Open Access

Identification of internalin-A-like virulent proteins in Leishmania donovani

  • Ishita Mukherjee1,
  • Abhijit Chakraborty1 and
  • Saikat Chakrabarti1Email author
Parasites & Vectors20169:557

https://doi.org/10.1186/s13071-016-1842-5

Received: 11 May 2016

Accepted: 9 October 2016

Published: 21 October 2016

Abstract

Background

An active immune surveillance and a range of barriers to infection allow the host to effectively eliminate microbial pathogens. However, pathogens may use diverse strategies to subdue such host defences. For instance, one such mechanism is the use of leucine-rich repeat (LRR) proteins by pathogens (microbial) to cause infection. In this study, we aimed at identifying novel virulence factor(s) in Leishmania donovani, based on the possibility of lateral gene transfers of bacterial virulence factor(s) to L. donovani.

Methods

Rigorous homology searching protocols including Hidden Markov Model (HMM) and BLASTp based searches were employed to detect remote but significant similarities between L. donovani proteins and bacterial virulence factors.

Results

We found that some L. donovani proteins are similar to internalin-A (Inl-A) protein of Listeria monocytogenes, a surface LRR protein that helps mediate host cell invasion by interacting with E-cadherin on the cell membrane. However, to date, no such invasion mechanism has been reported in Leishmania donovani, the causative agent of visceral leishmaniasis. Moreover, a comparative LRR motif analysis of L. donovani Inl-A-like proteins against the Inl-A protein of L. monocytogenes revealed existence of characteristic consensus LRR regions, suggesting a reliable evolutionary relationship between them. Further, through rigorous three dimensional (3D) modeling of L. donovani Inl-A-like proteins and subsequent molecular docking studies we suggest the probability of human E-cadherin binding with the L. donovani Inl-A-like proteins.

Conclusions

We have identified three potential candidates (UniProt ID: E9B7L9, E9BMT7 and E9BUL5) of Inl-A-like LRR containing proteins in L. donovani with the help of systematic whole genome sequence analysis. Thus, herein we propose the existence of a novel class of Inl-A-like virulence factor proteins in L. donovani and other Leishmania species based on sequence similarity, phylogenetic analysis and molecular modelling studies in L. donovani.

Keywords

Cell invasion LRR proteins Inl-A-like proteins Homology-based search HMM-profile based search Leishmania donovani

Background

Leishmania spp. (family Trypanosomatidae) are intracellular protozoan parasites that when transmitted to a mammalian host can cause a range of infectious diseases, collectively referred to as leishmaniasis. These parasites have two morphologically distinct variants during their life-cycle, a promastigote form in phlebotomine sand flies and an amastigote form in mammalian cells [1]. A complex array of processes is involved in the attachment and invasion of host cells, initially mediated by the promastigotes and subsequently by the amastigotes. In general, many proteins of Leishmania spp. have been identified as possible virulence factors. For instance, lipophosphoglycan and leishmanolysin are important in attachment, invasion and intracellular survival of the parasites [2, 3]. In addition, the leucine-rich repeat (LRR)-containing proteins proteophosphoglycan and parasite surface antigen 2 also participate in parasite attachment and invasion of host cells [2]. Moreover, the A2 protein is important for the survival of the pathogen in visceral organs [4]. Amastin, amastin-like surface protein and cysteine proteases are other groups of proteins that attribute virulence to this group of parasites [5, 6]. Thus, Leishmania spp. can utilize several polypeptide and polysaccharide ligands while interacting with the host cells and some of these interactions may be synergistically or alternatively involved in receptor-mediated endocytosis. A number of receptors on host cells, which include neutrophils, macrophages, monocytes or dendritic cells may interact with these parasites. However, once phagocytosed, Leishmania spp. can generally establish long term infections within phagosomal vesicles by subverting host immune responses [1]. Depending on the host’s immune response and the species that infects it, such an infection may result in the clinical presentation of cutaneous (L. major, L. mexicana, L. amazonensis and L. braziliensis), muco-cutaneous (L. braziliensis and L. panamensis) or visceral leishmaniasis (L. donovani and L. infantum) [1]. In particular, nearly 20,000–50,000 deaths occur annually from visceral leishmaniasis and about 200,000–400,000 new cases of visceral leishmaniasis occur worldwide each year. Nearly 90 % of these new cases occur in six countries including India [7].

In an approach to develop better intervention strategies against visceral leishmaniasis, the L. donovani genome has been sequenced, annotated and compared with other Leishmania spp. to understand the genome, its natural variation and genetic population structure [8]. Previously, genome comparisons of L. major, L. infantum and L. braziliensis showed great conservation of synteny and identified only a small number of genes to be differentially distributed [9] and these species-specific genes may be responsible for the difference in pathogenesis between the species. Further, comparison of the L. donovani genome with the other Leishmania species showed substantial genetic differentiation at the species level between the genomes of L. infantum and L. donovani [8]. Similarly, comparison between L. donovani clinical isolates (from Nepal and India) provided insights into emerging modes of drug resistance among them [8]. Therefore, a range of virulence factors leading to internalization and/or persistence of L. donovani in phagolysosomes and other visceral organs, have been elucidated. However, increasing instances of visceral leishmaniasis with emerging drug resistance is still persisting, especially in some parts of the Indian subcontinent. Hence, identification of new and more effective drug targets in L. donovani remains to be an important issue.

