Transcriptomic analysis delineates potential signature genes and miRNAs associated with the pathogenesis of asthma

Prithvi Singh, Archana Sharma, Rishabh Jha, Shweta Arora, Rafiq Ahmad, Arshad Husain Rahmani, Saleh A Almatroodi, Ravins Dohare, Mansoor Ali Syed, Prithvi Singh, Archana Sharma, Rishabh Jha, Shweta Arora, Rafiq Ahmad, Arshad Husain Rahmani, Saleh A Almatroodi, Ravins Dohare, Mansoor Ali Syed

Abstract

Asthma is a multifarious disease affecting several million people around the world. It has a heterogeneous risk architecture inclusive of both genetic and environmental factors. This heterogeneity can be utilised to identify differentially expressed biomarkers of the disease, which may ultimately aid in the development of more localized and molecularly targeted therapies. In this respect, our study complies with meta-analysis of microarray datasets containing mRNA expression profiles of both asthmatic and control patients, to identify the critical Differentially Expressed Genes (DEGs) involved in the pathogenesis of asthma. We found a total of 30 DEGs out of which 13 were involved in the pathway and functional enrichment analysis. Moreover, 5 DEGs were identified as the hub genes by network centrality-based analysis. Most hub genes were involved in protease/antiprotease pathways. Also, 26 miRNAs and 20 TFs having an association with these hub genes were found to be intricated in a 3-node miRNA Feed-Forward Loop. Out of these, miR-34b and miR-449c were identified as the key miRNAs regulating the expression of SERPINB2 gene and SMAD4 transcription factor. Thus, our study is suggestive of certain miRNAs and unexplored pathways which may pave a way to unravel critical therapeutic targets in asthma.

Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
PCA plot in the left panel represents the variation in the expression data between controls and asthmatics. Each point in the plot shows the overall expression value of 30 screened DEGs. The color of each point represents the disease status: magenta for controls and green for asthmatics. The percentage of total variation which is accounted for by the 1st and 2nd principal components are shown on the x and y axes respectively. Scree plot in the right panel shows the percentage of explained variances captured by their corresponding principal components.
Figure 2
Figure 2
Heatmap plot of top 10 up and downregulated DEGs in asthma. Expression values for each DEG (row) are normalized across all the samples (columns). Hierarchical clustering using Euclidean distances was employed for both rows and columns with cluster dendrograms presented along the left and top sides of the plot. The categorical annotation bars (above the heatmap) show the column annotations for age, gender and disease status. The location of each gene on its respective chromosome was shown in the right bar (color bands) as row annotation.
Figure 3
Figure 3
Circos plot representation of significantly enriched pathways linked with 9 asthma-associated DEGs. Outside the circle, 17 significantly enriched pathways (on the left) and 9 dysregulated genes (on the right) are indicated. Each DEG is denoted by a unique color band and the undirected colored edge inside the circle represents the association of a particular gene with their respective pathway(s).
Figure 4
Figure 4
Venn plots showing the significant hub genes obtained from (A) upregulated and (B) downregulated PPI networks, respectively. Areas with different colors correspond to different centrality measures. The cross-intersection area shows the hub genes in both networks. A total of 5 hub genes were obtained for the up and downregulated PPI networks.
Figure 5
Figure 5
Violin plots of 5 hub genes filtered based on functional enrichment analysis and PPI network centrality measures respectively. This plot represents the distribution density of the underlying expression data for hub genes. The top and bottom of the embedded box signify the 75th and 25th percentile of the distribution, respectively. The line inside the box represents the median. Endpoints of the axis are labelled by the minimum and maximum values. Control and asthmatic samples are distinguished by yellow and blue colors, respectively.
Figure 6
Figure 6
Asthma-specific 3-node miRNA FFL regulatory network with 51 nodes and 197 edges, respectively. The green-colored rectangular nodes represent asthma-associated miRNAs, red-colored triangular nodes represent transcription factors, and cyan-colored circular nodes represent asthma-associated hub genes.
Figure 7
Figure 7
Topological parameter graphical plots of the asthma-specific 3-node miRNA FFL representing (A) Node degree distribution, (B) Average clustering coefficient, (C) Topological coefficient, (D) Betweenness centrality, (E) Closeness centrality, (F) Shortest path length distribution. The lines are fitted with power laws.

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