An atlas of combinatorial transcriptional regulation in mouse and man

Reconstructing signaling pathways using regular language constrained paths

MOTIVATION

High-quality curation of the proteins and interactions in signaling pathways is slow and painstaking. As a result, many experimentally detected interactions are not annotated to any pathways. A natural question that arises is whether or not it is possible to automatically leverage existing pathway annotations to identify new interactions for inclusion in a given pathway.

RESULTS

We present RegLinker, an algorithm that achieves this purpose by computing multiple short paths from pathway receptors to transcription factors within a background interaction network. The key idea underlying RegLinker is the use of regular language constraints to control the number of non-pathway interactions that are present in the computed paths. We systematically evaluate RegLinker and five alternative approaches against a comprehensive set of 15 signaling pathways and demonstrate that RegLinker recovers withheld pathway proteins and interactions with the best precision and recall. We used RegLinker to propose new extensions to the pathways. We discuss the literature that supports the inclusion of these proteins in the pathways. These results show the broad potential of automated analysis to attenuate difficulties of traditional manual inquiry.

AVAILABILITY AND IMPLEMENTATION

https://github.com/Murali-group/RegLinker.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Molecular design of hypothalamus development

A wealth of specialized neuroendocrine command systems intercalated within the hypothalamus control the most fundamental physiological needs in vertebrates1,2. Nevertheless, we lack a developmental blueprint that integrates the molecular determinants of neuronal and glial diversity along temporal and spatial scales of hypothalamus development3. Here we combine single-cell RNA sequencing of 51,199 mouse cells of ectodermal origin, gene regulatory network (GRN) screens in conjunction with genome-wide association study-based disease phenotyping, and genetic lineage reconstruction to show that nine glial and thirty-three neuronal subtypes are generated by mid-gestation under the control of distinct GRNs. Combinatorial molecular codes that arise from neurotransmitters, neuropeptides and transcription factors are minimally required to decode the taxonomical hierarchy of hypothalamic neurons. The differentiation of γ-aminobutyric acid (GABA) and dopamine neurons, but not glutamate neurons, relies on quasi-stable intermediate states, with a pool of GABA progenitors giving rise to dopamine cells4. We found an unexpected abundance of chemotropic proliferation and guidance cues that are commonly implicated in dorsal (cortical) patterning5 in the hypothalamus. In particular, loss of SLIT-ROBO signalling impaired both the production and positioning of periventricular dopamine neurons. Overall, we identify molecular principles that shape the developmental architecture of the hypothalamus and show how neuronal heterogeneity is transformed into a multimodal neural unit to provide virtually infinite adaptive potential throughout life.

E2F transcription factor 8 promotes proliferation and radioresistance in glioblastoma

INTRODUCTION

Glioblastoma (GBM) is the most lethal brain tumor that has a median survival of less than 2 years. Tumor heterogeneity and high therapeutic resistance are hallmarks of GBM. Transcription factors (TFs) play a critical role in tumor progression by regulating the transcriptional events associated with tumor cells transition into more malignant cellular phenotypes. The E2 F transcription factor 8 (E2 F8) is a recently identified TF in the E2 F family. Studies have revealed that E2 F8 is involved in tumorigenesis of multiple cancer types; however, the oncogenic role of E2 F8 in GBM was rarely studied and the prognostic value of E2 F8 has not been explored.

AIMS

In this study, we investigated the expression profile, prognosis correlation and oncogenic role of E2 F8 to explore its potential use as a GBM therapeutic target.

RESULTS

E2 F8 was significantly enriched in GBM compared with normal brain tissues and low-grade glioma. E2 F8 high expression was strongly associated with worse outcome of GBM patients. E2 F8 silencing significantly attenuated the proliferation of tumor cells in vitro and tumorigenicity in vivo, while its overexpression promoted the proliferation of GBM tumor cells. Bioinformatics analysis revealed that E2 F8 was tightly linked to multiple oncogenic processes in GBM, including aggressive cell cycle, DNA repair, STAT3, TGFRβ and WNT pathways. E2 F8 high expression correlated with the expression of a variety of well-known oncogenes in GBM. E2 F8 was identified as a crucial transcriptional regulator of CHEK1 via its directly binding CHEK1 promoter area. Finally, E2 F8 conferred significant radioresistance to GBM tumor cells in vitro and in vivo.

CONCLUSION

E2 F8 is highly expressed in GBM and associated with worse outcome in GBM patients. It promotes tumorigenesis and radioresistance of GBM tumor cells and has oncogenic roles via its involvement in multiple oncogenic processes and pathways such as the regulation of CHEK1 transcriptional activity.

A single ChIP-seq dataset is sufficient for comprehensive analysis of motifs co-occurrence with MCOT package

Recognition of composite elements consisting of two transcription factor binding sites gets behind the studies of tissue-, stage- and condition-specific transcription. Genome-wide data on transcription factor binding generated with ChIP-seq method facilitate an identification of composite elements, but the existing bioinformatics tools either require ChIP-seq datasets for both partner transcription factors, or omit composite elements with motifs overlapping. Here we present an universal Motifs Co-Occurrence Tool (MCOT) that retrieves maximum information about overrepresented composite elements from a single ChIP-seq dataset. This includes homo- and heterotypic composite elements of four mutual orientations of motifs, separated with a spacer or overlapping, even if recognition of motifs within composite element requires various stringencies. Analysis of 52 ChIP-seq datasets for 18 human transcription factors confirmed that for over 60% of analyzed datasets and transcription factors predicted co-occurrence of motifs implied experimentally proven protein-protein interaction of respecting transcription factors. Analysis of 164 ChIP-seq datasets for 57 mammalian transcription factors showed that abundance of predicted composite elements with an overlap of motifs compared to those with a spacer more than doubled; and they had 1.5-fold increase of asymmetrical pairs of motifs with one more conservative 'leading' motif and another one 'guided'.

Meta Gene Regulatory Networks in Maize Highlight Functionally Relevant Regulatory Interactions

The regulation of gene expression is central to many biological processes. Gene regulatory networks (GRNs) link transcription factors (TFs) to their target genes and represent maps of potential transcriptional regulation. Here, we analyzed a large number of publically available maize (Zea mays) transcriptome data sets including >6000 RNA sequencing samples to generate 45 coexpression-based GRNs that represent potential regulatory relationships between TFs and other genes in different populations of samples (cross-tissue, cross-genotype, and tissue-and-genotype samples). While these networks are all enriched for biologically relevant interactions, different networks capture distinct TF-target associations and biological processes. By examining the power of our coexpression-based GRNs to accurately predict covarying TF-target relationships in natural variation data sets, we found that presence/absence changes rather than quantitative changes in TF gene expression are more likely associated with changes in target gene expression. Integrating information from our TF-target predictions and previous expression quantitative trait loci (eQTL) mapping results provided support for 68 TFs underlying 74 previously identified trans-eQTL hotspots spanning a variety of metabolic pathways. This study highlights the utility of developing multiple GRNs within a species to detect putative regulators of important plant pathways and provides potential targets for breeding or biotechnological applications.

Integrative genomics analysis of eQTL and GWAS summary data identifies PPP1CB as a novel bone mineral density risk genes

In recent years, multiple genome-wide association studies (GWAS) have identified numerous susceptibility variants and risk genes that demonstrate significant associations with bone mineral density (BMD). However, exploring how these genetic variants contribute risk to BMD remains a major challenge. We systematically integrated two independent expression quantitative trait loci (eQTL) data (N = 1890) and GWAS summary statistical data of BMD (N = 142,487) using Sherlock integrative analysis to reveal whether expression-associated variants confer risk to BMD. By using Sherlock integrative analysis and MAGMA gene-based analysis, we found there existed 36 promising genes, for example, PPP1CB, XBP1, and FDFT1, whose expression alterations may contribute susceptibility to BMD. Through a protein-protein interaction (PPI) network analysis, we further prioritized the PPP1CB as a hub gene that has interactions with predicted genes and BMD-associated genes. Two eSNPs of rs9309664 (PeQTL = 1.42 × 10-17 and PGWAS = 1.40 × 10-11) and rs7475 (PeQTL = 2.10 × 10-6 and PGWAS = 1.70 × 10-7) in PPP1CB were identified to be significantly associated with BMD risk. Consistently, differential gene expression analysis found that the PPP1CB gene showed significantly higher expression in low BMD samples than that in high BMD samples based on two independent expression datasets (P = 0.0026 and P = 0.043, respectively). Together, we provide a convergent line of evidence to support that the PPP1CB gene involves in the etiology of osteoporosis.

