Advertisement
  • Loading metrics

In-depth quantitative proteomics uncovers specie-specific metabolic programs in Leishmania (Viannia) species

  • Nathalia Pinho,

    Roles Data curation, Investigation, Visualization, Writing – original draft

    Affiliation Laboratório de Pesquisa em Leishmanioses, Instituto Oswaldo Cruz, Fiocruz, Rio de Janeiro, RJ, Brazil

  • Jacek R. Wiśniewski,

    Roles Formal analysis, Investigation, Methodology, Resources, Writing – review & editing

    Affiliation Biochemical Proteomics Group, Department of Proteomics and Signal Transduction, Max-Planck-Institute of Biochemistry, Martinsried, Germany

  • Geovane Dias-Lopes,

    Roles Formal analysis, Investigation

    Affiliation Laboratório de Biologia Molecular e Doenças Endêmicas, Instituto Oswaldo Cruz, Fiocruz, Rio de Janeiro, RJ, Brazil

  • Leonardo Saboia-Vahia,

    Roles Formal analysis, Investigation

    Current address: Laboratório Parasitologia Celular e Molecular, Instituto René Rachou, Fiocruz, Belo Horizonte, MG, Brazil

    Affiliation Laboratório de Pesquisa em Leishmanioses, Instituto Oswaldo Cruz, Fiocruz, Rio de Janeiro, RJ, Brazil

  • Ana Cristina Souza Bombaça,

    Roles Investigation, Validation, Writing – review & editing

    Affiliation Laboratório de Biologia Celular, Instituto Oswaldo Cruz, Fiocruz, Rio de Janeiro, RJ, Brazil

  • Camila Mesquita-Rodrigues,

    Roles Investigation

    Affiliation Laboratório de Biologia Celular, Instituto Oswaldo Cruz, Fiocruz, Rio de Janeiro, RJ, Brazil

  • Rubem Menna-Barreto,

    Roles Investigation, Resources

    Affiliation Laboratório de Biologia Celular, Instituto Oswaldo Cruz, Fiocruz, Rio de Janeiro, RJ, Brazil

  • Elisa Cupolillo,

    Roles Funding acquisition, Resources, Writing – review & editing

    Affiliation Laboratório de Pesquisa em Leishmanioses, Instituto Oswaldo Cruz, Fiocruz, Rio de Janeiro, RJ, Brazil

  • Jose Batista de Jesus,

    Roles Formal analysis, Funding acquisition, Resources, Writing – review & editing

    Affiliations Laboratório de Biologia Molecular e Doenças Endêmicas, Instituto Oswaldo Cruz, Fiocruz, Rio de Janeiro, RJ, Brazil, Departamento de Medicina–Universidade Federal de São João Del Rei, Campus Dom Bosco, São João del Rei, MG, Brazil

  • Gabriel Padrón,

    Roles Formal analysis, Investigation, Validation, Writing – review & editing

    Current address: Center for Genetic Engineering & Biotechnology, La Habana, Cuba

    Affiliation Laboratório de Pesquisa em Leishmanioses, Instituto Oswaldo Cruz, Fiocruz, Rio de Janeiro, RJ, Brazil

  • Patricia Cuervo

    Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Project administration, Resources, Supervision, Visualization, Writing – original draft, Writing – review & editing

    patricia.cuervo@fiocruz.br

    Affiliation Laboratório de Pesquisa em Leishmanioses, Instituto Oswaldo Cruz, Fiocruz, Rio de Janeiro, RJ, Brazil

In-depth quantitative proteomics uncovers specie-specific metabolic programs in Leishmania (Viannia) species

  • Nathalia Pinho, 
  • Jacek R. Wiśniewski, 
  • Geovane Dias-Lopes, 
  • Leonardo Saboia-Vahia, 
  • Ana Cristina Souza Bombaça, 
  • Camila Mesquita-Rodrigues, 
  • Rubem Menna-Barreto, 
  • Elisa Cupolillo, 
  • Jose Batista de Jesus, 
  • Gabriel Padrón
PLOS
x

Abstract

Leishmania species are responsible for a broad spectrum of diseases, denominated Leishmaniasis, affecting over 12 million people worldwide. During the last decade, there have been impressive efforts for sequencing the genome of most of the pathogenic Leishmania spp. as well as hundreds of strains, but large-scale proteomics analyses did not follow these achievements and the Leishmania proteome remained mostly uncharacterized. Here, we report a comprehensive comparative study of the proteomes of strains representing L. braziliensis, L. panamensis and L. guyanensis species. Proteins extracted by SDS-mediated lysis were processed following the multi-enzyme digestion-filter aided sample preparation (FASP) procedure and analysed by high accuracy mass spectrometry. “Total Protein Approach” and “Proteomic Ruler” were applied for absolute quantification of proteins. Principal component analysis demonstrated very high reproducibility among biological replicates and a very clear differentiation of the three species. Our dataset comprises near 7000 proteins, representing the most complete Leishmania proteome yet known, and provides a comprehensive quantitative picture of the proteomes of the three species in terms of protein concentration and copy numbers. Analysis of the abundance of proteins from the major energy metabolic processes allow us to highlight remarkably differences among the species and suggest that these parasites depend on distinct energy substrates to obtain ATP. Whereas L. braziliensis relies the more on glycolysis, L. panamensis and L. guyanensis seem to depend mainly on mitochondrial respiration. These results were confirmed by biochemical assays showing opposite profiles for glucose uptake and O2 consumption in these species. In addition, we provide quantitative data about different membrane proteins, transporters, and lipids, all of which contribute for significant species-specific differences and provide rich substrate for explore new molecules for diagnosing purposes. Data are available via ProteomeXchange with identifier PXD017696.

Author summary

Leishmania braziliensis, L. panamensis, and L. guyanensis are responsible for most of the cases of tegumentary leishmaniasis (TL) in the Americas. These species are associated with a variety of clinical manifestations of TL ranging from self-healing localized cutaneous lesions to disseminated and mucocutaneous presentations that may result in severe oropharyngeal mutilation. Here, we report a comprehensive quantitative comparison of the proteome of those species. Assessment of absolute titers of ~7000 proteins revealed a very clear differentiation among them. Significant differences in energy metabolism, membrane proteins, transporters, and lipids are contributing for species-specific traits and provide rich substrate for exploring new molecules for diagnosing purposes.

Introduction

Leishmania species are responsible for a broad spectrum of diseases denominated Leishmaniasis that affects over 12 million people worldwide [1]. Leishmania braziliensis, L. panamensis, and L. guyanensis are species of the Leishmania (Viannia) subgenus that comprises exclusively Neotropical parasites currently distributed across the American continent. These species are responsible for most of the cases of localized cutaneous leishmaniasis (LCL) in the Americas, but are also associated with the mucocutaneous form of the disease [1]. Mucocutaneous leishmaniasis (MCL) results from the parasite metastasis to the oropharyngeal mucosal tissues via lymphatic or haematogenous dissemination. In advanced stages, MCL may result in severe mutilation by destruction of the palate and nose cartilage [2]. Moreover, whereas L. panamensis is mainly associated to LCL, L. braziliensis may cause multiple disseminated cutaneous lesions, frequently refractory to treatment [1,3]. In addition, in some regions L. guyanensis is frequently isolated from patients who failed to response to pentavalent antimonial (SbV), the first line treatment for dermal leishmaniasis [4]. Despite that severity, currently there are no prognostic tools for the prediction of the clinical outcome after infection with those species, neither proper species-specific diagnostic tools that may orientate the therapeutic decision.

Heterogeneous clinical manifestations are the result of a complex interaction among host immune responses, host genetic background and parasite characteristics such as virulence, infectivity and pathogenicity [5]. Among others, to understand the parasite contribution for the clinical outcome, complete genome sequences of most of the pathogenic Leishmania species and hundreds of strains have been obtained [617], paving the way for the analyses of their proteomes. However, such a massive effort has not been followed by proteomics studies.

The first more comprehensive proteomics identification of a Leishmania species was accomplished using gel-free analysis of L. donovani and L. mexicana, with up to ~1800 proteins identified, representing ~20% of the Leishmania predicted proteome [18,19]. In addition, protein abundance was estimated for the proteins identified in the L. mexicana dataset by calculating the exponentially modified protein abundance index (emPAI) [19]. Recently, proteogenomic re-analysis of L. donovani proteome identified ~4000 unique proteins [20]; studies on the L. mexicana flagellar proteome identified ~2800 proteins, among which, 701 composed the flagellar proteome [21]; subcellular multi-step fractionation of L. donovani allowed identification of ~3900 proteins, included 645 integral membrane proteins [22]. Despite those important achievements, no Leishmania proteome has been described in large-scale nor deep quantitative data is available.

