Genomic medicine in Chagas disease

Acta Tropica 197 (2019) 105062

Contents lists available at ScienceDirect

Acta Tropica journal homepage: www.elsevier.com/locate/actatropica

Genomic medicine in Chagas disease a,⁎

b

a

a,⁎

T

Marialbert Acosta-Herrera , Mariana Strauss , Desiré Casares-Marfil , Javier Martín , Chagas Genetics CYTED Network a b

Instituto de Parasitología y Biomedicina López-Neyra, IPBLN-CSIC, PTS Granada, Granada, Spain Centro de Estudios e Investigación de la Enfermedad de Chagas y Leishmaniasis, FCM, INICSA-CONICET-Universidad Nacional de Córdoba, Córdoba, Argentina

A R T I C LE I N FO

A B S T R A C T

Keywords: Neglected tropical disease Personalized medicine Genetic risk factors Association studies

Genetic approaches have been proposed for improving the understanding of the causes of differential susceptibility to Trypanosoma cruzi infection and Chagas disease outcome. Polymorphisms in genes involved in the immune/inflammatory response are being studied in order to clarify their possible role in the occurrence or severity of the cardiac and/or gastrointestinal complications. However still today, the number of significant associated genes is limited and the pathophysiological mechanisms underlying this condition are unknown. This article review the information currently available from the published scientific literature regarding the genetic variants of molecules of the immune system and other variants that can contribute to the clinical presentation of the disease. Genomic medicine will improve our knowledge about the molecular basis of Chagas disease, will open new avenues for developing biomarkers of disease progression, new therapeutic strategies to suit the requirements of individual patients, and will contribute to the control of one of the infections with the greatest socio-economic impact in the Americas.

1. Genomic medicine: main goals and approaches According to the National Human Genome Research Institute (NHGRI, https://www.genome.gov/), genomic medicine is a developing field that uses the genomic information of individuals for diagnosis and/or therapeutic decision-making, including as well the health outcomes and policy implications of that clinical use. It is also known as personalized medicine, a discipline that tailors medical care to the genetic landscape of individuals. Since the completion of the Human Genome Project, the generated knowledge has allowed us to expand our understanding of biology and disease and to improve health. Humans share ˜99% of the nucleotide code in the DNA sequence and the remaining 1% helps to explain the diversity of human beings in the response to environmental stimuli and stress, predisposition to different diseases, differential drug metabolism and differential response to treatments (Subramanian et al., 2001). The single nucleotide polymorphisms (SNPs) are the most common human variation (Lander et al., 2001) and are simple substitutions of one nucleotide for another at a given DNA position. These variations are distinguished from mutations as they do not cause an abnormal phenotype and are present in more than 1% of the population. The most commonly studied are biallelic SNPs and they can map either into gene coding sequence or in non-coding sequence of the genome (1000 Genomes Project Consortium

⁎

et al., 2012). The knowledge provided by genomic medicine is assisting to uncover how subtle variations on the DNA sequence can cause large differences in health, by improving risk assessment, diagnosis, prognosis and tailored treatments (Subramanian et al., 2001). The main goal of genetic association studies is to detect associations between one or more genetic variants and a trait or disease (Timpson et al., 2018). Classically, geneticist performed linkage analysis that evaluated how genetic markers segregate among families affected of certain conditions. This approach has allowed the successful identification of genetic variants contributing to rare disorders. However, the success was limited when it was applied to common diseases. Under the common disease/common variant hypothesis, where common diseases are likely influenced by common genetic variants in the population, the genetic associations have been assessed by candidate gene association studies in unrelated individuals. The association is determined by comparing the allele frequency of the SNPs between cases (individuals affected by certain disease) and controls (unaffected individuals). If these differences are statistically significant (the observed differences are not due to random chance), then the SNP is considered as associated with the disease. Traditionally, these association studies have been performed in polymorphisms mapping in candidate genes, in which a small number of variants of this specific gene are tested for association. The selection of the genes is based on previous hypothesis of the

Corresponding authors at: Instituto de Parasitología y Biomedicina López-Neyra, CSIC, Av. del Conocimiento 17, Armilla, Granada, 18016, Spain. E-mail addresses: [email protected] (M. Acosta-Herrera), [email protected] (J. Martín).

https://doi.org/10.1016/j.actatropica.2019.105062 Received 11 March 2019; Accepted 11 June 2019 Available online 12 June 2019 0001-706X/ © 2019 Published by Elsevier B.V.

Acta Tropica 197 (2019) 105062

M. Acosta-Herrera, et al.

approaches different alleles of HLA have been described to be involved in the susceptibility and/or severity of diseases (Blackwell et al., 2009). More recently, GWAS have confirmed some previously reported associations, as well as identified novel genes and pathways associated with infectious diseases (Chapman & Hill, 2012). An example is the existing GWAS that examined different phenotypes of the Human Immunodeficiency Virus (HIV), including differential susceptibility, viral load and disease progression. Most of these studies have confirmed a central role for the HLA class I region and the CCR5–CCR2 locus in the pathogenesis of HIV infection (Fellay et al., 2007, 2009; International HIV Controllers Study, 2010). Another interesting example is the GWAS in leprosy, which have contributed significantly to improve our understanding of the pathophysiology of the disease. This chronic infection, caused by the bacteria Mycobacterium leprae, affects the skin and peripheral nervous system. These studies have led to the discovery of several genes clustering in innate immune pathways, as well as to confirm previously observed associations in the HLA-DR-DQ region (Cader et al., 2016; Wang et al., 2018; F. R. Zhang et al., 2009). Another major global public health threat is the infection caused by Mycobacterium tuberculosis, for which two GWAS have been published so far identifying new susceptibility loci in Moroccan, Gambian and Ghana populations (Grant et al., 2016; Thye et al., 2010). In addition, a pilot study from exome sequencing showed its potential to mine susceptibility genes in tuberculosis (Duncan et al., 2015) and another WES identified the association of HLA-DQA1 with spinal tuberculosis (Shen et al., 2018). On the other hand and in the context of parasitic diseases, great examples could be drawn from genetic studies in malaria, a tropical disease caused by seven species of Plasmodium (P. vivax, P. falciparum, P. ovale, P. malariae, P. knowlesi, P. cynomolgi and P. simium). GWAS of severe malaria pinpoint to the haemoglobin beta (HBB) gene, which contains the classic sickle haemoglobin variant polymorphism. The largest study in severe malaria analyzed 11,000 African children, with replication data in 14,000 individuals and reporting a novel locus near a group of genes encoding erythrocyte receptors, with high relevance in parasite invasion (Malaria Genomic Epidemiology et al., 2015). Similarly, GWAS has identified the HLA class II region as associated to visceral leishmaniasis in Indian and Brazilian population (LeishGEN Consortium et al., 2013). According to the above considerations, it can be concluded that host genetic factors are involved in the susceptibility to infection and to the development of different phenotypes in infectious diseases. In the present review we will focus in the current knowledge of the genetic basis of Chagas disease.

plausible biological mechanisms involved on disease pathogenesis (Subramanian et al., 2001). In contrast, the increasing knowledge of SNP data and haplotype structure allowed the design of genotyping arrays directed to the whole genome. These studies, called genomewide association studies (GWAS) interrogate common variants in the entire genome, thus increasing the probabilities of identifying the genetic component of common diseases (Manolio, 2013). These studies are hypothesis-free, where there is no need of a previous knowledge of the biological mechanisms involved on disease pathogenesis; and hypothesis-generating since the novel loci may highlight new molecular pathways never anticipated in the disease under study. However, large sample sizes are needed to fulfill the stringent threshold for statistical significance due to multiple testing adjustments (p < 5 × 10−08). Thanks to public information from the HapMap project (International HapMap et al., 2010) and The 1000 Genomes project (1000 Genomes Project Consortium et al., 2012), the determination of millions of SNPs through statistical inferences provide an excellent power to infer common genetic variation in different populations. Since the completion of the first GWAS in 2005 (Klein et al., 2005), thousands of genomic loci have been associated with common traits, however, despite the recruitment of unprecedented sample sizes and the consequent improvement on statistical power, the results from GWAS still cannot explain the complete heritability of diseases, which is known as the ‘missing heritability paradigm’ (Manolio et al., 2009). The contribution of rare variation (minor allele frequency < 0.5%) to this missing heritability has been largely discussed. These variations are not properly captured by genotyping arrays, therefore, whole-exome (WES) and whole-genome sequencing (WGS) generates millions of sequence reads in parallel, increasing the speed and the volume of the data. The analysis of rare variants requires composite tests to assess the overall ‘mutational load’ in cases and controls. With these technology is feasible a deeper characterization of the entire frequency spectrum of the genetic variation and their relationship with disease susceptibility (Manolio et al., 2009). Nevertheless, the impact of rare variation on complex diseases is still limited (Bomba et al., 2017). In addition, the genomic information is allowing the development of pharmacogenomic studies to identify differential abilities to metabolize drugs, and assess different drug reactions, to eventually develop individualized treatments (Lauschke et al., 2017). 2. Genomic medicine in the context of infectious diseases It is well known that not all individuals exposed to an infection develop a disease and when it occurs, there are inter-individual differences in the symptomatology and/or the degree of severity. One of the first evidences supporting genetic differences in risk of infections came from an epidemiological study reporting a strong association between death from infections in adopted persons and their biological parents, and not with their adoptive parents, suggesting a greater genetic than environmental effect (Sorensen et al., 1988). This finding was later reinforced by another in which adopted siblings presented an increased mortality risk when compared with half siblings or unrelated individuals (Petersen et al., 2010). Additionally, other studies have shown ethnical differences in the susceptibility to infectious diseases (Arama et al., 2015; Stead et al., 1990), highlighting the important role of the genetic landscape of the host in the variability of disease presentation (Chapman et al., 2012). The increasing number of candidate gene studies and GWAS has allowed the identification of common polymorphisms associated with a number of infectious diseases, including, viral, bacterial and parasitic infections. Given their relationship with immune-mediated diseases, a group of highly studied genes that have been identified as important in the susceptibility to infection and disease severity are the genes of the human major histocompatibility complex (MHC) and among these, the human leukocyte antigen (HLA) genes (Blackwell et al., 2009; Chapman & Hill, 2012; Vannberg et al., 2011). Using classical candidate gene