Genomic Scan for Selection Signature Reveals Fat Deposition in Chinese Indigenous Sheep with Extreme Tail Types

Simple Summary

According to the tail types, sheep can be briefly classified into three groups (fat-tailed, fat-rumped, and thin-tailed sheep). In this study, we used these three typical breeds from Chinese indigenous sheep breeds to perform a genome scan for selective sweeps using Ovine Infinium HD SNP BeadChip genotype data. Results showed that 25 genomic regions exhibited selection signals and harbored 73 positional candidate genes. These genes were documented not only to be associated with tail fat formation, but also be related to reproduction, body conformation, and appearance. Our findings contributed to understanding genetic basis of fat deposition in sheep tail and provide a reference for developing new sheep breeds with an ideal tail type.

Abstract

It is a unique feature that fat can be deposited in sheep tails and rumps. To elucidate the genetic mechanism underlying this trait, we collected 120 individuals from three Chinese indigenous sheep breeds with extreme tail types, namely large fat-tailed sheep (n = 40), Altay sheep (n = 40), and Tibetan sheep (n = 40), and genotyped them using the Ovine Infinium HD SNP BeadChip. Then genomic scan for selection signatures was performed using the hapFLK. In total, we identified 25 genomic regions exhibiting evidence of having been under selection. Bioinformatic analysis of the genomic regions showed that selection signatures related to multiple candidate genes had a demonstrated role in phenotypic variation. Nine genes have documented association with sheep tail types, including WDR92, TBX12, WARS2, BMP2, VEGFA, PDGFD, HOXA10, ALX4, and ETAA1. Moreover, a number of genes were of particular interest, including RXFP2 associated with the presence/absence and morphology of horns; MITF involved in coat color; LIN52 and SYNDIG1L related to the number of teats; MSRB3 gene associated with ear sizes; LTBP2 considered as a positional candidate genes for number of ribs; JAZF1 regulating lipid metabolism; PGRMC2, SPAG17, TSHR, GTF2A1, and LARP1B implicated with reproductive traits. Our findings provide insights into fat tail formation and a reference for carrying out molecular breeding and conservation in sheep.

Identification of a combination of transcription factors that synergistically increases endothelial cell barrier resistance

Endothelial cells (ECs) display remarkable plasticity during development before becoming quiescent and functionally mature. EC maturation is directed by several known transcription factors (TFs), but the specific set of TFs responsible for promoting high-resistance barriers, such as the blood-brain barrier (BBB), have not yet been fully defined. Using expression mRNA data from published studies on ex vivo ECs from the central nervous system (CNS), we predicted TFs that induce high-resistance barrier properties of ECs as in the BBB. We used our previously established method to  generate ECs from human pluripotent stem cells (hPSCs), and then we overexpressed the candidate TFs in hPSC-ECs and measured barrier resistance and integrity using electric cell-substrate impedance sensing, trans-endothelial electrical resistance and FITC-dextran permeability assays. SOX18 and TAL1 were the strongest EC barrier-inducing TFs, upregulating Wnt-related signaling and EC junctional gene expression, respectively, and downregulating EC proliferation-related genes. These TFs were combined with SOX7 and ETS1 that together effectively induced EC barrier resistance, decreased paracellular transport and increased protein expression of tight junctions and induce mRNA expression of several genes involved in the formation of EC barrier and transport. Our data shows identification of a transcriptional network that controls barrier resistance in ECs. Collectively this data may lead to novel approaches for generation of in vitro models of the BBB.

Single-cell transcriptome analysis reveals TOX as a promoting factor for T cell exhaustion and a predictor for anti-PD-1 responses in human cancer

Background

T cells exhibit heterogeneous functional states in the tumor microenvironment. Immune checkpoint inhibitors (ICIs) can reinvigorate only the stem cell-like progenitor exhausted T cells, which suggests that inhibiting the exhaustion progress will improve the efficacy of immunotherapy. Thus, regulatory factors promoting T cell exhaustion could serve as potential targets for delaying the process and improving ICI efficacy.

Methods

We analyzed the single-cell transcriptome data derived from human melanoma and non-small cell lung cancer (NSCLC) samples and classified the tumor-infiltrating (TI) CD8⁺ T cell population based on PDCD1 (PD-1) levels, i.e., PDCD1-high and PDCD1-low cells. Additionally, we identified differentially expressed genes as candidate factors regulating intra-tumoral T cell exhaustion. The co-expression of candidate genes with immune checkpoint (IC) molecules in the TI CD8⁺ T cells was confirmed by single-cell trajectory and flow cytometry analyses. The loss-of-function effect of the candidate regulator was examined by a cell-based knockdown assay. The clinical effect of the candidate regulator was evaluated based on the overall survival and anti-PD-1 responses.

Results

We retrieved many known factors for regulating T cell exhaustion among the differentially expressed genes between PDCD1-high and PDCD1-low subsets of the TI CD8⁺ T cells in human melanoma and NSCLC. TOX was the only transcription factor (TF) predicted in both tumor types. TOX levels tend to increase as CD8⁺ T cells become more exhausted. Flow cytometry analysis revealed a correlation between TOX expression and severity of intra-tumoral T cell exhaustion. TOX knockdown in the human TI CD8⁺ T cells resulted in downregulation of PD-1, TIM-3, TIGIT, and CTLA-4, which suggests that TOX promotes intra-tumoral T cell exhaustion by upregulating IC proteins in cancer. Finally, the TOX level in the TI T cells was found to be highly predictive of overall survival and anti-PD-1 efficacy in melanoma and NSCLC.

Conclusions

We predicted the regulatory factors involved in T cell exhaustion using single-cell transcriptome profiles of human TI lymphocytes. TOX promoted intra-tumoral CD8⁺ T cell exhaustion via upregulation of IC molecules. This suggested that TOX inhibition can potentially impede T cell exhaustion and improve ICI efficacy. Additionally, TOX expression in the TI T cells can be used for patient stratification during anti-tumor treatments, including anti-PD-1 immunotherapy.

The mammalian two-hybrid system as a powerful tool for high-throughput drug screening

Protein-protein interactions (PPIs) are the backbone of signaling pathways, responsible for the basis of cell communication and, when deregulated, several diseases. Consequently, identifying and modulating PPIs can unravel the pathophysiological mechanisms of diseases. The two-hybrid system, particularly the mammalian two-hybrid system (MTH), is an efficient technique to validate PPIs ex vivo. Combining MTH with high-throughput screening has a huge advantage in biomedical research. In this review, we describe methodologies developed from MTH and the role of these adaptations in PPI discovery. We also highlight the powerful contribution of MTH to the identification of disease-related PPIs and its use in the development of potential new drug screens.

Comprehensive Proteomic Characterization Reveals Subclass-Specific Molecular Aberrations within Triple-negative Breast Cancer

Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer lacking targeted therapies. This is attributed to its high heterogeneity that complicates elucidation of its molecular aberrations. Here, we report identification of specific proteome expression profiles pertaining to two TNBC subclasses, basal A and basal B, through in-depth proteomics analysis of breast cancer cells. We observed that kinases and proteases displayed unique expression patterns within the subclasses. Systematic analyses of protein-protein interaction and co-regulation networks of these kinases and proteases unraveled dysregulated pathways and plausible targets for each TNBC subclass. Among these, we identified kinases AXL, PEAK1, and TGFBR2 and proteases FAP, UCHL1, and MMP2/14 as specific targets for basal B subclass, which represents the more aggressive TNBC cell lines. Our study highlights intricate mechanisms and distinct targets within TNBC and emphasizes that these have to be exploited in a subclass-specific manner rather than a one-for-all TNBC therapy.

