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Article

Gut Microbiota Profiles Differ among Individuals Depending on Their Region of Origin: An Italian Pilot Study

1
Unit of Biostatistics, Fondazione IRCCS Casa Sollievo della Sofferenza, 71013 San Giovanni Rotondo, Italy
2
Division of Gastroenterology, Fondazione IRCCS Casa Sollievo della Sofferenza, 71013 San Giovanni Rotondo, Italy
3
Department of Clinical and Molecular Medicine, Sant’Andrea University Hospital, Sapienza University of Rome, 00185 Rome, Italy
4
Department of Biology, University of Florence, 50019 Florence, Italy
5
Dietetic and Clinical Nutrition Unit, Fondazione IRCCS Casa Sollievo della Sofferenza, 71013 San Giovanni Rotondo, Italy
6
Cancer Stem Cells Unit, Fondazione IRCCS Casa Sollievo della Sofferenza, 71013 San Giovanni Rotondo, Italy
*
Author to whom correspondence should be addressed.
These authors contributed equally to the manuscript.
Int. J. Environ. Res. Public Health 2019, 16(21), 4065; https://doi.org/10.3390/ijerph16214065
Received: 16 September 2019 / Revised: 14 October 2019 / Accepted: 19 October 2019 / Published: 23 October 2019
(This article belongs to the Special Issue The Impact of the Gut Microbiota on Human Health)

Abstract

:
Background and aims: Microbiota heterogeneity among humans is mainly due to genetic background, age, dietary habits, lifestyle and local environments. In this study we investigated whether the gut microbiota profile of Italian healthy volunteers could differ based on their geographical origin. Materials and Methods: 16S rRNA gene sequencing was employed to analyze the gut microbiota of 31 healthy volunteers from three different Italian regions: Apulia (South), Lazio (Center) and Lombardy (North). Results: Differences in microbiota composition were detected when the study participants were grouped by their region of origin and when they were classified based on age classes (p-values < 0.05). Also species richness was significantly different both according to Italian Regions (median richness: 177.8 vs. 140.7 vs. 168.0 in Apulia, Lazio and Lombardy; p < 0.001) and according to age classes (median richness: 140.1 vs. 177.8 vs. 160.0 in subjects < 32, 32–41 and > 41 years; p < 0.001), whereas the Shannon index and beta diversity did not change. Conclusions: This study identified differences in the gut microbiota composition and richness among individuals with the same ethnicity coming from three different Italian regions. Our results underline the importance of studies on population-specific variations in human microbiota composition leading to geographically tailored approaches to microbiota engineering.

1. Introduction

The human microbiota with its 1014 symbiotic and pathogen microorganisms living within host’s body, mostly (99%) in the gut [1], of almost 1.8 kg in weight, was considered the “forgotten” or “hidden” organ [2] due to its involvement in several physiological and pathological processes [3]. Although one third of our gut microbiota is in common with most of the people, the remaining two thirds is specific to each individual as its composition is rapidly and profoundly modulated by the diet, by host genotype, lifestyle, antibiotic and/or drugs use and by local environment [4,5]. Scientists are initiating numerous microbiota projects worldwide aimed to identify the presence of a ‘core gut microbiota’, within all individuals, that could be referred as “healthy profile” or “eubiotic state” [6,7] as opposed to gut microbiota “dysbiosis” which is associated with several non-communicable diseases [8,9]. Gut microbiota eubiosis is commonly considered when potentially beneficial species belonging mainly to the two bacterial phyla Firmicutes and Bacteroidetes are found in higher and well balanced percentage while potentially pathogenic species, such as some of those belonging to the phylum Proteobacteria are present in a minor percentage [10]. Hence, several studies were performed with the intent to decipher the content, diversity and function of the microbial gut community which has an impact on human health and well-being. Using data from several nations and continents, it was previously demonstrated the existence of three main enterotypes of the gut microbiota that vary in species and functional composition [11] and that they might allow classification of human groups that could respond differently to diet or drug therapy. Innovations in high-throughput next generation sequencing technologies have updated our knowledge of the gut microbiota revealing significant heterogeneity of the gut microbiota between individuals depending on the genetic background, gender, age, local environments, dietary habits, lifestyle and many other factors [12,13,14,15]. However, up to date, it is still not clear which factor plays a major role in modeling the microbiota content. Previous studies uncovered remarkable differences in microbiota composition depending on the geographical origin, comparing populations across broad international geographical areas, often living in different socio-economic and cultural contexts [16,17,18,19]. Whether a variability exists even within small-scale geographical regions has remained substantially unexplored. A report by Shin et al. identified significant changes in gut microbiota of elderly women living either in island or inland areas of South Korea, however these two groups differ from each other for lifestyle, dietary patterns and physical activities [14]. Similarly, the differences in microbiota composition observed between Russian urban and rural populations were likely due to diverse diets, lifestyle and environment [20]. Only recently a study by He et al., analyzing 7009 individuals homogeneous for ethnicity living in 14 random districts of the Chinese province of Guangdong, revealed that the gut microbiota composition and its relationship with metabolic disease was strongly dependent on the geography, with associations found in a district not applicable to other districts [21]. Given the increasing role that the microbiota is taking as a modulator of a number of pathological conditions and their response to treatments [22,23,24,25,26], studying the population-specific variations in its composition suggests for geographically tailored community approaches to microbiota engineering which may have a potential benefits in enhancing existing diagnostic and therapeutic strategies. In the light of these considerations, our study aimed to investigate whether the gut microbiota profile of healthy volunteers from three different Italian regions, namely Apulia (South of Italy), Lazio (Center of Italy) and Lombardy (North of Italy), relatively homogeneous as concerns physiological characteristics and lifestyle, could differ based on their geographical origin. Although we are aware of the limited number of our samples we intent to design a pilot study trialing a new procedure intended for use in a larger programme of research.

