The rise and evolving role of Fusobacterium nucleatum subspecies

Abstract

This review examines the classification, distribution, and detection methods of Fusobacterium nucleatum subspecies, focusing on their distinct roles in health and disease. The evolution of F. nucleatum classification is traced from early to modern protein-based and genomic methods, such as 16S rRNA and next generation sequencing and matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS). The review highlights the importance of distinguishing F. nucleatum subspecies due to their varying pathogenic potentials and ecological niches in both oral and extraoral environments. Different subspecies exhibit distinct prevalence and activity levels in specific clinical conditions, such as periodontitis and oral squamous cell carcinoma (OSCC). This highlights the importance of accurately identifying subspecies to understand their role in disease progression. Moreover, understanding the varying pathogenic potential of F. nucleatum subspecies, which is driven by genetic diversity and virulence factors, is also essential for advancing research and improving patient outcomes.

Keywords: Colorectal cancer (CRC); Detection methods; F. nucleatum classification; F. nucleatum subspecies; Oral squamous cell carcinoma (OSCC).

Graphical abstract

Fig. 1

The figures show scanning electron microscope images of the different subspecies of Fn for documentation of cellular diversity. The scale bars measure 2 µm. a) shows Fna C2 strain OMI 1357, b) shows Fna C2 strain OMI 1565, c) shows Fnv strain OMI 1416, d) shows Fnn strain OMI 415, and e) shows Fnp strain OMI 72. OMI: strain collection of the Division of Oral Microbiology and Immunology (OMI), Aachen, Germany.

Fig. 2

Phylogenetic tree of Fusobacteria of oral and non-oral origin based on a complete rpoB gene, with the exception of F. equinum and F. watanabei, for which only partial sequences are currently available. Evolutionary history was inferred by using the Maximum Likelihood method and Tamura-Nei model (Tamura and Nei, 1993). The tree with the highest log likelihood (−35665.18) is shown. The percentage of trees in which the associated taxa clustered together is shown above the branches. The initial tree(s) for the heuristic search were obtained automatically by applying the Neighbor-Join and BioNJ algorithms to a matrix of pairwise distances estimated using the Tamura-Nei model, and then selecting the topology with superior log likelihood value. The tree is drawn to scale, with branch lengths measured as the number of substitutions per site. This analysis included 47 nucleotide sequences. There were a total of 3600 positions in the final dataset. Evolutionary analyses were conducted in MEGA11 (Tamura et al., 2021). The CRC-associated Fna C2 is highlighted.

Fig. 3

Described disease associations of F. nucleatum subspecies: This illustration summarizes the described distribution patterns of the four Fn subspecies in healthy oral niches, periodontal and endodontic infections, and malignancies including OSCCs and CRCs. In healthy oral environments, Fnp and Fnv are most commonly detected, while Fnn is more frequently associated with periodontitis. In endodontic and alveolar infections, findings are mixed. In OSCCs, Fnp is most frequently detected, whereas Fna C2 appears enriched in CRC samples, suggesting a role in tumorigenesis.

Fig. 4

Upregulated Virulence Factors of F. nucleatum in OSCCs: The diagram summarizes upregulated virulence factors of OSCC-associated Fn. These include a high abundance of outer membrane proteins such as autotransporters, fibronectin-binding proteins, or von Willebrand domain-containing proteins, all of which facilitate adhesion and invasion. MORN2 domain-containing proteins, associated with invasive Fn strains are also depicted. Immune evasion is supported by lipooligosaccharide sialyltransferase and other lipopolysaccharide biosynthesis enzymes. Enriched iron acquisition systems and multidrug resistance transporters highlight adaptation to the TME and potential treatment challenges. Additionally, motility-related genes and chronic inflammation-inducing traits contribute to a tumor-supportive ‘inflammatory bacteriome’.

Fig. 5

CRC-associated virulence factors of F. animalis: The figure outlines the multifaceted pathogenic strategies employed by Fna in colorectal carcinogenesis. The process begins with Fna adhesion and invasion into intestinal epithelial and endothelial cells, mediated by multiple adhesins such as FadA, Fap2, CmpA, fusolisin, and RadD. Fna modulates immune responses e.g. by promoting the recruitment of immunosuppressive cells via CXCL8 and CCL20 signaling and by inhibiting natural killer and T-cell activity. Furthermore, metabolic reprogramming by Fna enhances tumorigenesis through increased prostaglandin and ceramide production, suppression of anti-oxidative polyamines (e.g., putrescine, spermidine, spermine), and enrichment of glutathione metabolism and γ-glutamyl amino acid pathways. Together, these adhesion-, immune-, and metabolism-based mechanisms highlight Fna’s ability to promote CRC progression.

Fig. 6

Coaggregation of F. animalis with partner species: This scheme illustrates the role of Fna in biofilm formation within the CRC. Fna co-aggregates with multiple CRC-associated taxa, including Campylobacter concisus, Gemella spp., Hungatella hathewayi, Parvimonas micra, and Streptococcus spp., forming complex, strain-specific biofilms. These interactions may enhance bacterial colonization, immune evasion, and tumor progression. Co-aggregation expands the diversity of surface molecules, potentially facilitating migration from the oral cavity to the gut and promoting a pro-oncogenic microbial community.

Fig. 7

F. animalis & CRC cells: This figure depicts key genomic features of Fna associated with CRC colonization and survival. Fna strains in CRC express stress response genes, enabling resistance to oxidative and thermal stress. These adaptations may reflect intracellular persistence strategies. Concurrent upregulation of host antimicrobial peptides S100A8 and S100A9 suggests immune recognition of Fna. CRC-derived Fna also expresses collagen-degrading proteases, potentially contributing to TME remodeling, consistent with elevated collagen-related gene expression in Fna-positive tumors.