In this study, we aimed at identifying novel virulence factor(s) and their possible invasion mechanism(s) in L. donovani. In this respect, we assumed that lateral gene transfers of bacterial virulence factor(s) to parasitic eukaryotes (L. donovani) is likely, since a recent report suggested the possibility of such transfers particularly of enzymatic genes belonging to core pathways such as the amino acid and sugar metabolism [10, 11]. Thus, preliminarily we searched for L. donovani proteins bearing significant resemblance to bacterial virulence factors or their virulence domains in particular with the help of the virulence factors database [12]. We identified a few L. donovani proteins possibly having an Internalin-A (Inl-A) like domain similar to Listeria monocytogenes Inl-A, which is a surface LRR protein that helps mediate host cell invasion by interacting with E-cadherin on the cell membrane [13]. We devised a strategy referred to as forward and reverse search analysis comprising of profile versus sequence (Hidden Markov Model (HMM) [14] based) and sequence versus sequence (BLASTp [15]) comparisons to confirm the existence of such Inl-A-like remote orthologs in L. donovani. With the help of this search strategy we identified remote but significant similarities between certain L. donovani proteins (L. donovani Inl-A-like proteins) and LRR containing Inl-A protein from Listeria monocytogenes. We next asked questions like, whether these remote orthologs shared a significant evolutionary relationship with Inl-A, or what is the extent of similarity between such L. donovani Inl-A-like proteins and their bacterial counterparts? Moreover, it would also be interesting to study whether these L. donovani Inl-A-like proteins share similar subversion mechanism in host cells as L. monocytogenes Inl-A? We addressed the first question by studying the orthologs of these proteins and by comparing the signature leucine rich repeat (LRR) motifs between the two sets of proteins. Whereas the second question was assessed by homology modeling of L. donovani Inl-A-like proteins and their subsequent docking studies with the protein interaction partner (human E-cadherin [hEC1]) of their bacterial ortholog (Inl-A). Based on these analyses we suggest the existence of a new group of virulence factors capable of employing a yet to be known mode of host invasion mechanism in L. donovani.

Methods

Dataset collection

For the purpose of this study, we have collected L. donovani proteome (8083 protein sequences) from TriTrypDB (release 8) [16, 17]. Selected protein sequences of L. donovani were also obtained from UniProt database [18] and three dimensional coordinates of the template protein were obtained from the protein data bank (PDB) [19].

Search for virulence factors in L. donovani

In order to search for virulence factors in L. donovani, the virulence factors database (VFDB, containing 34,230 complete sequences) was downloaded [12, 20]. Each of the L. donovani sequences was compared against this database using BLASTp algorithm (Basic Local Alignment Search Tool, BLAST, for searching protein databases using a protein query) [15]. Proteins satisfying the threshold, E-value of ≤ 1e-8, sequence identity of ≥ 20 %, query protein (L. donovani) length coverage of ≥ 25 % and subject protein (VFDB) length coverage of ≥ 40 % were selected as possible bacterial virulence factor-like proteins.

Bacterial virulence factor-like sequences in L. donovani

Existence of L. donovani proteins with reasonable similarity with bacterial Inl-A-like virulence proteins indicated the probability that these L. donovani proteins might be distant orthologs of Inl-A. To ascertain this similarity, we employed additional approaches referred to as forward and reverse search analysis (Fig. 1).
Fig. 1

Basic methodology for identification of L. donovani Inl-A-like proteins. a Outline of the basic search strategy to identify L. donovani Inl-A-like proteins. b Identified L. donovani Inl-A-like proteins with the help of ‘forward search analysis’ followed by ‘reverse search analysis’. Abbreviations: Inl-A, Internalin-A; LRR, Leucine Rich Repeat; HMM, Hidden Markov Model; BLAST [15], Basic Local Alignment Search Tool; HHblits, HMM-HMM-based lightning-fast iterative sequence search tool [30]

Forward search analysis

Initially, a BLASTp search was performed against the L. donovani proteome using the L. monocytogenes Inl-A LRR region (UniProt ID: P0DJM0 [region: 77–405]) as query [15]. Protein segments bearing an E-value of ≤ 1e-10, query length coverage of ≥ 60 % and sequence identity of ≥ 20 % with Inl-A LRR region were presumed to be similar to the Inl-A domain.

A hidden Markov model (HMM) profile representative of Inl-A was prepared to perform a sequence versus profile search. For this purpose, close homologs of Inl-A were determined via BLASTp search against all bacterial genome databases (homolog selection criteria: E-value of ≤ 1e-6, query length coverage of ≥ 70 %, sequence identity of ≥ 40 %) [15, 21]. The Inl-A-like region (± 10 residues) was extracted from these bacterial orthologs and representative non-redundant (< 95 % identical) sequences were selected for further analysis [22, 23]. Inl-A and its bacterial orthologs (81 sequences) were aligned with an iterative refinement method (MAFFT-L-INS-I version 7) giving preference to local alignment such that the conserved LRR motif regions are well aligned [24, 25]. The resultant multiple sequence alignment (MSA) was utilized to prepare HMM profile via HMMER 3.0 [14, 26]. This Inl-A profile along with other known Pfam [27, 28] family profiles (modified Pfam-A database) was used as profile database against which each L. donovani protein was scanned for similarity. For this purpose, we utilized the ‘hmmscan’ program of HMMER 3.0 package that allows one to search for similarity using sequence(s) against a profile (HMM) database [14, 26]. It was assumed that sequence(s)/domain(s) having an E-value of ≤ 1e-8, target coverage of ≥ 50 %, sequence identity of ≥ 20 %, conservation of ≥ 70 % with the Inl-A profile are probably Inl-A-like sequences. Among these proteins, the ones having considerably high scoring predicted Inl-A LRR domains (domain score ≥ 110) were selected for reverse search analysis. Moreover, an HHpred [29] analysis using HHblits (HMM-HMM-based lightning-fast iterative sequence search) algorithm [30] and maximum accuracy (MAC) alignment (MAC realignment threshold = 0.3) was done for each of these proteins against the protein data bank (PDB) HMM database to define the LRR regions in these proteins more reliably.

Reverse search analysis

Proteins from L. donovani which showed similarity to bacterial Inl-A sequence via forward search approaches were further matched against the NCBI non-redundant (NR) sequence database [31, 32] using BLASTp [15, 33] and against the UniProt and PDB databases using HHblits [30, 34]. For sequence-sequence comparisons using BLASTp, sequences having an E-value of ≤ 1e-6, query length coverage of ≥ 70 %, sequence identity of ≥ 20 % with the query were considered as homologous. Similarly, for the (HMM) profile-(HMM) profile comparison using HHblits search, sequences having probability of ≥ 97 % were assumed to be homologous. Here, probability reflects the percentage score that a database match is a true positive based on a comparison of non-homologous (negative) and homologous (positive) domain pairs in an all-against-all comparison of structural classification of proteins (SCOP) [35]. These proteins, which share similarity with the predicted Inl-A-like regions in the L. donovani proteins, were then further filtered to represent only non-Kinetoplastida and well-characterized proteins. This was done such that we can get a better estimate of a homologous relationship between these L. donovani proteins and other bacterial proteins or internalins. Such homologous proteins were then ranked based on the E-value and only the top 25 subject hits were taken for further analysis to determine if these L. donovani proteins bear any significant similarity to LRR proteins or Inl-A. Thus, L. donovani proteins showing significant sequence similarity with the bacterial Inl-A protein(s) in both forward and reverse search analysis were considered as L. donovani Inl-A-like proteins.

