The legume-rhizobium symbiosis is initiated through the activation of the Nodulation (Nod) factor-signaling cascade, leading to a rapid reprogramming of host cell developmental pathways. In this work, we combine transcriptome sequencing with molecular genetics and network analysis to quantify and categorize the transcriptional changes occurring in roots of Medicago truncatula from minutes to days after inoculation with Sinorhizobium medicae. To identify the nature of the inductive and regulatory cues, we employed mutants with absent or decreased Nod factor sensitivities (i.e. Nodulation factor perception and Lysine motif domain-containing receptor-like kinase3, respectively) and an ethylene (ET)-insensitive, Nod factor-hypersensitive mutant (sickle). This unique data set encompasses nine time points, allowing observation of the symbiotic regulation of diverse biological processes with high temporal resolution. Among the many outputs of the study is the early Nod factorinduced, ET-regulated expression of ET signaling and biosynthesis genes. Coupled with the observation of massive transcriptional derepression in the ET-insensitive background, these results suggest that Nod factor signaling activates ET production to attenuate its own signal. Promoter:b-glucuronidase fusions report ET biosynthesis both in root hairs responding to rhizobium as well as in meristematic tissue during nodule organogenesis and growth, indicating that ET signaling functions at multiple developmental stages during symbiosis. In addition, we identified thousands of novel candidate genes undergoing Nod factor-dependent, ET-regulated expression. We leveraged the power of this large data set to model Nod factor-and ET-regulated signaling networks using MERLIN, a regulatory network inference algorithm. These analyses predict key nodes regulating the biological process impacted by Nod factor perception. We have made these results available to the research community through a searchable online resource.

Stressed cells coordinate a multi-faceted response spanning many levels of physiology. Yet knowledge of the complete stress-activated regulatory network as well as design principles for signal integration remains incomplete. We developed an experimental and computational approach to integrate available protein interaction data with gene fitness contributions, mutant transcriptome profiles, and phospho-proteome changes in cells responding to salt stress, to infer the salt-responsive signaling network in yeast. The inferred subnetwork presented many novel predictions by implicating new regulators, uncovering unrecognized crosstalk between known pathways, and pointing to previously unknown ‘hubs’ of signal integration. We exploited these predictions to show that Cdc14 phosphatase is a central hub in the network and that modification of RNA polymerase II coordinates induction of stress-defense genes with reduction of growth-related transcripts. We find that the orthologous human network is enriched for cancer-causing genes, underscoring the importance of the subnetwork's predictions in understanding stress biology.

Long range regulatory interactions among distal enhancers and target genes are important for tissue-specific gene expression. Genome-scale identification of these interactions in a cell line-specific manner, especially using the fewest possible datasets, is a significant challenge. We develop a novel computational approach, Regulatory Interaction Prediction for Promoters and Long-range Enhancers (RIPPLE), that integrates published Chromosome Conformation Capture (3C) data sets with a minimal set of regulatory genomic data sets to predict enhancer-promoter interactions in a cell line-specific manner. Our results suggest that CTCF, RAD21, a general transcription factor (TBP) and activating chromatin marks are important determinants of enhancer-promoter interactions. To predict interactions in a new cell line and to generate genome-wide interaction maps, we develop an ensemble version of RIPPLE and apply it to generate interactions in five human cell lines. Computational validation of these predictions using existing ChIA-PET and Hi-C data sets showed that RIPPLE accurately predicts interactions among enhancers and promoters. Enhancer-promoter interactions tend to be organized into subnetworks representing coordinately regulated sets of genes that are enriched for specific biological processes and cis-regulatory elements. Overall, our work provides a systematic approach to predict and interpret enhancer-promoter interactions in a genome-wide cell-type specific manner using a few experimentally tractable measurements.

The three-dimensional (3D) organization of the genome plays an important role in gene regulation bringing distal sequence elements in 3D proximity to genes hundreds of kilobases away. Hi-C is a powerful genome-wide technique to study 3D genome organization. Owing to experimental costs, high resolution Hi-C datasets are limited to a few cell lines. Computational prediction of Hi-C counts can offer a scalable and inexpensive approach to examine 3D genome organization across multiple cellular contexts. Here we present HiC-Reg, an approach to predict contact counts from one-dimensional regulatory signals. HiC-Reg predictions identify topologically associating domains and significant interactions that are enriched for CCCTC-binding factor (CTCF) bidirectional motifs and interactions identified from complementary sources. CTCF and chromatin marks, especially repressive and elongation marks, are most important for HiC-Reg’s predictive performance. Taken together, HiC-Reg provides a powerful framework to generate high-resolution profiles of contact counts that can be used to study individual locus level interactions and higher-order organizational units of the genome.