2. Materials and Methods

2.1. Study Population

Thirty-one healthy control subjects were recruited at the outpatient Division of Immunology and Rheumatology, S. Andrea Hospital, Sapienza University of Rome [27], and at IRCCS “Casa Sollievo della Sofferenza” Hospital, with their respective Ethical committee approval numbers (N43/201 and N175/CE) and followed the same pre-analytical and analytical procedures, including samples collection and storage. All the subjects agreed to participate according to the ethical guidelines of the 2013 Declaration of Helsinki and signed an informed consent. Any subject under antibiotics or drugs treatment, consuming probiotics, or having a known history of inflammatory bowel disease or other autoimmune diseases were excluded. Only Caucasian as representative of the Italian population were recruited. Information regarding subject’s clinical variables (i.e., age, gender and BMI) as well as dietary, lifestyle and smoking habits were assessed the same day of the stool sample collection.

2.2. Sample Collection and DNA Extraction

Each participant provided the fresh stool sample in a collection tube filled with a DNA stabilization buffer (Canvax Biotech). The recruitment period spanned from 15th January 2018 to 10th May 2018. Since different DNA extraction/collection protocols can result in different diversity profiles due to variable cell resistance to common lysis methods, in order to reduce any possible bias, pre-analytical and analytical procedures, including samples storage and extraction was performed in only one center. DNA microbial extraction was performed using the QIAamp DNA Stool Mini Kit (Qiagen, Milan, Italy) starting from 250 µL of each sample according to the manufacturer’s protocol. After assessing DNA concentration and purity, samples were stored at −80° until processing.

2.3. Next-Generation Sequencing of Bacterial 16S rRNA Gene

The V3–V4 hypervariable region of the bacterial 16S rRNA gene was amplified from total DNA according to the Illumina 16S Metagenomic Sequencing Library Preparation instructions, as previously described [27]. In summary, the V3–V4 amplicon was obtained by PCR with universal primers, followed by purification. A second PCR was then performed to barcode the libraries using the Illumina dual-index system. Following a second step of purification, libraries were diluted to 4nM and pooled. Paired-end sequencing (2 × 300 cycles) was carried out on an Illumina MiSeq device (Illumina Inc., San Diego, CA, USA) according to the manufacturer’s specifications.

2.4. Bioinformatic Analysis

Sequence data generated as FASTQ files, deposited in the Arrayexpress repository under accession code E-MTAB-8136, were analyzed using the 16S Metagenomics GAIA 2.0 software (http://www.metagenomics.cloud, Sequentia Biotech, Barcelona, Spain, 2017; Benchmark of Gaia 2.0 using published datasets available online at: http://gaia.sequentiabiotech.com/benchmark) which performs the quality control of the reads/pairs (i.e., trimming, clipping and adapter removal steps) through FastQC and BBDuk. The reads/pairs are mapped with BWA-MEM against the custom databases (based on NCBI). The average number of reads per samples was 203,516.6 (SD +/− 81922). Rarefaction curves indicate that an adequate sequence depth was achieved (Supplementary Figure S1). For each sample, the software provided the calculation of the relative abundance of bacterial taxa, as well as the Shannon alpha diversity index, the Chao1 richness estimator and the Bray-Curtis beta-diversity index at the species level. Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) analysis to predict functional profiles of the three microbial communities (grouped by region) based on 16S rRNA sequencing data was performed [28]. Predicted functions were categorized at Kyoto Encyclopedia of Genes and Genome orthology (KEGG) level 2. One-way ANOVA test was performed to assess significant differences in functional categories among the three regions.

