Derangement of cell cycle markers in peripheral blood mononuclear cells of asthmatic patients as a reliable biomarker for asthma control

Abstract

In asthma, most of the identified biomarkers pertain to the Th2 phenotype and no known biomarkers have been verified for severe asthmatics. Therefore, identifying biomarkers using the integrative phenotype-genotype approach in severe asthma is needed. The study aims to identify novel biomarkers as genes or pathways representing the core drivers in asthma development, progression to the severe form, resistance to therapy, and tissue remodeling regardless of the sample cells or tissues examined. Comprehensive reanalysis of publicly available transcriptomic data that later was validated in vitro, and locally recruited patients were used to decipher the molecular basis of asthma. Our in-silicoanalysis revealed a total of 10 genes (GPRC5A, SFN, ABCA1, KRT8, TOP2A, SERPINE1, ANLN, MKI67, NEK2, and RRM2) related to cell cycle and proliferation to be deranged in the severe asthmatic bronchial epithelium and fibroblasts compared to their healthy counterparts. In vitro, RT qPCR results showed that (SERPINE1 and RRM2) were upregulated in severe asthmatic bronchial epithelium and fibroblasts, (SFN, ABCA1, TOP2A, SERPINE1, MKI67, and NEK2) were upregulated in asthmatic bronchial epithelium while (GPRC5A and KRT8) were upregulated only in asthmatic bronchial fibroblasts. Furthermore, MKI76, RRM2, and TOP2A were upregulated in Th2 high epithelium while GPRC5A, SFN, ABCA1 were upregulated in the blood of asthmatic patients. SFN, ABCA1 were higher, while MKI67 was lower in severe asthmatic with wheeze compared to nonasthmatics with wheezes. SERPINE1 and GPRC5A were downregulated in the blood of eosinophilic asthmatics, while RRM2 was upregulated in an acute attack of asthma. Validation of the gene expression in PBMC of locally recruited asthma patients showed that SERPINE1, GPRC5A, SFN, ABCA1, MKI67, and RRM2 were downregulated in severe uncontrolled asthma. We have identified a set of biologically crucial genes to the homeostasis of the lung and in asthma development and progression. This study can help us further understand the complex interplay between the transcriptomic data and the external factors which may deviate our understanding of asthma heterogeneity.

Figure 1

Schematic representation of the filtering of Genes Identified using our bioinformatic pipeline showing the resultant gene list that shares a common pathway mainly related to cell cycle, cell proliferation, and survival.

Figure 2

mRNA expression of cell cycle-related genes in healthy bronchial epithelium (NHLE, n = 3) versus asthmatic cells (DHLE, n = 3), and healthy lung fibroblasts (NHLF, n = 3) compared to asthmatic lung fibroblasts (DHLF, n = 3).

Figure 3

mRNA expression of the ten genes in bronchial epithelium using, GSE76227 transcriptomic dataset that contains the expression data of 190 bronchial biopsies (BB) and epithelial brushing (BRUSH) from Unbiased BIOmarkers in Prediction of REspiratory Disease outcomes (U-BIOPRED) Project. The normalized gene expression of each of the identified genes was extracted and compared between different subgroups. The datasets were subdivided into nonsevere asthmatic (MAS), severe asthmatics (SAS) (oral steroid naïve (N) vs. oral steroid users (OS).

Figure 4

mRNA expression of (MKI67, RRM2, and TOP2A) genes in bronchial epithelium using GSE67472 dataset to compare healthy controls (n = 43) to Th2-high asthmatics (n = 40) and Th2 low asthmatics (n = 22).

Figure 5

Normalized gene expression of genes identified in circulating T-cells (CD4 versus CD8) of asthmatic patients “MAS = mild to moderate and SAS = severe” compared to healthy (H) controls extracted from the expression profile of publicly available datasets (GSE31773).

Figure 6

Normalized gene expression of genes identified in the blood of asthmatic patients compared to healthy controls extracted from the expression profile of publicly available datasets (GSE69683) collected in the U-BIOPRED study, where (H) represents the 87 healthy controls, (M) represents the 77-moderate asthma, (S) represents the 246 nonsmoker’s severe asthma, and 88 smoker’s severe asthma, (A) represents the sum of all asthmatic patients in all severities.

Figure 7

Normalized gene expression of genes identified in whole blood of children with nonsevere and severe wheezes were compared to nonsevere and severe asthmatics extracted from the expression profile of publicly available datasets (GSE123750), where nonsevere asthmatic (MAS_Asthma) and severe asthmatic (SAS_Asthma) patients were compared to patients presented with nonsevere wheezes (MAS_Wheeze) and severe wheezes (SAS_Wheeze).

Figure 8

Normalized gene expression of genes identified in CD4 lymphocytes stimulated with HDM compared to media stimulation only in healthy, atopic, and asthmatic patients extracted from the expression profile of publicly available datasets (GSE73482).

Figure 9

Normalized gene expression of genes identified in blood taken from asthmatic children at baseline (Case_NV) and after they develop viral infections (Case_V) compared to healthy controls (Control_NV and Control_V) extracted from the expression profile of publicly available datasets (GSE115823).

Figure 10

Normalized gene expression of genes identified in PBMCs taken from asthmatic patients during the acute/exacerbation phase and the convalescent phase extracted from the expression profile of publicly available datasets (GSE16032).

Figure 11

Normalized gene expression of genes Identified in whole blood of eosinophilic asthma (> 500 cells) compared to non-eosinophilic (eosinophils count less than 500) extracted from the expression profile of publicly available datasets (GSE137394).

Figure 12

mRNA gene expression using RT qPCR of the ten genes in PBMC of the locally recruited cohort, healthy controls (n = 13), Nonsevere asthmatics (n = 20), and severe asthmatic (n = 18).

Figure 13

mRNA gene expression using RT qPCR of the ten genes in PBMC of a locally recruited cohort, the asthmatic patients were divided according to the ACT score into two groups: a well-controlled group (ACT > 20) and those with poor control (ACT < 20).