Aditi Chandra

Genetic and epigenetic basis of psoriasis pathogenesis

Psoriasis is a chronic inflammatory skin disease whose prevalence varies among different populations worldwide. It is a complex multi-factorial disease and the exact etiology is largely unknown. Family based studies have indicated a genetic predisposition; however they cannot fully explain the disease pathogenesis. In addition to genetic susceptibility, environmental as well as gender and age related factors were also been found to be associated. Recently, imbalances in epigenetic networks are indicated to be causative elements in psoriasis. The present knowledge of epigenetic involvement, mainly the DNA methylation, chromatin modifications and miRNA deregulation is surveyed here. An integrated approach considering genetic and epigenetic anomalies in the light of immunological network may explore the pathogenesis of psoriasis.

Increased Risk of Psoriasis due to combined effect of HLA-Cw6 and LCE3 risk alleles in Indian population

HLA-Cw6 is one of the most associated alleles in psoriasis. Recently, Late Cornified Envelop 3 (LCE3) genes were identified as a susceptibility factor for psoriasis. Some population showed epistatic interaction of LCE3 risk variants with HLA-Cw6, while some population failed to show any association. We determined the associations of a 32.2 kb deletion comprising LCE3C-3B genes and three SNPs (rs1886734, rs4112788; rs7516108) at the LCE3 gene cluster among the psoriasis patients in India. All three SNPs at the LCE3 gene cluster failed to show any association. In contrary, for patients with HLA-Cw6 allele, all three SNPs and the LCE3C-3B deletion showed significant associations. While, all five LCE3 genes were upregulated in psoriatic skin, only LCE3A showed significant overexpression with homozygous risk genotype compared to the non-risk genotype. LCE3B also showed significant overexpression in patients with HLA-Cw6 allele. Moreover, LCE3A showed significantly higher expression in patients bearing homozygous risk genotype in presence of HLA-Cw6 allele but not in those having non-risk genotype, demonstrating the combined effect of HLA-Cw6 allele and risk associated genotype near LCE3A gene. Integration of genetic and gene expression data thus allowed us to identify the actual disease variants at the LCE3 cluster among the psoriasis patients in India.

Genome-wide DNA methylation profile identified a unique set of differentially methylated immune genes in oral squamous cell carcinoma patients in India

BackgroundOral squamous cell carcinoma (OSCC) is one of the common malignancies in Southeast Asia. Epigenetic changes, mainly the altered DNA methylation, have been implicated in many cancers. Considering the varied environmental and genotoxic exposures among the Indian population, we conducted a genome-wide DNA methylation study on paired tumor and adjacent normal tissues of ten well-differentiated OSCC patients and validated in an additional 53 well-differentiated OSCC and adjacent normal samples.ResultsGenome-wide DNA methylation analysis identified several novel differentially methylated regions associated with OSCC. Hypermethylation is primarily enriched in the CpG-rich regions, while hypomethylation is mainly in the open sea. Distinct epigenetic drifts for hypo- and hypermethylation across CpG islands suggested independent mechanisms of hypo- and hypermethylation in OSCC development. Aberrant DNA methylation in the promoter regions are concomitant with gene expression. Hypomethylation of immune genes reflect the lymphocyte infiltration into the tumor microenvironment. Comparison of methylome data with 312 TCGA HNSCC samples identified a unique set of hypomethylated promoters among the OSCC patients in India. Pathway analysis of unique hypomethylated promoters indicated that the OSCC patients in India induce an anti-tumor T cell response, with mobilization of T lymphocytes in the neoplastic environment. Survival analysis of these epigenetically regulated immune genes suggested their prominent role in OSCC progression.ConclusionsOur study identified a unique set of hypomethylated regions, enriched in the promoters of immune response genes, and indicated the presence of a strong immune component in the tumor microenvironment. These methylation changes may serve as potential molecular markers to define risk and to monitor the prognosis of OSCC patients in India.

Severity grading of psoriatic plaques using deep CNN based multi-task learning

This paper addresses the problem of automatic machine analysis based severity scoring of psoriasis skin disease. Three different disease parameters namely, erythema, scaling and induration are considered for such severity grading. Given an image containing a psoriatic plaque the task is to predict severity scores for all the three parameters. This paper presents a novel deep CNN based architecture for achieving the task. Apart from viewing this task as three different single task learning (STL) problems (i.e. three different classification problems), a new multi-task learning (MTL) is also presented where the three classification tasks are treated as interdependent and thereby the neural net is trained accordingly. A new annotated dataset consisting of seven hundred and seven (707) images has been constructed on which the performance of the severity scoring algorithms have been reported. Several competing baselines are considered to compare the performance of STL and MTL approaches. Experimental result shows that the deep CNN based architectures (both the STL and MTL) achieve promising performances, MTL producing slightly superior results to that of STL.

