, or time point, and uses multitask learning to capture network dynamics across linearly and hierarchically related contexts. We applied DRMNs to study regulatory network dynamics in three developmental processes, each showing different temporal relationships and measuring a different combination of regulatory genomic data sets: cellular reprogramming, liver dedifferentiation, and forward differentiation. DRMN identified known and novel regulators driving cell type–specific expression patterns, showing its broad applicability to examine dynamics of gene regulatory networks from linearly and hierarchically related multi-omic data sets.

Editor's evaluation: Mutation of vsx genes in zebrafish highlights the robustness of the retinal specification network

Understanding the mechanisms of tissue-specific transcriptional regulation is crucial as mis-regulation can cause a broad range of diseases. Here, we investigated transcription factors (TF) that are indispensable for the topological control of tissue specific and cell-type specific regulatory networks as a function of their binding to regulatory elements on promoters and enhancers of corresponding target genes. In particular, we found that promoter-binding TFs that were indispensable for regulatory network control regulate genes that are tissue-specifically expressed and overexpressed in corresponding cancer types. In turn, indispensable, enhancer-binding TFs were enriched with disease and signaling genes as they control an increasing number of cell-type specific regulatory networks. Their target genes were cell-type specific for blood and immune-related cell-types and over-expressed in blood-related cancers. Notably, target genes of indispensable enhancer-binding TFs in cell-type specific regulatory networks were enriched with cancer drug targets, while target genes of indispensable promoter-binding TFs were bona-fide targets of cancer drugs in corresponding tissues. Our results emphasize the significant role control analysis of regulatory networks plays in our understanding of transcriptional regulation, demonstrating potential therapeutic implications in tissue-specific drug discovery research.

Prostate cancer (PCa) is one of the most common tumors in men and can be lethal, especially if left untreated. A substantial majority of PCa patients not only are diagnosed based on fine needle aspiration (FNA) biopsies, but their treatment choices are also largely driven by the pathological findings obtained with these FNA specimens. It is widely believed that lncRNAs have strong biological significance, but their specific functions and regulatory networks have not been elucidated. LncRNAs may serve as key players and regulators of PCa carcinogenesis and could be novel biomarkers of this cancer. To identify potential markers for early detection of PCa, in this study, we employed a competing endogenous RNA (ceRNA) microarray to identify differentially expressed lncRNAs (DelncRNAs) in PCa tissue and quantitative real-time PCR (qRT-PCR) analysis to validate these DelncRNAs in FNA biopsies. We demonstrated that a total of 451 lncRNAs were differentially expressed in four pairs of PCa/adjacent tissues, and upregulation of the lncRNAs RP11-33A14.1, RP11-423H2.3, and LAMTOR5-AS1 was confirmed in FNA biopsies of PCa by qRT-PCR and was consistent with the ceRNA array data. The association between the expression of the lncRNA LAMTOR5-AS1 and aggressive cancer was also investigated. Regulatory network analysis of DelncRNAs showed that the lncRNAs RP11-33A14.1 and RP11-423H2.3 targeted miR-7, miR-24-3p, and miR-30 and interacted with the RNA binding protein FUS. Knockdown of these DelncRNAs in PCa cells also demonstrated the effects of RP11-423H2.3 on miR-7/miR-24/miR-30 or LAMTOR5-AS1 on miR-942-5p/miR-542-3p via direct interaction. The results of these studies indicate that these three specific lncRNA signatures and regulatory networks might serve as risk prediction and diagnostic biomarkers for prostate cancer, even in biopsies obtained by FNA.

