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Cited by 10

The DOI matches its Crossref record; 50 of the 50 references it cites were found in a registry or an index.

miRNA-Mediated Priming of Macrophage M1 Differentiation Differs in Gram-Positive and Gram-Negative Settings

Riechert, Georg, Maucher, Daniel, Schmidt, Birte, Schumann, Julia·2022·Genes
DOI 10.3390/genes13020211PMC PMC8871789
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Abstract

A proper regulation of macrophage polarization is essential for the organism’s health and pathogen control. Differentiation control is known to occur at the transcriptional as well as the posttranscriptional levels. The mechanisms involved, however, have not yet been fully elucidated. In this study, we co-cultured macrophages with viable Gram-positive and Gram-negative bacteria to mimic macrophage differentiation to the M1-like type in an inflammatory milieu. We found that Gram-positive stimulation resulted in increased expressions of miR-7a-5p, miR-148a-3p, miR-155-5p, and miR-351-5p. Of note, these miRNAs were found to target inhibitory mediators of the Rac1-PI3K-Akt pathway and the MyD88-dependent pathway. In contrast, Gram-negative stimulation-induced downregulation of miR-9-5p, miR-27b-3p, miR-93-5p, and miR-106b-5p is known to target key members of the Rac1-PI3K-Akt pathway and the MyD88-dependent pathway. These results, taken together, point to a fine-tuning of macrophage polarization by TLR-induced changes in macrophage miRNA profiles. Here, the miRNA-mediated priming of M1 differentiation seems to differ in the Gram-positive and Gram-negative settings in terms of the mechanism and miRNAs involved.

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Figures

Figure 1 Murine macrophages (cell line RAW264.7, ATCC® number TIB-71) were evaluated following Gram-positive or -negative stimulation for 24 h and compared to unstimulated controls. (A) Workflow showing the cell culture conditions for miRNA sequencing and ddPCR. (B) Volcano plots showing the distribution of the fold changes and false discovery rates of all annotated miRNAs in a comparison between unstimulated and stimulated macrophages. For stimulation, either LTA (Gram-positive setting) or LPS (Gram-negative setting) was used. Red points above the horizontal line mark significant miRNAs (FDR > 0.05). The vertical lines mark the effect size threshold used. (C) Absolute miRNA quantification was performed by ddPCR analysis of isolated total RNA utilizing a QX200 Droplet Digital PCR System (Bio-Rad) and specific LNA PCR primers (Qiagen). The copy number of the target RNA in the total mixture was calculated by the system based on the number of positive droplets measured and assuming a Poisson distribution. In each ddPCR assay, a specific amount of RNA was used so that the copy number per ng of RNA can be calculated. The jittered box plots depict the median, the lower and upper quantile, the two extreme values, and the individual sample’s data points. Each group included five to six biological replicates and two technical replicates. Significant differences were identified utilizing an unpaired t-test. Statistical analysis was performed using GraphPad Prism 9 (GaphPad Software, La Jolla, CA, USA). A p-value < 0.05 was assumed to indicate significant differences. The p-values are specified in each diagram.
Figure 1 Murine macrophages (cell line RAW264.7, ATCC® number TIB-71) were evaluated following Gram-positive or -negative stimulation for 24 h and compared to unstimulated controls. (A) Workflow showing the cell culture conditions for miRNA sequencing and ddPCR. (B) Volcano plots showing the distribution of the fold changes and false discovery rates of all annotated miRNAs in a comparison between unstimulated and stimulated macrophages. For stimulation, either LTA (Gram-positive setting) or LPS (Gram-negative setting) was used. Red points above the horizontal line mark significant miRNAs (FDR > 0.05). The vertical lines mark the effect size threshold used. (C) Absolute miRNA quantification was performed by ddPCR analysis of isolated total RNA utilizing a QX200 Droplet Digital PCR System (Bio-Rad) and specific LNA PCR primers (Qiagen). The copy number of the target RNA in the total mixture was calculated by the system based on the number of positive droplets measured and assuming a Poisson distribution. In each ddPCR assay, a specific amount of RNA was used so that the copy number per ng of RNA can be calculated. The jittered box plots depict the median, the lower and upper quantile, the two extreme values, and the individual sample’s data points. Each group included five to six biological replicates and two technical replicates. Significant differences were identified utilizing an unpaired t-test. Statistical analysis was performed using GraphPad Prism 9 (GaphPad Software, La Jolla, CA, USA). A p-value < 0.05 was assumed to indicate significant differences. The p-values are specified in each diagram.CC BY · PubMed Central
Figure 2 Gene set enrichment analysis (GSEA) applied to expression changes of protein-coding genes found in the comparison of unstimulated and stimulated macrophages. For stimulation, either LTA (Gram-positive setting) or LPS (Gram-negative setting) was used.
Figure 2 Gene set enrichment analysis (GSEA) applied to expression changes of protein-coding genes found in the comparison of unstimulated and stimulated macrophages. For stimulation, either LTA (Gram-positive setting) or LPS (Gram-negative setting) was used.CC BY · PubMed Central

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Metadata

Authors
Riechert, G., Maucher, D., Schmidt, B., & Schumann, J.
Affiliations
University Clinic and Outpatient Clinic for Anesthesiology and Operative Intensive Care, University Medicine Halle (Saale), Franzosenweg 1a, 06112 Halle (Saale), Germany
ORCID
Schumann, Julia
Venue
Genes
Publisher
MDPI AG
Published
2022-01-24
Volume
13
Issue
2
Pages
211
ISSN
2073-4425
DOI
10.3390/genes13020211
PMC
PMC8871789
Cited by
10
References
50
Language
en
Licence
creativecommons.org/licenses/by/4.0/ (vor)

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