Regulatory impact factors: unraveling the transcriptional regulation of complex traits from expression data

Motivation: Although transcription factors (TF) play a central regulatory role, their detection from expression data is limited due to their low, and often sparse, expression. In order to fill this gap, we propose a regulatory impact factor (RIF) metric to identify critical TF from gene expression data. Results: To substantiate the generality of RIF, we explore a set of experiments spanning a wide range of scenarios including breast cancer survival, fat, gonads and sex differentiation. We show that the strength of RIF lies in its ability to simultaneously integrate three sources of information into a single measure: (i) the change in correlation existing between the TF and the differentially expressed (DE) genes; (ii) the amount of differential expression of DE genes; and (iii) the abundance of DE genes. As a result, RIF analysis assigns an extreme score to those TF that are consistently most differentially co-expressed with the highly abundant and highly DE genes (RIF1), and to those TF with the most altered ability to predict the abundance of DE genes (RIF2). We show that RIF analysis alone recovers well-known experimentally validated TF for the processes studied. The TF identified confirm the importance of PPAR signaling in adipose development and the importance of transduction of estrogen signals in breast cancer survival and sexual differentiation. We argue that RIF has universal applicability, and advocate its use as a promising hypotheses generating tool for the systematic identification of novel TF not yet documented as critical. Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online

Pneumomediastinum in COVID-19: a phenotype of severe COVID-19 pneumonitis? The results of the United Kingdom (POETIC) survey.

BACKGROUND: There is an emerging understanding that coronavirus disease 2019 (COVID-19) is associated with increased incidence of pneumomediastinum. We aimed to determine its incidence among patients hospitalised with COVID-19 in the United Kingdom and describe factors associated with outcome. METHODS: A structured survey of pneumomediastinum and its incidence was conducted from September 2020 to February 2021. United Kingdom-wide participation was solicited via respiratory research networks. Identified patients had SARS-CoV-2 infection and radiologically proven pneumomediastinum. The primary outcomes were to determine incidence of pneumomediastinum in COVID-19 and to investigate risk factors associated with patient mortality. RESULTS: 377 cases of pneumomediastinum in COVID-19 were identified from 58 484 inpatients with COVID-19 at 53 hospitals during the study period, giving an incidence of 0.64%. Overall 120-day mortality in COVID-19 pneumomediastinum was 195/377 (51.7%). Pneumomediastinum in COVID-19 was associated with high rates of mechanical ventilation. 172/377 patients (45.6%) were mechanically ventilated at the point of diagnosis. Mechanical ventilation was the most important predictor of mortality in COVID-19 pneumomediastinum at the time of diagnosis and thereafter (p<0.001) along with increasing age (p<0.01) and diabetes mellitus (p=0.08). Switching patients from continuous positive airways pressure support to oxygen or high flow nasal oxygen after the diagnosis of pneumomediastinum was not associated with difference in mortality. CONCLUSIONS: Pneumomediastinum appears to be a marker of severe COVID-19 pneumonitis. The majority of patients in whom pneumomediastinum was identified had not been mechanically ventilated at the point of diagnosis