The role of T-lymphocyte subsets and interleukin-5 blood levels among Indian subjects with autoimmune thyroid disease

A web server for predicting and scanning of IL-5 inducing peptides using alignment-free and alignment-based method

Interleukin-5 (IL-5) can act as an enticing therapeutic target due to its pivotal role in several eosinophil-mediated diseases. The aim of this study is to develop a model for predicting IL-5 inducing antigenic regions in a protein with high precision. All models in this study have been trained, tested and validated on experimentally validated 1907 IL-5 inducing and 7759 non-IL-5 inducing peptides obtained from IEDB. Our primary analysis indicates that IL-5 inducing peptides are dominated by certain residues like Ile, Asn, and Tyr. It was also observed that binders of a wide range of HLA alleles can induce IL-5. Initially, alignment-based methods have been developed using similarity and motif search. These alignment-based methods provide high precision but poor coverage. In order to overcome this limitation, we explore alignment-free methods which are mainly machine learning-based models. Firstly, models have been developed using binary profiles and eXtreme Gradient Boosting-based model achieved a maximum AUC of 0.59. Secondly, composition-based models have been developed and our dipeptide-based random forest model achieved a maximum AUC of 0.74. Thirdly, random forest model developed using selected 250 dipeptides and achieved AUC 0.75 and MCC 0.29 on validation dataset; best among alignment-free models. In order to improve the performance, we developed an ensemble or hybrid method that combined alignment-based and alignment-free methods. Our hybrid method achieved AUC 0.94 with MCC 0.60 on a validation/independent dataset. The best hybrid model developed in this study has been incorporated into the user-friendly web server and a standalone package named 'IL5pred' (https://webs.iiitd.edu.in/raghava/il5pred/).

Iodine Intake Increases IP-10 Expression in the Serum and Thyroids of Rats with Experimental Autoimmune Thyroiditis

Here, we sought to establish an experimental autoimmune thyroiditis rat model induced by bovine thyroglobulin (bTg) injection and to investigate pathological changes and variations in serum interferon- γ -inducible protein of 10 kDa (IP-10) in thyroid tissue following iodine treatment. Four-week-old female Lewis rats (n = 135) were randomly divided into normal (NC), thyroglobulin (TG), HI, HI+TG, HII, and HII+TG groups; rats in the NC and TG groups drank only distilled water (iodine concentration: 10  μ g/L), rats in the HI and HI+TG groups were given water containing 25.7 mg/L iodine, and rats in the HII and HII+TG groups were given water containing 423.3 mg/L iodine. Rats in the TG, HI+TG, and HII+TG groups were immunized with 0.1 mL bTg (8 mg/mL) in incomplete Freund's adjuvant once every 2 weeks for 6 weeks. Compared with the NC group, the TG, HI+TG, and HII+TG groups exhibited higher iodine intake and increased thyroid weights with increasing iodine doses (P < 0.05). The high iodine intake in the TG group was associated with increased CD4(+) T cells and serum IP-10. Thus, high iodine consumption aggravated the inflammatory reaction in the thyroid and mild high iodine consumption increased serum IP-10 levels after induction with bTg.