Opportunistic detection of Fusobacterium nucleatum as a marker for the early gut microbial dysbiosis

doi:10.21203/rs.2.19123/v1

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Opportunistic detection of Fusobacterium nucleatum as a marker for the early gut microbial dysbiosis

https://doi.org/10.21203/rs.2.19123/v1

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published 13 Jul, 2020

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Background Gastrointestinal microbiome has a great impact on human health and diseases. Fusobacterium nucleatum , an oral resident gram-negative microorganism, has been reported to differentially prevail in colorectal cancer (CRC). However, the effect of its enrichment was not clearly explored in terms of microbial homeostasis and stability at the early stage of disease development.

Result Our analysis on longitudinal metagenomic data generated by the Integrative Human Microbiome Project (iHMP) showed that F. nucleatum significantly proliferated in IBD subjects with decrease in microbial diversity. Using non-parametric LDA effect size (LEfSe) algorithm, 12 IBD- and 14 non-IBD-specific marker species were identified. IBD-specific marker species were found more in the F. nucleatum -experienced subjects than in not-experienced ones. In addition, F. nucleatum experience severely abrogated intra-personal stability of microbiome in IBD patients and induced highly variable gut microenvironment. From the longitudinal comparison between prior and posterior distributions to F. nucleatum detection, 41 species could be regarded as indicative “classifiers” of dysbiotic state. Multiple logistic regression models were built to predict the probability experiencing F. nucleatum and showed that the high probability at the points following F. nucleatum observation was significantly associated with microbial dysbiosis. Finally, clustering all detected microbes based on the longitudinal distribution revealed that marker species for IBD and non-IBD conditions as well as pre-documented signature biomarkers of CRC were well distinguishable and could be utilized for explaining gut symbiosis and dysbiosis.

Conclusion F. nucleatum opportunistically appeared under early gut dysbiosis, and a group of discriminative marker species associated with F. nucleatum , screened from the longitudinal analysis of microbial changes, was successfully applied to predict early microbiota alterations in both IBD and non-IBD conditions. Our prediction model and development of microbial biomarkers for estimating gut dysbiosis should provide a novel aspect of microbial homeostasis/dynamics and useful information on non-invasive biomarker screening.

General Microbiology

Gastroenterology & Hepatology

Fusobacterium nucleatum

inflammatory bowel diseases (IBD)

colorectal cancer (CRC)

opportunistic detection

microbial experience

Integrative Human Microbiome Project (iHMP)

The microbial communities in the gastrointestinal tract play pivotal roles in maintaining many biological functions such as food digestion, metabolism, and immunological regulations as well as developing diseases like ulcers, bowel perforation, inflammatory bowel diseases, irritable bowel syndrome, other inflammatory conditions, metabolic syndromes, and even colorectal cancer.

F. nucleatum was initially identified as a non-motile obligate anaerobe commonly residing on the tooth surface of healthy individuals and bridging bacterial species to form dental plaque biofilm [1, 2]. Many researches have indicated that F. nucleatum is also found in other organs and associated with several disorders such as oral inflammation, adverse pregnancy outcomes, IBD, Lemierre’s syndrome, cardiovascular diseases, atherosclerosis, Alzheimer’s disease, and cancer [3–8]. IBD refers to as chronic conditions describing a group of inflammatory disorders in intestines. Patients with IBD tend to show a high level of F. nucleatum in the colon and are at significantly high risk of CRC. It has been demonstrated that F. nucleatum is related with and promotes the growth of CRC [9–17].

CRC is the fourth most incident cancer in the world. The rates of CRC incidence and mortality are still rising in developing countries and in relatively young people in the United State. [18, 19]. Chronic inflammation at large intestine is a significant risk factor of CRC [30, 31]. CRC development is increased in IBD people by six times compared with control group and 1 out of 7 IBD patients died by CRC [32]. Furthermore, the occurrence of CRC after negative findings from a recent colonoscopy is three times higher in IBD patients than in non-IBD controls, indicating rapid carcinogenesis with chronic gut inflammation [33]. For early detection of CRC, endoscopic surveillance is required and accompanied by discomfort due to invasiveness. Thus, there is a realistic need for development of non-invasive biomarkers for the diagnosis [20]. Although F. nucleatum is detected significantly more in CRC tissues, the efficacy of fecal F. nucleatum as a potential non-invasive biomarker was not consistent because it is rarely detected [21–28]. However, corroborating evidences that F. nucleatum is a promising biomarker for CRC, have been accumulated and more effort have been made to understand its role in the carcinogenesis.

