Co-expression analysis of lncRNA and mRNA suggests a role for ncRNA-mediated regulation of host-parasite interactions in primary skin lesions of patients with American tegumentary leishmaniasis

Almeida, Mariana Cordeiro de; Felix, Juliana de Souza; Lopes, Maria Fernanda da Silva; Athayde, Flávia Regina Florencio de; Troiano, Jéssica A.; Scaramele, Natália Francisco; Furlan, Amanda de Oliveira; Lopes, Fernanda

doi:10.1016/j.actatropica.2023.106966

Cited by 2 publications

(1 citation statement)

References 49 publications

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“…Nevertheless, the precise pathogenic mechanism of SNCA in depression remains elusive. S100A9 (S100 calcium-binding protein A9) is a calcium-binding protein that has been shown to play a key role in the activation of immune cells and the release of inflammatory mediators (Almeida et al, 2023). A previous study indicated that S100A9 and the upstream gene Nax jointly regulate the inflammatory response of skin tissue.…”

Section: Discussionmentioning

confidence: 99%

The role of immune inflammatory response and inflammatory infiltrates in major depression disorder and atopic dermatitis: new insights from machine learning

Jiang,

Gong,

Yao

et al. 2023

Preprint

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Background: Major depression disorder (MDD) and atopic dermatitis (AD) are distinct disorders involving immune inflammatory responses. This study aimed to investigate the comorbid relationship between AD and MDD and to identify possible common mechanisms. Methods: We obtained AD and MDD data from the Gene Expression Omnibus (GEO) database. Differential expression analysis and the Genecard database were employed to identify shared genes associated with inflammatory diseases. These shared genes were then subjected to gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Hub genes were selected based on the protein-protein interactions using CytoHubba, and key regulatory genes were identified through enrichment analysis. Subsequently, we conducted immune infiltration and correlation analyses of the shared genes in AD. Finally, we employed three machine learning models to predict the significance of shared genes. Results: A total of 17 shared genes were identified in the AD_Inflammatory_MDD dataset (S100A9, PTGER2, PI3, SNCA, DAB2, PDGFA, FSTL1, CALD1, XK, UTS2, DHRS9, PARD3, NFIB, TMEM158, LIPH, RAB27B, and SH3BRL2). These genes were associated with biological processes such as the regulation of mesenchymal cell proliferation, mesenchymal cell proliferation, and glial cell differentiation. The neuroactive ligand-receptor interaction, IL-17 signaling, and Rap1 signaling pathways were significantly enriched in KEGG analysis. SNCA, S100A9, SH3BGRL2, RAB27B, TMEM158, DAB2, FSTL1, CALD1, and XK were identified as hub genes contributing to comorbid AD and MDD development. The three machine learning models consistently identified SNCA and PARD3 as important biomarkers. Conclusion: SNCA, S100A9, SH3BGRL2, RAB27B, TMEM158, DAB2, FSTL1, CALD1, and XK were identified as significant genes contributing to the development of AD and MDD comorbidities. Immune infiltration analysis showed a notable increase in the infiltration of various subtypes of CD4+ T cells, suggesting a potential association between the development of skin inflammation and the immune response. Across different machine learning models, SNCA and PARD3 consistently emerged as important biomarkers.

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Section: Discussionmentioning

confidence: 99%

The role of immune inflammatory response and inflammatory infiltrates in major depression disorder and atopic dermatitis: new insights from machine learning

Jiang,

Gong,

Yao

et al. 2023

Preprint

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Integrated analysis of lncRNA and mRNA expression profiles in cutaneous leishmaniasis lesions caused by Leishmania tropica

Hadifar,

Masoudzadeh,

Andersson

et al. 2024

Front. Cell. Infect. Microbiol.

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BackgroundCutaneous leishmaniasis (CL), caused by Leishmania (L.) species, remains a neglected tropical disease in many developing countries. We and others have shown that different Leishmania species can alter the gene expression profile of human host cells. Long non-coding RNAs (lncRNAs) have been found to play a role in the pathogenesis of leishmaniasis through dysregulation of transcriptome signatures. Understanding the regulatory roles of lncRNAs in the biological networks involved in leishmaniasis can improve our understanding of the disease.MethodsHerein, we used our previous RNA sequencing data (GSE216638) to investigate the profile of lncRNAs in the skin lesions of L. tropica-infected patients. We employed the weighted gene correlation network analysis (WGCNA) algorithm to establish co-expression networks of shared genes between CL patients and infer the potential role of lncRNAs in CL patients. We identified hub genes and trans- and cis-acting lncRNAs, and carried out functional enrichment analysis on a key co-expressed module related to L. tropica-infected patients.ResultsWe found substantial involvement of lncRNAs in the CL patient dataset. Using the WGCNA method, we classified all included genes into seven modules, with a module (turquoise) being significantly correlated with the studied clinical traits and identified as the key module. This module was mainly involved in the “interferon gamma signaling” and “cytokine signaling” pathways. We highlighted several lncRNAs and their co-expressed mRNA pairs, like SIRPG-AS1, IL21R-AS1, IL24, and TLDC2, as hub genes of the key module. Quantitative RT-PCR validated the expression of several genes in the lesions of an independent cohort of L. tropica-infected patients.ConclusionsThese findings enhance our understanding of the human skin response to L. tropica infection. Furthermore, the hub genes identified in this study are worthy of further evaluation as potential targets in the development of more effective treatments and preventive measures for CL caused by L. tropica.

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Co-expression analysis of lncRNA and mRNA suggests a role for ncRNA-mediated regulation of host-parasite interactions in primary skin lesions of patients with American tegumentary leishmaniasis

Cited by 2 publications

References 49 publications

The role of immune inflammatory response and inflammatory infiltrates in major depression disorder and atopic dermatitis: new insights from machine learning

The role of immune inflammatory response and inflammatory infiltrates in major depression disorder and atopic dermatitis: new insights from machine learning

Integrated analysis of lncRNA and mRNA expression profiles in cutaneous leishmaniasis lesions caused by Leishmania tropica

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