Deciphering molecular circuits from genetic variation underlying transcriptional responsiveness to stimuli

Individual genetic variation affects gene expression in response to stimuli, often by influencing complex molecular circuits. Here we combine genomic and intermediate-scale transcriptional profiling with computational methods to identify variants that affect the responsiveness of genes to stimuli (responsiveness QTLs; reQTLs) and to position these variants in molecular circuit diagrams. We apply this approach to study variation in transcriptional responsiveness to pathogen components in dendritic cells from recombinant inbred mouse strains. We identify reQTLs that correlate with particular stimuli and position them in known pathways. For example, in response to a virus-like stimulus, a trans-acting variant acts as an activator of the antiviral response; using RNAi, we identify Rgs16 as the likely causal gene. Our approach charts an experimental and analytic path to decipher the mechanisms underlying genetic variation in circuits that control responses to stimuli

Dissecting the effect of genetic variation on the hepatic expression of drug disposition genes across the collaborative cross mouse strains

A central challenge in pharmaceutical research is to investigate genetic variation in response to drugs. The Collaborative Cross (CC) mouse reference population is a promising model for pharmacogenomic studies because of its large amount of genetic variation, genetic reproducibility, and dense recombination sites. While the CC lines are phenotypically diverse, their genetic diversity in drug disposition processes, such as detoxification reactions, is still largely uncharacterized. Here we systematically measured RNA-sequencing expression profiles from livers of 29 CC lines under baseline conditions. We then leveraged a reference collection of metabolic biotransformation pathways to map potential relations between drugs and their underlying expression quantitative trait loci (eQTLs). By applying this approach on proximal eQTLs, including eQTLs acting on the overall expression of genes and on the expression of particular transcript isoforms, we were able to construct the organization of hepatic eQTL-drug connectivity across the CC population. The analysis revealed a substantial impact of genetic variation acting on drug biotransformation, allowed mapping of potential joint genetic effects in the context of individual drugs, and demonstrated crosstalk between drug metabolism and lipid metabolism. Our findings provide a resource for investigating drug disposition in the CC strains, and offer a new paradigm for integrating biotransformation reactions to corresponding variations in DNA sequences

Revealing the genetic basis of inter-individual variation in the abundance of immune cell types in a complex tissue.

<p>(<b>A</b>) A simple reference data. Shown are three cell types (<i>c1-c3</i>, left to right); each cell type consists of five genes (<i>g1-g5</i>), and its transcriptional profiling is described as a bar plot. (<b>B</b>,<b>C</b>) Each scheme consists of a certain composition of cell types in a tissue (left) and the transcriptional profiling of the same tissue (middle). The right panel provides the output of a computational deconvolution process (that is, the inferred abundance of each immune cell type in the tissue) given transcriptional profiling (middle), a reference data (from <b>A</b>), and a certain marker set. Here we exemplify the <i>g1-g4</i> marker set (top right) and the <i>g2-g4</i> marker set (bottom right). (<b>B</b>) A conceptual scheme of an iQTL acting on the quantity of cell type <i>c3</i> (left). Transcriptional profiling is exploited by the deconvolution procedure to correctly reveal difference in the abundance of cell type <i>c3</i> between genotypes, which is subsequently identified as a true positive iQTL (right). The two marker sets <i>g1-g4</i> and <i>g2-g4</i> are shown to provide similar predictions. (<b>C</b>) A scheme of an eQTL <i>v</i> acting on the RNA level of gene <i>g1</i> (middle). Using the eQTL target <i>g1</i> as a marker results in distinct quantities of <i>c3</i> between the genotypes, which leads to a spurious <i>v-c3</i> association (using <i>g1-g4</i> as markers, top right). Notably, this erroneous association can be eliminated by removing the eQTL target <i>g1</i> from the marker set (using <i>g2-g4</i> as markers, bottom right). In <b>B</b> and <b>C</b>, all RNA levels and inferred cell type abundance values were calculated using a linear regression model (using <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004856#pcbi.1004856.e002" target="_blank">Eq 1</a> in Methods).</p

Performance of the VoCAL algorithm in synthetic data.

<p>(<b>A</b>) Overview of our performance evaluation pipeline. The simulation takes as input a collection of cell type signatures (the 'data generation cell types') and provides both synthetic data and a 'ground truth' solution. VoCAL is applied on this synthetic data using a reference data carrying 'deconvolution cell types' (thus we do not use exactly the same cell types for both data generation and deconvolution). Predicted and ground truth associations are then compared to assess the performance of the VoCAL algorithm (based on an AUC score). (<b>B-D</b>) Effect of the number of eQTLs and iQTLs. Shown are AUC scores (<i>y</i>-axis) for varying numbers of iQTLs (<i>x-</i>axis) and different numbers of eQTL hotspots (color-coded) using the cell-tagging method. We applied VoCAL (<b>B</b>) without filtration, <i>k</i> = 1, (<b>C</b>) without filtration, <i>k</i> = 10, and (<b>D</b>) with filtration, <i>k</i> = 10. (<b>E</b>) Performance evaluation using different initialization methods (subpanels). Reported are AUC scores (<i>y</i>-axis) for varying numbers of iQTLs (<i>x-</i>axis) and 1 eQTL hotspot. VoCAL was applied without filtration and <i>k</i> = 1 (red), without filtration and <i>k</i> = 10 (blue), and using filtration with <i>k</i> = 10 (green). (<b>F</b>) Improved performance using a larger number of association maps. The AUC measure (<i>y</i>-axis) was obtained using different numbers of association maps (<i>k</i>; <i>x</i>-axis) with four alternative initialization methods (color coded), different numbers of iQTLs (subpanels) and 1 eQTL hotspot. (<b>B-F</b>) As expected, the complexity of the problem grows with the larger amount of iQTLs or eQTLs. The plots clearly show that VoCAL performance improves when reproducibility is tested over a larger number of association maps (a larger <i>k</i>), and when the filtration step is applied. In all cases, iQTL- and eQTL-effect size = 0.05.</p

