Pangenomic analysis identifies correlations between Akkermansia species and subspecies and human health outcomes
Abstract
Aim:Akkermansia are common members of the human gastrointestinal microbiota. The prevalence of these mucophilic bacteria, especially Akkermansia muciniphila (A. muciniphila), correlates with immunological and metabolic health. The genus Akkermansia in humans includes species with significantly larger genomes than
Methods: We conducted a pangenomic analysis of 234 Akkermansia complete or near-complete genomes. We also used high-resolution species and subspecies assignments to reanalyze publicly available metagenomic datasets to determine if there are relationships between Akkermansia species and A. muciniphila clades with various disease outcomes.
Results: Analysis of genome-wide average nucleotide identity, 16S rRNA gene identity, conservation of core Akkermansia genes, and analysis of the fatty acid composition of representative isolates support the partitioning of the genus Akkermansia into several species. In addition, A. muciniphila sensu stricto, the most prevalent Akkermansia species in humans, should be subdivided into two subspecies. For a pediatric cohort, we observed species-specific correlations between Akkermansia abundance with baseline obesity or after various interventions. For inflammatory bowel disease cohorts, we identified a decreased abundance of Akkermansia in patients with ulcerative colitis or Crohn’s disease, which was species and subspecies-dependent. In patients undergoing immune checkpoint inhibitor therapies for non-small cell lung carcinoma, we observed a significant association between one A. muciniphila subspecies and survival outcomes.
Conclusion: Our findings suggest that the prevalence of specific Akkermansia species and/or subspecies can be crucial in evaluating their association with human health, particularly in different disease contexts, and is an important consideration for their use as probiotics.
Keywords
INTRODUCTION
Akkermansia muciniphila (A. muciniphila) is a Gram-negative, mucin-degrading bacterium that is prevalent in the mammalian gastrointestinal tract[1,2]. The relative abundance of A. muciniphila is associated with improved human health in a variety of contexts, such as obesity, metabolic disorders, responsiveness to cancer therapy, seizures, neuroinflammation, inflammatory bowel disease, and healthy aging[3-12]. Indeed, these associations with positive health outcomes have generated interest in developing A. muciniphila into a new class of probiotic for the treatment and prevention of multiple metabolic and immunological disorders[13-16].
Most studies on the biology of A. muciniphila have focused on the original MucT (BAA-835) strain, which was isolated from human stool in the Netherlands[1,2,17,18]. When additional A. muciniphila isolates became available, Guo et al. first proposed three genetically distinct sub-species-level phylogroups, AmI, AmII, and AmIII[19]. As more isolates and genome sequences became available, additional phylogroups were proposed, including AmIV and AmV[20,21]. In addition, the A. muciniphila AmI phylogroup could be further subdivided into two sub-phylogroups, AmIa and AmIb[20]. Whole-genome analyses revealed potential phylogroup-specific metabolic differences. These were confirmed through the characterization of representative isolates demonstrating variations in growth rates in mucin medium, aerotolerance, ability to metabolize human milk oligosaccharides, vitamin B12 production, and activation of toll-like receptors (TLR)[20-23]. Based on the comparison of additional genomes from newly isolated human strains and near complete metagenome-assembled genomes (MAGs), there is an emerging consensus that genomes that were originally referred to as A. muciniphila should be sub-divided into several distinct species based on whole-genome nucleotide identity or conserved gene alignment[24-28]. Not all sub-species terminology used has been consistent with the original description of Akkermansia phylogroups [19], and the overlap in genomes used between studies has been limited. Recently, it was proposed that the AmIV phylogroup met the criteria of a new species of Akkermansia and was renamed Akkermansia biwaensis (A. biwaensis)[29]. A similar proposal was made for the AmII phylogroup to be renamed Akkermansia massiliensis (A. massiliensis)[30]. For consistency, we will refer to the AmII phylogroup as A. massiliensis and the AmIV phylogroup as A. biwaensis. Whether this assignment of new Akkermansia species or A. muciniphila phylogroups leads to different health outcomes remains an open question, especially since only one Akkermansia species at a time predominates within a single human host[20]. Of note, most reports exploring the role of A. muciniphila in human trials or preclinical murine models have focused on the AmIa strain MucT[15,31].
