New publication from National University of Singapore using Attana Technology to study Immune Response from Tuberculosis Exposed Healthy Individuals
In a new paper in npj Vaccines, 8, Article number: 127 (2023), the team at NUS, in collaboration with several research groups in USA, UK and, China has studied the immune response from healthy individuals that have been exposed to Tuberculosis (TB). TB is an airborne disease caused by Mycobacterium tuberculosis. Identifying the critical components in the antibody repertoires from immune, chronically exposed and, healthy individuals can be used for understanding how natural protection occurs. These findings have implications for future vaccine development. Attana technology was applied to determine the kinetics of the different involved antibodies and the IgA and IgG2 were found to provide the best protection.
The group at NUS has published several papers using Attana technology to identify correlation between protective immune response of human immune antibodies. Work has been published for e.g. Hepatitis B, Covid-19 and, now Tuberculosis. Whereas the two first diseases are virus born, TB is a bacterial disease. For Attana, the paper is important since it forms yet another example of the generic applicability of Attana technology in immune response research. It also demonstrates that more information than pure affinity is necessary to identify the best protecting antibodies. A crucial factor to secure a good protection is by the kinetics of the on-rate, where an on-rate for IgG antibodies equal to, or faster than 106Mol/s provides a good protection. Secondly the IgA antibodies, which are dimeric antibodies, probably have an initial function to inhibit the adhesion of bacteria, and hence slows down the infection process.
In the picture kinetic interaction results of immune antibodies are shown. Demonstrating larger variations in kinetics compared to traditional affinity, hence important information for understanding protection and optimizing vaccine candidates or for selecting optimal antibodies for therapeutics.
