Temporal Control of Macrophage Pro-inflammatory Phenotype by a Biphasic HIF-1α Regulatory Program

Hypoxia-inducible factor 1α (HIF-1α) broadly orchestrates metabolic reprogramming in inflammatory macrophages. However, how HIF-1α is activated in the earliest events following pattern recognition receptor activation remains unclear. We found that HIF-1α is functionally active shortly after macrophage inflammatory stimulation via a Rubicon (RUBCN)-NADPH-oxidase (NOX2) reactive oxygen species (ROS) axis, driving glycolysis, cytokine production, and bacterial killing before increased protein accumulation. Early HIF-1α stabilization primes inducible nitric oxide synthase (iNOS) expression and nitric oxide (NO) production, which subsequently suppresses electron transport chain function and induces mitochondrial dysfunction independently of RUBCN. These findings elucidate a temporally-coordinated HIF-1α pathway that integrates RUBCN–NOX2 redox signaling to control macrophage inflammation, metabolic adaptation, and antimicrobial defense. BMDMs were lysed for RNA extraction using the RNAeasy kit (Qiagen). RNA integrity was analyzed on a Bioanalyzer RNA Pico 6000 chip at the Core Facility for Scientific Research – University of São Paulo (CEFAP-USP). Complementary DNA library construction and sequencing were performed by BGI Global Genomics Services using the DNBSeq platform. The raw reads were filtered with the SOAPnuke software (-n 0.001 -l 20 -q 0.4 -A 0.25 --cutAdaptor -Q 2 -G --minLen 100). Reads quality control was summarized by FastQC (Andrews, 2017) and visualized using the MultiQC software (Ewels et al., 2016). Mus musculus reference genome GRCm38 and its annotation file were downloaded from ENSEMBL website (Yates, et al., 2020). The reads were mapped to the genome using the HISAT2 software (Kim, et al., 2019) with default parameters. Samtools (Li, et al., 2009) software was used for converting and generating statistics from the SAM/BAM files. A count table was generated using the featureCounts software (-t exon -g gene_id -Q 30 -f GTF) (Liao, et al., 2014). The analysis of differential gene expression was done with the R package DESeq2 (Love, et al., 2014) and the gene names were acquired by mapping ENSEMBL ids with the R package biomaRt (Smedley, et al., 2009). Pathway analysis was done using the webserver Pathview web (Luo and Brouwer, 2013; Luo, et al., 2017).