Given in vitro phenotypic differences between phylogroups that could be linked to interactions with its host, such as TLR stimulation and adherence to epithelial cells[20], we postulate that which Akkermansia species is present in the GI tract may influence the ultimate impact on its human host. Indeed, one study found that A. muciniphila could be distinguished from Akkermansia candidate species based on marker genes from metagenome-assembled genomes[25]. Based on these markers, the authors suggested that the association observed between A. muciniphila and body mass index (BMI) did not apply to the additional candidate species. However, current analysis tools and databases[32,33] do not, by default, distinguish between these Akkermansia phylogroups. In this study, we combined all the previously, but independently, described genomes from isolated strains into a single, large pangenome. Our pangenomic analysis defined a novel clade of Akkermansia and supported a species re-assignment for phylogroups AmII, IV, and V, which we corroborate with phenotypic characterizations of representative strains for each species and subspecies. Using these new classifications, we developed methods to identify Akkermansia species and subspecies from 16S rRNA and metagenomic sequences of stool samples. Finally, we provide three case studies to further support the premise that the relationship between the relative abundance of Akkermansia and health outcomes can correlate with specific Akkermansia phylogroups.
METHODS
Isolation of Akkermansia from fecal samples
Bacterial isolation and growth were performed in a Coy Laboratory anaerobic chamber under 5% hydrogen, 5% carbon dioxide, and 90% nitrogen. Eight Akkermansia strains were isolated from the stool of renal cell carcinoma (RCC) patients at Duke University Hospital (IRB protocol Pro00076768). Isolation of strains was performed as described previously[20]. Four Akkermansia strains were isolated from the stool of patients with amyotrophic lateral sclerosis (people with ALS, PALS) at Duke University Hospital (IRB protocol Pro00108282). To isolate new Akkermansia strains, approximately 100 mg of frozen stool was used to inoculate 1 mL of mucin medium as previously described[1,20], and supplemented with cysteine (0.5 mM), vancomycin (6 μg/mL), gentamicin (10 μg/mL), and kanamycin (12 μg/mL) and incubated at 37 °C for 48 h. Three sequential passages at 1:10 dilutions were performed, and a sample was streaked on 1% agar mucin medium plates, supplemented with cysteine (0.5 mM), to facilitate the isolation of single colonies. After 7 days at 37 °C, individual colonies were picked and cultured in synthetic media[5,20] supplemented with cysteine (0.5 mM).
Genome sequencing and annotation
Sequencing and annotation of genomes labeled as “RCC” were performed as described previously[20]. For “PALS” genomes, genomic DNA extractions were carried out using the DNeasy Blood & Tissue kit (Qiagen catalog 69504) following the manufacturer’s protocol. DNA concentrations were determined using the Qubit double-stranded DNA (dsDNA) broad-range kit (Thermo Scientific). Library preparation and sequencing were performed by SeqCenter (https://www.seqcenter.com/). The Oxford Nanopore Technologies (ONT) Ligation Sequencing Kit (SQK-NBD114.24) with NEBNext® Companion Module (E7180L) was used to prepare DNA sequencing libraries, adhering to the manufacturer’s specifications. Genome sequencing was carried out on Nanopore R10.4.1 flow cells using the MinION Mk1B device. The Oxford Nanopore data processing toolkit, Guppy (v6.3.8), was used for base calling and demultiplexing. The super accurate model was used for base calling, as it offers the highest raw read accuracy[34]. The resulting fastq files were assembled into a single contig using Flye[35]. In addition, we used an Illumina DNA Prep kit and IDT 10bp UDI indices to prepare additional libraries, which were then sequenced on an Illumina NextSeq 2000, generating 2 × 151 bp reads. The Illumina software app bcl-convert (v3.9.3) is provided to convert files produced by Illumina systems into FASTQ format. We used this software to perform demultiplexing, quality control, and adapter trimming of reads[36]. To refine the Flye assembled genome, we used Pilon in combination with the Illumina reads for autocorrection[37]. The assembly underwent annotation and quality evaluation using the PATRIC genome annotation service[38].
Additional data access
Additional Akkermansia genomes (n = 221), including that of MucT and excluding MAGs, were retrieved from NCBI. A summary of each isolate can be found in Supplementary Table 1. The 16S rRNA-based microbial community profiling results in the form of a phyloseq object[39] and raw metagenomic reads for validation of the proposed phylogroup prediction methods were obtained from the Pediatric Obesity Microbiome and Metabolism Study (POMMS)[40]. Metagenomic sequencing samples for the inflammatory bowel disease (IBD) and non-small cell lung cancer (NSCLC) datasets were obtained from the Sequence Read Archive using Bioproject accessions PRJNA398089, PRJNA400072, PRJEB42151, PRJEB42155, PRJNA751792 and PRJNA782662[41-44].