The Integrative Human Microbiome Project (iHMP) released extensive longitudinal datasets of disease-specific cohorts to understand the interaction between the microbiome and host using multi-omics technologies. Among them, there are whole-metagenome shotgun sequencing data from more than one hundred fecal samples of IBD and non-IBD participants over one-year collection [29]. These data seem to provide a comprehensive profiling on overall microbiome with IBD but the establishment of cancer-associated microbiome in IBD has not been investigated [34].

Here we explored whether F. nucleatum and its associated pathobionts might become prevalent under dysbiotic environment by analyzing the longitudinal metagenomic data and predicted if the occurrence of F. nucleatum could play a function as the indicator reflecting disease condition.

Metagenomic profiling of IBD or non-IBD participants

As shown in Fig. 1, the overall analysis includes filtering, profiling, longitudinal dissection, biomarker screening, modeling, and microbial dynamics. The data used in this study was the fecal metagenome dataset downloaded from the Inflammatory Bowel Disease Multi-’Omics Database (IBDMD) of iHMP, which were longitudinally generated from 130 participants. The data quality was tested and only significant samples were considered for the analysis (Additional file 9).

Microbial taxonomy was assessed at the species level using MetaPhlAn2, and the quality of the compositional data were controlled according to the specific conditions mentioned in the Method (Additional file 10) [35]. The number of filtered data was 1526 samples from 106 participants, and the basic information of the participants such as sex, age, and collection days were comparable between IBD patients and non-IBD subjects (Additional file 11: Table S1). The overall distribution did not show any specific tendency to sex, IBD-activity, subject, and data generation sites (Additional file 1: Figure S1).

Consistent with the previous reports, two major phyla in human gut, Firmicutes and Bacteroidetes displayed a reciprocal proportion in principal coordinate analysis (PCoA) (Fig. 2a) [36]. The metagenome samples from IBD and non-IBD subjects were clearly distinguished. Samples from non-IBD subjects mainly localized in a left-lower quadrant and samples from IBD patients were more widely distributed along PC1 axis (probability value of IBD vs. non-IBD, P_{IBD-Non (PC1)} < 2.2e–16, Fig. 2b). The representative subtypes of IBD, ulcerative colitis (UC) and Crohn’s diseases (CD), were not significantly discriminated by PC1 and PC2 axes (probability values of UC vs. CD, P_{UC-CD (PC1)} = 0.1726, P_{UC-CD (PC2)} = 0.0988), implying that the two idiopathic inflammatory disorders shares similar microbial community (Fig. 2b). Overall microbiome seemed to be largely distinct among subjects and stable over time (Fig. 2c). As grouped by K-means clustering, most of non-IBD samples belonged to cluster C3, suggesting that microbiome from non-IBD subjects should be relatively convergent relative to those from UC or CD (Odd Ratio, OR_nonIBD-C3 = 4.42, OR_UC-C3 = 2.30, OR_CD-C2 = 2.15).

Gut microbiota homeostasis is maintained under normal condition but stress conditions induce a decrease of microbial diversity, leading to dysbiosis [37]. Multiple measurements of alpha diversity indices like Shannon diversity, Pielou’s evenness, and richness (the number of detected species per sample) were lower in samples with IBD than in those without IBD. There were no significant differences in alpha diversity indices between CD and UC. Interestingly, the active state of IBD had lower Shannon diversity and richness (Fig. 2d, additional file 2: Figure S2a and b).

Additionally, the fraction of human sequence reads mixed with gut metagenomic data was higher in IBD and much higher in active state of IBD, which means that a leakage of human genome into gut lumen might reflect the severity of disorders (Fig. 2e). The fraction of human sequence reads was also positively correlated with diseases severity scores such as simple clinical colitis activity index (SCCAI) for UC and Harvey-Bradshaw index (HBI) for CD (Additional file 2: Figure S2c, d).

Detection of F. nucleatum and its longitudinal dissection

F. nucleatum is not frequently found in gut microbiome but its detection is important as an indicator of gut disorders. F. nucleatum occurred 41 times with marginal preference to chronic inflammation (OR = 1.79, Fisher’s one-sided P_detect = 0.1062) (Fig. 3a). Considering relative abundance upon detection, F. nucleatum was favorably detected in IBD samples (Wilcoxon test P_detect = 0.02891) (Fig. 3b).

