Accessible Region Conformation Capture (ARC-C) gives high resolution insights into genome architecture and regulation

Nuclear organization and chromatin interactions are important for genome function, yet determining chromatin connections at high-resolution remains a major challenge. To address this, we developed Accessible Region Conformation Capture (ARC-C), which profiles interactions between regulatory elements genome-wide without a capture step. Applied to C. elegans, we identify ~15,000 significant interactions between regulatory elements at 500bp resolution. Of 105 TFs or chromatin regulators tested, we find that the binding sites of 60 are enriched for interacting with each other, making them candidates for mediating interactions. These include cohesin and condensin II. Applying ARC-C to a mutant of transcription factor BLMP-1 detected changes in interactions between its targets. ARC-C simultaneously profiles domain level architecture, and we observe that C. elegans chromatin domains defined by either active or repressive modifications form topologically associating domains (TADs) which interact with A/B (active/inactive) compartment-like structure. Furthermore, we discovered that inactive compartment interactions are dependent on H3K9 methylation. ARC-C is a powerful new tool to interrogate genome architecture and regulatory interactions at high resolution.

A tandem duplication in Drosophila melanogaster shows enhanced expression beyond the gene copy number

Tandem duplicated genes are common features of genomes, but the phenotypic consequences of their origins are not well understood. It is not known whether a simple doubling of gene expression should be expected, or else some other expression outcome. This study describes an experimental framework using engineered deletions to assess any contribution of locally-acting cis- and globally-acting trans-regulatory factors to expression interactions of particular tandem duplicated genes. Acsx1L (CG6300) and Acsx1R (CG11659) are tandem duplicates of a putative acyl-CoA synthetase gene found in D. melanogaster. Experimental deletions of the duplicated segments were used to investigate whether the presence of one tandem duplicated block influences the expression of its neighbor. Acsx1L, the gene in the left block, shows much higher expression than either its duplicate Acsx1R or the single Acsx1 in D. simulans. Acsx1L expression decreases drastically upon deleting the right-hand duplicated block. Crosses among wildtype and deletion strains show that high tandem expression is primarily due to cis-acting interactions between the duplicated blocks. No effect of these genes on cuticular hydrocarbons was detected. Sequence and phylogenetic analysis suggest that the duplication rose to fixation in D. melanogaster and has been subject to extensive gene conversion. Some strains actually carry three tandem copies, yet strains with three Acsx1s do not have higher expression levels than strains with two. Surveys of tandem duplicate expression have typically not found the expected twofold increase in expression. This study suggests that cis-regulatory interactions between duplicated blocks could be responsible for this trend.

HOXC6-Mediated miR-188-5p Expression Induces Cell Migration through the Inhibition of the Tumor Suppressor FOXN2

Homeobox C6 (HOXC6) is a transcription factor that plays a role in the malignant progression of various cancers. However, the roles of HOXC6 and its regulatory mechanism remain unclear. In this study, we used microRNA (miRNA) regulatory networks to identify key regulatory interactions responsible for HOXC6-mediated cancer progression. In microarray profiling of miRNAs, the levels of miRNAs such as hsa-miR-188-5p, hsa-miR-8063, and hsa-miR-8064 were significantly increased in HOXC6-overexpressing cells. Higher positive expression rates of HOXC6 and miR-188-5p were observed in malignant cancer. We also found that HOXC6 significantly upregulated miR-188-5p expression. The underlying function of HOXC6-mediated miR-188-5p expression was predicted through TargetScan and the MiRNA Database. Overexpression of mir-188-5p inhibited the expression of forkhead box N2 (FOXN2), a tumor suppressor gene. Furthermore, in the luciferase assay, miR-188-5p bound to the 3′-UTR of FOXN2 and was mainly responsible for the dysregulation of FOXN2 expression. Silencing FOXN2 induced cell migration, and the effect of FOXN2 silencing was enhanced when the HOXC6/miR-188-5p axis was induced. These results suggest that HOXC6/miR-188-5p may induce malignant progression in cancer by inhibiting the activation of the FOXN2 signaling pathway.