Pangenomic analysis
A comparison of 234 A. muciniphila genomes was performed using the pangenomic workflow in Anvi’o version 7.1[45]. Briefly, this workflow included construction of a pangenome from genome fasta files[46,47], calculation of average nucleotide identity of assigned phylogroup[48], core gene cluster identification and single-copy core protein sequence alignment[49], estimation of functional enrichment[50], and extraction of full-length 16S rRNA sequences[51]. An in-depth description of these methods and the parameters used may be found at https://gitlab.oit.duke.edu/valdivia-lab/akkermansia-species-and-phylogroups.
Phenotypic characterization
Fatty acid methyl ester profiling of select strains was performed by EMSL Analytical, Inc. The API 20A system (bioMérieux catalog 20300) was used to measure 21 different biochemical phenotypes, including indole formation, urease and catalase activity, gelatin and esculin hydrolysis, and 16 acidification reactions. These tests were performed according to the manufacturer’s protocol under anaerobic conditions for 48 h.
Analysis of community profiling data
The 16S rRNA-based microbial community profiling of the POMMS cohort was obtained as a phyloseq object[40], which included the prevalence of identified amplicon sequence variants (ASVs). Identification of Akkermansia species and phylogroups from the data was performed using Phyloseq (v1.32.0)[39] in
Statistics and visualization
Figures were finalized using Inkscape[57]. Metabolic pathway completeness was visualized using the package pheatmap (v1.0.12) in R. Phylogenetic trees were visualized using the Interactive Tree of Life[58]. GraphPad Prism 10 (v10.1.0)[59] was used to generate all remaining graphs, calculate the average ANI and 16S rRNA sequence identity between phylogroups, and perform statistical analyses. Principal Component Analysis of FAME results used the standardized method in GraphPad Prism. Mann-Whitney U tests were used to compare relative abundances in datasets containing two disease groups, Kruskal-Wallis H tests were used to compare relative abundances in datasets containing more than two disease groups, and Cox regression was used for survival analyses.
RESULTS
Genomic analysis of multiple Akkermansia isolates supports the establishment of new species and A. muciniphila subspecies
To perform our analysis, we retrieved 221 publicly available genome sequences originally identified as
The average nucleotide identity (ANI) between two pairs of genomes has become the gold standard for identifying the genetic boundary of species from genome sequencing data[61,62]. Our pairwise analysis ANI between Akkermansia genomes reinforced the previously established phylogroup structure[19,20,22] and added a new phylogroup [Figure 1A and Supplementary Table 2]. This phylogroup, which we termed AmVI, is represented by Akkermansia isolated from a patient undergoing cancer immunotherapy and a patient with amyotrophic lateral sclerosis. Current standards use 95% ANI as a threshold to define new species[61,62], and a 98% ANI threshold has been used to define sub-species[63]. We calculated the average ANI between each pair of phylogroups and determined that the previously proposed phylogroups of A. muciniphila should be considered as separate species based on the 95% threshold, with Akkermansia AmI being considered
Figure 1. Phylogenetic analysis of Akkermansia genomes and evidence of speciation among seven major phylogroups. (A) ANI based on whole-genome comparisons, as calculated using pyANI in Anvi’o. The heatmap displays levels of ANI similarity between pairwise comparisons and grouping is set at a 95% similarity threshold. Red denotes a pairwise ANI of 95% or above. Major phylogroups are highlighted and labeled to maintain consistency with previous publications[19-22]. Note the presence of the new AmV and AmVI phylogroups; (B) Comparison of average ANI between Akkermansia phylogroups. Differences in average ANI below 95% were used to denote new species among phylogroups and an average ANI between 95% and 98% was defined to assign new sub-phylogroups within those new species (dotted lines on each subplot); (C) Phylogenetic analysis of core Akkermansia proteins. The predicted amino acid sequences encoded by single-copy Akkermansia genes were aligned and concatenated using anvi-get-sequences-for-gene-clusters. anvi-gen-phylogenomic-tree was used to generate a phylogenomic tree with FastTree. A. glycaniphila was used as an outgroup, and individual strains are highlighted by the proposed species and subspecies. Numbers denote main branch lengths; (D) Comparison of average (red line) genome sizes of high-quality complete genomes of representative members of Akkermansia phylogroups; (E) Phylogenetic grouping of full-length 16S rRNA Akkermansia sequences is consistent with genomic ANI comparisons. Full-length 16S rRNA gene sequences were extracted from genome sequences within Anvi’o. Clustal Omega was used to perform the alignment of those sequences, and the resulting phylogenetic tree was used for visualization. A. glycaniphila was used as an outgroup, and individual strains have been highlighted by phylogroup. ANI: Average nucleotide identity.