The low detection frequency of F. nucleatum in healthy state is not a favorable property for early diagnosis. Thus we tested whether the detection frequency and abundance are related with detection consistency using 44 replicated samples. Highly abundant species were mostly captured in both duplicated sample pair but less abundant ones were not. About one-fourth of total species appeared only in one sample of duplicate pairs. F. nucleatum was a relatively rare microbe observed only 4 times in three replicates of total 44 test samples. The abundance and the number of detection showed a positive correlation, as expected. The recovery rate, which is the ratio of the number of pairs having a certain microbe in both duplicated samples vs. total number of sample pairs with that microbe, was higher for abundant species. Recovery rate of F. nucleatum was only 33.3%, supporting inconsistent fecal detection (Additional file 3: Figure S3).

To overcome the limitation of snapshot-based approach, the samples collected from each subject over one year were organized according to their chronological point relative to F. nucleatum (Fig. 3c). At first, subjects were classified into F. nucleatum-experienced or –innocent (non-experienced) groups, and the samples from F. nucleatum-experienced subjects were sub-divided into prior or posterior distribution to the detection point of F. nucleatum as well as proximal or distal distribution toward F. nucleatum as illustrated. The samples from F. nucleatum-experienced subjects were displayed in PCoA plot and showed highly dispersed distribution (Fig. 3d, g). Experiencing F. nucleatum seemed to lead to reduced Shannon diversity and Pielou’s evenness. Particularly, the samples either proximal or posterior to F. nucleatum detection exhibited reduced alpha diversity and increased human read fraction (Fig. 3e, h, i, additional file 2: Figure S2e, f). Longitudinal tracking of F. nucleatum-experienced subjects revealed that the microbial diversity was reduced in non-IBD subjects as well (Fig. 3f). These results implies that F. nucleatum might appear under the perturbed gut microenvironment with low microbial diversity.

Identification of biomarkers in IBD/non-IBD and their correlation with F. nucleatum.

In order to clarify whether F. nucleatum was truly associated with inflammatory environment, we tried to screen biomarkers of IBD and non-IBD conditions. Using a non-parametric Linear discriminant analysis Effect Size (LEfSe) algorithm which emphasizes not only the differential abundance of features among the classes but also the biological consistency within the same class, 12 IBD and 14 non-IBD biomarkers were selected at species level. As expected, these markers were specifically detected in either IBD or non-IBD samples (Fig. 4a, b, additional file 12). In F. nucleatum-experienced subjects, the ratio of IBD markers to non-IBD markers was significantly increased, and non-IBD subjects who have experienced F. nucleatum held increased number of IBD biomarkers as well (Fig. 4c). The prevalence of IBD markers over non-IBD markers was also distinct in F. nucleatum-posterior samples. The subjects who have experienced F. nucleatum at least once over one year showed more IBD biomarkers in non-IBD conditions (the median of IBD marker #_{F. n.-}_unexp = 6.54, the median of IBD marker #_{F. n.-}_-exp = 9.23, P < 2.2e–16) (Fig. 4d, e). These results suggested that experience of F. nucleatum should be tightly connected with IBD. The association of biomarker species with F. nucleatum was assessed by calculating Spearman’s correlation coefficients. All 14 non-IBD biomarkers were negatively correlated with F. nucleatum, having very significant enrichment p-values, and IBD biomarkers showed mostly positive correlation with some exceptions (Fig. 4f). Collectively, the absolute correlation coefficient of a certain microbe with F. nucleatum had strong relationship with its enrichment p-values in either IBD or non-IBD conditions ( = 0.33, P = 3.8e–15), (Fig. 4f). When the longitudinal abundance of the biomarker species was examined, two representative non-IBD marker species, Alistipes shahii and Alistipes putridinis, showed the decreasing patterns of abundance along the temporal axis centered at F. nucleatum-detection. In contrast, IBD markers like Clostridium symbiosum and Clostridium bolteae had opposite abundance pattern, low at prior and high at posterior to F. nucleatum-detection along the temporal axis (Fig. 4g). The abundance of these four biomarkers was changed significantly before and after F. nucleatum experience only in IBD condition, which means that the abundance of the cardinal microbes should be perturbed by inflammatory circumstances. In addition to these biomarkers, two IBD markers, Flavonifractor plautii and a unclassified species in Oscillibater genus, and three non-IBD markers, Alistipes finegoldii, Roseburia hominis, Roseburia inulinivorans, exhibited similar patterns of abundance over time (Additional file 4: Figure S4).

Microbial destabilization after F. nucleatum detection

Human gut microbiota is a sort of indicators of human health and understanding of their behavior is important for diagnosis and prevention of various diseases. The microbial imbalance, called dysbiosis, is believed to cause or be associated with several metabolic and inflammatory diseases [38, 39]. Thus, we examined intra- and inter-individual perturbation of microbiome according to experience of F. nucleatum and temporal distribution relative to its detection.

