Characterizing Primary transcriptional responses to short term heat shock in paired fraternal lymphoblastoid lines with and without Down syndrome.

Heat shock stress induces genome wide changes in transcription regulation, activating a coordinated cellular response to enable survival. Using publicly available transcriptomic and proteomic data sets comparing individuals with and without trisomy 21, we noticed many heat shock genes are up-regulated in blood samples from individuals with trisomy 21. Yet no major heat shock response regulating transcription factor is encoded on chromosome 21, leaving it unclear why trisomy 21 itself would cause a heat shock response, or how it would impact the ability of blood cells to subsequently respond when faced with heat shock stress. To explore these issues in a context independent of any trisomy 21 associated co-morbidities or developmental differences, we characterized the response to heat shock of two lymphoblastoid cell lines derived from brothers with and without trisomy 21. To carefully compare the chromatin state and the transcription status of these cell lines, we measured nascent transcription, chromatin accessibility, and single cell transcript levels in the lymphoblastoid cell lines before and after acute heat shock treatment. The trisomy 21 cells displayed a more robust heat shock response after just one hour at 42°C than the matched disomic cells. We suggest multiple potential mechanisms for this increased heat shock response in lymphoblastoid cells with trisomy 21 including the possibility that cells with trisomy 21 may exist in a hyper-reactive state due to chronic stresses. Whatever the mechanism, abnormal heat shock response in individuals with Down syndrome may hobble immune responses during fever and contribute to health problems in these individuals.

Transcription factor enrichment analysis (TFEA) quantifies the activity of multiple transcription factors from a single experiment.

Detecting changes in the activity of a transcription factor (TF) in response to a perturbation provides insights into the underlying cellular process. Transcription Factor Enrichment Analysis (TFEA) is a robust and reliable computational method that detects positional motif enrichment associated with changes in transcription observed in response to a perturbation. TFEA detects positional motif enrichment within a list of ranked regions of interest (ROIs), typically sites of RNA polymerase initiation inferred from regulatory data such as nascent transcription. Therefore, we also introduce muMerge, a statistically principled method of generating a consensus list of ROIs from multiple replicates and conditions. TFEA is broadly applicable to data that informs on transcriptional regulation including nascent transcription (eg. PRO-Seq), CAGE, histone ChIP-Seq, and accessibility data (e.g., ATAC-Seq). TFEA not only identifies the key regulators responding to a perturbation, but also temporally unravels regulatory networks with time series data. Consequently, TFEA serves as a hypothesis-generating tool that provides an easy, rigorous, and cost-effective means to broadly assess TF activity yielding new biological insights.

Applying knowledge-driven mechanistic inference to toxicogenomics.

When considering toxic chemicals in the environment, a mechanistic, causal explanation of toxicity may be preferred over a statistical or machine learning-based prediction by itself. Elucidating a mechanism of toxicity is, however, a costly and time-consuming process that requires the participation of specialists from a variety of fields, often relying on animal models. We present an innovative mechanistic inference framework (MechSpy), which can be used as a hypothesis generation aid to narrow the scope of mechanistic toxicology analysis. MechSpy generates hypotheses of the most likely mechanisms of toxicity, by combining a semantically-interconnected knowledge representation of human biology, toxicology and biochemistry with gene expression time series on human tissue. Using vector representations of biological entities, MechSpy seeks enrichment in a manually curated list of high-level mechanisms of toxicity, represented as biochemically- and causally-linked ontology concepts. Besides predicting the canonical mechanism of toxicity for many well-studied compounds, we experimentally validated some of our predictions for other chemicals without an established mechanism of toxicity. This mechanistic inference framework is an advantageous tool for predictive toxicology, and the first of its kind to produce a mechanistic explanation for each prediction. MechSpy can be modified to include additional mechanisms of toxicity, and is generalizable to other types of mechanisms of human biology.