Learning an individual’s tissue-specific gene expression can be invaluable guiding diagnosis & monitoring progression of a wide range of diseases. Unfortunately, for most tissues, the transcriptome cannot be obtained without invasive procedures.
In contrast, measuring whole blood expression is minimally invasive and high time resolution.
Thus, we asked a natural but quite challenging question - whether and to what extent can we predict an individual’s tissue-expression (for 32 tissues) based solely on their whole blood gene expression.
Approach: Using Genotype-Tissue Expression (GTEx) database with expression profile of a wide-range of tissues and matched whole blood, we built a generalized linear model for each gene expression in tissue from Whole blood-expression.
We were significantly able to (P<0.05) predict tissue-specific expression levels for ~60% of the genes on average across 32 tissues.
In a downstream analysis, we found the tissue-specific expression inferred from the blood transcriptome is almost as good as the actual measured tissue expression in predicting disease state for six different complex disorders.
In sum, we present a computational approach and proof-of-concept for a pipeline to predict tissue-specific expression using whole blood expression. Will leave the rest for your read.
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Excited to share our preprint introducing the first pipeline to identify Cell type Specific Intracellular (CSI) microbes from single cell RNA-seq data. Here's a tweetorial summary. biorxiv.org/content/10.110…
Multiple recent studies have pointed to the functional importance of tumor microbiome. One eg., is the case of Fusobacterium in primary & metastatic colon tumors, which drives tumorigenesis, influences response to chemotherapy & binds to multiple human immune inhibitory receptors
Among many, the key challenges to systematically chart tumor microbiome are: 1. Remove reads due to contamination, 2. Whether microbes are intra/extra cellular and 3. if intra, which cell types within the tumor do they reside in?
We present (preprint) an approach for identifying drug treatments modulating the expression of a key protein required for the SARS-CoV-2 virus entry – the ACE2 (angiotensin-converting enzyme 2) receptor. preprints.org/manuscript/202…
Two weeks ago, Fang et al suggested that diabetes and hypertension patients treated with ACE-inhibitors & AR-blockers are at a higher risk for COVID-19 as these treatments may increase ACE2 expression.
This motivated us to ask this question – Which clinically approved drugs including antihypertensives may modulate ACE2 in lung tissue?
In a promising step for cancer precision medicine, we devised a novel strategy that identifies genetic interactions and robustly predicts drug response, spanning 21 targeted therapies and 11 immunotherapy datasets across 10 different cancer types (1/9) bit.ly/38Pzn9m
To predict treatment outcomes, we defined genetic interaction scores, which capture the likelihood of a given patient responding to a given drug. (2/9)
We predicted treatment response in 17 of the 21 targeted- and 8 of the 11 immuno- therapy datasets, with considerable accuracy (AUC > 0.7). (3/9)
We present the first computational strategy leveraging genetic interactions successfully predicting patients response to many different targeted and immunotherapy treatments based on their tumors transcriptome. Sharing the details here- biorxiv.org/content/10.110…
Current precision oncology is mainly performed by targeting actionable mutations & has a limited patient coverage. Here we present the first approach for systematically guiding patients treatment based on vulnerabilities identified across the whole exome.
We stratified responders in 17 out of 21 different targeted therapies cohorts with an AUC>0.7 across different cancer types better than previously published transcriptome-based signatures.
We identified for the first time a selection of three cancer driver mutations (P53, KRAS and VHL) during CRISPR-Cas9 gene editing and chart a comprehensive gene-wise editing risk map in our new work out today. This series summarizes it 1/X biorxiv.org/content/biorxi…
Recent reports in @NatureMedicine suggested that CRISPR-Cas9 gene editing induces a p53-dependent DNA damage response in primary cells, which may select for cells with oncogenic p53 mutations. These CRISPR-induced possible changes needs a systematic and thorough testing. 2/X
Leveraging computational and experimental techniques we asked whether CRISPR gene editing may select for other oncogenic mutations and if yes, which genes editing could severely induce this selection. 3/X