Seminal works on L. (Viannia) proteomes used 2DE of cell lysates with the aim to differentiate distinct Leishmania species causing American tegumentary leishmaniasis [23]. Since that, advances in MS protein identification driven the first proteomic maps of L. braziliensis, L. panamensis and L. guyanensis [2435]. Using different methods for sample preparation, up to some hundreds of proteins were identified in these species. Regardless these efforts, identification and quantitative organization of the L. (Viannia) species proteome remains mostly uncharacterized.

Despite the advances in mass spectrometry instrumentation, the quality of the sample preparation still determines the quality and depth of the proteomic analysis. The combination of SDS as denaturant for efficient protein extraction, followed by depletion of the detergent by successive washes with concentrated urea in a filter device and consecutive digestion with LysC and trypsin has proven to be an efficient method for sample preparation [3133].

Here, we describe a comprehensive quantitative analysis of the proteome of three strains that represent distinct and clinically/epidemiologically relevant species of L. (Viannia) subgenus: L. braziliensis (strain MHOM/BR/2000/LTCP 13396), L. panamensis (strain MHOM/CO/2009/6634) and L. guyanensis (strain MHOM/BR/1997/NMT-MAO 292P). We combined the filter-aided sample preparation (FASP) method with the consecutive multi-enzyme digestion [31,32] followed by high accuracy mass spectrometry and the “Total Protein Approach” and “Proteomic Ruler” [34,35] for calculation of absolute protein abundances in those species. Our dataset comprises near 7000 proteins, representing the most complete Leishmania proteome yet reported, and provides the first comprehensive quantitative analysis of the proteomes of the three species in terms of protein concentration and copy numbers. We describe the basic subcellular organization of the parasites and the cumulative abundance of proteins from the major metabolic processes and demonstrate the main differences among those three species.

Methods

Parasite culture

Leishmania braziliensis (MHOM/BR/2000/LTCP 13396), L. panamensis (MHOM/CO/2009/6634) and L. guyanensis (MHOM/BR/1997/NMT-MAO 292P) strains used in this study are open deposits and were provided by the Collection of Leishmania of the Oswaldo Cruz Foundation, CLIOC (http://clioc.fiocruz.br/). Through the text, tables and figures those strains will be mentioned as L. braziliensis, L. panamensis and L. guyanensis. Parasites were grown in Schneider’s medium containing 20% fetal bovine serum (heat inactivated at 56°C for 50 min) at 25°C. Parasites were collected in the late logarithmic growth phase by centrifugation at 1800 x g for 5 min, and washed twice in PBS, pH 7.2. In order to obtain meaningful data, we analyzed parasites collected from five independent biological assays (five biological replicates) of each L. braziliensis and L. panamensis and four independent biological assays (four biological replicates) of L. guyanensis. Variables such as the lots of culture medium and serum were controlled. According with the Brazilian Law of Biodiversity, this study was registered at SisGen (AA2236F).

Sample preparation

Parasites were lysed in a buffer containing 0.05 M Tris-HCl, pH 7.6, 0.05 M DTT and 2% SDS in a boiling water bath for 5 min. After chilling to room temperature, the lysates were clarified by centrifugation at 10,000×g for 5 min. Total protein was determined by BCA using a Nanodrop instrument. Aliquots containing 100 μg total protein were processed by the MED-FASP (Multi-Enzyme Digestion—Filter Aided Sample Preparation) method using consecutively LysC and trypsin [32,33]. Peptides were collected, concentrated and desalted on a C18 reverse phase column.

Liquid Chromatography–Tandem Mass Spectrometry

The peptide mixtures were analysed using a Q-Exactive HF mass spectrometer (Thermo-Fisher Scientific, Palo Alto). The chromatography was performed using 2 μg of total peptide on a 50 cm column with 75μm inner diameter packed with 1.8 μm C18 particles (100 Å pore size; Dr. Maisch, Ammerbuch-Entringen, Germany). Peptide separation was carried out using a 105 min acetonitrile gradient in 0.1% formic acid at a flow rate of 250 nl/min. The backpressure varied between 450 and 650 bar. The Q-Exactive HF mass spectrometer was operated in data-dependent mode with survey scans acquired at a resolution of 50 000 at m/z 400 (transient time 256 ms). Up to the top 15 most abundant isotope patterns with charge ≥ +2 from the survey scan (300–1650 m/z) were selected with an isolation window of 1.6 m/z and fragmented by HCD with normalized collision energies of 25. The maximum ion injection times for the survey scan and the MS/MS scans were 20 and 60 ms, respectively. The ion target value for MS1 and MS2 scan modes was set to 3 × 106 and 1 × 105, respectively. The dynamic exclusion was 25 s and 10 ppm.

Proteomic data analysis

The data were analyzed with MaxQuant Software using the L. braziliensis protein sequences available at UniProt database. The parameters of the search included a fragment ion mass tolerance of 0.5 Da and parent ion tolerance of 20 ppm. The maximum false peptide and protein discovery rate was set as 1%. Cysteine carbamidomethylation was set as fixed modification, methionine oxidation as variable modification, and a maximum of two missed cleavages was allowed. Protein abundances were calculated based on the spectral protein intensity (raw intensities) using the ‘Total Protein Approach’ (TPA) for absolute protein quantification and the ‘Proteomic Ruler’ approach [35] for assessing the protein copy number per cell, total protein, protein concentration and copy number were calculated in Microsoft Excel. The Perseus software (Ver. 1.6.5.0) [36] was used for statistical analysis. Minimal number of valid values was set to 4 per protein, for at least one species, and missing values were imputed from a normal distribution. Significances were calculated using the t-test with a threshold of false discovery rate (FDR) of 1%. The data are presented as the mean ± SD.

Enrichment analysis based on Gene Ontology and Metabolic Pathway annotations

In order to analyze if there were particular groups of proteins that contributed the most for distinguishing the L. (Viannia) species, only the proteins that presented simultaneously significant differences in concentration among the three species were used for enrichment analysis of gene ontology and metabolic pathway annotations using the enrichment tool at Tritrypdb (http://tritrypdb.org, v46). Parameters were set as follows: organism, L. braziliensis MHOM/BR/75/M2904 and p-value cutoff, 0.05.

Analysis of 2-NBDG uptake

Procyclic promastigotes of the late logarithmic growth phase (5×106 parasites/mL) were washed (2000 x g for 10 min) and pre-incubated in 10 mM phosphate buffered saline (PBS, pH 7.2, Sigma-Aldrich, St. Louis, USA) for 30 min at 28° C. Then, the cells were incubated with 300 μM 2-(N-(7-Nitrobenz-2-oxa-1,3-diazol-4-yl)Amino)-2-Deoxyglucose (2-NBDG, Molecular Probes, Massachusetts, USA), a fluorescent glucose analogue, for 30 min at 28° C. The 2-NBDG specificity was monitored by the parasite incubation at 4°C for 30 min, to decrease the glucose uptake. After this time, the parasites were washed and resuspended in PBS and 2-NBDG+ promastigotes were analyzed using a CytekDxP Multi-Color Upgrades flow cytometer (Cytek, Fremont, USA). A total of 10,000 events were acquired in the region previously established to correspond to protozoa and analyses were performed in Summit 6.1 software (Beckman Coulter, Brea, USA). Three biological replicates were analyzed. Student´s t test was used for analyzing the statistical significance of the differences observed among species, values of p< 0.05 were accepted and data are presented as the mean ± SD.

Analysis of oxygen uptake

Promastigotes (2.5 × 107 cells/mL) were resuspended in 2 mL of respiration buffer (125 mM sucrose, 65 mM KCl, 10 mM Tris-HCl pH 7.2, 1 mM MgCl2, and 2.5 mM potassium phosphate monobasic) at 26°C under continuous stirring and analyzed in a high-resolution oxygraph 2K (Oroboros Instruments, Innsbruck, Tyrol, Austria). Mitochondrial oxygen consumption was confirmed by the addition of 2 μM antimycin A (AA) (Sigma-Aldrich, St. Louis, Missouri, USA) to reach residual oxygen consumption (ROX). Oxygen concentration and flux were acquired and analyzed using the DatLab software. Statistical significance was assessed by Student´s t test and results are shown as the mean ± SD of three independent experiments (three biological replicates).

