Sample matching by inferred agonal stress in gene expression analyses of the brain

<p>Abstract</p> <p>Background</p> <p>Gene expression patterns in the brain are strongly influenced by the severity and duration of physiological stress at the time of death. This agonal effect, if not well controlled, can lead to spurious findings and diminished statistical power in case-control comparisons. While some recent studies match samples by tissue pH and clinically recorded agonal conditions, we found that these indicators were sometimes at odds with observed stress-related gene expression patterns, and that matching by these criteria still sometimes results in identifying case-control differences that are primarily driven by residual agonal effects. This problem is analogous to the one encountered in genetic association studies, where self-reported race and ethnicity are often imprecise proxies for an individual's actual genetic ancestry.</p> <p>Results</p> <p>We developed an Agonal Stress Rating (ASR) system that evaluates each sample's degree of stress based on gene expression data, and used ASRs in <it>post hoc </it>sample matching or covariate analysis. While gene expression patterns are generally correlated across different brain regions, we found strong region-region differences in empirical ASRs in many subjects that likely reflect inter-individual variabilities in local structure or function, resulting in region-specific vulnerability to agonal stress.</p> <p>Conclusion</p> <p>Variation of agonal stress across different brain regions differs between individuals, revealing a new level of complexity for gene expression studies of brain tissues. The Agonal Stress Ratings quantitatively assess each sample's extent of regulatory response to agonal stress, and allow a strong control of this important confounder.</p

Automated Discovery of Tissue-Targeting Enhancers and Transcription Factors from Binding Motif and Gene Function Data

<div><p>Identifying enhancers regulating gene expression remains an important and challenging task. While recent sequencing-based methods provide epigenomic characteristics that correlate well with enhancer activity, it remains onerous to comprehensively identify all enhancers across development. Here we introduce a computational framework to identify tissue-specific enhancers evolving under purifying selection. First, we incorporate high-confidence binding site predictions with target gene functional enrichment analysis to identify transcription factors (TFs) likely functioning in a particular context. We then search the genome for clusters of binding sites for these TFs, overcoming previous constraints associated with biased manual curation of TFs or enhancers. Applying our method to the placenta, we find 33 known and implicate 17 novel TFs in placental function, and discover 2,216 putative placenta enhancers. Using luciferase reporter assays, 31/36 (86%) tested candidates drive activity in placental cells. Our predictions agree well with recent epigenomic data in human and mouse, yet over half our loci, including 7/8 (87%) tested regions, are novel. Finally, we establish that our method is generalizable by applying it to 5 additional tissues: heart, pancreas, blood vessel, bone marrow, and liver.</p></div

Fraction of unique placenta TFBS clusters associated with placenta terms compared to placenta experimental sets.

<p>For all terms, the fraction of unique placenta TFBS clusters (not found in the other two sets) found next to a placenta annotated target gene was higher than the fraction of unique mouse/human experimentally annotated placenta enhancer sets associated with the same placenta term. First two columns show the term, the third column shows the ratio of the fraction of unique placenta TFBS clusters to the fraction of unique mouse placenta experimental enhancers, and the fourth column shows the ratio of the fraction of unique placenta TFBS clusters to the fraction of unique human placenta experimental enhancers.</p

Identification of placenta TFBS clusters.

<p>(A) Three stages of data processing are used to identify placenta TFBS clusters. In stage 1, the top 10,000 predictions for the top 50 TFs were compiled, and binding site predictions in close proximity were clustered. In the second stage, clusters containing less than 5 non-overlapping binding sites were removed. In stage 3, regions that are rich in binding site predictions for TFs below rank 100 were removed. The filtering steps strongly enrich for placenta terms in GREAT, as shown by plotting the fold enrichment in each stage for six different terms. (B) Null model showing that choosing clusters with ≥5 non-overlapping TFBS enriches for clusters in the regulatory domain of genes involved in placenta development. Gray bars represent distribution of −log₁₀(q-value) for “placenta development” term when 3,014 (size-matched to stage 2 in (A)) clusters are selected randomly from stage 1 in (A). Arrow points to −log₁₀(q-value) when only clusters with ≥5 non-overlapping TFs are selected.</p

Placenta TFBS clusters in the regulatory domain of genes with important roles in placenta development.

