Dynamic trans-acting factor colocalization in human cells

A Large Multiethnic Genome-Wide Association Study of Adult Body Mass Index Identifies Novel Loci

Body mass index (BMI), a proxy measure for obesity, is determined by both environmental (including ethnicity, age, and sex) and genetic factors, with > 400 BMI-associated loci identified to date. However, the impact, interplay, and underlying biological mechanisms among BMI, environment, genetics, and ancestry are not completely understood. To further examine these relationships, we utilized 427,509 calendar year-averaged BMI measurements from 100,418 adults from the single large multiethnic Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort. We observed substantial independent ancestry and nationality differences, including ancestry principal component interactions and nonlinear effects. To increase the list of BMI-associated variants before assessing other differences, we conducted a genome-wide association study (GWAS) in GERA, with replication in the Genetic Investigation of Anthropomorphic Traits (GIANT) consortium combined with the UK Biobank (UKB), followed by GWAS in GERA combined with GIANT, with replication in the UKB. We discovered 30 novel independent BMI loci (P < 5.0 × 10^-8) that replicated. We then assessed the proportion of BMI variance explained by sex in the UKB using previously identified loci compared to previously and newly identified loci and found slight increases: from 3.0 to 3.3% for males and from 2.7 to 3.0% for females. Further, the variance explained by previously and newly identified variants decreased with increasing age in the GERA and UKB cohorts, echoed in the variance explained by the entire genome, which also showed gene-age interaction effects. Finally, we conducted a tissue expression QTL enrichment analysis, which revealed that GWAS BMI-associated variants were enriched in the cerebellum, consistent with prior work in humans and mice.

TLR10 and NFKBIA contributed to the risk of hip osteoarthritis: systematic evaluation based on Han Chinese population

Multiple lines of evidence have confirmed the importance of genetic factors for hip osteoarthritis (HOA). Our study aimed to investigate the associations of TLR10 and NFKBIA with respect to the HOA risk in Han Chinese individuals. A total of 1,043 HOA patients and 2,664 controls were recruited. Then, 23 tag single-nucleotide polymorphisms (SNPs) in the TLR10 and NFKBIA genes were selected for genotyping. Genetic association analyses were conducted in both single-marker and haplotype-based ways. Gene by gene, two-way interactions were analysed using a case-only method. Multiple bioinformatics tools were utilised to examine the potential functional significance of the SNPs. Two significant SNPs, rs11096957 (OR = 1.26, P = 1.35 × 10⁻⁵) and rs2273650 (OR = 1.2, P = 1.57 × 10⁻³), were significantly associated with HOA risk. Rs11096957 was also associated with the severity of the HOA. Bioinformatics analysis indicated that the allele T of rs2273650 would create new miRNA/SNP target duplexes, which suggests that rs2273650 could alter the NFKBIA expression by affecting the miRNA/SNP target duplexes. Our study identified significant association signals from NFKBIA with HOA for the first time, and it also confirmed the contribution of TLR10 to the HOA risk. These findings would provide clues for identifying individuals at high risk of HOA.

Novel genetic associations for blood pressure identified via gene-alcohol interaction in up to 570K individuals across multiple ancestries

Heavy alcohol consumption is an established risk factor for hypertension; the mechanism by which alcohol consumption impact blood pressure (BP) regulation remains unknown. We hypothesized that a genome-wide association study accounting for gene-alcohol consumption interaction for BP might identify additional BP loci and contribute to the understanding of alcohol-related BP regulation. We conducted a large two-stage investigation incorporating joint testing of main genetic effects and single nucleotide variant (SNV)-alcohol consumption interactions. In Stage 1, genome-wide discovery meta-analyses in ≈131K individuals across several ancestry groups yielded 3,514 SNVs (245 loci) with suggestive evidence of association (P < 1.0 x 10-5). In Stage 2, these SNVs were tested for independent external replication in ≈440K individuals across multiple ancestries. We identified and replicated (at Bonferroni correction threshold) five novel BP loci (380 SNVs in 21 genes) and 49 previously reported BP loci (2,159 SNVs in 109 genes) in European ancestry, and in multi-ancestry meta-analyses (P < 5.0 x 10-8). For African ancestry samples, we detected 18 potentially novel BP loci (P < 5.0 x 10-8) in Stage 1 that warrant further replication. Additionally, correlated meta-analysis identified eight novel BP loci (11 genes). Several genes in these loci (e.g., PINX1, GATA4, BLK, FTO and GABBR2) have been previously reported to be associated with alcohol consumption. These findings provide insights into the role of alcohol consumption in the genetic architecture of hypertension.

A Positive Feedback Amplifier Circuit That Regulates Interferon (IFN)-Stimulated Gene Expression and Controls Type I and Type II IFN Responses

Interferon (IFN)-I and IFN-II both induce IFN-stimulated gene (ISG) expression through Janus kinase (JAK)-dependent phosphorylation of signal transducer and activator of transcription (STAT) 1 and STAT2. STAT1 homodimers, known as γ-activated factor (GAF), activate transcription in response to all types of IFNs by direct binding to IFN-II activation site (γ-activated sequence)-containing genes. Association of interferon regulatory factor (IRF) 9 with STAT1–STAT2 heterodimers [known as interferon-stimulated gene factor 3 (ISGF3)] or with STAT2 homodimers (STAT2/IRF9) in response to IFN-I, redirects these complexes to a distinct group of target genes harboring the interferon-stimulated response element (ISRE). Similarly, IRF1 regulates expression of ISGs in response to IFN-I and IFN-II by directly binding the ISRE or IRF-responsive element. In addition, evidence is accumulating for an IFN-independent and -dependent role of unphosphorylated STAT1 and STAT2, with or without IRF9, and IRF1 in basal as well as long-term ISG expression. This review provides insight into the existence of an intracellular amplifier circuit regulating ISG expression and controlling long-term cellular responsiveness to IFN-I and IFN-II. The exact timely steps that take place during IFN-activated feedback regulation and the control of ISG transcription and long-term cellular responsiveness to IFN-I and IFN-II is currently not clear. Based on existing literature and our novel data, we predict the existence of a multifaceted intracellular amplifier circuit that depends on unphosphorylated and phosphorylated ISGF3 and GAF complexes and IRF1. In a combinatorial and timely fashion, these complexes mediate prolonged ISG expression and control cellular responsiveness to IFN-I and IFN-II. This proposed intracellular amplifier circuit also provides a molecular explanation for the existing overlap between IFN-I and IFN-II activated ISG expression.

The Crosstalk of Endoplasmic Reticulum (ER) Stress Pathways with NF-κB: Complex Mechanisms Relevant for Cancer, Inflammation and Infection

Stressful conditions occuring during cancer, inflammation or infection activate adaptive responses that are controlled by the unfolded protein response (UPR) and the nuclear factor of kappa light polypeptide gene enhancer in B-cells (NF-κB) signaling pathway. These systems can be triggered by chemical compounds but also by cytokines, toll-like receptor ligands, nucleic acids, lipids, bacteria and viruses. Despite representing unique signaling cascades, new data indicate that the UPR and NF-κB pathways converge within the nucleus through ten major transcription factors (TFs), namely activating transcription factor (ATF)4, ATF3, CCAAT/enhancer-binding protein (CEBP) homologous protein (CHOP), X-box-binding protein (XBP)1, ATF6α and the five NF-κB subunits. The combinatorial occupancy of numerous genomic regions (enhancers and promoters) coordinates the transcriptional activation or repression of hundreds of genes that collectively determine the balance between metabolic and inflammatory phenotypes and the extent of apoptosis and autophagy or repair of cell damage and survival. Here, we also discuss results from genetic experiments and chemical activators of endoplasmic reticulum (ER) stress that suggest a link to the cytosolic inhibitor of NF-κB (IκB)α degradation pathway. These data show that the UPR affects this major control point of NF-κB activation through several mechanisms. Taken together, available evidence indicates that the UPR and NF-κB interact at multiple levels. This crosstalk provides ample opportunities to fine-tune cellular stress responses and could also be exploited therapeutically in the future.