Gene expression analysis

Gene expression levels were analyzed by qPCR for 10 genes encoding for proteins that presented statistical differences of abundance among at least two species. Oligonucleotide sequences of the target and reference genes (S1 Table) were designed using the Primer-BLAST designing tool from available public sequences for L. braziliensis. Total RNA was extracted from cells by using Trizol Reagent according to the manufacturer’s instruction. The quantity and integrity of RNA were determined by spectrophotometer (Nanodrop ND-1000, NanoDrop Technologies); cDNA was synthesized from 2 μg of total RNA using SuperScript III reverse transcription system and oligo-dT primers in total reaction volumes of 20 μL. Real-time PCR was carried out in duplicate using SYBR Green PCR Master Mix (Applied Biossystems) and the Vii7 Sequence Detection System (Applied Biossystems), following the manufacturer’s procedures. PCR conditions for all primers were optimized and specificities were verified by melting curve analyses and agarose gel electrophoresis. The cDNAs were amplified during 40 cycles consisting of 10 s of denaturation at 95°C, 20 s of annealing at 56°C, and 20 s of extension at 72°C. Gene expression was quantified by means of the comparative Ct method (ΔΔCt). The linearity of the assay was evaluated using a standard curve for each gene from serial dilutions of pooled cDNAs from all samples. The efficiency of amplification for each gene was calculated from the standard curve of each gene. Data are shown as normalized ratios between target gene expression and geometric media of the reference genes. Three independent assays were performed following the MIQE guidelines [37].

Results and discussion

The Leishmania (Viannia) proteome is distributed in about six orders of magnitude and L. braziliensis, L. panamensis and L. guyanensis are clearly separated by differences in their protein concentrations

To obtain an in-depth quantitative description of the L. (Viannia) proteome, whole cell lysates, of four-five independent biological replicates, from each of the species were processed by MED-FASP and analyzed by LC-MS/MS. Searches with MaxQuant software resulted in the identification of 120224 peptides corresponding to 6930 proteins, of which 95% (6530) were identified with at least three peptides and 80% (5544) with at least six peptides (S1 Fig, S2 Table). Our dataset comprises ~85% of the Leishmania predicted proteome (~8000 protein-coding genes predicted, considering one protein per gene) [6,7,9,12] and therefore represents the most complete proteome for a Leishmania species yet described. More than 3000 proteins were identified for the first time (S2 Table). Pearson’s correlation analysis revealed high reproducibility of proteomics data between biological replicates (coefficient > 94%) (S1 Fig). More than 6600 proteins were identified in each replicate and 6033 proteins were identified in all 14 samples (S3 Table).

Analysis of the data showed that only 9% of identified proteins have Gene Ontology annotations for cellular component, molecular function and biological process together (S2 Fig). Considering only the ontological annotation for cellular component we found that about 4900 proteins in our data set appear as uncharacterized. Despite efforts for improving L. braziliensis annotations [38], functional annotation of Leishmania spp. is still very limited and represents a bottleneck in the biological interpretation of transcriptomics and proteomics data.

After statistical analysis using Perseus, a total of 6821 proteins were statistically validated in at least 10 out of the 14 samples (FDR 1%) and these were used for estimation of total protein contents, protein concentrations and protein copy numbers using the total protein approach (TPA) and the histone ruler methods. Principal component analysis (PCA) of the protein concentrations showed tight clustering for each set of biological replicates and revealed a clear separation among the strains representing L. braziliensis, L. panamensis and L. guyanensis samples (Fig 1A). Although proteomes are highly dynamic and may vary according to parasite stage, culture conditions and probably to intra-specific diversity of Leishmania, our results show that global protein abundances obtained by deep proteomics are able to clearly distinguish the three species, in a similar way that deep genome sequencing allows such distinction [9,12].

thumbnail
Fig 1. Description of the Leishmania (Viannia) proteomes.

Whole cell lysates from L. braziliensis, L. panamensis and L. guyanensis were processed by MED-FASP and analyzed by LC-MS/MS. Protein abundances were calculated based on the raw spectral intensities. (A) Principal component analysis of the protein concentration values of all identified proteins determined by the Total Protein Approach, (TPA), method. The close clustering of each set of biological replicates reveals accurate reproducibility among independent biological samples. (B) Total protein content per cell, n = 5 biological replicates for L. braziliensis and L. panamensis and n = 4 for L. guyanensis. Values are expressed in pg ± SD. (C) Dynamic range of protein copy numbers across all proteins identified in each species. Protein abundances were ranked from the highest to the lowest copy number in the Leishmania samples. (D) Venn plot with number of proteins identified in each species.

https://doi.org/10.1371/journal.pntd.0008509.g001

The total protein contents per cell were calculated using the histone ruler method based on the genome data for L. braziliensis, L. panamensis and L. guyanensis (strains M2904, PSC-1 and CL085, respectively) and that report 32Mb, 30Mb and 31Mb DNA, respectively [6,9,12]. These values correspond to 0.035 pg, 0.033 pg and 0.034 pg of DNA, respectively, per haploid genome. Assuming diploidy, L. braziliensis, L. panamensis and L. guyanensis strains used in this study contain 3.86 ± 0.1 pg, 3.89 ± 0.08 pg and 3.55 ± 0.2 pg of total protein per cell, respectively (Fig 1B, Table 1). These values are similar to the protein estimation reported by Aebischer [39] for L. mexicana, which corresponds to ~4 pg. Although we are aware that these calculations could vary according to the ploidy of the genome, the ploidy status of the strains here studied is unknown. Because it has been shown that the chromosome number in Leishmania sp. (i) varies among species and strains [7,8,12,13]; (ii) varies in response to external stimuli [13,15,16, 40,41]; (iii) is present as a mosaic, i.e., different chromosomes have different ploidy [42]; (iv) is highly variable in vitro-cultivated strains but does not seem to vary as much in intracellular parasites from mammal hosts [17,41], and (v) has been barely studied in species of the subgenus L. (Viannia), we opted for a parsimonious assumption of a diploid state for the three species here studied. Nevertheless, the power of the quantitative method used here can at any time revisit the calculations and adjust the protein concentrations according to the observed ploidy for each chromosome in each species, since it does not depend on chemical markers or external standards.

thumbnail
Table 1. Summary of general information about the proteomes of Leishmania braziliensis, L. panamensis and L. guyanensis promastigotes.

https://doi.org/10.1371/journal.pntd.0008509.t001

Using the values of total protein contents, we calculated protein copy numbers per species and estimated the total protein molecules per parasite to be 6.4 ± 0.20 x 107 for L. braziliensis, 6.3 ± 0.13 x 107 for L. panamensis, and 5.9 ± 0.35 x 107 for L. guyanensis (Table 1). The protein copy number values span 6 orders of magnitude (Fig 1C). A total of 6779 proteins were identified in L. braziliensis, 6783 in L. panamensis and 6764 in L. guyanensis. A total of 6708 proteins were identified as common to the three species (Fig 1D).

Among the top 20 most abundant proteins we found histones H3, H4, H2B, alpha- and beta-tubulin, elongation factor 1-alpha, HSP70 and HSP83, among others, with an estimated mean among the three species around 750000, 580000, 680000, 560000, 545000, 530000, 400000, 300000 copies per cell, respectively (Table 2). These proteins are in fact the most abundant in the proteomes of other Leishmania species, [43,44] and, probably for this reason, they are frequently identified in proteomic studies independently of the phenomenon studied. Interestingly, those proteins are coded by gene arrays with more than 10 gene copies each one [7,9,12], suggesting a correlation between gene copy number and protein copy number, at least for those genes, in the strains of L. (Viannia) species studied here. Among other highly abundant molecules were various ribosomal proteins including several 40S and 60S subunits involved in translation of mRNA. Such molecules were also identified among the top 30 most highly expressed genes in different isolates of L. tropica [11]. However, some of the most abundant gene arrays reported to date in L. braziliensis, L. panamensis and L. guyanensis, such as amastin-like surface protein, TATE DNA transposon, GP63, folate/biopterin transporter and tuzin [7,9,12], were not among the most abundant proteins (top 20, Table 2, or even top 100, S2 Table) in our dataset, evidencing that for those proteins there is a poor correlation between gene and protein copy numbers. Nevertheless, such lack of correlation could be associated, among others, with the parasite stage, i.e., some gene arrays are more recruited during procyclic or metacyclic promastigote stage whereas others should be preferentially recruited during amastigote stage. Indeed, it has been shown that amastin is preferentially expressed by amastigotes during the interaction with macrophages [45]. On the other hand, deep proteome data as that obtained here allowed us to detect, identify and quantify low abundant proteins such as a protein kinase (A4HLK1), a subtilisin-like serine peptidase (A4H792) and a lot of uncharacterized proteins (ex: A4HM75, A4HP44 and A4H8S3), all of them presenting around 50–80 copies per cell (S2 Table). Interestingly, the quantitative data here provided may impact the selection of vaccine candidate antigens. Indeed, because protein abundance will determine its chance to be processed and loaded onto MHC molecules, it has been proposed that protein abundance is the single most important parameter to take into account when a vaccine candidate antigen is being selected [39].

thumbnail
Table 2. Top 20 most abundant protein groups in Leishmania (Viannia) proteomes in terms of protein copy number.

