Discovery of Novel Human Gene Regulatory Modules from Gene Co-expression and Promoter Motif Analysis

Loss of RREB1 in pancreatic beta cells reduces cellular insulin content and affects endocrine cell gene expression

AIMS/HYPOTHESIS

Genome-wide studies have uncovered multiple independent signals at the RREB1 locus associated with altered type 2 diabetes risk and related glycaemic traits. However, little is known about the function of the zinc finger transcription factor Ras-responsive element binding protein 1 (RREB1) in glucose homeostasis or how changes in its expression and/or function influence diabetes risk.

METHODS

A zebrafish model lacking rreb1a and rreb1b was used to study the effect of RREB1 loss in vivo. Using transcriptomic and cellular phenotyping of a human beta cell model (EndoC-βH1) and human induced pluripotent stem cell (hiPSC)-derived beta-like cells, we investigated how loss of RREB1 expression and activity affects pancreatic endocrine cell development and function. Ex vivo measurements of human islet function were performed in donor islets from carriers of RREB1 type 2 diabetes risk alleles.

RESULTS

CRISPR/Cas9-mediated loss of rreb1a and rreb1b function in zebrafish supports an in vivo role for the transcription factor in beta cell mass, beta cell insulin expression and glucose levels. Loss of RREB1 also reduced insulin gene expression and cellular insulin content in EndoC-βH1 cells and impaired insulin secretion under prolonged stimulation. Transcriptomic analysis of RREB1 knockdown and knockout EndoC-βH1 cells supports RREB1 as a novel regulator of genes involved in insulin secretion. In vitro differentiation of RREB1KO/KO hiPSCs revealed dysregulation of pro-endocrine cell genes, including RFX family members, suggesting that RREB1 also regulates genes involved in endocrine cell development. Human donor islets from carriers of type 2 diabetes risk alleles in RREB1 have altered glucose-stimulated insulin secretion ex vivo, consistent with a role for RREB1 in regulating islet cell function.

CONCLUSIONS/INTERPRETATION

Together, our results indicate that RREB1 regulates beta cell function by transcriptionally regulating the expression of genes involved in beta cell development and function.

The USR domain of USF1 mediates NF-Y interactions and cooperative DNA binding

The trimeric CCAAT-binding NF-Y is a "pioneer" Transcription Factor -TF- known to cooperate with neighboring TFs to regulate gene expression. Genome-wide analyses detected a precise stereo-alignment -10/12 bp- of CCAAT with E-box elements and corresponding colocalization of NF-Y with basic-Helix-Loop-Helix (bHLH) TFs. We dissected here NF-Y interactions with USF1 and MAX. USF1, but not MAX, cooperates in DNA binding with NF-Y. NF-Y and USF1 synergize to activate target promoters. Reconstruction of complexes by structural means shows independent DNA binding of MAX, whereas USF1 has extended contacts with NF-Y, involving the USR, a USF-specific amino acid sequence stretch required for trans-activation. The USR is an intrinsically disordered domain and adopts different conformations based on E-box-CCAAT distances. Deletion of the USR abolishes cooperative DNA binding with NF-Y. Our data indicate that the functionality of certain unstructured domains involves adapting to small variation in stereo-alignments of the multimeric TFs sites.

Different SP1 binding dynamics at individual genomic loci in human cells

Using a tamoxifen-inducible time-course ChIP-sequencing (ChIP-seq) approach, we show that the ubiquitous transcription factor SP1 has different binding dynamics at its target sites in the human genome. SP1 very rapidly reaches maximal binding levels at some sites, but binding kinetics at other sites is biphasic, with rapid half-maximal binding followed by a considerably slower increase to maximal binding. While ∼70% of SP1 binding sites are located at promoter regions, loci with slow SP1 binding kinetics are enriched in enhancer and Polycomb-repressed regions. Unexpectedly, SP1 sites with fast binding kinetics tend to have higher quality and more copies of the SP1 sequence motif. Different cobinding factors associate near SP1 binding sites depending on their binding kinetics and on their location at promoters or enhancers. For example, NFY and FOS are preferentially associated near promoter-bound SP1 sites with fast binding kinetics, whereas DNA motifs of ETS and homeodomain proteins are preferentially observed at sites with slow binding kinetics. At promoters but not enhancers, proteins involved in sumoylation and PML bodies associate more strongly with slow SP1 binding sites than with the fast binding sites. The speed of SP1 binding is not associated with nucleosome occupancy, and it is not necessarily coupled to higher transcriptional activity. These results with SP1 are in contrast to those of human TBP, indicating that there is no common mechanism affecting transcription factor binding kinetics. The biphasic kinetics at some SP1 target sites suggest the existence of distinct chromatin states at these loci in different cells within the overall population.

New Drug Targets to Prevent Death Due to Stroke: A Review Based on Results of Protein-Protein Interaction Network, Enrichment, and Annotation Analyses

This study used established biomarkers of death from ischemic stroke (IS) versus stroke survival to perform network, enrichment, and annotation analyses. Protein-protein interaction (PPI) network analysis revealed that the backbone of the highly connective network of IS death consisted of IL6, ALB, TNF, SERPINE1, VWF, VCAM1, TGFB1, and SELE. Cluster analysis revealed immune and hemostasis subnetworks, which were strongly interconnected through the major switches ALB and VWF. Enrichment analysis revealed that the PPI immune subnetwork of death due to IS was highly associated with TLR2/4, TNF, JAK-STAT, NOD, IL10, IL13, IL4, and TGF-β1/SMAD pathways. The top biological and molecular functions and pathways enriched in the hemostasis network of death due to IS were platelet degranulation and activation, the intrinsic pathway of fibrin clot formation, the urokinase-type plasminogen activator pathway, post-translational protein phosphorylation, integrin cell-surface interactions, and the proteoglycan-integrin extracellular matrix complex (ECM). Regulation Explorer analysis of transcriptional factors shows: (a) that NFKB1, RELA and SP1 were the major regulating actors of the PPI network; and (b) hsa-mir-26-5p and hsa-16-5p were the major regulating microRNA actors. In conclusion, prevention of death due to IS should consider that current IS treatments may be improved by targeting VWF, the proteoglycan-integrin-ECM complex, TGF-β1/SMAD, NF-κB/RELA and SP1.

