A protein signature associated with active tuberculosis identified by plasma profiling and network-based analysis

Zaynab Mousavian; Elin Folkesson; Gabrielle Fröberg; Fariba Foroogh; Margarida Correia-Neves; Judith Bruchfeld; Gunilla Källenius; Christopher Sundling

doi:10.1016/j.isci.2022.105652

A protein signature associated with active tuberculosis identified by plasma profiling and network-based analysis

iScience. 2022 Nov 22;25(12):105652. doi: 10.1016/j.isci.2022.105652. eCollection 2022 Dec 22.

Authors

Zaynab Mousavian^{1

2

3}, Elin Folkesson^{1

4}, Gabrielle Fröberg^{1

5}, Fariba Foroogh^{1

2

4}, Margarida Correia-Neves^{1

6

7}, Judith Bruchfeld^{1

4}, Gunilla Källenius^{1

2

4}, Christopher Sundling^{1

2

4}

Affiliations

¹ Division of Infectious Diseases, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden.
² Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden.
³ School of Mathematics, Statistics and Computer Science, College of Science, University of Tehran, Tehran, Iran.
⁴ Department of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden.
⁵ Department of Clinical Microbiology, Karolinska University Laboratory, Karolinska University Hospital, Stockholm, Sweden.
⁶ Life and Health Sciences Research Institute, School of Medicine, University of Minho, Braga, Portugal.
⁷ ICVS/3B's, PT Government Associate Laboratory, Braga, Portugal.

Abstract

Annually, approximately 10 million people are diagnosed with active tuberculosis (TB), and 1.4 million die of the disease. If left untreated, each person with active TB will infect 10-15 new individuals. The lack of non-sputum-based diagnostic tests leads to delayed diagnoses of active pulmonary TB cases, contributing to continued disease transmission. In this exploratory study, we aimed to identify biomarkers associated with active TB. We assessed the plasma levels of 92 proteins associated with inflammation in individuals with active TB (n = 20), latent TB (n = 14), or healthy controls (n = 10). Using co-expression network analysis, we identified one module of proteins with strong association with active TB. We removed proteins from the module that had low abundance or were associated with non-TB diseases in published transcriptomic datasets, resulting in a 12-protein plasma signature that was highly enriched in individuals with pulmonary and extrapulmonary TB and was further associated with disease severity.

Keywords: clinical finding; disease; infectious disease; proteomics.