Protein IDs according to UniProt. Detailed information in S2 Table. Lb: L. braziliensis; Lp: L. panamensis; Lg: L. guyanensis.

https://doi.org/10.1371/journal.pntd.0008509.t002

The protein copy number has low correlation with the protein molecular weight

We analyzed the correlation of the protein abundance in terms of copy number, using the mean value obtained for the three species, against the molecular weight of the identified proteins. Although, in general, smaller proteins present more copies than larger proteins, we observed that there is a low correlation between the number of molecules per protein and the molecular weight (R2 = 0.129) (Fig 2A). For example, the putative calpain-like cysteine peptidase (A4HFH6) with 701.07 kDa, one of the largest proteins in our proteome dataset, presented approximately 29400 copies, representing ~0.05% of total parasite protein mass, in contrast to smaller putative calpain-like cysteine peptidases (A4HJ22 and A4HJ21) with around 100 kDa that presented 200–600 copies per cell. Our data agree with previous reports on Saccharomyces cerevisiae and Escherichia coli protein abundance that found moderate or no correlation between protein size and protein abundance [46,47].

thumbnail
Fig 2. Quantitative analysis of the proteomes of Leishmania (Viannia) species.

(A) Correlation between protein copy numbers and molecular weight, R2 = 0.129. Every blue dot represents a single protein. Red dotted line = tendency line. (B) Protein abundance of basic components of promastigotes architecture based on the current gene ontology annotations of cellular component. The values represent the mean of percent of total cellular protein content ± SD (n = 14). (C) Abundance of ribosomal proteins of the large and small subunits. The values represent the mean of protein copy numbers ± SD (n = 14). (D) Cumulative abundance of proteins involved in the major energy metabolic processes. Bars show the total sum of the number of copies of proteins involved in each metabolic pathway ± SD (n = 14).

https://doi.org/10.1371/journal.pntd.0008509.g002

We also analyzed the protein abundance of basic components of promastigotes architecture and metabolism, in terms of percentage of total cellular protein or copy number. Based on the current gene ontology annotations of cellular component, ribosome subunits compose ~12% of the total protein mass (Fig 2B), whereas membrane, mitochondrion, flagellum and nucleus proteins compose ~5.7, 5.3, 3.8, and 4.1% of the proteome, respectively (Fig 2B). The abundance of ribosomal subunits in our dataset is in agreement with the high activity of translation and synthesis of proteins observed in organisms in the logarithmic growth phase, such as the parasites used here (S3 Fig). In our dataset, ribosomal proteins of the large and small subunits consist of 64 and 36 proteins respectively. In agreement with the 1:1 stoichiometry, the average copy numbers of those proteins were 1.3 ± 0.06 × 105 and 1.5 ± 0.09 × 105, for the large and small subunits, respectively (Fig 2C).

Energy metabolism molecules comprise about 8% of all molecules per cell

Quantitative proteomic data allowed us to determine the abundances of proteins involved in the main metabolic processes for energy production in those parasites. In our dataset, both glycosomal and cytosolic glycolytic enzymes were detected and quantified, representing near 1.2 million molecules per cell (Fig 2D, S2 Table). In addition, oxidative phosphorylation enzymes were represented by near 1.1 million molecules per cell. It is known that generation of ATP in promastigotes involves mainly glycolysis and oxidative phosphorylation [48]. In agreement, our dataset reveled that abundance of the proteins involved in those metabolic processes represents 3.7% of all protein molecules per cell. We also observed an important contribution of enzymes from other processes involved in ATP generation such as tricarboxylic acid cycle with an estimate of ~1.0 million molecules per cell, as well as amino acid oxidation and fatty acid beta-oxidation, comprising an estimate of 860.000, and 410.000 molecules per cell, respectively (Fig 2D). Together, all those energy metabolic processes comprise ~ 4.6 million of molecules per cell, which corresponds to about 7.4% of all molecules per promastigote (Fig 2D, S2 Table).

Leishmania braziliensis, L. panamensis and L. guyanensis exhibit significant differences in protein abundance

Statistical significance of differences in protein abundance among the species was determined by t-test analyses. In total, at FDR of 1%, the concentrations of 2112 proteins were significantly different between L. braziliensis and L. panamensis, 1639 between L. braziliensis and L. guyanensis and 1089 between L. panamensis and L. guyanensis (Fig 3A, S4 Table). Remarkably, the concentration of 185 proteins was different across the three species (Fig 3A), and near 45% of them are annotated as uncharacterized. Despite that, with the integration of omics data, the “uncharacterized” universe should represent a rich source of information in the future. Gene expression levels were analyzed by qPCR for 10 genes encoding for proteins that presented statistical differences of abundance among at least two species. We observed low correlation between mRNA levels assessed by qPCR and protein abundance (S4 Fig). This result is not unexpected since lack of correlation between transcript and protein levels is commonly observed both in prokaryotes and eukaryotes, including, E. coli, Saccharomyces cerevisiae, Leishmania spp. and mammalian cells [4952]. In fact, in Leishmania spp. there is a constitutive expression of most of the genome and gene regulation mainly occurs at posttranscriptional level by differences on mRNA maturation and stability [53,54]. However, we are aware that our comparison was limited to 10 genes and we cannot rule out the possibility that deep pairwise comparison between transcriptomics and proteomics data could reveal potential correlations among mRNA and protein levels in Leishmania, at least for some genes in the strains studied here.

thumbnail
Fig 3. Differences in protein abundance in the proteomes of Leishmania (Viannia) species.

(A) Venn diagram shows the number of differentially abundant proteins among L. braziliensis, L. panamensis and L. guyanensis. (B) Differential protein concentration of diverse isoenzymes used for Leishmania species identification (n = 5 biological replicates for L. braziliensis and L. panamensis and n = 4 for L. guyanensis). Asterisks denote significant differences in the proteomics dataset (FDR < 0.01). (C) Principal component analysis of the concentration values of enzymes of the main energy metabolic pathways including glycolysis, TCA, oxidative phosphorylation, fatty acid beta-oxidation and amino acid oxidation (S4 Table).

https://doi.org/10.1371/journal.pntd.0008509.g003

To analyze if there were particular groups of proteins that contributed the most for distinguishing the L. (Viannia) species, the 185 proteins were submitted to enrichment analysis. The beta-alanine metabolism (4.00-fold), valine, leucine and isoleucine degradation (3.73-fold) and fatty acid degradation (3.17-fold) were annotations of metabolic pathways significantly overrepresented. In addition, biological processes including sterol biosynthetic process (15-fold), sulfur amino acid metabolic process (11.9-fold), monosaccharide metabolic process (6.83-fold) cellular amino acid metabolic process (3.47-fold), phospholipid biosynthetic process (4.08-fold) and lipid metabolic process (2.49-fold), among others, were also significantly enriched. In general, this analysis suggest that the abundance of proteins related to energy metabolism is distinct between the species here studied.

In order to know whether would have a chromosome that could contribute the most for those differences in protein abundance, we analyzed the chromosomal localization of the 185 proteins. With the exception of chromosomes 14 and 22, all the other chromosomes contribute with at least one differentially abundant protein. Interestingly, chromosomes 31, 35, 18 and 16 contributed with most of the differentially abundant proteins (16, 15, 14 and 13 proteins, respectively) (S5 Fig). Remarkably, proteins coded by genes located on that chromosomes are mainly transporters, peptidases and uncharacterized integral components of membrane, among others. Studies on Leishmania ploidy have revealed that chromosome copy number varies significantly between species, and even among strains, and that chromosome 31 is supernumerary in all species which ploidy state has been studied to date, including L. braziliensis, L. guyanensis and L. panamensis [716]. The ploidy of the chromosomes 35, 18 and 16 varies between disomic and trisomic states among different strains of L. braziliensis, L. panamensis and L. guyanensis [9,12,15,16]. Recently it was reported for L. panamensis that chromosomes 16 and 18 suffered changes in their somy after Sb-resistance selection [15, 16]. Whether the significant differences in protein abundance observed in our proteome dataset could be reflecting the genetic heterogeneity among those species, resulting from variation in chromosome number, remains to be clarified. However, the availability of in-depth quantitative protein data, as the one provided here, is a peremptory condition to answer that question.

