In Search of Biomarkers for Pathogenesis and Control of Leishmaniasis by Global Analyses of Leishmania-Infected Macrophages

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.

Virtual Screening and the In Vitro Assessment of the Antileishmanial Activity of Lignans

“…The set of 160 Lignans and Neolignans from different subclasses (14 Furans, 10 Furofurans, 14 Dibenzylbutyrolactols, 22 Dibenzylbutanes, 21 Dibenzocycloocyadienes, 17 Aryltetralins, 3 Arylnaphthalenes, 8 Neolignans alkyl aryl ethers, 16 Neolignans benzofurans, and 9 Neolignans benzodiones) were used in this study. Lignans and Neolignans comprise a class of secondary metabolites, with diverse chemical structures, that are found in more than 70 families of plants and exhibit several significant and potent biological activities, including antioxidant [21,22], anti-inflammatory, hepatoprotective, anticancer [22,23], antimicrobial [22], trypanocidal [24,25], neuroprotective [26], and larvicidal [27] activities.…”

Proteomic Analysis Reveals a Predominant NFE2L2 (NRF2) Signature in Canonical Pathway and Upstream Regulator Analysis of Leishmania-Infected Macrophages

“…In the field of Leishmania research, many proteomic studies have focused on a variety of aspects related to parasite biology and host interactions, such as parasite life-cycle, drug resistance mechanisms, identification of immunogenic proteins for vaccine development and the identification of targets for chemotherapeutic treatment, as well as biomarkers that hold potential for use in diagnosis (9–11, 35–39). However, the mechanisms involved in interactions between Leishmania parasites and the host, as well as the factors that drive disease outcome, warrant comprehensive investigation [revised by Veras et al (6)].…”

“…This advanced technology has proven to be highly efficient in the study of individual molecules, as it permits the identification of hundreds or even thousands of signature proteins that characterize elements of microbial and host response during infection (1, 3–5). In the context of this large-scale analysis, bioinformatics contributes through the use of advanced computational algorithms to search databases [revised by Veras et al (6)]. Several studies have demonstrated the power of proteomics with respect to providing valuable information regarding the identification of proteins and pathways involved in host-parasite interactions, both in parasites (7–10) as well as in the host (11, 12).…”

Leveraging User-Friendly Network Approaches to Extract Knowledge From High-Throughput Omics Datasets

“…Applications of correlation networks include hub genes identification in several diseases such as cancer (Oh et al, 2015), chronic fatigue syndrome (Presson et al, 2008), diabetes (Keller et al, 2008), and in the multivariate disease autism (Voineagu et al, 2011). The use of networks in the context of the neglected tropical disease leishmaniasis was also recently reviewed (Veras et al, 2018). Also performed were the stratification of breast cancer subtypes using human plasma metabolomics (Fan et al, 2016), the study of extracellular proteins in serum to disclose information on human disease states (Emilsson et al, 2018), and the evaluation of coordinated expression patterns in different brain regions in Alzheimer’s disease (Wang et al, 2016a).…”