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We carried out an integrative analysis of enhancer landscape and gene expression dynamics during hematopoietic differentiation using DNase-seq, histone mark ChIP-seq and RNA sequencing to model how the early establishment of enhancers and regulatory locus complexity govern gene expression changes at cell state transitions. We found that high-complexity genes-those with a large total number of DNase-mapped enhancers across the lineage-differ architecturally and functionally from low-complexity genes, achieve larger expression changes and are enriched for both cell type-specific and transition enhancers, which are established in hematopoietic stem and progenitor cells and maintained in one differentiated cell fate but lost in others. We then developed a quantitative model to accurately predict gene expression changes from the DNA sequence content and lineage history of active enhancers. Our method suggests a new mechanistic role for PU.1 at transition peaks during B cell specification and can be used to correct assignments of enhancers to genes.

MicroRNAs repress mRNA translation by guiding Argonaute proteins to partially complementary binding sites, primarily within the 3' untranslated region (UTR) of target mRNAs. In cell lines, Argonaute-bound microRNAs exist mainly in high molecular weight RNA-induced silencing complexes (HMW-RISC) associated with target mRNA. Here we demonstrate that most adult tissues contain reservoirs of microRNAs in low molecular weight RISC (LMW-RISC) not bound to mRNA, suggesting that these microRNAs are not actively engaged in target repression. Consistent with this observation, the majority of individual microRNAs in primary T cells were enriched in LMW-RISC. During T-cell activation, signal transduction through the phosphoinositide-3 kinase-RAC-alpha serine/threonine-protein kinase-mechanistic target of rapamycin pathway increased the assembly of microRNAs into HMW-RISC, enhanced expression of the glycine-tryptophan protein of 182 kDa, an essential component of HMW-RISC, and improved the ability of microRNAs to repress partially complementary reporters, even when expression of targeting microRNAs did not increase. Overall, data presented here demonstrate that microRNA-mediated target repression in nontransformed cells depends not only on abundance of specific microRNAs, but also on regulation of RISC assembly by intracellular signaling.

Here we present HiC-DC, a principled method to estimate the statistical significance (P values) of chromatin interactions from Hi-C experiments. HiC-DC uses hurdle negative binomial regression account for systematic sources of variation in Hi-C read counts—for example, distance-dependent random polymer ligation and GC content and mappability bias—and model zero inflation and overdispersion. Applied to high-resolution Hi-C data in a lymphoblastoid cell line, HiC-DC detects significant interactions at the sub-topologically associating domain level, identifying potential structural and regulatory interactions supported by CTCF binding sites, DNase accessibility, and/or active histone marks. CTCF-associated interactions are most strongly enriched in the middle genomic distance range (∼700 kb–1.5 Mb), while interactions involving actively marked DNase accessible elements are enriched both at short (1.5 Mb) genomic distances. There is a striking enrichment of longer-range interactions connecting replication-dependent histone genes on chromosome 6, potentially representing the chromatin architecture at the histone locus body., Genome-wide chromosome conformation capture has helped us identify features of genome topology influencing biology but requires careful statistical analysis. Here the authors present HiC-DC, a bioinformatics method that can detect statistically significant regulatory interactions.

Precision oncology seeks to predict the best therapeutic option for individual patients based on the molecular characteristics of their tumors. To assess the preclinical feasibility of drug sensitivity prediction, several studies have measured drug responses for cytotoxic and targeted therapies across large collections of genomically and transcriptomically characterized cancer cell lines and trained predictive models using standard methods like elastic net regression. Here we use existing drug response data sets to demonstrate that multitask learning across drugs strongly improves the accuracy and interpretability of drug prediction models. Our method uses trace norm regularization with a highly efficient ADMM (alternating direction method of multipliers) optimization algorithm that readily scales to large data sets. We anticipate that our approach will enhance efforts to exploit growing drug response compendia in order to advance personalized therapy.

Eukaryotic cells can "remember" transient encounters with a wide range of stimuli, inducing lasting states of altered responsiveness. Regulatory T (Treg) cells are a specialized lineage of suppressive CD4 T cells that act as critical negative regulators of inflammation in various biological contexts. Treg cells exposed to inflammatory conditions acquire strongly enhanced suppressive function. Using inducible genetic tracing, we analyzed the long-term stability of activation-induced transcriptional, epigenomic, and functional changes in Treg cells. We found that the inflammation-experienced Treg cell population reversed many activation-induced changes and lost its enhanced suppressive function over time. The "memory-less" potentiation of Treg suppressor function may help avoid a state of generalized immunosuppression that could otherwise result from repeated activation.

