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Seasonal changes in gene expression represent cell-type composition in whole blood.

ABSTRACT: Seasonal patterns in behavior and biological parameters are widespread. Here, we examined seasonal changes in whole blood gene expression profiles of 233 healthy subjects. Using weighted gene co-expression network analysis, we identified three co-expression modules showing circannual patterns. Enrichment analysis suggested that this signal stems primarily from red blood cells and blood platelets. Indeed, a large clinical database with 51 142 observations of blood cell counts over 3 years confirmed a corresponding seasonal pattern of counts of red blood cells, reticulocytes and platelets. We found no direct evidence that these changes are linked to genes known to be key players in regulating immune function or circadian rhythm. It is likely, however, that these seasonal changes in cell counts and gene expression profiles in whole blood represent biological and clinical relevant phenomena. Moreover, our findings highlight possible confounding factors relevant to the study of gene expression profiles in subjects collected at geographical locations with disparaging seasonality patterns.

SUBMITTER: De Jong S

PROVIDER: S-EPMC3990170 | biostudies-literature | 2014 May

REPOSITORIES: biostudies-literature

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Seasonal changes in gene expression represent cell-type composition in whole blood.

De Jong Simone S Neeleman Marjolein M Luykx Jurjen J JJ ten Berg Maarten J MJ Strengman Eric E Den Breeijen Hanneke H HH Stijvers Leon C LC Buizer-Voskamp Jacobine E JE Bakker Steven C SC Kahn René S RS Horvath Steve S Van Solinge Wouter W WW Ophoff Roel A RA

Human molecular genetics 20140107 10

Seasonal patterns in behavior and biological parameters are widespread. Here, we examined seasonal changes in whole blood gene expression profiles of 233 healthy subjects. Using weighted gene co-expression network analysis, we identified three co-expression modules showing circannual patterns. Enrichment analysis suggested that this signal stems primarily from red blood cells and blood platelets. Indeed, a large clinical database with 51 142 observations of blood cell counts over 3 years confirm ...[more]

PMID: 24399446

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Project description:Measuring genome-wide changes in transcript abundance in circulating peripheral whole blood is a useful way to study disease pathobiology and may help elucidate the molecular mechanisms of disease, or discovery of useful disease biomarkers. The sensitivity and interpretability of analyses carried out in this complex tissue, however, are significantly affected by its dynamic cellular heterogeneity. It is therefore desirable to quantify this heterogeneity, either to account for it or to better model interactions that may be present between the abundance of certain transcripts, specific cell types and the indication under study. Accurate enumeration of the many component cell types that make up peripheral whole blood can further complicate the sample collection process, however, and result in additional costs. Many approaches have been developed to infer the composition of a sample from high-dimensional transcriptomic and, more recently, epigenetic data. These approaches rely on the availability of isolated expression profiles for the cell types to be enumerated. These profiles are platform-specific, suitable datasets are rare, and generating them is expensive. No such dataset exists on the Affymetrix Gene ST platform.We present 'Enumerateblood', a freely-available and open source R package that exposes a multi-response Gaussian model capable of accurately predicting the composition of peripheral whole blood samples from Affymetrix Gene ST expression profiles, outperforming other current methods when applied to Gene ST data.'Enumerateblood' significantly improves our ability to study disease pathobiology from whole blood gene expression assayed on the popular Affymetrix Gene ST platform by allowing a more complete study of the various components of this complex tissue without the need for additional data collection. Future use of the model may allow for novel insights to be generated from the ~400 Affymetrix Gene ST blood gene expression datasets currently available on the Gene Expression Omnibus (GEO) website.

Dataset Information

Seasonal changes in gene expression represent cell-type composition in whole blood.

Publications

Seasonal changes in gene expression represent cell-type composition in whole blood.

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