Integrative analysis reveals functional and regulatory roles of H3K79me2 in mediating alternative splicing

Background

Accumulating evidence suggests alternative splicing (AS) is a co-transcriptional splicing process not only controlled by RNA-binding splicing factors, but also mediated by epigenetic regulators, such as chromatin structure, nucleosome density, and histone modification. Aberrant AS plays an important role in regulating various diseases, including cancers.

Methods

In this study, we integrated AS events derived from RNA-seq with H3K79me2 ChIP-seq data across 34 different normal and cancer cell types and found the higher enrichment of H3K79me2 in two AS types, skipping exon (SE) and alternative 3′ splice site (A3SS).

Results

Interestingly, by applying self-organizing map (SOM) clustering, we unveiled two clusters mainly comprised of blood cancer cell types with a strong correlation between H3K79me2 and SE. Remarkably, the expression of transcripts associated with SE was not significantly different from that of those not associated with SE, indicating the involvement of H3K79me2 in splicing has little impact on full mRNA transcription. We further showed that the deletion of DOT1L1, the sole H3K79 methyltransferase, impeded leukemia cell proliferation as well as switched exon skipping to the inclusion isoform in two MLL-rearranged acute myeloid leukemia cell lines. Our data demonstrate H3K79me2 was involved in mediating SE processing, which might in turn influence transformation and disease progression in leukemias.

Conclusions

Collectively, our work for the first time reveals that H3K79me2 plays functional and regulatory roles through a co-transcriptional splicing mechanism.

Electronic supplementary material

The online version of this article (10.1186/s13073-018-0538-1) contains supplementary material, which is available to authorized users.

Analytical Strategy to Prioritize Alzheimer's Disease Candidate Genes in Gene Regulatory Networks Using Public Expression Data

Alzheimer’s disease (AD) progressively destroys cognitive abilities in the aging population with tremendous effects on memory. Despite recent progress in understanding the underlying mechanisms, high drug attrition rates have put a question mark behind our knowledge about its etiology. Re-evaluation of past studies could help us to elucidate molecular-level details of this disease. Several methods to infer such networks exist, but most of them do not elaborate on context specificity and completeness of the generated networks, missing out on lesser-known candidates. In this study, we present a novel strategy that corroborates common mechanistic patterns across large scale AD gene expression studies and further prioritizes potential biomarker candidates. To infer gene regulatory networks (GRNs), we applied an optimized version of the BC3Net algorithm, named BC3Net10, capable of deriving robust and coherent patterns. In principle, this approach initially leverages the power of literature knowledge to extract AD specific genes for generating viable networks. Our findings suggest that AD GRNs show significant enrichment for key signaling mechanisms involved in neurotransmission. Among the prioritized genes, well-known AD genes were prominent in synaptic transmission, implicated in cognitive deficits. Moreover, less intensive studied AD candidates (STX2, HLA-F, HLA-C, RAB11FIP4, ARAP3, AP2A2, ATP2B4, ITPR2, and ATP2A3) are also involved in neurotransmission, providing new insights into the underlying mechanism. To our knowledge, this is the first study to generate knowledge-instructed GRNs that demonstrates an effective way of combining literature-based knowledge and data-driven analysis to identify lesser known candidates embedded in stable and robust functional patterns across disparate datasets.

Genomic positional conservation identifies topological anchor point RNAs linked to developmental loci

BACKGROUND

The mammalian genome is transcribed into large numbers of long noncoding RNAs (lncRNAs), but the definition of functional lncRNA groups has proven difficult, partly due to their low sequence conservation and lack of identified shared properties. Here we consider promoter conservation and positional conservation as indicators of functional commonality.

RESULTS

We identify 665 conserved lncRNA promoters in mouse and human that are preserved in genomic position relative to orthologous coding genes. These positionally conserved lncRNA genes are primarily associated with developmental transcription factor loci with which they are coexpressed in a tissue-specific manner. Over half of positionally conserved RNAs in this set are linked to chromatin organization structures, overlapping binding sites for the CTCF chromatin organiser and located at chromatin loop anchor points and borders of topologically associating domains (TADs). We define these RNAs as topological anchor point RNAs (tapRNAs). Characterization of these noncoding RNAs and their associated coding genes shows that they are functionally connected: they regulate each other's expression and influence the metastatic phenotype of cancer cells in vitro in a similar fashion. Furthermore, we find that tapRNAs contain conserved sequence domains that are enriched in motifs for zinc finger domain-containing RNA-binding proteins and transcription factors, whose binding sites are found mutated in cancers.

CONCLUSIONS

This work leverages positional conservation to identify lncRNAs with potential importance in genome organization, development and disease. The evidence that many developmental transcription factors are physically and functionally connected to lncRNAs represents an exciting stepping-stone to further our understanding of genome regulation.

Genetic polymorphisms of NOS2 and predisposition to fracture non-union: A case control study based on Han Chinese population

A non-union, especially atrophic non-unions, is a permanent failure of healing following a fracture and can be difficult to treat. Approximately 5–10% of fractures will result in a non-union during the healing process. non-unions can be classified into two types: atrophic non-union which is often due to impaired bone healing with a potential biological mechanism, and hypertrophic non-union which is due to inadequate fixation after fracture. Genetic variations also play an important role in the fracture healing response. Previous studies based on animal models have indicated that NOS2 might be greatly involved in the bone fracture healing process. In this case-control study, 346 nonunion patients were compared to 883 patients with normal fracture healing to investigate the potential genetic association between NOS2 and the fracture healing process using study subjects of Chinese Han ancestry. Twenty-seven single nucleotide polymorphisms (SNPs) covering NOS2 were genotyped in our study subjects and analyzed. In addition to the single marker-based analysis, we performed a gene-by-environment analysis to examine the potential interactions between genetic polymorphisms and some environmental factors. SNP rs2297514 showed significant association with the fracture healing process after adjusting for age and gender (OR = 1.38, P = 0.0005). Our results indicated that the T allele of rs2297514 significantly increased the risk of a non-union during the fracture healing process by 38% compared to the C allele. Further stratification analyses conducted for this SNP using data from subgroups classified by different sites of fracture indicated that significance could only be observed in the tibial diaphysis subgroup (N = 428, OR = 1.77, P = 0.0007) but not other groups including femur diaphysis, humeral shaft, ulnar shaft, and femur neck. Gene-by-environment interaction analyses of the three environmental factors showed no significant results. In this study, rs2297514 was significantly associated with the non-union status of fracture healing using a large Chinese population-based study sample. Our findings replicated those of a previous preliminary study and offered strong evidence linking NOS2 and fracture healing.

Integrating ChIP-seq with other functional genomics data

Transcription is regulated by transcription factor (TF) binding at promoters and distal regulatory elements and histone modifications that control the accessibility of these elements. Chromatin immunoprecipitation followed by sequencing (ChIP-seq) has become the standard assay for identifying genome-wide protein-DNA interactions in vitro and in vivo. As large-scale ChIP-seq data sets have been collected for different TFs and histone modifications, their potential to predict gene expression can be used to test hypotheses about the mechanisms of gene regulation. In addition, complementary functional genomics assays provide a global view of chromatin accessibility and long-range cis-regulatory interactions that are being combined with TF binding and histone remodeling to study the regulation of gene expression. Thus, ChIP-seq analysis is now widely integrated with other functional genomics assays to better understand gene regulatory mechanisms. In this review, we discuss advances and challenges in integrating ChIP-seq data to identify context-specific chromatin states associated with gene activity. We describe the overall computational design of integrating ChIP-seq data with other functional genomics assays. We also discuss the challenges of extending these methods to low-input ChIP-seq assays and related single-cell assays.