2.5. Statistical Analysis

Participants’ characteristics were reported as mean ± standard deviation or absolute frequency and percentages for continuous and categorical variables, respectively. For each continuous variable, the assumption of normality distribution was checked by means of Q-Q plots and Shapiro-Wilks test. For skewed continuous variables, medians along with interquartile range (i.e., IQR, first-third quartiles) were reported instead of means and non-parametric tests were performed. Overall comparisons between groups were assessed using ANOVA models (or Kruskal-Wallis test as appropriate) or Fisher exact test for continuous and categorical variables, respectively. The participants of the study were divided into three age classes with equal size (i.e., tertiles) (<32 years, between 32 and 41 years, and >41 years, respectively) and the relative abundances of microbial taxa were compared among region of origin, age classes and other lifestyle factors. The presence of a linear trend between the subject’s age (as continuous variable) and relative abundance was estimated by Spearman correlation coefficient. p-values from all statistical tests were adjusted for multiple comparisons, within each taxonomic level, controlling the False Discovery Rate (FDR) at level 0.05, using Benjamini-Hochberg step-up procedure. Since participants’ age distributions were not similar among regions (i.e., subjects in Lazio were significantly younger than the others), evidence of statistically differences among regions can be wrongly inferred. To this purpose, all those abundances of microbial taxa which were significantly associated with the region of origin were considered as “spurious results” if they also were significantly associated with subject’s age classes. If found, all spurious results were discarded. In order to evaluate the species richness and diversity of the microbial communities in the fecal samples of the study participants, the Chao1 and the Shannon indices were calculated for each sample, respectively. Moreover, to measure the inter-individual dissimilarity of the gut microbiota, beta-diversity measures were calculated, through the Bray-Curtis metric, which describes how many species are shared between samples. All Bray-Curtis dissimilarities were summarized by means of Kruskal’s non-metric MultiDimensional Scaling (MDS) method and were graphically represented into a bi-dimensional plot, where each point (defined by X and Y coordinates) represents an individual. Subjects who belonged to different groups (e.g., regions) were marked by different colors. To measure how similar an individual is to its own group (cohesion) compared to other groups (separation), the individual silhouette was estimated and the mean of all individual silhouettes within each group was computed. The individual silhouette ranges from −1 to +1, where a high value indicates that the individual is well matched to its own group and poorly matched to neighboring groups. If most individuals have a high silhouette value, then the clustering configuration is appropriate. On the contrary, if many individuals have a low or negative value, then the clustering configuration may have too many or too few groups.
A p-value < 0.05 was considered for statistical significance. Statistical analyses and plots were performed using the computing environment R (R Development Core Team, Vienna, Austria 2008, version 3.5.1.)

