Mechanisms underlying divergent responses of genetically distinct macrophages to IL-4

Mechanisms by which noncoding genetic variation influences gene expression remain only partially understood but are considered to be major determinants of phenotypic diversity and disease risk. Here, we evaluated effects of >50 million single-nucleotide polymorphisms and short insertions/deletions provided by five inbred strains of mice on the responses of macrophages to interleukin-4 (IL-4), a cytokine that plays pleiotropic roles in immunity and tissue homeostasis. Of >600 genes induced >2-fold by IL-4 across the five strains, only 26 genes reached this threshold in all strains. By applying deep learning and motif mutation analyses to epigenetic data for macrophages from each strain, we identified the dominant combinations of lineage-determining and signal-dependent transcription factors driving IL-4 enhancer activation. These studies further revealed mechanisms by which noncoding genetic variation influences absolute levels of enhancer activity and their dynamic responses to IL-4, thereby contributing to strain-differential patterns of gene expression and phenotypic diversity.

Stripenn detects architectural stripes from chromatin conformation data using computer vision

Architectural stripes tend to form at genomic regions harboring genes with salient roles in cell identity and function. Therefore, the accurate identification and quantification of these features is essential for the understanding of lineage-specific gene regulation. Here, we present Stripenn, an algorithm rooted in computer vision to systematically detect and quantitate architectural stripes from chromatin conformation measurements of various technologies. We demonstrate that Stripenn outperforms existing methods, highlight its biological applications in the context of B and T lymphocytes, and examine the role of sequence variation on architectural stripes by studying the conservation of these features in inbred strains of mice. In summary, Stripenn is a computational method which borrows concepts from widely used image processing techniques for demarcation and quantification of architectural stripes.

Mechanisms underlying divergent responses of genetically distinct macrophages to IL-4

Mechanisms by which non-coding genetic variation influences gene expression remain only partially understood but are considered to be major determinants of phenotypic diversity and disease risk. Here, we evaluated effects of >50 million SNPs and InDels provided by five inbred strains of mice on the responses of macrophages to interleukin 4 (IL-4), a cytokine that plays pleiotropic roles in immunity and tissue homeostasis. Remarkably, of >600 genes induced >2-fold by IL-4 across the five strains, only 26 genes reached this threshold in all strains. By applying deep learning and motif mutation analyses to epigenetic data for macrophages from each strain, we identified the dominant combinations of lineage determining and signal-dependent transcription factors driving late enhancer activation. These studies further revealed mechanisms by which non-coding genetic variation influences absolute levels of enhancer activity and their dynamic responses to IL-4, thereby contributing to strain-differential patterns of gene expression and phenotypic diversity.

Speeding up eQTL scans in the BXD population using GPUs

The BXD recombinant inbred strains of mice are an important reference population for systems biology and genetics that have been full sequenced and deeply phenotyped. To facilitate inter-active use of genotype-phenotype relations using many massive omics data sets for this and other segregating populations, we have developed new algorithms and code that enables near-real time whole genome QTL scans for up to 1 million traits. By using easily parallelizable operations including matrix multiplication, vectorized operations, and element-wise operations, we have decreased run-time to a few seconds for large transcriptome data sets. Our code is ideal for interactive web services, such as GeneNetwork.org. We used parallelization of different CPU threads as well as GPUs. We found that the speed advantage of GPUs is dependent on problem size and shape (number of cases, number of genotypes, number of traits). Our results provide a path for speeding up eQTL scans using linear mixed models (LMMs). Our implementation is in the Julia programming language.

Differential Effects of Rapamycin on Glucose Metabolism in Nine Inbred Strains

Studies in mice suggest that rapamycin has a negative impact on glucose homeostasis by inducing insulin resistance. However, results have been inconsistent and difficult to assess because the strains, methods of treatment, and analysis vary among studies. Using a consistent protocol, we surveyed nine inbred strains of mice for the effect of rapamycin on various aspects of glucose metabolism. Across all strains, rapamycin significantly delayed glucose clearance after challenge. However, rapamycin showed no main effect on systemic insulin sensitivity. Analysis of individual strains shows that rapamycin induced higher glucose values at 15 minutes post-challenge in 7/9 strains. However, only three strains show rapamycin-induced reduction in glucose clearance from 15 to 120 minutes. Although pancreatic insulin content was reduced by rapamycin in seven strains, none showed reduced serum insulin values. Although one strain showed no effects of rapamycin on glucose metabolism (129), another showed increased systemic insulin sensitivity (B6). We suggest that rapamycin likely inhibits insulin production and secretion in most strains while having strain-specific effects on glucose clearance without altering systemic insulin sensitivity. This strain survey indicates that genetic differences greatly influence the metabolic response to rapamycin.