Interferon regulatory factor 1 (IRF1) and anti-pathogen innate immune responses

The eponymous member of the interferon regulatory factor (IRF) family, IRF1, was originally identified as a nuclear factor that binds and activates the promoters of type I interferon genes. However, subsequent studies using genetic knockouts or RNAi-mediated depletion of IRF1 provide a much broader view, linking IRF1 to a wide range of functions in protection against invading pathogens. Conserved throughout vertebrate evolution, IRF1 has been shown in recent years to mediate constitutive as well as inducible host defenses against a variety of viruses. Fine-tuning of these ancient IRF1-mediated host defenses, and countering strategies by pathogens to disarm IRF1, play crucial roles in pathogenesis and determining the outcome of infection.

On the NF-Y regulome as in ENCODE (2019)

NF-Y is a trimeric Transcription Factor -TF- which binds with high selectivity to the conserved CCAAT element. Individual ChIP-seq analysis as well as ENCODE have progressively identified locations shared by other TFs. Here, we have analyzed data introduced by ENCODE over the last five years in K562, HeLa-S3 and GM12878, including several chromatin features, as well RNA-seq profiling of HeLa cells after NF-Y inactivation. We double the number of sequence-specific TFs and co-factors reported. We catalogue them in 4 classes based on co-association criteria, infer target genes categorizations, identify positional bias of binding sites and gene expression changes. Larger and novel co-associations emerge, specifically concerning subunits of repressive complexes as well as RNA-binding proteins. On the one hand, these data better define NF-Y association with single members of major classes of TFs, on the other, they suggest that it might have a wider role in the control of mRNA production.

The ongoing ENCODE consortium represents a useful compendium of locations of TFs, chromatin marks, gene expression data. In previous reports, we identified modules of CCAAT-binding NF-Y with individual TFs. Here, we analyzed all 363 factors currently present: 68 with enrichment of CCAAT in their locations, 38 with overlap of peaks. New sequence-specific TFs, co-activators and co-repressors are reported. Co-association patterns correspond to specific targeted genes categorizations and gene expression changes, as assessed by RNA-seq after NF-Y inactivation. These data widen and better define a coherent model of synergy of NF-Y with selected groups of TFs and co-factors.

Modeling the Role of the Microbiome in Evolution

There is undeniable evidence showing that bacteria have strongly influenced the evolution and biological functions of multicellular organisms. It has been hypothesized that many host-microbial interactions have emerged so as to increase the adaptive fitness of the holobiont (the host plus its microbiota). Although this association has been corroborated for many specific cases, general mechanisms explaining the role of the microbiota in the evolution of the host are yet to be understood. Here we present an evolutionary model in which a network representing the host adapts in order to perform a predefined function. During its adaptation, the host network (HN) can interact with other networks representing its microbiota. We show that this interaction greatly accelerates and improves the adaptability of the HN without decreasing the adaptation of the microbial networks. Furthermore, the adaptation of the HN to perform several functions is possible only when it interacts with many different bacterial networks in a specialized way (each bacterial network participating in the adaptation of one function). Disrupting these interactions often leads to non-adaptive states, reminiscent of dysbiosis, where none of the networks the holobiont consists of can perform their respective functions. By considering the holobiont as a unit of selection and focusing on the adaptation of the host to predefined but arbitrary functions, our model predicts the need for specialized diversity in the microbiota. This structural and dynamical complexity in the holobiont facilitates its adaptation, whereas a homogeneous (non-specialized) microbiota is inconsequential or even detrimental to the holobiont's evolution. To our knowledge, this is the first model in which symbiotic interactions, diversity, specialization and dysbiosis in an ecosystem emerge as a result of coevolution. It also helps us understand the emergence of complex organisms, as they adapt more easily to perform multiple tasks than non-complex ones.

Classifying human promoters by occupancy patterns identifies recurring sequence elements, combinatorial binding, and spatial interactions

Background

Characterizing recurring sequence patterns in human promoters has been a challenging undertaking even nowadays where a near-complete overview of promoters exists. However, with the more recent availability of genomic location (ChIP-seq) data, one can approach that question through the identification of characteristic patterns of transcription factor occupancy and histone modifications.

Results

Based on the ENCODE annotation and integration of sequence motifs as well as three-dimensional chromatin data, we have undertaken a re-analysis of occupancy and sequence patterns in human promoters. We identify clear groups of CAAT-box and E-box sequence motif containing promoters, as well as a group of promoters whose interaction with an enhancer appears to be mediated by CCCTC-binding factor (CTCF) binding on the promoter. We also extend our analysis to inactive promoters, showing that only a surprisingly small number of inactive promoters is repressed by the polycomb complex. We also identify combinatorial patterns of transcription factor interactions indicated by the ChIP-seq signals.

Conclusion

Our analysis defines subgroups of promoters characterized by stereotypic patterns of transcription factor occupancy, and combinations of specific sequence patterns which are required for their binding. This grouping provides new hypotheses concerning the assembly and dynamics of transcription factor complexes at their respective promoter groups, as well as questions on the evolutionary origin of these groups.

Electronic supplementary material

The online version of this article (10.1186/s12915-018-0585-5) contains supplementary material, which is available to authorized users.