Hoffman, N. | Search bioRxiv

bioRxiv2020

PhosR enables processing and functional analysis of phosphoproteomic data

Mass spectrometry (MS)-based phosphoproteomics has revolutionised our ability to profile phosphorylation-based signalling in cells and tissues on a global scale. To infer the action of kinases and signalling pathways in phosphoproteomic experiments, we present PhosR, a set of tools and methodologies implemented in a suite of R packages facilitating comprehensive analysis of phosphoproteomic data. By applying PhosR to both published and new phosphoproteomic datasets, we demonstrate capabilities in data imputation and normalisation using a novel set of stably phosphorylated sites, and in functional analysis for inferring active kinases and signalling pathways. In particular, we introduce a signalome construction method for identifying a collection of signalling modules to summarise and visualise the interaction of kinases and their collective actions on signal transduction. Together, our data and findings demonstrate the utility of PhosR in processing and generating novel biological knowledge from MS-based phosphoproteomic data.

systems biology↗

bioRxiv2018

Maternal psychosocial risk factors and offspring gestational epigenetic age acceleration in a South African birth cohort study

Epigenetic age (EA) acceleration is associated with higher risk of chronic disease and mortality in adults. However, little is known about whether and how in utero exposures might shape gestational EA acceleration at birth. We aimed to explore associations between maternal psychosocial risk factors and offspring gestational EA acceleration at birth in a South African birth cohort study - the Drakenstein Child Health Study. Maternal psychosocial risk factors included trauma/stressor exposure; posttraumatic stress disorder (PTSD); depression, psychological distress; and alcohol/tobacco use. Offspring gestational EA acceleration at birth was calculated using an epigenetic clock previously devised for neonates. Bivariate linear regression was used to explore unadjusted associations between maternal risk factors and offspring gestational EA acceleration at birth. A stepwise regression method was then used to determine the best multivariable model for adjusted associations. Data from 272 maternal-offspring dyads were included in the current analysis. In the stepwise regression model, maternal trauma exposure ({beta}=7.92; p<0.01) or PTSD ({beta}=7.46; p<0.01) were significantly associated with offspring gestational EA acceleration at birth, controlling for ethnicity, offspring sex, head circumference at birth, maternal HIV status, and prenatal tobacco or alcohol use. In site-stratified models, these associations retained statistical significance and direction of effect. Maternal trauma exposure or PTSD may thus be associated with offspring gestational EA acceleration at birth. Given the novelty of this preliminary finding, and its potential translational relevance, further studies to delineate underlying biological pathways and to explore clinical implications of EA acceleration are warranted.

genomics↗