Conserved and species-specific transcription factor co-binding patterns drive divergent gene regulation in human and mouse

The mouse is widely used as system to study human genetic mechanisms. However, extensive rewiring of transcriptional regulatory networks often confounds translation of findings between human and mouse. Site-specific gain and loss of individual transcription factor binding sites (TFBS) has caused functional divergence of orthologous regulatory loci, and so we must look beyond this positional conservation to understand common themes of regulatory control. Fortunately, transcription factor co-binding patterns shared across species often perform conserved regulatory functions. These can be compared to 'regulatory sentences' that retain the same meanings regardless of sequence and species context. By analyzing TFBS co-occupancy patterns observed in four human and mouse cell types, we learned a regulatory grammar: the rules by which TFBS are combined into meaningful regulatory sentences. Different parts of this grammar associate with specific sets of functional annotations regardless of sequence conservation and predict functional signatures more accurately than positional conservation. We further show that both species-specific and conserved portions of this grammar are involved in gene expression divergence and human disease risk. These findings expand our understanding of transcriptional regulatory mechanisms, suggesting that phenotypic divergence and disease risk are driven by a complex interplay between deeply conserved and species-specific transcriptional regulatory pathways.

A proximity-based graph clustering method for the identification and application of transcription factor clusters

BACKGROUND

Transcription factors (TFs) form a complex regulatory network within the cell that is crucial to cell functioning and human health. While methods to establish where a TF binds to DNA are well established, these methods provide no information describing how TFs interact with one another when they do bind. TFs tend to bind the genome in clusters, and current methods to identify these clusters are either limited in scope, unable to detect relationships beyond motif similarity, or not applied to TF-TF interactions.

METHODS

Here, we present a proximity-based graph clustering approach to identify TF clusters using either ChIP-seq or motif search data. We use TF co-occurrence to construct a filtered, normalized adjacency matrix and use the Markov Clustering Algorithm to partition the graph while maintaining TF-cluster and cluster-cluster interactions. We then apply our graph structure beyond clustering, using it to increase the accuracy of motif-based TFBS searching for an example TF.

RESULTS

We show that our method produces small, manageable clusters that encapsulate many known, experimentally validated transcription factor interactions and that our method is capable of capturing interactions that motif similarity methods might miss. Our graph structure is able to significantly increase the accuracy of motif TFBS searching, demonstrating that the TF-TF connections within the graph correlate with biological TF-TF interactions.

CONCLUSION

The interactions identified by our method correspond to biological reality and allow for fast exploration of TF clustering and regulatory dynamics.

A genome-wide interactome of DNA-associated proteins in the human liver

Large-scale efforts like the ENCODE Project have made tremendous progress in cataloging the genomic binding patterns of DNA-associated proteins (DAPs), such as transcription factors (TFs). However, most chromatin immunoprecipitation-sequencing (ChIP-seq) analyses have focused on a few immortalized cell lines whose activities and physiology differ in important ways from endogenous cells and tissues. Consequently, binding data from primary human tissue are essential to improving our understanding of in vivo gene regulation. Here, we identify and analyze more than 440,000 binding sites using ChIP-seq data for 20 DAPs in two human liver tissue samples. We integrated binding data with transcriptome and phased WGS data to investigate allelic DAP interactions and the impact of heterozygous sequence variation on the expression of neighboring genes. Our tissue-based data set exhibits binding patterns more consistent with liver biology than cell lines, and we describe uses of these data to better prioritize impactful noncoding variation. Collectively, our rich data set offers novel insights into genome function in human liver tissue and provides a valuable resource for assessing disease-related disruptions.

Multiple Gene-Environment Interactions on the Angiogenesis Gene-Pathway Impact Rectal Cancer Risk and Survival

Characterization of gene-environment interactions (GEIs) in cancer is limited. We aimed at identifying GEIs in rectal cancer focusing on a relevant biologic process involving the angiogenesis pathway and relevant environmental exposures: cigarette smoking, alcohol consumption, and animal protein intake. We analyzed data from 747 rectal cancer cases and 956 controls from the Diet, Activity and Lifestyle as a Risk Factor for Rectal Cancer study. We applied a 3-step analysis approach: first, we searched for interactions among single nucleotide polymorphisms on the pathway genes; second, we searched for interactions among the genes, both steps using Logic regression; third, we examined the GEIs significant at the 5% level using logistic regression for cancer risk and Cox proportional hazards models for survival. Permutation-based test was used for multiple testing adjustment. We identified 8 significant GEIs associated with risk among 6 genes adjusting for multiple testing: TNF (OR = 1.85, 95% CI: 1.10, 3.11), TLR4 (OR = 2.34, 95% CI: 1.38, 3.98), and EGR2 (OR = 2.23, 95% CI: 1.04, 4.78) with smoking; IGF1R (OR = 1.69, 95% CI: 1.04, 2.72), TLR4 (OR = 2.10, 95% CI: 1.22, 3.60) and EGR2 (OR = 2.12, 95% CI: 1.01, 4.46) with alcohol; and PDGFB (OR = 1.75, 95% CI: 1.04, 2.92) and MMP1 (OR = 2.44, 95% CI: 1.24, 4.81) with protein. Five GEIs were associated with survival at the 5% significance level but not after multiple testing adjustment: CXCR1 (HR = 2.06, 95% CI: 1.13, 3.75) with smoking; and KDR (HR = 4.36, 95% CI: 1.62, 11.73), TLR2 (HR = 9.06, 95% CI: 1.14, 72.11), EGR2 (HR = 2.45, 95% CI: 1.42, 4.22), and EGFR (HR = 6.33, 95% CI: 1.95, 20.54) with protein. GEIs between angiogenesis genes and smoking, alcohol, and animal protein impact rectal cancer risk. Our results support the importance of considering the biologic hypothesis to characterize GEIs associated with cancer outcomes.

The methyltransferase SETDB1 regulates a large neuron-specific topological chromatin domain

We report locus-specific disintegration of megabase-scale chromosomal conformations in brain after neuronal ablation of Kmt1e/Setdb1 histone H3-lysine 9 methyltransferase, including a large topologically associated 1.2Mb domain conserved in human and mouse and encompassing >70 genes at the clustered Protocadherin (cPcdh) locus. TAD^cPcdh in mutant neurons showed abnormal accumulations of CTCF transcriptional regulator and 3D genome organizer at cryptic binding sites, converted into permissive state with DNA cytosine hypomethylation and histone hyperacetylation. Broadly upregulated expression across cPcdh included defective S-type Protocadherin single-cell stochastic constraint. Setdb1-dependent loop formations, bypassing 0.2–1Mb of linear genome, radiated from TAD^Pcdh fringes towards cPcdh cis-regulatory sequences, counterbalanced shorter-range facilitative promoter-enhancer contacts and carried loop-bound polymorphisms associated with genetic risk for schizophrenia. We show that KRAB-zinc finger Setdb1 repressor complex, shielding neuronal 3D genomes from excess CTCF binding, is critically required for structural maintenance of TAD^cPcdh.

