Transcription is controlled by interactions of cis -acting DNA elements with diffusible trans -acting factors. Changes in cis or trans factors can drive expression divergence within and between species, and their relative prevalence can reveal the evolutionary history and pressures that drive expression variation. Previous work delineating the mode of expression divergence in animals has largely used whole-body expression measurements in one condition. Because cis -acting elements often drive expression in a subset of cell types or conditions, these measurements may not capture the complete contribution of cis -acting changes. Here, we quantify the mode of expression divergence in the Drosophila fat body, the primary immune organ, in several conditions, using two geographically distinct lines of D. melanogaster and their F1 hybrids. We measured expression in the absence of infection and in infections with Gram-negative S. marcescens or Gram-positive E. faecalis bacteria, which trigger the two primary signaling pathways in the Drosophila innate immune response. The mode of expression divergence strongly depends on the condition, with trans -acting effects dominating in response to Gram-negative infection and cis -acting effects dominating in Gram-positive and preinfection conditions. Expression divergence in several receptor proteins may underlie the infection-specific trans effects. Before infection, when the fat body has a metabolic role, there are many compensatory effects, changes in cis and trans that counteract each other to maintain expression levels. This work shows that within a single tissue, the mode of expression divergence varies between conditions and suggests that these differences reflect the diverse evolutionary histories of host–pathogen interactions.
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Mixed-Layer Deep Modeling of Genotypes and Cross-Tissue Expression Uncovers Trans-Eqtls
Motivation: Modeling genetics of gene expression had been effective at highlighting cis-eQTLs, variants that control nearby transcripts. Yet, incorporation of long-range effects has been hampered by unfavora- ble statistical considerations. On the other end, expression alone has been modeled across tissues by decomposition into contributing factors, without any connection to genetics. Results: We develop MIxed-Layer Analysis of Genetics and Expression (MILAGE), a model that combines direct effects of cis-SNPs on nearby transcripts with trans-effects that control global factors of expression in a tissue-specific pattern. We develop judicious initialization of the model, followed by gradient descent learning. We present GPU-based implementation of the learner to enable computational feasibility in this otherwise intractably-large parameter space. We show the model to explain > 59% of test-set variation in GTEx data. The inferred genetically-regulated factors are consistent with expected tissue similarity.
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- Award ID(s):
- 1547120
- PAR ID:
- 10026361
- Date Published:
- Journal Name:
- RECOMB Satellite on Genetics
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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