Evaluating sequence based similarity between bacterial Inl-A and L. donovani Inl-A-like proteins

Bacterial Inl-A and L. donovani Inl-A-like proteins were aligned pairwise using the Needleman-Wunsch algorithm available in EMBOSS (v6.2.0) [36] for the computation of sequence identities between them. Sequences sharing an E-value of ≤ 1e-6, query length coverage of ≥ 70 % and sequence identity of ≥ 45 % with L. donovani Inl-A-like proteins and bacterial Inl-A protein from NCBI non-redundant (NR) database [31, 32] were taken as close orthologs of L. donovani Inl-A-like proteins and bacterial Inl-A proteins, respectively. MSA containing the close orthologs of L. donovani Inl-A-like and bacterial Inl-A protein having sequence identity of ≤ 95 % was used for creating a phylogenetic tree by the Randomized Axelerated Maximum Likelihood (RAxML version 8.1.6) [37] program utilizing the maximum likelihood algorithm. The generated tree was edited in ETE (version 2.3.8) [38].

Further, the similarity between Inl-A LRR regions and the predicted LRR regions in L. donovani Inl-A-like proteins were compared using the HHalign [39] program. The HMM profile for each LRR region of L. donovani Inl-A-like proteins was constructed as mentioned before (criteria for selecting orthologs: E-value ≤ 1e-6, query length coverage ≥ 70 % and sequence identity ≥ 30 %). Additionally, distantly orthologous bacterial internalin sequences were assimilated (bacterial internalin sequences having sequence identities in the range of 20–30 % with L. monocytogenes Inl-A were determined using PSI-BLAST version 2.2.28+ [40]). Similar HMM-HMM comparison between LRR regions from distant internalin sequences and Inl-A LRRs was done.

Homology modeling

Three dimensional (3D) coordinates for each of the selected L. donovani Inl-A-like proteins were predicted by MODELLER 9.15 [4144] with the help of L. monocytogenes Inl-A [PDB ID: 1O6S, chain A] as template and structure-sequence alignment provided by the HHpred server [29]. 3D models were ranked on the basis of energy parameters like DOPE score and structures were validated using VERIFY3D [45, 46], RAMPAGE [47]. Certain selected models were further refined and loop modeling was performed in MODELLER 9.15 to predict most plausible 3D models with least stereo-chemical violations. The homology models were visualized and analyzed in chimera 1.10.1 [48]. Since calcium (Ca2+) is important for the interaction of Inl-A with hEC1 [13], Ca2+was coordinated with the corresponding residues in the L. donovani models. In models where corresponding Ca2+coordinating [49] residues were missing, other similar residues within 5 Å were considered and ligand Ca2+ was incorporated in the models with a coordination sphere of 2.45 Å (similar to Inl-A structure) in MODELLER 9.15. These models were further filtered based on DOPE score and validated using previously mentioned structure validation tools.

Docking and selection of predicted interaction pose

The modeled L. donovani Inl-A-like structures were superimposed onto the template Inl-A structure and residues within 5 Å of hEC1 were taken as possible interacting residues while for hEC1 residues known to interact with Inl-A were taken as possible interacting residues. Docking of L. donovani Inl-A-like proteins with hEC1 was guided with this a priori interaction information while performing molecular docking via multiple programs namely, PatchDock [50, 51], HADDOCK 2.0 (High Ambiguity Driven protein-protein Docking) [5254] and ClusPro 2.0 [5557], respectively. Similarly, re-docking of Inl-A with hEC1 was also performed with these multiple programs to study whether the docking programs are capable of capturing the native Inl-A-hEC1 interaction conformation reliably (Additional file 1: Figure S1). PatchDock [50, 51] allows geometric shape complementarity matching with the help of geometric hashing and pose-clustering techniques and FireDock [58] allow further refinement of the complexes. Top 100 solutions from PatchDock-based on the receptor-ligand geometric score were clustered according to root mean square deviation (RMSD) in chimera (version 1.10.1) [48] to determine largest docked clusters. Solutions present in each cluster were further refined in FireDock [58]. HADDOCK [5254] is mainly a data driven (ambiguous interaction restraints) rigid-body docking process with solvent based refinement of complexes where the predicted interacting residues were taken as active residues and passive residues were defined automatically around the active residues. ClusPro [5557] utilizes Fast-Fourier Transform (FFT)-based approach and correlation between binding site free energy attractor and cluster size to provide an approximation of the native binding conformation. While docking in ClusPro the residues, which possibly interact, were assumed to share attractive forces.

Assuming that clusters having higher number of similar frames with more average negative docking scores (better energetics) are more likely to possess the best possible interaction pose between two proteins, top three clusters (Cluster I, Cluster II and Cluster III) were selected from each docking program for further evaluation. A representative frame from each cluster was aligned with Inl-A crystal structure and ligand RMSD (l-RMSD) was calculated in chimera v1.10.1 [48]. The docking scores (FireDock refined glob score, HADDOCK score, ClusPro balanced score) of these ranked clusters from each program were plotted with l-RMSD for better enumeration of similar interaction poses. Here, FireDock refined glob score represents the binding energy score of the complexes calculated considering desolvation energy (atomic contact energy, ACE), van der Waals interactions, partial electrostatics, hydrogen and disulfide bonds, π-stacking and aliphatic interactions, rotamer’s probabilities etc. [50, 51, 58], whereas, HADDOCK score is a weighted sum of intermolecular electrostatic, van der Waals, desolvation, ambiguous interaction restraints (AIR) energies, and a buried surface area (BSA) term [5254]. Moreover, ClusPro balanced score is given as the sum of terms representing shape complementarity (attractive and repulsive interactions), electrostatic and desolvation contributions [in terms of pairwise potentials like DARS (Decoys As the Reference State) and ACP (atomic contact potential)] [5557].

