Biology subjects

Sterk, P. J.

Publications and source records attributed to Sterk, P. J..

3 recordsLinked to original sources

bioRxiv2020

Novel Type 2-high gene clusters associated with corticosteroid sensitivity in COPD

RationaleSevere asthma and COPD share common pathophysiologic traits such as relative corticosteroid insensitivity. We recently published three transcriptome-associated clusters (TACs) using hierarchical analysis of the sputum transcriptome in asthmatics from the U-BIOPRED cohort with one Type 2-high signature (TAC1) and 2 Type 2-low signatures. ObjectiveWe examined whether gene-expression signatures obtained in asthma can be used to identify the subgroup of COPD patients with steroid sensitivity. MethodsUsing gene set variation analysis (GSVA), we examined the distribution and enrichment scores (ES) of the 3 TACs in the transcriptome of bronchial biopsies from 46 patients who participated in the GLUCOLD COPD study that received 30 months of treatment with inhaled corticosteroids (ICS) with and without an added long-acting {beta}-agonist (LABA). The identified signatures were then associated with longitudinal clinical variables after treatment. Measurements and main resultsBronchial biopsies in COPD patients at baseline showed a wide range of expression of the 3 TACs. After ICS{+/-}LABA treatment, the ES of TAC1 was significantly reduced at 30 months, but those of TAC2 and TAC3 were unaffected. A corticosteroid-sensitive TAC1 (sub)signature was developed from the TAC1 ICS-responsive genes. This signature consisted of mast cell-specific genes identified by single-cell RNA-seq and positively correlated with bronchial biopsy mast cell numbers following ICS{+/-}LABA. Baseline levels of gene transcription predicted change in FEV1 %predicted following 30-month ICS{+/-}LABA. ConclusionsSputum-derived transcriptomic signatures from an asthma cohort can be recapitulated in bronchial biopsies of COPD patients and identified airway wall mast cells as a predictor of those COPD patients who will benefit from inhaled corticosteroids.

bioinformatics↗

bioRxiv2019

Loss of adaptive capacity in asthmatics revealed by biomarker fluctuation dynamics on experimental rhinovirus challenge

Asthma is a dynamic disease, in which lung mechanical and inflammatory processes often interact in a complex, unpredictable manner. We hypothesize that this may be explained by respiratory disease-related systems instability and loss of adaptability to changing environmental conditions, resulting in highly fluctuating biomarkers and symptoms. Using time series of inflammatory (eosinophils, neutrophils, FeNO), clinical and lung function biomarkers (PEF, FVC, and FEV1), we estimated this loss of adaptive capacity (AC) during an experimental perturbation with a rhinovirus in 24 healthy and asthmatic volunteers. Loss of AC was estimated by comparing similarities between pre- and post-challenge time series. Unlike healthy participants, the asthmatics post-viral-challenge state resembled significantly more other rhinovirus-infected asthmatics than their own pre-viral-challenge state (hypergeometric-test: p=0.029). This reveals loss of AC, and supports the novel concept that not only single physiological mechanisms, but interacting dynamic disease properties are altered in asthma and contribute to a more vulnerable phenotype.

physiology↗

bioRxiv2019

A Topological Data Analysis Network Model of Asthma Based on Blood Gene Expression Profiles

Stratified medicine requires discretisation of disease populations for targeted treatments. We have developed and applied a discrete Morse theory clustering algorithm to a Topological Data Analysis (TDA) network model of 498 gene expression profiles of peripheral blood from asthma and healthy participants. The Morse clustering algorithm defined nine clusters, BC1-9, representing molecular phenotypes with discrete phenotypes including Type-1, 2 & 17 cytokine inflammatory pathways. The TDA network model and clusters were also characterised by activity of glucocorticoid receptor signalling associated with different expression profiles of glucocorticoid receptor (GR), according to microarray probesets targeted to the start or end of the GR mRNAs 3 UTR; suggesting differential GR mRNA processing as a possible driver of asthma phenotypes including steroid insensitivity.

molecular biology↗