3. Chagas disease: disease pathogenesis Chagas disease, also known as American Tripanosomiasis, is an infectious disease caused by the protoazon parasite Trypanosoma cruzi and transmitted by hematophagous bug vectors from the Reduviidae family (Perez-Molina et al., 2018; Stanaway et al., 2015). According to the World Health Organization (http://www.who.int/chagas/en/) and the Center for Disease Control and Prevention (https://www.cdc.gov/ parasites/chagas/), 8 million people suffer from Chagas disease worldwide, mainly in Latin America and 75 million people are at risk of infection (Messenger et al., 2015). Although its endemic areas are spread out in 21 countries of Latin America and the southern of the United States, it is becoming a public health problem in non-endemic areas such as Canada, Australia and some European countries, due to globalization and migratory movements (Flores-Ferrer et al., 2018). A co-evolution between the parasite and the host has been described induced by human modifications (Noireau et al., 2009) and this coevolution might be reflected in the host immune genes; therefore, the immune response is key to understand the mechanisms underlying Chagas disease. The differences on the individuals’ genetic landscape may allow the setting of a differential and effective immune response 2

Acta Tropica 197 (2019) 105062

M. Acosta-Herrera, et al.

4.1. MHC region and HLA genes

against the parasite and could explain differential genetic susceptibility in endemic populations, as detected in other diseases (Arama et al., 2015). However, and given the complexity of the genetic structure in Hispanic/Latino population due to their recent admixture, these genetic diversity increases population heterogeneity and might challenge the characterization of the disease in different Hispanic/Latino populations (Bryc et al., 2010).

The MHC region is located on chromosome 6 and encode different sets of proteins that play key roles in the immune system and that are responsible for mediating host-pathogen interactions (Cruz-Tapias et al., 2013). The most relevant molecules in this context are the HLA, which are cell-surface glycoproteins that bind peptide fragments that either have been synthesized within the cell (HLA class Ia; A, B, and C) or that have been ingested and processed by the cell (HLAclass II; DR, DQ, and DP). Although the classical HLA molecules are responsible for presenting antigens derived from pathogens to protect cells, there is a subset of HLA genes that account for encoding non-classical class Ib molecules that play a role on the modulation of the immune response (Persson et al., 2017; Sabbagh et al., 2018). The type of the presentation could affect the clinical course of diseases because patients may respond differently to the same antigen, depending on their HLA repertoire (Cardozo et al., 2014). MHC class III genes are located between the class I and class II regions and are very heterogeneous in respect to their function. Genes encoding the components of the complement system such as C2, C4 and factor B (Gruen et al., 2001) and TNF, which plays an important role in inflammation, viral infection, tumor cachexia and the immune response (Coornaert et al., 2009). Further gene products include LTA, with functions very similar to TNF, but with a considerably restricted mode of expression (Remouchamps et al., 2011). Several HLA alleles and haplotypes have been reported to be associated with the susceptibility to T. cruzi infection and to the chronic manifestation of Chagas disease (Ayo et al., 2013; Ortega Zamora et al., 2017) (Table 1). There are a variety of HLA alleles that have presented an association with the disease; most of these were associated with the chronic form of the disease. The most studied genes are HLA-B and HLA-DRB1. A study conducted in Peruvian population found that DRB1*14-DQB1*0301 haplotype was associated to protection against T. cruzi infection in a highly endemic area (Nieto et al., 2000). Another study conducted in Mexican population found that the frequencies of HLA-DR4 and HLAB39 were significantly increased in seropositive individuals when compared to healthy controls. Additionally, the authors observed an increased frequency of HLA-B35 in patients with cardiomyopathy when compared as well to healthy controls (Cruz-Robles et al., 2004). Furthermore, a study performed in Bolivian population found that the frequencies of HLA-B*14:02 and HLA-DRB1*01 were significantly lower in patients suffering from megacolon as well as in those with ECG alteration and/or megacolon compared with a group of patients with indeterminate symptoms (del Puerto et al., 2012). The variability of HLA alleles associated with Chagas disease in the mentioned studies may reflect the genetic heterogeneity presented among the different population in Latin America. Interestingly, one study assessed the association of the HLA-B*3505 allele with moderate to severe cutaneous reaction in response to Benznidazole, produced by a delayed hypersensitivity reaction with a Th2 response (Salvador et al., 2015). Another less polymorphic HLA-G, MICA and MICB have also been studied (Ayo et al., 2015a; del Puerto et al., 2012; Dias et al., 2015). It is remarkable that HLA-G gene was also studied in other parasitic disorders. The 3′ untranslated region of the HLA-G gene has been the main focus of studies on malaria and human African trypanosomiasis; in Chagas disease this region exhibited distinct patterns of associations with CCC and digestive forms in Brazilian population (Dias et al., 2015; Sabbagh et al., 2018). Within the MHC class III region, the TNF and LTA genes are the most widely investigated regarding the susceptibility to the infection and CCC. Some studies demonstrated that the TNF gene polymorphisms could modify the genetic risk of developing heart disease (Dedoussis et al., 2005; P. Zhang et al., 2017). Despite this, conflicting results are reported regarding the role of TNF gene polymorphism in the disease. In this sense although genetic associations were reported in Colombian,

3.1. Disease pathogenesis and immune response After the first contact with infected vectors an acute phase starts and is characterized by high parasitemia rate in blood (Hernandez et al., 2016). In this phase, the immune response to the infection depends on both the innate and the adaptive immune system. This response involves several immune cells such as macrophages, dendritic cells, natural killer cells, B and T lymphocytes. Additionally, various proinflammatory cytokines such as interferon gamma (IFNG), tumor necrosis factor (TNF) and interleukin 12 (IL-12), among others, play an important role in regulating parasite invasion (Junqueira et al., 2010; Machado et al., 2012). Afterwards, an adaptive and persistent Th1 response dependent of IL-12 takes place, which facilitates the parasite clearance in the host (Leon Rodriguez et al., 2018). When parasitemia decreases, patients can keep asymptomatic for years or even the rest of their life in a chronic asymptomatic (or indeterminate) phase (Angheben et al., 2015). Nevertheless, around 30% of patients can develop a chronic phase, which can affect colon, esophagus and heart (Zingales, 2018), being the cardiac chronic form the most common and severe form of the disease, known as Chronic Chagas Cardiomyopathy (CCC) (Perez-Molina & Molina, 2018). The molecular mechanisms behind the chronic forms are still unknown, although both the host genetic and the genetic diversity of the parasite could influence the different outcomes of the disease (Zingales et al., 2012). An infiltration of a plethora of immune cells and the production of proinflammatory cytokines (Bonney et al., 2015; Cunha-Neto et al., 2014) causes a chronic inflammation and development of myocardial dysfunction (Bonney & Engman, 2015). This exacerbated inflammatory response can exceed self-tolerance threshold and trigger an immune reaction against self-molecules, under an autoimmunity hypothesis (De Bona et al., 2018). However, the possible role of parasite persistence by evading the immune response has also been proposed (Bonney & Engman, 2015; Cardoso et al., 2015; De Bona et al., 2018; Matzaraki et al., 2017). Taking into account the particularities of the profile of the immune response in Chagas disease, especially the inflammatory response, and that the appearance of chronic symptoms largely depends on the immune balance in individuals exposed to infection, genetic susceptibility studies have been focused in analyzing genes of the immune response and how these can confer differential susceptibility to both infection and the development of CCC (Ortega Zamora et al., 2017). 4. Genetic susceptibility to Chagas disease Chagas’s disease etiology is considered as multifactorial or complex including the interaction of host genes, environmental and parasite factors. The vast majority of the studies on Chagas disease susceptibility and progression have been carried out using qualitative, dichotomous traits, such as seropositive versus seronegative for T. cruzi antigens (in both groups the individuals were exposed), or presence or absence of CCC and/or digestive manifestation (Ayo et al., 2013; Ortega Zamora et al., 2017; Shaw, 2017). In this work we will update the current knowledge of the genetic basis of Chagas disease (recently reviewed in (Ayo et al., 2013; CunhaNeto & Chevillard, 2014; Ortega Zamora et al., 2017); Shaw, 2017), including information of epigenetic and gene expression data and the need of integration of the existing omics data in order to advance in the genomic medicine of this neglected disease. 3