Since a consensus pose predicted from all three programs utilizing different scoring functions ascertains more confidence to such an interaction pose, these solutions were considered as the best docked conformations. Finally, the free energy of binding of L. donovani Inl-A-like proteins and hEC1 as well as the probable hydrogen-bonding interaction patterns of these complexes (the selected best docked conformations) were determined using PISA (available in ccp4mg (version 2.8.1) [59]). Moreover, residues involved in probable hydrophobic interactions were determined with the help of Protein Inter-Chain Interaction (PICI) web server [60].

Results

Similarity between bacterial virulence factors and L. donovani proteins

An in-depth analysis aiming to establish possible existence of bacterial virulence factor-like proteins was undertaken based on the observation that lateral gene transfer from microbes to Trypanosomatidae may have played a role in their evolution [10, 11]. Since, we were interested in identifying remote orthologs, carrying representative virulence factor-like domains in the L. donovani proteome, the threshold for selection of similar proteins was implemented accordingly. On searching the virulence factor database (VFDB) [12], we found 232 L. donovani proteins that possess significant sequence similarity with bacterial virulence factor-like proteins (Fig. 2b). However, functions for some of these proteins (108) are already known (Fig. 2a), whereas some have putative functions and still others have no known functions. Based on the similarity to VFDB proteins predicted functions were assigned to L. donovani proteins without functional classification (Fig. 2c; Additional file 2: Table S1). It was interesting to note that 32 % of the proteins with unknown functions were found to be similar to bacterial (L. monocytogenes) Inl-A proteins possibly sharing an internalin domain.
Fig. 2

Search for bacterial virulence factor-like proteins in L. donovani. The virulence factor database (VFDB) [12] was searched to identify whether any L. donovani proteins bear significant similarity (E-value ≤ 1e-8, sequence identity ≥ 20 %, query length coverage ≥ 25 %, subject length coverage ≥ 40 %) to bacterial virulence factor proteins. a Functional distribution of L. donovani proteins listed in the UniProt database [18] bearing significant similarity to bacterial virulence factors. b Distribution of L. donovani proteins having significant matches with the VFDB [12] proteins according to the search. c Predicted functional distribution of L. donovani proteins without any known functions as determined by similarity to VFDB [12] proteins

Establishment of distant orthology between L. donovani proteins and bacterial Inl-A

Identification of homologs can be done based on database searches using sequence-sequence comparisons. In order to ascertain the orthologous relationship between bacterial internalin-A and L. donovani proteins, we utilized a strategy referred to as forward and reverse search analysis as outlined in Fig. 1. In the forward search analysis, BLASTp [15] search against L. donovani proteome suggested two parasite proteins (UniProt ID: E9B7L9, E9BMT7) to be Inl-A-like (Table 1). However, a number of studies have suggested that profile-profile and sequence-profile-based searches are more sensitive than sequence-sequence searches in identifying remote orthology since profiles include more information than one sequence alone about a related family of proteins [15, 39, 61]. For instance, given a multiple sequence alignment (MSA) of bacterial Inl-A homologs (only for LRR regions) probability theory was used to define an HMM profile containing position specific information about the degree of conservation of the internalin domain. Correspondingly, while comparing the L. donovani proteome with the modified Pfam-A database (containing the Inl-A HMM profile) using ‘hmmscan’ [14, 2628] a range of domains were predicted including LRRs in many L. donovani proteins. Since we were interested in establishing the existence of Inl-A-like proteins in L. donovani, we selected proteins having an Inl-A-like LRR region for further analysis. Thus, we identified five L. donovani proteins (UniProt ID: E9BUL5, E9BEF7, E9B7L9, E9B8L0, E9BMT7) sharing significant similarity with the Inl-A profile (Table 2). This finding is intriguing because the Inl-A LRR region is mainly involved in interaction with human host cell receptor E-cadherin (hEC1) and in turn plays a major role in invasion of the host cells in Listeria pathogenesis [62].
Table 1

Forward search analysis (BLASTp)

Subjecta ID (Gene symbol)

Alternate protein IDs (Subject)a (Ensembl, RefSeq GI, UniProt)

E-valueb

Queryc (aligned region)

Queryc coverage (%)

Subjecta (aligned region)

Subjecta coverage (%)

Sequence identity (%)

LdBPK_030010.1

emb|CBZ31242; gi|398009536; E9B7L9

3.94e-19

10–342

96.8

439–831

38.95

25.25

LdBPK_311630.1

emb|CBZ36565.1; gi|398020181; E9BMT7

6.35e-14

16–339

94.19

301–684

47.35

24.68

Predicted distant Inl-A orthologs in L. donovani proteome identified based on BLASTp [15] search

aSubject: corresponds to L. donovani proteins which are probably Inl-A-like

bE-value (expect-value): the average expected number of non-homologous proteins with a score higher than the one obtained for the database match; E-values closer to 0 are statistically significant

cQuery: corresponds to Inl-A LRR region

Table 2

Forward search analysis (hmmscan)

Querya ID (Gene symbol)

Alternate protein IDs [Query]a (UniProt, Ensembl, RefSeq GI)

Subjectb coverage (%)

Querya length

Domain scorec

c-Evalued

i-Evaluee

Querya (Aligned region)

Sequence identity (%)

Conservationf (%)