Intra-individual dissimilarity of microbiome was measured by calculating pairwise Bray-Curtis distance after random sampling in a given participant (Fig. 5a). Consistent with the previous literature, IBD subjects regardless of F. nucleatum experience, showed much longer microbial distance than non-IBD subjects at any given time intervals, supporting that IBD is related with microbial destabilization [34]. More importantly, IBD patients who have experienced F. nucleatum had more unstable microbiomes than F. nucleatum-unexperienced group (Fig. 5b). For temporal analysis of microbial stability, the time when F. nucleatum was detected was set as the initial point of the pairwise distance (Fig. 5c). F. nucleatum-experienced subjects showed significantly higher dissimilarity in samples ahead of F. nucleatum-detection than in those behind the detection, which was not shown in F. nucleatum-innocent subjects with random initial points (P_|x|<20w = 3.5e–05 in F. nucleatum-experienced; P_|x|<20w = 0.1905 in F. nucleatum-innocent; Fig. 5d). When examining 20 F. nucleatum-experienced subjects, all samples from non-IBD subjects were localized in a confined area of PCoA plot but certain IBD subjects (for example, C3009, H4015, M2034, and P6009) showed dramatic shift of microbiome prior to F. nucleatum detection (Additional file 5: Figure 56).

Two different participants in the same class were randomly selected to compare inter-individual microbial distance (Fig. 5e). The dissimilarity between IBD patients was higher than non-IBD subjects (d_IBD = 0.5696, d_non-IBD = 0.5000), and that of F. nucleatum-experienced subjects also higher than non-experienced ones (d_exp = 0.5927, d_non-exp = 0.5401). The microbial distance on the temporal distribution was higher at posterior than prior to F. nucleatum-detection (d_posterior = 0.5816, d_prior = 0.5372) (Fig. 5f-h).When F. nucleatum-detected samples were compared with another sample from other F. nucleatum-experienced subjects, the inter-personal microbial distance was gradually elevated until 20 weeks after F. nucleatum detection (Fig. 5i, j).

Collectively, these results suggested that highly variable microbiome might be pre-established in F. nucleatum-colonizing environment, and potentiate dysbiosis with chronic inflammation. On the other hand, a convergent microbiome before F. nucleatum detection is transformed into unstable and divergent one with F. nucleatum detection, possibly leading to the formation of pathogenic microbiome.

Identification of classifier microbes prior and posterior to F. nucleatum detection

To figure out representative microbes in samples before and after F. nucleatum-detection, 317 samples from F. nucleatum-experienced subjects were partitioned by iterating createDataPartition function in caret R package 1,000 times. A total of 258 microbes, identified at least 5 times across F. nucleatum-experienced subjects, were tested for their discriminative ability for samples prior or posterior to F. nucleatum-detection. The values of area under the Receiver Operating Character (ROC) curve (AUC) was calculated, and 41 significant species with average AUC value above 0.5 in multiple logistic regression models (FDR < 0.001) were regarded as classifiers (Fig. 6a). Among the classifier microbes, 26 species were enriched in F. nucleatum-posterior samples and 15 species in F. nucleatum-prior samples (Fig. 6b, additional file 13: Table S2). The posterior-enriched classifiers, including 3 IBD marker species, were found more in IBD samples, and the prior-enriched classifier with 4 non-IBD marker species were preferentially observed in non-IBD samples (Fig. 6b, c).

A recent fecal metagenome analysis suggested 29 core signature bacteria enriched in CRC metagenomes including three F. nucleatum strains [40]. Among them, 18 signature species were detected in our data, and most of them (13 out of 17 signatures except F. nucleatum) were positively correlated with F. nucleatum (additional file 14: Table S3). Of note, five CRC signature species were ranked as potent classifiers enriched in F. nucleatum-posterior samples (P_CRC = 0.0164) (Fig. 6b). The CRC-enriched classifiers included three Clostridium species, C. symbiosum, C. bolteae, C. clostridioforme, F. nucleatum, and Peptostreptococcus stomatis (AUC_{C. sym.} = 0.6574, AUC_{C. bolt.} = 0.6427, AUC_{C. clostri.} = 0.6102, AUC_{F. nuc.} = 0.6043, AUC_{P. sto.} = 0.5406). Especially, C. symbiosum, the most significant F. nucleatum-posterior classifier in our study, was proposed as a potent fecal biomarker of CRC even superior to F. nucleatum [41].

