The search for diagnostic biomarkers has been profiting from a growing number of high quality sequenced genomes and freely available bioinformatic tools. These can be combined with wet lab experiments for a rational search. Improved, point-of-care diagnostic tests for visceral leishmaniasis (VL), early case detection and surveillance are required. Previous investigations demonstrated the potential of IgG1 as a biomarker for monitoring clinical status in rapid diagnostic tests (RDTs), although using a crude lysate antigen (CLA) as capturing antigen. Replacing the CLA by specific antigens would lead to more robust RDTs. Immunoblots revealed L. donovani protein bands detected by IgG1 from VL patients. Upon confident identification of these antigens by mass spectrometry (MS), we searched for evidence of constitutive protein expression and presence of antigenic domains or high accessibility to B-cells. Selected candidates had their linear epitopes mapped with in silico algorithms. Multiple high-scoring predicted epitopes from the shortlisted proteins were screened in peptide arrays. The most promising candidate was tested in RDT prototypes using VL and nonendemic healthy control (NEHC) patient sera. Over 90% of the proteins identified from the immunoblots did not satisfy the selection criteria and were excluded from the downstream epitope mapping. Screening of predicted epitope peptides from the shortlisted proteins identified the most reactive, for which the sensitivity for IgG1 was 84% (95% CI 60—97%) with Sudanese VL sera on RDT prototypes. None of the sera from NEHCs were positive. We employed in silico searches to reduce drastically the output of wet lab experiments, focusing on promising candidates containing selected protein features. By predicting epitopes in silico we screened a large number of peptides using arrays, identifying the most promising one, for which IgG1 sensitivity and specificity, with limited sample size, supported this proof of concept strategy for diagnostics discovery, which can be applied to the development of more robust IgG1 RDTs for monitoring clinical status in VL.
The leishmaniases comprise a group of vector-borne diseases caused by parasites of the genus Leishmania. The visceral form—visceral leishmaniasis (VL), also known as kala-azar (Hindi for ‘black fever’) affects internal organs such as liver, spleen and bone marrow, and leads to death if left untreated. VL is caused by parasites of the Leishmania donovani complex and in 2015 over 23. 000 new cases were reported to the world health organization (WHO) worldwide [1] while an overall case-fatality rate of 10% has been estimated [2]. VL is diagnosed by a combination of clinical symptoms, including prolonged fever, weight loss, hepatosplenomegaly and malaise, and the detection of parasite-specific immunoglobulins (Igs). The recombinant protein rK39, a fragment of a Leishmania infantum kinesin-like gene, was described in 1993 [3] and remains the most widely used antigen for VL serodiagnosis. Nonetheless, novel and improved antigens are still required to complement the use of the rK39, to improve sensitivity in eastern Africa [4,5] and to determine cure after successful chemotherapy (versus relapse). The recombinant chimeric protein rK28, which is a derivative of the rK39, incorporating the first two repeats of a Sudanese L. donovani kinesin flanked by proteins HASPB1 and HASPB2, was engineered to address low sensitivity values reported from eastern Africa. Although a slight better performance compared to the rK39, both in terms of sensitivity and specificity, was reported with Sudanese VL patients [6], important variations in sensitivity amongst different rK39 rapid diagnostic test (RDT) manufacturers were also reported from that region [7]. Moreover, false positive rates as high as 19. 5% in eastern African patients were described [8], meaning that further investigations are still required to confirm the relevance of the rK28 as well as its superiority over the rK39-based diagnostic tests for VL in eastern African patients. Due to Igs persistence even after complete parasite clearance [9–13], neither the rK39 nor the rK28 commercial diagnostic kits can be employed to determine cure after successful chemotherapy. There has been a dramatic reduction in genome sequencing costs, accompanied by an exponential increase in the number of available sequences in public repositories [14,15]. The first Leishmania spp. genome sequencing was completed in 2005 [16]. More recently, the advent of high-throughput technologies has enabled the completion of the whole genome sequencing of L. donovani, the causative agent of VL in the Indian subcontinent [17]. Moreover, the availability of next-generation sequencing made it possible to perform whole transcriptome sequencing. RNA sequencing (RNA-seq) generates data on the transcriptome at a specific stage of a pathogen life cycle or in a specific culture condition of an organism. The growing number of available, high quality, whole genome sequences has become a central element in the area of comparative genomics, which has contributed greatly to a better understanding of multiple aspects of the leishmaniases, including determinants of disease phenotype [18], mode of action of drugs [19] and parasite biology [20]. Igs are a major component of the immune system. They bind specific regions of pathogens’ proteins (epitopes), tagging them for clearance by the immune system. Epitopes can be divided into linear (a continuous stretch of amino acids (AAs) ) and discontinuous (where non-proximal residues are brought together by protein folding) and can be identified by functional and structural studies (e. g. X-ray crystallography), while in silico epitope prediction algorithms are gaining popularity. The early prediction methods of linear B-cell epitopes were mainly based on propensity scales [21]. More recently, machine learning methods have been employed to improve prediction performance [22–24]. The prediction of discontinuous epitopes still depends on the availability of 3D structures. In silico tools can also be used to locate antigenic domains from DNA sequences or peptide sequences influencing protein localisation within cells. Diagnostic research can incorporate genomics, transcriptomics, proteomics as well as bioinformatic prediction algorithms for the discovery of new biomarkers. Such a systematic ‘omics’ approach has been applied alone for the discovery of vaccine candidates [25–27] as well as for diagnostic biomarkers [28]. These in silico searches can also be applied downstream of wet lab experiments, refining their output for a rationalised search. Mass spectrometry (MS) can be used to identify proteins from wet lab experiments (e. g. immunoblots). Comparative genomics enables identification of species-specific genes, while levels of life-stage specific proteins can be estimated using publicly available RNA-seq data. In silico prediction algorithms can be employed to infer protein localisation, search for antigenic domains as well as to predict linear B-cell epitopes. Synthetic peptides can then be incorporated into arrays, enabling the screening of a large number of candidates in pilot serological assays. Promising candidates can be adapted and tested in RDTs, a format suitable for field use. Here we employed comparative genomics as well as in silico algorithms to reduce drastically the excessive number of L. donovani protein candidates recognised by human IgG1, which has been shown to be a potential indicator of post-chemotherapeutic relapse in VL, using RDT prototypes sensitised with a L. donovani crude lysate antigen (CLA) [29]. By screening a large number of predicted epitopes from selected candidate antigens containing desired protein features, we identified one peptide specifically recognised by IgG1 in arrays. This peptide was also tested in prototype RDTs with VL patient sera as well as sera from nonendemic healthy controls (NEHCs), showing promising results of both sensitivity and specificity on a small sample size. Thus we propose that this approach is a valid proof of concept for the discovery of diagnostic peptides, which can be used to replace the CLA in IgG1 RDTs, leading to the production of cheaper and more robust RDTs for monitoring clinical status in VL.
In India, the collection of samples was approved by the Ethics Committee of Banaras Hindu University, Varanasi while in Sudan the approval for collection and research was granted by the Ethical Research Committee, Faculty of Medicine, University of Khartoum and the National Health Research Ethics Committee, Federal Ministry of Health, Sudan. Written informed consent was obtained from all adult subjects included in the study or from the parents or guardians of individuals less than 18 years of age. This research was also approved, as part of the EC NIDIAG project, by the London School of Hygiene and Tropical Medicine Ethics Committee as well as by the Ethics Committee of the Antwerp University Hospital. A detailed description of all serum samples used in this work can be found below while a summary is shown in Table 1. L. donovani promastigote antigens recognised by human IgG1 were profiled using western blots as described in the repository protocols. io (dx. doi. org/10. 17504/protocols. io. u8rezv6). Briefly, the cytosolic proteins from a L. donovani (strain MHOM/IN/80/DD8) whole cell lysate were separated by SDS-PAGE, blotted onto a nitrocellulose (NC) membrane and sliced into individual strips, to be immunoassayed with sera from individual patients from India. Upon visual identification of bands of interest on the individual strips using HRP-labelled anti-human IgG1 as secondary antibody, new gels were run and corresponding immunogenic bands were excised and analysed by mass spectrometry (MS) (syn. liquid chromatography tandem mass spectrometry (LC-MS/MS) ), according to the methods described in S1 Text. Protein hits were identified by matching peptide fragments against the L. donovani reference genome-derived proteome only (ENA accession nos. FR799588-FR799623 [17]), henceforth referred to as LdBPK v1 genome. These proteins were submitted to the in silico filter detailed below. Desired protein features were searched in silico in order to decrease the number of candidates from the MS output while shortlisting the proteins more likely to be antigenic, to have their B-cell epitopes mapped with in silico prediction algorithms. Some protein features were searched in series while others in parallel, meaning that all shortlisted proteins satisfy the criteria described in either branch as shown in Fig 1. These shortlisted v1 IDs were then merged with the IDs from an improved LdBPK282 reference genome (ENA accession number ERP022358, henceforth referred to as LdBPK v2) according to the correspondence in Data Set 2 from [30]. Hypothetical proteins were excluded, unless containing tandem repeats (TRs). Additional information on each step of this in silico filter are detailed in the following subsections. Four in silico algorithms were used independently to define B-cell epitopes: ABCPred [23], Bepipred1. 