LdBPK_365070.1

E9BUL5, emb|CBZ38944|, gi|398024920

81.49

1208

117

3.30e-37

6.10e-34

394–776

28.18

75.26

LdBPK_191670.1

E9BEF7, emb|CBZ33633, gi|398014298

55.74

560

141.6

1.10e-44

2.00e-41

265–526

26.98

77.78

LdBPK_030010.1

E9B7L9, emb|CBZ31242|, gi|398009536

56.6

1009

117.4

3.10e-37

4.50e-34

202–467

23.05

75.78

LdBPK_030010.1

E9B7L9 emb|CBZ31242|, gi|398009536

60.64

1009

145

1.30e-45

1.90e-42

563–847

28.52

78.15

LdBPK_051200.1

E9B8L0, emb|CBZ31583, gi|398010218

59.57

396

146

5.70e-46

9.40e-43

77–356

29.06

76.23

LdBPK_311630.1

E9BMT7, emb|CBZ36565, gi|398020181

72.13

811

213.8

1.60e-66

2.70e-63

394–732

30.65

84.21

Predicted distant Inl-A orthologs in L. donovani proteome identified based on hmmscan [14, 26] against a modified Pfam database [27, 28] (including an Inl-A profile)

aQuery: corresponds to L. donovani proteins

bSubject: corresponds to Internalin-A internalin domain profile

cDomain Score: the calculated domain score for the best aligned profile (Inl-A)-sequence (L. donovani) matched states of the Inl-A-like internalin domain predicted in L. donovani proteins

dc-Evalue (conditional E-value): measures the statistical significance of each domain, given that the target sequence is a true homolog. It is the expected number of additional domains one would find with a domain score this high in the set of sequences reported in the top hits list, if those sequences consisted only of random non-homologous sequence outside the region that sufficed to define them as homologs

ei-Evalue (independent E-value): the significance of the sequence in the whole database search, if this were the only domain one had identified

fConservation: estimation of identical or similar matching states over the alignment length

Four of these L. donovani proteins contain one Inl-A LRR-like region while E9B7L9 possesses two possible Inl-A LRR-like regions. Further, HMM-HMM-based search was performed using HHblits algorithm [30] and MAC realignment was done for each L. donovani Inl-A-like protein to delineate the LRR regions in these proteins more appropriately (Additional file 3: Table S2). The reverse search analysis, compared the predicted Inl-A-like LRR regions of the identified L. donovani proteins against different sequence databases [NCBI non redundant database (NR), UniProt database] [18, 31, 32] and structure database (PDB) [19] to confirm whether these predicted Inl-A-like LRR regions bear reliable similarity to LRR-containing proteins or internalins. These comparisons were done with the help of BLASTp [15] against NR [31, 32] database and HHblits against UniProt and PDB HMM databases [30, 34]. We found indications that E9BMT7 (region: 394–757) as well as E9B7L9 (region: 212–573) are highly similar to different internalins as ~71 and ~70 %, respectively of their significant orthologous ‘hits’ retrieved via BLASTp [15] belong to internalins (Fig. 3a). Similarly, E9B7L9 (region: 583–971), E9BUL5 (region: 386–782), E9BEF7 (region: 135–513) also contain LRR rich regions, which bear similarity to internalins. Moreover, the HMM-HMM-based search results when ranked according to E-value show that Inl-A and Inl-A-like proteins are among the top ranked candidates for L. donovani Inl-A-like regions (Fig. 3b). However, during the reverse search analysis, it was found that E9B8L0 (region: 39–381) is less likely to possess an internalin domain. Thus, based on the search for virulent proteins in the VFDB database [12] and the forward as well as reverse search analyses utilizing different sequence-sequence, sequence-profile and profile-profile comparison methods we propose that E9BUL5, E9BEF7, E9B7L9 and E9BMT7 proteins possibly contain Inl-A-like LRR regions within their sequence.
Fig. 3

Reverse search analysis. a The percentage of major functional classes of homologous proteins (hits) obtained from BLASTp [15] search for each probable Inl-A-like region of L. donovani proteins. b The ranks of Inl-A/Inl-A-like proteins which share similarity with each of the probable Inl-A-like region of L. donovani proteins based on HHblits (HMM-HMM-based lightning-fast iterative sequence search) search against PDB and UniProt HMM databases [30, 34]. Ranks of the Inl-A and Inl-A-like proteins within top 25 similar proteins are shown

It is also possible that similar Inl-A-like proteins are present in other trypanosomatids and/or Kinetoplastida. Using similar approaches we have identified 8 non-Leishmania probable internalin-A-like proteins within trypanosomatids and/or Kinetoplastida proteome (see Additional file 4: Figure S2 for details). Similarly, identification of 13 leishmanial orthologs of L. donovani Inl-A-like proteins indicates that these Inl-A-like proteins are not unique to L. donovani and it is likely that an Inl-A-like class of proteins is present in Leishmania spp. and other kinetoplastids (Additional file 4: Figure S2).

Establishment of LRR motif similarity between bacterial Inl-A and L. donovani Inl-A-like proteins

Pairwise sequence identities and phylogenetic analysis suggest distant orthology between Inl-A orthologs and L. donovani Inl-A-like proteins (Fig. 4a, b). Hence, distant orthologs (sequence identity between 20 and 30 %) of L. monocytogenes Inl-A proteins were identified and average sequence identities of the equivalent LRR motifs (LRR1-15) among them were calculated for comparison. A varied range (8–42 %) of sequence identities was observed for most of the LRR motifs between L. donovani Inl-A-like proteins and L. monocytogenes Inl-A sequence (Fig. 5a). Further, HMM profiles for each LRR motif extracted from the bacterial Inl-A distant orthologs and L.donovani Inl-A-like proteins were constructed and compared across these two sets of sequences (Fig. 5b). This exercise indicated that the probability of a orthologous relationship between bacterial Inl-A LRRs and predicted LRRs of E9B7L9 (region: 212–573), E9BMT7 (region: 394–757) and E9BUL5 (region: 386–782) are reasonably high, since a profile-profile comparison yielded probability of ≥ 90 % and an E-value of ≤ 1e-5 for all the LRRs except one which had significant E-value but probability ≤ 90 %. However, profiles of predicted LRRs for E9B7L9 (region: 583–971) and E9BEF7 (region: 135–513) when compared with profiles of bacterial Inl-A LRR motifs either do not show significant E-values (E-value of ≥ 1e-5) or probabilities (probability ≤ 90 %) for some of the LRRs (Fig. 5b). Since, for a sequence to be like Inl-A, it must have a reasonable degree of similarity in all of the LRRs, the Inl-A-like proteins from L. donovani were further pruned at this stage and only E9B7L9 (region: 212–573), E9BMT7 (region: 394–757) and E9BUL5 (region: 386–782) were selected for further study.
Fig. 4