Acta Tropica 197 (2019) 105062

M. Acosta-Herrera, et al.

Table 1 HLA genes. Gene

Alleles/Genotyping

HLA class I and II HLA-A A*68 A*30 HLA-B B*39 B*35 B*40 in the presence of Cw3 B*14:02 HLA-C C*03 in LD with B*40 and B*15 KIR2DS2 in the presence of HLA-C1 HLA-DRB1 DRB1*14-DQB1*0301 haplotype DR16 and DR4 DRB1*1503 and DRB1*1103 DRB1*0409 DRB1*01, DRB1*08 and DRB1*1501 DRB1*01 and DQB1*0501 DRB1*01 HLA-DQB1 DQB1*0303 DQB1* 0302 and DQB1*06 HLA-DPB1 DPB1* 0401, DPB1*2301and DPB1*3901 MICA MICA*011 DRB1*01-B*14-MICA*011 haplotype MICA-129 MICB MICB*008 HLA-G 3' UTR HLA class III TNF TNFa -238A TNFa -1031C and -308A TNFa-308 TNFa microsatellite and -308 TNFd3, TNFb7, TNFa8; TNFa2-b4-c2-d3-e2 and TNFa8-b1-c1-d3-e3 haplotypes TNFa microsatellite and -308 TNFa -308, -244 and -238 and TNFb TNFa -308 TNFa -308 LTA LTA +80 and +252 LTA +252 LTA +80 and +252 BAT1 BAT1 − 22C/G and −348C/T BAT1 − 22C/G CYP21A2 V281L IKBL IKBL−62A/T and −262A/G

Population

Association

Reference

Mexico Brazil Mexico Mexico Chile Bolivia Venezuela Brazil Peru Mexico Argentina Argentina Venezuela Venezuela Bolivia Venezuela Brazil Venezuela Bolivia Bolivia Brazil Bolivia Brazil

CCC All chronic forms Infection and CCC CCC CCC All chronic forms CCC CCC Infection Infection and CCC Infection and CCC Infection CCC CCC All chronic forms CCC All chronic forms CCC Infection All chronic forms CCC No association All chronic forms

(Cunha-Neto et al., 2014) (Deghaide et al., 1998) (Cruz-Robles et al., 2004) (Cruz-Robles et al., 2004) (Llop et al., 1988) (del Puerto et al., 2012) (Layrisse et al., 2000) (Ayo et al., 2015b) (Nieto et al., 2000) (Cruz-Robles et al., 2004) (Garcia Borras et al., 2009) (Garcia Borras et al., 2006) (Fernandez-Mestre et al., 1998) (Colorado et al., 2000) (del Puerto et al., 2012) (Fernandez-Mestre et al., 1998) (Deghaide et al., 1998) (Colorado et al., 2000) (del Puerto et al., 2012) (del Puerto et al., 2012) (Ayo et al., 2015b) (del Puerto et al., 2012) (Dias et al., 2015)

Brazil Colombia Mexico Brazil Brazil

Infection CCC CCC CCC Infection and All chronic forms

(Pissetti et al., 2011) (Criado et al., 2012) (Rodriguez-Perez et al., 2005) (Drigo et al., 2006) (Campelo et al., 2007)

Brazil Peru Brazil Brazil Brazil Brazil Brazil Brazil Brazil Bolivia Brazil

No association No association No association No association CCC CCC No association CCC CCC CCC CCC

(Drigo et al., 2007) (Beraun et al., 1998) (Alvarado-Arnez et al., 2018) (Lima et al., 2018) (Ramasawmy et al., 2007) (Pissetti et al., 2013) (Alvarado-Arnez et al., 2018) (Ramasawmy et al., 2006a, 2006b) (Alvarado-Arnez et al., 2018) (del Puerto et al., 2013) (Ramasawmy et al., 2008)

involved in controlling T. cruzi infection (Sugawara, 2000) and, also, has been described a local expression of IL-18 in myocardial tissue of patients with Chagas cardiomyopathy (Nogueira et al., 2015). IL-18 mediates IFNγ induction in T cells, which in turn can increase the production of IL-18 creating a positive feedback mechanism (Esper et al., 2014). Interestingly, polymorphisms of IL-18 and IFNG were associated with risk of infection but not with CCC in Colombian patients (Leon Rodriguez et al., 2016a, 2016b; Torres et al., 2010); while in Brazilians it was associated with CCC (Alvarado-Arnez et al., 2018; Nogueira et al., 2015). The authors explained that these discrepancies may be the result of a difference in genotypic distributions between Brazilian and Colombian populations, and this is not surprising given the genetic diversity present in Latin America. Chemokines and their receptors control the migration of leukocytes during the inflammatory process and the modulation of T cell subpopulations. Chemokines have been directly related to cardiac effects, involving processes such as heart tissue repair, arrhythmia and cardiac insufficiency (Ortega Zamora et al., 2017). The chemokine receptor 5 (CCR5) is a receptor for the chemokines CCL2, CCL3, CCL4 and CCL5 and is one of the most replicated gene, which have been associated mainly with the chronic forms of the disease. Interestingly, patients with cardiomyopathy exhibited higher expression of this gene, which resulted in increased inflammation (de Oliveira et al., 2016). To date, there are a total of eight studies that found a significant association with CCC in different Latin American populations (Calzada et al.,

Mexican and Brazilian populations (Campelo et al., 2007; Criado et al., 2012; Drigo et al., 2006; Rodriguez-Perez et al., 2005), other authors did not find a significant association between the TNF gene variants with the disease (Alvarado-Arnez et al., 2018; Beraun et al., 1998; Drigo et al., 2007). The discordant results observed in these studies may be due to the limited power of most of them, the different TNF genetic variations analyzed and the heterogeneity among populations.

4.2. Cytokines, chemokines and their receptors During parasitic infections, the inflammatory response is crucial and it depends on cytokine secretion. Cytokines are mediators of the immune response and help to modulate the progression of the disease by inhibiting parasitic replication in different cell types (Alvarado-Arnez et al., 2018). Most candidate gene studies carried out in Chagas disease have been focused on these molecules and were replicated in different Latin American populations (Table 2). Interleukin 17 (IL-17), interleukin 18 (IL-18) and interferon-gamma (IFNG) are critical molecules for host defense against a variety of intracellular pathogens. Several studies shown that, polymorphisms located in the genes encoding for these cytokines were associated with the susceptibility to T. cruzi infection and to CCC, mainly in Colombian and Brazilian population (Alvarado-Arnez et al., 2018; Leon Rodriguez et al., 2016a, 2016b; Leon Rodriguez et al., 2015; Torres et al., 2010). IL-18 encodes a proinflammatory cytokine that has been proposed to be 4

Acta Tropica 197 (2019) 105062

M. Acosta-Herrera, et al.

Table 2 Cytokines, chemokines and their receptors. Gene

Chr

Gene name

Population

Association

Reference

Cytokines and their receptors IL-1A 2 IL-1B 2

Interleukin 1 alpha Interleukin 1 beta

IL-1RN

2

Interleukin 1 receptor antagonist

IL-4

5

Interleukin 4

IL-4R IL-6 IL-10

16 7 1

Interleukin 4 receptor Interleukin 6 Interleukin 10

IL-12B IL-17A IL-18

5 6 11

Interleukin 12B Interleukin 17A Interleukin 18

IFNG

12

Interferon gamma

TGFB

19

Transforming growth factor beta

Colombia Colombia Mexico Mexico Colombia Bolivia Colombia Colombia Colombia/Peru Brazil Colombia Brazil Colombia Colombia Colombia Brazil Colombia Brazil Colombia/Peru Brazil

CCC CCC No association CCC No association Infection No association CCC No association CCC No association No association CCC Infection and CCC Infection CCC Infection CCC Infection No association

(Florez et al., 2006) (Florez et al., 2006) (Cruz-Robles et al., 2009) (Cruz-Robles et al., 2009) (Florez et al., 2006) (Alvarado Arnez et al., 2011) (Florez et al., 2011) (Florez et al., 2011) (Torres et al., 2010a) (Costa et al., 2009) (Florez et al., 2011) (Alvarado-Arnez et al., 2018) (Zafra et al., 2007) (Leon Rodriguez et al., 2015) (Leon Rodriguez et al., 2016) (Nogueira et al., 2015) (Torres et al., 2010b) (Alvarado-Arnez et al., 2018) (Calzada et al., 2009) (Alvarado-Arnez et al., 2018)

Peru Colombia Venezuela Argentina Brazil Brazil Brazil Brazil Colombia Argentina Brazil Brazil Brazil Colombia/Peru Brazil Brazil Brazil

CCC CCC CCC CCC CCC CCC All chronic forms No association CCC CCC CCC CCC CCC Infection No association No association No association

(Calzada et al., 2001a) (Florez et al., 2012; Machuca et al., 2014) (Fernandez-Mestre et al., 2004) (Juiz et al., 2019) (Nogueira et al., 2012) (Frade et al., 2013) (de Oliveira et al., 2015) (Lima et al., 2018) (Florez et al., 2012; Machuca et al., 2014) (Juiz et al., 2019) (Frade et al., 2013; Ramasawmy et al., 2009) (Nogueira et al., 2012) (Nogueira et al., 2012) (Torres et al., 2009) (Nogueira et al., 2012) (Nogueira et al., 2012) (Nogueira et al., 2012)

Chemokines and their receptors CCR5 3 C-C motif chemokine receptor 5

CCR2

3

C-C motif chemokine receptor 2

CCL2 CXCL9 CXCL10 MIF CCL5 CCL17 CCL19

17 4 4 22 17 16 9

C-C motif chemokine ligand 2 C-X-C motif chemokine ligand 9 C-X-C motif chemokine ligand 10 Macrophage migration inhibitory factor C-C motif chemokine ligand 5 C-C motif chemokine ligand 17 C-C motif chemokine ligand 179

4.3. Other genes

2001a, 2001b; de Oliveira et al., 2015; Fernandez-Mestre et al., 2004; Florez et al., 2012; Frade et al., 2013; Juiz et al., 2019; Machuca et al., 2014; Nogueira et al., 2012) and one of them also found a significant association with the digestive form of the disease (de Oliveira et al., 2015). Additionally, CCR2 belongs to the same chemokine receptor family and has also been associated with CCC after T. cruzi infection. CCR5, CCR2 and their haplotypes were associated with the development of this chronic cardiomyopathy (Juiz et al., 2019; Machuca et al., 2014). A variant of the chemokine CXCL9 gene was associated with reduced risk of developing severe CCC in Brazilian patients. Moreover, CCC patients with ventricular dysfunction displayed reduced genotypic frequencies of variants in CXCL9, CXCL10, and increased in CCR5 as compared to those without the dysfunction (Nogueira et al., 2012). Another noteworthy gene is CCL2, which was significantly associated with CCC in Brazilian population with replicable results. Plasma levels of this protein have been correlated with myocardial dysfunction in patients with acute myocardial infarction (Parissis et al., 2002) and severe CCC (Talvani et al., 2004). An integral component of the host antimicrobial alarm system is the chemokine macrophage migration inhibitory factor (MIF). Serum levels of this protein were significantly increased in parasitic diseases such as leishmaniasis and malaria (Alonso et al., 2019; Awandare et al., 2006; Jose et al., 2018). In Chagas disease this gene was associated with the infection by T. cruzi in Colombian and Peruvian population (Torres et al., 2009).

