Inflammatory related plasma proteins involved in acute preschool wheeze

BACKGROUND

Preschool wheeze is a risk factor for asthma development. However, the molecular mechanism behind a wheezing episode is not well understood.

OBJECTIVE

Our aims were to assess the association of plasma proteins with acute preschool wheeze and to study the proteins with differential expression at the acute phase at revisit after 3 months. Additionally, to investigate the relationship between protein expression and clinical parameters.

METHOD

We measured 92 inflammatory proteins in plasma and clinical parameters from 145 children during an episode of preschool wheeze (PW) and at the revisit after 3 months (PW-R, n = 113/145) and 101 healthy controls (HC) aged 6-48 months in the GEWAC cohort using the antibody-mediated proximity extension-based assay (Olink Proteomics, Uppsala).

RESULTS

Of the 74 analysed proteins, 52 were differentially expressed between PW and HC. The expression profiles of the top 10 proteins, Oncostatin M (OSM), IL-10, IL-6, Fibroblast growth factor 21 (FGF21), AXIN1, CXCL10, SIRT2, TNFSF11, Tumour necrosis factor β (TNF-β) and CASP8, could almost entirely separate PW from HC. Five out of 10 proteins were associated with intake of oral corticosteroids (OCS) 24 h preceding blood sampling (OSM, CASP8, IL-10, TNF-β and CXCL10). No differences in protein expression were seen between PWs with or without OCS in comparison to HC. At the revisit after 3 months, differential protein expressions were still seen between PW-R and HC for three (IL-10, SIRT2 and FGF21) of the 10 proteins.

CONCLUSION

Our results contribute to unravelling potential immunopathological pathways shared between preschool wheeze and asthma.

Multivariate strategy for the sample selection and integration of multi-batch data in metabolomics

INTRODUCTION

Availability of large cohorts of samples with related metadata provides scientists with extensive material for studies. At the same time, recent development of modern high-throughput 'omics' technologies, including metabolomics, has resulted in the potential for analysis of large sample sizes. Representative subset selection becomes critical for selection of samples from bigger cohorts and their division into analytical batches. This especially holds true when relative quantification of compound levels is used.

OBJECTIVES

We present a multivariate strategy for representative sample selection and integration of results from multi-batch experiments in metabolomics.

METHODS

Multivariate characterization was applied for design of experiment based sample selection and subsequent subdivision into four analytical batches which were analyzed on different days by metabolomics profiling using gas-chromatography time-of-flight mass spectrometry (GC-TOF-MS). For each batch OPLS-DA^® was used and its p(corr) vectors were averaged to obtain combined metabolic profile. Jackknifed standard errors were used to calculate confidence intervals for each metabolite in the average p(corr) profile.

RESULTS

A combined, representative metabolic profile describing differences between systemic lupus erythematosus (SLE) patients and controls was obtained and used for elucidation of metabolic pathways that could be disturbed in SLE.

CONCLUSION

Design of experiment based representative sample selection ensured diversity and minimized bias that could be introduced at this step. Combined metabolic profile enabled unified analysis and interpretation.