Associations of ERAP1 coding variants and domain specific interaction with HLA-C∗06 in the early onset psoriasis patients of India

Interferon-γ-induced aminopeptidase ERAP1 trims peptides within the endoplasmic reticulum so that they can be loaded onto MHC class I and presented to the CD8+ T-cells. ERAP1 association and its interaction with HLA-C∗06 is controversial across different populations. We have investigated the association and possible functional role of non-synonymous SNPs at different exons of ERAP1 (rs26653: Arg127Pro, rs30187: Lys528Arg and rs27044: Gln730Glu) and their interactions with HLA-C∗06 in psoriasis. Significant associations of HLA-C∗06 (OR=5.47, P<2.2×10-16), rs30187 (OR 1.35, P=7.4×10-4) and rs27044 (OR=1.24, P=5.8×10-3) were observed. All three ERAP1 SNPs showed significant association only for HLA-C∗06 positive patients, while rs30187 and rs27044 showed significant association only for early onset patients (rs30187: OR=1.47, P=9.6×10-5; rs27044: OR=1.36, P=3.3×10-4). No differential expression of ERAP1 was observed either between paired uninvolved and involved skin tissues of psoriasis patients or between non-risk and risk variants in the involved skin. Significant epistatic interaction was observed between HLA-C∗06 and the SNP (rs27044) located at the peptide-binding cavity of ERAP1. Evolutionary conservation analysis among mammals showed confinement of Lys528 and Gln730 within highly conserved regions of ERAP1 and suggested the possible detrimental effect of this allele in ERAP1 regulation.

Association of IL12B risk haplotype and lack of interaction with HLA-Cw6 among the psoriasis patients in India

Psoriasis is a complex multifactorial chronic inflammatory skin disorder involving both genetic and environmental susceptibility factors. It is strongly associated with HLA-Cw6, but several studies suggested that further genetic factors may confer additional risk. We investigated the association of two single-nucleotide polymorphisms (SNPs), rs3212227 at the 3′-untranslated region and rs7709212 located at ~6.7 kb upstream from the transcription start site of IL12B gene in a case-control study comprising 1702 individuals from India. We found both SNPs were significantly associated with psoriasis (rs7709212: odds ratio (OR)=1.37, P-value=1.09 × 10−5; rs3212227: OR=1.38, P-value=8.88 × 10−6). IL12B gene was significantly upregulated in involved skin of psoriasis patients with risk genotype carriers (rs7709212_TT and rs3212227_TT) compared with non-risk genotype carriers (rs7709212_CC and rs3212227_GG). Significantly higher serum protein concentration of IL12 was also observed among risk allele carriers compared with non-risk allele carriers irrespective of the presence of HLA-Cw6 allele. Haplotype analysis suggested significant increased risk (OR=1.50, P-value=5.01 × 10−8) to the disease when both risk alleles of IL12B were present. IL12 serum protein concentration of risk haplotype (TT-TT) carriers showed significant upregulation compared with the non-risk carriers independent of HLA-Cw6 alleles. Our data suggested the association of IL12B with the psoriasis, however no evidence was observed for the epistatic effect of IL12B with HLA-Cw6 among the psoriasis patients in India.

Psoriasis skin biopsy image segmentation using Deep Convolutional Neural Network

BACKGROUND AND OBJECTIVE Development of machine assisted tools for automatic analysis of psoriasis skin biopsy image plays an important role in clinical assistance. Development of automatic approach for accurate segmentation of psoriasis skin biopsy image is the initial prerequisite for developing such system. However, the complex cellular structure, presence of imaging artifacts, uneven staining variation make the task challenging. This paper presents a pioneering attempt for automatic segmentation of psoriasis skin biopsy images. METHODS Several deep neural architectures are tried for segmenting psoriasis skin biopsy images. Deep models are used for classifying the super-pixels generated by Simple Linear Iterative Clustering (SLIC) and the segmentation performance of these architectures is compared with the traditional hand-crafted feature based classifiers built on popularly used classifiers like K-Nearest Neighbor (KNN), Support Vector Machine (SVM) and Random Forest (RF). A U-shaped Fully Convolutional Neural Network (FCN) is also used in an end to end learning fashion where input is the original color image and the output is the segmentation class map for the skin layers. RESULTS An annotated real psoriasis skin biopsy image data set of ninety (90) images is developed and used for this research. The segmentation performance is evaluated with two metrics namely, Jaccards Coefficient (JC) and the Ratio of Correct Pixel Classification (RCPC) accuracy. The experimental results show that the CNN based approaches outperform the traditional hand-crafted feature based classification approaches. CONCLUSIONS The present research shows that practical system can be developed for machine assisted analysis of psoriasis disease.