We next refined the evolutionary relatedness among Akkermansia genomes by identifying a total of 137 single-copy, core gene clusters among Akkermansia groups. We included A. glycaniphila to anchor into a deeper branch of the Akkermansia evolutionary tree. The aligned amino acid sequences predicted to be encoded by these gene clusters were concatenated to generate a phylogenetic tree. After rooting the tree to A. glycaniphila, we found a similar clustering of Akkermansia core proteins into the same phylogroups as predicted by ANI [Figure 1C].
As further evidence of the distinctiveness of Akkermansia species, an ANOVA test indicated that there are statistically significant differences in their average genome sizes [F(6,225) = 92.77, P < 0.0001].
The divergence of 16S rRNA sequence identity is commonly used to distinguish between species[64,65].We successfully extracted full-length representative 16S rRNA gene sequences for 217 genomes and generated a phylogenetic tree rooted on A. glycaniphila. Genomes for which our analysis did not yield full-length 16S rRNA gene sequences were of contig or scaffold assembly levels, suggesting incomplete assembly or low sequencing quality for these genomes. As with genome level ANI and core protein divergence, Akkermansia 16S rRNA sequences clustered by phylogroup, except for AmIa and AmIb sub-phylogroups, which could not be resolved. While these observations further support the phylogroup-level separation by ANI and core phylogeny, the average 16S rRNA gene sequence identity between any pair of phylogroups does not meet the commonly used threshold of 97%. At a higher species demarcating threshold of 98.65%, only
Akkermansia species are differentiated by their core genomes and predicted metabolic function
To further differentiate between A. muciniphila, A. massiliensis, and A. biwaensis, we first identified gene clusters that were present in every genome of a single species, but not in the other species. We next identified gene clusters that were present in all genomes of two species, but not the remaining, and gene clusters present in all genomes of all three species [Figure 2A]. We find that 43% of these gene clusters (1,192/2,748) are present in all three species. A. biwaensis contains 664 gene clusters which are not core to
Figure 2. Akkermansia species are distinguishable by their estimated metabolic capabilities and fatty acid composition. (A) Unique core gene clusters were defined as those present in all genomes of one species, but not present in every genome of another species. Shared core gene clusters (n = 1,192) were defined as those present in all genomes of multiple species. All core gene clusters were categorized by these definitions and tallied to define the sizes of the A. muciniphila, A. massiliensis, and A. biwaensis core genomes; (B) Estimated metabolic enrichment analysis indicates the gain or loss of some metabolic pathways in Akkermansia phylogroups. Blue indicates pathway presence, calculated by pathway completeness of 50% or greater; (C) A principal components analysis was performed to compare fatty acid composition across species. The principal component scores distinguish three clusters comprised primarily of either A. muciniphila and AmV, A. massiliensis, or A. biwaensis and AmVI, as indicated by dotted ellipses. Isolates are colored by species; (D) Average fatty acid composition by C14:0 iso and C15:0 iso distinguishes A. massiliensis from other Akkermansia species.
To define the predicted metabolic capabilities of the new Akkermansia species and phylogroups, we assigned functions to predicted ORFs based on the KEGG Orthology (KO). The resulting KOs were filtered to include only those annotated as present or absent in at least three genomes in each phylogroup [Figure 2B]. As previously reported, gene clusters involved in assimilatory sulfate reduction are absent in
Phenotypic characterization of Akkermansia species
Analysis of Akkermansia pangenomes suggests there is significant diversity among Akkermansia species in terms of metabolic function[20,22,66]. Additional studies corroborated the phenotypic diversity between isolates of these species in vitro[20,23]. Two studies further characterized a few selected isolates using biochemical test kits and fatty acid methyl ester (FAME) analysis[29,30]. However, these studies were restricted to a single strain of A. muciniphila (MucT), A. glycaniphila (PytT, isolated from python), and one isolate of either
Fatty acid composition is a recommended means to phenotype microorganisms and has been used to differentiate closely related species, such as those of Legionella[67,68]. FAME analysis was performed on six representative A. muciniphila isolates (including MucT, all isolated from humans), in addition to two AmV (isolated from mice), three A. massiliensis (isolated from humans), three A. biwaensis (isolated from humans), and two AmVI (isolated from humans) isolates. To compare the results across phylogroup, we then performed a Principal Component Analysis (PCA) [Figure 2C and Supplementary Table 4]. Three clusters of isolates were distinguished from PC1 and PC2, which account for 61.61% of the overall variance. These clusters primarily consist of (i) A. muciniphila and AmV, (ii) A. massiliensis, and (iii) A. biwaensis and AmVI. Of the fatty acids present at an abundance greater than 1%, we found that C14:0 iso and C15:0 iso distinguished A. massiliensis from the other species [Figure 2D and Supplementary Figure 3]. Distinguishing between the A. muciniphila and A. biwaensis by individual fatty acids was not possible.
