Salvatore Loguercio

Detection and interpretation of expression quantitative trait loci (eQTL).

Analysis of expression quantitative trait loci (eQTL) provides a means for detecting transcriptional regulatory relationships at a genome-wide scale. Here we explain the eQTL analysis pipeline, we introduce publicly available tools for the statistical analysis, and we discuss issues that might complicate the eQTL mapping process. The detection and interpretation of eQTL requires careful consideration of a range of potentially confounding effects. Particularly population substructure and batch effects may lead to the detection of many false-positive eQTL if not accounted for. Traditionally, most eQTL mapping methods only check for the correlation of single loci with gene expression. In order to detect (epistatic) interactions between distant genetic loci one has to take into account several loci simultaneously. Here, we present the Random Forest regression method as a way of accounting for interacting loci. Next, we introduce analysis methods aiding the biological interpretation of detected eQTL. For example, the notion of local (cis) and distant (trans) eQTL has been very useful for interpreting the causes and implications of eQTL in many studies. In addition, Bayesian networks have been used extensively to infer causal relationships among eQTL and between eQTL and other genetic associations (e.g. disease associated loci). Also, the integration of eQTL with complementary information such as physical protein interaction data may significantly improve statistical power and provide insight into possible molecular mechanisms linking the regulator to its target gene. The eQTL approach is potentially very powerful for the analysis of regulatory pathways affecting disease susceptibility and other relevant traits. However, careful analysis is required to unleash its full potential.

Deep Sequencing of the Murine Igh Repertoire Reveals Complex Regulation of Nonrandom V Gene Rearrangement Frequencies

A diverse Ab repertoire is formed through the rearrangement of V, D, and J segments at the IgH (Igh) loci. The C57BL/6 murine Igh locus has >100 functional VH gene segments that can recombine to a rearranged DJH. Although the nonrandom usage of VH genes is well documented, it is not clear what elements determine recombination frequency. To answer this question, we conducted deep sequencing of 5′-RACE products of the Igh repertoire in pro-B cells, amplified in an unbiased manner. Chromatin immunoprecipitation–sequencing results for several histone modifications and RNA polymerase II binding, RNA-sequencing for sense and antisense noncoding germline transcripts, and proximity to CCCTC-binding factor (CTCF) and Rad21 sites were compared with the usage of individual V genes. Computational analyses assessed the relative importance of these various accessibility elements. These elements divide the Igh locus into four epigenetically and transcriptionally distinct domains, and our computational analyses reveal different regulatory mechanisms for each region. Proximal V genes are relatively devoid of active histone marks and noncoding RNA in general, but having a CTCF site near their recombination signal sequence is critical, suggesting that being positioned near the base of the chromatin loops is important for rearrangement. In contrast, distal V genes have higher levels of histone marks and noncoding RNA, which may compensate for their poorer recombination signal sequences and for being distant from CTCF sites. Thus, the Igh locus has evolved a complex system for the regulation of V(D)J rearrangement that is different for each of the four domains that comprise this locus.

Unifying model for molecular determinants of the preselection Vβ repertoire

Significance The assembly of immunoglobulin and T-cell receptor genes by V(D)J (variable, diversity, joining) recombination must strike a balance between maximum diversification of antigen receptors and favoring gene segments with specialized functions. We quantified the use of V gene segments in the primary T-cell receptor β repertoire, defining the relative contribution of 13 parameters in shaping their recombination efficiencies. Computational analysis of these data provides a unifying model, revealing a minimal set of five parameters that predict Vβ use. This model building approach will help predict how natural alterations of large V clusters impact immune receptor repertoires. The primary antigen receptor repertoire is sculpted by the process of V(D)J recombination, which must strike a balance between diversification and favoring gene segments with specialized functions. The precise determinants of how often gene segments are chosen to complete variable region coding exons remain elusive. We quantified Vβ use in the preselection Tcrb repertoire and report relative contributions of 13 distinct features that may shape their recombination efficiencies, including transcription, chromatin environment, spatial proximity to their DβJβ targets, and predicted quality of recombination signal sequences (RSSs). We show that, in contrast to functional Vβ gene segments, all pseudo-Vβ segments are sequestered in transcriptionally silent chromatin, which effectively suppresses wasteful recombination. Importantly, computational analyses provide a unifying model, revealing a minimum set of five parameters that are predictive of Vβ use, dominated by chromatin modifications associated with transcription, but largely independent of precise spatial proximity to DβJβ clusters. This learned model-building strategy may be useful in predicting the relative contributions of epigenetic, spatial, and RSS features in shaping preselection V repertoires at other antigen receptor loci. Ultimately, such models may also predict how designed or naturally occurring alterations of these loci perturb the preselection use of variable gene segments.