Several other genes, selected for their previous association with other infectious diseases, have been studied for Chagas disease. These include genes coding for toll-like receptors (TLRs) and related molecules such as Toll-interleukin-1 receptor domain containing adapter protein (TIRAP). This molecule encodes an adapter protein associated with TLRs, which recognizes microbial pathogens. In the context of Chagas disease two studies have reported an association with the susceptibility of developing CCC (Table 3) (Frade et al., 2013; Ramasawmy et al., 2009). Others studies have addressed the association between haptoglobin (HP) polymorphisms with T. cruzi infection and its chronic forms, including cardiac and digestive forms in Brazilian and Venezuelan population (Jorge et al., 2010; Mundaray Fernandez et al., 2014). HP is an acute-phase protein synthesized mainly by the liver during inflammatory processes and also possesses anti-inflammatory and antioxidant properties (Sadrzadeh et al., 2004). On the other hand, two studies in the mannose-binding lectin 2 (MBL2) showed association with an increased risk of severe CCC, related with high MBL serum levels in Brazilian population (Luz et al., 2010), and a moderate association with the risk of infection in Chilean population (Weitzel et al., 2012). Genes such us TLR5, TLR6, TLR9, NOS2, FOXO3, TYK2 among others were also studied in the context of Chagas disease, however, no significant association were observed with these genes (Calzada et al., 2002; Ramasawmy et al., 2009; Weitzel et al., 2012; Leon Rodriguez, Gonzalez, et al., 2016a, 2016b; Leon Rodriguez et al., 2018).

5

Acta Tropica 197 (2019) 105062

M. Acosta-Herrera, et al.

Table 3 : Others genes. Gene

Chr

Gene name

Population

Association

Reference

HP

16

Haptoglobin

MBL2

10

Mannose binding lectin 2

TIRAP VDR MASP2 ACTC1 FCN2 CTLA4

11 12 1 15 9 2

Toll-interleukin receptor domain containing adaptor protein Vitamin D3 receptor Mannan binding lectin serine peptidase 2 Actin alpha cardiac muscle 1 Ficolin 2 Cytotoxic T-lymphocyte associated protein 4

TLR1/TLR2

4

Toll like receptor 1 / Toll like receptor 2

TLR2 TLR4

4 9

Toll like receptor 2 Toll like receptor 4

TLR5/TLR9 TLR6 PTPN22 NRAMP1 NOS2 FOXO3 TYK2

1/3 4 1 2 17 6 19

Toll like receptor 5 / Toll like receptor 9 Toll like receptor 6 Tyrosine-protein phosphatase non-receptor type 22 Human natural resistance‐associated macrophage protein 1 Nitric oxide synthase Forkhead box protein 3 Non-receptor tyrosine-protein kinase

Brazil Venezuela Brazil Chile Brazil Colombia Brazil Brazil Brazil Brazil Venezuela Chile Brazil Colombia Colombia Brazil Brazil Chile Colombia/Peru Peru Peru Colombia Colombia

CCC Infection and CCC Infection and CCC Infection CCC CCC CCC CCC All chronic forms CCC No association No association No association No association No association No association No association No association No association No association No association No association No association

(Jorge et al., 2010) (Mundaray Fernandez et al., 2014) (Luz et al., 2016) (Weitzel et al., 2012) (Frade et al., 2013; Ramasawmy et al., 2009) Leon Rodriguez et al., 2016) (Boldt et al., 2011) (Frade et al., 2013) (Luz et al., 2013) (Dias et al., 2013) (Fernandez-Mestre et al., 2009) (Weitzel et al., 2012) (Ramasawmy et al., 2009) (Zafra et al., 2008) (Zafra et al., 2008) (Ramasawmy et al., 2009) (Ramasawmy et al., 2009) (Weitzel et al., 2012) (Robledo et al., 2007) (Calzada et al., 2001a) (Calzada et al., 2002) (Leon Rodriguez et al., 2016) (Leon Rodriguez et al., 2018)

5. Other “genomics-based” studies towards precision medicine in Chagas disease

to understand host systemic responses to infections to identify markers for disease diagnosis and prognosis (Yamagishi et al., 2014). Additionally they have been applied to assess drug resistance (Coll et al., 2018) and differential response to treatments, which is very important for patient management and vaccine development (Tientcheu et al., 2015). Nowadays, single-cell RNA sequencing is renewing the study of cell to cell heterogeneity. Specifically, it led to the identification of new variations in gene expression involved in host-parasite interactions in malaria (Reid et al., 2018). All of the above highlights the enormous potential of genomics and genomics-based approaches as a cornerstone in contemporary research on infectious diseases, which should be eventually transferred to improve our understanding of Chagas disease dynamics and pathophysiology (Bah et al., 2018).

The National Academy of Sciences has recently coined the term ‘precision medicine’, where each individual’s diagnosis would be accurately carried out by using genomics, epigenomics, environmental exposures and other clinical information. In addition to establishing genetic associations with T. cruzi infection and Chagas disease outcomes, transcriptomic studies analyzing differential gene expression, are key tools to understand host responses to infection and to establish diagnostic and prognostic markers. In this sense, Ferreira et al. assessed the whole-blood transcriptome of 120 patients with Chagas disease and 30 uninfected controls. A gene signature of 27 genes was identified, mainly related to natural killer (NK)/CD8+ T-cell cytotoxicity and potentially determining disease progression (Ferreira et al., 2017). Another interesting study assessing a whole transcriptome analysis of heart biopsies of CCC patients conducted by Frade et al., found the overexpression of a long noncoding RNA that was previously associated with myocardial infarction, and suggesting its role as a CCC biomarker (Frade et al., 2016). Additional strategies include the assessment of epigenomic modifications at the level of DNA methylation. Such analyses provide important information regarding the regulation of gene expression. In this sense, Laugier et al. investigated the impact of whole-genome methylation and expression in myocardial samples from patients with CCC compared to samples from healthy donors. The authors found 399 genes differentially expressed and methylated that were related to heart function or to the immune response, and were able to pinpoint novel potential disease pathways and therapeutic targets (Laugier et al., 2017). The effect of these epigenetic alterations in Chagas disease, switching genes on or off, could be mimicked by small molecules and drugs that target and modulate their action. Most of the evidence exploiting the therapeutic potential of epigenetic targets comes from cancer research, where the combination of conventional and epigenetic drugs is the gold standard (Dzobo, 2019; Kim et al., 2019; Wu et al., 2019); these results may be eventually transferred to other immunoinflammatory and infectious diseases. Genomics-based tools have been successfully applied in other infectious diseases to determine the evolutionary history of different strains to monitor transmission dynamics in otubreaks (Merker et al., 2015), to investigate host pathway biology and multigene families associated with immune evasion (Bah et al., 2018; Bopp et al., 2013), and

6. Future perspectives Still today, the evidence supporting the genetic associations with T. cruzi infection and CCC is limited, given the low number of genes confidently associated, in comparison with other infectious diseases (Chapman & Hill, 2012; Klebanov, 2018). To date, only one GWAS in Chagas disease have been performed, without significant associations at the genomic level (Deng et al., 2013). Currently, as part of the Science and Technology program for development “CYTED”, an Ibero-American Network of Genomic Medicine in Chagas Disease from different endemic countries and Spain, is committed to the genomic assessment of disease susceptibility and outcomes. This will allow the establishment of key genetic factors in the susceptibility to both infection and the development of severe symptoms. Moreover, as detected in other complex diseases, it is anticipated that the observed associations will only explain a small fraction of the genetic component of the disease (Manolio et al., 2009). Therefore, further assessments of rare genetic variation by WES and WGS strategies will provide a deeper characterization of all genetic variants and their relationship with disease susceptibility. Establishing the association of genes with Chagas disease should continue with the discovery of the functional implication of such associations. As commented before, genetic variants alone do not fully explain the risk to the disease; the underlying molecular mechanisms of how the associated genes contribute to the disease pathogenesis remain largely unknown, due to the lack of an efficient strategy to identify the causal variants (Meng et al., 2018). Also, the vast majority of 6