Deciphering the genetic links between NAFLD and co-occurring conditions using a liver gene regulatory network

Background & Aims: Non-alcoholic fatty liver disease (NAFLD) is a multi-system metabolic disease that co-occurs with various hepatic and extra-hepatic diseases. The phenotypic manifestation of NAFLD is primarily observed in the liver. Therefore, identifying liver-specific gene regulatory interactions between variants associated with NAFLD and multimorbid conditions may help to improve our understanding of underlying shared aetiology. Methods: Here, we constructed a liver-specific gene regulatory network (LGRN) consisting of genome-wide spatially constrained expression quantitative trait loci (eQTLs) and their target genes. The LGRN was used to identify regulatory interactions involving NAFLD-associated genetic modifiers and their inter-relationships to other complex traits. Results and Conclusions: We demonstrate that MBOAT7 and IL32, which are associated with NAFLD progression, are regulated by spatially constrained eQTLs that are enriched for an association with liver enzyme levels. MBOAT7 transcript levels are also linked to eQTLs associated with cirrhosis, and other traits that commonly co-occur with NAFLD. In addition, genes that encode interacting partners of NAFLD-candidate genes within the liver-specific protein-protein interaction network were affected by eQTLs enriched for phenotypes relevant to NAFLD (e.g. IgG glycosylation patterns, OSA). Furthermore, we identified distinct gene regulatory networks formed by the NAFLD-associated eQTLs in normal versus diseased liver, consistent with the context-specificity of the eQTLs effects. Interestingly, genes targeted by NAFLD-associated eQTLs within the LGRN were also affected by eQTLs associated with NAFLD-related traits (e.g. obesity and body fat percentage). Overall, the genetic links identified between these traits expand our understanding of shared regulatory mechanisms underlying NAFLD multimorbidities.

Reconstruction of a global gene regulatory network for Streptomyces coelicolor: Curation, inference, and assessment

Streptomyces coelicolor A3(2) is a model microorganism for the study of Streptomycetes, antibiotic production, and secondary metabolism in general. However, little effort to globally study its transcription has been made even though S. coelicolor has an outstanding variety of regulators among bacteria. We manually curated 29 years of literature and databases to assemble a meta-curated experimentally-validated gene regulatory network (GRN) with 5386 genes and 9707 regulatory interactions (~41% of the total expected interactions). This provides the most extensive and up-to-date reconstruction available for the regulatory circuitry of this organism. We found a low level of direct experimental validation for the regulatory interactions reported in the literature and curated in this work. Only ~6% (533/9687) are supported by experiments confirming the binding of the transcription factor to the upstream region of the target gene, the so-called "strong" evidence. To tackle network incompleteness, we performed network inference using several methods (including two proposed here) for motif detection in DNA sequences and GRN inference from transcriptomics. Further, we contrasted the structural properties and functional architecture of the networks to assess the predictions' reliability, finding the inference from DNA sequence data to be the most trustworthy. Finally, we show two possible applications of the inferred and the curated network. The inferred one allowed us to identify putative novel transcription factors for the key Streptomyces antibiotic regulatory proteins (SARPs). The curated one allows us to study the conservation of the system-level components between S. coelicolor and Corynebacterium glutamicum. There we identified the basal machinery as the common signature between the two organisms. The curated networks were deposited in Abasy Atlas (https://abasy.ccg.unam.mx/) while the inferences are available as Supplementary Material.

Systematic inference of regulation by protein kinases finds surprising level of transcription factor deactivation