Omics Pipe: a community-based framework for reproducible multi-omics data analysis

MOTIVATION Omics Pipe (http://sulab.scripps.edu/omicspipe) is a computational framework that automates multi-omics data analysis pipelines on high performance compute clusters and in the cloud. It supports best practice published pipelines for RNA-seq, miRNA-seq, Exome-seq, Whole-Genome sequencing, ChIP-seq analyses and automatic processing of data from The Cancer Genome Atlas (TCGA). Omics Pipe provides researchers with a tool for reproducible, open source and extensible next generation sequencing analysis. The goal of Omics Pipe is to democratize next-generation sequencing analysis by dramatically increasing the accessibility and reproducibility of best practice computational pipelines, which will enable researchers to generate biologically meaningful and interpretable results. RESULTS Using Omics Pipe, we analyzed 100 TCGA breast invasive carcinoma paired tumor-normal datasets based on the latest UCSC hg19 RefSeq annotation. Omics Pipe automatically downloaded and processed the desired TCGA samples on a high throughput compute cluster to produce a results report for each sample. We aggregated the individual sample results and compared them to the analysis in the original publications. This comparison revealed high overlap between the analyses, as well as novel findings due to the use of updated annotations and methods. AVAILABILITY AND IMPLEMENTATION Source code for Omics Pipe is freely available on the web (https://bitbucket.org/sulab/omics_pipe). Omics Pipe is distributed as a standalone Python package for installation (https://pypi.python.org/pypi/omics_pipe) and as an Amazon Machine Image in Amazon Web Services Elastic Compute Cloud that contains all necessary third-party software dependencies and databases (https://pythonhosted.org/omics_pipe/AWS_installation.html).

Extensive Mass Spectrometry-based Analysis of the Fission Yeast Proteome THE SCHIZOSACCHAROMYCES POMBE PeptideAtlas*

We report a high quality and system-wide proteome catalogue covering 71% (3,542 proteins) of the predicted genes of fission yeast, Schizosaccharomyces pombe, presenting the largest protein dataset to date for this important model organism. We obtained this high proteome and peptide (11.4 peptides/protein) coverage by a combination of extensive sample fractionation, high resolution Orbitrap mass spectrometry, and combined database searching using the iProphet software as part of the Trans-Proteomics Pipeline. All raw and processed data are made accessible in the S. pombe PeptideAtlas. The identified proteins showed no biases in functional properties and allowed global estimation of protein abundances. The high coverage of the PeptideAtlas allowed correlation with transcriptomic data in a system-wide manner indicating that post-transcriptional processes control the levels of at least half of all identified proteins. Interestingly, the correlation was not equally tight for all functional categories ranging from rs >0.80 for proteins involved in translation to rs <0.45 for signal transduction proteins. Moreover, many proteins involved in DNA damage repair could not be detected in the PeptideAtlas despite their high mRNA levels, strengthening the translation-on-demand hypothesis for members of this protein class. In summary, the extensive and publicly available S. pombe PeptideAtlas together with the generated proteotypic peptide spectral library will be a useful resource for future targeted, in-depth, and quantitative proteomic studies on this microorganism.

YY1 plays an essential role at all stages of B-cell differentiation

Significance Ying Yang 1 (YY1) is a ubiquitously expressed transcription factor that has been demonstrated to be essential for pro–B-cell development as well as lymphoma. It has recently been proposed that YY1 regulates the germinal center B-cell transcriptional program. We confirm this hypothesis and additionally show that YY1 is equally essential for all stages of B-cell differentiation. Through ChIP-sequencing analysis of YY1 binding, and analysis of differentially expressed genes from RNA-sequencing, our data show that, in addition to the regulation of several B-cell–specific genes, YY1 regulates many genes and pathways important in basic cellular functions, such as mitochondrial bioenergetics, transcription, ribosomal function, and cellular proliferation, thus explaining the requirement for YY1 at all stages of B-cell differentiation. Ying Yang 1 (YY1) is a ubiquitously expressed transcription factor shown to be essential for pro–B-cell development. However, the role of YY1 in other B-cell populations has never been investigated. Recent bioinformatics analysis data have implicated YY1 in the germinal center (GC) B-cell transcriptional program. In accord with this prediction, we demonstrated that deletion of YY1 by Cγ1-Cre completely prevented differentiation of GC B cells and plasma cells. To determine if YY1 was also required for the differentiation of other B-cell populations, we deleted YY1 with CD19-Cre and found that all peripheral B-cell subsets, including B1 B cells, require YY1 for their differentiation. Transitional 1 (T1) B cells were the most dependent upon YY1, being sensitive to even a half-dosage of YY1 and also to short-term YY1 deletion by tamoxifen-induced Cre. We show that YY1 exerts its effects, in part, by promoting B-cell survival and proliferation. ChIP-sequencing shows that YY1 predominantly binds to promoters, and pathway analysis of the genes that bind YY1 show enrichment in ribosomal functions, mitochondrial functions such as bioenergetics, and functions related to transcription such as mRNA splicing. By RNA-sequencing analysis of differentially expressed genes, we demonstrated that YY1 normally activates genes involved in mitochondrial bioenergetics, whereas it normally down-regulates genes involved in transcription, mRNA splicing, NF-κB signaling pathways, the AP-1 transcription factor network, chromatin remodeling, cytokine signaling pathways, cell adhesion, and cell proliferation. Our results show the crucial role that YY1 plays in regulating broad general processes throughout all stages of B-cell differentiation.