3. Results

3.1. Microbiota Profile in Italian Subjects from Different Regions

Demographic and behavioral characteristics of the study participants grouped by regions were reported in Table 1.
The microbiota profile at all taxonomic levels was characterized for each of the 31 healthy individuals, and the mean relative abundance of bacterial taxa in Apulia, Lazio and Lombardy were represented in Figure 1 ((A)—phylum level, (B)—class level, (C)—order level) and in Figure 2 ((A)—family level, (B)—genus level; species level not shown)).
No significant differences were found among the three groups as for gender distribution, body mass index (BMI), type of diet, smoking habits, alcohol consumption and physical activity whereas a statistically significant difference was found with respect to participants’ age (p = 0.005), where subjects in Lazio were significantly younger than the subjects who belonged to the other regions (median: 37 vs. 26 vs. 42 years in Apulia, Lazio and Lombardy, respectively). When the participants were divided according to the three age classes, significant associations between relative abundances of microbial taxa and age classes were found for the families of Acholeplasmataceae, Bacillaceae, Peptostreptococcaceae, Pseudomonadaceae, for the genera of Acetivibrio, Bacillus, Defluviitalea, Eggerthella, Fenollaria, Hydrogenoanaerobacterium, Lachnotalea, Lutispora, Natranaerovirga, Paludibacter, Porphyromonas, Pseudomonas, Raoultibacter and for the species of Alistipes finegoldii, Anaerotruncus rubiinfantis, Bacteroides acidifaciens, Bacteroides clarus, Bacteroides sp. ANH 2438, Bifidobacterium sp. 113, Blautia luti, Butyricimonas sp. 180-3, Butyrivibrio crossotus, Clostridium sp. Culture Jar-19, Dialister sp. GBA27, Eubacterium coprostanoligenes, Faecalibacterium prausnitzii, Fenollaria timonensis, Flintibacter butyricus, Prevotella sp. 109, Robinsoniella sp. MCWD5, Ruminococcus sp. DJF_VR70k1 and Ruminococcus sp. ID1 (Table 2).
Furthermore, Spearman’s correlation coefficients were estimated to investigate the presence of a linear association between subjects’ age (considered as continuous variable) and the relative abundances of microorganisms. Indeed, the correlation analysis corroborates group comparisons analysis (with respect to age classes): while correlation achieves the highest statistical power in the detection of a linear trend, the group comparison analysis allow the detection of any potential non-linear associations. Statistically significant positive correlations with the age were found for Acholeplasmataceae, Bacillaceae, Peptostreptococcaceae, Acetivibrio, Bacillus, Defluviitalea, Eggerthella, Lachnotalea, Natranaerovirga, Paludibacter, Raoultibacter, Bacteroides clarus, Bacteroides sp. ANH. 2438, Eubacterium coprostanoligenes, Flintibacter butyricus, Robinsoniella sp. MCWD5 and Ruminococcus sp. ID, while a negative correlation was found for Alistipes finegoldii, Bifidobacterium sp. 113, Blautia luti, Butyricimonas sp. 180-3, Butyrivibrio crossotus, Dialister sp. GBA27 and Ruminococcus sp. DJF_VR70k1 (Table 2). These results strongly support that age influences gut microbiota composition. Conversely, no statistically significant difference in the microbial pattern was detected according to gender, alcohol consumption, BMI, type of diet (if Mediterranean or other), smoking habits and physical activity (data not shown). Once assessed that no other registered/collected demographic or behavioral characteristics of the subjects but age affected the fecal microbiota, we next analyzed its composition in relation to the geographical origin of the participants. The phyla of Cyanobacteria and Nitrospirae, the classes of Epsilonproteobacteria, Nitrospira, Oligosphaeria and Sphingobacteriia and the orders of Alteromonadales, Anaeroplasmatales, Bacillales, Corynebacteriales, Desulfobacterales, Desulfurellales, Micrococcales, Myxococcales, Nautiliales, Nitrospirales, Sphingobacteriales, Streptomycetales, Synergistales, Syntrophobacterales, Thermoanaerobacterales, Tissierellales, Veillonellales and Vibrionales resulted differently represented among the Apulia, Lazio and Lombardy groups (see Table 3).
Many differences emerged also at lower taxonomic levels, some of which completely overlapping with the ones found in the classification by age groups (Figure 3).
Having excluded possible spurious results (i.e., all those associations which were also significant with respect to age classes), Aeromonadaceae, Anaeroplasmataceae, Atopobiaceae, Catabacteriaceae, Christensenellaceae, Clostridiales Family XII Incertae Sedis, Clostridiales Family XIII Incertae Sedis, Cytophagaceae, Desulfohalobiaceae, Desulfurellaceae, Enterococcaceae, Erwiniaceae, Flavobacteriaceae, Nautiliaceae, Nitrospiraceae, Oxalobacteraceae, Paenibacillaceae, Peptococcaceae, Polyangiaceae, Propionibacteriaceae, Puniceicoccaceae, Ruminococcaceae, Selenomonadaceae, Sphingobacteriaceae, Streptomycetaceae, Succinivibrionaceae, Synergistaceae, Syntrophorhabdaceae, Thermoanaerobacterales, Family III Incertae Sedis, Tissierellaceae, Veillonellaceae and Vibrionaceae remained differently represented at the family level among the three regions. At the genus level, significant differences were found for Acetanaerobacterium, Acetobacteroides, Aeromonas, Alkaliphilus, Alloprevotella, Anaerofilum, Anaeroplasma, Anaerostipes, Candidatus Phytoplasma, Candidatus Soleaferrea, Catabacter, Christensenella, Coprobacillus, Dakarella, Denitrobacterium, Desulfohalobium, Desulfotomaculum, Dialister, Dysgonomonas, Escherichia, Eubacterium, Faecalibacterium, Falcatimonas, Fastidiosipila, Gabonibacter, Gorbachella, Gordonibacter, Haemophilus, Harryflintia, Hespellia, Hippea, Howardella, Hungatella, Ihubacter, Intestinibacter, Lachnobacterium, Libanicoccus, Mediterranea, Megamonas, Megasphaera, Mitsuokella, Mucilaginibacter, Nautilia, Nitrospira, Olsenella, Oribacterium, Oxalobacter, Paenibacillus, Pantoea, Parasporobacterium, Peptococcus, Prevotellamassilia, Propionibacterium, Provencibacterium, Robinsoniella, Saccharofermentans, Selenomonas, Succinivibrio, Synergistes, Syntrophorhabdus and Wigglesworthia. Finally, a total of 117 bacterial species were significantly different among the three regions (see Table 3, possible spurious results at the species level are underlined).

3.2. Bacterial Diversity

The species richness (i.e., Chao1 index) significantly differed when subjects were classified by their region of origin (median richness: 177.8 vs. 140.7 vs. 168.0 in Apulia, Lazio and Lombardy; p = 5.7 × 10−5), age classes (median richness: 140.1 vs. 177.8 vs. 160.0 in subjects < 32, 32–41 and > 41 years; p = 1.6 × 10−4) and physical activity (median richness: 179.5 vs. 162.5 vs. 149.2 in subjects who performed “none”, “little” and “moderate” physical activity; p = 6.8 × 10−4), whereas an interesting trend was observed when individuals were grouped according to their adherence to Mediterranean diet rather than to other non-Mediterranean styles (p = 0.07) (Figure 4).
Conversely, independently from the classification criterion, no statistically significant difference was found for the Shannon index, which takes into account the number and the relative abundance of the species within each sample (data not shown). Furthermore, as for beta-diversity, Kruskal’s non-metric MultiDimensional Scaling (MDS) plots of the Bray-Curtis dissimilarities did not reveal any significant clustering neither by regions (Figure 5A) and age classes (Figure 5B) nor by other classification criteria, as indicated by mean silhouette values around zero. The lack of Shannon index and beta diversity association may due to the sampling of multiple village sites and multiple cities within the study.
In order to understand the functional meaning of the microbiota differences observed among the three regions, a PICRUSt prediction of the metagenomes was performed. A total of five pathways, namely infectious diseases, membrane transport, metabolism, replication and repair and signaling molecules and interactions emerged as significantly changed (Figure 6).

