Immunogenicity Profile in African Green Monkeys of a Vaccine Candidate Based on a Mutated Form of Human Interleukin-15

Rodríguez-Álvarez Yunier,Batista-Roche Lino Gerardo,Llopiz-Arzuaga Alexey,Puente-Pérez Pedro,Martínez-Castillo Rafael,Castro-Velazco Jorge,Santos-Savio Alicia

BMC immunology（2021）SCI 4区

Center for Genetic Engineering and Biotechnology

Cited 0|Views3

Abstract

Background Interleukin (IL)-15 is a proinflammatory T-cell growth factor overexpressed in several autoimmune diseases such as rheumatoid arthritis. Our initial strategy to neutralize the increased levels of IL-15 consisted in a vaccine candidate based on the recombinant modified human IL-15 (mhIL-15) mixed with the alum adjuvant. A previous study in non-human primates Macaca fascicularis has shown that vaccination induces neutralizing antibodies against native IL-15, without affecting animal behavior, clinical status, or the percentage of IL-15-dependent cell populations. However, the mhIL-15 used as an antigen was active in the IL-2-dependent cytotoxic T-cell line CTLL-2, which could hinder its therapeutic application. The current article evaluated the immunogenicity in African green monkeys of a vaccine candidate based on IL-15 mutant D8SQ108S, an inactive form of human IL-15. Results IL-15 D8SQ108S was inactive in the CTLL-2 bioassay but was able to competitively inhibit the biological activity of human IL-15. Immunization with 200 µg of IL-15 mutant combined with alum elicited anti-IL-15 IgG antibodies after the second and third immunizations. The median values of anti-IL-15 antibody titers were slightly higher than those generated in animals immunized with 200 µg of mhIL-15. The highest antibody titers were induced after the third immunization in monkeys vaccinated with 350 µg of IL-15 D8SQ108S. In addition, sera from immunized animals inhibited the biological activity of human IL-15 in CTLL-2 cells. The maximum neutralizing effect was observed after the third immunization in sera of monkeys vaccinated with the highest dose of the IL-15 mutant. These sera also inhibited the proliferative activity of simian IL-15 in the CTLL-2 bioassay and did not affect the IL-2-induced proliferation of the aforementioned T-cell line. Finally, it was observed that vaccination neither affects the animal behavior nor the general clinical parameters of immunized monkeys. Conclusion Immunization with inactive IL-15 D8SQ108S mixed with alum generated neutralizing antibodies specific for human IL-15 in African green monkeys. Based on this fact, the current vaccine candidate could be more effective than the one based on biologically active mhIL-15 for treating autoimmune disorders involving an uncontrolled overproduction of IL-15.

Translated text

Key words

IL-15,IL-15 D8SQ108S,Vaccine candidate,Neutralizing antibodies,African green monkeys,CTLL-2 cells

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

Try using models to generate summary,it takes about 60s

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper