ABSTRACT: Purpose: The goals of this study are to compare transcriptomic profiling (RNA-seq) of BMDMs activated by CM generated from CO-Fibs and PH-Fibs and to evaluate the mechanisms how healthy and pulmonary hypertensive adventitial microenvironment maintain or activate macrophage phenotypes. Methods: Mouse BMDMs were treated with media conditioned by CO-Fibs (CO-CM) or PH-Fibs (PH-CM) or left untreated (UNX). Each CM was pooled from 5 different fibroblast populations. Total RNA was purified using NucleoSpin RNA Isolation kit (Machery-Nagel, www.mn-net.com) according to the manufacturer’s instructions. RNA quantity and quality were evaluated using NanoDrop and Bioanalyzer. For Transcriptomics analysis, RNA-Seq library preparation and sequencing were conducted using a NuGen universal plus mRNA-Seq kit. Sequencing was performed on Illumina NovaSEQ6000 system, using the paired-read 2x150 cycle option. RNA-Seq reads were generated using Illumina NovaSEQ6000 analysis pipeline. The read quality of all the samples were checked using FastQC v0.11.5. Adapter trimming, quality control, and base correction were performed by AfterQC (Chen et al., 2017). The sequencing reads in the FASTQ files of all the samples were aligned to the Mus musculus reference genome (Mus_musculus. GRCm38v91) with Spliced Transcripts Alignment to a Reference (STAR) version 2.6.0c. We next applied featureCounts v1.6.2 to assign reads to genes using mouse transcript annotations as a guide. EdgeR in R was used for data normalization and differential expression analysis of RNA-Seq expression profiles with biological replication. The Benjamini-Hochberg procedure was used to control the false discovery rate (FDR), and a cut-off criterion of FDR ≤ 0.05 was applied to identify differentially expressed genes. Individual gene expression was calculated as ‘reads per kilobase per million mapped reads’ (RPKM). Results: Using a standard data analysis workflow (STAR-featureounts-edgeR), our results showed that both CO-CM and PH-CM had significant, yet clearly different, effects on gene expression in BMDMs. Principal component analysis (PCA) of all 12,191 genes showed overall separation of CO-CM and PH-CM treated BMDMs from untreated BMDMs. To obtain insight into the underlying mechanisms through which microenvironments regulate macrophage phenotype and function in control and diseased conditions, we analyzed genes uniquely regulated by CO-CM or PH-CM vs. naïve BMDM using Ingenuity Pathway Analysis (IPA). Canonical signaling pathways identified by IPA demonstrated that CO-CM significantly inhibited inflammatory pathways (role of pattern recognition receptors), immune responses (PD-1, PD-L1 cancer immunotherapy pathway, Tec Kinase signal, and T cell exhaustion signal), lipid (3-phosphoinositide) metabolism and glycolysis suggesting a metabolically quiescent and anti-inflammatory phenotype. In contrast, when the microenvironment was dictated by disease (i.e., PH-CM), macrophages exhibited distinct upregulated canonical pathways, particularly those related to inflammation (iNOS, Toll-like receptor, NF-kB, TNFR, etc.) and activated immune responses (Th1 pathway, T cell exhaustion signaling pathway, dendritic cell maturation). Pathways involved in metabolism were also notably altered with increased NO and ROS production, and decreased oxidative phosphorylation indicating altered mitochondrial function. Conclusions: Results from our study provide the basis for a more systematic mechanistic understanding of the cross-talk between adventitial fibroblasts and macrophages in health and disease by revealing a comprehensive view of the macrophage transcriptomic and metabolomic landscape.