Among those proteins that have abundancies significantly different among at least two species we identified HSP70, GP63, CPB, RNA pol II, DNA pol, CYB and even several of them have significantly different concentration among the three L. (Viannia) species studied here (S4 Table). Various enzymes such as malate dehydrogenase (MDH), isocitrate dehydrogenase (ICDH), glucose-6-phosphate dehydrogenase (G6PDH), 6-phosphogluconate dehydrogenase (6PGD) nucleoside hydrolase (NH), glutamate dehydrogenase (GDH), phosphoglucomutase (PGM), mannose phosphate isomerase (MPI), and glucose-6-phosphate isomerase (GPI) presented also significant differences in our dataset (S4 Table). All of those molecules are used for L. (Viannia) species typing using seminal approaches such as multilocus enzyme electrophoresis (MLEE) [55,56] or PCR variants including PCR-RFLP and multilocus sequence typing (MLST) [5760]. Thus, our proteomics dataset shows that the differences in the abundance of those proteins between the L. braziliensis, L. guyanensis and L. panamensis strains analyzed here agree with the molecular polymorphisms commonly observed by MLEE, MLST and/or PCR-variant analyses. In addition, the depth of our data set allows us to recover the contribution of many other molecules not previously known for species differentiation.

Leishmania (Viannia) species differ in the abundance of energy metabolism enzymes

Many of the isoenzymes used as gold standards for Leishmania species identification [55,56,61] presented significant differential abundance in our proteome dataset (Fig 3B). Since some of them are essential enzymes of the main energy metabolic pathways we decided to analyzed if would be possible to differentiate the three species relying only on the titers of enzymes of the energy metabolic pathways. The “energy profile” based on proteins concentration was first analyzed by PCA and we clearly observed three distinct clusters, each one corresponding to each species analyzed here (Fig 3C). Almost 80% of variability is explained by components 1 and 2, showing that there are clear differences in the energy metabolism among species. In addition, this result also shows that, in terms of energy metabolism, the L. panamensis and L. guyanensis strains are more related together compared to the L. braziliensis one. This result is in agreement with the phylogenetic relationships deduced from different molecular approaches [5558].

Further, we specifically compared the abundance of the proteins involved in glycolysis and oxidative phosphorylation, which are considered as the main metabolic pathways involved in energy generation in Leishmania promastigotes [20,48]. Remarkably, we observed statistical differences in the cumulative concentration of proteins involved in the glycolytic pathway among the species (Fig 4A and S4 Table). Such abundance is significantly higher in L. braziliensis (mean 513 pmol/mg) than in L. panamensis (mean 465 pmol/mg) or L. guyanensis (mean 473 pmol/mg), reinforcing the idea that these parasites seem to depend differentially on distinct energy substrates.

thumbnail
Fig 4. Leishmania (Viannia) species significantly differ in the abundance of energy metabolism enzymes.

(A) Cumulative concentration of the proteins involved in glycolysis. Each symbol (dots, squares or triangles) show the total sum of the concentration values of proteins involved in glycolysis in each of the 5 biological replicates for L. braziliensis and L. panamensis or 4 biological replicates for L. guyanensis. Statistical differences by t test (p<0.003 Lb x Lp; p<0.006 Lb x Lg). (B) 2-NBDG uptake levels. Promastigotes were incubated with 300 μM 2-NBDG, a fluorescent glucose analogue, and further analysed by flow cytometry. Bars show mean ± SD of at least three independent experiments. Statistical differences by t test (p<0.001). (C) Oxygen consumption under routine conditions (open bars) or in the presence of 2 μM antimycin A (residual oxygen consumption-ROX, filled bars). ROX control shows a reduction up to 95% in mitochondrial functionality. Bars show mean and standard deviations of at least three independent experiments. Significant differences by t test. ** p<0.001; *** p<0.0001. (D) Concentrations of specific proteins involved in oxidative phosphorylation. Complex I (green background), complex IV (purple background) and complex V (blue background) enzymes are identified with the UniProt code (S4 Table). Values represent mean ± SD of protein concentration (pmol/mg) [n = 5 biological replicates for L. braziliensis (dots) and L. panamensis (squares) and n = 4 for L. guyanensis (triangles)]. *Statistical differences according to Perseus analysis (FDR < 0.01) and further analyzed by the Holm-Sidak method (p<0.01).

https://doi.org/10.1371/journal.pntd.0008509.g004

This result prompted us to analyze the levels of glucose uptake by each species through the evaluation of 2-NBDG consumption, a fluorescent glucose analogue [62]. Confirming our proteomics data, L. braziliensis exhibit significant 3.7-fold higher 2-NBDG uptake than L. panamensis (Fig 4B). In addition, this assay revealed that L. guyanensis also uptakes higher levels of 2-NBDG than L. panamensis (2.9-fold). L. guyanensis showed a lower 2-NBDG consumption level than L. braziliensis, although the difference was not significant (Fig 4B). Conversely, analysis of mitochondrial O2 consumption showed that L. panamensis strain consumes 2-fold and 2.5-fold more O2 than L. braziliensis and L. guyanensis, respectively (Fig 4C). In agreement with that results, proteomics data revealed significant differences in the abundance of enzymes involved in oxidative phosphorylation. Enzymes of the complex I (NADH dehydrogenase), complex IV (cytochrome c oxidase) and complex V (ATP synthase) exhibited higher concentration in L. panamensis than in the other two species (Fig 4D). In addition, in L. guyanensis we observed significantly higher titers for complex I and complex IV enzymes compared to L. braziliensis (Fig 4D). Taking together, our results suggest that L. (Viannia) species differ in the ways of ATP production. In addition, the analysis of the cumulative concentration of the enzymes from the other main energy pathways confirmed that both L. guyanensis and L. panamensis rely more on other metabolic pathways such amino acid oxidation and fatty acid beta-oxidation to obtain ATP than L. braziliensis (S6 Fig). Our results are in good agreement with early works showing that different Leishmania species consume glucose at different rates and that glycolysis, mitochondrial respiration and amino acid oxidation are the main ATP sources for promastigotes, whereas fatty acid beta-oxidation is the main source for amastigotes [48,63,64]. Further analysis of sugar uptake and oxygen consumption should be performed over the course of the parasites’ growth curve in order to obtain indisputable data on the metabolic traits of each L. (Viannia) species during parasite differentiation. Although parasites change their metabolic profile during differentiation from procyclic to metacyclic stages [65, 66], the differences between species observed here are not related with a discrepancy in the rate of differentiation of parasites because: (i) all replicates of each species were collected at the same point of the growth curve (late log phase, S3 Fig); (ii) proteins such as SHERP/HASPB and META1, used as differentiation markers because their abundance increases highly in metacyclic parasites (stationary phase) [6770], were not detected in our dataset; (iii) proteins involved in translation and synthesis of proteins, which are processes very active in organisms in the log phase (i.e., dividing promastigotes) were highly abundant in our dataset; and (iv) differences in the protein abundance of ribosomal subunits, which are decreased in non-dividing metacyclic parasites (stat phase) [71], were not observed among the species studied here. Together, our data support the proposition that the three species of L. (Viannia) studied here have consistent differences in their energy metabolism. Our findings are in agreement with previous studies on the metabolome of different L. (Leishmania) species that demonstrated that L. donovani, L. mexicana and L. major are quite distinct in their metabolism [72]. Remarkably, large differences in consumption of amino acids were observed between those species [72]. Thus, potentially, the differences in energy metabolism sources could be a common trait in Leishmania spp.

Differences on energy metabolism probably reflect the different invertebrate hosts involved in transmission of each parasite species and their variable feeding sources. Indeed, the geographical origin of the L. braziliensis strain used here is the Atlantic forest at Brazilian’ Bahia state where parasites can be transmitted by Lutzomyia whitmani and Lu. intermedia, whereas L. panamensis comes from the Colombian Andean mountain’s region where the main vector is Lu. gomezi, and L. guyanensis strain was isolated in the Amazon region where it is mainly transmitted by Lu. umbratilis [7375]. Such distinct landscapes exhibit different flora and fauna for sandflies feeding and those distinct ecological scenarios should influence the preference for a particular metabolic pathway in parasites. The study of more strains of each species will corroborate whether those remarkably differences on energy metabolism are species-specific traits.