The logic of transcriptional regulator recruitment architecture at cis-regulatory modules controlling liver functions

Control of gene transcription relies on concomitant regulation by multiple transcriptional regulators (TRs). However, how recruitment of a myriad of TRs is orchestrated at cis-regulatory modules (CRMs) to account for coregulation of specific biological pathways is only partially understood. Here, we have used mouse liver CRMs involved in regulatory activities of the hepatic TR, NR1H4 (FXR; farnesoid X receptor), as our model system to tackle this question. Using integrative cistromic, epigenomic, transcriptomic, and interactomic analyses, we reveal a logical organization where trans-regulatory modules (TRMs), which consist of subsets of preferentially and coordinately corecruited TRs, assemble into hierarchical combinations at hepatic CRMs. Different combinations of TRMs add to a core TRM, broadly found across the whole landscape of CRMs, to discriminate promoters from enhancers. These combinations also specify distinct sets of CRM differentially organized along the genome and involved in regulation of either housekeeping/cellular maintenance genes or liver-specific functions. In addition to these TRMs which we define as obligatory, we show that facultative TRMs, such as one comprising core circadian TRs, are further recruited to selective subsets of CRMs to modulate their activities. TRMs transcend TR classification into ubiquitous versus liver-identity factors, as well as TR grouping into functional families. Hence, hierarchical superimpositions of obligatory and facultative TRMs bring about independent transcriptional regulatory inputs defining different sets of CRMs with logical connection to regulation of specific gene sets and biological pathways. Altogether, our study reveals novel principles of concerted transcriptional regulation by multiple TRs at CRMs.

The Role of Osteopontin and Its Gene on Glucocorticoid Response in Myasthenia Gravis

Biomarkers that assess treatment response for patients with the autoimmune disorder, myasthenia gravis (MG), have not been evaluated to a significant extent. We hypothesized the pro-inflammatory cytokine, osteopontin (OPN), may be associated with variability of response to glucocorticoids (GCs) in patients with MG. A cohort of 250 MG patients treated with standardized protocol of GCs was recruited, and plasma OPN and polymorphisms of its gene, secreted phosphoprotein 1 (SPP1), were evaluated. Mean OPN levels were higher in patients compared to healthy controls. Carriers of rs11728697*T allele (allele definition: one of two or more alternative forms of a gene) were more frequent in the poorly GC responsive group compared to the GC responsive group indicating an association of rs11728697*T allele with GC non-responsiveness. One risk haplotype (AGTACT) was identified associated with GC non-responsiveness compared with GC responsive MG group. Genetic variations of SPP1 were found associated with the response to GC among MG patients.

Emerging functional roles of nuclear receptors in breast cancer

Nuclear receptors (NRs) have been targets of intensive drug development for decades due to their roles as key regulators of multiple developmental, physiological and disease processes. In breast cancer, expression of the estrogen and progesterone receptor remains clinically important in predicting prognosis and determining therapeutic strategies. More recently, there is growing evidence supporting the involvement of multiple nuclear receptors other than the estrogen and progesterone receptors, in the regulation of various processes important to the initiation and progression of breast cancer. We review new insights into the mechanisms of action of NRs made possible by recent advances in genomic technologies and focus on the emerging functional roles of NRs in breast cancer biology, including their involvement in circadian regulation, metabolic reprogramming and breast cancer migration and metastasis.

Targeted sequencing identifies genetic polymorphisms of flavin-containing monooxygenase genes contributing to susceptibility of nicotine dependence in European American and African American

BACKGROUND

Smoking is a leading cause of preventable death. Early studies based on samples of twins have linked the lifetime smoking practices to genetic predisposition. The flavin-containing monooxygenase (FMO) protein family consists of a group of enzymes that metabolize drugs and xenobiotics. Both FMO1 and FMO3 were potentially susceptible genes for nicotine metabolism process.

METHODS

In this study, we investigated the potential of FMO genes to confer risk of nicotine dependence via deep targeted sequencing in 2,820 study subjects comprising 1,583 nicotine dependents and 1,237 controls from European American and African American. Specifically, we focused on the two genomic segments including FMO1,FMO3, and pseudo gene FMO6P, and aimed to investigate the potential association between FMO genes and nicotine dependence. Both common and low-frequency/rare variants were analyzed using different algorithms. The potential functional significance of SNPs with association signal was investigated with relevant bioinformatics tools.

RESULTS

We identified different clusters of significant common variants in European (with most significant SNP rs6674596, p = .0004, OR = 0.67, MAF_EA = 0.14, FMO1) and African Americans (with the most significant SNP rs6608453, p = .001, OR = 0.64, MAF_AA = 0.1, FMO6P). No significant signals were identified through haplotype-based analyses. Gene network investigation indicated that both FMO1 and FMO3 have a strong relation with a variety of genes belonging to CYP gene families (with combined score greater than 0.9). Most of the significant variants identified were SNPs located within intron regions or with unknown functional significance, indicating a need for future work to understand the underlying functional significance of these signals.

CONCLUSIONS

Our findings indicated significant association between FMO genes and nicotine dependence. Replications of our findings in other ethnic groups were needed in the future. Most of the significant variants identified were SNPs located within intronic regions or with unknown functional significance, indicating a need for future work to understand the underlying functional significance of these signals.

Properties of STAT1 and IRF1 enhancers and the influence of SNPs

BACKGROUND

STAT1 and IRF1 collaborate to induce interferon-γ (IFNγ) stimulated genes (ISGs), but the extent to which they act alone or together is unclear. The effect of single nucleotide polymorphisms (SNPs) on in vivo binding is also largely unknown.

RESULTS

We show that IRF1 binds at proximal or distant ISG sites twice as often as STAT1, increasing to sixfold at the MHC class I locus. STAT1 almost always bound with IRF1, while most IRF1 binding events were isolated. Dual binding sites at remote or proximal enhancers distinguished ISGs that were responsive to IFNγ versus cell-specific resistant ISGs, which showed fewer and mainly single binding events. Surprisingly, inducibility in one cell type predicted ISG-responsiveness in other cells. Several dbSNPs overlapped with STAT1 and IRF1 binding motifs, and we developed methodology to rapidly assess their effects. We show that in silico prediction of SNP effects accurately reflects altered binding both in vitro and in vivo.

CONCLUSIONS

These data reveal broad cooperation between STAT1 and IRF1, explain cell type specific differences in ISG-responsiveness, and identify genetic variants that may participate in the pathogenesis of immune disorders.

SZDB: A Database for Schizophrenia Genetic Research

Schizophrenia (SZ) is a debilitating brain disorder with a complex genetic architecture. Genetic studies, especially recent genome-wide association studies (GWAS), have identified multiple variants (loci) conferring risk to SZ. However, how to efficiently extract meaningful biological information from bulk genetic findings of SZ remains a major challenge. There is a pressing need to integrate multiple layers of data from various sources, eg, genetic findings from GWAS, copy number variations (CNVs), association and linkage studies, gene expression, protein-protein interaction (PPI), co-expression, expression quantitative trait loci (eQTL), and Encyclopedia of DNA Elements (ENCODE) data, to provide a comprehensive resource to facilitate the translation of genetic findings into SZ molecular diagnosis and mechanism study. Here we developed the SZDB database (http://www.szdb.org/), a comprehensive resource for SZ research. SZ genetic data, gene expression data, network-based data, brain eQTL data, and SNP function annotation information were systematically extracted, curated and deposited in SZDB. In-depth analyses and systematic integration were performed to identify top prioritized SZ genes and enriched pathways. Multiple types of data from various layers of SZ research were systematically integrated and deposited in SZDB. In-depth data analyses and integration identified top prioritized SZ genes and enriched pathways. We further showed that genes implicated in SZ are highly co-expressed in human brain and proteins encoded by the prioritized SZ risk genes are significantly interacted. The user-friendly SZDB provides high-confidence candidate variants and genes for further functional characterization. More important, SZDB provides convenient online tools for data search and browse, data integration, and customized data analyses.