Inference of cell type specific regulatory networks on mammalian lineages

A Validated Regulatory Network for Th17 Cell Specification

The gene regulatory network inference method based on bulk sequencing data not only confuses different types of cells, but also ignores the phenomenon of network dynamic changes with cell state. Single cell transcriptome sequencing technology provides data support for constructing cell type and state specific gene regulatory networks. This study proposes a method for inferring cell type and state specific gene regulatory networks based on scRNA-seq data, called inferCSN. Firstly, inferCSN infers pseudo temporal information from scRNA-seq data and reorders cells based on this information. Because of the uneven distribution of cells in pseudo temporal information, the regulatory relationship tends to lean towards the high-density areas of cells. Therefore, based on the cell state, we divide the cells into different windows to eliminate the temporal information differences caused by cell density. Then, a sparse regression model, combined with reference network information, is used to construct a cell type-specific regulatory network (CSN) for each window. The experimental results on both simulated and real scRNA-seq datasets show that inferCSN outperforms other methods in multiple performance metrics. In addition, experimental results on datasets of different types (such as steady-state and linear datasets) and scales (different cell and gene numbers) show that inferCSN is robust. To further demonstrate the effectiveness and application prospects of inferCSN, we analyzed the gene regulatory network of T cells in different states and different tumor subclons within the tumor microenvironment, and we found that comparing the regulatory networks in different states can reveal immune suppression related signaling pathways.

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/).

Systems Biology, Integrative Biology, Predictive Biology

Fungal-bacterial symbioses range from antagonisms to mutualisms and remain one of the least understood interdomain interactions despite their ubiquity as well as ecological and medical importance. To build a predictive conceptual framework for understanding interactions between fungi and bacteria in different types of symbioses, we surveyed fungal and bacterial transcriptional responses in the mutualism between Rhizopus microsporus (Rm) (ATCC 52813, host) and its Mycetohabitans (formerly Burkholderia) endobacteria versus the antagonism between a nonhost Rm (ATCC 11559) and Mycetohabitans isolated from the host, at two time points, before and after partner physical contact. We found that bacteria and fungi sensed each other before contact and altered gene expression patterns accordingly. Mycetohabitans did not discriminate between the host and nonhost and engaged a common set of genes encoding known as well as novel symbiosis factors. In contrast, responses of the host versus nonhost to endobacteria were dramatically different, converging on the altered expression of genes involved in cell wall biosynthesis and reactive oxygen species (ROS) metabolism. On the basis of the observed patterns, we formulated a set of hypotheses describing fungal-bacterial interactions and tested some of them. By conducting ROS measurements, we confirmed that nonhost fungi increased production of ROS in response to endobacteria, whereas host fungi quenched their ROS output, suggesting that ROS metabolism contributes to the nonhost resistance to bacterial infection and the host ability to form a mutualism. Overall, our study offers a testable framework of predictions describing interactions of early divergent Mucoromycotina fungi with bacteria.IMPORTANCE Animals and plants interact with microbes by engaging specific surveillance systems, regulatory networks, and response modules that allow for accommodation of mutualists and defense against antagonists. Antimicrobial defense responses are mediated in both animals and plants by innate immunity systems that owe their functional similarities to convergent evolution. Like animals and plants, fungi interact with bacteria. However, the principles governing these relations are only now being discovered. In a study system of host and nonhost fungi interacting with a bacterium isolated from the host, we found that bacteria used a common gene repertoire to engage both partners. In contrast, fungal responses to bacteria differed dramatically between the host and nonhost. These findings suggest that as in animals and plants, the genetic makeup of the fungus determines whether bacterial partners are perceived as mutualists or antagonists and what specific regulatory networks and response modules are initiated during each encounter.

Mammalian first lineage segregation generates trophectoderm (TE) and pluripotent inner cell mass (ICM), which provides an ideal model for studying the mechanisms of maintenance and loss of pluripotency. In mouse, the transcription factor OCT4 restricts to ICM and plays a key role in TE/ICM specification and pluripotent regulatory networks. However, in pig, OCT4 does not restrict to ICM cells, suggesting a different molecular basis in TE/ICM specification and pluripotent regulatory networks. To explore molecular basis of porcine TE/ICM specification and pluripotent regulatory networks, we examined expression pattern of pluripotency factors, including SOX2, REX1, SALL4, ESG1, NANOG, TBX3, LIN28, KLF2, and KLF5, in porcine blastocysts. We found that SOX2 is a faithful pluripotent marker that anchored to the pluripotent cells including embryonic part cells, ICM cells and newly EPI cells along with developmental progress, whereas OCT4 expressed in almost all the cells at the same time. Consistently, analysis of spatiotemporal distribution of SOX2 and the TE marker CDX2 revealed an exclusive expression pattern in D6 blastocysts, whereas no correlation was observed between OCT4 and CDX2 at the same stage. Our results provide a molecular basis in porcine embryonic patterning and a clue for further studying porcine pluripotent regulatory networks.