Chantier qualité GA; International audience; Legumes (Fabaceae or Leguminosae) are unique among cultivated plants for their ability to carry out endosymbiotic nitrogen fixation with rhizobial bacteria, a process that takes place in a specialized structure known as the nodule. Legumes belong to one of the two main groups of eurosids, the Fabidae, which includes most species capable of endosymbiotic nitrogen fixation1. Legumes comprise several evolutionary lineages derived from a common ancestor 60 million years ago (Myr ago). Papilionoids are the largest clade, dating nearly to the origin of legumes and containing most cultivated species2. Medicago truncatula is a long-established model for the study of legume biology. Here we describe the draft sequence of the M. truncatula euchromatin based on a recently completed BAC assembly supplemented with Illumina shotgun sequence, together capturing ~94% of all M. truncatula genes. A whole-genome duplication (WGD) approximately 58 Myr ago had a major role in shaping the M. truncatula genome and thereby contributed to the evolution of endosymbiotic nitrogen fixation. Subsequent to the WGD, the M. truncatula genome experienced higher levels of rearrangement than two other sequenced legumes, Glycine max and Lotus japonicus. M. truncatula is a close relative of alfalfa (Medicago sativa), a widely cultivated crop with limited genomics tools and complex autotetraploid genetics. As such, the M. truncatula genome sequence provides significant opportunities to expand alfalfa’s genomic toolbox.

Polycistronic microRNA (miRNA) clusters are a common feature of vertebrate genomes. The coordinated expression of miRNAs belonging to different seed families from a single transcriptional unit suggests functional cooperation, but this hypothesis has not been experimentally tested. Here we report the characterization of an allelic series of genetically engineered mice harboring selective targeted deletions of individual components of the miR-17 ∼ 92 cluster. Our results demonstrate the coexistence of functional cooperation and specialization among members of this cluster, identify a previously undescribed function for the miR-17 seed family in controlling axial patterning in vertebrates and show that loss of miR-19 selectively impairs Myc-driven tumorigenesis in two models of human cancer. By integrating phenotypic analysis and gene expression profiling, we provide a genome-wide view of how the components of a polycistronic miRNA cluster affect gene expression in vivo. The reagents and data sets reported here will accelerate exploration of the complex biological functions of this important miRNA cluster.

Identification of interacting residues involved in protein-protein and protein-ligand interaction is critical for the prediction and understanding of the interaction and has practical impact on mutagenesis and drug design. In this work, we introduce a new decoding algorithm, ETB-Viterbi, with an early traceback mechanism, and apply it to interaction profile hidden Markov models (ipHMMs) to enable optimized incorporation of long-distance correlations between interacting residues, leading to improved prediction accuracy. The method was applied and tested to a set of domain-domain interaction families from the 3DID database, and showed statistically significant improvement in accuracy measured by F-score. To gauge and assess the method's effectiveness and robustness in capturing the correlation signals, sets of simulated data based on the 3DID dataset with controllable correlation between interacting residues were also used, as well as reversed sequence orientation. It was demonstrated that the prediction consistently improves as the correlations increase and is not significantly affected by sequence orientation.

Motivation: Prediction of protein–protein interaction has become an important part of systems biology in reverse engineering the biological networks for better understanding the molecular biology of the cell. Although significant progress has been made in terms of prediction accuracy, most computational methods only predict whether two proteins interact but not their interacting residues—the information that can be very valuable for understanding the interaction mechanisms and designing modulation of the interaction. In this work, we developed a computational method to predict the interacting residue pairs—contact matrix for interacting protein domains, whose rows and columns correspond to the residues in the two interacting domains respectively and whose values (1 or 0) indicate whether the corresponding residues (do or do not) interact. Results: Our method is based on supervised learning using support vector machines. For each domain involved in a given domain–domain interaction (DDI), an interaction profile hidden Markov model (ipHMM) is first built for the domain family, and then each residue position for a member domain sequence is represented as a 20-dimension vector of Fisher scores, characterizing how similar it is as compared with the family profile at that position. Each element of the contact matrix for a sequence pair is now represented by a feature vector from concatenating the vectors of the two corresponding residues, and the task is to predict the element value (1 or 0) from the feature vector. A support vector machine is trained for a given DDI, using either a consensus contact matrix or contact matrices for individual sequence pairs, and is tested by leave-one-out cross validation. The performance averaged over a set of 115 DDIs collected from the 3 DID database shows significant improvement (sensitivity up to 85%, and specificity up to 85%), as compared with a multiple sequence alignment-based method (sensitivity 57%, and specificity 78%) previously reported in the literature. Contact: lliao@cis.udel.edu or wuc@cis.udel.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Identification of interacting residues involved in protein-protein and protein-ligand interaction is critical for the prediction and understanding of the interaction and has practical impact on mutagenesis and drug design. In this work, we introduce a new decoding algorithm, ETB-Viterbi, with early trace back mechanism built into interaction profile hidden Markov models (ipHMMs) that can incorporate the longdistance correlations between interacting residues to improve prediction accuracy. The method was applied and tested to a set of domain-domain interaction families from the 3DID database, and showed statistically significant improvement in accuracy measured by F-score. To gauge and assess the method's effectiveness in capturing the correlation signals, sets of simulated data based on the 3DID dataset with controllable correlation between interacting residues were also used, and it was demonstrated that the prediction consistently improves as the correlations increase.