Imputation for transcription factor binding predictions based on deep learning

Understanding the cell-specific binding patterns of transcription factors (TFs) is fundamental to studying gene regulatory networks in biological systems, for which ChIP-seq not only provides valuable data but is also considered as the gold standard. Despite tremendous efforts from the scientific community to conduct TF ChIP-seq experiments, the available data represent only a limited percentage of ChIP-seq experiments, considering all possible combinations of TFs and cell lines. In this study, we demonstrate a method for accurately predicting cell-specific TF binding for TF-cell line combinations based on only a small fraction (4%) of the combinations using available ChIP-seq data. The proposed model, termed TFImpute, is based on a deep neural network with a multi-task learning setting to borrow information across transcription factors and cell lines. Compared with existing methods, TFImpute achieves comparable accuracy on TF-cell line combinations with ChIP-seq data; moreover, TFImpute achieves better accuracy on TF-cell line combinations without ChIP-seq data. This approach can predict cell line specific enhancer activities in K562 and HepG2 cell lines, as measured by massively parallel reporter assays, and predicts the impact of SNPs on TF binding.

Transcription factors play a central role in regulating various cellular processes. They bind to DNA in a cell-specific way. To study where a TF would bind to DNA, ChIP-seq experiment has been developed and widely adopted by the science community to study genome-wide in vivo protein-DNA interactions. However, for each TF, only a limited number of cell types have been explored by ChIP-seq experiments. To study the binding of a TF to a DNA sequence in a cell line without corresponding ChIP-seq data, researchers would check whether there is a motif for the TF in the sequence. However, motif alone contains only sequence information and therefore cannot reflect the cell specificity of TF binding. In this work, we demonstrate how to model the TF binding problem using deep learning and achieve cell specific binding prediction for TF-cell line combinations without ChIP-seq data.

Combinatorial bZIP dimers display complex DNA-binding specificity landscapes

How transcription factor dimerization impacts DNA-binding specificity is poorly understood. Guided by protein dimerization properties, we examined DNA binding specificities of 270 human bZIP pairs. DNA interactomes of 80 heterodimers and 22 homodimers revealed that 72% of heterodimer motifs correspond to conjoined half-sites preferred by partnering monomers. Remarkably, the remaining motifs are composed of variably-spaced half-sites (12%) or 'emergent' sites (16%) that cannot be readily inferred from half-site preferences of partnering monomers. These binding sites were biochemically validated by EMSA-FRET analysis and validated in vivo by ChIP-seq data from human cell lines. Focusing on ATF3, we observed distinct cognate site preferences conferred by different bZIP partners, and demonstrated that genome-wide binding of ATF3 is best explained by considering many dimers in which it participates. Importantly, our compendium of bZIP-DNA interactomes predicted bZIP binding to 156 disease associated SNPs, of which only 20 were previously annotated with known bZIP motifs.

Modular combinatorial binding among human trans-acting factors reveals direct and indirect factor binding

Background

The combinatorial binding of trans-acting factors (TFs) to the DNA is critical to the spatial and temporal specificity of gene regulation. For certain regulatory regions, more than one regulatory module (set of TFs that bind together) are combined to achieve context-specific gene regulation. However, previous approaches are limited to either pairwise TF co-association analysis or assuming that only one module is used in each regulatory region.

Results

We present a new computational approach that models the modular organization of TF combinatorial binding. Our method learns compact and coherent regulatory modules from in vivo binding data using a topic model. We found that the binding of 115 TFs in K562 cells can be organized into 49 interpretable modules. Furthermore, we found that tens of thousands of regulatory regions use multiple modules, a structure that cannot be observed with previous hard clustering based methods. The modules discovered recapitulate many published protein-protein physical interactions, have consistent functional annotations of chromatin states, and uncover context specific co-binding such as gene proximal binding of NFY + FOS + SP and distal binding of NFY + FOS + USF. For certain TFs, the co-binding partners of direct binding (motif present) differs from those of indirect binding (motif absent); the distinct set of co-binding partners can predict whether the TF binds directly or indirectly with up to 95% accuracy. Joint analysis across two cell types reveals both cell-type-specific and shared regulatory modules.

Conclusions

Our results provide comprehensive cell-type-specific combinatorial binding maps and suggest a modular organization of combinatorial binding.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-3434-3) contains supplementary material, which is available to authorized users.

Probabilistic Inference on Multiple Normalized Genome-Wide Signal Profiles With Model Regularization

Understanding genome-wide protein-DNA interaction signals forms the basis for further focused studies in gene regulation. In particular, the chromatin immunoprecipitation with massively parallel DNA sequencing technology (ChIP-Seq) can enable us to measure the in vivo genome-wide occupancy of the DNA-binding protein of interest in a single run. Multiple ChIP-Seq runs thus inherent the potential for us to decipher the combinatorial occupancies of multiple DNA-binding proteins. To handle the genome-wide signal profiles from those multiple runs, we propose to integrate regularized regression functions (i.e., LASSO, Elastic Net, and Ridge Regression) into the well-established SignalRanker and FullSignalRanker frameworks, resulting in six additional probabilistic models for inference on multiple normalized genome-wide signal profiles. The corresponding model training algorithms are devised with computational complexity analysis. Comprehensive benchmarking is conducted to demonstrate and compare the performance of nine related probabilistic models on the ENCODE ChIP-Seq datasets. The results indicate that the regularized SignalRanker models, in contrast to the original SignalRanker models, can demonstrate excellent inference performance comparable to the FullSignalRanker models with low model complexities and time complexities. Such a feature is especially valuable in the context of the rapidly growing genome-wide signal profile data in the recent years.

Systematic Discovery of Chromatin-Bound Protein Complexes from ChIP-seq Datasets

Chromatin immunoprecipitation followed by sequencing is an invaluable assay for identifying the genomic binding sites of transcription factors. However, transcription factors rarely bind chromatin alone but often bind together with other cofactors, forming protein complexes. Here, we describe a computational method that integrates multiple ChIP-seq and RNA-seq datasets to discover protein complexes and determine their role as activators or repressors. This chapter outlines a detailed computational pipeline for discovering and predicting binding partners from ChIP-seq data and inferring their role in regulating gene expression. This work aims at developing hypotheses about gene regulation via binding partners and deciphering the combinatorial nature of DNA-binding proteins.

Computational discovery of transcription factors associated with drug response

This study integrates gene expression, genotype and drug response data in lymphoblastoid cell lines with transcription factor (TF)-binding sites from ENCODE (Encyclopedia of Genomic Elements) in a novel methodology that elucidates regulatory contexts associated with cytotoxicity. The method, GENMi (Gene Expression iN the Middle), postulates that single-nucleotide polymorphisms within TF-binding sites putatively modulate its regulatory activity, and the resulting variation in gene expression leads to variation in drug response. Analysis of 161 TFs and 24 treatments revealed 334 significantly associated TF-treatment pairs. Investigation of 20 selected pairs yielded literature support for 13 of these associations, often from studies where perturbation of the TF expression changes drug response. Experimental validation of significant GENMi associations in taxanes and anthracyclines across two triple-negative breast cancer cell lines corroborates our findings. The method is shown to be more sensitive than an alternative, genome-wide association study-based approach that does not use gene expression. These results demonstrate the utility of GENMi in identifying TFs that influence drug response and provide a number of candidates for further testing.

NF-Y Binding Site Architecture Defines a C-Fos Targeted Promoter Class

ChIP-seq experiments detect the chromatin occupancy of known transcription factors in a genome-wide fashion. The comparisons of several species-specific ChIP-seq libraries done for different transcription factors have revealed a complex combinatorial and context-specific co-localization behavior for the identified binding regions. In this study we have investigated human derived ChIP-seq data to identify common cis-regulatory principles for the human transcription factor c-Fos. We found that in four different cell lines, c-Fos targeted proximal and distal genomic intervals show prevalences for either AP-1 motifs or CCAAT boxes as known binding motifs for the transcription factor NF-Y, and thereby act in a mutually exclusive manner. For proximal regions of co-localized c-Fos and NF-YB binding, we gathered evidence that a characteristic configuration of repeating CCAAT motifs may be responsible for attracting c-Fos, probably provided by a nearby AP-1 bound enhancer. Our results suggest a novel regulatory function of NF-Y in gene-proximal regions. Specific CCAAT dimer repeats bound by the transcription factor NF-Y define this novel cis-regulatory module. Based on this behavior we propose a new enhancer promoter interaction model based on AP-1 motif defined enhancers which interact with CCAAT-box characterized promoter regions.

