Computational Approach to Identifying Universal Macrophage Biomarkers

Dharanidhar Dang; Sahar Taheri; Soumita Das; Pradipta Ghosh; Lawrence S Prince; Debashis Sahoo

doi:10.3389/fphys.2020.00275

Computational Approach to Identifying Universal Macrophage Biomarkers

Front Physiol. 2020 Apr 8:11:275. doi: 10.3389/fphys.2020.00275. eCollection 2020.

Authors

Dharanidhar Dang^{1

2}, Sahar Taheri¹, Soumita Das³, Pradipta Ghosh^{4

5}, Lawrence S Prince^{2

6}, Debashis Sahoo^{1

2

5}

Affiliations

¹ Department of Computer Science and Engineering, University of California, San Diego, San Diego, CA, United States.
² Department of Pediatrics, University of California, San Diego, San Diego, CA, United States.
³ Department of Pathology, University of California, San Diego, San Diego, CA, United States.
⁴ Departments of Medicine and Cellular and Molecular Medicine, University of California, San Diego, San Diego, CA, United States.
⁵ Moores Cancer Center, San Diego, CA, United States.
⁶ Rady Children's Hospital, San Diego, CA, United States.

Abstract

Macrophages engulf and digest microbes, cellular debris, and various disease-associated cells throughout the body. Understanding the dynamics of macrophage gene expression is crucial for studying human diseases. As both bulk RNAseq and single cell RNAseq datasets become more numerous and complex, identifying a universal and reliable marker of macrophage cell becomes paramount. Traditional approaches have relied upon tissue specific expression patterns. To identify universal biomarkers of macrophage, we used a previously published computational approach called BECC (Boolean Equivalent Correlated Clusters) that was originally used to identify conserved cell cycle genes. We performed BECC analysis using the known macrophage marker CD14 as a seed gene. The main idea behind BECC is that it uses massive database of public gene expression dataset to establish robust co-expression patterns identified using a combination of correlation, linear regression and Boolean equivalences. Our analysis identified and validated FCER1G and TYROBP as novel universal biomarkers for macrophages in human and mouse tissues.

Keywords: Boolean analysis; CAD; biomarker; gene expression; macrophage.