DOI: 10.5061/dryad.bzkh189kq
title: Identification of signaling networks associated with lactate modulation of macrophages and dendritic cells

description: 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.
description: <strong>1.1       RNA Isolation Procedures and
 Sequencing</strong> 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. <strong>1.2       Downstream
 Analysis of Differentially Expressed Genes</strong>
 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.
description: RNAseq Dataset The attached folder contains all RNAseq datasets related to
 transcriptomic experiments measuring the differential gene expression of
 Macrophage and Dendritic Cell in response to lactate treatment. \[Folder]
 archive_2020_03_02__22_59_13-macrophage: Contains macrophage RNAseq
 datasets &gt; analysis.html: &gt; \&gt;Contains a summary of the RNAseq
 experiment performed with macrophages, provided by the UC Davis
 Bioinformatics Core &gt; \&gt;Multidimensional scaling (MDS) plots shown
 by treatment group and metadata parameters &gt; \&gt;Analysis method &
 R settings outlined &gt; \&gt;Output of resulting differential gene
 expression data columns: &gt; \&gt;logFC: log2 fold change, with the group
 listed first in the comparison or file name being the numerator of the
 fold change &gt; \&gt;AveExpr: Average expression across all samples, in
 log2 counts per million reads &gt; \&gt;P.Value: Raw p-value from the test
 that the log fold change differs from 0 &gt; \&gt;adj.P.Val:
 Benjamini-Hochberg false discovery rate adjusted p-value &gt; analysis.md:
 Same as analysis.html, as a markdown file &gt;
 gene_annotations2020-02-11.txt: Annotations of genes for reference &gt;
 Lactate_v_Control.txt: Differential gene expression data for lactate vs.
 control in the macrophage groups. The columns included are:
 ensemblr_gene_id_version, external_gene_name, logFC, P.Value, adj.P.Val,
 and description &gt; Lewis-Sangsuwan_TagSeq_Metadata_updated2020-01-29:
 Metadata information about each RNA samples (e.g. mouse age, sex,
 treatment, batch) &gt; normalized_counts.txt: normalized count data from
 RNAseq &gt; raw_counts_for_stats.txt: raw count data from RNAseq &gt;
 preproc_mapping_stats_with_STAR.nb: HTML file report contains
 preprocessing and mapping statistics for the Lewis-Sangsuwan Mouse Batch
 TagSeq dataset, including contaminant screening, read trimming,
 deduplication, and mapping metrics, generated using HTStream for data
 quality assurance in gene expression analysis. &gt;
 preproc_mapping_stats_with_STAR.rmd: R markdown format of the same
 preproc_mapping_stats_with_STAR.nb HTML file \[Folder]
 archive_2020_03_02__22_59_28-Dendritic cells: Contains dendritic cell
 RNAseq datasets &gt; analysis.html: &gt; \&gt;Contains a summary of the
 RNAseq experiment performed with dendritic cells, provided by the UC Davis
 Bioinformatics Core &gt; \&gt;Multidimensional scaling (MDS) plots shown
 by treatment group and metadata parameters &gt; \&gt;Analysis method &
 R settings outlined &gt; \&gt;Output of resulting differential gene
 expression data columns: &gt; \&gt;logFC: log2 fold change, with the group
 listed first in the comparison or file name being the numerator of the
 fold change &gt; \&gt;AveExpr: Average expression across all samples, in
 log2 counts per million reads &gt; \&gt;P.Value: Raw p-value from the test
 that the log fold change differs from 0 &gt; \&gt;adj.P.Val:
 Benjamini-Hochberg false discovery rate adjusted p-value &gt; analysis.md:
 Same as analysis.html, as a markdown file &gt;
 gene_annotations2020-02-11.txt: Annotations of genes for reference &gt;
 Lactate_v_Control.txt: Differential gene expression data for lactate vs.
 control in the dendritic cell groups. The columns included are:
 ensemblr_gene_id_version, external_gene_name, logFC, P.Value, adj.P.Val,
 and description &gt; Lewis-Sangsuwan_TagSeq_Metadata_updated2020-01-29:
 Metadata information about each RNA samples (e.g. mouse age, sex,
 treatment, batch) &gt; normalized_counts.txt: normalized count data from
 RNAseq &gt; raw_counts_for_stats.txt: raw count data from RNAseq &gt;
 preproc_mapping_stats_with_STAR.nb: HTML file report contains
 preprocessing and mapping statistics for the Lewis-Sangsuwan Mouse Batch
 TagSeq dataset, including contaminant screening, read trimming,
 deduplication, and mapping metrics, generated using HTStream for data
 quality assurance in gene expression analysis. &gt;
 preproc_mapping_stats_with_STAR.rmd: R markdown format of the same
 preproc_mapping_stats_with_STAR.nb HTML file