Beneficial effects of fecal microbiota transplantation in recurrent Clostridioides difficile infection

Abstract

Fecal microbiota transplantation (FMT) is highly effective in preventing recurrent Clostridioides difficile infection (rCDI). However, the mechanisms underpinning its clinical efficacy are incompletely understood. Herein, we provide an overview of rCDI pathogenesis followed by a discussion of potential mechanisms of action focusing on the current understanding of trans-kingdom microbial, metabolic, immunological, and epigenetic mechanisms. We then outline the current research gaps and offer methodological recommendations for future studies to elevate the quality of research and advance knowledge translation. By combining interventional trials with multiomics technology and host and environmental factors, analyzing longitudinally collected biospecimens will generate results that can be validated with animal and other models. Collectively, this will confirm causality and improve translation, ultimately to develop targeted therapies to replace FMT.

Keywords: fecal microbiota transplantation; host-microbial interactions; intestinal microbial transfer; recurrent Clostridioides difficile infection.

Figure 1.. Main mechanisms of action underlying C. difficile pathogenesis

Antibiotic-induced dysbiosis can establish a favorable condition for the germination of C. difficile spores. The vegetative form of C. difficile, thereafter, can induce pathogenesis mainly by toxin production (TcdA, TcdB, and CDT), SlpA, flagellin, and PG. C. difficile-derived PG interacts with NOD1 to stimulate neutrophil recruitment and CXCL1 production. Neutrophil infiltration and translocation to inflamed tissue result in NET formation. SlpA interaction with TLR4 can accelerate DC maturation and activation. Similar to the stimulation of TLR2 by CDT, flagellin-mediated activation of TLR5, which is promoted by TcdB, triggers transcriptional factors NF-κB and MAPK to provoke the expression of inflammatory cytokines. TcdA, however, mainly triggers programmed cell death pathways and eventually leads to apoptosis, necrosis, and pseudomembrane formation. CDT, C. difficile transferase; CXCL1, CXC chemokine ligand 1; DC, dendritic cell; MAPK, mitogen-activated protein kinase; NET, neutrophil extracellular trap; NF-κB, nuclear factor kappa B; NOD1, nucleotide-binding oligomerization domain 1; PG, peptidoglycan; SlpA, surface layer protein A; TcdA, toxin A; TcdB, toxin B; TLR, Toll-like receptor.

Figure 2.. Pre- and post-FMT mechanisms underpinning interactions between C. difficile, intestinal microbiota, and immune system

During CDI and prior to FMT administration, the gut environment is predominantly accumulated with conjugated bile acids that mostly accelerate C. difficile germination. This is accompanied by the loss of the gut barrier integrity, abundance of inflammation mediators (e.g., ROS, IL-1β, TNF-α, and IFN-γ), impoverishment of Bacteroidetes and Firmicutes phyla, and enrichment of Proteobacteria species. Post-FMT alteration of the gut microbiota composition is characterized by the increased presence of Bacteroidetes and Firmicutes species that promote the production of secondary bile acids (LCA and DCA) from primary bile acids (CDCA, CA). The vegetative growth of C. difficile, the release of TcdB, and the production of PBA are suppressed by LCA and DCA. TcdB interaction with DC leads to the accumulation of TcdB-specific Th17 cells and the subsequent production of IL-17A and IL-22. Furthermore, bacterial production of butyrate can increase the production of tight junction proteins, expand Treg cells, and attenuate the release of pro-inflammatory cytokines. Valerate, however, can interfere with C. difficile colonization. Post-FMT condition also features the abundance of anti-inflammatory cytokines, IgG, and IgA, expansion of eosinophils, and ILC2 cell activation. BSH, bile salt hydrolase; CA, cholic acid; CDCA, chenodeoxycholic acid; DCA, deoxycholic acid; FGF, fibroblast growth factor; HIF, hypoxia-inducible factor; IFN-γ, interferon gamma; IgA, immunoglobulin A; IgG, immunoglobulin G; IL, interleukin; ILC2, innate lymphoid type 2 cells; LCA, lithocholic acid; PBA, primary bile acid; ROS, reactive oxygen species; SCFA, short-chain fatty acid; TCA, taurocholic acid; TcdB, toxin B; Th 17, T helper 17; TNF-α, tumour necrosis factor α; Treg, regulatory T cell.

Figure 3.. Post-FMT interaction of miRNAs and glycans with the immune system and the gut microbiota

C. difficile-derived TcdB can reduce the expression of miR-150, miR-26b, miR-23a, and miR-28-5p, which downregulate the expression levels of IL-18, FGF-21, IL-12B, and TNFRSF9 inflammatory gene targets, respectively. IL-12B suppression by miR23a prevents the proliferation of Th1 cell. Likewise, the overexpression of miR-28-5p prevents TNFRSF9 production and subsequently reduces NK cell activation and inflammatory cytokine secretion. TcdB further inhibits the activity of intestinal ion transporter NHE3 and Cl⁻/HCO₃⁻ exchanger protein DRA. TcdB, along with TcdA, can induce vascular permeability and thereby increase the accumulation of VEGF-A and probably the activation of the VEGFR-2. The elevated presence of glycans, in return, accelerates the growth of Bacteroides and Bifidobacteria genera. Bacteroides fragilis can reduce the proportion of N-linked glycans. Prevotella strains decrease the Bacilli/Clostridia ratio, suppress the expression of AFAP1, and catabolize PLP to attenuate inflammation. AFAP1, actin filament-associated protein 1; DRA, down-regulated in adenoma; FGF-21, fibroblast growth factor 21; NHE3, Na+/H+ exchanger 3; NK, natural killer; PLP, pyridoxal-5-phosphate; TcdA, toxin A; TcdB, toxin B; Th 1, T helper 1; TNFRSF9, TNF receptor superfamily member 9; VEGF-A, vascular endothelial growth factor A; VEGFR-2, VEGF-A receptor.

Figure 4.. Using integrated multiomics approaches to FMT

An overview of key omics technologies used in FMT research to capture the relevant molecular signatures. (A) Multiomics data can be obtained from phenomic inputs and a wide spectrum of in vivo and/or in vitro research study designs. (B) Epigenomics, genomics, transcriptomics, proteomics, and metabolomics are complementary to each other, providing a comprehensive framework for research on FMT. Together, multiple pieces of information from a multiomics method can provide a comprehensive cellular readout that is absent in the outcomes of a single omic approach. (C) The acquired data from different multiomics technologies should be processed and analyzed accordingly, namely data exploration, clustering, network mapping, enrichment analysis, gene expression analysis, dimensionality reduction techniques, and statistical analysis. (D) Machine learning, deep learning, and prediction models have paved the way for the integration and validation of separated layers of multiomics data. AI, artificial intelligence.