Next, we used the API 20A system to perform biochemical phenotyping on six A. muciniphila (including MucT, isolated from humans), two AmV (isolated from mice), three A. massiliensis (isolated from humans), four A. biwaensis (isolated from humans), and one AmVI (isolated from humans) isolate [Supplementary Table 5]. Out of the 21 assays performed, 15 yielded negative results for all isolates. All isolates were positive for glucose and lactose utilization, and the remaining four assays yielded varied results. Mannitol utilization was absent in all AmV, A. massiliensis, and AmVI isolates, but varied between isolates of A. muciniphila and A. biwaensis. Maltose utilization occurred in both AmV isolates, not in the AmVI isolate, and varied between isolates of A. muciniphila, A. massiliensis, and A. biwaensis. All isolates of AmV and A. biwaensis could use mannose but not the AmVI isolate, and mannose utilization varied between isolates of
Akkermansia species and subspecies-level assignments can be made from metagenomic sequences and 16S rRNA V3-V4 regions
Akkermansia species and phylogroups have distinct metabolic, in vitro phenotypic, and in vivo competitive characteristics[20-23] that could be linked to health or disease risks. We next asked if we could use existing 16S rRNA and metagenomic sequencing data generated from various patient cohorts to clarify the relationships between Akkermansia species and human health. We leveraged our pangenomic analysis results to enhance existing metagenomic sequencing tools, enabling us to achieve species-level resolution of the Akkermansia in these published datasets.
To determine if existing strain-finding programs can distinguish between Akkermansia species, we used the POMMS stool samples[20,40], for which we have matching 16S rRNA amplicon and shotgun metagenomic sequencing datasets. We also previously isolated Akkermansia strains from several of these samples[20]. We identified Akkermansia phylogroups from metagenomic sequencing data using a combination of three strain-finding programs, StrainPhlAn3, StrainR, and SMEG[53,55,56].
StrainPhlAn3 is broadly used to identify bacterial strains from metagenomic data[69]. To identify Akkermansia phylogroups, we ran metagenomic sequencing data generated from POMMS samples through our StrainPhlAn3 workflow, which included 31 samples from which we had previously identified and isolated the dominant Akkermansia species[20,70]. StrainPhlAn3 frequently failed to detect Akkermansia in samples where the relative abundance was less than 0.5% of total bacterial sequences and was able to assign a phylogroup to only 85 out of 146 POMMS metagenomes with levels of Akkermansia detectable by MetaPhlAn3 [Supplementary Figure 4A].
SMEG is designed to identify bacterial strains and measure their growth rate based on metagenomic sequences by calculating the coverage of SNPs closer to the origin of replication as compared to those closer to the terminus region[56]. We generated a SMEG database using 34 Akkermansia genomes representing different species and strains and used the growth_est module to predict Akkermansia species. SMEG was able to assign a species or phylogroup to 108 out of 146 POMMS metagenomic samples with detectable (greater than 0% relative abundance) levels of Akkermansia, which allowed for improved sensitivity in assigning phylogroups and for the estimation of growth rates in vivo. The results of a simple linear regression indicate that the relative abundance of A. muciniphila is a significant predictor of its estimated growth rate [R2 = 0.1281, F(1,52) = 7.642, P = 0.0079] [Figure 3A]. However, no significant association between growth rate and relative abundance was found for either A. massiliensis [R2 = 0.0043,
Figure 3. The relative abundance of the three main Akkermansia species and A. muciniphila sub-phylogroup in two different patient cohorts can correlate with disease outcomes. (A) In vivo growth rate is a minor contributor to the relative abundance of Akkermansia species in stool samples. A simple linear regression was performed to determine if the relative abundance of Akkermansia in a metagenomics sequencing sample is predictive of the estimated growth rate as determined by SMEG. Each regression line is colored by, and represents, either A. muciniphila, A. massiliensis, or A. biwaensis; (B) Comparison of the relative abundances of A. muciniphila,
StrainR is designed for the relative quantification of highly related strains from metagenomic datasets[55]. We generated a StrainR database using one representative Akkermansia genome per species and phylogroup, and StrainR assigned a species or phylogroup to 139 out of 146 POMMS metagenomic samples with detectable levels of Akkermansia, nearly doubling the sensitivity of StrainPhlAn3. Out of those 146 POMMS samples with detectable levels of Akkermansia, 138 (94.52%) were found to contain a single Akkermansia species. The remaining eight samples all contained A. muciniphila in addition to A. massiliensis and/or
To validate the effectiveness of these methods, the Akkermansia species and subspecies in POMMS patients were compared against strains isolated from the corresponding stool samples. Out of 31 POMMS metagenomic samples with matching isolates, we identified 26 samples with measurable levels of Akkermansia, as assessed by MetaPhlAn3, for which we could predict the prevalent phylogroup by a combination of StrainPhlAn3, StrainR, and SMEG. For these samples, 24/26 (92%) of the isolated strains matched the predicted phylogroup. Eight samples in the entire dataset were predicted to contain more than one Akkermansia species, accounting for a range of 1.57%-33.50% and a median of 13.83% of Akkermansia abundance of the additional minor species. Note that this method has only been validated to distinguish between the A. muciniphila, A. massiliensis, and A. biwaensis species, as the validation dataset did not include Akkermansia AmIII, AmV, or AmVI isolates.