Gene activation by metazoan enhancers: Diverse mechanisms stimulate distinct steps of transcription

Enhancers can stimulate transcription by a number of different mechanisms which control different stages of the transcription cycle of their target genes, from recruitment of the transcription machinery to elongation by RNA polymerase. These mechanisms may not be mutually exclusive, as a single enhancer may act through different pathways by binding multiple transcription factors. Multiple enhancers may also work together to regulate transcription of a shared target gene. Most of the evidence supporting different enhancer mechanisms comes from the study of single genes, but new high-throughput experimental frameworks offer the opportunity to integrate and generalize disparate mechanisms identified at single genes. This effort is especially important if we are to fully understand how sequence variation within enhancers contributes to human disease.

A high-resolution transcriptome map of cell cycle reveals novel connections between periodic genes and cancer

Progression through the cell cycle is largely dependent on waves of periodic gene expression, and the regulatory networks for these transcriptome dynamics have emerged as critical points of vulnerability in various aspects of tumor biology. Through RNA-sequencing of human cells during two continuous cell cycles (>2.3 billion paired reads), we identified over 1 000 mRNAs, non-coding RNAs and pseudogenes with periodic expression. Periodic transcripts are enriched in functions related to DNA metabolism, mitosis, and DNA damage response, indicating these genes likely represent putative cell cycle regulators. Using our set of periodic genes, we developed a new approach termed "mitotic trait" that can classify primary tumors and normal tissues by their transcriptome similarity to different cell cycle stages. By analyzing >4 000 tumor samples in The Cancer Genome Atlas (TCGA) and other expression data sets, we found that mitotic trait significantly correlates with genetic alterations, tumor subtype and, notably, patient survival. We further defined a core set of 67 genes with robust periodic expression in multiple cell types. Proteins encoded by these genes function as major hubs of protein-protein interaction and are mostly required for cell cycle progression. The core genes also have unique chromatin features including increased levels of CTCF/RAD21 binding and H3K36me3. Loss of these features in uterine and kidney cancers is associated with altered expression of the core 67 genes. Our study suggests new chromatin-associated mechanisms for periodic gene regulation and offers a predictor of cancer patient outcomes.

Blood and Intestine eQTLs from an Anti-TNF-Resistant Crohn's Disease Cohort Inform IBD Genetic Association Loci

OBJECTIVES:

Genome-wide association studies (GWAS) have identified loci reproducibly associated with inflammatory bowel disease (IBD) and other immune-mediated diseases; however, the molecular mechanisms underlying most of genetic susceptibility remain undefined. Expressional quantitative trait loci (eQTL) of disease-relevant tissue can be employed in order to elucidate the genes and pathways affected by disease-specific genetic variance.

METHODS:

In this study, we derived eQTLs for human whole blood and intestine tissues of anti-tumor necrosis factor-resistant Crohn's disease (CD) patients. We interpreted these eQTLs in the context of published IBD GWAS hits to inform on the disease process.

RESULTS:

At 10% false discovery rate, we discovered that 5,174 genes in blood and 2,063 genes in the intestine were controlled by a nearby single-nucleotide polymorphism (SNP) (i.e., cis-eQTL), among which 1,360 were shared between the two tissues. A large fraction of the identified eQTLs were supported by the regulomeDB database, showing that the eQTLs reside in regulatory elements (odds ratio; OR=3.44 and 3.24 for blood and intestine eQTLs, respectively) as opposed to protein-coding regions. Published IBD GWAS hits as a whole were enriched for blood and intestine eQTLs (OR=2.88 and 2.05; and P value=2.51E-9 and 0.013, respectively), thereby linking genetic susceptibility to control of gene expression in these tissues. Through a systematic search, we used eQTL data to inform 109 out of 372 IBD GWAS SNPs documented in National Human Genome Research Institute catalog, and we categorized the genes influenced by eQTLs according to their functions. Many of these genes have experimentally validated roles in specific cell types contributing to intestinal inflammation.

CONCLUSIONS:

The blood and intestine eQTLs described in this study represent a powerful tool to link GWAS loci to a regulatory function and thus elucidate the mechanisms underlying the genetic loci associated with IBD and related conditions. Overall, our eQTL discovery approach empirically identifies the disease-associated variants including their impact on the direction and extent of expression changes in the context of disease-relevant cellular pathways in order to infer the functional outcome of this aspect of genetic susceptibility.

Modeling the causal regulatory network by integrating chromatin accessibility and transcriptome data

Cell packs a lot of genetic and regulatory information through a structure known as chromatin, i.e. DNA is wrapped around histone proteins and is tightly packed in a remarkable way. To express a gene in a specific coding region, the chromatin would open up and DNA loop may be formed by interacting enhancers and promoters. Furthermore, the mediator and cohesion complexes, sequence-specific transcription factors, and RNA polymerase II are recruited and work together to elaborately regulate the expression level. It is in pressing need to understand how the information, about when, where, and to what degree genes should be expressed, is embedded into chromatin structure and gene regulatory elements. Thanks to large consortia such as Encyclopedia of DNA Elements (ENCODE) and Roadmap Epigenomic projects, extensive data on chromatin accessibility and transcript abundance are available across many tissues and cell types. This rich data offer an exciting opportunity to model the causal regulatory relationship. Here, we will review the current experimental approaches, foundational data, computational problems, interpretive frameworks, and integrative models that will enable the accurate interpretation of regulatory landscape. Particularly, we will discuss the efforts to organize, analyze, model, and integrate the DNA accessibility data, transcriptional data, and functional genomic regions together. We believe that these efforts will eventually help us understand the information flow within the cell and will influence research directions across many fields.

A high definition look at the NF-Y regulome reveals genome-wide associations with selected transcription factors

NF-Y is a trimeric transcription factor (TF), binding the CCAAT box element, for which several results suggest a pioneering role in activation of transcription. In this work, we integrated 380 ENCODE ChIP-Seq experiments for 154 TFs and cofactors with sequence analysis, protein–protein interactions and RNA profiling data, in order to identify genome-wide regulatory modules resulting from the co-association of NF-Y with other TFs. We identified three main degrees of co-association with NF-Y for sequence-specific TFs. In the most relevant one, we found TFs having a significant overlap with NF-Y in their DNA binding loci, some with a precise spacing of binding sites with respect to the CCAAT box, others (FOS, Sp1/2, RFX5, IRF3, PBX3) mostly lacking their canonical binding site and bound to arrays of well spaced CCAAT boxes. As expected, NF-Y binding also correlates with RNA Pol II General TFs and with subunits of complexes involved in the control of H3K4 methylations. Co-association patterns are confirmed by protein–protein interactions, and correspond to specific functional categorizations and expression level changes of target genes following NF-Y inactivation. These data define genome-wide rules for the organization of NF-Y-centered regulatory modules, supporting a model of distinct categorization and synergy with well defined sets of TFs.

Towards a predictive model of chromatin 3D organization

Architectural proteins mediate interactions between distant regions in the genome to bring together different regulatory elements while establishing a specific three-dimensional organization of the genetic material. Depletion of specific architectural proteins leads to miss regulation of gene expression and alterations in nuclear organization. The specificity of interactions mediated by architectural proteins depends on the nature, number, and orientation of their binding site at individual genomic locations. Knowledge of the mechanisms and rules governing interactions among architectural proteins may provide a code to predict the 3D organization of the genome.