4. Discussion

It is well recognized that ethnicity and geographical locations are key factors influencing the composition and diversity of the gut microbiota [5]. However, beside understandable divergences across wide international geographical areas characterized by different socio-economic settings [16,17,18,19], important differences are emerging even in people with similar genetic and cultural background [21]. In this regard, the aim of the present study was to characterize the gut microbiota composition of healthy people belonging to three different Italian regions, namely Apulia, Lazio and Lombardy from the South, Center and North of the peninsula, respectively. All the study participants had the same ethnicity and were quite homogeneously distributed across the three regions as regards gender, BMI, physical activity, dietary, smoking and drinking habits. Only the age was not distributed similarly among the three regions, and since the age is an established factor influencing the composition of microbiota [12,19,29], it was taken into account as a potential confounding factor. However, it should be considered that, although gut microbiota composition changes throughout life, major shifts are described to occur in the transition from infancy to adulthood and then to old age, while it is documented to be quite stable through different stages of adult life [30,31,32]. Furthermore, differences emerged in the species richness of gut microbial communities according to the region, age class and physical activity criterion. Nevertheless, when we grouped our study population by age classes, differences in composition within the bacterial families, genera and species emerged. Although divided in three classes of age, our study population is mainly composed of young adults and adults (between 24 and 47 years of age with the exception of two individuals of 64 and 65 years old respectively), and includes only 1 subject older than 70 (which is considered the threshold age for defining an individual as elderly). We speculate that the significant differences observed in Chao1 index according to age are likely due to the acquisition of a mature (not aged) microbiota (Figure 4B).
Physical exercise is known to be associated to a healthier and more diverse microbiota, which seems to be in contrast with the results from our study. It should be considered, however, that in our study population people performing little and moderate physical activity overlapped with younger age, which would explain the reduced richness observed. All these and many other differences, also within higher taxonomic levels, were observed when the 31 subjects were grouped by regions. Noteworthy is that many taxa, which were represented in almost all the members of one or two regions, were instead completely absent in the other(s), supporting that gut microbiota and geographic localization are closely interlinked; this is, for example, the case of Cyanobacteria which were unique to Lombards, Nitrospirae only found in Apulians, and many other lower taxa. A predictive analysis of the functional pathways affected by the microbiota diversity among the three regions revealed five categories significantly changed. It should be kept in mind, however, that PICRUSt only performs predictions and that more accurate functional profiles require metagenomic approaches which will be the subject of future studies.
Our data, emphasize the importance of the selective pressure in shaping gut microbial ecology by numerous common environmental exposures. This could be the reflection of the different exposure of these individuals to industrial presence [33] (considering the north part more industrialized as compared to the south of Italy) agricultural chemicals like fertilizers (as the southern part of Italy is an agriculture-based Economy) or the natural source of water people drinks. As specified above, our study population was homogeneous from the ethnical point of view, so we can isolate the geographical effect from the ethnical factor. A very interesting point of view in trying to understand the bases of such a spatial microbiota variability comes from the application of the ecological theory according to which local diversification of microbiota could be introduced by processes of community ecology, such as dispersal, diversification, environmental selection and ecological drift, as exhaustively discussed by Costello et al. [34]. Further investigation would be required to shed light on the factors underlying such a difference in microbiota composition among the Italian regions.

5. Conclusions

Overall, our results point out the existence of a variability in the microbiota composition of populations closely related from geographical point of view. This interesting link between small-scale geography and gut microbiota deserves further investigation and poses important implications for the development of microbiota-based clinical approaches. Indeed, the role of microbiota in the onset, progression and response to therapies in a large number of diseases, is increasingly recognized both as a diagnostic marker [22,24,26] and as a manipulable target for improving the clinical course of pathologies [23,35]. Therefore, considering the existence of a variability within a limited geographic area could be particularly important in order to set up tailored therapeutic approaches. Moreover, attention should be paid when setting a specific microbial pattern as a reference for health or disease, since it may be strongly influenced by the population used to generate the data [21]. Further efforts should be devoted to identify the factors underlying the association between microbiota and small-scale geography [36].

Supplementary Materials

The following are available online at https://www.mdpi.com/1660-4601/16/21/4065/s1, Figure S1: Rarefaction curve revealing the number of identified OTUs as a function of the number of reads by regions’ group.

Author Contributions

Conceived the study and designed the experiments: V.P. Performed the microbiota profiles: V.P., C.P. Statistical analysis: A.F. and M.C. Analyzed the data: A.F., M.C., C.P., R.P., E.B. and V.P. Contributed reagents/materials/analysis tools: A.P.-D., B.L., D.C., A.P., E.B. Wrote the paper: all authors.

