Back

In-Depth Immune Profiling Reveals Cell-Type, Sex and Infection-Stage Dependent Differences for Tuberculosis Control

Watt, J.; Sokolowski, D. J.; Liu, J.

2026-10-07 immunology
10.64898/2026.09.30.755733 bioRxiv
Show abstract

Tuberculosis (TB) disease consistently causes the highest number of annual deaths from infectious disease and is responsible for over 100 million disability-adjusted-life-years annually. Despite the dramatic impact on human health, TB treatments are intensive and moderately effective, while Bacille Calmette-Guerin (BCG), the primary TB vaccine strategy and the oldest vaccine in circulation, needs updating. One gap in knowledge is that the vaccine primed lung immune environment has not been systematically profiled following TB challenge, making it challenging to interpret how various vaccine strategies provide protection. Here we profiled the male and female mouse lung immune environment using Mass Cytometry by Time of Flight (CyTOF) and bacterial burden (colony forming units, CFU), to systematically identify immune cells contributing to Mycobacterium tuberculosis (Mtb) infection or vaccine-induced protection. We investigated the mouse immune system after various infection outcomes including primary Mtb infection, Mtb reinfection, Mtb challenge after BCG vaccination and Mtb challenge after vaccination with a live attenuated Mtb vaccine. Across all conditions we identified Thy1.2 expression on NK cells as a new candidate for a sex-specific correlate of protection. Our results further replicate and support known immune responses for vaccine-induced protection, such as T-cell activation and infiltration. These findings highlight the pan-immune response to vaccine-induced protection, showcase the importance of sex in rodent models of TB, and identify novel host immune candidates to protect the lung against TB disease.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.