CapsDeMM: Capsule Network for Detection of Munro’s Microabscess in Skin Biopsy Images

This paper presents an approach for automatic detection of Munro’s Microabscess in stratum corneum (SC) of human skin biopsy in order to realize a machine assisted diagnosis of Psoriasis. The challenge of detecting neutrophils in presence of nucleated cells is solved using the recent advances of deep learning algorithms. Separation of SC layer, extraction of patches from the layer followed by classification of patches with respect to presence or absence of neutrophils form the basis of the overall approach which is effected through an integration of a U-Net based segmentation network and a capsule network for classification. The novel design of the present capsule net leads to a drastic reduction in the number of parameters without any noticeable compromise in the overall performance. The research further addresses the challenge of dealing with Mega-pixel images (in 10X) vis-a-vis Giga-pixel ones (in 40X). The promising result coming out of an experiment on a dataset consisting of 273 real-life images shows that a practical system is possible based on the present research. The implementation of our system is available at https://github.com/Anabik/CapsDeMM.

Severity Assessment of Psoriatic Plaques Using Deep CNN Based Ordinal Classification

Development of computer-aided diagnosis (CAD) tool for severity assessment of psoriatic plaques is important to assist the dermatologists to overcome the human limitation. In this paper, a pioneering attempt is made to build a Convolutional Neural Network (CNN) model to classify a skin image with respect to its severity class. However, the commonly used loss functions like categorical cross entropy and mean square error ignores the underlying ordinal class relationships (distance between predicted and actual class) which are important for the present problem. In this paper, the Earth Mover’s Distance based loss function is proposed for training CNN since it takes into account the corresponding ordinal class relationships. Separate CNNs are trained for severity scoring corresponding to three plaque characteristics- erythema (redness), scaling (silveryness) and induration (elevation). Mean accuracy (MA), mean absolute error (MAE) and Kendall’s \(\tau _b\) are used for performance evaluation. The experimental result shows that the proposed ordinal classification technique outperforms the traditional approaches.

Epigenome-wide DNA methylation regulates cardinal pathological features of psoriasis

BackgroundPsoriasis is a chronic inflammatory autoimmune skin disorder. Several studies suggested psoriasis to be a complex multifactorial disease, but the exact triggering factor is yet to be determined. Evidences suggest that in addition to genetic factors, epigenetic reprogramming is also involved in psoriasis development. Major histopathological features, like increased proliferation and abnormal differentiation of keratinocytes, and immune cell infiltrations are characteristic marks of psoriatic skin lesions. Following therapy, histopathological features as well as aberrant DNA methylation reversed to normal levels. To understand the role of DNA methylation in regulating these crucial histopathologic features, we investigated the genome-wide DNA methylation profile of psoriasis patients with different histopathological features.ResultsGenome-wide DNA methylation profiling of psoriatic and adjacent normal skin tissues identified several novel differentially methylated regions associated with psoriasis. Differentially methylated CpGs were significantly enriched in several psoriasis susceptibility (PSORS) regions and epigenetically regulated the expression of key pathogenic genes, even with low-CpG promoters. Top differentially methylated genes overlapped with PSORS regions including S100A9, SELENBP1, CARD14, KAZN and PTPN22 showed inverse correlation between methylation and gene expression. We identified differentially methylated genes associated with characteristic histopathological features in psoriasis. Psoriatic skin with Munro’s microabscess, a distinctive feature in psoriasis including parakeratosis and neutrophil accumulation at the stratum corneum, was enriched with differentially methylated genes involved in neutrophil chemotaxis. Rete peg elongation and focal hypergranulosis were also associated with epigenetically regulated genes, supporting the reversible nature of these characteristic features during remission and relapse of the lesions.ConclusionOur study, for the first time, indicated the possible involvement of DNA methylation in regulating the cardinal pathophysiological features in psoriasis. Common genes involved in regulation of these pathologies may be used to develop drugs for better clinical management of psoriasis.