A task-based approach for Gene Ontology evaluation.

BackgroundThe Gene Ontology and its associated annotations are critical tools for interpreting lists of genes. Here, we introduce a method for evaluating the Gene Ontology annotations and structure based on the impact they have on gene set enrichment analysis, along with an example implementation. This task-based approach yields quantitative assessments grounded in experimental data and anchored tightly to the primary use of the annotations.ResultsApplied to specific areas of biological interest, our framework allowed us to understand the progress of annotation and structural ontology changes from 2004 to 2012. Our framework was also able to determine that the quality of annotations and structure in the area under test have been improving in their ability to recall underlying biological traits. Furthermore, we were able to distinguish between the impact of changes to the annotation sets and ontology structure.ConclusionOur framework and implementation lay the groundwork for a powerful tool in evaluating the usefulness of the Gene Ontology. We demonstrate both the flexibility and the power of this approach in evaluating the current and past state of the Gene Ontology as well as its applicability in developing new methods for creating gene annotations.

Pathway and time-resolved benzo[a]pyrene toxicity on Hepa1c1c7 cells at toxic and subtoxic exposure

Benzo[a]pyrene (B[a]P) is an environmental contaminant mainly studied for its toxic/carcinogenic effects. For a comprehensive and pathway orientated mechanistic understanding of the effects directly triggered by a toxic (5 μM) or a subtoxic (50 nM) concentration of B[a]P or indirectly by its metabolites, we conducted time series experiments for up to 24 h to study the effects in murine hepatocytes. These cells rapidly take up and actively metabolize B[a]P, which was followed by quantitative analysis of the concentration of intracellular B[a]P and seven representative degradation products. Exposure with 5 μM B[a]P led to a maximal intracellular concentration of 1604 pmol/5 × 10(4) cells, leveling at 55 pmol/5 × 10(4) cells by the end of the time course. Changes in the global proteome (>1000 protein profiles) and metabolome (163 metabolites) were assessed in combination with B[a]P degradation. Abundance profiles of 236 (both concentrations), 190 (only 5 μM), and 150 (only 50 nM) proteins were found to be regulated in response to B[a]P in a time-dependent manner. At the endogenous metabolite level amino acids, acylcarnitines and glycerophospholipids were particularly affected by B[a]P. The comprehensive chemical, proteome and metabolomic data enabled the identification of effects on the pathway level in a time-resolved manner. So in addition to known alterations, also protein synthesis, lipid metabolism, and membrane dysfunction were identified as B[a]P specific effects.

Dizeez: an online game for human gene-disease annotation.

Structured gene annotations are a foundation upon which many bioinformatics and statistical analyses are built. However the structured annotations available in public databases are a sparse representation of biological knowledge as a whole. The rate of biomedical data generation is such that centralized biocuration efforts struggle to keep up. New models for gene annotation need to be explored that expand the pace at which we are able to structure biomedical knowledge. Recently, online games have emerged as an effective way to recruit, engage and organize large numbers of volunteers to help address difficult biological challenges. For example, games have been successfully developed for protein folding (Foldit), multiple sequence alignment (Phylo) and RNA structure design (EteRNA). Here we present Dizeez, a simple online game built with the purpose of structuring knowledge of gene-disease associations. Preliminary results from game play online and at scientific conferences suggest that Dizeez is producing valid gene-disease annotations not yet present in any public database. These early results provide a basic proof of principle that online games can be successfully applied to the challenge of gene annotation. Dizeez is available at http://genegames.org.

Integrative Analysis of Low- and High-Resolution eQTL

The study of expression quantitative trait loci (eQTL) is a powerful way of detecting transcriptional regulators at a genomic scale and for elucidating how natural genetic variation impacts gene expression. Power and genetic resolution are heavily affected by the study population: whereas recombinant inbred (RI) strains yield greater statistical power with low genetic resolution, using diverse inbred or outbred strains improves genetic resolution at the cost of lower power. In order to overcome the limitations of both individual approaches, we combine data from RI strains with genetically more diverse strains and analyze hippocampus eQTL data obtained from mouse RI strains (BXD) and from a panel of diverse inbred strains (Mouse Diversity Panel, MDP). We perform a systematic analysis of the consistency of eQTL independently obtained from these two populations and demonstrate that a significant fraction of eQTL can be replicated. Based on existing knowledge from pathway databases we assess different approaches for using the high-resolution MDP data for fine mapping BXD eQTL. Finally, we apply this framework to an eQTL hotspot on chromosome 1 (Qrr1), which has been implicated in a range of neurological traits. Here we present the first systematic examination of the consistency between eQTL obtained independently from the BXD and MDP populations. Our analysis of fine-mapping approaches is based on ‘real life’ data as opposed to simulated data and it allows us to propose a strategy for using MDP data to fine map BXD eQTL. Application of this framework to Qrr1 reveals that this eQTL hotspot is not caused by just one (or few) ‘master regulators’, but actually by a set of polymorphic genes specific to the central nervous system.