Investigating the Taenia solium Fatty Acid Binding Protein Superfamily for Their Immunological Outlook and Prospect for Therapeutic Targets

Abstract

Taenia solium, like other helminthic parasites, lacks key components of cellular machinery required for endogenous lipid biosynthesis. This deficiency compels the parasite to obtain all of its lipid requirements from its host. The passage of lipids across the cell membrane is tightly regulated. To facilitate effective lipid transport, the cestode parasite utilizes certain lipid binding proteins called FABPs. These FABPs bind with the lipid ligands and allow the transport of lipids across the membranes and into the cytosol. Here, by integrating a computational with homology protein prediction tools, we had identified five FABPs in the T. solium proteome. We confirmed their presence by RNA expression analysis of respective genes from the parasite's cysticerci transcript. During the molecular modeling and MD simulation studies, two of them, TsM_000544100 and TsM_001185100, were most stable. Furthermore, they had a robust interaction with the IgG1 molecule, as evidenced by MD simulation. In addition, by employing in silico screening, we had identified potential ligand interacting residues that are present on the probable druggable site. In combination with in vitro cysticidal assays, enalaprilat dihydrate showed efficacy against cysticerci, which suggests that FABPs play a significant role in the cysticercus life cycle. Together, we provided a detailed distribution of all FABPs expressed by T. solium cysticerci and the critical role of TsM_001185100 in cysticercus viability.

Figure 1

Pipeline for predicting the Taenia solium fatty acid binding protein superfamily and analysis of immunological property and use as potential therapeutic targets.

Figure 2

T. solium expresses five fatty acid binding proteins in its proteome. The secondary structure of the proteins was predicted using the Phyre2 server where all proteins have 2 α-helices and 10 β-sheets (except TsM_000713700 that has 9 β-sheets). For secondary structure prediction, a query on the hidden Markov model was applied, i.e., looking for similar structure in the existing database.

Figure 3

All five predicted proteins were compared with FABP proteins from other species including H. sapiens. (A) Multiple sequence alignment was performed using the clustalW module of the MEGA software. Amino acid extensions are indicated in red boxes, whereas the C-terminal gap in TsM_000713700 is indicated with a black box. (B) Maximum likelihood phylogenetic tree analysis of all predicted proteins was constructed using the MEGA software (version 10). The maximum likelihood tree was constructed for all five T. solium FABPs (C) and all five T. solium FABPS, E. granulosus FABP, M. corti hypothetical protein, T. asiatica hypothetical protein, S. mansoni Sm14, F. hepatica Fh15 protein, H. sapiens FABP9, and H. sapiens PMP2 protein. The Jones–Taylor–Thornton (JTT) model was used with the nearest-neighbor-interchange (NNI) as the tree inference method to construct the phylogenetic tree.

Figure 4

Molecular modeling was done using AlphaFold in automated mode. (A) All modeled structures were visualized using the UCSF chimera software, and secondary structures were represented using different colors (green: α-helix, red: β-sheets, blue: loop region) along with their Ramachandran plot allowed percentages. As shown in the figure, all five predicted TsMFABPs have a common β-barrel-like structure with two extended α-helices. (B) Structural error analysis (negative value indicates stable structures) of respective proteins.

Figure 5

Docking study was performed of modeled FABPs with the immunoglobulin IgG1 Fab region (3FZU: PDB ID) using the HawkDock server. (A) FABPs were docked on IgG1 Fab regions using the HawkDock server. (B) Docked complexes are shown in the figure with their respective hydrogen bonding status and amino acid residues involved in hydrogen bonding using the LigPlot software.

Figure 6

TsMFABPs (TsM_000544100 and TsM_001185100) showed stability with the IgG1-Fab region and was highly immunoreactive to NCC positive patient serum samples. The stability of predicted proteins complexed with the IgG1 Fab region was analyzed using molecular dynamics simulation. (A–E) These figures show the comparative MD simulation trajectories of all five predicted proteins, all five proteins complexed with IgG1 immunoglobulin, and IgG1 alone over the time period of 100 ns (0.15 M NaCl solution as the buffer system). (F) The cumulative stability plots of all complexes for comparative analysis. RMSD data were plotted using the Origin 9.0 software.

Figure 7

Recombinant TsM_001185100 (TsFABP1) showed immunoreactivity against NCC positive serum samples. r-TsM_001185100 was purified using Ni-NTA affinity chromatography. (A). SDS-PAGE and Coomassie brilliant blue (CBB) stained image of different fractions purified using increasing concentrations of imidazole. (B). Purified r-TsM_001185100 is an approximately 16 kDa protein in CBB stained SDS-PAGE gel electrophoresis. (C). EITB blot of r-TsM_001185100 showed that the protein is highly cross-reactive against NCC positive patients’ serum samples.

Figure 8

FABP expression at the RNA stage and investigation as therapeutic targets (A). Cyst isolation, RNA extraction, and agarose gel electrophoresis image showing the expression of each TsMFABP after PCR of respective cDNA from the total RNA of T. solium RNA. The druggable site was predicted using the sitemap module of the Schrödinger software. Post druggable site prediction, four known fatty acid ligands were docked to identify ligand binding residues. Docking of ligands into the predicted site was done using the Maestro Glide module. (B) As seen in the snapshot images, Arg107, Arg127, and Tyr129 of TsM_001185100 form interaction with the ligands, and Arg140 of TsM_000544100 forms interaction with the ligands. We did not see any ligand binding residue of TsM_000544100 forming interaction with the palmitate. The druggable site was predicted using the sitemap module of the Schrödinger software. DrugBank-approved and investigational drug library was screened against the druggable site using the Glide module, and resultant drugs were then searched in the literature for their blood–brain barrier penetration abilities. (C). Interaction diagram of riboflavin against TsM_000544100. (D) Interaction diagram of enalaprilat and β-nicotinamide mononucleotide against the TsM_001185100.

Figure 9

FABP effectively serves as a potential therapeutic target. (A). Cyst viability was decreased with enalaprilat dihydrate (4 μg/mL) and β-nicotinamide mononucleotide (16 μg/mL) but not with riboflavin. (B). Scanning electron microscopy revealed the altered surface morphology of the cyst wall after incubation with compounds as indicated.

Figure 10

MD simulation analysis suggests the stable interaction between TsM_001185100 and enalaprilat dihydrate and β-nicotinamide mononucleotide where the compounds are predominantly interacting in the β-barrel region. (A) RMSD plot of protein ligand complexes for 100 ns time period. (B) RMSF plot showing minor fluctuations in amino acids of proteins during simulation. (C) Colored histogram plot of multiple interactions formed by amino acid residues of FABPs with selected ligands. (D) The 100 ns contact-based timeline depiction of entire contacts made by TsMFABPs with selected compounds. In the MD (molecular dynamics) simulation study, both the ligands, β-nicotinamide mononucleotide and enalaprilat dihydrate, showed stable interaction with the TsM_001185100 protein for 100 ns time, and Arg107, Arg129, Tyr129, and Glu73 were the predominant ligand interacting residues for the entire time frame of simulation (A–D).