Funding

This research was supported by the ‘‘Ricerca Corrente RC2018” and the “Ricerca Corrente RC2019” fundings granted by the Italian Ministry of Health to VP.

Conflicts of Interest

The authors declare no conflict of interest related to the manuscript.

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Figure 1. Microbiota composition of Italian healthy subjects grouped by regions, at the phylum (A), class (B) and order (C) levels. The mean value of all the detected taxa at each level is represented.
Figure 1. Microbiota composition of Italian healthy subjects grouped by regions, at the phylum (A), class (B) and order (C) levels. The mean value of all the detected taxa at each level is represented.
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Figure 2. Microbiota composition of Italian healthy subjects grouped by regions, at the family (A) and genus (B) levels. The mean value of all the detected taxa at each level is represented.
Figure 2. Microbiota composition of Italian healthy subjects grouped by regions, at the family (A) and genus (B) levels. The mean value of all the detected taxa at each level is represented.
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Figure 3. Venn diagrams showing the number of distinct and shared families (A), genera (B) and species (C) between subjects grouped by age classes and by regions.
Figure 3. Venn diagrams showing the number of distinct and shared families (A), genera (B) and species (C) between subjects grouped by age classes and by regions.
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Figure 4. Box-plots of Chao1 index of species richness in Italian healthy subjects grouped by regions of origin (A), age classes (B), physical activity (C) and type of diet (D).
Figure 4. Box-plots of Chao1 index of species richness in Italian healthy subjects grouped by regions of origin (A), age classes (B), physical activity (C) and type of diet (D).
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Figure 5. Kruskal’s non-metric MultiDimensional Scaling (MDS) plots of the Bray-Curtis dissimilarities in Italian healthy subjects grouped by regions of origin (A) and age classes (B). Each point plotted into the bi-dimensional space (as X, Y coordinates) represents an individual. Individuals that are more similar to one another are ordinated closer together and those who belong to different groups were marked by different colors. Mean silhouette value was 0.0069 for clustering by regions and −0.0225 for clustering by age classes.
Figure 5. Kruskal’s non-metric MultiDimensional Scaling (MDS) plots of the Bray-Curtis dissimilarities in Italian healthy subjects grouped by regions of origin (A) and age classes (B). Each point plotted into the bi-dimensional space (as X, Y coordinates) represents an individual. Individuals that are more similar to one another are ordinated closer together and those who belong to different groups were marked by different colors. Mean silhouette value was 0.0069 for clustering by regions and −0.0225 for clustering by age classes.
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Figure 6. PICRUSt prediction of the functional composition of microbial communities grouped by region. A total of five KEGG pathways were significantly changed among the three regions.
Figure 6. PICRUSt prediction of the functional composition of microbial communities grouped by region. A total of five KEGG pathways were significantly changed among the three regions.
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Table 1. Subjects’ demographic and behavioral characteristics by Italian regions.
Table 1. Subjects’ demographic and behavioral characteristics by Italian regions.
Apulia (N = 12)Lazio (N = 10)Lombardy (N = 9)p-Value
Age (years)Median [IQR]37.0 [35.0–44.2]26.0 [24.0–29.0]42.0 [39.0–47.0]0.005 *
Range28–6524–4629–74
Gender—N (%)Males6 (50.0)6 (60.0)3 (33.3)0.542 #
BMI (Kg/m2)Mean ± SD22.8 ± 2.322.3 ± 3.021.7 ± 3.50.723 $
Range19.1–28.117.2–26.916.5–28.4
BMI (WHO classification)—N (%)Underweight (<18.5)0 (0.0)2 (20.0)2 (22.2)0.528 #