Comparison of internalin-A (Inl-A) and L. donovani Inl-A-like proteins. a Pairwise sequence identities between L. monocytogenes Inl-A and L. donovani Inl-A-like complete sequences. b Orthologs of Inl-A and L. donovani Inl-A-like proteins were compared to establish the extent of orthology they share. The generated tree in circular layout shown here exemplifies the distant orthology between Inl-A (a) and E9BMT7 (b), E9BUL5 (c), E9B7L9 (d), E9BEF7 (e), respectively

Fig. 5

LRR motif similarity between internalin-A (Inl-A) and L. donovani Inl-A-like proteins. a The pairwise sequence identities between each Inl-A LRR and the corresponding predicted LRR of L. donovani Inl-A-like proteins along with the average LRR sequence identities between Inl-A LRRs and distant internalin-like sequences. b Probability of similarity [calculated based on (HMM)profile-(HMM)profile comparisons] between Inl-A LRRs with the corresponding predicted LRRs of L. donovani Inl-A-like proteins are shown along with average probability of similarity with respect to distant internalin-like sequences. Probability values >90 % indicate a homologous relationship that is either globally homologous or locally similar in structure to the Inl-A LRRs

Homology modeling of L. donovani Inl-A-like proteins

Based on the previous analysis, we found that similarity between E9B7L9, E9BMT7 and E9BUL5 and L. monocytogenes Inl-A (P0DJM0) falls within the twilight zone (20–30 % sequence identity) of sequence similarity. However, the other analyses strongly indicate that E9B7L9 (region: 212–573), E9BMT7 (region: 394–757) and E9BUL5 (region: 386–782) probably have Inl-A-like LRR repeat stretches. Thus, we prepared three-dimensional (3D) model structures for these proteins based on the LRR region of L. monocytogenes Inl-A crystal structure (PDB ID: 1O6S, chain A). Since, HMM-HMM comparison yields high quality alignments in the twilight zone of sequence similarity, we used the previously generated alignment from HHpred for template-based modeling [29, 39].

Generally, internalins have an N-terminal cap domain, a LRR-domain (22 amino acid repeats), an inter-repeat region (IR), and C-terminal repeats. The internalin domain (region 36–496 of mature Inl-A) is comprised of an α-helical domain (residues: 36–78), LRR domain (residues: 79–414) and an immunoglobulin-like domain (residues: 415–495). In particular, the LRR domain has 15 full and a half 22 residue repeats with each repeat containing a β strand (xxLxL, L: leucine, valine, or isoleucine; x: any amino acid) followed by a loop (xxNxLxx), a 310- helix (LxxLx), and a second loop (xLxxL). These repeats create a right-handed solenoid with a stretch of conserved aliphatic hydrophobic residues and an asparagine directed toward the solenoid core [13]. The L. donovani Inl-A-like proteins mostly have 23 residue repeats in contrast to the 22 residue repeats present in L. monocytogenes Inl-A. 3D homology models were prepared for E9B7L9 (region: 212–573), E9BMT7 (region: 394–757) and E9BUL5 (region: 386–782). Parameters describing these model structures are outlined in Table 3. In general, the predicted 3D structures have 15 LRR repeats each of which have a β sheet, followed by a loop, a helical region and another loop creating a solenoid. The solenoid created by these repeats exhibits a similar pattern of aliphatic hydrophobic residues towards its core (Fig. 6).
Table 3

Validation of homology models of L. donovani Inl-A-like proteins

L. donovani Inl-A-like proteins

E9B7L9

E9BMT7

E9BUL5

Predicted Inl-A-like Region

212–573

394–757

386–782

Sequence similarity index

 Whole Sequence Identity with Inl-A (UniProt ID: PODJMO)

18 %

15 %

16.8 %

 LRR Region Sequence Identity with Inl-A (UniProt ID: PODJMO)

21 %

20 %

22 %

 LRR Region Sequence Similarity with Inl-A (UniProt ID: PODJMO)

41.7 %

41.2 %

33.4 %

Structure Similarity Index

 

 Root Mean Square Deviation (RMSD) [Å] with Inl-A (PDB ID: 1O6S, chain A)

1.2

1

1.5

Stereo-chemical Evaluation

 Ramachandran Plot [47]

Favored

320 (88.9 %)

319 (88.1 %)

334 (84.6 %)

Allowed

37 (10.3 %)

39 (10.8 %)

53 (13.4 %)

Outlier

3 (0.8 %)

4 (1.1 %)

8 (2.0 %)

 Verify3d [45, 46]

Passed (93.65 % of the residues had an averaged 3D–1D score > = 0.2)

Passed (91.21 % of the residues had an averaged 3D–1D score > = 0.2)

Passed (88.92 % of the residues had an averaged 3D–1D score > = 0.2)

Fig. 6

Homology models of L. donovani Inl-A-like proteins. Ribbon representation of L. monocytogenes Inl-A internalin domain (Uniprot ID: P0DJM0, PDB ID: 1O6S, chain A) (a) and of homology models of E9B7L9 (region: 212–573) (b), E9BMT7 (region: 394–757) (c) and E9BUL5 (region: 386–782) (d). Amino acid residues are coloured based on the nature of residues

Docking of L. donovani Inl-A-like proteins with internalin receptor E-cadherin (hEC1)

Since, homologous proteins may share protein interaction partners [63] here we have explored the possibility that the L. donovani Inl-A-like proteins might possibly interact with hEC1 as the L. monocytogenes Inl-A is known to interact with hEC1. E-cadherin fills the cavity created by the curved repeat domain of Inl-A and most of the repeats bind hEC1 with the help of hydrogen bonds but LRR3 and LRR10 do not bind, while LRR12-15 have a patch of aromatic amino acids forming extensive hydrophobic interactions with hEC1 [13].