When considering discriminative significance of all microbes found in F. nucleatum-experienced subjects, all 11 CRC biomarkers, detected more than 5 times in F. nucleatum-experienced subjects, could discriminate F. nucleatum-posterior from F. nucleatum-prior samples (P_CRC = 0.00091). Additionally, a majority of IBD and non-IBD markers showed a discriminative power (P_IBD = 0.02285, P_non-IBD = 0.0732) (Fig. 6d).

Among prior-enriched classifier species, Dorea longicatena with the highest AUC (AUC = 0.7224) was recently proposed as potential probiotic of metabolic disorder and also reported to be over-represented in remissive CD patients after ileocolonic resection when compared to recurrent cases [42, 43]. Coprococcus comes (AUC = 0.7143)was reported to show down-regulation in CRC patients, and three Roseburia speciesincluding R. hominis, R. inulinivorans, and R. intestinalis (AUC_R.hom. = 0.6594, AUC_R.inul. = 0.6576, AUC_R.intest. = 0.6140), were well-documented to shape beneficial gut microflora by fermenting dietary polysaccharides [44–47]. Most biomarkers found in this analysis exhibited meaningful p-values and were differentially enriched in either F. nucleatum-posterior or -prior samples, supporting that F. nucleatum-oriented approach is advantageous to the identification of effective biomarkers (Fig. 6e).

Estimation of F. nucleatum experience and dysbiosis level in F. nucleatum-innocent subjects

Among 41 classifiers, top 13 classifiers except F. nucleatum, satisfying average AUC > 0.6 and FDR < 1e–07, were used to construct a prediction model for the estimation of the probability experiencing F. nucleatum as illustrated (Fig. 7a). Multiple general linear modeling (GLM) was tested with 100 randomly partitioned training datasets. The 10^th model was chosen as the best by considering AUC, Akaike information criterion (AIC), accuracy, sensitivity, precision, and specificity (Fig. 7b, additional file 6: Figure S6). The 10 species used for building the 10^th model were Dorea longicatena, Coprococcus comes, Lachnospiraceae bacterium 3_1_46FAA, Clostridium symbiosum, Roseburia hominis, Roseburia inulinivorans, Alistipes shahii, Bacteroides stercoris, Clostridium bolteae, and Veillonella parvula in descending order of mean AUC (Additional file 13: Table S2). When applying this model to F. nucleatum-experienced subjects for validation, the probability experiencing F. nucleatum, so called “posterior probability”, was gradually increased and reached decision threshold to 0.5 before actual F. nucleatum-detected points, which means that this model can predict the exact point of F. nucleatum detection (Fig. 7c). Interestingly, samples with posterior probability above 0.5 from F. nucleatum-innocent subjects were assigned as putative F. nucleatum-posterior group, where we observed clear manifestations of dysbiosis: decreased alpha-diversity, increased biomarkers of IBD and CRC, and decreased non-IBD biomarkers (Fig. 7d). The posterior probability was significantly correlated with both Shannon diversity and the ratio of IBD to non-IBD markers (Spearman correlation, _shannon = –0.29, _ratio = 0.53; Fig. 7e). As shown in PCoA plots of top 12 “dynamic” subjects selected according to the standard deviation of posterior probability, several IBD patients including E5009, H4015, H4032, H4044, P6009, P6010, and P6025, displayed dramatic microbial shift with increasing posterior probability (Additional file 7: Figure S7). In addition to top 12 dynamic subjects, top 32 subjects corresponding to 70^th percentile, also confirmed the negative correlation between posterior probability and Shannon diversity with intra-personal perspective along timeline (Fig. 7f, additional file 8: Figure S8). The samples with low posterior probability were converged in the lower left side but the samples with high probability were scattered, indicating that our prediction model explained microbial variance well (Fig. 7g).

Application of potential biomarkers to the evaluation of fecal microbiome

To see the distribution of individual species, we formulated 5 features associated with F. nucleatum and inflammation as follows; 1) microbial abundance correlation with F. nucleatum, 2) enrichment in IBD condition, 3) enrichment in F. nucleatum-experienced subjects, 4) enrichment in samples posterior to F. nucleatum detection, 5) discriminative significance for samples prior or posterior to F. nucleatum. The biomarkers of IBD and non-IBD conditions were distinguishable in PCA plot, and the pre–documented CRC marker species were largely distributed at the right side of plot with moderate dispersion (Fig. 8a, b).

To verify the validity of our IBD/non-IBD biomarkers as well as CRC markers in the longitudinal analysis, all microbes detected were grouped by k-mean clustering. Among 9 clusters, cluster 1 harbored most non-IBD biomarkers and cluster 6 had many of both CRC and IBD biomarkers. Cluster 4 had both CRC and non-IBD markers. Cluster 8 and 9 contained IBD markers (Fig. 8c). Notably, the cluster 1 and 6 were positioned in distal area of PCA plot, and the cluster 8 and 9 were localized near cluster 6, which indicated that the microbes belonging to the disease-marker containing cluster share similar correlation and dynamic characteristics (Fig. 8d).