Gene expression is controlled by pathways of regulatory factors often involving the activity of protein kinases on transcription factor proteins. Despite this well established mechanism, the number of well described pathways that include the regulatory role of protein kinases on transcription factors is surprisingly scarce in eukaryotes.To address this, PhosTF was developed to infer functional regulatory interactions and pathways in both simulated and real biological networks, based on linear cyclic causal models with latent variables. GeneNetWeaverPhos, an extension of GeneNetWeaver, was developed to allow the simulation of perturbations in known networks that included the activity of protein kinases and phosphatases on gene regulation. Over 2000 genome-wide gene expression profiles, where the loss or gain of regulatory genes could be observed to perturb gene regulation, were then used to infer the existence of regulatory interactions, and their mode of regulation in the budding yeast Saccharomyces cerevisiae.Despite the additional complexity, our inference performed comparably to the best methods that inferred transcription factor regulation assessed in the DREAM4 challenge on similar simulated networks. Inference on integrated genome-scale data sets for yeast identified ∼8800 protein kinase/phosphatase-transcription factor interactions and ∼6500 interactions among protein kinases and/or phosphatases. Both types of regulatory predictions captured statistically significant numbers of known interactions of their type. Surprisingly, kinases and phosphatases regulated transcription factors by a negative mode or regulation (deactivation) in over 70% of the predictions.Author summaryIn this work we addressed the challenging problem of inferring regulation by protein kinases and phosphatases via their activity on transcription factors. Although many protein kinase activity predictors have been developed for classes of protein kinases on specific amino acids within target sequences, our approach (PhosTF) provides predictions of regulatory activity for specific protein kinases and phosphatases on specific transcription factors. Our inference approach achieves this using the functional output observed in gene expression data of gene knock out stains, along with known transcription factor regulatory interactions. We formulated and tested a model for inference of regulation as well as a model for simulation of genes expression, transcription and translation. The simulation was used for in-silico validation of the inference method, which performed comparably to the best performers on simpler inference problem in the DREAM4 competition. The inference method was then applied to yeast expression data, with significant validation by known kinase/phosphatase interactions. Over 15300 novel regulatory interactions were predicted, suggesting that kinase activity provided a surprising level of repression of gene expression, either through the deactivation of activators or the activation of repressors.

Network Inference of Transcriptional Regulation in Germinating Low Phytic Acid Soybean Seeds

The low phytic acid (lpa) trait in soybeans can be conferred by loss-of-function mutations in genes encoding myo-inositol phosphate synthase and two epistatically interacting genes encoding multidrug-resistance protein ATP-binding cassette (ABC) transporters. However, perturbations in phytic acid biosynthesis are associated with poor seed vigor. Since the benefits of the lpa trait, in terms of end-use quality and sustainability, far outweigh the negatives associated with poor seed performance, a fuller understanding of the molecular basis behind the negatives will assist crop breeders and engineers in producing variates with lpa and better germination rate. The gene regulatory network (GRN) for developing low and normal phytic acid soybean seeds was previously constructed, with genes modulating a variety of processes pertinent to phytic acid metabolism and seed viability being identified. In this study, a comparative time series analysis of low and normal phytic acid soybeans was carried out to investigate the transcriptional regulatory elements governing the transitional dynamics from dry seed to germinated seed. GRNs were reverse engineered from time series transcriptomic data of three distinct genotypic subsets composed of lpa soybean lines and their normal phytic acid sibling lines. Using a robust unsupervised network inference scheme, putative regulatory interactions were inferred for each subset of genotypes. These interactions were further validated by published regulatory interactions found in Arabidopsis thaliana and motif sequence analysis. Results indicate that lpa seeds have increased sensitivity to stress, which could be due to changes in phytic acid levels, disrupted inositol phosphate signaling, disrupted phosphate ion (Pi) homeostasis, and altered myo-inositol metabolism. Putative regulatory interactions were identified for the latter two processes. Changes in abscisic acid (ABA) signaling candidate transcription factors (TFs) putatively regulating genes in this process were identified as well. Analysis of the GRNs reveal altered regulation in processes that may be affecting the germination of lpa soybean seeds. Therefore, this work contributes to the ongoing effort to elucidate molecular mechanisms underlying altered seed viability, germination and field emergence of lpa crops, understanding of which is necessary in order to mitigate these problems.

dynDeepDRIM: a dynamic deep learning model to infer direct regulatory interactions using single cell time-course gene expression data

Time-course single-cell RNA sequencing (scRNA-seq) data have been widely applied to reconstruct the cell-type-specific gene regulatory networks by exploring the dynamic changes of gene expression between transcription factors (TFs) and their target genes. The existing algorithms were commonly designed to analyze bulk gene expression data and could not deal with the dropouts and cell heterogeneity in scRNA-seq data. In this paper, we developed dynDeepDRIM that represents gene pair joint expression as images and considers the neighborhood context to eliminate the transitive interactions. dynDeepDRIM integrated the primary image, neighbor images with time-course into a four-dimensional tensor and trained a convolutional neural network to predict the direct regulatory interactions between TFs and genes. We evaluated the performance of dynDeepDRIM on five time-course gene expression datasets. dynDeepDRIM outperformed the state-of-the-art methods for predicting TF-gene direct interactions and gene functions. We also observed gene functions could be better performed if more neighbor images were involved.