Exploiting Gene-Expression Deconvolution to Probe the Genetics of the Immune System

<div><p>Sequence variation can affect the physiological state of the immune system. Major experimental efforts targeted at understanding the genetic control of the abundance of immune cell subpopulations. However, these studies are typically focused on a limited number of immune cell types, mainly due to the use of relatively low throughput cell-sorting technologies. Here we present an algorithm that can reveal the genetic basis of inter-individual variation in the abundance of immune cell types using only gene expression and genotyping measurements as input. Our algorithm predicts the abundance of immune cell subpopulations based on the RNA levels of informative marker genes within a complex tissue, and then provides the genetic control on these predicted immune traits as output. A key feature of the approach is the integration of predictions from various sets of marker genes and refinement of these sets to avoid spurious signals. Our evaluation of both synthetic and real biological data shows the significant benefits of the new approach. Our method, VoCAL, is implemented in the freely available R package ComICS.</p></div

Flow chart of the VoCAL algorithm.

<p>(<b>A</b>). An overview of the analysis. VoCAL takes as input gene expression profiles in complex tissues from a population of genotyped individuals and a reference data containing expression profiles of isolated immune cell types (top box). VoCAL utilizes this input to identify significant 'immune trait associations'—the associations between immune cell type abundance levels and their underlying iQTLs. (<b>B</b>) The VoCAL pipeline. VoCAL proceeds in five steps: step 1—choosing the initial <i>k</i> sets of marker genes; step 2—predicting immune traits (namely, cell type quantities across individuals) based on the reference data and gene expression of the marker genes in a tissue; step 3—mapping the association between each immune trait and each genomic locus. VoCAL generates replicates of this association map by applying steps 2–3 repeatedly using each of the <i>k</i> (non-overlapping) marker sets from step 1; step 4—consolidating the collection of association maps into combined association <i>P</i>-values between each immune trait and each locus. Significantly associated loci are referred to as iQTLs; step 5—filtration of the <i>k</i> marker sets to exclude potentially confounding eQTL targets. If at least one marker is filtered out, VoCAL returns to step 2.</p

Analysis of lung transcriptomes reveals genetic control on mucosal Langerhans cells, demonstrating the benefits of the VoCAL algorithm.

<p>(<b>A</b>) Associations in the presence or absence of the filtration step. Shown are −log <i>P</i>-values of association generated by the VoCAL algorithm with (blue) or without (red) the filtration step (<i>y-</i>axis) across genomic positions (<i>x</i>-axis). Exemplified are natural killer (NK) cells (top left), lung macrophages (top right), B cells (bottom left) and mucosal Langerhans cells (bottom right). The results suggest a promising association of mucosal Langerhans cells (permutation FDR < 0.025; detailed in panel <b>B</b>), and highlights the ability of marker filtration to eliminate spurious associations in the remaining cell types. (<b>B</b>) Langerhans cells demonstrate the advantages of using multiple association maps. Presented are 8 association maps that were constructed by the VoCAL algorithm for mucosal Langerhans cells. Left panel:–log <i>P-</i>values of association tests (<i>y</i>-axis) of the 8 association maps (gray) and of the final VoCAL output (black) across genomic positions (<i>x</i>-axis). Scores were normalized by the maximal score of each association map. The plot demonstrates agreement between the different association maps. Right panel: Predicted abundance of mucosal Langerhans cells (<i>y</i>-axis), which were utilized to generate the 8 association maps (<i>x</i>-axis) in individuals carrying the B6 (black) or D2 (green) allele in the associated iQTL (the rs3705833 locus; *<i>P</i> < 0.01; **<i>P</i> < 0.001). In all cases, D2-carrying individuals exhibited a higher predicted abundance of Langerhans cells than did B6-carrying individuals.</p

Pleural macrophages translocate to the lung during infection to promote improved influenza outcomes.

Seasonal influenza results in 3 to 5 million cases of severe disease and 250,000 to 500,000 deaths annually. Macrophages have been implicated in both the resolution and progression of the disease, but the drivers of these outcomes are poorly understood. We probed mouse lung transcriptomic datasets using the Digital Cell Quantifier algorithm to predict immune cell subsets that correlated with mild or severe influenza A virus (IAV) infection outcomes. We identified a unique lung macrophage population that transcriptionally resembled small serosal cavity macrophages and whose presence correlated with mild disease. Until now, the study of serosal macrophage translocation in the context of viral infections has been neglected. Here, we show that pleural macrophages (PMs) migrate from the pleural cavity to the lung after infection with IAV. We found that the depletion of PMs increased morbidity and pulmonary inflammation. There were increased proinflammatory cytokines in the pleural cavity and an influx of neutrophils within the lung. Our results show that PMs are recruited to the lung during IAV infection and contribute to recovery from influenza. This study expands our knowledge of PM plasticity and identifies a source of lung macrophages independent of monocyte recruitment and local proliferation