Normal weight (18.5–24.9)10 (83.3)7 (70.0)6 (66.7)
Overweight (25.0–29.9)2 (16.7)1 (10.0)1 (11.1)
Diet (%)Mediterranean11 (91.7)5 (50.0)7 (77.8)0.149 #
Hypercaloric1 (8.3)0 (0.0)1 (11.1)
Hypocaloric0 (0.0)1 (10.0)1 (11.1)
Vegetarian0 (0.0)1 (10.0)0 (0.0)
Iperproteic (meat)0 (0.0)1 (10.0)0 (0.0)
Carbohydrates and proteins, little vegetables0 (0.0)2 (20.0)0 (0.0)
Smoking (n.cigarettes/day)Median [IQR]0.0 [0.0–0.8]0.0 [0.0–1.5]0.0 [0.0–11.0]0.764 *
Range0–120–50–17
Smokers (yes vs. no)—N (%)Yes3 (25.0)3 (30.0)3 (33.3)1.000 #
Alcohol—N (%)None3 (25.0)4 (40.0)1 (11.1)0.336 #
Little (1 occasional glass)9 (75.0)5 (50.0)6 (66.7)
Moderate (1–2 glasses/day)0 (0.0)1 (10.0)2 (22.2)
Physical activity—N (%)None4 (33.3)0 (0.0)5 (55.6)0.051 #
Little5 (41.7)3 (30.0)2 (22.2)
Moderate3 (25.0)7 (70.0)2 (22.2)
SD: Standard deviation; IQR: Interquartile range (i.e., first-third quartiles); * p-value from Kruskal-Wallis test; $ p-value from ANOVA model; # p-value from Fisher exact test.
Table 2. Gut microbiota in subjects grouped by age classes (i.e., tertiles) and Spearman correlation coefficients. Data were reported as median along with interquartile range (first-third quartiles). Only significant results (i.e., p-values < 0.05 from any statistical test) were reported. All p-values were adjusted controlling for the False Discovery Rate at 0.05 level within each taxonomic level.
Table 2. Gut microbiota in subjects grouped by age classes (i.e., tertiles) and Spearman correlation coefficients. Data were reported as median along with interquartile range (first-third quartiles). Only significant results (i.e., p-values < 0.05 from any statistical test) were reported. All p-values were adjusted controlling for the False Discovery Rate at 0.05 level within each taxonomic level.
Spearman Correlation
Biological ClassificationMicrobiotaAge < 32 Years (N = 10)Age 32–41 Years (N = 11)Age > 41 Years (N = 10)Test for Overall Difference (p-Value #)Coefficientp-Value
FamilyAcholeplasmataceae0.0000 [0.0000, 0.0000]0.0027 [0.0006, 0.0155]0.0073 [0.0035, 0.0514]0.0250.6040.021
Bacillaceae0.0000 [0.0000, 0.0000]0.0184 [0.0094, 0.0219]0.0188 [0.0071, 0.0295]0.0250.5660.029
Peptostreptococcaceae0.0000 [0.0000, 0.0000]0.0274 [0.0193, 0.0426]0.0364 [0.0222, 0.0432]0.0410.5360.041
Pseudomonadaceae0.0000 [0.0000, 0.0000]0.0168 [0.0090, 0.0185]0.0129 [0.0095, 0.0263]0.0250.4570.090
GenusAcetivibrio0.0000 [0.0000, 0.0000]0.0142 [0.0086, 0.0238]0.0128 [0.0075, 0.0222]0.0280.5510.041
Bacillus0.0000 [0.0000, 0.0000]0.0092 [0.0045, 0.0144]0.0059 [0.0032, 0.0170]0.0280.5430.041
Defluviitalea0.0000 [0.0000, 0.0000]0.0114 [0.0032, 0.0197]0.0225 [0.0064, 0.0257]0.0280.6490.012
Eggerthella0.0000 [0.0000, 0.0000]0.0083 [0.0019, 0.0379]0.0112 [0.0066, 0.0182]0.0560.5240.048
Fenollaria0.0000 [0.0000, 0.0000]0.0176 [0.0131, 0.0201]0.0085 [0.0060, 0.0145]0.0390.4710.089
Hydrogenoanaerobacterium0.0000 [0.0000, 0.0000]0.0193 [0.0070, 0.0257]0.0099 [0.0062, 0.0163]0.0340.4260.131
Lachnotalea0.0000 [0.0000, 0.0000]0.0088 [0.0063, 0.0143]0.0087 [0.0047, 0.0269]0.0280.5800.041
Lutispora0.0000 [0.0000, 0.0000]0.0054 [0.0030, 0.0186]0.0068 [0.0020, 0.0246]0.0340.4990.067
Natranaerovirga0.0000 [0.0000, 0.0000]0.0183 [0.0110, 0.0311]0.0200 [0.0123, 0.0215]0.0280.5280.048
Paludibacter0.0000 [0.0000, 0.0000]0.0048 [0.0024, 0.0078]0.0045 [0.0025, 0.0060]0.0280.5580.041
Porphyromonas0.0000 [0.0000, 0.0000]0.0251 [0.0156, 0.0273]0.0114 [0.0071, 0.0239]0.0400.4180.136
Pseudomonas0.0000 [0.0000, 0.0000]0.0143 [0.0079, 0.0171]0.0119 [0.0094, 0.0215]0.0330.4820.085
Raoultibacter0.0000 [0.0000, 0.0000]0.0043 [0.0011, 0.0139]0.0113 [0.0045, 0.0212]0.0500.5480.041
SpeciesAlistipes finegoldii0.1246 [0.0709, 0.6407]0.0038 [0.0010, 0.0653]0.0022 [0.0001, 0.0313]0.049−0.5590.043
Anaerotruncus rubiinfantis0.0000 [0.0000, 0.0000]0.0081 [0.0034, 0.0272]0.0076 [0.0034, 0.0120]0.0490.4660.091
Bacteroides acidifaciens0.0000 [0.0000, 0.0000]0.0193 [0.0138, 0.0321]0.0242 [0.0162, 0.0305]0.0490.5210.056
Bacteroides clarus0.0000 [0.0000, 0.0000]0.0035 [0.0028, 0.0559]0.0066 [0.0015, 0.0176]0.0490.5510.043
