Cargando…

Computational Approach to Identifying Universal Macrophage Biomarkers

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, id...

Descripción completa

Detalles Bibliográficos
Autores principales: Dang, Dharanidhar, Taheri, Sahar, Das, Soumita, Ghosh, Pradipta, Prince, Lawrence S., Sahoo, Debashis
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7156600/
https://www.ncbi.nlm.nih.gov/pubmed/32322218
http://dx.doi.org/10.3389/fphys.2020.00275
_version_ 1783522244191846400
author Dang, Dharanidhar
Taheri, Sahar
Das, Soumita
Ghosh, Pradipta
Prince, Lawrence S.
Sahoo, Debashis
author_facet Dang, Dharanidhar
Taheri, Sahar
Das, Soumita
Ghosh, Pradipta
Prince, Lawrence S.
Sahoo, Debashis
author_sort Dang, Dharanidhar
collection PubMed
description 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.
format Online
Article
Text
id pubmed-7156600
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-71566002020-04-22 Computational Approach to Identifying Universal Macrophage Biomarkers Dang, Dharanidhar Taheri, Sahar Das, Soumita Ghosh, Pradipta Prince, Lawrence S. Sahoo, Debashis Front Physiol Physiology 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. Frontiers Media S.A. 2020-04-08 /pmc/articles/PMC7156600/ /pubmed/32322218 http://dx.doi.org/10.3389/fphys.2020.00275 Text en Copyright © 2020 Dang, Taheri, Das, Ghosh, Prince and Sahoo. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Physiology
Dang, Dharanidhar
Taheri, Sahar
Das, Soumita
Ghosh, Pradipta
Prince, Lawrence S.
Sahoo, Debashis
Computational Approach to Identifying Universal Macrophage Biomarkers
title Computational Approach to Identifying Universal Macrophage Biomarkers
title_full Computational Approach to Identifying Universal Macrophage Biomarkers
title_fullStr Computational Approach to Identifying Universal Macrophage Biomarkers
title_full_unstemmed Computational Approach to Identifying Universal Macrophage Biomarkers
title_short Computational Approach to Identifying Universal Macrophage Biomarkers
title_sort computational approach to identifying universal macrophage biomarkers
topic Physiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7156600/
https://www.ncbi.nlm.nih.gov/pubmed/32322218
http://dx.doi.org/10.3389/fphys.2020.00275
work_keys_str_mv AT dangdharanidhar computationalapproachtoidentifyinguniversalmacrophagebiomarkers
AT taherisahar computationalapproachtoidentifyinguniversalmacrophagebiomarkers
AT dassoumita computationalapproachtoidentifyinguniversalmacrophagebiomarkers
AT ghoshpradipta computationalapproachtoidentifyinguniversalmacrophagebiomarkers
AT princelawrences computationalapproachtoidentifyinguniversalmacrophagebiomarkers
AT sahoodebashis computationalapproachtoidentifyinguniversalmacrophagebiomarkers