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RNA-binding proteins (RNA-BPs) play critical roles in development and disease to regulate gene expression. However, genome-wide identification of their targets in primary human cells has been challenging. Here, we applied a modified CLIP-seq strategy to identify genome-wide targets of the FMRP translational regulator 1 (FMR1), a brain-enriched RNA-BP, whose deficiency leads to Fragile X Syndrome (FXS), the most prevalent inherited intellectual disability. We identified FMR1 targets in human dorsal and ventral forebrain neural progenitors and excitatory and inhibitory neurons differentiated from human pluripotent stem cells. In parallel, we measured the transcriptomes of the same four cell types upon FMR1 gene deletion. We discovered that FMR1 preferentially binds long transcripts in human neural cells. FMR1 targets include genes unique to human neural cells and associated with clinical phenotypes of FXS and autism. Integrative network analysis using graph diffusion and multitask clustering of FMR1 CLIP-seq and transcriptional targets reveals critical pathways regulated by FMR1 in human neural development. Our results demonstrate that FMR1 regulates a common set of targets among different neural cell types but also operates in a cell type-specific manner targeting distinct sets of genes in human excitatory and inhibitory neural progenitors and neurons. By defining molecular subnetworks and validating specific high-priority genes, we identify novel components of the FMR1 regulation program. Our results provide new insights into gene regulation by a critical neuronal RNA-BP in human neurodevelopment.

Cells function and respond to changes in their environment by the coordinated activity of their molecular components, including mRNAs, proteins and metabolites. At the heart of proper cellular function are molecular networks connecting these components to process extra-cellular environmental signals and drive dynamic, context-specific cellular responses. Network-based computational approaches aim to systematically integrate measurements from high-throughput experiments to gain a global understanding of cellular function under changing environmental conditions. We provide an overview of recent methodological developments toward solving two major computational problems within this field in the past two years (2013-2015): network reconstruction and network-based interpretation. Looking forward, we envision development of methods that can predict phenotypes with high accuracy as well as provide biologically plausible mechanistic hypotheses.

Running title: Regulatory genomics of GWAS SNPsAbbreviations: GWAS -genome-wide association study; eQTL -expression quantitative trait loci; ASE -allele-specific expression; TF -transcription factor; LDlinkage disequilibrium; FPKM -fragments per kilobase of transcript per million mapped reads; LCASE -local chromosome allele-specific expression; DHS -DNase I hypersensitive sites; PWM -position weight matrix; TCGA -The Cancer Genome Atlas; ER+ -estrogen receptor positive; TAD -topologically associated domain; MAF -minor allele frequency; RPKM -reads per kilobase of transcript per million mapped reads; SNP -single nucleotide polymorphism; MAPQ -mapping quality;ChIP-seq -chromatin immunoprecipitation sequencing; ASB -allele-specific binding;ncRNA -non-coding RNA; TSS -transcription start site; DNase-seq -DNase I Major FindingsResearch. Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on January 19, 2018; DOI: 10.1158/0008-5472.CAN-17-3486 Cancer Research Mathematical Oncology 4We developed a computational framework for integrating GWAS results with heterogeneous cancer genomic data and tissue-specific epigenetic data to facilitate the discovery of causative variants functioning through long-distance gene regulation.Applied to a breast cancer susceptibility region in 5p12, our method provides strong support for a putative causative SNP that is predicted to modulate GATA3 binding and regulate the expression of MRPS30 and nearby lncRNAs. Quick Guide to Equations and AssumptionsSince the majority of GWAS variants lie in non-coding regions of the human genome where a direct link to gene function is not obvious, we searched for (causative SNP, TF, target gene) triplets under the model of gene regulation by enhancers, in which the SNP interferes with the binding affinity of a key transcription factor (TF). With this assumption, we built a regulation model for a breast cancer susceptibility locus harboring three GWAS SNPs in the 5p12 region. To infer candidate target genes, we first performed expression quantitative trait loci (eQTL) analysis by regressing gene expression levels against two co-variates: genotype status at a given GWAS SNP and copy number of the gene. For each pair of ∈ {GWAS SNPs in 5p12} and ∈ {genes in 5p12 TAD}, the eQTL model can be expressed as: DOI: 10.1158/0008-5472.CAN-17-3486 Cancer Research Mathematical Oncology 5 hypothesis testing was further applied using ≤ , where is the total number of genes tested in the TAD ( = 22, thus = 0.0023).Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on JanuaryTo identify cis-regulated target genes, we tested local chromosome allele-specific expression (LCASE) using exonic SNPs that were properly phased with the GWAS SNP . For each exonic SNP , we obtained a subset of patients who had heterozygous genotypes at both the GWAS SNP and the exonic SNP . For each patient ( ∈ {1, ...