SynthesisPLoS neglected tropical diseases2024
Using meta-analysis and machine learning to investigate the transcriptional response of immune cells to Leishmania infection.
Synthesis in PLoS neglected tropical diseases, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed, 5 citations in OpenAlex.
- Integrative cross-platform transcriptomic analysis coupled with machine learning identifies candidate biomarkers of abiotic stress resilience in Arabidopsis thaliana.BMC plant biology · 2026Article
- Insights into genetic heterogeneity and drug resistance in Leishmania donovani of the Indian sub-continent from genomic data.Antonie van Leeuwenhoek · 2025Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLeishmaniasis is a parasitic disease caused by the Leishmania protozoan affecting millions of people worldwide, especially in tropical and subtropical regions. The immune response involves the activation of various cells to eliminate the infection. Understanding the complex interplay between Leishmania and the host immune system is crucial for developing effective treatments against this disease.
methodsThis study collected extensive transcriptomic data from macrophages, dendritic, and NK cells exposed to Leishmania spp. Our objective was to determine the Leishmania-responsive genes in immune system cells by applying meta-analysis and feature selection algorithms, followed by co-expression analysis.
resultsAs a result of meta-analysis, we discovered 703 differentially expressed genes (DEGs), primarily associated with the immune system and cellular metabolic processes. In addition, we have substantiated the significance of transcription factor families, such as bZIP and C2H2 ZF, in response to Leishmania infection. Furthermore, the feature selection techniques revealed the potential of two genes, namely G0S2 and CXCL8, as biomarkers and therapeutic targets for Leishmania infection. Lastly, our co-expression analysis has unveiled seven hub genes, including PFKFB3, DIAPH1, BSG, BIRC3, GOT2, EIF3H, and ATF3, chiefly related to signaling pathways.
conclusionsThese findings provide valuable insights into the molecular mechanisms underlying the response of immune system cells to Leishmania infection and offer novel potential targets for the therapeutic goals.
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