Genome-Wide Screening of mRNA Expression in Leprosy Patients

Belone, Andréa de Faria Fernandes; Rosa, Patrícia Sammarco; Trombone, Ana Paula; Fachin, Luciana Raquel Vincenzi; Guidella, Cássio Cesar; Ura, Shogo; Barreto, Jaison Antônio; Pinilla, Mabel G.; Carvalho, Alex F.; Carraro, Dirce Maria; Soares, Fernando Augusto; Soares, Cléverson Teixeira

doi:10.3389/fgene.2015.00334

Cited by 28 publications

(67 citation statements)

References 23 publications

(33 reference statements)

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“…We observed that two risk SNPs of PARL and all their captured SNPs were cis eQTLs for PARL mRNA expression in human blood ( P value from 5.6 × 10 −5 to 5.6 × 10 −24 ), and two risk SNPs of PINK1 were cis and trans eQTLs for PINK1 mRNA expression in skin ( P = 1.5 × 10 −9 ) and blood ( P = 3.1 × 10 −30 ), respectively. Nevertheless, we only found a significantly different expression of PINK1 mRNA, but not PARL mRNA, between leprotic lesions (leprosy per se or its subtypes) and control tissues based on the re-analysis of reported datasets1040. The exact reason for the discrepancy of PARL mRNA expression remains unknown and awaits future study.…”

Section: Discussionmentioning

confidence: 60%

“…We noticed that PARL mRNA expression level was extremely high in immune cells, but PINK1 had an extremely high mRNA expression in central nervous system. We observed a significantly differential mRNA expression of PINK1 , but not PARL , in leprosy skin lesions of 66 patients from the Gene Expression Omnibus dataset (GEO; http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE74481)40 (Table S4). …”

Section: Resultsmentioning

confidence: 99%

“…We performed eQTL analysis in different human tissues by using two publically available expression data sets1040. We first investigated whether the PARL and PINK1 variants affect gene expression in human whole blood, brain and skin tissues using the Genotype-Tissue Expression project (GTEx, http://www.gtexportal.org/home/38) data and the HaploReg dataset (http://www.broadinstitute.org/mammals/haploreg/haploreg.php)54.…”

Section: Methodsmentioning

confidence: 99%

“…The data was retrieved from GEO under accession series GSE74481 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE74481)40.…”

Section: Methodsmentioning

confidence: 99%

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Common variants in the PARL and PINK1 genes increase the risk to leprosy in Han Chinese from South China

Wang

Feng

et al. 2016

Sci Rep

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Leprosy is a chronic infectious and neurological disease caused by Mycobacterium leprae, an unculturable pathogen with massive genomic decay and dependence on host metabolism. We hypothesized that mitochondrial genes PARL and PINK1 would confer risk to leprosy. Thirteen tag SNPs of PARL and PINK1 were analyzed in 3620 individuals with or without leprosy from China. We also sequenced the entire exons of PARL, PINK1 and PARK2 in 80 patients with a family history of leprosy by using the next generation sequencing technology (NGS). We found that PARL SNP rs12631031 conferred a risk to leprosy (Padjusted = 0.019) and multibacillary leprosy (MB, Padjusted = 0.020) at the allelic level. rs12631031 and rs7653061 in PARL were associated with leprosy and MB (dominant model, Padjusted < 0.05) at the genotypic level. PINK1 SNP rs4704 was associated with leprosy at the genotypic level (Padjusted = 0.004). We confirmed that common variants in PARL and PINK1 were associated with leprosy in patients underwent NGS. Furthermore, PARL and PINK1 could physically interact with each other and were involved in the highly connected network formed by reported leprosy susceptibility genes. Together, our results showed that PARL and PINK1 genetic variants are associated with leprosy.