In addition to the differences in metabolism, we observed that concentration values of many proteins annotated as integral components of membrane, including transporters, surface antigens-like proteins, plasma membrane ATPases, and GP63, as well as proteins related to the metabolism of plasma membrane’ lipids were significantly different across the three L. (Viannia) species (S4 Table, S7 Fig) and allowed distinguishing the three species studied here. Together, our results reveal a species-specific pattern of protein abundances that might be contributing to specific host adaptation and differentiation. Such remarkable differences could be exploited for better and suitable diagnosis tools. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE [76] partner repository with the dataset identifier PXD017696.

Conclusions

We provide the first in-depth quantitative proteomic analysis of L. braziliensis, L. panamensis and L. guyanensis, the etiological agents of cutaneous leishmaniasis in the Americas. Comprehensive comparison of the proteomes of strains representing these species allowed us to show the extension of the differences among those close related species and how such differences shape specie-specific traits. Our results clearly show that deep quantitative proteomics analysis is able to distinguish the three species, in a similar way that deep genome sequencing allows such distinction. Regarding that, we provided information of the total protein content of those species, their main cellular components, and differences in the abundance of specific proteins and energy metabolic pathways. Together our results indicate that differences observed at the major energy metabolic processes, among others, could be contributing for species-specific fitness. Notably, we accomplished that without laborious and expensive protocols for sample preparation and absolute protein quantification. Indeed, the TPA and proteomic ruler methods allowed the determination of absolute protein concentrations and copy numbers without the introduction of peptide or protein modifications. Remarkably, the rich quantitative data here provided is available for exploring new diagnostic systems that hopefully can be translated in affordable tools for using in the field.

Supporting information

S1 Fig. Peptide coverage and protein abundance correlation among replicates.

(A) 95% proteins were identified with at least three peptides. Profiles of Pearson’s correlation coefficient of protein abundance among (B) L. braziliensis replicates, (C) L. panamensis replicates, (D) L. guyanensis replicates. Plotted values represent the total protein fraction of each individual protein calculated by the total protein approach.

https://doi.org/10.1371/journal.pntd.0008509.s001

(TIF)

S2 Fig. Functional annotations of protein identified in L. (Viannia) species proteome.

(A) Number and percentage of proteins in our dataset with any functional annotation for the main gene ontology (GO) categories of Cellular component, Molecular function and Biological process. (B) Number of proteins identified in each one of the main cellular components of Leishmania promastigotes, according to GO annotation for cellular component.

https://doi.org/10.1371/journal.pntd.0008509.s002

(TIF)

S3 Fig. Growth curves of L. braziliensis, L. panamensis and L. guyanensis.

Every dot, square or triangle represents mean ± SD of biological triplicates for L. braziliensis, L. panamensis and L. guyanensis, respectively. Growth curves started by adding 1 x 105 parasites/mL in 10 mL of Schnneider's medium supplemented with 10% of FBS. Parasites proliferation was evaluated every 24 h during two weeks by optical microscopy using a hemocytometer.

https://doi.org/10.1371/journal.pntd.0008509.s003

(TIF)

S4 Fig. Protein abundance and corresponding mRNA levels for 10 selected genes.

Values represent mean ± SD of protein concentration (pmol/mg) [n = 5 biological replicates for L. braziliensis (dots) and L. panamensis (squares) and n = 4 for L. guyanensis (triangles)]. * Statistical differences according to Perseus analysis (FDR < 0.01). Bars represent mean ± SD of mRNA relative levels determined by qPCR. * Significant differences by t test.

https://doi.org/10.1371/journal.pntd.0008509.s004

(TIF)

S5 Fig. Chromosomal localization of differentially abundant proteins among the three species.

https://doi.org/10.1371/journal.pntd.0008509.s005

(TIF)

S6 Fig. Cumulative concentration of the proteins involved in other energy metabolic pathways.

Bars show the mean of total sum of the concentration values in each species (n = 5 biological replicates for L. braziliensis and L. panamensis and n = 4 for L. guyanensis). Statistical differences by t test (p<0.01 Lb x Lb; p<0.005 Lb x Lg; p<0.0001 Lp x Lg).

https://doi.org/10.1371/journal.pntd.0008509.s006

(TIF)

S7 Fig. Proteins involved in lipids metabolism show significant differences in abundance.

(A) Concentration values of different plasma membrane transporters. (B) Concentration values of different plasma membrane proteins. (C) Concentration values of proteins involved in biosynthesis/metabolism of sphingolipid (SL), phospholipid (PL) (green background), isoprenoid and sterols (blue background) and other lipids (purple background). Values represent mean of pmol/mg ± SD (n = 5 biological replicates for L. braziliensis and L. panamensis and n = 4 for L. guyanensis). All plotted values were statistically different across the three species in our proteomics dataset (FDR < 0.01) (S4 Table).

https://doi.org/10.1371/journal.pntd.0008509.s007

(TIF)

S1 Table. Primer sequences used for qPCR amplification.

https://doi.org/10.1371/journal.pntd.0008509.s008

(XLSX)

S2 Table. Protein groups identification and histone ruler.

https://doi.org/10.1371/journal.pntd.0008509.s009

(XLSX)

S3 Table. Reproducibility among biological replicates.

https://doi.org/10.1371/journal.pntd.0008509.s010

(XLSX)

S4 Table. Statistical significance of differences in protein abundance among the species.

https://doi.org/10.1371/journal.pntd.0008509.s011

(XLSX)

Acknowledgments

The authors are grateful to Prof. Matthias Mann for continuous support. We thank Katharina Zettl for technical help with mass spectrometric measurements, and Dr. Rosane Temporal -quality manager of LPL-FIOCRUZ-RJ and all the staff of CLIOC for the technical assistance and quality advising.

