The integration of large-scale public data and network analysis uncovers molecular characteristics of psoriasis

Abstract

In recent years, a growing interest in the characterization of the molecular basis of psoriasis has been observed. However, despite the availability of a large amount of molecular data, many pathogenic mechanisms of psoriasis are still poorly understood. In this study, we performed an integrated analysis of 23 public transcriptomic datasets encompassing both lesional and uninvolved skin samples from psoriasis patients. We defined comprehensive gene co-expression network models of psoriatic lesions and uninvolved skin. Moreover, we curated and exploited a wide range of functional information from multiple public sources in order to systematically annotate the inferred networks. The integrated analysis of transcriptomics data and co-expression networks highlighted genes that are frequently dysregulated and show aberrant patterns of connectivity in the psoriatic lesion compared with the unaffected skin. Our approach allowed us to also identify plausible, previously unknown, actors in the expression of the psoriasis phenotype. Finally, we characterized communities of co-expressed genes associated with relevant molecular functions and expression signatures of specific immune cell types associated with the psoriasis lesion. Overall, integrating experimental driven results with curated functional information from public repositories represents an efficient approach to empower knowledge generation about psoriasis and may be applicable to other complex diseases.

Integrative network analysis suggests prioritised drugs for atopic dermatitis

“…Two distinct co-expression networks were inferred by using the gene expression profiles of the lesional and non-lesional skin samples from all the included studies and selecting the genes common to all the platforms.The co-expression networks were inferred as already described in [ 20 ] through the use of the INfORM algorithm [ 21 ]. We set up INfORM with the same parameters used in [ 20 ].…”

“…Two distinct co-expression networks were inferred by using the gene expression profiles of the lesional and non-lesional skin samples from all the included studies and selecting the genes common to all the platforms.The co-expression networks were inferred as already described in [ 20 ] through the use of the INfORM algorithm [ 21 ]. We set up INfORM with the same parameters used in [ 20 ]. We used the clr [ 22 ], aracne [ 23 ] and mrnet [ 24 ] algorithms with the following correlation and mutual information measures: Pearson correlation, Kendall correlation, Spearman correlation, empirical mutual information, Miller-Madow asymptotic bias corrected empirical estimator, Schurmann-Grassberger estimate of the entropy of a Dirichlet probability distribution and a shrinkage estimate of the entropy of a Dirichlet probability distribution, as implemented in the minet Bioconductor package [ 25 ].…”

“…The final consensus gene rank was generated through the Borda function of the TopKlists R package [ 33 ]. Differential centrality analysis: We performed differential centrality analysis as already described in [ 20 ]. In detail, for each of the networks, their node betweenness, closeness and degree centralities were calculated with the Python’s NetworkX package (Python 3.6, NetworkX 2.3).…”

“…To compare the network of the lesional skin with the non-lesional one, the absolute difference between the median ranks of the two networks was calculated and the genes were ranked accordingly. Bridge gene analysis: The identification of the bridge genes was conducted as reported in [ 20 ]. In our previous study, we hypothesized that, by studying the connectivity patterns among known disease genes, it is possible to identify additional associated genes.…”

Multi-Omics Research Strategies for Psoriasis and Atopic Dermatitis

“…For example, transcriptomics data have been repeatedly utilized to double-check the reliability of biomarkers with genomic data (CARD14) [110] or merged transcriptomic data (CCL20) by the sva package in R software (R package version 3.46.0, R version 3.2) [111]. Public transcriptomic data were integrated into a large-scale dataset to systematically annotate the gene co-expression network and reach a more persuasive conclusion [112]. More often, proteomics and transcriptomics are combined to verify the consistency of the expression of biomarkers consisting of psoriasisrelated proteins and their respective coding genes.…”