The Importance of Being Specified: Cell Fate Decisions and Their Role in Cell Biology

BackgroundRecent advances in sequencing technologies have enabled parallel assays of chromatin accessibility and gene expression for major human cell lines. Such innovation provides a great opportunity to decode phenotypic consequences of genetic variation via the construction of predictive gene regulatory network models. However, there still lacks a computational method to systematically integrate chromatin accessibility information with gene expression data to recover complicated regulatory relationships between genes in a tissue-specific manner.ResultsWe propose a Markov random field (MRF) model for constructing tissue-specific transcriptional regulatory networks via integrative analysis of DNase-seq and RNA-seq data. Our method, named CSNets (cell-line specific regulatory networks), first infers regulatory networks for individual cell lines using chromatin accessibility information, and then fine-tunes these networks using the MRF based on pairwise similarity between cell lines derived from gene expression data. Using this method, we constructed regulatory networks specific to 110 human cell lines and 13 major tissues with the use of ENCODE data. We demonstrated the high quality of these networks via comprehensive statistical analysis based on ChIP-seq profiles, functional annotations, taxonomic analysis, and literature surveys. We further applied these networks to analyze GWAS data of Crohn’s disease and prostate cancer. Results were either consistent with the literature or provided biological insights into regulatory mechanisms of these two complex diseases. The website of CSNets is freely available at http://bioinfo.au.tsinghua.edu.cn/jianglab/CSNETS/.ConclusionsCSNets demonstrated the power of joint analysis on epigenomic and transcriptomic data towards the accurate construction of gene regulatory network. Our work provides not only a useful resource of regulatory networks to the community, but also valuable experiences in methodology development for multi-omics data integration.

BackgroundLong noncoding RNAs (lncRNAs) play important roles in the regulation of immunological and apoptotic function. This study aimed to explore the critical immune- and apoptosis-related lncRNAs in the occurrence and development of Henoch-Schönlein purpura nephritis (HSPN) in children.MethodsDifferential analysis was employed to identify the differentially expressed lncRNAs, as well as the immune- and apoptosis-related mRNAs in children with HSPN. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were used to validate the immunological and apoptotic roles of the differentially expressed immune- and apoptosis-related lncRNAs and mRNAs. Spearman’s correlation analysis was performed to analyze the differentially expressed lncRNAs and immune- and apoptosis-related messenger RNAs (mRNAs). Based on the competing endogenous RNA (ceRNA) mechanism, the immune- and apoptosis-related lncRNA-microRNA (miRNA)-mRNA regulatory network was then constructed in children with HSPN. The expression levels of the lncRNAs in the lncRNA-miRNA-mRNA regulatory network were further confirmed by quantitative real-time polymerase chain in the peripheral blood samples of children with HSPN.ResultsBy intersecting the differentially expressed immune-related and apoptosis-related genes through GO and KEGG analyses, a total of 43 genes were identified in children with HSPN, and 100 lncRNAs highly correlated with the above genes were identified by correlation analysis. The immune- and apoptosis-related lncRNA-miRNA-mRNA regulatory network was then established based on ceRNA mechanism. Dysregulation of a total of 11 lncRNAs were discovered, including upregulated SNHG3, LINC00152, TUG1, GAS5, FGD5-AS1, DLEU2, and SCARNA9; and downregulated SNHG1, NEAT1, DISC1-IT1, and PVT1. The validation conducted in the clinical samples also suggested that the above lncRNAs in the specific regulatory network may act as potential biomarkers with prognosis in children with HSPN.ConclusionsLncRNAs may play essential regulatory roles in the occurrence and development of HSPN in children, and the immune- and apoptosis-related lncRNA-miRNA-mRNA regulatory network might be the underlying molecular mechanism that dissects the disease pathogenesis. In addition, the dysregulated lncRNAs in the regulatory network may be novel biomarkers for the diagnosis and therapy of HSPN in children.