0 [22], EpiQuest-B [36] and lbtope [37]. For all epitopes predicted from each algorithm individually (generally a 15 or 16mer), a ‘core sequence’, stretching from the fifth to the eleventh AA residue was defined and scanned against the rest of the predicted antigenic peptides, in order to identify peptides of similar sequences. These peptides sharing core sequences with other predicted peptides at any position in the AA sequence are henceforth referred as partially overlapping peptides. While ABCPred, lbtope and EpiQuest-B were run on default values, predicting antigenic sequences of 16,15 and 16 AA residues, respectively, shorter sequences with high antigenic scores were also considered for EpiQuest-B. The highest scoring peptides predicted from Bepipred were obtained by calculating the area under the curve (AUC) from plots of AA residue position vs. individual residue score for all possible combinations of 16mers with one AA offset, considering all contiguous predicted antigenic sequences from all submitted proteins. Additional details about the prediction algorithms employed can be found in Table 2. Desalted peptides i. e. of varying purity grades were synthesised with an N-terminal biotin molecule linked via AAG spacer so that they could bind to NC membranes (1620215, Bio-Rad, USA), which had been previously soaked with neutralite avidin (NLA) (NLA30, e-proteins, Belgium) and dried at 50°C. Lyophilised peptides were dissolved according to standard protocols [38]. Where the solvent mixture in which the peptides were dissolved exhibited stable background values, the peptide concentration was calculated based on their molar absorbances, measured either at 280 nm, for tyrosine (Y) and tryptophan (W) containing peptides [39], or at 205 nm [40], for those without W or Y. For the remaining peptides, the concentration was calculated by dividing the peptide amount reported by the manufacturer by the volume of the solvent mixture in which they were dissolved. Any promising candidate identified from the pilot peptide screening was synthesised with higher purity grade (>90%) and with a N-terminal biotin—polyethylene glycol-glycine spacer in order to improve water solubility as well as to increase rotation and ensure that the full amino acid sequence could freely interact with Igs, as opposed to being adsorbed onto the solid support and therefore unavailable for recognition by Ig [41]. RDTs were composed of a NC strip sensitised with NLA at 3. 5 mg/ml and a conjugate pad, impregnated with anti-human IgG1-specific antibody conjugated to 40nm gold beads (nanoQ, Belgium). The strip was either housed within a plastic cassette, with a buffer application well and a test/reading window (cassette), or not (dipstick). Prior to application on the RDT, equal volumes of serum and the biotinylated peptide at stock concentration were mixed and incubated at 37°C for 15 minutes. 3. 5 μl of the mix was then pipetted onto the sample application zone, just above the top of the sample pad and at the bottom end of the NC strip. 150 μl of buffer solution was dispensed into the buffer application well (cassette) or dipsticks were dipped into a recipient vessel filled with same volume of running buffer. After 15 minute incubation, a test was deemed valid if a clear red control band was present in line with the ‘C’, and deemed positive if a second band was present in line with the ‘T’. If no band was visible at the ‘T’ and a clear control band at ‘C’ developed, then the test was deemed ‘negative’. Invalid tests were assessed by the absence of the red control band ‘C’ or by any other clear migration problem. All statistical analyses were performed using the computing environment R [43]. The final fluorescence of each spot on the peptide arrays (spot fluorescence) was expressed in arbitrary units and calculated by subtracting the mean background value of a given acquired image from the individual fluorescence values. Mean background value was calculated from the mean of six-eight fluorescence measures in random positions on the arrays where no fluorescence signal was detected. The peptide specificity was expressed as 95% Fiellers confidence intervals (CIs) of the ratio between the mean spot fluorescence of eight replicates from the arrays hybridised with VL serum and that of arrays hybridised with NEHC serum (ratio VL/NEHC). CIs were calculated with the mratios R package [44]. RDT results were compared with defined clinical status to establish sensitivity and 95% CIs with Clopper-Pearson exact method using the PropCIs R package [45].