<p><i>Hand1</i> (A) and <i>Dll4</i> (B) contain placenta TFBS clusters in their regulatory domains. Placenta TFBS clusters that were tested are shown in the lower panel of each figure along with representative binding sites that were predicted over them. Binding sites for TFs that have a known role in placenta development are shaded dark gray, whereas bindings sites for TFs that have a predicted role in placenta development are shaded in light gray.</p

TFBS clusters for other tissues.

<p>We ran our pipeline on 5 additional tissues: heart, pancreas, blood vessel, bone marrow, and liver (Supplementary <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003449#pcbi.1003449.s018" target="_blank">Tables S8</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003449#pcbi.1003449.s019" target="_blank">S9</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003449#pcbi.1003449.s020" target="_blank">S10</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003449#pcbi.1003449.s021" target="_blank">S11</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003449#pcbi.1003449.s022" target="_blank">S12</a>), and show the top enriched term for the MGI Phenotype Single KO ontology for each. Pancreas (in italics) is the only tissue for which the top term reported through GREAT was not relevant to the tissue analyzed (see text).</p

Prediction of transcription factors likely to have a role in placenta development.

<p>(A) For a library of 917 motifs, genome-wide binding site predictions were generated using the excess conservation method <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003449#pcbi.1003449-Wenger1" target="_blank">[7]</a>. The top 10,000 predictions for each motif were analyzed using GREAT <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003449#pcbi.1003449-McLean1" target="_blank">[8]</a> and TFs were ranked by significance of association with a placenta term. The top 50 TFs were further analyzed to determine if their role in placenta development has already been characterized. (B) The top 50 TFs most enriched for placenta terms, the TF DNA-binding domains, whether the TF is known to have a role in placenta development and the corresponding placenta term q-values.</p

Overlap with large-scale experimental data.

<p>Overlap of placenta TFBS clusters with mouse placenta enhancer-associated ChIP-Seq data (A) and human placenta DNase-Seq data (B). Placenta TFBS clusters had a significant overlap with both the mouse (C) and human (D) experimental data. (E) Each dataset had a large proportion of regions that were not identified in the other two datasets. Over 800 placenta TFBS clusters were not found in either the mouse or human experimental data. Counts for human data are after conversion to mouse mm9.</p

The Enhancer Landscape during Early Neocortical Development Reveals Patterns of Dense Regulation and Co-option

<div><p>Genetic studies have identified a core set of transcription factors and target genes that control the development of the neocortex, the region of the human brain responsible for higher cognition. The specific regulatory interactions between these factors, many key upstream and downstream genes, and the enhancers that mediate all these interactions remain mostly uncharacterized. We perform p300 ChIP-seq to identify over 6,600 candidate enhancers active in the dorsal cerebral wall of embryonic day 14.5 (E14.5) mice. Over 95% of the peaks we measure are conserved to human. Eight of ten (80%) candidates tested using mouse transgenesis drive activity in restricted laminar patterns within the neocortex. GREAT based computational analysis reveals highly significant correlation with genes expressed at E14.5 in key areas for neocortex development, and allows the grouping of enhancers by known biological functions and pathways for further studies. We find that multiple genes are flanked by dozens of candidate enhancers each, including well-known key neocortical genes as well as suspected and novel genes. Nearly a quarter of our candidate enhancers are conserved well beyond mammals. Human and zebrafish regions orthologous to our candidate enhancers are shown to most often function in other aspects of central nervous system development. Finally, we find strong evidence that specific interspersed repeat families have contributed potentially key developmental enhancers via co-option. Our analysis expands the methodologies available for extracting the richness of information found in genome-wide functional maps.</p></div

Monomer and dimer transcription factor motif predictions most enriched in the E14.5 p300 ChIP-seq set.

<p>Motif fold enrichment is relative to length and GC-matched regions of the mouse genome.</p