Modeling co-occupancy of transcription factors using chromatin features

Regulation of gene expression requires both transcription factor (TFs) and epigenetic modifications, and interplays between the two types of factors have been discovered. However study of relationships between chromatin features and TF–TF co-occupancy remains limited. Here, we revealed the relationship by first illustrating distinct profile patterns of chromatin features related to different binding events, including single TF binding and TF–TF co-occupancy of 71 TFs from five human cell lines. We further implemented statistical analyses to demonstrate the relationship by accurately predicting co-occupancy genome-widely using chromatin features including DNase I hypersensitivity, 11 histone modifications (HMs) and GC content. Remarkably, our results showed that the combination of chromatin features enables accurate predictions across the five cells. For individual chromatin features, DNase I enables high and consistent predictions. H3K27ac, H3K4me 2, H3K4me3 and H3K9ac are more reliable predictors than other HMs. Although the combination of 11 HMs achieves accurate predictions, their predictive ability varies considerably when a model obtained from one cell is applied to others, indicating relationship between HMs and TF–TF co-occupancy is cell type dependent. GC content is not a reliable predictor, but the addition of GC content to any other features enhances their predictive ability. Together, our results elucidate a strong relationship between TF–TF co-occupancy and chromatin features.

Probabilistic Inference on Multiple Normalized Signal Profiles from Next Generation Sequencing: Transcription Factor Binding Sites

With the prevalence of chromatin immunoprecipitation (ChIP) with sequencing (ChIP-Seq) technology, massive ChIP-Seq data has been accumulated. The ChIP-Seq technology measures the genome-wide occupancy of DNA-binding proteins in vivo. It is well-known that different DNA-binding protein occupancies may result in a gene being regulated in different conditions (e.g. different cell types). To fully understand a gene's function, it is essential to develop probabilistic models on multiple ChIP-Seq profiles for deciphering the gene transcription causalities. In this work, we propose and describe two probabilistic models. Assuming the conditional independence of different DNA-binding proteins' occupancies, the first method (SignalRanker) is developed as an intuitive method for ChIP-Seq genome-wide signal profile inference. Unfortunately, such an assumption may not always hold in some gene regulation cases. Thus, we propose and describe another method (FullSignalRanker) which does not make the conditional independence assumption. The proposed methods are compared with other existing methods on ENCODE ChIP-Seq datasets, demonstrating its regression and classification ability. The results suggest that FullSignalRanker is the best-performing method for recovering the signal ranks on the promoter and enhancer regions. In addition, FullSignalRanker is also the best-performing method for peak sequence classification. We envision that SignalRanker and FullSignalRanker will become important in the era of next generation sequencing. FullSignalRanker program is available on the following website: http://www.cs.toronto.edu/~wkc/FullSignalRanker/.

Integrative analysis of RNA, translation, and protein levels reveals distinct regulatory variation across humans

Elucidating the consequences of genetic differences between humans is essential for understanding phenotypic diversity and personalized medicine. Although variation in RNA levels, transcription factor binding, and chromatin have been explored, little is known about global variation in translation and its genetic determinants. We used ribosome profiling, RNA sequencing, and mass spectrometry to perform an integrated analysis in lymphoblastoid cell lines from a diverse group of individuals. We find significant differences in RNA, translation, and protein levels suggesting diverse mechanisms of personalized gene expression control. Combined analysis of RNA expression and ribosome occupancy improves the identification of individual protein level differences. Finally, we identify genetic differences that specifically modulate ribosome occupancy—many of these differences lie close to start codons and upstream ORFs. Our results reveal a new level of gene expression variation among humans and indicate that genetic variants can cause changes in protein levels through effects on translation.

Global SUMOylation on active chromatin is an acute heat stress response restricting transcription

BACKGROUND

Cells have developed many ways to cope with external stress. One distinctive feature in acute proteotoxic stresses, such as heat shock (HS), is rapid post-translational modification of proteins by SUMOs (small ubiquitin-like modifier proteins; SUMOylation). While many of the SUMO targets are chromatin proteins, there is scarce information on chromatin binding of SUMOylated proteins in HS and the role of chromatin SUMOylation in the regulation of transcription.

RESULTS

We mapped HS-induced genome-wide changes in chromatin occupancy of SUMO-2/3-modified proteins in K562 and VCaP cells using ChIP-seq. Chromatin SUMOylation was further correlated with HS-induced global changes in transcription using GRO-seq and RNA polymerase II (Pol2) ChIP-seq along with ENCODE data for K562 cells. HS induced a rapid and massive rearrangement of chromatin SUMOylation pattern: SUMOylation was gained at active promoters and enhancers associated with multiple transcription factors, including heat shock factor 1. Concomitant loss of SUMOylation occurred at inactive intergenic chromatin regions that were associated with CTCF-cohesin complex and SETDB1 methyltransferase complex. In addition, HS triggered a dynamic chromatin binding of SUMO ligase PIAS1, especially onto promoters. The HS-induced SUMOylation on chromatin was most notable at promoters of transcribed genes where it positively correlated with active transcription and Pol2 promoter-proximal pausing. Furthermore, silencing of SUMOylation machinery either by depletion of UBC9 or PIAS1 enhanced expression of HS-induced genes.

CONCLUSIONS

HS-triggered SUMOylation targets promoters and enhancers of actively transcribed genes where it restricts the transcriptional activity of the HS-induced genes. PIAS1-mediated promoter SUMOylation is likely to regulate Pol2-associated factors in HS.

Single-cell chromatin accessibility reveals principles of regulatory variation

Cell-to-cell variation is a universal feature of life that impacts a wide range of biological phenomena, from developmental plasticity^1,2 to tumor heterogeneity³. While recent advances have improved our ability to document cellular phenotypic variation^4–8 the fundamental mechanisms that generate variability from identical DNA sequences remain elusive. Here we reveal the landscape and principles of cellular DNA regulatory variation by developing a robust method for mapping the accessible genome of individual cells via assay for transposase-accessible chromatin using sequencing (ATAC-seq). Single-cell ATAC-seq (scATAC-seq) maps from hundreds of single-cells in aggregate closely resemble accessibility profiles from tens of millions of cells and provides insights into cell-to-cell variation. Accessibility variance is systematically associated with specific trans-factors and cis-elements, and we discover combinations of trans-factors associated with either induction or suppression of cell-to-cell variability. We further identify sets of trans-factors associated with cell-type specific accessibility variance across 8 cell types. Targeted perturbations of cell cycle or transcription factor signaling evoke stimulus-specific changes in this observed variability. The pattern of accessibility variation in cis across the genome recapitulates chromosome topological domains⁹de novo, linking single-cell accessibility variation to three-dimensional genome organization. All together, single-cell analysis of DNA accessibility provides new insight into cellular variation of the “regulome.”