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Section: Discussionmentioning

confidence: 60%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…The data was retrieved from GEO under accession series GSE74481 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE74481)40.…”

Section: Methodsmentioning

confidence: 99%

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Common variants in the PARL and PINK1 genes increase the risk to leprosy in Han Chinese from South China

Wang

Feng

et al. 2016

Sci Rep

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“…All datasets analyzed during the current study are available under NCBI's Gene Expression Omnibus repository accessions: GSE40950 [28], GSE35423 [14], GSE24280 [unknown], GSE443 [80], GSE17763 [81], GSE16844 [47], GSE74481 [82], GSE95748 [52], and GSE100853 [83].…”

Section: Availability Of Data and Materialsmentioning

confidence: 99%

Reanalysis And Integration Of Public Microarray Datasets Reveals Novel Host Genes Modulated In Leprosy

Leal-Calvo

Moraes

2019

Preprint

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Background:Leprosy is an insidious disease caused primarily by mycobacteria. The difficulties in culturing this slow-growing bacteria together with the chronic progression of the disease have hampered the development of accurate methods for diagnosis. Host gene expression profiling is an important tool to assess overall tissue activity, whether in health or disease conditions. High-throughput gene expression experiments have become popular over the last decade or so, and public databases have been created to easily store and retrieve these data. This has enabled researchers to reuse and reanalyze existing datasets with the aim of generating novel and or more robust information. In this work, after a systematic search, nine microarray datasets evaluating host gene expression in leprosy were reanalyzed and the information was integrated to strengthen evidence of differential expression for several genes. Results: Reanalysis of individual datasets revealed several differentially expressed genes (DEGs). Then, five integration methods were tested, both at the P-value and effect size level. In the end, random effects model (REM) and ratio association (sdef) were selected as the main methods to pinpoint DEGs. Overall, some classic gene/pathways were found corroborating previous findings and validating this approach for analysis. Also, various original DEGs related to poorly understood processes in leprosy were described. Nevertheless, some of the novel genes have already been associated with leprosy pathogenesis by genetic or functional studies, whilst others are, as yet, unrelated or poorly studied in these contexts. Conclusions:This study reinforces evidences of differential expression of several genes and presents novel genes and pathways associated with leprosy pathogenesis. Altogether, these data are useful in better understanding host responses to the disease and, at the same time, provide a list of potential host biomarkers that could be useful in complementing leprosy diagnosis based on transcriptional levels.

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Derivation of marker gene signatures from human skin and their use in the interpretation of the transcriptional changes associated with dermatological disorders

Shih

Nirmal

Headon

et al. 2017

The Journal of Pathology

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Numerous studies have explored the altered transcriptional landscape associated with skin diseases to understand the nature of these disorders. However, data interpretation represents a significant challenge due to a lack of good maker sets for many of the specialized cell types that make up this tissue, whose composition may fundamentally alter during disease. Here we have sought to derive expression signatures that define the various cell types and structures that make up human skin, and demonstrate how they can be used to aid the interpretation of transcriptomic data derived from this organ. Two large normal skin transcriptomic datasets were identified, one RNA‐seq (n = 578), the other microarray (n = 165), quality controlled and subjected separately to network‐based analyses to identify clusters of robustly co‐expressed genes. The biological significance of these clusters was then assigned using a combination of bioinformatics analyses, literature, and expert review. After cross comparison between analyses, 20 gene signatures were defined. These included expression signatures for hair follicles, glands (sebaceous, sweat, apocrine), keratinocytes, melanocytes, endothelia, muscle, adipocytes, immune cells, and a number of pathway systems. Collectively, we have named this resource SkinSig. SkinSig was then used in the analysis of transcriptomic datasets for 18 skin conditions, providing in‐context interpretation of these data. For instance, conventional analysis has shown there to be a decrease in keratinization and fatty metabolism with age; we more accurately define these changes to be due to loss of hair follicles and sebaceous glands. SkinSig also highlighted the over‐/under‐representation of various cell types in skin diseases, reflecting an influx in immune cells in inflammatory disorders and a relative reduction in other cell types. Overall, our analyses demonstrate the value of this new resource in defining the functional profile of skin cell types and appendages, and in improving the interpretation of disease data. © 2016 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.

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Genome-Wide Screening of mRNA Expression in Leprosy Patients

Cited by 28 publications

References 23 publications

Common variants in the PARL and PINK1 genes increase the risk to leprosy in Han Chinese from South China

Common variants in the PARL and PINK1 genes increase the risk to leprosy in Han Chinese from South China

Reanalysis And Integration Of Public Microarray Datasets Reveals Novel Host Genes Modulated In Leprosy

Derivation of marker gene signatures from human skin and their use in the interpretation of the transcriptional changes associated with dermatological disorders

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