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Proteome profiling reveals functionally distinct subclasses within TNBC

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Kinases and proteases underlie unique functional signatures among the subclasses

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Kinase-protease-centric networks highlight subclass-specific molecular rewiring

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Protein association dysregulations reveal TNBC subclass-specific protein targets

Gene Regulatory Strategies that Decode the Duration of NFκB Dynamics Contribute to LPS- versus TNF-Specific Gene Expression

Pathogen-derived lipopolysaccharide (LPS) and cytokine tumor necrosis factor (TNF) activate NFκB with distinct duration dynamics, but how immune response genes decode NFκB duration to produce stimulus-specific expression remains unclear. Here, detailed transcriptomic profiling of combinatorial and temporal control mutants identified 81 genes that depend on stimulus-specific NFκB duration for their stimulus-specificity. Combining quantitative experimentation with mathematical modeling, we found that for some genes a long mRNA half-life allowed effective decoding, but for many genes this was insufficient to account for the data; instead, we found that chromatin mechanisms, such as a slow transition rate between inactive and RelA-bound enhancer states, could also decode NFκB dynamics. Chromatin-mediated decoding is favored by genes acting as immune effectors (e.g., tissue remodelers and T cell recruiters) rather than immune regulators (e.g., signaling proteins and monocyte recruiters). Overall, our results delineate two gene regulatory strategies that decode stimulus-specific NFκB dynamics and determine distinct biological functions.

Cap Analysis of Gene Expression (CAGE): A Quantitative and Genome-Wide Assay of Transcription Start Sites

Cap analysis of gene expression (CAGE) is an approach to identify and monitor the activity (transcription initiation frequency) of transcription start sites (TSSs) at single base-pair resolution across the genome. It has been effectively used to identify active promoter and enhancer regions in cancer cells, with potential utility to identify key factors to immunotherapy. Here, we overview a series of CAGE protocols and describe detailed experimental steps of the latest protocol based on the Illumina sequencing platform; both experimental steps (see Subheadings 3.1-3.11) and computational processing steps (see Subheadings 3.12-3.20) are described.

Flexible k-mers with variable-length indels for identifying binding sequences of protein dimers

Many DNA-binding proteins interact with partner proteins. Recently, based on the high-throughput consecutive affinity-purification systematic evolution of ligands by exponential enrichment (CAP-SELEX) method, many such protein pairs have been found to bind DNA with flexible spacing between their individual binding motifs. Most existing motif representations were not designed to capture such flexibly spaced regions. In order to computationally discover more co-binding events without prior knowledge about the identities of the co-binding proteins, a new representation is needed. We propose a new class of sequence patterns that flexibly model such variable regions and corresponding algorithms that identify co-bound sequences using these patterns. Based on both simulated and CAP-SELEX data, features derived from our sequence patterns lead to better classification performance than patterns that do not explicitly model the variable regions. We also show that even for standard ChIP-seq data, this new class of sequence patterns can help discover co-bound events in a subset of sequences in an unsupervised manner. The open-source software is available at https://github.com/kevingroup/glk-SVM.

Immunity-associated long non-coding RNA and expression in response to bacterial infection in large yellow croaker (Larimichthys crocea)

Long non-coding RNA refers to an RNA transcript of a non-coding protein with a sequence length greater than 200 bp. More and more reports indicated that lncRNA was involved in the regulation of gene expression as a signalling molecule, an inducing molecule, a leader molecule and a scaffold molecule. Previous studies have sequenced the draft genome and several transcriptome data sets for protein-coding genes of the large yellow croaker (Larimichthys crocea), but little is known about the expression and function of lncRNAs in this species. In order to obtain a catalogue of lncRNAs for this croaker, Vibrio parahaemolyticus infection challenge experiment was conducted and long non-coding RNA sequences were obtained. Using high-throughput sequencing of lncRNA, a total of 73,233 high-confidence transcripts were reconstructed in 32,726 loci, recovering most of the expressed reference transcripts, and 6473 novel expressed loci were identified. The tissue expression profile revealed that most lacunas were specifically enriched in distinct tissues. A set of 163 lncRNAs were identified as being specifically expressed in the spleen and may be involved in the immune response. It is the first time to identify specific lncRNAs in the L. crocea systematically in this croaker, aiming to benefit the future genomic study of this species.

Transcription Factors That Govern Development and Disease: An Achilles Heel in Cancer

Development requires the careful orchestration of several biological events in order to create any structure and, eventually, to build an entire organism. On the other hand, the fate transformation of terminally differentiated cells is a consequence of erroneous development, and ultimately leads to cancer. In this review, we elaborate how development and cancer share several biological processes, including molecular controls. Transcription factors (TF) are at the helm of both these processes, among many others, and are evolutionarily conserved, ranging from yeast to humans. Here, we discuss four families of TFs that play a pivotal role and have been studied extensively in both embryonic development and cancer—high mobility group box (HMG), GATA, paired box (PAX) and basic helix-loop-helix (bHLH) in the context of their role in development, cancer, and their conservation across several species. Finally, we review TFs as possible therapeutic targets for cancer and reflect on the importance of natural resistance against cancer in certain organisms, yielding knowledge regarding TF function and cancer biology.

Assays to Interrogate the Ability of Compounds to Inhibit the AF-2 or AF-1 Transactivation Domains of the Androgen Receptor

Prostate cancer is the leading cause of cancer and second leading cause of cancer-related death in men in the United States. Twenty percent of patients receiving the standard of care androgen deprivation therapy (ADT) eventually progress to metastatic and incurable castration-resistant prostate cancer (CRPC). Current FDA-approved drugs for CRPC target androgen receptor (AR) binding or androgen production, but only provide a 2- to 5-month survival benefit due to the emergence of resistance. Overexpression of AR coactivators and the emergence of AR splice variants, both promote continued transcriptional activation under androgen-depleted conditions and represent drug resistance mechanisms that contribute to CRPC progression. The AR contains two transactivation domains, activation function 2 (AF-2) and activation function 1 (AF-1), which serve as binding surfaces for coactivators involved in the transcriptional activation of AR target genes. Full-length AR contains both AF-2 and AF-1 surfaces, whereas AR splice variants only have an AF-1 surface. We have recently prosecuted a high-content screening campaign to identify hit compounds that can inhibit or disrupt the protein-protein interactions (PPIs) between AR and transcriptional intermediary factor 2 (TIF2), one of the coactivators implicated in CRPC disease progression. Since an ideal inhibitor/disruptor of AR-coactivator PPIs would target both the AF-2 and AF-1 surfaces, we describe here the development and validation of five AF-2- and three AF-1-focused assays to interrogate and prioritize hits that disrupt both transactivation surfaces. The assays were validated using a test set of seven known AR modulator compounds, including three AR antagonists and one androgen synthesis inhibitor that are FDA-approved ADTs, two investigational molecules that target the N-terminal domain of AR, and an inhibitor of the Hsp90 (heat shock protein) molecular chaperone.

Imputed gene associations identify replicable trans-acting genes enriched in transcription pathways and complex traits

Regulation of gene expression is an important mechanism through which genetic variation can affect complex traits. A substantial portion of gene expression variation can be explained by both local (cis) and distal (trans) genetic variation. Much progress has been made in uncovering cis-acting expression quantitative trait loci (cis-eQTL), but trans-eQTL have been more difficult to identify and replicate. Here we take advantage of our ability to predict the cis component of gene expression coupled with gene mapping methods such as PrediXcan to identify high confidence candidate trans-acting genes and their targets. That is, we correlate the cis component of gene expression with observed expression of genes in different chromosomes. Leveraging the shared cis-acting regulation across tissues, we combine the evidence of association across all available Genotype-Tissue Expression Project tissues and find 2,356 trans-acting/target gene pairs with high mappability scores. Reassuringly, trans-acting genes are enriched in transcription and nucleic acid binding pathways and target genes are enriched in known transcription factor binding sites. Interestingly, trans-acting genes are more significantly associated with selected complex traits and diseases than target or background genes, consistent with percolating trans effects. Our scripts and summary statistics are publicly available for future studies of trans-acting gene regulation.

