Network-Based Approaches Reveal Potential Therapeutic Targets for Host-Directed Antileishmanial Therapy Driving Drug Repurposing

Abstract

Leishmania parasites are the causal agent of leishmaniasis, an endemic disease in more than 90 countries worldwide. Over the years, traditional approaches focused on the parasite when developing treatments against leishmaniasis. Despite numerous attempts, there is not yet a universal treatment, and those available have allowed for the appearance of resistance. Here, we propose and follow a host-directed approach that aims to overcome the current lack of treatment. Our approach identifies potential therapeutic targets in the host cell and proposes known drug interactions aiming to improve the immune response and to block the host machinery necessary for the survival of the parasite. We started analyzing transcription factor regulatory networks of macrophages infected with Leishmania major. Next, based on the regulatory dynamics of the infection and available gene expression profiles, we selected potential therapeutic target proteins. The function of these proteins was then analyzed following a multilayered network scheme in which we combined information on metabolic pathways with known drugs that have a direct connection with the activity carried out by these proteins. Using our approach, we were able to identify five host protein-coding gene products that are potential therapeutic targets for treating leishmaniasis. Moreover, from the 11 drugs known to interact with the function performed by these proteins, 3 have already been tested against this parasite, verifying in this way our novel methodology. More importantly, the remaining eight drugs previously employed to treat other diseases, remain as promising yet-untested antileishmanial therapies. IMPORTANCE This work opens a new path to fight parasites by targeting host molecular functions by repurposing available and approved drugs. We created a novel approach to identify key proteins involved in any biological process by combining gene regulatory networks and expression profiles. Once proteins have been selected, our approach employs a multilayered network methodology that relates proteins to functions to drugs that alter these functions. By applying our novel approach to macrophages during the Leishmania infection process, we both validated our work and found eight drugs already approved for use in humans that to the best of our knowledge were never employed to treat leishmaniasis, rendering our work as a new tool in the box available to the scientific community fighting parasites.

Keywords: drug repurposing; gene regulatory networks; host-direct therapy; leishmaniasis; multilayered network.

FIG 1

Global transcriptomic profiles of Leishmania-infected human macrophages and genes related to immune response and host-pathogen interaction. Distribution of DEGs between different specific times postinfection. The box width indicates the number of DEGs downregulated (purple) and upregulated (orange) at adjusted P value of 0.05 and −0.5 > logFC > 0.5. Numbers at the end of each bar correspond to total DEGs obtained after paired-samples analysis. (A) Distribution of ncRNAs differentially expressed in Leishmania major-infected macrophages. (B) Distribution of protein-coding genes differentially expressed in Leishmania major-infected macrophages. (C) Venn diagrams exploring the conservation of ncRNAs (top) and protein-coding genes related to the immune system (bottom) in Leishmania major-infected macrophages. (D) Top 20 biological process GO terms enrichment related to immune response, stress, or host-pathogen interaction.

FIG 2

Network comparison of non infected against infected macrophage at 4 h postinfection. (A) The network shown is formed by 942 nodes (167 TFs) and 3,847 edges colored according to their existence in the non infected macrophage network, infected-macrophage network, or both networks. (B) Subnetwork represents all edges presented only in the 4 hpi network. The colors of edges and nodes are the same as those in the upper network.

FIG 3

Pipeline to identify potential therapeutic targets for leishmaniasis host-directed treatment in human macrophage from RNA-seq data. (A) First, we processed a set of RNA-seq data derived from Leishmania major-infected macrophages. This data set is composed of 4 time points: 4 h postinfection (hpi), 24 hpi, 48 hpi, and 72 hpi. Raw reads were analyzed using an in-house-developed pipeline that takes raw reads as input, and as output we obtained bona fide read counts per gene. Then, counts were used to obtain a normalized counts matrix and detect the differentially expressed genes. Next, we filtered a reference human GRN using normalized data to contextualize the GRN and get infected and non infected contexts simultaneously. After that, we applied a pairwise comparison of infected against non infected contextualized networks to obtain the nodes and connections present in a disease condition. Next, we used the list of nodes to keep only genes involved in processes related to immune response, response to stress, or host-pathogen interaction and that were evidenced as differentially expressed. (B) Schematic workflow was applied to identify the drug targets using the Multipath package. With the filtered list, we mapped the gene set of interest to their gene products and related biological pathways in which these proteins participate and obtained the drug-gene product direct connection. Finally, drug-target interactions were literature filtered to select the best candidate targets for host-directed antileishmanial treatment.

FIG 4

Context-specific gene regulatory networks of Leishmania-infected macrophage and multilayered network analysis reveal potential new therapeutic targets and drug repurposing for host-directed antileishmanial therapies. Our network analysis reveals a final set of 5 possible drug targets; these 5 targets interact with 11 different drugs. Our literature mining reveals that at least 3 drugs were validated in in vitro or in vivo models to test their potential as antileishmanial drugs (46, 47, 80, 99, 100).