References

  1. 1. Alvar J, Vélez ID, Bern C, Herrero M, Desjeux P, Cano J, et al. Leishmaniasis worldwide and global estimates of its incidence. PLoS One. 2012;7(5):e35671. pmid:22693548
  2. 2. Marsden PD. Mucosal leishmaniasis ("espundia" Escomel, 1911). Trans R Soc Trop Med Hyg. 1986;80(6):859–76. pmid:3037735
  3. 3. Jirmanus L, Glesby MJ, Guimarães LH, Lago E, Rosa ME, Machado PR, et al. Epidemiological and clinical changes in American tegumentary leishmaniasis in an area of Leishmania (Viannia) braziliensis transmission over a 20-year period. Am J Trop Med Hyg. 2012;86(3):426–33. pmid:22403312
  4. 4. Romero GA, Guerra MV, Paes MG, Macêdo VO. Comparison of cutaneous leishmaniasis due to Leishmania (Viannia) braziliensis and L. (V.) guyanensis in Brazil: therapeutic response to meglumine antimoniate. Am J Trop Med Hyg. 2001;65(5):456–65. pmid:11716098
  5. 5. Murray HW, Berman JD, Davies CR, Saravia NG. Advances in leishmaniasis. Lancet. 2005;366(9496):1561–77. pmid:16257344
  6. 6. Peacock CS, Seeger K, Harris D, Murphy L, Ruiz JC, Quail MA, et al. Comparative genomic analysis of three Leishmania species that cause diverse human disease. Nat Genet. 2007;39(7):839–47. pmid:17572675
  7. 7. Rogers MB, Hilley JD, Dickens NJ, Wilkes J, Bates PA, Depledge DP, et al. Chromosome and gene copy number variation allow major structural change between species and strains of Leishmania. Genome Res. 2011;21(12):2129–42. pmid:22038252
  8. 8. Downing T, Imamura H, Decuypere S, Clark TG, Coombs GH, Cotton JA, et al. Whole genome sequencing of multiple Leishmania donovani clinical isolates provides insights into population structure and mechanisms of drug resistance. Genome Res. 2011;21(12):2143–56. pmid:22038251
  9. 9. Llanes A, Restrepo CM, Del Vecchio G, Anguizola FJ, Lleonart R. The genome of Leishmania panamensis: insights into genomics of the L. (Viannia) subgenus. Sci Rep. 2015;5:8550. pmid:25707621
  10. 10. Imamura H, Downing T, Van den Broeck F, Sanders MJ, Rijal S, Sundar S, et al. Evolutionary genomics of epidemic visceral leishmaniasis in the Indian subcontinent. Elife. 2016;5:e12613. pmid:27003289
  11. 11. Iantorno SA, Durrant C, Khan A, Sanders MJ, Beverley SM, Warren WC, et al. Gene Expression in Leishmania is regulated predominantly by gene dosage. mBio. 2017;8(5):e01393–17. pmid:28900023
  12. 12. Coughlan S, Taylor AS, Feane E, Sanders M, Schonian G, Cotton JA, et al. Leishmania naiffi and Leishmania guyanensis reference genomes highlight genome structure and gene evolution in the Viannia subgenus. R Soc Open Sci. 2018;5(4):172212. pmid:29765675
  13. 13. Bussotti G, Gouzelou E, Côrtes Boité M, Kherachi I, Harrat Z, Eddaikra N, et al. Leishmania genome dynamics during environmental adaptation reveal strain-specific differences in gene copy number variation, karyotype instability, and telomeric amplification. MBio. 2018;9(6): e01399–18. pmid:30401775
  14. 14. Urrea DA, Duitama J, Imamura H, Álzate JF, Gil J, Muñoz N, Villa JA, et al. Genomic Analysis of Colombian Leishmania panamensis strains with different level of virulence. Sci Rep. 2018;8(1):17336. pmid:30478412
  15. 15. Restrepo CM, Llanes A, Cedeño EM, Chang JH, Álvarez J, Ríos M, et al. Environmental conditions may shape the patterns of genomic variations in Leishmania panamensis. Genes (Basel) 2019;10(11):838.
  16. 16. Patiño LH, Imamura H, Cruz-Saavedra L, Pavia P, Muskus C, Méndez C, et al. Major changes in chromosomal somy, gene expression and gene dosage driven by SbIII in Leishmania braziliensis and Leishmania panamensis. Sci Rep. 2019;9(1):9485. pmid:31263131
  17. 17. Domagalska MA, Imamura H, Sanders M, Van den Broeck F, Bhattarai NR, Vanaerschot M, et al. Genomes of Leishmania parasites directly sequenced from patients with visceral leishmaniasis in the Indian subcontinent. PLoS Negl Trop Dis. 2019;13(12):e0007900. pmid:31830038
  18. 18. Rosenzweig D, Smith D, Opperdoes F, Stern S, Olafson RW, Zilberstein D. Retooling Leishmania metabolism: from sand fly gut to human macrophage. FASEB J. 2008;22(2):590–602. pmid:17884972
  19. 19. Paape D, Barrios-Llerena ME, Le Bihan T, Mackay L, Aebischer T. Gel free analysis of the proteome of intracellular Leishmania mexicana. Mol Biochem Parasitol. 2010;169(2):108–14. pmid:19900490
  20. 20. Nirujogi RS, Pawar H, Renuse S, Kumar P, Chavan S, Sathe G, et al. Moving from unsequenced to sequenced genome: reanalysis of the proteome of Leishmania donovani. J Proteomics. 2014;97:48–61. pmid:23665000
  21. 21. Beneke T, Demay F, Hookway E, Ashman N, Jeffery H, Smith J, et al. Genetic dissection of a Leishmania flagellar proteome demonstrates requirement for directional motility in sand fly infections. PLoS Pathog. 2019;15(6):e1007828. pmid:31242261
  22. 22. Jardim A, Hardie DB, Boitz J, Borchers CH. Proteomic Profiling of Leishmania donovani promastigote subcellular organelles. J Proteome Res. 2018;17(3):1194–1215. pmid:29332401
  23. 23. Saravia NG, Gemmell MA, Nance SL, Anderson NL. Two-dimensional electrophoresis used to differentiate the causal agents of American tegumentary leishmaniasis. Clin Chem. 1984;30(12 Pt 1):2048–52.
  24. 24. Acestor N, Masina S, Walker J, Saravia NG, Fasel N, Quadroni M. Establishing two-dimensional gels for the analysis of Leishmania proteomes. Proteomics. 2002;2(7):877–9. pmid:12124933
  25. 25. Góngora R, Acestor N, Quadroni M, Fasel N, Saravia NG, Walker J. Mapping the proteome of Leishmania Viannia parasites using two-dimensional polyacrylamide gel electrophoresis and associated technologies. Biomedica. 2003;23(2):153–60. pmid:12872554
  26. 26. Walker J, Acestor N, Gongora R, Quadroni M, Segura I, Fasel N, Saravia NG. Comparative protein profiling identifies elongation factor-1beta and tryparedoxin peroxidase as factors associated with metastasis in Leishmania guyanensis. Mol Biochem Parasitol. 2006;145(2):254–64. pmid:16325936
  27. 27. Cuervo P, de Jesus JB, Junqueira M, Mendonça-Lima L, González LJ, Betancourt L, et al. Proteome analysis of Leishmania (Viannia) braziliensis by two-dimensional gel electrophoresis and mass spectrometry. Mol Biochem Parasitol. 2007;154(1):6–21. pmid:17499861
  28. 28. Cuervo P, De Jesus JB, Saboia-Vahia L, Mendonça-Lima L, Domont GB, Cupolillo E. Proteomic characterization of the released/secreted proteins of Leishmania (Viannia) braziliensis promastigotes. J Proteomics. 2009;73(1):79–92. pmid:19703603
  29. 29. Cuervo P, Domont GB, De Jesus JB. Proteomics of trypanosomatids of human medical importance. J Proteomics. 2010;73(5):845–867. pmid:20056176
  30. 30. Moreira Dde S, Pescher P, Laurent C, Lenormand P, Späth GF, Murta SM. Phosphoproteomic analysis of wild-type and antimony-resistant Leishmania braziliensis lines by 2D-DIGE technology. Proteomics. 2015;15(17):2999–3019. pmid:25959087
  31. 31. Wiśniewski JR, Zougman A, Nagaraj N, Mann M. Universal sample preparation method for proteome analysis. Nat Methods. 2009;6(5):359–362. pmid:19377485
  32. 32. Wiśniewski JR. Filter-Aided Sample Preparation: The Versatile and Efficient Method for Proteomic Analysis. Methods Enzymol. 2017;585:15–27. pmid:28109427
  33. 33. Wiśniewski JR, Mann M. Consecutive proteolytic digestion in an enzyme reactor increases depth of proteomic and phosphoproteomic analysis. Anal Chem. 2012;84(6):2631–7. pmid:22324799
  34. 34. Wiśniewski JR, Hein MY, Cox J, Mann M. A "proteomic ruler" for protein copy number and concentration estimation without spike-in standards. Mol Cell Proteomics. 2014;13(12):3497–506. pmid:25225357
  35. 35. Wiśniewski J. R. Label-Free and Standard-Free Absolute Quantitative Proteomics Using the "Total Protein" and "Proteomic Ruler" Approaches. Methods Enzymol. 2017; 585, 49–60. pmid:28109442
  36. 36. Tyanova S, Temu T, Sinitcyn P, Carlson A, Hein MY, Geiger T, et al. The Perseus computational platform for comprehensive analysis of (prote)omics data. Nat Methods. 2016;13(9):731–40. pmid:27348712
  37. 37. Bustin SA, Benes V, Garson JA, Hellemans J, Huggett J, Kubista M, et al. The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. Clin Chem. 2009;55(4):611–22. pmid:19246619
  38. 38. Torres F, Arias-Carrasco R, Caris-Maldonado JC, Barral A, Maracaja-Coutinho V, De Queiroz ATL. LeishDB: a database of coding gene annotation and non-coding RNAs in Leishmania braziliensis. Database (Oxford). 2017;2017:bax047.
  39. 39. Aebischer T. Leishmania spp. proteome data sets: a comprehensive resource for vaccine development to target visceral leishmaniasis. Front Immunol. 2014;5:260. pmid:24959165