Heterodimeric DNA motif synthesis and validations

Bound by transcription factors, DNA motifs (i.e. transcription factor binding sites) are prevalent and important for gene regulation in different tissues at different developmental stages of eukaryotes. Although considerable efforts have been made on elucidating monomeric DNA motif patterns, our knowledge on heterodimeric DNA motifs are still far from complete. Therefore, we propose to develop a computational approach to synthesize a heterodimeric DNA motif from two monomeric DNA motifs. The approach is sequentially divided into two components (Phases A and B). In Phase A, we propose to develop the inference models on how two DNA monomeric motifs can be oriented and overlapped with each other at nucleotide level. In Phase B, given the two monomeric DNA motifs oriented, we further propose to develop DNA-binding family-specific input-output hidden Markov models (IOHMMs) to synthesize a heterodimeric DNA motif. To validate the approach, we execute and cross-validate it with the experimentally verified 618 heterodimeric DNA motifs across 49 DNA-binding family combinations. We observe that our approach can even "rescue" the existing heterodimeric DNA motif pattern (i.e. HOXB2_EOMES) previously published on Nature. Lastly, we apply the proposed approach to infer previously uncharacterized heterodimeric motifs. Their motif instances are supported by DNase accessibility, gene ontology, protein-protein interactions, in vivo ChIP-seq peaks, and even structural data from PDB. A public web-server is built for open accessibility and scientific impact. Its address is listed as follows: http://motif.cs.cityu.edu.hk/custom/MotifKirin.

Rapamycin-upregulated miR-29b promotes mTORC1-hyperactive cell growth in TSC2-deficient cells by downregulating tumor suppressor retinoic acid receptor β (RARβ)

miR-29b has been identified as a rapamycin-induced microRNA (miRNA) in Tsc2-deficient, mTORC1-hyperactive cells. The biological significance of this induction of miR-29b is unknown. We have found that miR-29b acts as an oncogenic miRNA in Tsc2-deficient cells: inhibition of miR-29b suppressed cell proliferation, anchorage-independent cell growth, cell migration, invasion, and the growth of Tsc2-deficient tumors in vivo. Importantly, the combination of miR-29b inhibition with rapamycin treatment further inhibited these tumor-associated cellular processes. To gain insight into the molecular mechanisms by which miR-29b promotes tumorigenesis, we used RNA sequencing to identify the tumor suppressor retinoid receptor beta (RARβ) as a target gene of miR-29b. We found that miR-29b directly targeted the 3'UTR of RARβ. Forced expression of RARβ reversed the effects of miR-29b overexpression in proliferation, migration, and invasion, indicating that it is a critical target. miR-29b expression correlated with low RARβ expression in renal clear cell carcinomas and bladder urothelial carcinomas, tumors associated with TSC gene mutations. We further identified growth family member 4 (ING4) as a novel interacting partner of RARβ. Overexpression of ING4 inhibited the migration and invasion of Tsc2-deficient cells while silencing of ING4 reversed the RARβ-mediated suppression of cell migration and invasion. Taken together, our findings reveal a novel miR-29b/RARβ/ING4 pathway that regulates tumorigenic properties of Tsc2-deficient cells, and that may serve as a potential therapeutic target for TSC, lymphangioleiomyomatosis (LAM), and other mTORC1-hyperactive tumors.

Sox2: A Regulatory Factor in Tumorigenesis and Metastasis

The transcription factor Sox2 plays an important role in various phases of embryonic development, including cell fate and differentiation. These key regulatory functions are facilitated by binding to specific DNA sequences in combination with partner proteins to exert their effects. Recently, overexpression and gene amplification of Sox2 has been associated with tumor aggression and metastasis in various cancer types, including breast, prostate, lung, ovarian and colon cancer. All the different roles for Sox2 involve complicated regulatory networks consisting of protein-protein and protein-nucleic acid interactions. Their involvement in the EMT modulation is possibly enabled by Wnt/ β-catenin and other signaling pathways. There are number of in vivo models which show Sox2 association with increased cancer aggressiveness, resistance to chemo-radiation therapy and decreased survival rate suggesting Sox2 as a therapeutic target. This review will focus on the different roles for Sox2 in metastasis and tumorigenesis. We will also review the mechanism of action underlying the cooperative Sox2- DNA/partner factors binding where Sox2 can be potentially explored for a therapeutic opportunity to treat cancers.

Exploring induced pluripotency in human fibroblasts via construction, validation, and application of a gene regulatory network

Reprogramming of somatic cells to induced pluripotent stem cells, by overexpressing certain factors referred to as the reprogramming factors, can revolutionize regenerative medicine. To provide a coherent description of induced pluripotency from the gene regulation perspective, we use 35 microarray datasets to construct a reprogramming gene regulatory network. Comprising 276 nodes and 4471 links, the resulting network is, to the best of our knowledge, the largest gene regulatory network constructed for human fibroblast reprogramming and it is the only one built using a large number of experimental datasets. To build the network, a model that relates the expression profiles of the initial (fibroblast) and final (induced pluripotent stem cell) states is proposed and the model parameters (link strengths) are fitted using the experimental data. Twenty nine additional experimental datasets are collectively used to test the model/network, and good agreement between experimental and predicted gene expression profiles is found. We show that the model in conjunction with the constructed network can make useful predictions. For example, we demonstrate that our approach can incorporate the effect of reprogramming factor stoichiometry and that its predictions are consistent with the experimentally observed trends in reprogramming efficiency when the stoichiometric ratios vary. Using our model/network, we also suggest new (not used in training of the model) candidate sets of reprogramming factors, many of which have already been experimentally verified. These results suggest our model/network can potentially be used in devising new recipes for induced pluripotency with higher efficiencies. Additionally, we classify the links of the network into three classes of different importance, prioritizing them for experimental verification. We show that many of the links in the top ranked class are experimentally known to be important in reprogramming. Finally, comparing with other methods, we show that using our model is advantageous.

VirtualCytometry: a webserver for evaluating immune cell differentiation using single-cell RNA sequencing data

MOTIVATION

The immune system has diverse types of cells that are differentiated or activated via various signaling pathways and transcriptional regulation upon challenging conditions. Immunophenotyping by flow and mass cytometry are the major approaches for identifying key signaling molecules and transcription factors directing the transition between the functional states of immune cells. However, few proteins can be evaluated by flow cytometry in a single experiment, preventing researchers from obtaining a comprehensive picture of the molecular programs involved in immune cell differentiation. Recent advances in single-cell RNA sequencing (scRNA-seq) have enabled unbiased genome-wide quantification of gene expression in individual cells on a large scale, providing a new and versatile analytical pipeline for studying immune cell differentiation.

RESULTS

We present VirtualCytometry, a web-based computational pipeline for evaluating immune cell differentiation by exploiting cell-to-cell variation in gene expression with scRNA-seq data. Differentiating cells often show a continuous spectrum of cellular states rather than distinct populations. VirtualCytometry enables the identification of cellular subsets for different functional states of differentiation based on the expression of marker genes. Case studies have highlighted the usefulness of this subset analysis strategy for discovering signaling molecules and transcription factors for human T-cell exhaustion, a state of T-cell dysfunction, in tumor and mouse dendritic cells activated by pathogens. With more than 226 scRNA-seq datasets precompiled from public repositories covering diverse mouse and human immune cell types in normal and disease tissues, VirtualCytometry is a useful resource for the molecular dissection of immune cell differentiation.

AVAILABILITY AND IMPLEMENTATION

www.grnpedia.org/cytometry.