We noted that full-length 16S rRNA gene sequences clustered by the new proposed Akkermansia species nomenclature. We determined that this clustering is maintained if we restrict our analysis to the V3-V4 subregion, which is commonly used in 16S rRNA gene-based microbial community profiling
Correlations between Akkermansia species and health in metagenomic studies
We reanalyzed metagenomic datasets of patients with obesity, inflammatory bowel disease, and responsiveness to cancer immunotherapies - instances where Akkermansia has been reported to influence health - to determine if there are correlations between specific species of Akkermansia and the health status of their host. Among all the samples analyzed where Akkermansia sequences were detectable, we found
There is a negative correlation between Akkermansia abundance and obesity in adult humans and mouse models of diet-induced obesity[3-5,7,71]. We performed Mann-Whitney U tests to compare the relative abundances of A. muciniphila, A. massiliensis, or A. biwaensis between children with obesity (case) and control samples from this same dataset [Figure 3B]. There was no significant difference in the relative abundance of A. muciniphila (U = 6,971, P = 0.1569) or A. massiliensis between cases and controls
Akkermansia abundance has been linked to varying risk for IBD. Akkermansia has been found to be depleted in adult human cases of Crohn’s disease (CD) and ulcerative colitis (UC), while other studies have found Akkermansia to only be depleted in the case of CD (in pediatric patients), or to be associated with responsiveness to treatment rather than with disease occurrence[31,72-74]. Other multi-omics studies have found no significant association between Akkermansia and IBD[43,44]. We combined three of the largest adult-IBD metagenomic studies[42-44] and processed them to make Akkermansia species and subspecies assignments. In this combined dataset, 34.64% of samples (124/358) contained detectable levels of Akkermansia.
Kruskal-Wallis H tests were performed to compare the relative abundances of A. muciniphila,
We next asked if the relative abundance of A. muciniphila subspecies (AmIa and AmIb), influenced their correlation with health. In the POMMS dataset, there was no significant difference in the relative abundance of A. muciniphila AmIa (U = 7,574, P = 0.5084) or AmIb (U = 7,115, P = 0.2384) between case (obese) and control samples [Figure 3D]. For the combined IBD dataset [Figure 3E], we found that there is a significant difference in both AmIa [H(3) = 11.75, P = 0.0028] and AmIb [H(3) = 11.08, P = 0.0039] between healthy and IBD groups. Post hoc pairwise comparisons indicated that AmIa is significantly decreased in both the CD and UC groups (control mean rank = 192.8, CD mean rank = 177.9, P = 0.0178, UC mean rank = 173.8, P = 0.0015), while AmIb is only significantly decreased in the UC group relative to healthy controls (control mean rank = 192.4, UC mean rank = 163.3, P = 0.0121).
Akkermansia is associated with improved outcomes following PD-1 blockade treatment of epithelial tumors[6] and the relative abundance of Akkermansia is predictive of clinical responsiveness to similar treatment of non-small-cell lung cancer (NSCLC)[41]. In particular, the presence of Akkermansia in patient stools was associated with improved responses to immunotherapy and overall survival, compared to patients with no Akkermansia. We reanalyzed stool metagenomes from one of these studies and assigned a dominant Akkermansia species and subspecies to each sample[41]. Based on a Cox regression model, adjusted for sex and age of patients [Figures 4A and B], we found a significant survival difference between patients colonized predominantly by A. muciniphila (AmI) and those without Akkermansia (HR = 0.580, 95%CI: 0.408-0.820, P = 0.002). There was no significant survival difference between patients colonized predominantly by A. massiliensis or A. biwaensis.
