Abstract

Leishmaniasis is a vector-borne, neglected tropical disease with a worldwide distribution that can present in a variety of clinical forms, depending on the parasite species and host genetic background. The pathogenesis of this disease remains far from being elucidated because the involvement of a complex immune response orchestrated by host cells significantly affects the clinical outcome. Among these cells, macrophages are the main host cells, produce cytokines and chemokines, thereby triggering events that contribute to the mediation of the host immune response and, subsequently, to the establishment of infection or, alternatively, disease control. There has been relatively limited commercial interest in developing new pharmaceutical compounds to treat leishmaniasis. Moreover, advances in the understanding of the underlying biology of Leishmania spp. have not translated into the development of effective new chemotherapeutic compounds. As a result, biomarkers as surrogate disease endpoints present several potential advantages to be used in the identification of targets capable of facilitating therapeutic interventions considered to ameliorate disease outcome. More recently, large-scale genomic and proteomic analyses have allowed the identification and characterization of the pathways involved in the infection process in both parasites and the host, and these analyses have been shown to be more effective than studying individual molecules to elucidate disease pathogenesis. RNA-seq and proteomics are large-scale approaches that characterize genes or proteins in a given cell line, tissue, or organism to provide a global and more integrated view of the myriad biological processes that occur within a cell than focusing on an individual gene or protein. Bioinformatics provides us with the means to computationally analyze and integrate the large volumes of data generated by high-throughput sequencing approaches. The integration of genomic expression and proteomic data offers a rich multi-dimensional analysis, despite the inherent technical and statistical challenges. We propose that these types of global analyses facilitate the identification, among a large number of genes and proteins, those that hold potential as biomarkers. The present review focuses on large-scale studies that have identified and evaluated relevant biomarkers in macrophages in response to Leishmania infection.

Highlights

  • Leishmaniasis is a neglected parasitic disease that is distributed worldwide and is often associated with poverty

  • A number of biological questions have been approached using this framework, and some applications in the context of leishmaniasis include the study of distinct states of infection with Leishmania infantum (Gardinassi et al, 2016) and the evaluation of the host-parasite interplay in localized cutaneous leishmaniasis caused by L. braziliensis (Christensen et al, 2016)

  • Evaluation of the phagocytotic effect on gene transcription demonstrated a lack of response in bead-containing macrophages at 4 hpi, with no differentially expressed genes (DEGs) observed between these macrophages and uninfected cells, highly pronounced differences were detected at later time points. These findings indicate that, in contrast to the response exhibited by uninfected macrophages and macrophages that internalized the latex beads, Leishmania triggers a unique transcriptomic response shortly after phagocytosis, with reduced communication between the parasite and host cell at later stages of infection (Fernandes et al, 2016)

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Summary

INTRODUCTION

Leishmaniasis is a neglected parasitic disease that is distributed worldwide and is often associated with poverty. The present review focuses on recent large-scale studies detailing the host-related genes analyzed by RNA-seq and the proteins identified by proteomics, as well as describes the types of bioinformatics analyses used to integrate the large volumes of data generated by these high-throughput sequencing techniques. A number of biological questions have been approached using this framework, and some applications in the context of leishmaniasis include the study of distinct states of infection with Leishmania infantum (Gardinassi et al, 2016) and the evaluation of the host-parasite interplay in localized cutaneous leishmaniasis caused by L. braziliensis (Christensen et al, 2016) Both studies used expression data as input to construct weighted gene-gene correlation networks, a particular case of a correlation network in which the edges have associated weights and no strict conditional on the correlation values is set, which characterizes a soft-thresholding approach. Chen et al, 2013; Kuleshov et al, 2016 http://amp

Statistical methods
CONCLUSIONS
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