Using meta-analysis and machine learning to investigate the transcriptional response of immune cells to Leishmania infection

Background: Leishmaniasis 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. Methods: This 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. Results: As 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. Conclusions: These 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. Author summary: Leishmaniasis is a group of diseases caused by protozoan parasites belonging to the genus Leishmania. The infection involves various cells, and effective diagnosis and treatment require a clear understanding of its multiple aspects. We conducted a study using meta-analysis and feature selection algorithms combined with co-expression analysis to shed light on the key responsive genes and mechanisms involved in Leishmania infection. Our findings revealed transcriptional signatures associated with essential cellular metabolic functions and immune system response. We also confirmed the critical role of transcription factor families, including bZIP and C2H2 ZF, in response to Leishmania infection. The applied feature selection techniques identified two genes that show potential as Leishmania infection biomarkers and treatment targets. Moreover, our co-expression analysis identified seven crucial hub genes in signaling pathways. These findings provide valuable insights into the molecular mechanisms underlying the immune system cells’ response to Leishmania infection and present novel potential targets for therapeutic intervention.