Biology subjects

Venkatraman, V.

Publications and source records attributed to Venkatraman, V..

3 recordsLinked to original sources

bioRxiv2018

Identifying high-priority proteins across the human diseasome using semantic similarity

Knowledge of \"popular proteins\" has been a focus of multiple Human Proteome Organization (HUPO) initiatives and can guide the development of proteomics assays targeting important disease pathways. We report here an updated method to identify prioritized protein lists from the research literature, and apply it to catalog lists of important proteins across multiple cell types, sub-anatomical regions, and disease phenotypes of interest. We provide a systematic collection of popular proteins across 10,129 human diseases as defined by the Disease Ontology, 10,642 disease phenotypes defined by Human Phenotype Ontology, and 2,370 cellular pathways defined by Pathway Ontology. This strategy allows instant retrieval of popular proteins across the human \"diseasome\", and further allows reverse queries from protein to disease, enabling functional analysis of experimental protein lists using bibliometric annotations.

bioinformatics↗

bioRxiv2017

Proteomic Architecture of Human Coronary and Aortic Atherosclerosis

The inability to detect premature atherosclerosis significantly hinders implementation of personalized therapy to prevent coronary heart disease. A comprehensive understanding of arterial protein networks and how they change in early atherosclerosis could identify new biomarkers for disease detection and improved therapeutic targets. Here we describe the human arterial proteome and the proteomic features strongly associated with early atherosclerosis based on mass-spectrometry analysis of coronary artery and aortic specimens from 100 autopsied young adults (200 arterial specimens). Convex analysis of mixtures, differential dependent network modeling and bioinformatic analyses defined the composition, network re-wiring and likely regulatory features of the protein networks associated with early atherosclerosis. Among other things the results reveal major differences in mitochondrial protein mass between the coronary artery and distal aorta in both normal and atherosclerotic samples - highlighting the importance of anatomic specificity and dynamic network structures in in the study of arterial proteomics. The publicly available data resource and the description of the analysis pipeline establish a new foundation for understanding the proteomic architecture of atherosclerosis and provide a template for similar investigations of other chronic diseases characterized by multi-cellular tissue phenotypes.\n\nHighlightsO_LILC MS/MS analysis performed on 200 human aortic or coronary artery samples\nC_LIO_LINumerous proteins, networks, and regulatory pathways associated with early atherosclerosis\nC_LIO_LIMitochondrial proteins mass and selected metabolic regulatory pathways vary dramatically by disease status and anatomic location\nC_LIO_LIPublically available data resource and analytic pipeline are provided or described in detail\nC_LI

molecular biology↗

bioRxiv2017

Reason’s Enemy Is Not Emotion: Engagement of Cognitive Control Networks Explains Biases in Gain/Loss Framing

In the classic gain/loss framing effect, describing a gamble as a potential gain or loss biases people to make risk-averse or risk-seeking decisions, respectively. The canonical explanation for this effect is that frames differentially modulate emotional processes - which in turn leads to irrational choice behavior. Here, we evaluate the source of framing biases by integrating functional magnetic resonance imaging (fMRI) data from 143 human participants performing a gain/loss framing task with meta-analytic data from over 8000 neuroimaging studies. We found that activation during choices consistent with the framing effect were most correlated with activation associated with the resting or default brain, while activation during choices inconsistent with the framing effect most correlated with the task-engaged brain. Our findings argue against the common interpretation of gain/loss framing as a competition between emotion and control. Instead, our study indicates that this effect results from differential cognitive engagement across decision frames.\n\nSignificance StatementThe biases frequently exhibited by human decision-makers have often been attributed to the presence of emotion. Using a large fMRI sample and analysis of whole-brain networks defined with the meta-analytic tool Neurosynth, we find that neural activity during frame-biased decisions are more significantly associated with default behaviors (and the absence of executive control) than with emotion. These findings point to a role for neuroscience in shaping longstanding psychological theories in decision science.

neuroscience↗