Multiple sclerosis-associated CLEC16A controls HLA class II expression via late endosome biogenesis

C-type lectins are key players in immune regulation by driving distinct functions of antigen-presenting cells. The C-type lectin CLEC16A gene is located at 16p13, a susceptibility locus for several autoimmune diseases, including multiple sclerosis. However, the function of this gene and its potential contribution to these diseases in humans are poorly understood. In this study, we found a strong upregulation of CLEC16A expression in the white matter of multiple sclerosis patients (n = 14) compared to non-demented controls (n = 11), mainly in perivascular leukocyte infiltrates. Moreover, CLEC16A levels were significantly enhanced in peripheral blood mononuclear cells of multiple sclerosis patients (n = 69) versus healthy controls (n = 46). In peripheral blood mononuclear cells, CLEC16A was most abundant in monocyte-derived dendritic cells, in which it strongly co-localized with human leukocyte antigen class II. Treatment of these professional antigen-presenting cells with vitamin D, a key protective environmental factor in multiple sclerosis, downmodulated CLEC16A in parallel with human leukocyte antigen class II. Knockdown of CLEC16A in distinct types of model and primary antigen-presenting cells resulted in severely impaired cytoplasmic distribution and formation of human leucocyte antigen class II-positive late endosomes, as determined by immunofluorescence and electron microscopy. Mechanistically, CLEC16A participated in the molecular machinery of human leukocyte antigen class II-positive late endosome formation and trafficking to perinuclear regions, involving the dynein motor complex. By performing co-immunoprecipitations, we found that CLEC16A directly binds to two critical members of this complex, RILP and the HOPS complex. CLEC16A silencing in antigen-presenting cells disturbed RILP-mediated recruitment of human leukocyte antigen class II-positive late endosomes to perinuclear regions. Together, we identify CLEC16A as a pivotal gene in multiple sclerosis that serves as a direct regulator of the human leukocyte antigen class II pathway in antigen-presenting cells. These findings are a first step in coupling multiple sclerosis-associated genes to the regulation of the strongest genetic factor in multiple sclerosis, human leukocyte antigen class II.

ZNF143 provides sequence specificity to secure chromatin interactions at gene promoters

Chromatin interactions connect distal regulatory elements to target gene promoters guiding stimulus- and lineage-specific transcription. Few factors securing chromatin interactions have so far been identified. Here, by integrating chromatin interaction maps with the large collection of transcription factor-binding profiles provided by the ENCODE project, we demonstrate that the zinc-finger protein ZNF143 preferentially occupies anchors of chromatin interactions connecting promoters with distal regulatory elements. It binds directly to promoters and associates with lineage-specific chromatin interactions and gene expression. Silencing ZNF143 or modulating its DNA-binding affinity using single-nucleotide polymorphisms (SNPs) as a surrogate of site-directed mutagenesis reveals the sequence dependency of chromatin interactions at gene promoters. We also find that chromatin interactions alone do not regulate gene expression. Together, our results identify ZNF143 as a novel chromatin-looping factor that contributes to the architectural foundation of the genome by providing sequence specificity at promoters connected with distal regulatory elements.

Chromatin interactions can connect distal regulatory elements to promoters via protein factors, but few such factors have been identified. Here, the authors show that zinc-finger protein ZNF143 is a sequence-specific chromatin-looping factor that connects promoters with distal regulatory elements.

DNA-mediated cooperativity facilitates the co-selection of cryptic enhancer sequences by SOX2 and PAX6 transcription factors

Sox2 and Pax6 are transcription factors that direct cell fate decision during neurogenesis, yet the mechanism behind how they cooperate on enhancer DNA elements and regulate gene expression is unclear. By systematically interrogating Sox2 and Pax6 interaction on minimal enhancer elements, we found that cooperative DNA recognition relies on combinatorial nucleotide switches and precisely spaced, but cryptic composite DNA motifs. Surprisingly, all tested Sox and Pax paralogs have the capacity to cooperate on such enhancer elements. NMR and molecular modeling reveal very few direct protein-protein interactions between Sox2 and Pax6, suggesting that cooperative binding is mediated by allosteric interactions propagating through DNA structure. Furthermore, we detected and validated several novel sites in the human genome targeted cooperatively by Sox2 and Pax6. Collectively, we demonstrate that Sox-Pax partnerships have the potential to substantially alter DNA target specificities and likely enable the pleiotropic and context-specific action of these cell-lineage specifiers.

Integrative analysis of public ChIP-seq experiments reveals a complex multi-cell regulatory landscape

The large collections of ChIP-seq data rapidly accumulating in public data warehouses provide genome-wide binding site maps for hundreds of transcription factors (TFs). However, the extent of the regulatory occupancy space in the human genome has not yet been fully apprehended by integrating public ChIP-seq data sets and combining it with ENCODE TFs map. To enable genome-wide identification of regulatory elements we have collected, analysed and retained 395 available ChIP-seq data sets merged with ENCODE peaks covering a total of 237 TFs. This enhanced repertoire complements and refines current genome-wide occupancy maps by increasing the human genome regulatory search space by 14% compared to ENCODE alone, and also increases the complexity of the regulatory dictionary. As a direct application we used this unified binding repertoire to annotate variant enhancer loci (VELs) from H3K4me1 mark in two cancer cell lines (MCF-7, CRC) and observed enrichments of specific TFs involved in biological key functions to cancer development and proliferation. Those enrichments of TFs within VELs provide a direct annotation of non-coding regions detected in cancer genomes. Finally, full access to this catalogue is available online together with the TFs enrichment analysis tool (http://tagc.univ-mrs.fr/remap/).

Genome-wide map of regulatory interactions in the human genome

Increasing evidence suggests that interactions between regulatory genomic elements play an important role in regulating gene expression. We generated a genome-wide interaction map of regulatory elements in human cells (ENCODE tier 1 cells, K562, GM12878) using Chromatin Interaction Analysis by Paired-End Tag sequencing (ChIA-PET) experiments targeting six broadly distributed factors. Bound regions covered 80% of DNase I hypersensitive sites including 99.7% of TSS and 98% of enhancers. Correlating this map with ChIP-seq and RNA-seq data sets revealed cohesin, CTCF, and ZNF143 as key components of three-dimensional chromatin structure and revealed how the distal chromatin state affects gene transcription. Comparison of interactions between cell types revealed that enhancer-promoter interactions were highly cell-type-specific. Construction and comparison of distal and proximal regulatory networks revealed stark differences in structure and biological function. Proximal binding events are enriched at genes with housekeeping functions, while distal binding events interact with genes involved in dynamic biological processes including response to stimulus. This study reveals new mechanistic and functional insights into regulatory region organization in the nucleus.

Regulatory analysis of the C. elegans genome with spatiotemporal resolution

Discovering the structure and dynamics of transcriptional regulatory events in the genome with cellular and temporal resolution is crucial to understanding the regulatory underpinnings of development and disease. We determined the genomic distribution of binding sites for 92 transcription factors (TFs) and regulatory proteins across multiple stages of C. elegans development by performing 241 ChIP-seq experiments. Integrating regulatory binding and cellular-resolution expression data yielded a spatiotemporally-resolved metazoan TF binding map. Using this map, we explore developmental regulatory circuits that encode combinatorial logic at the levels of co-binding and co-expression of TFs, characterizing (1) the genomic coverage and clustering of regulatory binding, (2) the binding preferences of and biological processes regulated by TFs, (3) the global TF co-associations and genomic subdomains that suggest shared patterns of regulation, and (4) key TFs and TF co-associations for fate specification of individual lineages and cell-types.

Comparative analysis of regulatory information and circuits across distant species

Despite the large evolutionary distances between metazoan species, they can show remarkable commonalities in their biology, and this has helped to establish fly and worm as model organisms for human biology. Although studies of individual elements and factors have explored similarities in gene regulation, a large-scale comparative analysis of basic principles of transcriptional regulatory features is lacking. Here we map the genome-wide binding locations of 165 human, 93 worm and 52 fly transcription regulatory factors, generating a total of 1,019 data sets from diverse cell types, developmental stages, or conditions in the three species, of which 498 (48.9%) are presented here for the first time. We find that structural properties of regulatory networks are remarkably conserved and that orthologous regulatory factor families recognize similar binding motifs in vivo and show some similar co-associations. Our results suggest that gene-regulatory properties previously observed for individual factors are general principles of metazoan regulation that are remarkably well-preserved despite extensive functional divergence of individual network connections. The comparative maps of regulatory circuitry provided here will drive an improved understanding of the regulatory underpinnings of model organism biology and how these relate to human biology, development and disease.