Integration of ChIP-seq and machine learning reveals enhancers and a predictive regulatory sequence vocabulary in melanocytes.

We take a comprehensive approach to the study of regulatory control of gene expression in melanocytes that proceeds from large-scale enhancer discovery facilitated by ChIP-seq; to rigorous validation in silico, in vitro, and in vivo; and finally to the use of machine learning to elucidate a regulatory vocabulary with genome-wide predictive power. We identify 2489 putative melanocyte enhancer loci in the mouse genome by ChIP-seq for EP300 and H3K4me1. We demonstrate that these putative enhancers are evolutionarily constrained, enriched for sequence motifs predicted to bind key melanocyte transcription factors, located near genes relevant to melanocyte biology, and capable of driving reporter gene expression in melanocytes in culture (86%; 43/50) and in transgenic zebrafish (70%; 7/10). Next, using the sequences of these putative enhancers as a training set for a supervised machine learning algorithm, we develop a vocabulary of 6-mers predictive of melanocyte enhancer function. Lastly, we demonstrate that this vocabulary has genome-wide predictive power in both the mouse and human genomes. This study provides deep insight into the regulation of gene expression in melanocytes and demonstrates a powerful approach to the investigation of regulatory sequences that can be applied to other cell types.

kmer-SVM: a web server for identifying predictive regulatory sequence features in genomic data sets.

Massively parallel sequencing technologies have made the generation of genomic data sets a routine component of many biological investigations. For example, Chromatin immunoprecipitation followed by sequence assays detect genomic regions bound (directly or indirectly) by specific factors, and DNase-seq identifies regions of open chromatin. A major bottleneck in the interpretation of these data is the identification of the underlying DNA sequence code that defines, and ultimately facilitates prediction of, these transcription factor (TF) bound or open chromatin regions. We have recently developed a novel computational methodology, which uses a support vector machine (SVM) with kmer sequence features (kmer-SVM) to identify predictive combinations of short transcription factor-binding sites, which determine the tissue specificity of these genomic assays (Lee, Karchin and Beer, Discriminative prediction of mammalian enhancers from DNA sequence. Genome Res. 2011; 21:2167-80). This regulatory information can (i) give confidence in genomic experiments by recovering previously known binding sites, and (ii) reveal novel sequence features for subsequent experimental testing of cooperative mechanisms. Here, we describe the development and implementation of a web server to allow the broader research community to independently apply our kmer-SVM to analyze and interpret their genomic datasets. We analyze five recently published data sets and demonstrate how this tool identifies accessory factors and repressive sequence elements. kmer-SVM is available at http://kmersvm.beerlab.org.

Recombinant MVA-prime elicits neutralizing antibody responses by inducing antigen-specific B cells in the germinal center.

The RV144 HIV-1 vaccine trial has been the only clinical trial to date that has shown any degree of efficacy and associated with the presence of vaccine-elicited HIV-1 envelope-specific binding antibody and CD4+ T-cell responses. This trial also showed that a vector-prime protein boost combined vaccine strategy was better than when used alone. Here we have studied three different priming vectors-plasmid DNA, recombinant MVA, and recombinant VSV, all encoding clade C transmitted/founder Env 1086 C gp140, for priming three groups of six non-human primates each, followed by a protein boost with adjuvanted 1086 C gp120 protein. Our data showed that MVA-priming favors the development of higher antibody binding titers and neutralizing activity compared with other vectors. Analyses of the draining lymph nodes revealed that MVA-prime induced increased germinal center reactivity characterized by higher frequencies of germinal center (PNAhi) B cells, higher frequencies of antigen-specific B-cell responses as well as an increased frequency of the highly differentiated (ICOShiCD150lo) Tfh-cell subset.