Figure 4. The association between Akkermansia and improved survival following PD-1 blockade treatment depends on the dominant Akkermansia species and sub-phylogroup. (A) A Cox regression model was used to determine the factors associated with prolonged survival after treatment for non-small-cell lung cancer. Cumulative survival is displayed using dominant Akkermansia species as the predictive variable, colored by species. The line for A. massiliensis is dotted to make the line for Akkermansia-negative samples visible; (B) The tabular results of the Cox regression model reveal that the relationship between Akkermansia and prolonged survival is specific to
We also found a significant difference between A. muciniphila AmIa and AmIb in the survival of patients with NSCLC undergoing immune checkpoint inhibitor treatment. After restricting our analysis to those samples with detectable A. muciniphila, we generated a Cox regression model [Figures 4C and D] and found that patients colonized predominantly by AmIa had a significant increase in survival compared to patients with no detectable A. muciniphila (HR = 0.492, 95%CI: 0.319-0.744, P = 0.001). We did not detect this enhanced survival in patients predominantly colonized with A. muciniphila AmIb (HR = 0.689, 95%CI: 0.434-1.062, P = 0.101).
Overall, our findings indicate that in circumstances where A. muciniphila is associated with an impact on host physiology or immunity, which subspecies predominates can make a difference in its association with disease outcomes. In IBD, AmIa abundance is linked to protection from both CD and UC, while AmIb was only associated with protection from UC. Likewise, the increased survival noted in NSCLC patients is only significant for those patients who were colonized by predominantly AmIa.
DISCUSSION
We performed a detailed analysis of genomic variance among human Akkermansia isolates and reinforced the existence of at least six phylogroups (AmI-AmVI)[19-22]. We also provide further support for the assignments of new Akkermansia species, A. massiliensis and A. biwaensis, and for at least two
Proposed nomenclature for Akkermansia phylogroups
| Previous nomenclature | Proposed nomenclature | Type strain | Type strain availability | Average genome size (Mbp) |
| A. muciniphila AmIa | A. muciniphila subspecies muciniphila | MucT | ATCC BAA-835 = DSM 22959 | 2.743 |
| A. muciniphila AmIb | A. muciniphila subspecies communis | Akk1570T | 2.816 | |
| A. muciniphila AmII | A. massiliensis | Marseille-P6666T | CSUR P6666 = CECT 30548 | 3.112 |
| A. muciniphila AmIII | 2.956 | |||
| A. muciniphila AmIV | A. biwaensis | WON2089T | NBRC 115679 = DSM 114407 | 3.213 |
| A. muciniphila AmV | A. ignis | MmAkk2T | Submitted to ATCC and DSMZ | 2.809 |
| A. muciniphila AmVI | A. durhamii | RCC_12PDT | Submitted to ATCC and DSMZ | 3.161 |
We noted marked differences in the genome sizes of the genus Akkermansia, with A. massiliensis,
While different methodologies have been applied for the pangenomic analysis of the Akkermansia genus to define relationships between Akkermansia and health, these have not generally taken into consideration subspecies and strain assignments[22,25,27,66,77]. This could be particularly relevant when considering the potential of Akkermansia strains to be developed into next-generation probiotics[13-16]. The new species outlined, both as previously suggested[20,22,23,60] and reinforced by the results presented in this study, highlight that there are clear phenotypic differences that may be relevant to the interaction between Akkermansia and the host. Indeed, we find species and subspecies level assignments refine the outcome of associations between Akkermansia and humans, where one sub-species may drive the associations observed in the past, as in the case of increased responsiveness to PD-1 blockade in patients colonized by AmIa Akkermansia. Thus, targeting specific strains to be developed into future probiotics should include an analysis of which phylogroup is most strongly associated with the health benefit of interest. Additionally, if one or more phylogroups of Akkermansia are determined to be negatively associated with health outcomes in a particular context, knowing what strains to avoid during therapeutic development will decrease the risk of failure or development of adverse effects.