Genetic Variation in Human Gene Regulatory Factors Uncovers Regulatory Roles in Local Adaptation and Disease

Differences in gene regulation have been suggested to play essential roles in the evolution of phenotypic changes. Although DNA changes in cis-regulatory elements affect only the regulation of its corresponding gene, variations in gene regulatory factors (trans) can have a broader effect, because the expression of many target genes might be affected. Aiming to better understand how natural selection may have shaped the diversity of gene regulatory factors in human, we assembled a catalog of all proteins involved in controlling gene expression. We found that at least five DNA-binding transcription factor classes are enriched among genes located in candidate regions for selection, suggesting that they might be relevant for understanding regulatory mechanisms involved in human local adaptation. The class of KRAB-ZNFs, zinc-finger (ZNF) genes with a Krüppel-associated box, stands out by first, having the most genes located on candidate regions for positive selection. Second, displaying most nonsynonymous single nucleotide polymorphisms (SNPs) with high genetic differentiation between populations within these regions. Third, having 27 KRAB-ZNF gene clusters with high extended haplotype homozygosity. Our further characterization of nonsynonymous SNPs in ZNF genes located within candidate regions for selection, suggests regulatory modifications that might influence the expression of target genes at population level. Our detailed investigation of three candidate regions revealed possible explanations for how SNPs may influence the prevalence of schizophrenia, eye development, and fertility in humans, among other phenotypes. The genetic variation we characterized here may be responsible for subtle to rough regulatory changes that could be important for understanding human adaptation.

Evidence against tetrapod-wide digit identities and for a limited frame shift in bird wings

In crown group tetrapods, individual digits are homologized in relation to a pentadactyl ground plan. However, testing hypotheses of digit homology is challenging because it is unclear whether digits represent distinct and conserved gene regulatory states. Here we show dramatic evolutionary dynamism in the gene expression profiles of digits, challenging the notion that five digits have conserved developmental identities across amniotes. Transcriptomics shows diversity in the patterns of gene expression differentiation of digits, although the anterior-most digit of the pentadactyl limb has a unique, conserved expression profile. Further, we identify a core set of transcription factors that are differentially expressed among the digits of amniote limbs; their spatial expression domains, however, vary between species. In light of these results, we reevaluate the frame shift hypothesis of avian wing evolution and conclude only the identity of the anterior-most digit has shifted position, suggesting a 1,3,4 digit identity in the bird wing.

The orphan nuclear receptor SHP regulates ER stress response by inhibiting XBP1s degradation

In this study, Sun et al. investigated the role of the orphan nuclear receptor SHP, a well-known transcriptional corepressor of bile acid and lipid metabolism in the liver, in other tissues. They report that SHP functions as a regulator of ER stress in the exocrine pancreas, specifically via the regulation of XBP1s stability.

The orphan nuclear receptor SHP (small heterodimer partner) is a well-known transcriptional corepressor of bile acid and lipid metabolism in the liver; however, its function in other tissues is poorly understood. Here, we report an unexpected role for SHP in the exocrine pancreas as a modulator of the endoplasmic reticulum (ER) stress response. SHP expression is induced in acinar cells in response to ER stress and regulates the protein stability of the spliced form of X-box-binding protein 1 (XBP1s), a key mediator of ER stress response. Loss of SHP reduces XBP1s protein level and transcriptional activity, which in turn attenuates the ER stress response during the fasting–feeding cycle. Consequently, SHP-deficient mice also are more susceptible to cerulein-induced pancreatitis. Mechanistically, we show that SHP physically interacts with the transactivation domain of XBP1s, thereby inhibiting the polyubiquitination and degradation of XBP1s by the Cullin3–SPOP (speckle-type POZ protein) E3 ligase complex. Together, our data implicate SHP in governing ER homeostasis and identify a novel posttranslational regulatory mechanism for the key ER stress response effector XBP1.

Inferring Regulatory Programs Governing Region Specificity of Neuroepithelial Stem Cells during Early Hindbrain and Spinal Cord Development

Neuroepithelial stem cells (NSC) from different anatomical regions of the embryonic neural tube's rostrocaudal axis can differentiate into diverse central nervous system tissues, but the transcriptional regulatory networks governing these processes are incompletely understood. Here, we measure region-specific NSC gene expression along the rostrocaudal axis in a human pluripotent stem cell model of early central nervous system development over a 72-h time course, spanning the hindbrain to cervical spinal cord. We introduce Escarole, a probabilistic clustering algorithm for non-stationary time series, and combine it with prior-based regulatory network inference to identify genes that are regulated dynamically and predict their upstream regulators. We identify known regulators of patterning and neural development, including the HOX genes, and predict a direct regulatory connection between the transcription factor POU3F2 and target gene STMN2. We demonstrate that POU3F2 is required for expression of STMN2, suggesting that this regulatory connection is important for region specificity of NSCs.

Global landscape of mouse and human cytokine transcriptional regulation

Cytokines are cell-to-cell signaling proteins that play a central role in immune development, pathogen responses, and diseases. Cytokines are highly regulated at the transcriptional level by combinations of transcription factors (TFs) that recruit cofactors and the transcriptional machinery. Here, we mined through three decades of studies to generate a comprehensive database, CytReg, reporting 843 and 647 interactions between TFs and cytokine genes, in human and mouse respectively. By integrating CytReg with other functional datasets, we determined general principles governing the transcriptional regulation of cytokine genes. In particular, we show a correlation between TF connectivity and immune phenotype and disease, we discuss the balance between tissue-specific and pathogen-activated TFs regulating each cytokine gene, and cooperativity and plasticity in cytokine regulation. We also illustrate the use of our database as a blueprint to predict TF-disease associations and identify potential TF-cytokine regulatory axes in autoimmune diseases. Finally, we discuss research biases in cytokine regulation studies, and use CytReg to predict novel interactions based on co-expression and motif analyses which we further validated experimentally. Overall, this resource provides a framework for the rational design of future cytokine gene regulation studies.

Genetic and Epigenetic Fine Mapping of Complex Trait Associated Loci in the Human Liver

Deciphering the impact of genetic variation on gene regulation is fundamental to understanding common, complex human diseases. Although histone modifications are important markers of gene regulatory elements of the genome, any specific histone modification has not been assayed in more than a few individuals in the human liver. As a result, the effects of genetic variation on histone modification states in the liver are poorly understood. Here, we generate the most comprehensive genome-wide dataset of two epigenetic marks, H3K4me3 and H3K27ac, and annotate thousands of putative regulatory elements in the human liver. We integrate these findings with genome-wide gene expression data collected from the same human liver tissues and high-resolution promoter-focused chromatin interaction maps collected from human liver-derived HepG2 cells. We demonstrate widespread functional consequences of natural genetic variation on putative regulatory element activity and gene expression levels. Leveraging these extensive datasets, we fine-map a total of 74 GWAS loci that have been associated with at least one complex phenotype. Our results reveal a repertoire of genes and regulatory mechanisms governing complex disease development and further the basic understanding of genetic and epigenetic regulation of gene expression in the human liver tissue.

A comprehensive single cell transcriptional landscape of human hematopoietic progenitors

Hematopoietic Stem/Progenitor cells (HSPCs) are endowed with the role of maintaining a diverse pool of blood cells throughout the human life. Despite recent efforts, the nature of the early cell fate decisions remains contentious. Using single-cell RNA-Seq, we show that existing approaches to stratify bone marrow CD34+ cells reveal a hierarchically-structured transcriptional landscape of hematopoietic differentiation. Still, this landscape misses important early fate decisions. We here provide a broader transcriptional profiling of bone marrow lineage negative hematopoietic progenitors that recovers a key missing branchpoint into basophils and expands our understanding of the underlying structure of early adult human haematopoiesis. We also show that this map has strong similarities in topology and gene expression to that found in mouse. Finally, we identify the sialomucin CD164, as a reliable marker for the earliest branches of HSPCs specification and we showed how its use can foster the design of alternative transplantation cell products.

Human Hematopoietic stem and progenitor cells (HSPCs) are commonly defined by CD34 expression. Here, the authors map single-cell RNA states both inside and outside the CD34 compartment, uncovering previously unappreciated branchpoints and validating CD164 as an efficient marker for early HSPCs.

Tick-tock hedgehog-mutual crosstalk with liver circadian clock promotes liver steatosis

BACKGROUND & AIMS

The mammalian circadian clock controls various aspects of liver metabolism and integrates nutritional signals. Recently, we described Hedgehog (Hh) signaling as a novel regulator of liver lipid metabolism. Herein, we investigated crosstalk between hepatic Hh signaling and circadian rhythm.