Acta Tropica 197 (2019) 105062

M. Acosta-Herrera, et al.

genes and association with clinical outcome in a Chagas disease case-control study from Brazil. Mem. Inst. Oswaldo Cruz 113 (6), e170489. https://doi.org/10.1590/ 0074-02760170489. Alvarado Arnez, L.E., et al., 2011. Sequence variation in the IL-4 gene and resistance to Trypanosoma cruzi infection in Bolivians. J. Allergy Clin. Immunol. 127 (1), 279–282. https://doi.org/10.1016/j.jaci.2010.10.026. 282 e271-273. Angheben, A., et al., 2015. Chagas disease and transfusion medicine: a perspective from non-endemic countries. Blood Transfus. 13 (4), 540–550. https://doi.org/10.2450/ 2015.0040-15. Arama, C., et al., 2015. Ethnic differences in susceptibility to malaria: what have we learned from immuno-epidemiological studies in West Africa? Acta Trop. 146, 152–156. https://doi.org/10.1016/j.actatropica.2015.03.023. Awandare, G.A., et al., 2006. Decreased circulating macrophage migration inhibitory factor (MIF) protein and blood mononuclear cell MIF transcripts in children with Plasmodium falciparum malaria. Clin. Immunol. 119 (2), 219–225. https://doi.org/ 10.1016/j.clim.2005.12.003. Ayo, C.M., et al., 2013. Genetic susceptibility to Chagas disease: an overview about the infection and about the association between disease and the immune response genes. Biomed Res. Int. 2013, 284729. https://doi.org/10.1155/2013/284729. Ayo, C.M., et al., 2015a. Association of the functional MICA-129 polymorphism with the severity of chronic chagas heart disease. Clin. Infect. Dis. 61 (8), 1310–1313. https:// doi.org/10.1093/cid/civ540. Ayo, C.M., et al., 2015b. Killer cell immunoglobulin-like receptors and their HLA ligands are related with the immunopathology of chagas disease. PLoS Negl. Trop. Dis. 9 (5), e0003753. https://doi.org/10.1371/journal.pntd.0003753. Bah, S.Y., et al., 2018. Highlights on the application of genomics and bioinformatics in the fight against infectious diseases: challenges and opportunities in Africa. Front. Genet. 9, 575. https://doi.org/10.3389/fgene.2018.00575. Beraun, Y., et al., 1998. Polymorphisms at tumor necrosis factor (TNF) loci are not associated with Chagas’ disease. Tissue Antigens 52 (1), 81–83. Blackwell, J.M., et al., 2009. HLA and infectious diseases. Clin. Microbiol. Rev. 22 (2), 370–385. https://doi.org/10.1128/CMR.00048-08. Table of Contents. Boldt, A.B., et al., 2011. MASP2 haplotypes are associated with high risk of cardiomyopathy in chronic Chagas disease. Clin. Immunol. 140 (1), 63–70. https://doi.org/10. 1016/j.clim.2011.03.008. Bomba, L., Walter, K., Soranzo, N., 2017. The impact of rare and low-frequency genetic variants in common disease. Genome Biol. 18 (1), 77. https://doi.org/10.1186/ s13059-017-1212-4. Bonney, K.M., et al., 2015. Autoimmune pathogenesis of Chagas heart disease: looking back, looking ahead. Am. J. Pathol. 185 (6), 1537–1547. https://doi.org/10.1016/j. ajpath.2014.12.023. Bopp, S.E., et al., 2013. Mitotic evolution of Plasmodium falciparum shows a stable core genome but recombination in antigen families. PLoS Genet. 9 (2), e1003293. https:// doi.org/10.1371/journal.pgen.1003293. Boyle, E.A., Li, Y.I., Pritchard, J.K., 2017. An expanded view of complex traits: from polygenic to omnigenic. Cell 169 (7), 1177–1186. https://doi.org/10.1016/j.cell. 2017.05.038. Bryc, K., Velez, C., Karafet, T., Moreno-Estrada, A., Reynolds, A., Auton, A., et al., 2010. Colloquium paper: genome-wide patterns of population structure and admixture among Hispanic/Latino populations. Proc Natl Acad Sci U S A 107 (Suppl 2), 8954–8961. https://doi.org/10.1073/pnas.0914618107. Buscaglia, C.A., et al., 2015. Neglected tropical diseases in the post-genomic era. Trends Genet. 31 (10), 539–555. https://doi.org/10.1016/j.tig.2015.06.002. Cader, M.Z., et al., 2016. C13orf31 (FAMIN) is a central regulator of immunometabolic function. Nat. Immunol. 17 (9), 1046–1056. https://doi.org/10.1038/ni.3532. Calzada, J.E., et al., 2009. Transforming growth factor beta 1 (TGFbeta1) gene polymorphisms and Chagas disease susceptibility in Peruvian and Colombian patients. Cytokine 45 (3), 149–153. https://doi.org/10.1016/j.cyto.2008.11.013. Calzada, J.E., et al., 2002. No evidence for association of the inducible nitric oxide synthase promoter polymorphism with Trypanosoma cruzi infection. Tissue Antigens 59 (4), 316–319. Calzada, J.E., et al., 2001a. Chemokine receptor CCR5 polymorphisms and Chagas’ disease cardiomyopathy. Tissue Antigens 58 (3), 154–158. Calzada, J.E., et al., 2001b. Lack of association between NRAMP1 gene polymorphisms and Trypanosoma cruzi infection. Tissue Antigens 57 (4), 353–357. Campelo, V., et al., 2007. TNF microsatellite alleles in Brazilian Chagasic patients. Dig. Dis. Sci. 52 (12), 3334–3339. https://doi.org/10.1007/s10620-006-9699-7. Cardoso, M.S., et al., 2015. Evasion of the immune response by Trypanosoma cruzi during acute infection. Front. Immunol. 6, 659. https://doi.org/10.3389/fimmu.2015. 00659. Cardozo, D.M., et al., 2014. Evidence of HLA-DQB1 contribution to susceptibility of dengue serotype 3 in dengue patients in Southern Brazil. J Trop Med 2014, 968262. https://doi.org/10.1155/2014/968262. Colorado, I.A., et al., 2000. HLA class II DRB1, DQB1, DPB1 polymorphism and cardiomyopathy due to Trypanosoma cruzi chronic infection. Hum. Immunol. 61 (3), 320–325. Coll, F., et al., 2018. Genome-wide analysis of multi- and extensively drug-resistant Mycobacterium tuberculosis. Nat. Genet. 50 (2), 307–316. https://doi.org/10.1038/ s41588-017-0029-0. Coornaert, B., et al., 2009. A20: central gatekeeper in inflammation and immunity. J. Biol. Chem. 284 (13), 8217–8221. https://doi.org/10.1074/jbc.R800032200. Costa, G.C., et al., 2009. Functional IL-10 gene polymorphism is associated with Chagas disease cardiomyopathy. J. Infect. Dis. 199 (3), 451–454. https://doi.org/10.1086/ 596061. Criado, L., et al., 2012. Genetic polymorphisms in TNFA/TNFR2 genes and Chagas disease in a Colombian endemic population. Cytokine 57 (3), 398–401. https://doi.org/10.

Fig. 1. The integration of omics data will offer a wide representation of disease complexity and detailed assessment of the molecular determinants.

significantly associated genetic variants identified through GWAS fall outside of coding regions, complicating the understanding and increasing the need to determine their biological functions (Boyle et al., 2017; Edwards et al., 2013; Hindorff et al., 2009). Currently, the integration of genomic, methylomic and transcriptomic data will provide deeper characterization of the associated variants and the levels of gene expression/methylation, and if the variants are correlated with the activation or inhibition of certain genes, that is, if they constitute expression or methylation quantitative trait loci (eQTLs and meQTLs) (Buscaglia et al., 2015; Smith et al., 2014) (Fig. 1). In addition, different functional tests are being addressed to try to understand the function of genes and the functional consequences of genetic variants (functional genomics). To establish the function of a specific variant, genome editing techniques such as rescue experiments, CRISPR-Cas9 or promoter capture Hi-C in cell cultures and animal models are the mostly used. (Martin et al., 2016; Rodenburg, 2018). Other new strategy, termed integrated transcriptome and epigenome analysis (iTEA) was successfully applied to identify functional genetic variants in non-coding elements, in type 2 diabetes, which can be extended to the study of other complex diseases (Meng et al., 2018). Finally, follow-up studies could be of great interest, such studies include longitudinal information at different levels (genomic, transriptomic, epigenomic and clinical information) that will help us establish accurate biomarkers for the development of the infection and/or severe complications. Acknowledgments We would like to thank the “Red Iberoamericana de Medicina Genómica en Enfermedad de Chagas” (RIMGECH-217RT0524) from CYTED. Additionally, we would like to acknowledge the assistance of Consejo Nacional de Investigaciones Cientificas y Técnicas (CONICET) and Universidad Nacional de Córdoba, Argentina. MAH was funded under Juan de la Cierva Formación fellowship (FJCI-2015-24028). References Genomes Project Consortium, et al., 2012. An integrated map of genetic variation from 1,092 human genomes. Nature 491 (7422), 56–65. https://doi.org/10.1038/ nature11632. Alonso, D., et al., 2019. HIF-1alpha-regulated MIF activation and Nox2-dependent ROS generation promote Leishmania amazonensis killing by macrophages under hypoxia. Cell. Immunol. 335, 15–21. https://doi.org/10.1016/j.cellimm.2018.10.007. Alvarado-Arnez, L.E., et al., 2018. Single nucleotide polymorphisms of cytokine-related

