Dataset Information

Computational Approach to Identifying Universal Macrophage Biomarkers.

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.

SUBMITTER: Dang D

PROVIDER: S-EPMC7156600 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Computational Approach to Identifying Universal Macrophage Biomarkers.

Dang Dharanidhar D Taheri Sahar S Das Soumita S Ghosh Pradipta P Prince Lawrence S LS Sahoo Debashis D

Frontiers in physiology 20200408

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 macroph ...[more]

PMID: 32322218

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Project description:BackgroundPathogenicity islands (PAIs), distinct genomic segments of pathogens encoding virulence factors, represent a subgroup of genomic islands (GIs) that have been acquired by horizontal gene transfer event. Up to now, computational approaches for identifying PAIs have been focused on the detection of genomic regions which only differ from the rest of the genome in their base composition and codon usage. These approaches often lead to the identification of genomic islands, rather than PAIs.ResultsWe present a computational method for detecting potential PAIs in complete prokaryotic genomes by combining sequence similarities and abnormalities in genomic composition. We first collected 207 GenBank accessions containing either part or all of the reported PAI loci. In sequenced genomes, strips of PAI-homologs were defined based on the proximity of the homologs of genes in the same PAI accession. An algorithm reminiscent of sequence-assembly procedure was then devised to merge overlapping or adjacent genomic strips into a large genomic region. Among the defined genomic regions, PAI-like regions were identified by the presence of homolog(s) of virulence genes. Also, GIs were postulated by calculating G+C content anomalies and codon usage bias. Of 148 prokaryotic genomes examined, 23 pathogenic and 6 non-pathogenic bacteria contained 77 candidate PAIs that partly or entirely overlap GIs.ConclusionSupporting the validity of our method, included in the list of candidate PAIs were thirty four PAIs previously identified from genome sequencing papers. Furthermore, in some instances, our method was able to detect entire PAIs for those only partial sequences are available. Our method was proven to be an efficient method for demarcating the potential PAIs in our study. Also, the function(s) and origin(s) of a candidate PAI can be inferred by investigating the PAI queries comprising it. Identification and analysis of potential PAIs in prokaryotic genomes will broaden our knowledge on the structure and properties of PAIs and the evolution of bacterial pathogenesis.

Project description:Macrophage polarization is increasingly recognised as an important pathogenetic factor in inflammatory and neoplastic diseases. Proinflammatory M1 macrophages promote T helper (Th) 1 responses and show tumoricidal activity. M2 macrophages contribute to tissue repair and promote Th2 responses. CD68 and CD163 are used to identify macrophages in tissue sections. However, characterisation of polarised macrophages in situ has remained difficult. Macrophage polarisation is regulated by transcription factors, pSTAT1 and RBP-J for M1, and CMAF for M2. We reasoned that double-labelling immunohistochemistry for the detection of macrophage markers together with transcription factors may be suitable to characterise macrophage polarisation in situ. To test this hypothesis, we have studied conditions associated with Th1- and Th2-predominant immune responses: infectious mononucleosis and Crohn's disease for Th1 and allergic nasal polyps, oxyuriasis, wound healing and foreign body granulomas for predominant Th2 response. In all situations, CD163+ cells usually outnumbered CD68+ cells. Moreover, CD163+ cells, usually considered as M2 macrophages, co-expressing pSTAT1 and RBP-J were found in all conditions examined. The numbers of putative M1 macrophages were higher in Th1- than in Th2-associated diseases, while more M2 macrophages were seen in Th2- than in Th1 related disorders. In most Th1-related diseases, the balance of M1 over M2 cells was shifted towards M1 cells, while the reverse was observed for Th2-related conditions. Hierarchical cluster analysis revealed two distinct clusters: cluster I included Th1 diseases together with cases with high numbers of CD163+pSTAT1+, CD68+pSTAT1+, CD163+RBP-J+ and CD68+RBP-J+ macrophages; cluster II comprised Th2 conditions together with cases displaying high numbers of CD163+CMAF+ and CD68+CMAF+ macrophages. These results suggest that the detection of pSTAT1, RBP-J, and CMAF in the context of CD68 or CD163 expression is a suitable tool for the characterisation of macrophage polarisation in situ. Furthermore, CD163 cannot be considered a reliable M2 marker when used on its own.

Dataset Information

Computational Approach to Identifying Universal Macrophage Biomarkers.

Publications

Computational Approach to Identifying Universal Macrophage Biomarkers.

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