The number of detected microbes along temporal proximity to F. nucleatum was decreased in clusters 1, 4, and 7 where non-IBD biomarkers are engaged (Fig. 8e). Clusters 6 and 8, which have both CRC and IBD biomarkers, increased in IBD condition. Interestingly, although the cluster 2 and 3 showed significant decrease in detected microbe number regardless of inflammatory conditions, they did not contain any biomarkers. In accordance with Fig. 4e, dysbiosis-associated biomarkers changed only in IBD condition. Clusters 1, 4, and 7 were negatively associated with the posterior probability, but the clusters 6 and 8 were positively related (Fig. 8f). Furthermore, the clusters 1 and 6 were reciprocally distributed each other in terms of microbial abundance and detection frequency (Fig. 8g).

Together, our work illuminated previously unrecognized knowledge to understand microbial dynamics as dysbiosis is underway by focusing on the opportunistic colonization of F. nucleatum. Although further experiments were needed to prove detailed microbial interactions, we anticipated that analyzing chronological alteration of microbiome would greatly improve biomarker screening and diagnosis of microbiota-associated diseases.

Data curation and taxonomy assignment

A total of 1,638 fecal metagenomic dataset (1,338 HMP data and 300 HMP pilot data), longitudinally collected from 130 participants was downloaded from IBDMD (https://ibdmdb.org/) [29]. (Additional file 15). To keep the validity of longitudinal sampling, the data from participants who provided fecal samples more than 5 times was considered. Technical replicates were not used in this study. After filtering, metagenomic analysis of 1,560 fecal sample data (243 HMP pilot and 1,317 HMP) from 106 participants was performed at species-level resolution by MetaPhlAn2 software [35]. To improve the resolution of metagenomic data and to reduce outlier-driven statistical distortion, the following three conditions for quality control were examined and finally 1,526 samples were selected for the further analysis.

Species level explains more than 90% of total microbiome.
Total bacterial abundance accounts for 70% of whole metagenome.
Minimum number of bacterial species is greater than 17.

Sample categorization based on diseases activity indices

Simple complex colitis activity index (SCCAI) and Harvey-Bradshaw index (HBI) were available in 413 UC-derived samples and 650 CD-derived samples, respectively (Supplementary data 1). HBI is a simpler version of the Crohn’s disease activity index (CDAI), which enables patients to self-diagnose diseases severity. To match two different activity indices, we generated a combined criteria like below to categorize them, considering common guidelines (https://www.igibdscores.it/en/info-hbi.html) [59, 60]. 1) Remission: SCCAI ≤2, and HBI ≤3; 2) Border: 3≤ SCCAI ≤5, and 4≤ HBI ≤7; 3) Active: SCCAI ≥6, and HBI ≥8.

Principal Coordinate Analysis

Microbial abundance data was log10-transformed after adding 1e–05 pseudo-abundance, where 1e–05 is the half of minimum abundance detected across whole samples. Then, integer 5 was added to remove negative values and use Bray-Curtis dissimilarity. Principal coordinates analysis were conducted using vegdist and cmdscale function in R.

To examine whether samples are distinguishable in 2-dimensional PCoA plot by participant categories, we performed analysis of variance (ANOVA) for the particular classes against principal coordinates. Then, samples from IBD/non-IBD subjects, UC/CD patients, or F. nucleatum-innocent/experienced subjects were examined whether they were differentially distributed according to their classes.

To conduct principal component analysis (PCA) between microbes, five microbial features in distributional dynamics were used as described below. These features were used for k-mean clustering of microbes as well.

1) P-value for Spearman correlation coefficients with F. nucleatum

2) P-value for the differential enrichment in IBD condition

3) P-value for the differential enrichment in samples from F. nucleatum-experienced subjects

4) P-value for the differential enrichment in samples after F. nucleatum detection

5) P-value for discriminating samples posterior to F. nucleatum detection from those prior to F. nucleatum detection in 100 random sub-samples.

Because the significances for F. nucleatum-posterior enrichment and classifying samples were measured for microbes detected in F. nucleatum-experienced subjects at least 5 times, 258 out of 533 total species were plotted in PCA plot.

K-mean clustering

To test whether three origins of samples, non-IBD, UC or CD subjects, were distinguishable by their microbial composition, we conducted k-mean clustering using kmean function in R after adding 1e–05 pseudo-abundance and log10 transformation. Then, we tested whether each condition is over-represented in a particular cluster compared to others using Fisher’s exact test, and odd ratios for each cluster were compared.