7

Acta Tropica 197 (2019) 105062

M. Acosta-Herrera, et al.

Florez, O., et al., 2006. Interleukin-1 gene cluster polymorphism in chagas disease in a Colombian case-control study. Hum. Immunol. 67 (9), 741–748. https://doi.org/10. 1016/j.humimm.2006.06.004. Frade, A.F., et al., 2016. Myocardial infarction-associated transcript, a long noncoding RNA, is overexpressed during dilated cardiomyopathy due to chronic chagas disease. J. Infect. Dis. 214 (1), 161–165. https://doi.org/10.1093/infdis/jiw095. Frade, A.F., et al., 2013. Genetic susceptibility to Chagas disease cardiomyopathy: involvement of several genes of the innate immunity and chemokine-dependent migration pathways. BMC Infect. Dis. 13, 587. https://doi.org/10.1186/1471-2334-13587. Grant, A.V., Sabri, A., Abid, A., Abderrahmani Rhorfi, I., Benkirane, M., Souhi, H., et al., 2016. A genome-wide association study of pulmonary tuberculosis in Morocco. Hum. Genet. 135 (3), 299–307. https://doi.org/10.1007/s00439-016-1633-2. Garcia Borras, S., et al., 2006. HLA class II DRB1 polymorphism in Argentinians undergoing chronic Trypanosoma cruzi infection. Ann. Clin. Biochem. 43 (Pt 3), 214–216. https://doi.org/10.1258/000456306776865205. Garcia Borras, S., et al., 2009. Distribution of HLA-DRB1 alleles in Argentinean patients with Chagas’ disease cardiomyopathy. Immunol. Invest. 38 (3-4), 268–275. Gruen, J.R., et al., 2001. Human MHC class III and IV genes and disease associations. Front Biosci 6, D960–972. Hernandez, C., et al., 2016. Molecular diagnosis of chagas disease in Colombia: parasitic loads and discrete typing units in patients from acute and chronic phases. PLoS Negl. Trop. Dis. 10 (9), e0004997. https://doi.org/10.1371/journal.pntd.0004997. Hindorff, L.A., Sethupathy, P., Junkins, H.A., Ramos, E.M., Mehta, J.P., Collins, F.S., Manolio, T.A., 2009. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc Natl Acad Sci U S A 106 (23), 9362–9367. https://doi.org/10.1073/pnas.0903103106. International HapMap, C., et al., 2010. Integrating common and rare genetic variation in diverse human populations. Nature 467 (7311), 52–58. https://doi.org/10.1038/ nature09298. International HIV Controllers Study, et al., 2010. The major genetic determinants of HIV1 control affect HLA class I peptide presentation. Science 330 (6010), 1551–1557. https://doi.org/10.1126/science.1195271. Jorge, S.E., et al., 2010. Haptoglobin genotypes in Chagas’ disease. Clin. Biochem. 43 (3), 314–316. https://doi.org/10.1016/j.clinbiochem.2009.09.020. Jose, S., et al., 2018. Neutralization of macrophage migration inhibitory factor improves host survival after Clostridium difficile infection. Anaerobe 53, 56–63. https://doi. org/10.1016/j.anaerobe.2018.06.014. Juiz, N.A., et al., 2019. Association study between CCR2-CCR5 genes polymorphisms and chronic Chagas heart disease in Wichi and in admixed populations from Argentina. PLoS Negl. Trop. Dis. 13 (1), e0007033. https://doi.org/10.1371/journal.pntd. 0007033. Junqueira, C., et al., 2010. The endless race between Trypanosoma cruzi and host immunity: lessons for and beyond Chagas disease. Expert Rev. Mol. Med. 12, e29. https://doi.org/10.1017/S1462399410001560. Kim, D.H., Kim, H.M., Huong, P.T.T., Han, H.J., Hwang, J., Cha-Molstad, H., et al., 2019. Enhanced anticancer effects of a methylation inhibitor by inhibiting a novel DNMT1 target, CEP 131, in cervical cancer. BMB Rep. Klebanov, N., 2018. Genetic predisposition to infectious disease. Cureus 10 (8), e3210. https://doi.org/10.7759/cureus.3210. Klein, R.J., et al., 2005. Complement factor H polymorphism in age-related macular degeneration. Science 308 (5720), 385–389. https://doi.org/10.1126/science. 1109557. Lander, E.S., et al., 2001. Initial sequencing and analysis of the human genome. Nature 409 (6822), 860–921. https://doi.org/10.1038/35057062. Laugier, L., et al., 2017. Whole-genome cardiac DNA methylation fingerprint and gene expression analysis provide new insights in the pathogenesis of chronic chagas disease cardiomyopathy. Clin. Infect. Dis. 65 (7), 1103–1111. https://doi.org/10.1093/ cid/cix506. Lauschke, V.M., et al., 2017. Pharmacogenomic biomarkers for improved drug therapyrecent progress and future developments. AAPS J. 20 (1), 4. https://doi.org/10. 1208/s12248-017-0161-x. Layrisse, Z., et al., 2000. HLA-C(*)03 is a risk factor for cardiomyopathy in Chagas disease. Hum. Immunol. 61 (9), 925–929. Consortium, Leish G.E.N., et al., 2013. Common variants in the HLA-DRB1-HLA-DQA1 HLA class II region are associated with susceptibility to visceral leishmaniasis. Nat. Genet. 45 (2), 208–213. https://doi.org/10.1038/ng.2518. Leon Rodriguez, D.A., et al., 2018. Comprehensive analysis of three TYK2 gene variants in the susceptibility to Chagas disease infection and cardiomyopathy. PLoS One 13 (1), e0190591. https://doi.org/10.1371/journal.pone.0190591. Leon Rodriguez, D.A., et al., 2016. IL-18 gene variants influence the susceptibility to chagas disease. PLoS Negl. Trop. Dis. 10 (3), e0004583. https://doi.org/10.1371/ journal.pntd.0004583. Leon Rodriguez, D.A., et al., 2016a. Evaluation of VDR gene polymorphisms in Trypanosoma cruzi infection and chronic Chagasic cardiomyopathy. Sci. Rep. 6, 31263. https://doi.org/10.1038/srep31263. Leon Rodriguez, D.A., et al., 2015. Investigation of the role of IL-17A gene variants in Chagas disease. Genes Immun. 16 (8), 536–540. https://doi.org/10.1038/gene. 2015.42. Leon Rodriguez, D.A., et al., 2016b. Analysis of association of FOXO3 gene with Trypanosoma cruzi infection and chronic Chagasic cardiomyopathy. HLA. https:// doi.org/10.1111/tan.12808. Lima, A.P.B., et al., 2018. Evaluation of parasite and host genetics in two generations of a family with Chagas disease. Parasitol. Res. 117 (9), 3009–3013. https://doi.org/10. 1007/s00436-018-5969-5. Llop, E., et al., 1988. HLA antigens in cardiomyopathic Chilean chagasics. Am. J. Hum.

