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

Abstract

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.

Fig 1. Schematic overview of the strategy for understanding aspects of response of immune system cells to Leishmania infection.

Fig 2. Gene expression comparison between infected and healthy controls.

Volcano plot displaying combined effect size (x-axis) and negative log10 of the false discovery rate value (y-axis). The significant up and down-regulated genes are plotted as red dots.

Fig 3. The top significant Gene Ontology (GO) categories of the differentially expressed genes (DEGs) in three ontologies: BP, biological process; MF, molecular function; and CC, cellular component.

Fig 4. The pathway enrichment analysis of up and down-regulated genes between infected and healthy controls.

The circles size and color mean the gene ratio and the adjusted p-value, respectively. The top pathways are shown.

Fig 5. The number of differentially expressed genes (DEGs) in different transcription factor families.

The number of up- or down-regulated are shown for each transcription factor family.

Fig 6. Weighted gene co-expression network analysis (WGCNA) of differentially expressed genes (DEGs).

Hierarchical cluster tree representing seven modules of co-expressed genes. The gene dendrogram was constructed by clustering dissimilarity using consensus Topological Overlap. The color row indicates the corresponding module colors. Each colored row represents a module color-coded to highlight a group of genes with strong interconnections.