To classify the microbes based on their distributional features, we clustered 258 species, detected at least 5 times in F. nucleatum-experienced subjects, using k-mean clustering. The best number of cluster was determined using NbClust package in R, and cluster number was chosen by vote. Features used for clustering are same as annotated in PCA plot section.

Screening microbial marker species for IBD or non-IBD condition

To identify microbial biomarkers that were differentially enriched in IBD or non-IBD conditions, we applied a web-based linear discriminant analysis effect size (LEfSe) algorithm (http://huttenhower.sph.harvard.edu/galaxy/), which emphasizes not only the differential abundance of features among the classes but also the biological consistency within a same class [61]. Here, by setting IBD subtypes (UC and CD) as a sub-class of IBD, we could obtain common inflammatory biomarkers to capture shared intestinal circumstances by chronic inflammation rather than the specific alteration in UC or CD. Significance thresholds were 0.05 for both Krustal-Wallis test among classes and pairwise Wilcoxon test between sub-classes. LDA score threshold of discriminative microbes was 2.5. Detailed results was included in an additional file 12.

Microbial dissimilarity analysis

Microbial distance was calculated by pairwise Bray-Curtis dissimilarity. For intra-individual dissimilarity of microbiome, one subject was randomly selected for 10,000 times and two samples were chosen from the same subject. For inter-individual dissimilarity test, the subjects were divided into three groups based on their classification categories such as inflammatory condition, F. nucleatum experience, or longitudinal distribution toward F. nucleatum observation, and selected each sample from two random subjects. As a control of inter-individual distance, two samples were randomly selected regardless of categories. To examine microbial distance by temporal proximity toward F. nucleatum-detected point, the F. nucleatum-detected samples was set as the initial point and another random sample was selected from the same subject. Two randomly picked samples from a F. nucleatum-innocent subject were served as control. Detailed codes for the analysis are included in a supplementary material, and in GitHub (https://github.com/JW-Huh/F.nucleatum-project).

Logistic regression model

To construct a prediction model for F. nucleatum experience, we first screened “classifier” microbes that discriminate between F. nucleatum-prior and F. nucleatum-posterior samples. After partitioning 317 samples from F. nucleatum-experienced subjects 1,000 times using a createDataPartition function in caret R package, 41 classifiers, having average AUC value significantly higher than 0.5 (FDR < 0.001), were selected among 258 microbes which were detected at least 5 times across F. nucleatum-experienced subjects. Here, to improve the number of samples, 41 F. nucleatum-detected samples were considered as F. nucleatum-posterior group. Detailed information for classifier species were included in an additional file 13.

Among 41 classifiers, top–13 potent classifiers except F. nucleatum (average AUC > 0.6 & classifying FDR < 1e–07) and inflammatory condition of subjects were selected as model features. To find out the best set of classifiers, we added up the microbes from the top to the 13^th in a decreasing order of average AUC, resulting in 13 different feature sets. In a similar way of classifier screening, samples from F. nucleatum-experienced subjects were divided into training and test set 100 times using caret R package, and multiple logistic regression models were generated (total 1,300 models; one model/training set with 100 training sets and 13 feature combinations). The best performer was selected by averaging performance ranks of cross-validation AUC with training set, AUC with test set, Akaike information criterion (AIC), and four prediction statistics with decision threshold at 0.5 (accuracy, sensitivity, specificity, precision). The selected model 10 was used for subsequent analysis. Detailed codes for the classifier screening and modeling were presented in GitHub (https://github.com/JW-Huh/F.nucleatum-project).

This study revealed that sporadic observation of F. nucleatum in fecal metagenome reflected dysbiotic environment in the gut. Distribution of 12 IBD and 14 non-IBD biomarkers was significantly altered by F. nucleatum experience. Among the longitudinal metagenomes, samples posterior to the F. nucleatum detection showed high intra- and inter-individual dissimilarity, indicating that occurrence of F. nucleatum might serve as a trigger for perturbed stability and increased divergence of microbiome. The 41 classifier species related with prediction of F. nucleatum occurrence were identified and their effectiveness was validated in F. nucleatum-innocent subjects. They also included the previously suggested CRC core signature species. A prediction model constructed with the classifier species successfully estimated microbial dysbiotic state and colonization of diseases-associated microbes. Thepotentialprobability experiencing F. nucleatum was significantly associated with the distribution of our markers, microbial diversity and variance. Based on the distribution characteristics, all microbes were classified to suggest potential biomarkers for symbiosis and dysbiosis. Our results provide a novel layer of information to develop conditional biomarkers focused on a specific microbe and to understand the microbial dynamics during perturbation of metagenomic stability.

