Signal integration and transcriptional regulation of the inflammatory response mediated by the GM-/MCSF signaling axis in human monocytes

In recent years, the macrophage colony-stimulating factor (M-CSF) and granulocyte-macrophage CSF (GM-CSF) cytokines have been identified as opposing regulators of the inflammatory program. However, the two cytokines are simultaneously present in the inflammatory milieu, and it is not clear how cells integrate these signals. In order to understand the regulatory networks associated with the GM/M-CSF signaling axis, we analyzed DNA methylation in human monocytes. Our results indicate that GM-CSF induces activation of the inflammatory program and extensive DNA methylation changes, while M-CSF-polarized cells are in a less differentiated state. This inflammatory program is mediated via JAK2 associated with the GM-CSF receptor and the downstream extracellular signal-regulated (ERK) signaling. However, PI3K signaling is associated with a negative regulatory loop of the inflammatory program and M-CSF autocrine signaling in GM-CSF-polarized monocytes. Our findings describe the regulatory networks associated with the GM/M-CSF signaling axis and how they contribute to the establishment of the inflammatory program associated with monocyte activation.This work was supported by grants from the Plan Nacional de I+D+I 2013– 2016 ISCIII (Institute of Health Carlos III; PI16/01318, PI17/01244, PI17/ 0119, PI16/1900, and PI19/00184); the Gobierno del Principado de Asturias; the PCTI-Plan de Ciencia, Tecnologı´a e Innovacio´ n 2013-2017 (grant IDI/ 2018/144); FEDER ‘‘Funding Program of the European Union’’; the Red Española de Investigación Renal (REDinREN) (RD16/0009/0020, RD016/0009/002, and RD016/0009/001); the Agencia Estatal de Investigación (AEI) (ayuda Juan de la Cierva-Incorporaciόn; IJCI-2017-33347 to R.M.R.); and the Instituto de Salud Carlos III (Contratos Sara Borrell; CD16/00033 to C.H.). CIC bioGUNE support was provided by the Basque Department of Industry, Tourism and Trade (Etortek and Elkartek programs), the Innovation Technology Department of Bizkaia County, the CIBERehd Network, and Spanish MINECO, the Severo Ochoa Excellence Accreditation (SEV-2016-0644

Clinical characteristics related to disease severity with respect to the rs4819554 genotype distribution.

<p>Clinical characteristics related to disease severity with respect to the rs4819554 genotype distribution.</p

Clinical characteristics related to disease severity with respect to the rs4819554 genotype distribution.

<p>Clinical characteristics related to disease severity with respect to the rs4819554 genotype distribution.</p

Genotyping and frequencies of <i>KIR2DL2</i> and <i>KIR2DL3</i> alleles in relation to treatment outcome.

<p>Genotyping and frequencies of <i>KIR2DL2</i> and <i>KIR2DL3</i> alleles in relation to treatment outcome.</p

Pairwise D' LD based on Cramer's V correlation coefficient between the presence and absence of different KIR genes in the two groups of patients (A: Non Sustained Viral Responders, NSVR; B: Sustained Viral Responders, SVR).

<p>In this approach to assessing KIR LD, the KIR cluster genetic polymorphism is considered simply as the presence (POS) or absence (NEG) of KIR genes. Note: Several conditions were applied for this analysis: 1) <i>KIR3DL3, KIR3DP1, KIR2DL4</i> and <i>KIR3DL2</i> were not included because they were present in all patients (framework genes), 2) <i>KIR2DL2</i> and <i>KIR2DL3</i> were considered as alleles of the same locus, 3) <i>KIR2DL5</i> was differentiated in <i>KIR2DL5A</i> and <i>KIR2DL5B</i> because they are located at different positions, and 4) <i>KIR2DS3</i> was included in the centromeric region.</p

KIR genotype distribution in the study cohort.

<p>The genotypes were deduced from KIR profiles as previously described <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0099426#pone.0099426-Pyo1" target="_blank">[10]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0099426#pone.0099426-Hsu2" target="_blank">[33]</a>. Note: ¹ID assigned by the Allele Frequency Net Database (<a href="http://www.allelefrequencies.net" target="_blank">http://www.allelefrequencies.net</a>)</p

Note: Cen-AA (X1), Cen-BB (X2), IFNL3-G/G (X3), IFNL3-G/T (X4), IFNL3-T/T (X5), HOMOKIR2DL3-C1C1 (X6), HOMOKIR2DL2-C1C1 (X7), HOMOKIR2DL2-C1C2 (X8) viral load less than 400000 UI/ml (X9).

<p>Note: Cen-AA (X1), Cen-BB (X2), IFNL3-G/G (X3), IFNL3-G/T (X4), IFNL3-T/T (X5), HOMOKIR2DL3-C1C1 (X6), HOMOKIR2DL2-C1C1 (X7), HOMOKIR2DL2-C1C2 (X8) viral load less than 400000 UI/ml (X9).</p

KIR genotypes and haplotype frequencies of the studied population, and combinations of Cen and Tel haplotypes with HLA-B and HLA-C.

<p><b>Note</b>: ¹B/B and A/B genotypes were included in this group.</p><p>Tel-HLA-C, Cen-HLA-B and Cen-HLAB combinations were not significant.</p

ROC curve of logic regression model using <i>IFNL3</i> C/C and KIR/HLA gene determination (<i>KIR2DL2/3</i>, <i>KIR2DL2/3-HLA</i> and KIR haplotype study) (solid line).

<p>We also included the ROC curves of <i>IFNL3</i> (dotted line) and <i>KIR2DL2/3-HLA</i> (dashed line) logic model determinations. It can be observed they do not overlap with the best logic regression model. Note: Area under the curve (AUC) of the best model: 0.729 (95% CI, 0.692–0.772).</p