The development of the western blot strips immunoassayed with active Indian VL serum samples and relapsed patients revealed the protein bands detected by human IgG1. New gels were run in order to get three selected antigenic bands (B1, B2 and B3) excised (Fig 2), to ultimately have their constituent proteins revealed by MS. Criteria to select the antigenic bands were (I) strong recognition, visually assessed by their intensity, (II) ubiquitous recognition i. e. recognised by the majority of the patients and (III) confident match between the bands from the western blots and the new gels. A total of 1357 putative L. donovani proteins were identified from the three selected antigenic bands, with some identified in multiple bands (redundant matches). All these candidate antigen proteins were submitted to the in silico filter (Fig 1) to reduce the number of promising candidates while restricting to those possessing protein features that correlate with antigenicity. The initial 1357 IDs from the three selected antigenic bands (Fig 2) contained 678 unique IDs that matched the LdBPK v1 genome (we only matched the peptides identified by MS against the LdBPK v1 genome, such that the analysis returned L. donovani protein hits only). Out of these, 538 were considered to be constitutively expressed in both amastigote and promastigote life stages following gene expression analysis, of which 209 had a MASCOT score ≥ 100. Sixty six of these candidates were found to be secretory, 60 of which as part of exosomes while 6 where actively secreted to the outside of the cell via the presence of a signal peptide. These 66 unique v1 IDs were merged with the corresponding IDs from the improved LdBPK v2 reference genome, making 62 annotated extracellular and constitutively expressed proteins, identified with confidence by MS, as shown in Fig 3. Further three unique candidates harbouring TR domains were found to be constitutively expressed and were identified with confidence by MS from the excised bands. Although none of the three was found to be extracellular, they were kept for downstream epitope mapping as such proteins containing TR domains are often B-cell antigens [46]. A single TR protein satisfying all the criteria described remained unannotated in the improved LdBPK v2 reference genome. Results including the number of hits satisfying all the criteria applied are schematically shown in Fig 3 while the detailed list with all the 65 shortlisted proteins can be found in S1 Table. All 65 shortlisted proteins were profiled with the B-cell prediction algorithms as detailed in the methods section. Considering partially overlapping peptides, only that of highest score was shortlisted. The top 20 scoring peptides, with no shared core sequences within a given algorithm, were selected for synthesis, making a total of 80 high scoring peptides from four different prediction algorithms, as shown in S2 Table.
Based on the interpretation of the results of our experiments we conclude that:
Visceral leishmaniasis (VL) is a neglected tropical disease caused by protozoan parasites of the Leishmania donovani complex. Without treatment, VL is fatal. Although diagnostic techniques, mainly based on the detection of anti-Leishmania antibodies are available, invasive procedures such as microscopy from spleen or bone marrow aspirates are still required for the diagnosis of seronegative VL suspects, for the detection of recurrent cases and to confirm cure after successful treatment. Previous investigations showed the potential of IgG1 as a biomarker of post-chemotherapeutic relapse for VL in rapid diagnostic tests (RDTs) sensitised with crude lysate antigen (CLA). Here we employed in silico tools to search for desired protein features in a large number of L. donovani antigens detected by human IgG1 in western blots. We then employed prediction algorithms to profile epitopes from the shortlisted proteins. We screened a panel of high-scoring peptides in a high-throughput manner using arrays, with low reagent consumption. The most reactive peptide was adapted to RDTs, showing promising results of both sensitivity and specificity. This peptide has the potential of replacing the CLAs in IgG1 RDTs. Thus we believe that in silico tools can be used to optimise wet lab experiments for a rational search of biomarkers.
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Refining wet lab experiments with in silico searches: A rational quest for diagnostic peptides in visceral leishmaniasis