  40. 40. Laffitte MN, Leprohon P, Papadopoulou B, Ouellette M. Plasticity of the Leishmania genome leading to gene copy number variations and drug resistance. F1000Res. 2016;5:2350. pmid:27703673
  41. 41. Dumetz F, Imamura H, Sanders M, Seblova V, Myskova J, Pescher P, et al. Modulation of aneuploidy in Leishmania donovani during adaptation to different in vitro and in vivo environments and its impact on gene expression. MBio. 2017;8(3): e00599–17. pmid:28536289
  42. 42. Sterkers Y, Lachaud L, Crobu L, Bastien P, Pagès M. FISH analysis reveals aneuploidy and continual generation of chromosomal mosaicism in Leishmania major. Cell Microbiol. 2011;13(2):274–83. pmid:20964798
  43. 43. de Jesus JB, Mesquita-Rodrigues C, Cuervo P. Proteomics advances in the study of Leishmania parasites and leishmaniasis. Subcell Biochem. 2014;74:323–349. pmid:24264252
  44. 44. Paape D, Aebischer T. Contribution of proteomics of Leishmania spp. to the understanding of differentiation, drug resistance mechanisms, vaccine and drug development. J Proteomics. 2011; 74(9):1614–24. pmid:21621022
  45. 45. Rochette A, McNicoll F, Girard J, Breton M, Leblanc E, Bergeron MG, et al. Characterization and developmental gene regulation of a large gene family encoding amastin surface proteins in Leishmania spp. Mol Biochem Parasitol. 2005;140(2):205–20. pmid:15760660
  46. 46. Warringer J, Blomberg A. Evolutionary constraints on yeast protein size. BMC Evol Biol. 2006;6:61 pmid:16911784
  47. 47. Wiśniewski JR, Rakus D. Multi-enzyme digestion FASP and the 'Total Protein Approach'-based absolute quantification of the Escherichia coli proteome. J Proteomics. 2014 23;109:322–31. pmid:25063446
  48. 48. Opperdoes FR, Coombs GH. Metabolism of Leishmania: proven and predicted. Trends Parasitol. 2007;23(4):149–58. pmid:17320480
  49. 49. Vogel C, Marcotte EM. Insights into the regulation of protein abundance from proteomic and transcriptomic analyses. Nat Rev Genet. 2012;13(4):227–32. pmid:22411467
  50. 50. Taniguchi Y, Choi PJ, Li GW, Chen H, Babu M, Hearn J, et al. Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells. Science. 2010;329(5991):533–8. pmid:20671182
  51. 51. Griffin TJ, Gygi SP, Ideker T, Rist B, Eng J, Hood L, et al. Complementary profiling of gene expression at the transcriptome and proteome levels in Saccharomyces cerevisiae. Mol Cell Proteomics. 2002;1(4):323–33. pmid:12096114
  52. 52. Lahav T, Sivam D, Volpin H, Ronen M, Tsigankov P, Green A, et al. Multiple levels of gene regulation mediate differentiation of the intracellular pathogen Leishmania. FASEB J. 2011;25(2):515–25. pmid:20952481
  53. 53. Leifso K, Cohen-Freue G, Dogra N, Murray A, McMaster WR. Genomic and proteomic expression analysis of Leishmania promastigote and amastigote life stages: the Leishmania genome is constitutively expressed. Mol Biochem Parasitol. 2007; 152(1):35–46. pmid:17188763
  54. 54. Requena JM. Lights and shadows on gene organization and regulation of gene expression in Leishmania. Front Biosci. 2011;17:2069–2085.
  55. 55. Cupolillo E, Grimaldi G Jr, Momen H. A general classification of New World Leishmania using numerical zymotaxonomy. Am J Trop Med Hyg. 1994;50(3):296–311. pmid:8147488
  56. 56. Saravia NG, Segura I, Holguin AF, Santrich C, Valderrama L, Ocampo C. Epidemiologic, genetic, and clinical associations among phenotypically distinct populations of Leishmania (Viannia) in Colombia. Am J Trop Med Hyg. 1998;59(1):86–94. pmid:9684634
  57. 57. Graça GC, Volpini AC, Romero GA, Oliveira Neto MP, Hueb M, Porrozzi R, et al., Development and validation of PCR-based assays for diagnosis of American cutaneous leishmaniasis and identification of the parasite species. Mem Inst Oswaldo Cruz. 2012;107(5):664–74. pmid:22850958
  58. 58. Boité MC, Mauricio IL, Miles MA, Cupolillo E. New insights on taxonomy, phylogeny and population genetics of Leishmania (Viannia) parasites based on multilocus sequence analysis. PLoS Negl Trop Dis. 2012;6(11):e1888. pmid:23133690
  59. 59. Herrera G, Hernández C, Ayala MS, Flórez C, Teherán AA, Ramírez JD. Evaluation of a Multilocus Sequence Typing (MLST) scheme for Leishmania (Viannia) braziliensis and Leishmania (Viannia) panamensis in Colombia. Parasit Vectors. 2017;10(1):236. pmid:28499458
  60. 60. Kato H, Gomez EA, Seki C, Furumoto H, Martini-Robles L, Muzzio J, et al. PCR-RFLP analyses of Leishmania species causing cutaneous and mucocutaneous leishmaniasis revealed distribution of genetically complex strains with hybrid and mito-nuclear discordance in Ecuador. PLoS Negl Trop Dis. 2019;13(5):e0007403. pmid:31059516
  61. 61. Ramírez JD, Hernández C, León CM, Ayala MS, Flórez C, González C. Taxonomy, diversity, temporal and geographical distribution of Cutaneous Leishmaniasis in Colombia: A retrospective study. Sci Rep. 2016;6:28266. pmid:27328969
  62. 62. Bombaça ACS, Dias FA, Ennes-Vidal V, Garcia-Gomes ADS, Sorgine MHF, d'Avila-Levy CM, et al. Hydrogen peroxide resistance in Strigomonas culicis: Effects on mitochondrial functionality and Aedes aegypti interaction. Free Radic Biol Med. 2017;113:255–266. pmid:28993269
  63. 63. Steiger RF, Meshnick SR. Amino-acid and glucose utilization of Leishmania donovani and L. braziliensis. Trans R Soc Trop Med Hyg. 1977;71(5):441–3. pmid:595100
  64. 64. Hart DT, Coombs GH. Leishmania mexicana: energy metabolism of amastigotes and promastigotes. Exp Parasitol. 1982;54(3):397–409. pmid:7151947
  65. 65. Silva AM, Cordeiro-da-Silva A, Coombs GH. Metabolic variation during development in culture of Leishmania donovani promastigotes. PLoS Negl Trop Dis. 2011;5: e1451. pmid:22206037
  66. 66. Berg M, Vanaerschot M, Jankevics A, Cuypers B, Maes I, Mukherjee S, et al. Metabolic adaptations of Leishmania donovani in relation to differentiation, drug resistance, and drug pressure. Mol Microbiol. 2013;90: 428–442. pmid:24020363
  67. 67. Berberich C, Marín M, Ramírez JR, Muskus C, Vélez ID. The metacyclic stage-expressed meta-1 gene is conserved between Old and New World Leishmania species. Mem Inst Oswaldo Cruz. 1998;93(6):819‐21. pmid:9921309
  68. 68. Depledge DP, MacLean LM, Hodgkinson MR, Smith BA, Jackson AP, Ma S, et al. Leishmania-specific surface antigens show sub-genus sequence variation and immune recognition. PLoS Negl Trop Dis. 2010;4(9):e829. pmid:20927190
  69. 69. Sádlová J, Price HP, Smith BA, Votýpka J, Volf P, Smith DF. The stage-regulated HASPB and SHERP proteins are essential for differentiation of the protozoan parasite Leishmania major in its sand fly vector, Phlebotomus papatasi. Cell Microbiol. 2010;12(12):1765‐79. pmid:20636473
  70. 70. Giraud E, Martin O, Yakob L, Rogers M. Quantifying Leishmania metacyclic promastigotes from individual sandfly bites reveals the efficiency of vector transmission. Commun Biol. 2019;2:84. pmid:30854476
  71. 71. Jara M, Berg M, Caljon G, de Muylder G, Cuypers B, Castillo D, et al. Macromolecular biosynthetic parameters and metabolic profile in different life stages of Leishmania braziliensis: Amastigotes as a functionally less active stage. PLoS One. 2017;12(7):e0180532. pmid:28742826
  72. 72. Westrop GD, Williams RA, Wang L, Zhang T, Watson DG, Silva AM, et al. Metabolomic analyses of Leishmania reveal multiple species differences and large differences in amino acid metabolism. PLoS One. 2015;10(9):e0136891. pmid:26368322
  73. 73. Miranda JC, Reis E, Schriefer A, Goncalves M, Reis MG, Carvalho L, et al. Frequency of infection of Lutzomyia phlebotomines with Leishmania braziliensis in a Brazilian endemic area as assessed by pinpoint capture and polymerase chain reaction. Mem Inst Oswaldo Cruz. 2002;97(2):185–8. pmid:12016439
  74. 74. Ferro C, López M, Fuya P, Lugo L, Cordovez JM, González C. Spatial distribution of sand fly vectors and eco-epidemiology of cutaneous leishmaniasis transmission in Colombia. PLoS One. 2015;10(10):e0139391. pmid:26431546
  75. 75. de Souza Freitas MT, Ríos-Velasquez CM, Costa CR Jr, Figueirêdo CA Jr, Aragão NC, da Silva LG, et al. Phenotypic and genotypic variations among three allopatric populations of Lutzomyia umbratilis, main vector of Leishmania guyanensis. Parasit Vectors. 2015;8:448. pmid:26338469
  76. 76. Perez-Riverol Y, Csordas A, Bai J, Bernal-Llinares M, Hewapathirana S, Kundu DJ, et al. The PRIDE database and related tools and resources in 2019: improving support for quantification data. Nucleic Acids Res 2019;47(D1):D442–D450. pmid:30395289