Epitope mapping from Mycobacterium leprae proteins: Convergent data from in silico and in vitro approaches for serodiagnosis of leprosy
Introduction
Leprosy, also known as Hansen’s disease, is a chronic granulomatous infection caused by two acid-fast bacilli: Mycobacterium leprae and the recently described M. lepromatosis. This infection mainly affects the skin and peripheral nerves, although the eyes, mucous membranes, and bones may be affected (Maymone et al., 2020).
As an obligate intracellular microorganism, the bacillus induces a dedifferentiation process of Schwann cells into mesenchymal cells resulting in the demyelination of the peripheral nerves and, consequently, in anaesthesia or hypoesthesia (White and Franco-Paredes, 2015). This phenomenon explains why the loss of sensation in a hypopigmented skin lesion is a key signature for clinical diagnosis in addition to thickening of the peripheral nerves or the presence of acid-fast bacilli in skin smear examination. Currently, these indicators remain the basis for the clinical diagnosis of leprosy (Maymone et al., 2020).
Based on the host-pathogen interaction, the spectrum of leprosy presentation is dependent on patient immunity at the level of the cell-mediated response to bacillus, i.e., whether this is a Th1 or Th2 phenotype. In the Th1 phenotype, associated with paucibacillary (PB) leprosy, the patient generally presents low antibody titres following INF-γ release and a powerful cell-mediated immunity that restricts the bacilli growth and the number of lesions. Whereas, the Th2 profile, referred to as multibacillary (MB), is characterized by release of IL-4 and IL-10, detectable antibody titres to M. leprae antigens, and reduced cell-mediated immunity leading to the widespread dissemination of the bacilli in addition to numerous lesions (Walker and Lockwood, 2006).
M. leprae infection is highly responsive to the therapy but late diagnosis may result in irreparable physical disabilities due to neural impairment. Thus, serological biomarkers that promote early detection of leprosy may complement clinical diagnosis, help conduct therapy, or guide epidemiological control measures (de Souza et al., 2016).
Epitope-based synthetic peptides identified from the reactivity to patient antibodies have been exploited as alternative targets to serodiagnosis of neglected tropical diseases, including those of parasitic (Feliciano et al., 2016; Link et al., 2017; Prado et al., 2018), viral (Amrun et al., 2019; Bergamaschi et al., 2019), and bacterial origin (Joung et al., 2020; Sachse et al., 2018) such as leprosy (Alban et al., 2014; de Santana et al., 2018).
The B-cell epitope, also known as the antigenic determinant, refers to the restricted portion of a molecule against which specific antibodies react to. Based on the spatial structure, the epitope can be conformational (discontinuous) or linear (continuous), i.e. when amino acid residues are close together because of protein folding or disposed contiguously in a peptide stretch, respectively (Atassi and Smith, 1978).
Experimental B-cell epitope mapping may provide molecular information on the interactions between the epitope and paratope that are implicated in the formation of the antigen-antibody complex and help define parameters to develop more accurate algorithms for in silico prediction of immunodominant domains of antigens (Potocnakova et al., 2016).
Accordingly, this study aimed to identify linear B-cell epitopes from two M. leprae-derived antigens by using in silico prediction tools and compare these results to in vitro experiments. Therefore, linear epitopes from the M. leprae proteins (Ag85B or ML2028 and ML2055) were experimentally mapped using peptide-scanning techniques where overlapping peptides spanning the entire sequence are individually tested for antibody interacting residues.
Both molecules are secreted proteins involved in pathogenesis and in the stimulation of immune responses during mycobacterial infections. Ag85B (ML2028), the most abundant protein of the antigen 85 complex (Ag85A, Ag85B and Ag85C), is characterized as a 30 kDa fibronectin- and elastin-binding protein with mycolyltransferase activity highly conserved across all mycobacterial species (Content et al., 1991; Lima et al., 1991). ML2055, also known as alanine and proline-rich protein (Apa) or MPT32, is another fibronectin attachment protein which is involved in adherence to host cells and internalization of mycobacteria (Schorey et al., 1995).
Our in vitro findings related the antigenicity of Ag85B and ML2055 peptides with antibodies from patients with leprosy have identified several immunodominant regions that were also indicated by the in silico prediction, thereby demonstrating agreement between data obtained by experiment and simulation.
Section snippets
In vitro analysis of B-cell linear epitopes of Ag85B and ML2055 proteins
Immunoglobulin G (IgG) from serum pooled from either healthy or leprosy patients were precipitated as previously described (Harlow and Lane, 1988). Using the SPOT synthesis technique (Frank, 1992), overlapping pentadecapeptides frameshifted by 3 residues that covered the primary sequence from Ag85B (ML2028, GenBank accession number: CAA43269.1) and ML2055 (GenBank accession number: P46842) proteins were synthesized on cellulose membrane with an automated synthesizer (Intavis Bioanalytical
Epitope mapping using IgG antibodies from patients with leprosy
The M. leprae proteins Ag85B and ML2055 and were synthesized over cellulose membranes as overlapping pentadecapeptides and assayed against IgG antibodies derived from sera from patients with leprosy and from healthy volunteers. Epitope mapping identified two main immunodominant regions in both proteins corresponding to the reactive spots numbered as 6–8 for protein Ag85B (Fig. 1A) and 5–7 for protein ML2055 (Fig. 2A). Densitometry analysis of the Ag85B (Fig. 1B) and ML2055 (Fig. 2B) membranes
Discussion
The ability of antibodies to discriminate between diverse pathogens has aroused interest in identifying specific antigenic determinants (epitope mapping) for the development of immunodiagnostic tools (Kapingidza et al., 2020). Advances in this field with computational prediction have provided molecular understanding about the antigen-antibody interaction (Potocnakova et al., 2016).
The role of immunoinformatics is even more relevant when information concerning the immunogenic targets is limited,
CRediT authorship contribution statement
B.A. Soares and K.N. Teixeira: Conceptualization, Formal analysis, Investigation, Methodology, Writing - original draft. J.F. de Santana: Methodology, Formal analysis, Investigation, Writing - original draft. B.L.M. de Assis: Methodology, Investigation. C. Z. Ribeiro: Methodology, Investigation, Writing - original draft. J.P.S. Scandelari: Methodology, Investigation. V. Thomaz-Soccol: Conceptualization, Formal analysis, Funding acquisition, Review. R.A. Machado-de-Ávila: Methodology, Formal
Funding
This study was supported by the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Brazil (grant No. 409728/2018-7) and by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), Brazil - Finance Code 001.
Declaration of Competing Interest
The authors declare no competing financial interest.
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These authors contributed equally to this work.