Bacterial lipopolysaccharide modulates immune response in the colorectal tumor microenvironment

Abstract

Immune responses can have opposing effects in colorectal cancer (CRC), the balance of which may determine whether a cancer regresses, progresses, or potentially metastasizes. These effects are evident in CRC consensus molecular subtypes (CMS) where both CMS1 and CMS4 contain immune infiltrates yet have opposing prognoses. The microbiome has previously been associated with CRC and immune response in CRC but has largely been ignored in the CRC subtype discussion. We used CMS subtyping on surgical resections from patients and aimed to determine the contributions of the microbiome to the pleiotropic effects evident in immune-infiltrated subtypes. We integrated host gene-expression and meta-transcriptomic data to determine the link between immune characteristics and microbiome contributions in these subtypes and identified lipopolysaccharide (LPS) binding as a potential functional mechanism. We identified candidate bacteria with LPS properties that could affect immune response, and tested the effects of their LPS on cytokine production of peripheral blood mononuclear cells (PBMCs). We focused on Fusobacterium periodonticum and Bacteroides fragilis in CMS1, and Porphyromonas asaccharolytica in CMS4. Treatment of PBMCs with LPS isolated from these bacteria showed that F. periodonticum stimulates cytokine production in PBMCs while both B. fragilis and P. asaccharolytica had an inhibitory effect. Furthermore, LPS from the latter two species can inhibit the immunogenic properties of F. periodonticum LPS when co-incubated with PBMCs. We propose that different microbes in the CRC tumor microenvironment can alter the local immune activity, with important implications for prognosis and treatment response.

Fig. 1. Immune-related enriched gene sets in CMS1 and CMS4.

a Immune related enriched gene sets common to CMS1 and CMS4. b Representative enriched immune-related gene sets unique to CMS1. c Representative enriched immune-related gene sets unique to CMS4. NES normalized enrichment score. Gene Ratio genes contributing to enrichment of the gene set from our dataset divided by total set size of the gene set in question. All gene sets have adjusted p-values < 0.05, as calculated through GSEA analysis.

Fig. 2. Abundant Microbes in CMS1 and CMS4 with lipopolysaccharide processes.

a Differentially abundant Bacteria in CMS1 and CMS4 with LPS processes annotations, showing their fold changes in CMS1 (left) or CMS4 (right) against the average abundance of the other three subtypes. b F. periodonticum and B. fragilis normalized counts in CMS1 compared to the other subtypes. log2FC log₂Fold Change, FC log₂Fold Change, p.adj adjusted p-value as calculated in DESeq2.

Fig. 3. Changes in cytokine expression in peripheral blood mononuclear cells (PBMCs) following treatment with F. periodonticum alone (red), or in combination with B. fragilis (blue) or P. asaccharolytica (yellow) for cytokines IL-18, IL-10, and IL-1β.

For these experiments we used the lowest concentration of F. periodonticum LPS (6 ng/mL) and the highest concentration of LPS from B. fragilis or P. asaccharolytica (600 ng/mL), as these respective concentrations had the largest effects on cytokine production in earlier experiments. Values are shown as percentages of PBMC baseline secretion, which is set at 100%. Dashed lines indicate a single experimental run. Colored, solid horizontal lines represent the means of repeat experiments. Y-axes of IL-10 and IL-1β are in log₁₀ scale, while Y-axis of IL-18 is in the linear scale. Fpe = F. periodonticum (6 ng/mL), Fpe + Bfr = F. periodonticum (6 ng/mL) + B. fragilis (600 ng/mL), Fpe + Pas = F. periodonticum (6 ng/mL) + P. asaccharolytica (600 ng/mL); * = Paired Student’s t tests p-value < 0.05 (F. periodonticum vs PBMC, F. periodonticum + B. fragilis vs F. periodonticum alone, or F. periodonticum + P. asaccharolytica vs F. periodonticum alone).