Naslov
Immune and inflammatory responses to freediving calculated from leukocyte gene expression profiles

Autori
Eftedal, Ingrid ; Flatberg, Arnar ; Drviš, Ivan ; Dujić, Željko

Izvornik
Physiological genomics (1094-8341) 48 (2016); 795-802

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
apnea ; hypoxia ; inflammation ; leukocyte ; transcriptome deconvolution

Sažetak
Freedivers hold their breath while diving, causing blood oxygen levels to decrease (hypoxia) while carbon dioxide increases (hypercapnia). Whereas blood gas changes are presumably involved in the progression of respiratory diseases, less is known about their effect on healthy individuals. Here we have used gene expression profiling to analyze elite athletes' immune and inflammatory responses to freediving. Blood was collected before and 1 and 3 h after a series of maximal dynamic and static freediving apneas in a pool, and peripheral blood gene expression was mapped on genome-wide microarrays. Fractions of phenotypically distinct immune cells were computed by deconvolution of the gene expression data using Cibersort software. Changes in gene activity and associated biological pathways were determined using R and GeneGo software. The results indicated a temporary increase of neutrophil granulocytes, and a decrease of cytotoxic lymphocytes ; i.e., CD8+ T cells and resting NK cells. Biological pathway associations indicated possible protective reactions: genes involved in anti- inflammatory responses to proresolving lipid mediators were upregulated, whereas central factors involved in granule-mediated lymphocyte cytotoxicity were downregulated. While it remains unresolved whether freediving alters the immune system's defensive function, these results provide new insight into leukocyte responses and the protection of homeostasis in healthy athletes. freedivers dive on a single breath, and their performance hinges on their ability to suppress breathing voluntarily while floating face down (static apnea) or swimming horizontally (dynamic apnea) or vertically (constant weight apnea, free immersion apnea, no-limits apnea). To improve their performance, they use adaptive techniques that increase their lung capacity, reduce metabolic rates, and improve their tolerance to apnea, i.e., to hypoxia and hypercapnia (22). Freediving performance is often further enhanced by hyperventilation to reduce carbon dioxide levels prior to the dive and by glossopharyngeal insufflation (“lung packing”) for extra volumes of air or breathing pure oxygen to add to the body's oxygen reserves (19). During dives, an initial easy- going phase is followed by a physiological breaking point after which the urge to breath causes a struggle phase with displays of involuntary movements of the respiratory muscles that are thought to increase cardiac output (12, 31). This effect restores oxygen supply to the vital organs, such as the brain and the heart. Still, hypoxia can be severe at the end of a dive ; oxygen levels that are considered pathological in untrained individuals have been measured in freediving athletes' first expired breaths and arterial blood after diving (21, 30, 52). Adaption to hypercapnia also permits freedivers to prolong their breath holding, and the level of carbon dioxide first expired after breaking off a dive is considerably elevated (30). In light of an emerging understanding of the role of the immune system and inflammatory signaling in maintaining tissue and organ homeostasis (24), it is of interest to understand the responses of the white blood cells, leukocytes, to physiologically stressful changes in blood gas during voluntary apnea. The possibility of genome-wide measurements of gene expression on microarrays has expedited research into the molecular basis of biological states and responses. For studies of the immune system, peripheral blood is an obvious choice for gene expression analysis (5). Blood is a highly heterogeneous tissue. Of its formed elements, the erythrocytes, platelets, and leukocytes, only leukocytes have chromosome- containing nuclei ; genome-wide gene expression in peripheral blood therefore ideally represent the biological state of its leukocytes. However, the interpretation of gene expression data from blood is complicated by the heterogeneity of the leukocyte compartment, which consists of a number of phenotypically different cell types. The main leukocytes, the neutrophils, eosinophils, basophils, lymphocytes, and monocytes, are further divided into subsets of cells with different function in the immune system. Each leukocyte subset derives its phenotype from the particular set of genes it expresses, and the cell types are present in blood in variable amounts (51). In practical terms, this means that a measured change in the abundance of any transcript in blood does not immediately tell us whether the activity of its gene has changed, or whether there has been a change in the relative abundance of cells in which this gene is expressed (39). Traditional microarray analysis does not take sample composition into account, but recent papers have presented methods where transcriptome contributions from phenotypically distinct cell types are separated by signal deconvolution on the basis of cell type- specific gene expression (1, 25, 38). Deconvolution of microarray signals extracts cell type-specific information from system-wide data and has been found to corroborate results from flow cytometric phenotyping. Also, since deconvolution is done after the genome-wide data are collected, it eliminates the need for fractioning of samples and facilitates unbiased detection of cell types for which the patterns of gene expression are known. In this study we examine the effects of freediving on cells of the immune system. Genome-wide cDNA microarrays were used to analyze the peripheral blood transcriptome of elite freedivers who performed a series of dynamic and static apnea dives in a pool with their respiratory tract immersed. The proportions of major immune cell types in the participant's blood before and after dives were calculated by cell type-specific deconvolution of the microarray data. Changes in biological pathways were predicted on basis of differentially expressed genes.

Izvorni jezik
Engleski

Znanstvena područja
Kineziologija

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