Identification of signaling networks associated with lactate modulation of macrophages and dendritic cells

The advancement in the understanding of cancer immune evasion has manifested the development of cancer immunotherapeutic approaches such as checkpoint inhibitors and interleukin agonists. Although cancer immunotherapy breakthroughs have demonstrated improved potency for cancer treatment, only a fraction of patients effectively respond to these treatments. Moreover, there is compelling evidence indicating that cancer cells develop a unique microenvironment through adaptive metabolic reprogramming, which aberrantly modulates host immunity to evade immunosurveillance. As part of the tumor cell adaptive metabolic switch, lactate is produced and released into the tumor environment. Recent studies have shown that lactate significantly modulates immune functions, especially in innate immune cells. Dendritic cells (DCs) and macrophages (MΦs) are specialized antigen-presenting cells serving as key players in innate immunity and anticancer-associated immune responses. Although, most studies have shown that lactate affects immune phenotypes (e.g., surface protein expression and cytokine production), the cell signaling network mediated by lactate is not fully understood. In the present study, we identified the key signaling pathways in bone marrow-derived DCs and MΦs that are changed by cancer-relevant concentrations of lactate. First, transcriptome analysis was used to guide notable signaling pathways mediated by lactate. Subsequently, biomolecular techniques including immunoblotting, flow cytometry, and immunofluorescence imaging were performed to further investigate and confirm the changes in these key signaling pathways. The result indicated that lactate differentially impacted the biochemical networks of DCs and MΦs. While lactate mainly altered STAT3, ERK, and MAPK p38 signaling cascades in DCs, the STAT1 and GSK-3β signaling in MΦs are the major pathways significantly impacted by lactate. This study identifies key signal transductions impacted by lactate, which advances our understanding of the interplay between the tumor microenvironment and innate immunity.

Methods
1.1       RNA Isolation Procedures and Sequencing

RNA was harvested from DCs and MΦs (1 million cells/well) treated with either media (control) or 50 mM sLA for 48 h at 37°C using 1 mL of TRIzol Reagent (Thermo Fisher Scientific). Chloroform was added to each TRIzol sample for phase separation and RNA was precipitated from the aqueous phase using a 1:1 ratio of ethanol. The isolated nucleic acid material was purified, DNAse-treated, and concentrated using an RNA Clean & Concentrator kit (Zymo Research). The samples were sent to the UC Davis DNA Core for quality assessment using LabChip GX Nucleic Acid Analyzer. The top-scoring technical replicates were selected and sent to the DNA Core via 3’-Tag-Seq (QuantSeq) Library Preparation and Illumina HiSeq 4000. The gene expression levels were quantified and compared between sLA and control groups on R using limma-voom, and adjusted p-values were calculated via Bejamini-Hochberg Procedure. For each comparison, the log fold change (Log2FC) and adjusted p-value levels for each comparison were used for further analysis.

1.2       Downstream Analysis of Differentially Expressed Genes

The genes for each comparison group were threshold-filtered by an adjusted p-value less than or equal to 0.05 and a magnitude of Log2FC greater than 1.0. The significant genes of sLA vs. control comparisons in DCs and MΦs were compared using an online bioinformatics tool (https://bioinformatics.psb.ugent.be/webtools/Venn/) to yield a condensed list of 63 differentially expressed genes. Enrichment analysis of this gene set was performed using an online Enrichr tool (https://maayanlab.cloud/Enrichr/). This analysis was used to check for significant terms within Wikipathways and Gene Ontology Biological Processes enrichment lists for potential key pathway candidates. For graphical representation of gene expression via volcano plots and heatmaps, R packages EnhancedVolcano and pheatmap were used, respectively. Visualization of pathway expression change was performed via Omicsoft bioinformatics software.