Dr Zaynab Mousavian
Karolinska Institute, Sweden
Network-based analysis reveals a plasma protein signature associated with active Tuberculosis
Tuberculosis (TB) is a bacterial infectious disease caused by Mycobacterium tuberculosis. It is estimated that approximately 25% of the global population has been exposed to the infection with many carrying it in a latent form. Annually, an estimated 10 million people are diagnosed with active tuberculosis and approximately 1.4 million die of the disease. If left untreated, each person with active TB will infect 10 to 15 new individuals every year, highlighting the importance of accurate early detection to interrupt disease transmission. In an exploratory study, using collected plasma samples from a Swedish cohort (Active: 20, Latent: 14, Healthy controls: 10), we assessed the relative plasma concentration of 92 proteins associated with inflammation and then constructed a weighted protein co-expression network. After clustering network into four modules, we identified a protein module containing 16 proteins associated with active TB. We used multiple independent transcriptomic datasets from studies investigating respiratory infections and non-TB diseases (approximately 600 individuals) to remove proteins within the module that were associated with non-TB diseases. Further removing low-expressed proteins in active TB, resulted in a 12-protein plasma signature that was highly associated with disease severity and highly enriched in individuals with active TB in several TB transcriptomic datasets (>3000 individuals from four continents).The 12marker signature has now been further validated (AUC=0.95) in an independent Italian TB cohort (Active: 30, Latent: 54, Controls: 20) with promising data indicating the signature can be condensed to 6-markers providing similar sensitivity and specificity as 12 markers, which increases feasibility to design a blood-based clinical test for diagnosis of active TB. Further validation in larger patient cohorts as well as evaluation for performance in suspect TB cases will be necessary to validate the signature as a possible screening test for active TB as specified in WHO target product profile.
I obtained my Ph.D. in Bioinformatics from the University of Tehran, Iran in 2017 and then I was collaborating with the University of Tehran as a researcher for about five years. Now I’m working as a postdoctoral researcher at the Department of Medicine, Solna at Karolinska Institute in the laboratory of Dr. Christopher Sundling and Prof. Gunilla Källenius since August 2021. We are working on how Mycobacterium Tuberculosis (Mtb) affect the immune response during active and latent TB, to identify signatures associated with TB disease progression, using methods such as T cell FluoroSpot, Olink/plasma protein measurement, and mass cytometry.