We have used different approaches (varying protocols and scoring functions) for docking L. donovani Inl-A-like proteins with hEC1. Thus different scoring functions with various parameters allow us to evaluate the docked conformations, whereas ranking clusters based on higher number of similar frames is also suggestive of most likely interaction conformation between two proteins. Similar poses of L. donovani Inl-A-like proteins and hEC1 among the top three possible interaction poses (representative poses from clusters with higher number of frames or better docking scores namely Cluster I, Cluster II and Cluster III) were identified from the docking solutions of all the programs (Additional file 5: Table S3). Quality of protein-protein interaction predictions is generally assessed based on the Critical Assessment of Predicted Interactions (CAPRI) evaluation criteria, which utilize fraction of native contacts (Fnat) and ligand root mean square deviation (l-RMSD) to evaluate the performance of docking programs. In general, Fnat is determined by counting the number of contact residues that are retrieved in the predicted conformations between the two proteins with respect to the reference structure. Similarly, l-RMSD determined by calculating the RMSD on the backbone atoms of the smaller protein after fitting the model and the reference on the backbone atoms of the larger protein can be used to determine near native predictions. However, CAPRI criteria dictate 0.1 ≤ Fnat < 0.3 and l-RMSD ≤ 10 Å as acceptable predictions; 0.3 ≤ Fnat < 0.5 and l-RMSD ≤ 5 Å as medium quality predictions; and Fnat ≥ 0.5 and l-RMSD ≤ 1 Å as high quality predictions [52, 64]. Extending this paradigm based on the l-RMSD of the docked L. donovani Inl-A-like proteins with Inl-A structure we were able to postulate the likelihood of possible interaction between L. donovani Inl-A-like proteins and hEC1. Docked conformations when compared to L. monocytogenes Inl-A structure in complex with hEC1 were found to be within 5 Å for E9B7L9 (region: 212–573) and within 10 Å for E9BMT7 (region: 394–757) and E9BUL5 (region: 386–782), respectively (Fig. 7). These findings suggest the possibility of such interaction between L. donovani Inl-A-like proteins and hEC1. Further, the free energies of complex formation are -7.4, -8.8 and -10.4 kcal/mol for E9B7L9 (region: 212–573), E9BMT7 (region: 394–757), and E9BUL5 (region: 386–782) with hEC1, respectively, while it is -4.4 kcal/mol for Inl-A-hEC1 complex. However, on exploring the probable interaction forces that make these complexes stable we found hydrogen bond interactions as well as hydrophobic interactions in the predicted complexes of E9B7L9 (region: 212–573)-hEC1, E9BMT7 (region: 394–757)-hEC1 and E9BUL5 (region: 386–782)-hEC1 docked complexes (Fig. 8bd; Additional file 6: Tables S4 and S5). Thus, all the molecular docking based observations indicate that the selected L. donovani Inl-A-like proteins might interact with hEC1 in a similar manner as L. monocytogenes Inl-A (Fig. 8).
Fig. 7

Molecular docking of L. donovani Inl-A-like proteins with human E-cadherin (hEC1). Directed docking solutions from each program were ranked and compared to identify consensus poses from all three programs (PatchDock [50, 51] followed by FireDock [58] refinement, HADDOCK [5254] and ClusPro [5557]). Average docking scores (FireDock refined glob scores, HADDOCK scores and ClusPro balanced scores) for the top three significant clusters (Cluster I, Cluster II and Cluster III) are plotted against the ligand root mean square deviations (RMSD) between the crystal complex (PDB ID: 1O6S) and representative docked poses of hEC1 with the L. donovani Inl-A-like proteins a E9B7L9 (region: 212–573), b E9BMT7 (region: 394–757) and c E9BUL5 (region: 386–782)

Fig. 8

Probable docking conformations of hEC1 with L. donovani Inl-A-like proteins. a L. monocytogenes Inl-A internalin domain crystal structure in complex with hEC1 [PDB ID: 1O6S]. b-d Representative best-docked conformation of L. donovani Inl-A-like proteins with hEC1 similar to Inl-A-hEC1 interaction for E9B7L9 (region: 212–573) (b), E9BMT7 (region: 394–757) (c), and E9BUL5 (region: 386–782) (d). Key interacting residues probably involved in forming hydrogen bond interactions or hydrophobic interactions in E9B7L9-hEC1, E9BMT7-hEC1 and E9BUL5-hEC1 complexes are also shown. Ranked solutions from each program having ligand RMSD (l-RMSD) with Inl-A crystal structure within 10 Å were compared and best poses as predicted by HADDOCK [5254] are shown here as representatives. In cases where hydrogen bond forming residue overlaps with hydrophobic interaction forming residue precedence is given to hydrogen bond color code while the residue is shown in ball and stick conformation to indicate that it is probably involved in hydrophobic interaction as well

Discussion

Leishmania donovani, an intracellular protozoan, is one of the major pathogens causing visceral leishmaniasis worldwide [7]. In this context, previous studies have elucidated a number of proteins participating in the establishment of this infection, such as lipophosphoglycan, leishmanolysin, parasite surface antigen 2, A2 protein, amastin, amastin-like surface protein, cysteine protease B, etc. [26]. Genome sequencing studies and annotations may predict additional virulent proteins involved in this process [8]. During the establishment of parasitic infections, a number of different proteins may contribute to host-pathogen interactions. Therefore, herein we used sequence-based studies and phylogenetic analysis to identify potential virulence factors in L. donovani based on similarity to other known bacterial virulence factors. This study proposes the existence of an internalin-A-like class of proteins in L. donovani. Moreover, further studies in Kinetoplastida also indicated the presence of Inl-A-like class of proteins in other Leishmania spp. Elucidation of such a class of Inl-A-like virulence factors in L. donovani and/or other Leishmania spp. may provide a better understanding of the mechanism of infection(s) mediated by Leishmania spp. or L. donovani in particular.

