In silico identification of novel therapeutic targets from secretome analysis of parasites
thesisposted on 28.03.2022, 15:22 authored by Gagan Garg
The secretome of an organism is defined as the subset of proteins secreted by its cell, usually known as excretory/secretory (ES) proteins. These proteins play an important role in producing clinical infections inside the host organism during parasite attack. ES proteins are the choice of new therapeutic solutions for different clinical infections, especially in the case of parasitic and fungal infections because these proteins are present at the hostparasite interface and act as immunoregulators to host immune recognition for parasite survival inside the host organism. An in-depth study of ES proteins will lead to better understanding of host-parasite relationships and the molecular biology of parasites while their functional annotation can help identify therapeutic molecular targets to control parasitic infections with minimum host side effects. This thesis focusses on the role of the secretome in the molecular interplay between parasites and their respective hosts. As the involvement of ES proteins as a key factor in parasitism, my focus was to predict, annotate and analyse parasitic excretory/secretory (ES) proteins for the prediction of novel therapeutic solutions against parasitic infections using transcriptomic data. To achieve these goals, I have carried out an initial review of the different approaches for secretome analysis, compiling recent secretome data available for parasites and application of bioinformatic tools to parasites Based on this review, we carried out a preliminary analysis on Echinococcus multilocularis and Echinococcus granulosus by integrating SecretomeP, the most widely used program for non-classical ES protein prediction, into an existing pipeline, EST2Secretome. However in case of parasites, SecretomeP is not able to completely predict non-classical secretory proteins, as shown in the other parasitic secretome studies. To address these issues, we developed a new secretome analysis approach, which use sequence similarity search against experimentally identified ES proteins collected from literature along with computational prediction.The updated approach was applied to 454 transcriptomic data of Strongyloides ratti, which is a gastrointestinal nematode that infects rats and used as a model to study human stronglyloidasis. By integrating different computational tools together we were able to study ES proteins comprehensively in S. ratti transcriptome. The analysis revealed the involvement of S. ratti ES proteins in pathways such as purine metabolism and glutathione metabolism, which are important for parasite survival inside the host Our updated computational approach was applied to analyse largest publicly available helminth transcriptome data from dbEST. From 870,223 ESTs for 78 helminth species, predicted ES proteins along with annotation results were compiled as a database (Helminth Secretome Database). This unique resource is a collection of 18,992 helminth ES proteins and freely available to scientific community. The bioinformatics approach developed has been applied to novel transcriptome datasets of two parasitic organisms. Our primary computational application was first applied to the analysis of transcriptome data from the infective third larval stage (L3i) of Strongyloides stercoralis. This dataset is the first transcriptome of L3i of S. stercoralis, using 454 sequencing coupled with a semi-automated bioinformatic analyses. Along with ES proteins we carried out functional annotation of all putative proteins translated from 11,250 contiguous sequences, of which most were novel. Secondly, we studied the transcriptome of the adult stage of Echinostoma caproni generated using 454 sequencing. Our bioinformatic workflow was employed to predict 3,415 putative ES proteins and potential therapeutic targets. With the advent of next-generation sequencing technologies, massive sequencing datasets have been generated. Despite this increase in sequence data, several proteins remain unannotated, as hypothetical proteins. To fill this gap we have extended our bioinformatics workflow with other relevant computational tools for the annotation of hypothetical proteins and the prediction and analysis of secreted proteins as therapeutic targets. This protocol was applied to pathogenic fungi, Cryptococcus gattii and Cryptococcus neoformans var. grubii, causative agents of disease (cryptococcosis) in healthy, immunocompetent and immunosuppressive humans. In conclusion, an updated computational secretome analysis approach using transcriptomic or proteomic data was developed to significantly reduce the time taken for secretome analysis, improved annotations at the protein function levels and identified key ES proteins inferred to be involved in parasite-host interactions. Several novel candidates for parasite intervention were discovered during these analyses.