METHODS

Diurnal rhythms of Hh signaling were investigated in liver and hepatocytes from mice with ablation of Smoothened (SAC-KO) and crossbreeds with PER2::LUC reporter mice. By using genome-wide screening, qPCR, immunostaining, ELISA and RNAi experiments in vitro we identified relevant transcriptional regulatory steps. Shotgun lipidomics and metabolic cages were used for analysis of metabolic alterations and behavior.

RESULTS

Hh signaling showed diurnal oscillations in liver and hepatocytes in vitro. Correspondingly, the level of Indian Hh, oscillated in serum. Depletion of the clock gene Bmal1 in hepatocytes resulted in significant alterations in the expression of Hh genes. Conversely, SAC-KO mice showed altered expression of clock genes, confirmed by RNAi against Gli1 and Gli3. Genome-wide screening revealed that SAC-KO hepatocytes showed time-dependent alterations in various genes, particularly those associated with lipid metabolism. The clock/hedgehog module further plays a role in rhythmicity of steatosis, and in the response of the liver to a high-fat diet or to differently timed starvation.

CONCLUSIONS

For the first time, Hh signaling in hepatocytes was found to be time-of-day dependent and to feed back on the circadian clock. Our findings suggest an integrative role of Hh signaling, mediated mainly by GLI factors, in maintaining homeostasis of hepatic lipid metabolism by balancing the circadian clock.

LAY SUMMARY

The results of our investigation show for the first time that the Hh signaling in hepatocytes is time-of-day dependent, leading to differences not only in transcript levels but also in the amount of Hh ligands in peripheral blood. Conversely, Hh signaling is able to feed back to the circadian clock.

The differences in brain stem transcriptional profiling in hypertensive ISIAH and normotensive WAG rats

BACKGROUND

The development of essential hypertension is associated with a wide range of mechanisms. The brain stem neurons are essential for the homeostatic regulation of arterial pressure as they control baroreflex and sympathetic nerve activity. The ISIAH (Inherited Stress Induced Arterial Hypertension) rats reproduce the human stress-sensitive hypertensive disease with predominant activation of the neuroendocrine hypothalamic-pituitary-adrenal and sympathetic adrenal axes. RNA-Seq analysis of the brain stems from the hypertensive ISIAH and normotensive control WAG (Wistar Albino Glaxo) rats was performed to identify the differentially expressed genes (DEGs) and the main central mechanisms (biological processes and metabolic pathways) contributing to the hypertensive state in the ISIAH rats.

RESULTS

The study revealed 224 DEGs. Their annotation in databases showed that 22 of them were associated with hypertension and blood pressure (BP) regulation, and 61 DEGs were associated with central nervous system diseases. In accordance with the functional annotation of DEGs, the key role of hormonal metabolic processes and, in particular, the enhanced biosynthesis of aldosterone in the brain stem of ISIAH rats was proposed. Multiple DEGs associated with several Gene Ontology (GO) terms essentially related to modulation of BP were identified. Abundant groups of DEGs were related to GO terms associated with responses to different stimuli including response to organic (hormonal) substance, to external stimulus, and to stress. Several DEGs making the most contribution to the inter-strain differences were detected including the Ephx2, which was earlier defined as a major candidate gene in the studies of transcriptional profiles in different tissues/organs (hypothalamus, adrenal gland and kidney) of ISIAH rats.

CONCLUSIONS

The results of the study showed that inter-strain differences in ISIAH and WAG brain stem functioning might be a result of the imbalance in processes leading to the pathology development and those, exerting the compensatory effects. The data obtained in this study are useful for a better understanding of the genetic mechanisms underlying the complexity of the brain stem processes in ISIAH rats, which are a model of stress-sensitive form of hypertension.

Exploration of prognosis-related microRNA and transcription factor co-regulatory networks across cancer types

The study of cancer prognosis serves as an important part of cancer research. Large-scale cancer studies have identified numerous genes and microRNAs (miRNAs) associated with prognosis. These informative genes and miRNAs represent potential biomarkers to predict survival and to elucidate the molecular mechanism of tumour progression. MiRNAs and transcription factors (TFs) can work cooperatively as essential mediators of gene expression, and their dysregulation affects cancer prognosis. A panoramic view of cancer prognosis at the system level, considering the co-regulation roles of miRNA and TF, remains elusive. Here, we establish 12 prognosis-related miRNA-TF co-regulatory networks. The characteristics of prognostic target genes and their regulators in the network are depicted. Although the target genes and co-regulatory patterns exhibit cancer-specific properties, some miRNAs and TFs are highly conserved across cancers. We illustrate and interpret the roles of these conserved regulators by building a model associated with cancer hallmarks, functional enrichment analysis, network community detection, and exhaustive literature research. The elaborated system-level prognostic miRNA-TF co-regulation landscape, including the highlighted roles of conserved regulators, provides a novel and powerful insights into further biological and medical discoveries.

A Network-guided Association Mapping Approach from DNA Methylation to Disease

Aberrant DNA methylation may contribute to development of cancer. However, understanding the associations between DNA methylation and cancer remains a challenge because of the complex mechanisms involved in the associations and insufficient sample sizes. The unprecedented wealth of DNA methylation, gene expression and disease status data give us a new opportunity to design machine learning methods to investigate the underlying associated mechanisms. In this paper, we propose a network-guided association mapping approach from DNA methylation to disease (NAMDD). Compared with existing methods, NAMDD finds methylation-disease path associations by integrating analysis of multiple data combined with a stability selection strategy, thereby mining more information in the datasets and improving the quality of resultant methylation sites. The experimental results on both synthetic and real ovarian cancer data show that NAMDD substantially outperforms former disease-related methylation site research methods (including NsRRR and PCLOGIT) under false positive control. Furthermore, we applied NAMDD to ovarian cancer data, identified significant path associations and provided hypothetical biological path associations to explain our findings.

Sequence Characteristics Distinguish Transcribed Enhancers from Promoters and Predict Their Breadth of Activity

Enhancers and promoters both regulate gene expression by recruiting transcription factors (TFs); however, the degree to which enhancer vs. promoter activity is due to differences in their sequences or to genomic context is the subject of ongoing debate. We examined this question by analyzing the sequences of thousands of transcribed enhancers and promoters from hundreds of cellular contexts previously identified by cap analysis of gene expression. Support vector machine classifiers trained on counts of all possible 6-bp-long sequences (6-mers) were able to accurately distinguish promoters from enhancers and distinguish their breadth of activity across tissues. Classifiers trained to predict enhancer activity also performed well when applied to promoter prediction tasks, but promoter-trained classifiers performed poorly on enhancers. This suggests that the learned sequence patterns predictive of enhancer activity generalize to promoters, but not vice versa. Our classifiers also indicate that there are functionally relevant differences in enhancer and promoter GC content beyond the influence of CpG islands. Furthermore, sequences characteristic of broad promoter or broad enhancer activity matched different TFs, with predicted ETS- and RFX-binding sites indicative of promoters, and AP-1 sites indicative of enhancers. Finally, we evaluated the ability of our models to distinguish enhancers and promoters defined by histone modifications. Separating these classes was substantially more difficult, and this difference may contribute to ongoing debates about the similarity of enhancers and promoters. In summary, our results suggest that high-confidence transcribed enhancers and promoters can largely be distinguished based on biologically relevant sequence properties.

Kidney organoids in translational medicine: Disease modeling and regenerative medicine

The kidney is one of the most complex organs composed of multiple cell types, functioning to maintain homeostasis by means of the filtering of metabolic wastes, balancing of blood electrolytes, and adjustment of blood pressure. Recent advances in 3D culture technologies in vitro enabled the generation of "organoids" which mimic the structure and function of in vivo organs. Organoid technology has allowed for new insights into human organ development and human pathophysiology, with great potential for translational research. Increasing evidence shows that kidney organoids are a useful platform for disease modeling of genetic kidney diseases when derived from genetic patient iPSCs and/or CRISPR-mutated stem cells. Although single cell RNA-seq studies highlight the technical difficulties underlying kidney organoid generation reproducibility and variation in differentiation protocols, kidney organoids still hold great potential to understand kidney pathophysiology as applied to kidney injury and fibrosis. In this review, we summarize various studies of kidney organoids, disease modeling, genome-editing, and bioengineering, and additionally discuss the potential of and current challenges to kidney organoid research.