We present a new computational method for predicting ligand binding residues and functional sites in protein sequences. These residues and sites tend to be not only conserved, but also exhibit strong correlation due to the selection pressure during evolution in order to maintain the required structure and/or function. To explore the effect of correlations among multiple positions in the sequences, the method uses graph theoretic clustering and kernel-based canonical correlation analysis (kCCA) to identify binding and functional sites in protein sequences as the residues that exhibit strong correlation between the residues' evolutionary characterization at the sites and the structure-based functional classification of the proteins in the context of a functional family. The results of testing the method on two well-curated data sets show that the prediction accuracy as measured by Receiver Operating Characteristic (ROC) scores improves significantly when multipositional correlations are accounted for.

Legumes and many nonleguminous plants enter symbiotic interactions with microbes, and it is poorly understood how host plants respond to promote beneficial, symbiotic microbial interactions while suppressing those that are deleterious or pathogenic. Trans-acting siRNAs (tasiRNAs) negatively regulate target transcripts and are characterized by siRNAs spaced in 21-nucleotide (nt) “phased” intervals, a pattern formed by DICER-LIKE 4 (DCL4) processing. A search for phased siRNAs (phasiRNAs) found at least 114 Medicago loci, the majority of which were defense-related NB-LRR-encoding genes. We identified three highly abundant 22-nt microRNA (miRNA) families that target conserved domains in these NB-LRRs and trigger the production of trans-acting siRNAs. High levels of small RNAs were matched to >60% of all ∼540 encoded Medicago NB-LRRs; in the potato, a model for mycorrhizal interactions, phasiRNAs were also produced from NB-LRRs. DCL2 and SGS3 transcripts were also cleaved by these 22-nt miRNAs, generating phasiRNAs, suggesting synchronization between silencing and pathogen defense pathways. In addition, a new example of apparent “two-hit” phasiRNA processing was identified. Our data reveal complex tasiRNA-based regulation of NB-LRRs that potentially evolved to facilitate symbiotic interactions and demonstrate miRNAs as master regulators of a large gene family via the targeting of highly conserved, protein-coding motifs, a new paradigm for miRNA function.

Protein functional site prediction plays a key role in understanding protein function and in protein engineering. In this work we developed a novel method using canonical correlation analysis to predict protein ligand binding sites. The method was tested with a well-known benchmark dataset and consistently outperformed the existing method Xdet, which is based on Pearson correlation, by improving the lowest and highest ranked positives for more than 18% and 22% respectively.

High-throughput DNA sequencing has enabled systems biology to begin to address areas in health, agricultural and basic biological research. Concomitant with the opportunities is an absolute necessity to manage significant volumes of high-dimensional and inter-related data and analysis. Alpheus is an analysis pipeline, database and visualization software for use with massively parallel DNA sequencing technologies that feature multi-gigabase throughput characterized by relatively short reads, such as Illumina-Solexa (sequencing-by-synthesis), Roche-454 (pyrosequencing) and Applied Biosystem’s SOLiD (sequencing-by-ligation). Alpheus enables alignment to reference sequence(s), detection of variants and enumeration of sequence abundance, including expression levels in transcriptome sequence. Alpheus is able to detect several types of variants, including non-synonymous and synonymous single nucleotide polymorphisms (SNPs), insertions/deletions (indels), premature stop codons, and splice isoforms. Variant detection is aided by the ability to filter variant calls based on consistency, expected allele frequency, sequence quality, coverage, and variant type in order to minimize false positives while maximizing the identification of true positives. Alpheus also enables comparisons of genes with variants between cases and controls or bulk segregant pools. Sequence-based differential expression comparisons can be developed, with data export to SAS JMP Genomics for statistical analysis.