1016/j.cyto.2011.12.007. Cruz-Robles, D., et al., 2009. Association between IL-1B and IL-1RN gene polymorphisms and Chagas’ disease development susceptibility. Immunol. Invest. 38 (3-4), 231–239. Cruz-Robles, D., et al., 2004. MHC class I and class II genes in Mexican patients with Chagas disease. Hum. Immunol. 65 (1), 60–65. Cruz-Tapias, P., et al., 2013. Major histocompatibility complex: antigen processing and presentation. In: Anaya, J.M., Shoenfeld, Y., Rojas-Villarraga, A. (Eds.), Autoimmunity: From Bench to Bedside. El Rosario University Press, Bogota (Colombia). Cunha-Neto, E., et al., 2014. Chagas disease cardiomyopathy: immunopathology and genetics. Mediators Inflamm. 2014, 683230. https://doi.org/10.1155/2014/683230. Chapman, S.J., et al., 2012. Human genetic susceptibility to infectious disease. Nat. Rev. Genet. 13 (3), 175–188. https://doi.org/10.1038/nrg3114. De Bona, E., et al., 2018. Autoimmunity in Chronic Chagas Disease: A Road of Multiple Pathways to Cardiomyopathy? Front. Immunol. 9, 1842. https://doi.org/10.3389/ fimmu.2018.01842. de Oliveira, A.P., et al., 2016. The role of CCR5 in Chagas disease - a systematic review. Infect. Genet. Evol. 45, 132–137. https://doi.org/10.1016/j.meegid.2016.08.012. de Oliveira, A.P., et al., 2015. Genetic susceptibility to cardiac and digestive clinical forms of chronic chagas disease: involvement of the CCR5 59029 A/G polymorphism. PLoS One 10 (11), e0141847. https://doi.org/10.1371/journal.pone.0141847. Dedoussis, G.V., et al., 2005. Association between TNF-alpha -308G&A polymorphism and the development of acute coronary syndromes in Greek subjects: the CARDIO2000-GENE Study. Genet. Med. 7 (6), 411–416. 10.109701.GIM. 0000170993.75385.F4. Deghaide, N.H., et al., 1998. HLA class I and II profiles of patients presenting with Chagas’ disease. Dig. Dis. Sci. 43 (2), 246–252. del Puerto, F., et al., 2013. 21-Hydroxylase gene mutant allele CYP21A2*15 strongly linked to the resistant HLA haplotype B*14:02-DRB1*01:02 in chronic Chagas disease. Hum. Immunol. 74 (6), 783–786. https://doi.org/10.1016/j.humimm.2013.01. 023. del Puerto, F., et al., 2012. Protective human leucocyte antigen haplotype, HLADRB1*01-B*14, against chronic Chagas disease in Bolivia. PLoS Negl. Trop. Dis. 6 (3), e1587. https://doi.org/10.1371/journal.pntd.0001587. Deng, X., et al., 2013. Genome wide association study (GWAS) of Chagas cardiomyopathy in Trypanosoma cruzi seropositive subjects. PLoS One 8 (11), e79629. https://doi. org/10.1371/journal.pone.0079629. Dias, F.C., et al., 2013. Polymorphic sites at the immunoregulatory CTLA-4 gene are associated with chronic chagas disease and its clinical manifestations. PLoS One 8 (10), e78367. https://doi.org/10.1371/journal.pone.0078367. Dias, F.C., et al., 2015. Human leucocyte antigen-G (HLA-G) and its murine functional homolog Qa2 in the Trypanosoma cruzi Infection. Mediators Inflamm. 2015, 595829. https://doi.org/10.1155/2015/595829. Drigo, S.A., et al., 2006. TNF gene polymorphisms are associated with reduced survival in severe Chagas’ disease cardiomyopathy patients. Microbes Infect. 8 (3), 598–603. https://doi.org/10.1016/j.micinf.2005.08.009. Drigo, S.A., et al., 2007. Lack of association of tumor necrosis factor-alpha polymorphisms with Chagas disease in Brazilian patients. Immunol. Lett. 108 (1), 109–111. https:// doi.org/10.1016/j.imlet.2006.10.008. Duncan, C., Jamieson, F., Mehaffy, C., 2015. Preliminary evaluation of exome sequencing to identify genetic markers of susceptibility to tuberculosis disease. BMC Res. Notes 8, 750. https://doi.org/10.1186/s13104-015-1740-5. Dzobo, K., 2019. Epigenomics-guided drug development: recent advances in solving the Cancer treatment "jigsaw puzzle. OMICS 23 (2), 70–85. https://doi.org/10.1089/ omi.2018.0206. Edwards, S.L., Beesley, J., French, J.D., Dunning, A.M., 2013. Beyond GWASs: illuminating the dark road from association to function. Am. J. Hum. Genet. 93 (5), 779–797. https://doi.org/10.1016/j.ajhg.2013.10.012. Esper, L., et al., 2014. Regulatory effects of IL-18 on cytokine profiles and development of myocarditis during Trypanosoma cruzi infection. Microbes Infect. 16 (6), 481–490. https://doi.org/10.1016/j.micinf.2014.03.007. Fellay, J., et al., 2009. Common genetic variation and the control of HIV-1 in humans. PLoS Genet. 5 (12), e1000791. https://doi.org/10.1371/journal.pgen.1000791. Fellay, J., et al., 2007. A whole-genome association study of major determinants for host control of HIV-1. Science 317 (5840), 944–947. https://doi.org/10.1126/science. 1143767. Fernandez-Mestre, M., et al., 2009. Influence of CTLA-4 gene polymorphism in autoimmune and infectious diseases. Hum. Immunol. 70 (7), 532–535. https://doi.org/ 10.1016/j.humimm.2009.03.016. Fernandez-Mestre, M.T., et al., 1998. Influence of the HLA class II polymorphism in chronic Chagas’ disease. Parasite Immunol. 20 (4), 197–203. Fernandez-Mestre, M.T., et al., 2004. Is the CCR5-59029-G/G genotype a protective factor for cardiomyopathy in Chagas disease? Hum. Immunol. 65 (7), 725–728. https://doi. org/10.1016/j.humimm.2004.05.002. Ferreira, L.R., et al., 2017. Blood gene signatures of chagas cardiomyopathy with or without ventricular dysfunction. J. Infect. Dis. 215 (3), 387–395. https://doi.org/10. 1093/infdis/jiw540. Flores-Ferrer, A., et al., 2018. Evolutionary ecology of Chagas disease; what do we know and what do we need? Evol. Appl. 11 (4), 470–487. https://doi.org/10.1111/eva. 12582. Florez, O., et al., 2011. Interleukin 4, interleukin 4 receptor-alpha and interleukin 10 gene polymorphisms in Chagas disease. Parasite Immunol. 33 (9), 506–511. https://doi. org/10.1111/j.1365-3024.2011.01314.x. Florez, O., et al., 2012. Genetic variants in the chemokines and chemokine receptors in Chagas disease. Hum. Immunol. 73 (8), 852–858. https://doi.org/10.1016/j. humimm.2012.04.005.

8

Acta Tropica 197 (2019) 105062

M. Acosta-Herrera, et al.

doi.org/10.1086/523653. Reid, A.J., et al., 2018. Single-cell RNA-seq reveals hidden transcriptional variation in malaria parasites. Elife 7. https://doi.org/10.7554/eLife.33105. Remouchamps, C., et al., 2011. Biology and signal transduction pathways of the Lymphotoxin-alphabeta/LTbetaR system. Cytokine Growth Factor Rev. 22 (5–6), 301–310. https://doi.org/10.1016/j.cytogfr.2011.11.007. Robledo, G., et al., 2007. Association study of PTPN22 C1858T polymorphism in Trypanosoma cruzi infection. Tissue Antigens 69 (3), 261–264. https://doi.org/10. 1111/j.1399-0039.2007.00800.x. Rodenburg, R.J., 2018. The functional genomics laboratory: functional validation of genetic variants. J. Inherit. Metab. Dis. 41 (3), 297–307. https://doi.org/10.1007/ s10545-018-0146-7. Rodriguez-Perez, J.M., et al., 2005. Tumor necrosis factor-alpha promoter polymorphism in Mexican patients with Chagas’ disease. Immunol. Lett. 98 (1), 97–102. https://doi. org/10.1016/j.imlet.2004.10.017. Sabbagh, A., et al., 2018. The role of HLA-G in parasitic diseases. HLA 91 (4), 255–270. https://doi.org/10.1111/tan.13196. Sadrzadeh, S.M., et al., 2004. Haptoglobin phenotypes in health and disorders. Am. J. Clin. Pathol. 121 (Suppl), S97–S104. https://doi.org/10.1309/ 8GLX5798Y5XHQ0VW. Salvador, F., et al., 2015. Evaluation of cytokine profile and HLA association in benznidazole related cutaneous reactions in patients with Chagas disease. Clin. Infect. Dis. 61 (11), 1688–1694. https://doi.org/10.1093/cid/civ690. Shaw, M.A., 2017. Human genetic susceptibility to chagas disease. In: Telleria, J., Tibayrenc, M. (Eds.), American Trypanosomiasis. Chagas Disease. One Hundred Years of Research. Elsevier, pp. 629–652. Shen, J., Shi, S., Lai, Z., 2018. Identification of HLA-DQA1 as a susceptibility gene for spinal tuberculosis by exome sequencing. Med. Sci. Monit. 24, 3442–3449. https:// doi.org/10.12659/MSM.907864. Smith, A.K., et al., 2014. Methylation quantitative trait loci (meQTLs) are consistently detected across ancestry, developmental stage, and tissue type. BMC Genomics 15, 145. https://doi.org/10.1186/1471-2164-15-145. Sorensen, T.I., et al., 1988. Genetic and environmental influences on premature death in adult adoptees. N. Engl. J. Med. 318 (12), 727–732. https://doi.org/10.1056/ NEJM198803243181202. Stanaway, J.D., et al., 2015. The burden of Chagas disease: estimates and challenges. Glob. Heart 10 (3), 139–144. https://doi.org/10.1016/j.gheart.2015.06.001. Stead, W.W., et al., 1990. Racial differences in susceptibility to infection by Mycobacterium tuberculosis. N. Engl. J. Med. 322 (7), 422–427. https://doi.org/10. 1056/NEJM199002153220702. Subramanian, G., et al., 2001. Implications of the human genome for understanding human biology and medicine. JAMA 286 (18), 2296–2307. Sugawara, I., 2000. Interleukin-18 (IL-18) and infectious diseases, with special emphasis on diseases induced by intracellular pathogens. Microbes Infect. 2 (10), 1257–1263. Talvani, A., et al., 2004. Elevated concentrations of CCL2 and tumor necrosis factor-alpha in chagasic cardiomyopathy. Clin. Infect. Dis. 38 (7), 943–950. https://doi.org/10. 1086/381892. Thye, T., Vannberg, F.O., Wong, S.H., Owusu-Dabo, E., Osei, I., Gyapong, J., et al., 2010. Genome-wide association analyses identifies a susceptibility locus for tuberculosis on chromosome 18q11.2. Nat. Genet. 42 (9), 739–741. https://doi.org/10.1038/ng.639. Tientcheu, L.D., et al., 2015. Differential transcriptomic and metabolic profiles of M. africanum- and M. Tuberculosis-infected patients after, but not before, drug treatment. Genes Immun. 16 (5), 347–355. https://doi.org/10.1038/gene.2015.21. Timpson, N.J., et al., 2018. Genetic architecture: the shape of the genetic contribution to human traits and disease. Nat. Rev. Genet. 19 (2), 110–124. https://doi.org/10. 1038/nrg.2017.101. Torres, O.A., et al., 2010a. Lack of association between IL-6-174G/C gene polymorphism and Chagas disease. Tissue Antigens 76 (2), 131–134. https://doi.org/10.1111/j. 1399-0039.2010.01478.x. Torres, O.A., et al., 2010b. Role of the IFNG +874T/A polymorphism in Chagas disease in a Colombian population. Infect. Genet. Evol. 10 (5), 682–685. https://doi.org/10. 1016/j.meegid.2010.03.009. Torres, O.A., et al., 2009. Association of the macrophage migration inhibitory factor -173G/C polymorphism with Chagas disease. Hum. Immunol. 70 (7), 543–546. https://doi.org/10.1016/j.humimm.2009.04.022. Vannberg, F.O., et al., 2011. Human genetic susceptibility to intracellular pathogens. Immunol. Rev. 240 (1), 105–116. https://doi.org/10.1111/j.1600-065X.2010. 00996.x. Wang, D., et al., 2018. Missense variants in HIF1A and LACC1 contribute to leprosy risk in han chinese. Am. J. Hum. Genet. 102 (5), 794–805. https://doi.org/10.1016/j.ajhg. 2018.03.006. Weitzel, T., et al., 2012. Mannose-binding lectin and Toll-like receptor polymorphisms and Chagas disease in Chile. Am. J. Trop. Med. Hyg. 86 (2), 229–232. https://doi. org/10.4269/ajtmh.2012.11-0539. Wu, B., Pan, X., Chen, X., Chen, M., Shi, K., Xu, J., et al., 2019. Epigenetic drug library screening identified an LSD1 inhibitor to target UTX-deficient cells for differentiation therapy. Signal Transduct. Target. Ther. 4, 11. https://doi.org/10.1038/s41392-0190040-2. Yamagishi, J., et al., 2014. Interactive transcriptome analysis of malaria patients and infecting Plasmodium falciparum. Genome Res. 24 (9), 1433–1444. https://doi.org/ 10.1101/gr.158980.113. Zafra, G., et al., 2008. Polymorphisms of toll-like receptor 2 and 4 genes in Chagas disease. Mem. Inst. Oswaldo Cruz 103 (1), 27–30. Zafra, G., et al., 2007. Polymorphism in the 3′ UTR of the IL-12B gene is associated with Chagas’ disease cardiomyopathy. Microbes Infect. 9 (9), 1049–1052. https://doi.org/ 10.1016/j.micinf.2007.04.010.