A Comprehensive Survey of Tools and Software for Active Subnetwork Identification

A recent focus of computational biology has been to integrate the complementary information available in molecular profiles as well as in multiple network databases in order to identify connected regions that show significant changes under different conditions. This allows for capturing dynamic and condition-specific mechanisms of the underlying phenomena and disease stages. Here we review 22 such integrative approaches for active module identification published over the last decade. This article only focuses on tools that are currently available for use and are well-maintained. We compare these methods focusing on their primary features, integrative abilities, network structures, mathematical models, and implementations. We also provide real-world scenarios in which these methods have been successfully applied, as well as highlight outstanding challenges in the field that remain to be addressed. The main objective of this review is to help potential users and researchers to choose the best method that is suitable for their data and analysis purpose.

Multi-study inference of regulatory networks for more accurate models of gene regulation

Gene regulatory networks are composed of sub-networks that are often shared across biological processes, cell-types, and organisms. Leveraging multiple sources of information, such as publicly available gene expression datasets, could therefore be helpful when learning a network of interest. Integrating data across different studies, however, raises numerous technical concerns. Hence, a common approach in network inference, and broadly in genomics research, is to separately learn models from each dataset and combine the results. Individual models, however, often suffer from under-sampling, poor generalization and limited network recovery. In this study, we explore previous integration strategies, such as batch-correction and model ensembles, and introduce a new multitask learning approach for joint network inference across several datasets. Our method initially estimates the activities of transcription factors, and subsequently, infers the relevant network topology. As regulatory interactions are context-dependent, we estimate model coefficients as a combination of both dataset-specific and conserved components. In addition, adaptive penalties may be used to favor models that include interactions derived from multiple sources of prior knowledge including orthogonal genomics experiments. We evaluate generalization and network recovery using examples from Bacillus subtilis and Saccharomyces cerevisiae, and show that sharing information across models improves network reconstruction. Finally, we demonstrate robustness to both false positives in the prior information and heterogeneity among datasets.

Due to increasing availability of biological data, methods to properly integrate data generated across the globe become essential for extracting reproducible insights into relevant research questions. In this work, we developed a framework to reconstruct gene regulatory networks from expression datasets generated in separate studies—and thus, because of technical variation (different dates, handlers, laboratories, protocols etc…), challenging to integrate. Since regulatory mechanisms are often shared across conditions, we hypothesized that drawing conclusions from various data sources would improve performance of gene regulatory network inference. By transferring knowledge among regulatory models, our method is able to detect weaker patterns that are conserved across datasets, while also being able to detect dataset-unique interactions. We also allow incorporation of prior knowledge on network structure to favor models that are somewhat similar to the prior itself. Using two model organisms, we show that joint network inference outperforms inference from a single dataset. We also demonstrate that our method is robust to false edges in the prior and to low condition overlap across datasets, and that it can outperform current data integration strategies.

A De Novo Shape Motif Discovery Algorithm Reveals Preferences of Transcription Factors for DNA Shape Beyond Sequence Motifs

DNA shape adds specificity to sequence motifs but has not been explored systematically outside this context. We hypothesized that DNA-binding proteins (DBPs) preferentially occupy DNA with specific structures ("shape motifs") regardless of whether or not these correspond to high information content sequence motifs. We present ShapeMF, a Gibbs sampling algorithm that identifies de novo shape motifs. Using binding data from hundreds of in vivo and in vitro experiments, we show that most DBPs have shape motifs and can occupy these in the absence of sequence motifs. This "shape-only binding" is common for many DBPs and in regions co-bound by multiple DBPs. When shape and sequence motifs co-occur, they can be overlapping, flanking, or separated by consistent spacing. Finally, DBPs within the same protein family have different shape motifs, explaining their distinct genome-wide occupancy despite having similar sequence motifs. These results suggest that shape motifs not only complement sequence motifs but also facilitate recognition of DNA beyond conventionally defined sequence motifs.

Cistrome Data Browser: expanded datasets and new tools for gene regulatory analysis

The Cistrome Data Browser (DB) is a resource of human and mouse cis-regulatory information derived from ChIP-seq, DNase-seq and ATAC-seq chromatin profiling assays, which map the genome-wide locations of transcription factor binding sites, histone post-translational modifications and regions of chromatin accessible to endonuclease activity. Currently, the Cistrome DB contains approximately 47,000 human and mouse samples with about 24,000 newly collected datasets compared to the previous release two years ago. Furthermore, the Cistrome DB has a new Toolkit module with several features that allow users to better utilize the large-scale ChIP-seq, DNase-seq, and ATAC-seq data. First, users can query the factors which are likely to regulate a specific gene of interest. Second, the Cistrome DB Toolkit facilitates searches for factor binding, histone modifications, and chromatin accessibility in any given genomic interval shorter than 2Mb. Third, the Toolkit can determine the most similar ChIP-seq, DNase-seq, and ATAC-seq samples in terms of genomic interval overlaps with user-provided genomic interval sets. The Cistrome DB is a user-friendly, up-to-date, and well maintained resource, and the new tools will greatly benefit the biomedical research community. The database is freely available at http://cistrome.org/db, and the Toolkit is at http://dbtoolkit.cistrome.org.

Next-Generation Genome-Scale Models Incorporating Multilevel 'Omics Data: From Yeast to Human

Genome-scale modelling in eukaryotes has been pioneered by the yeast Saccharomyces cerevisiae. Early metabolic networks have been reconstructed based on genome sequence and information accumulated in the literature on biochemical reactions. Protein-protein interaction networks have been constructed based on experimental observations such as yeast-2-hybrid method. Gene regulatory networks were based on a variety of data types, including information on TF-promoter binding and gene coexpression. The aforementioned networks have been improved gradually, and methods for their integration were developed. Incorporation of omics data including genomics, metabolomics, transcriptomics, fluxome, and phosphoproteome led to next-generation genome-scale models. The methods tested on yeast have later been implemented in human, further, cellular components found to be important in yeast physiology under (ab)normal conditions, and (dis)regulation mechanisms in yeast shed light to the healthy and disease states in human. This chapter provides a historical perspective on next-generation genome-scale models incorporating multilevel 'omics data, from yeast to human.

Integrative Approaches for Inference of Genome-Scale Gene Regulatory Networks

Transcriptional regulatory networks specify the regulatory proteins of target genes that control the context-specific expression levels of genes. With our ability to profile the different types of molecular components of cells under different conditions, we are now uniquely positioned to infer regulatory networks in diverse biological contexts such as different cell types, tissues, and time points. In this chapter, we cover two main classes of computational methods to integrate different types of information to infer genome-scale transcriptional regulatory networks. The first class of methods focuses on integrative methods for specifically inferring connections between transcription factors and target genes by combining gene expression data with regulatory edge-specific knowledge. The second class of methods integrates upstream signaling networks with transcriptional regulatory networks by combining gene expression data with protein-protein interaction networks and proteomic datasets. We conclude with a section on practical applications of a network inference algorithm to infer a genome-scale regulatory network.

In Silico Evolution of Signaling Networks Using Rule-Based Models: Bistable Response Dynamics

One of the ultimate goals in biology is to understand the design principles of biological systems. Such principles, if they exist, can help us better understand complex, natural biological systems and guide the engineering of de novo ones. Toward deciphering design principles, in silico evolution of biological systems with proper abstraction is a promising approach. Here, we demonstrate the application of in silico evolution combined with rule-based modeling for exploring design principles of cellular signaling networks. This application is based on a computational platform, called BioJazz, which allows in silico evolution of signaling networks with unbounded complexity. We provide a detailed introduction to BioJazz architecture and implementation and describe how it can be used to evolve and/or design signaling networks with defined dynamics. For the latter, we evolve signaling networks with switch-like response dynamics and demonstrate how BioJazz can result in new biological insights into network structures that can endow bistable response dynamics. This example also demonstrated both the power of BioJazz in evolving and designing signaling networks and its limitations at the current stage of development.

