Causative Agent Resistant To Wear Cyst Fluid Protein Purific | 93098
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Causative Agent Resistant To Wear Cyst Fluid Protein Purification Investigation By Shotgun LC-MS/MS Taenia Solium Cysts From Pigs' Skeletal Muscle And Central Nervous System Were Subjected To Quantitative Multiplexed Proteomics.


Shuchang Yichen*

The larval stage (cysts) of the tapeworm Taenia solium, which causes human and porcine cysticercosis, can infest a variety of tissues, including the skeletal muscles and the central nervous system (CNS) (SM). The proteome alterations brought on by the cyst's tissue localisation in the host tissues have received less attention. The ability to assess global proteome alterations in response to various situations is strength of quantitative multiplexed proteomics. Here, we used a TMT-multiplexed method to identify and quantify more than 4,200 proteins, 891 of which were host proteins, in cysts isolated from the SM and CNS of pigs. This is the most comprehensive host and parasite protein mixing associated with tapeworm infections that have been documented to our knowledge. Skeletal muscle cysts were enriched in a number of cysticercosis antigens, including GP50, paramyosin, and a calcium-binding protein. Our findings pointed to the existence of tissue-enriched antigens that could help advance cysticercosis immunodiagnosis (Stryi?ski R et al., 2020). We chose 42 highly antigenic proteins enriched for each tissue localization of the cysts using a variety of epitope identification techniques. We chose 10 proteins and created synthetic peptides from the top 10 epitopes, taking into consideration fold alterations and antigen/ epitope contents. Serum antibodies from cysticercotic pigs identified nine peptides, indicating that these peptides constitute antigens. Combinations of peptides from SM and CNS cysts produced better outcomes than combinations of peptides from a single tissue site, although it is yet unknown which tissue-enriched antigens are the ideal. We found that at least five distinct antigenic determinants were necessary for a valid immunodiagnostic test for porcine cysticercosis using machine learning techniques (Victor B 2014).

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