GLI proteins convert Sonic hedgehog (Shh) signaling into a transcriptional output in a tissue-specific fashion. The Shh pathway has been extensively studied in the limb bud, where it helps regulate growth through a SHH-FGF feedback loop. However, the transcriptional response is still poorly understood. We addressed this by determining the gene expression patterns of approximately 200 candidate GLI-target genes, and identified three discrete SHH-responsive expression domains. GLI-target genes expressed in the three domains are predominately regulated by derepression of GLI3 but have different temporal requirements for SHH. The GLI binding regions associated with these genes harbor both distinct and common DNA motifs. Given the potential for interaction between the SHH and FGF pathways, we also measured the response of GLI-target genes to inhibition of FGF signaling and found the majority were either unaffected or upregulated. These results provide the first characterization of the spatiotemporal response of a large group of GLI-target genes and lay the foundation for a systems-level understanding of the gene regulatory networks underlying SHH-mediated limb patterning.

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.

BackgroundThere is a growing interest in Jatropha curcas L. (jatropha) as a biodiesel feedstock plant. Variations in its morphology and seed productivity have been well documented. However, there is the lack of systematic comparative evaluation of distinct collections under same climate and agronomic practices. With the several reports on low genetic diversity in jatropha collections, there is uncertainty on genetic contribution to jatropha morphology.ResultIn this study, five populations of jatropha plants collected from China (CN), Indonesia (MD), Suriname (SU), Tanzania (AF) and India (TN) were planted in one farm under the same agronomic practices. Their agronomic traits (branching pattern, height, diameter of canopy, time to first flowering, dormancy, accumulated seed yield and oil content) were observed and tracked for two years. Significant variations were found for all the agronomic traits studied. Genetic diversity and epigenetic diversity were evaluated using florescence Amplified Fragment Length Polymorphism (fAFLP) and methylation sensitive florescence AFLP (MfAFLP) methods. Very low level of genetic diversity was detected (polymorphic band <0.1%) within and among populations. In contrast, intermediate but significant epigenetic diversity was detected (25.3% of bands were polymorphic) within and among populations. More than half of CCGG sites surveyed by MfAFLP were methylated with significant difference in inner cytosine and double cytosine methylation among populations. Principal coordinates analysis (PCoA) based on Nei's epigenetic distance showed Tanzania/India group distinct from China/Indonesia/Suriname group. Inheritance of epigenetic markers was assessed in one F1 hybrid population between two morphologically distinct parent plants and one selfed population. 30 out of 39 polymorphic markers (77%) were found heritable and followed Mendelian segregation. One epiallele was further confirmed by bisulphite sequencing of its corresponding genomic region.ConclusionOur study confirmed climate and practice independent differences in agronomic performance among jatropha collections. Such agronomic trait variations, however, were matched by very low genetic diversity and medium level but significant epigenetic diversity. Significant difference in inner cytosine and double cytosine methylation at CCGG sites was also found among populations. Most epigenetic differential markers can be inherited as epialleles following Mendelian segregation. These results suggest possible involvement of epigenetics in jatropha development.

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, ...