Leishmania spp. as avirulent or metacyclic promastigotes can interact with different receptors like complement receptors (CR1 and CR3), mannose receptor (MR), fibronectin receptors (FnRs) on primary macrophages and dendritic cell-specific intercellular adhesion molecule-3-grabbing non-integrin (DC-SIGN) on dendritic cells for invasion [65]. However, they interact with Fc gamma receptors (FcγRs) in primary macrophages and dendritic cells for phagocytosis in amastigote stage [65]. It is plausible that these Inl-A-like proteins in Leishmania interact with E-cadherin or other receptors structurally similar to E-cadherin (like cadherin superfamily members) on host cell surface to promote their internalization. However, one might question the plausibility of such an interaction depending on whether cadherin is widely expressed on the skin or whether any of these leishmanial Inl-A-like proteins are expressed during initial stages of infection to be able to subsequently interact with host cell surface receptors. In particular, CDH1 (E-cadherin) is widely expressed in the skin, specifically in epidermal cells, langerhans cells, keratinocytes and melanocytes [66, 67]. Further, in this regard based on literature studies we have found that a close L. major homolog of E9BMT7 (95 % identical to E9BMT7) is present in the exosomes secreted in the midgut of the sand fly that are co-egested in the inocula with the parasite during the insect’s bite [68]. This observation indicates that leishmanial Inl-A-like proteins may be expressed in the promastigote stage and can possibly interact with host cell receptors to promote cell invasion during the initial stages of infection. However, direct experimental evidence to support this hypothesis, for instance, lower infectivity on knock down of these protein(s) or binding affinity assessment, on actual receptor or cadherin binding is lacking.

Moreover, following interaction with host cell receptors Leishmania spp. become internalized and utilize different mechanisms to infect and establish long term infections within these host cells by effectively suppressing and evading host immune responses. These different mechanisms of evasion may include modification of the complement system and phagocytosis, strategies to survive within phagosomes, interference in antigen presentation, modulation of cytokine and chemokine levels and modification of T cell responses. Additionally, Leishmania spp. alter host cell signalling pathways like toll-like receptor pathways, protein kinase C mediated signalling, JAK/STAT pathway activation via IFN-γ, MAPK signalling pathway etc. for establishing infection [69]. However, it is also likely that the leishmanial Inl-A-like virulence factors may aid in establishment and/or maintenance of infection by evading or suppressing host immune responses. In this respect one of our collaborators has studied the RNAseq expression profile of intracellular amastigotes isolated from infected murine resident peritoneal macrophages nearly 12 h post-infection. Interestingly in this unpublished work it has been found that the three L. donovani Inl-A-like genes are over-expressed when more virulent parasites are used to bring about infection as compared to infection by less virulent parasites. Moreover, the level of E9BMT7 gene shows significant fold change (> 1.5) in these two conditions and therefore it is likely that E9BMT7 or other L. donovani Inl-A-like proteins are associated with higher infectivity of L. donovani. However, direct experimental evidence elucidating the exact function of these proteins in the amastigote stage is lacking.

Our findings based on sequence and structure analyses raise the possibility that L. donovani Inl-A-like proteins may facilitate host cell infection by interaction with host cell receptors (for example E-cadherin). This possibility receives further support from experimental finding such as the leishmanial Inl-A-like proteins being secreted or exported in vesicles by promastigotes present in infected sand fly inocula. Additionally, these proteins could also aid in suppressing/evading host immune responses when expressed in amastigote stage. However, we believe further direct experimental characterization of the L. donovani Inl-A-like proteins and identification of their host-protein interaction partners (e.g. E-cadherin) will create new avenues for study. Moreover, in this way if one or more of the L. donovani Inl-A-like proteins are proven to be indeed important for the virulence of Leishmania spp., we may be able to develop other intervention strategies in leishmaniasis.

Conclusion

We used extensive sequence-profile and profile-profile comparison-based methods to predict the existence of L. donovani Inl-A-like proteins, which are possibly remote orthologs of L. monocytogenes Inl-A. We further explored the possibility whether L. donovani can adopt similar subversion mechanism in host cells utlizing the Inl-A-like protein-based interaction with human E-cadherin [hEC1]. We addressed this issue based on homology modeling of L. donovani Inl-A-like proteins and their subsequent docking studies with the host interaction partner (human E-cadherin [hEC1]) of their bacterial ortholog (Inl-A). Based on these analyses, we suggest the existence of a new group of virulence factor in L. donovani and other Leishmania spp. capable of employing a yet to be known mode of host invasion mechanism.

Abbreviations

ACP: 

Atomic contact potential

AIR: 

Ambiguous interaction restraints

BLAST: 

Basic local alignment search tool

BSA: 

Buried surface area

CAPRI: 

Critical assessment of predicted interactions

c-Evalue: 

Conditional E-value

CR1: 

First complement receptor

CR3: 

Third complement receptor

DARS: 

Decoys as the reference state

DC-SIGN: 

Dendritic cell-specific intercellular adhesion molecule-3-grabbing non-integrin

E-value: 

Expect-value

FcγRs: 

Fc gamma receptors

FFT: 

Fast-fourier transform

Fnat: 

Fraction of native contacts

FnRs: 

Fibronectin receptors

HADDOCK: 

High ambiguity driven protein-protein docking

hEC1: 

Human E-cadherin

HHblits: 

HMM-HMM–based lightning–fast iterative sequence search

HMM: 

Hidden Markov Model

i-Evalue: 

Independent E-value

Inl-A: 

Internalin-A

l-RMSD: 

Ligand root mean square deviation

LRR: 

Leucine rich repeat

MAC: 

Maximum accuracy

MR: 

Mannose receptor

NR: 

Non redundant

PDB: 

Protein data bank

RMSD: 

Root mean square deviation

SCOP: 

Structural classification of proteins

VFDB: 

Virulence factor database

Declarations

Acknowledgements

Authors thank Shreemoyee Dutta Majumdar for her initial involvement in the project. SC acknowledges CSIR-Indian Institute of Chemical Biology (IICB) for infrastructural support. We would also like to thank our colleagues Dr Nahid Ali and Md Shadab (CSIR-IICB) for sharing gene expression information regarding the L. donovani internalin-A-like proteins from their transcriptomic profile study.

Funding

This work was supported from the network project (BSC0114) of Council of Scientific and Industrial Research, New Delhi, India. IM and AC are thankful to CSIR for their fellowship. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Availability of data and materials

The dataset(s) supporting the conclusions of this article are included within the article and its additional file(s).

Authors’ contributions

SC, IM and AC formulated the study design. IM performed the experiments. SC and IM analyzed and interpreted the results. The manuscript was prepared by IM and SC. All authors read and approved the final version of the manuscript.

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Not applicable.

Ethics approval and consent to participate

Not applicable.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Structural Biology and Bioinformatics Division, Council for Scientific and Industrial Research (CSIR) - Indian Institute of Chemical Biology (IICB)

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