Acknowledgements

The authors thank Dr. Seokjin Ham and Byung Hee Kang for their kind advices in designing the project.

Funding

This work is supported by the grant from the National Research Foundation of Korea (NRF–2014M3C9A3064548, NRF–2017M3C9A6047625), BK21 Plus funded by the Ministry of Education, Republic of Korea (10Z20130012243), and the Technology Development Program of MSS (S2632274)

Authors contributions

J.-W. H. and T.-Y. R. conceived and designed this project. J.-W. H. collected the data and performed the analysis. J.-W. H. and T.-Y. R. wrote and edited manuscript.

All authors read and approved the final manuscript.

Ethnic approval and consent to participate

Not applicable

Competing interest

The authors declare that they have no competing interests.

Availability of data and materials

All publically available datasets were describe in Methods. Supplementary figures are provided separately.

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Additional file 1: Figure S1. Microbial variation by sample categories. (a) Sex. (b) Disease severity. The severity was classified based on their diseases scores. (c) Participant. (d) Institutes. Five different institutes have collected fecal samples of IBD and non-IBD participants.

Additional file 2: Figure S2. Microbial diversity and human read fraction. (a) Pielou's evenness, (b) Richness, (c) simple clinical colitis activity index (SCCAI) for UC, (d) Harvey-Bradshaw index (HBI) for CD, (e) Pielou's evenness for F. nucleatum-experience, (f) Richness for F. nucleatum-experience.

Additional file 3: Figure S3. Low detection probability of opportunistic microbes. (a) Microbial abundance and its detection frequency in 44 duplicated samples, (b) Proportion of half-recovered species among total detected species, (c) Correlation between microbial abundance and detection number. Dot color indicates recovery rate of a certain microbe in pairs.

Additional file 4: Figure S4. Abundance changes for microbial biomarkers. (a) non-IBD markers, (b) IBD markers. Line color indicates sample conditions (red line for IBD, blue line for non-IBD). * indicates p-value < 0.05, ** p < 0.011, *** p < 0.001, **** p < 0.0001

Additional file 5: Figure S5. PCoA plot of 20 F. nucleatum-experienced subjects. Line color indicates temporal proximity to F. nucleatum.

Additional file 6: Figure S6. Model performance comparison.

(a) AUC, (b) AIC, (c) accuracy, (d) sensitivity, (e) precision, and (f) specificity

Additional file 7: Figure S7. Individual alteration of microbiome in 12 dynamic subjects by inflammatory conditions and posterior probability. Line color indicates posterior probability.

Additional file 8: Figure S8. Intra-individual change of posterior probability and Shannon diversity in 70th percentile dynamic subjects. Pearson correlation coefficients were shown at the bottom of each participant panel.

Additional file 9: Filtering step: removing replicated samples or participants with insufficient number of collections.

Additional file 10: Quality control: removing samples with poor taxonomic assignment.

Additional file 11: Table S1. Basic information of participants.

Additional file 12: LEfSe biomarker screening results.

Additional file 13: Table S2. Classifier species enriched prior or posterior to the detection point of F. nucleatum.

Additional file 14: Table S3. Correlation coefficients with F. nucleatum and multiple enrichment tests for global biomarker species of colorectal cancer (CRC).

Additional file 15: Metadata.

Additional file 16: Summary of microbial correlation with F. nucleatum and enrichment tests.

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Opportunistic detection of Fusobacterium nucleatum as a marker for the early gut microbial dysbiosis

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Abstract

Figures

Background

Results and Discussion

Metagenomic profiling of IBD or non-IBD participants

Detection of F. nucleatum and its longitudinal dissection

Identification of biomarkers in IBD/non-IBD and their correlation with F. nucleatum.

Microbial destabilization after F. nucleatum detection

Identification of classifier microbes prior and posterior to F. nucleatum detection

Estimation of F. nucleatum experience and dysbiosis level in F. nucleatum-innocent subjects

Application of potential biomarkers to the evaluation of fecal microbiome

Methods

Data curation and taxonomy assignment

Sample categorization based on diseases activity indices

Principal Coordinate Analysis

K-mean clustering

Screening microbial marker species for IBD or non-IBD condition

Microbial dissimilarity analysis

Logistic regression model

Conclusions

Declarations

Acknowledgements

Funding

Authors contributions

Ethnic approval and consent to participate

Competing interest

Availability of data and materials

Reference

Additional files

Supplementary Files

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