Genet. 43 (5), 770–773. Luz, P.R., et al., 2013. Association of L-ficolin levels and FCN2 genotypes with chronic Chagas disease. PLoS One 8 (4), e60237. https://doi.org/10.1371/journal.pone. 0060237. Luz, P.R., et al., 2016. Genetically determined MBL deficiency is associated with protection against chronic cardiomyopathy in chagas disease. PLoS Negl. Trop. Dis. 10 (1), e0004257. https://doi.org/10.1371/journal.pntd.0004257. Luz, P.R., et al., 2010. High levels of mannose-binding lectin are associated with the risk of severe cardiomyopathy in chronic Chagas Disease. Int. J. Cardiol. 143 (3), 448–450. https://doi.org/10.1016/j.ijcard.2009.09.467. Machado, F.S., et al., 2012. Current understanding of immunity to Trypanosoma cruzi infection and pathogenesis of Chagas disease. Semin. Immunopathol. 34 (6), 753–770. https://doi.org/10.1007/s00281-012-0351-7. Machuca, M.A., et al., 2014. SNP/haplotype associations of CCR2 and CCR5 genes with severity of chagasic cardiomyopathy. Hum. Immunol. 75 (12), 1210–1215. https:// doi.org/10.1016/j.humimm.2014.09.023. Malaria Genomic Epidemiology, et al., 2015. A novel locus of resistance to severe malaria in a region of ancient balancing selection. Nature 526 (7572), 253–257. https://doi. org/10.1038/nature15390. Manolio, T.A., 2013. Bringing genome-wide association findings into clinical use. Nat. Rev. Genet. 14 (8), 549–558. https://doi.org/10.1038/nrg3523. Manolio, T.A., et al., 2009. Finding the missing heritability of complex diseases. Nature 461 (7265), 747–753. https://doi.org/10.1038/nature08494. Martin, P., McGovern, A., Massey, J., Schoenfelder, S., Duffus, K., Yarwood, A., et al., 2016. Identifying causal genes at the multiple sclerosis associated region 6q23 using capture Hi-C. PLoS One 11 (11), e0166923. https://doi.org/10.1371/journal.pone. 0166923. Matzaraki, V., et al., 2017. The MHC locus and genetic susceptibility to autoimmune and infectious diseases. Genome Biol. 18 (1), 76. https://doi.org/10.1186/s13059-0171207-1. Meng, F., Yuan, G., Zhu, X., Zhou, Y., Wang, D., Guo, Y., 2018. Functional variants identified efficiently through an integrated transcriptome and epigenome analysis. Sci. Rep. 8 (1), 2959. https://doi.org/10.1038/s41598-018-21024-6. Merker, M., et al., 2015. Evolutionary history and global spread of the Mycobacterium tuberculosis Beijing lineage. Nat. Genet. 47 (3), 242–249. https://doi.org/10.1038/ ng.3195. Messenger, L.A., et al., 2015. Between a bug and a hard place: trypanosoma cruzi genetic diversity and the clinical outcomes of Chagas disease. Expert Rev. Anti. Ther. 13 (8), 995–1029. https://doi.org/10.1586/14787210.2015.1056158. Mundaray Fernandez, N., et al., 2014. The role of haptoglobin genotypes in Chagas disease. Dis. Markers 793646. https://doi.org/10.1155/2014/793646. Nieto, A., et al., 2000. HLA haplotypes are associated with differential susceptibility to Trypanosoma cruzi infection. Tissue Antigens 55 (3), 195–198. Nogueira, L.G., et al., 2015. Functional IL-18 polymorphism and susceptibility to chronic chagas disease. Cytokine 73 (1), 79–83. https://doi.org/10.1016/j.cyto.2015.01.037. Nogueira, L.G., et al., 2012. Myocardial chemokine expression and intensity of myocarditis in Chagas cardiomyopathy are controlled by polymorphisms in CXCL9 and CXCL10. PLoS Negl. Trop. Dis. 6 (10), e1867. https://doi.org/10.1371/journal.pntd. 0001867. Noireau, F., et al., 2009. Trypanosoma cruzi: adaptation to its vectors and its hosts. Vet. Res. 40 (2), 26. https://doi.org/10.1051/vetres/2009009. Ortega Zamora, Y., et al., 2017. Chagas disease immunogenetics: elusive markers of disease progression. Expert Rev. Cardiovasc. Ther. 1–10. https://doi.org/10.1080/ 14779072.2017.1317591. Parissis, J.T., et al., 2002. Serum profiles of C-C chemokines in acute myocardial infarction: possible implication in postinfarction left ventricular remodeling. J. Interferon Cytokine Res. 22 (2), 223–229. https://doi.org/10.1089/ 107999002753536194. Perez-Molina, J.A., et al., 2018. Chagas disease. Lancet 391 (10115), 82–94. https://doi. org/10.1016/S0140-6736(17)31612-4. Persson, G., et al., 2017. HLA class Ib in pregnancy and pregnancy-related disorders. Immunogenetics 69 (8-9), 581–595. https://doi.org/10.1007/s00251-017-0988-4. Petersen, L., et al., 2010. Genetic influences on incidence and case-fatality of infectious disease. PLoS One 5 (5), e10603. https://doi.org/10.1371/journal.pone.0010603. Pissetti, C.W., et al., 2011. Genetic and functional role of TNF-alpha in the development Trypanosoma cruzi infection. PLoS Negl. Trop. Dis. 5 (3), e976. https://doi.org/10. 1371/journal.pntd.0000976. Pissetti, C.W., et al., 2013. Association between the lymphotoxin-alpha gene polymorphism and chagasic cardiopathy. J. Interferon Cytokine Res. 33 (3), 130–135. https://doi.org/10.1089/jir.2012.0024. Ramasawmy, R., et al., 2009. Heterozygosity for the S180L variant of MAL/TIRAP, a gene expressing an adaptor protein in the Toll-like receptor pathway, is associated with lower risk of developing chronic Chagas cardiomyopathy. J. Infect. Dis. 199 (12), 1838–1845. https://doi.org/10.1086/599212. Ramasawmy, R., et al., 2006a. The monocyte chemoattractant protein-1 gene polymorphism is associated with cardiomyopathy in human chagas disease. Clin. Infect. Dis. 43 (3), 305–311. https://doi.org/10.1086/505395. Ramasawmy, R., et al., 2006b. BAT1, a putative anti-inflammatory gene, is associated with chronic Chagas cardiomyopathy. J. Infect. Dis. 193 (10), 1394–1399. https:// doi.org/10.1086/503368. Ramasawmy, R., et al., 2008. Variants in the promoter region of IKBL/NFKBIL1 gene may mark susceptibility to the development of chronic Chagas’ cardiomyopathy among Trypanosoma cruzi-infected individuals. Mol. Immunol. 45 (1), 283–288. https://doi. org/10.1016/j.molimm.2007.04.015. Ramasawmy, R., et al., 2007. Polymorphisms in the gene for lymphotoxin-alpha predispose to chronic Chagas cardiomyopathy. J. Infect. Dis. 196 (12), 1836–1843. https://

9

Acta Tropica 197 (2019) 105062

M. Acosta-Herrera, et al.

known about Chagas disease manifestations, serodiagnosis and drug sensitivity. Acta Trop. 184, 38–52. https://doi.org/10.1016/j.actatropica.2017.09.017. Zingales, B., et al., 2012. The revised Trypanosoma cruzi subspecific nomenclature: rationale, epidemiological relevance and research applications. Infect. Genet. Evol. 12 (2), 240–253. https://doi.org/10.1016/j.meegid.2011.12.009.

Zhang, F.R., et al., 2009. Genomewide association study of leprosy. N. Engl. J. Med. 361 (27), 2609–2618. https://doi.org/10.1056/NEJMoa0903753. Zhang, P., et al., 2017. Tumor necrosis factor-alpha gene polymorphisms and susceptibility to ischemic heart disease: a systematic review and meta-analysis. Medicine (Baltimore) 96 (14), e6569. https://doi.org/10.1097/MD.0000000000006569. Zingales, B., 2018. Trypanosoma cruzi genetic diversity: something new for something

10