Our study presents certain limitations. First, the novel species described in our analysis of the Akkermansia pangenome are relatively rare in the human populations we have characterized and appear to be present in lower abundance than other Akkermansia species. There are currently only five isolates of A. ignis and two of A. durhamii. Two strains, BAA-2860 and CSUN-56, may also represent novel species. Prescreening of MAGs generated from metagenomic sequencing of fresh stool samples would allow for more efficient, targeted isolation of these rare species. The functional characterization of Akkermansia strain by standard methods like the API 20A system is of limited use as the composition of the assay medium is not compatible with culturing Akkermansia. Indeed, the species identification table provided by bioMérieux does not include Akkermansia. There are also some limitations to the use of StrainR in reliably distinguishing between the A. muciniphila AmIa and AmIb subspecies.
Finally, the rarity of the non-A. muciniphila species can decrease the statistical power of any associations between these species and human health conditions. As illustrated in cases of pediatric obesity, the proportion of individuals with a detectable level of A. biwaensis was low, which, compounded with the low prevalence of these new species compared to A. muciniphila, makes determining relevant disease associations more challenging. Thus, much larger datasets will be necessary to properly investigate the importance of these new Akkermansia species to human health.
DECLARATIONS
Authors’ contributions
Conception, data acquisition, data analysis and interpretation, and writing: Mueller KD
Akkermansia isolation and genome sequencing: Davey L, Panzetta ME
Research guidance and manuscript revision: Valdivia RH
Manuscript revision: Rawls JF, McCann JR, Flores GE
Availability of data and materials
Genomes retrieved from NCBI and their accession numbers are listed in Supplementary Table 1. 16S and metagenomic datasets for method validation were provided by the POMMS[40]. Metagenomic sequencing samples case studies were obtained from the SRA using Bioproject accessions PRJNA398089, PRJNA400072, PRJEB42151, PRJEB42155, PRJNA751792, and PRJNA782662[41-44]. Sequences for the Akkermansia durhamii isolates have been deposited in GenBank under BioProject accession number PRJNA1066260.
Financial support and sponsorship
The work described here was supported by NIH grants (AI42376 to R.H.V and R24-DK110492 to Rawls JF). Additional support from the HHMI EPI program to Valdivia RH and Flores GE was provided by the National Institutes of Health through the National Institute of General Medical Sciences (NIGMS) (grant number SC1GM136546). Mueller KD was partially supported by the National Science Foundation under Grant Number DGE 1545220.
Conflicts of interest
Valdivia RH is a co-founder of Bloom Sciences (San Diego, CA). The company was not involved in sponsoring or analyzing and interpreting the data presented. Other authors declared that there are no conflicts of interest.
Ethical approval and consent to participate
Isolation of Akkermansia strains from the stool of RCC patients at Duke University Hospital (IRB protocol Pro00076768) and from the stool of patients with amyotrophic lateral sclerosis (People with ALS, PALS) patients at Duke University Hospital (IRB protocol Pro00108282) was both approved by the Duke Institutional Review Board. Signed informed consent was obtained from individuals enrolled in these studies, indicating awareness that their stool would be used for scientific research.
Consent for publication
Not applicable.
Copyright
© The Author(s) 2024.
Supplementary Materials
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OAE Style
Mueller KD, Panzetta ME, Davey L, McCann JR, Rawls JF, Flores GE, Valdivia RH. Pangenomic analysis identifies correlations between Akkermansia species and subspecies and human health outcomes. Microbiome Res Rep 2024;3:33. http://dx.doi.org/10.20517/mrr.2024.09
AMA Style
Mueller KD, Panzetta ME, Davey L, McCann JR, Rawls JF, Flores GE, Valdivia RH. Pangenomic analysis identifies correlations between Akkermansia species and subspecies and human health outcomes. Microbiome Research Reports. 2024; 3(3): 33. http://dx.doi.org/10.20517/mrr.2024.09
Chicago/Turabian Style
Katherine D. Mueller, M. Emilia Panzetta, Lauren Davey, Jessica R. McCann, John F. Rawls, Gilberto E. Flores, Raphael H. Valdivia. 2024. "Pangenomic analysis identifies correlations between Akkermansia species and subspecies and human health outcomes" Microbiome Research Reports. 3, no.3: 33. http://dx.doi.org/10.20517/mrr.2024.09
ACS Style
Mueller, KD.; Panzetta ME.; Davey L.; McCann JR.; Rawls JF.; Flores GE.; Valdivia RH. Pangenomic analysis identifies correlations between Akkermansia species and subspecies and human health outcomes. Microbiome. Res. Rep. 2024, 3, 33. http://dx.doi.org/10.20517/mrr.2024.09
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