Gene Regulatory Networks: A Primer in Biological Processes and Statistical Modelling

Modelling gene regulatory networks requires not only a thorough understanding of the biological system depicted, but also the ability to accurately represent this system from a mathematical perspective. Throughout this chapter, we aim to familiarize the reader with the biological processes and molecular factors at play in the process of gene expression regulation. We first describe the different interactions controlling each step of the expression process, from transcription to mRNA and protein decay. In the second section, we provide statistical tools to accurately represent this biological complexity in the form of mathematical models. Among other considerations, we discuss the topological properties of biological networks, the application of deterministic and stochastic frameworks, and the quantitative modelling of regulation. We particularly focus on the use of such models for the simulation of expression data that can serve as a benchmark for the testing of network inference algorithms.

Bioinformatics analysis of novel transcription factors and related differentially regulated modules in non-union skeletal fractures

OBJECTIVE

This study aimed to further clarify the underlying pathomechanism of non-union skeletal fractures.

METHODS

Gene expression profile dataset GSE494 obtained from six non-union skeletal fracture and six normal samples was downloaded from the Gene Expression Omnibus database. Overlapping genes in at least two platforms were analyzed, and differentially expressed genes (DEGs) between normal and disease groups were screened. Transcriptional regulatory relationships and differentially regulated modules of various transcription factors (TFs) were determined. Differentially regulated modules with unknown functions were subjected to functional enrichment analysis.

RESULTS

Overall, 4,252 overlapping genes in at least two platforms and 77 DEGs, including 31 up and 46 downregulated genes, were obtained. Overall, 64,623 transcriptional regulatory relationships, including 49 TFs and 3,900 target genes, and 9 significant modules for differential regulation were identified. Three modules with unknown functions regulated by TFs, including zinc finger, ZZ-type containing 3 (ZZZ3), nuclear TF Y, alpha (NFYA), and POU class 2 homeobox 2 (POU2F2), were identified. Enriched GO-BP terms of NFYA and POU2F2 modules included cell adhesion and related terms and those of ZZ3 included cell cycle, cell proliferation, and associated terms.

CONCLUSION

Three TFs, including ZZZ3, POU2F2, and NFYA, and their regulated modules may have important effects on non-union skeletal fractures. Cell proliferation may be related with ZZZ3; cell adhesion and its similar process may be related with POU2F2 and NFYA.

Signatures of selection and environmental adaptation across the goat genome post-domestication

BACKGROUND

Since goat was domesticated 10,000 years ago, many factors have contributed to the differentiation of goat breeds and these are classified mainly into two types: (i) adaptation to different breeding systems and/or purposes and (ii) adaptation to different environments. As a result, approximately 600 goat breeds have developed worldwide; they differ considerably from one another in terms of phenotypic characteristics and are adapted to a wide range of climatic conditions. In this work, we analyzed the AdaptMap goat dataset, which is composed of data from more than 3000 animals collected worldwide and genotyped with the CaprineSNP50 BeadChip. These animals were partitioned into groups based on geographical area, production uses, available records on solid coat color and environmental variables including the sampling geographical coordinates, to investigate the role of natural and/or artificial selection in shaping the genome of goat breeds.

RESULTS

Several signatures of selection on different chromosomal regions were detected across the different breeds, sub-geographical clusters, phenotypic and climatic groups. These regions contain genes that are involved in important biological processes, such as milk-, meat- or fiber-related production, coat color, glucose pathway, oxidative stress response, size, and circadian clock differences. Our results confirm previous findings in other species on adaptation to extreme environments and human purposes and provide new genes that could explain some of the differences between goat breeds according to their geographical distribution and adaptation to different environments.

CONCLUSIONS

These analyses of signatures of selection provide a comprehensive first picture of the global domestication process and adaptation of goat breeds and highlight possible genes that may have contributed to the differentiation of this species worldwide.

An intersection network based on combining SNP coassociation and RNA coexpression networks for feed utilization traits in Japanese Black cattle

Genome-wide association studies (GWAS) of quantitative traits have detected numerous genetic associations, but they encounter difficulties in pinpointing prominent candidate genes and inferring gene networks. The present study used a systems genetics approach integrating GWAS results with external RNA-expression data to detect candidate gene networks in feed utilization and growth traits of Japanese Black cattle, which are matters of concern. A SNP coassociation network was derived from significant correlations between SNPs with effects estimated by GWAS across 7 phenotypic traits. The resulting network genes contained significant numbers of annotations related to the traits. Using bovine transcriptome data from a public database, an RNA coexpression network was inferred based on the similarity of expression patterns across different tissues. An intersection network was then generated by superimposing the SNP and RNA networks and extracting shared interactions. This intersection network contained 4 tissue-specific modules: nervous system, reproductive system, muscular system, and glands. To characterize the structure (topographical properties) of the 3 networks, their scale-free properties were evaluated, which revealed that the intersection network was the most scale-free. In the subnetwork containing the most connected transcription factors (URI1, ROCK2, and ETV6), most genes were widely expressed across tissues, and genes previously shown to be involved in the traits were found. Results indicated that the current approach might be used to construct a gene network that better reflects biological information, providing encouragement for the genetic dissection of economically important quantitative traits.

wTO: an R package for computing weighted topological overlap and a consensus network with integrated visualization tool

BACKGROUND

Network analyses, such as of gene co-expression networks, metabolic networks and ecological networks have become a central approach for the systems-level study of biological data. Several software packages exist for generating and analyzing such networks, either from correlation scores or the absolute value of a transformed score called weighted topological overlap (wTO). However, since gene regulatory processes can up- or down-regulate genes, it is of great interest to explicitly consider both positive and negative correlations when constructing a gene co-expression network.

RESULTS

Here, we present an R package for calculating the weighted topological overlap (wTO), that, in contrast to existing packages, explicitly addresses the sign of the wTO values, and is thus especially valuable for the analysis of gene regulatory networks. The package includes the calculation of p-values (raw and adjusted) for each pairwise gene score. Our package also allows the calculation of networks from time series (without replicates). Since networks from independent datasets (biological repeats or related studies) are not the same due to technical and biological noise in the data, we additionally, incorporated a novel method for calculating a consensus network (CN) from two or more networks into our R package. To graphically inspect the resulting networks, the R package contains a visualization tool, which allows for the direct network manipulation and access of node and link information. When testing the package on a standard laptop computer, we can conduct all calculations for systems of more than 20,000 genes in under two hours. We compare our new wTO package to state of art packages and demonstrate the application of the wTO and CN functions using 3 independently derived datasets from healthy human pre-frontal cortex samples. To showcase an example for the time series application we utilized a metagenomics data set.

CONCLUSION

In this work, we developed a software package that allows the computation of wTO networks, CNs and a visualization tool in the R statistical environment. It is publicly available on CRAN repositories under the GPL -2 Open Source License ( https://cran.r-project.org/web/packages/wTO/ ).

A graphical model approach visualizes regulatory relationships between genome-wide transcription factor binding profiles

Integrated analysis of multiple genome-wide transcription factor (TF)-binding profiles will be vital to advance our understanding of the global impact of TF binding. However, existing methods for measuring similarity in large numbers of chromatin immunoprecipitation assays with sequencing (ChIP-seq), such as correlation, mutual information or enrichment analysis, are limited in their ability to display functionally relevant TF relationships. In this study, we propose the use of graphical models to determine conditional independence between TFs and showed that network visualization provides a promising alternative to distinguish ‘direct’ versus ‘indirect’ TF interactions. We applied four algorithms to measure ‘direct’ dependence to a compendium of 367 mouse haematopoietic TF ChIP-seq samples and obtained a consensus network known as a ‘TF association network’ where edges in the network corresponded to likely causal pairwise relationships between TFs. The ‘TF association network’ illustrates the role of TFs in developmental pathways, is reminiscent of combinatorial TF regulation, corresponds to known protein–protein interactions and indicates substantial TF-binding reorganization in leukemic cell types. With the rapid increase in TF ChIP-Seq data sets, the approach presented here will be a powerful tool to study transcriptional programmes across a wide range of biological systems.