Bacteroides sp. ANH. 24380.0000 [0.0000, 0.0000]0.0005 [0.0000, 0.0012]0.0010 [0.0000, 0.0161]0.1890.5370.043
Bifidobacterium sp. 1130.0012 [0.0001, 0.0197]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.048−0.5800.043
Blautia luti0.0032 [0.0003, 0.1185]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.035−0.6370.012
Butyricimonas sp. 180 30.0013 [0.0000, 0.0079]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.072−0.5380.043
Butyrivibrio crossotus0.0017 [0.0007, 0.0027]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.048−0.5540.043
Clostridium sp. Culture Jar 190.0000 [0.0000, 0.0000]0.0177 [0.0053, 0.0322]0.0066 [0.0036, 0.0087]0.0480.4470.097
Dialister sp. GBA270.0008 [0.0000, 0.0090]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.049−0.5360.043
Eubacterium coprostanoligenes0.0000 [0.0000, 0.0000]0.0326 [0.0159, 0.0655]0.0817 [0.0316, 0.3701]0.0580.5730.043
Faecalibacterium prausnitzii12.7134 [10.7228, 14.3182]3.3569 [2.0414, 5.4147]5.3014 [3.4161, 6.9285]0.035−0.5170.057
Fenollaria timonensis0.0000 [0.0000, 0.0000]0.0154 [0.0118, 0.0183]0.0074 [0.0053, 0.0129]0.0490.4770.082
Flintibacter butyricus0.0000 [0.0000, 0.0000]0.0099 [0.0011, 0.0127]0.0184 [0.0145, 0.0311]0.0350.6640.008
Prevotella sp. 1090.0000 [0.0000, 0.0000]0.0025 [0.0002, 0.0068]0.0055 [0.0012, 0.2678]0.0480.5030.064
Robinsoniella sp. MCWD50.0000 [0.0000, 0.0000]0.0143 [0.0044, 0.0239]0.0156 [0.0081, 0.0296]0.0490.5640.043
Ruminococcus sp. DJF_VR70k10.0011 [0.0002, 0.0087]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.048−0.5370.043
Ruminococcus sp. ID10.0000 [0.0000, 0.0000]0.0221 [0.0109, 0.0261]0.0292 [0.0172, 0.0674]0.0480.6590.008
# KW: p-value from Kruskal-Wallis test.
Table 3. Gut microbiota in subjects grouped by Italian regions. Data were reported as median along with interquartile range (first-third quartiles). Only significant results (i.e., p-values < 0.05 from any statistical test) were reported. All p-values were adjusted controlling for the False Discovery Rate at 0.05 level within each taxonomic level.
Table 3. Gut microbiota in subjects grouped by Italian regions. Data were reported as median along with interquartile range (first-third quartiles). Only significant results (i.e., p-values < 0.05 from any statistical test) were reported. All p-values were adjusted controlling for the False Discovery Rate at 0.05 level within each taxonomic level.
Biological ClassificationMicrobiotaApulia (N = 12)Lazio (N = 10)Lombardy (N = 9)Test for Overall Difference (p-Value *)
PhylumCyanobacteria 0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.0031 [0.0011, 0.0064]<0.001
Nitrospirae 0.0037 [0.0019, 0.0061]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]<0.001
ClassEpsilonproteobacteria 0.0241 [0.0070, 0.0465]0.0000 [0.0000, 0.0000]0.0086 [0.0080, 0.0258]<0.001
Nitrospira 0.0036 [0.0019, 0.0061]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]<0.001
Oligosphaeria 0.0007 [0.0000, 0.0152]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.024
Sphingobacteriia 0.0127 [0.0100, 0.0253]0.0000 [0.0000, 0.0000]0.0078 [0.0074, 0.0156]<0.001
OrderAlteromonadales 0.0132 [0.0091, 0.0170]0.0000 [0.0000, 0.0000]0.0125 [0.0108, 0.0188]<0.001
Anaeroplasmatales 0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.0009 [0.0000, 0.0183]0.003
Bacillales 0.0911 [0.0763, 0.1214]0.0255 [0.0195, 0.0356]0.0464 [0.0263, 0.0607]0.006
Corynebacteriales 0.0062 [0.0046, 0.0112]0.0000 [0.0000, 0.0000]0.0086 [0.0063, 0.0125]<0.001
Desulfobacterales 0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.0064 [0.0009, 0.0260]<0.001
Desulfurellales 0.0023 [0.0004, 0.0130]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]<0.001
Micrococcales 0.0180 [0.0133, 0.0248]0.0000 [0.0000, 0.0000]0.0151 [0.0108, 0.0184]<0.001
Myxococcales 0.0008 [0.0000, 0.0083]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.001
Nautiliales 0.017 [0.0035, 0.0287]0.0000 [0.0000, 0.0000]0.0048 [0.0018, 0.0168]0.001
Nitrospirales 0.0033 [0.0017, 0.0060]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]<0.001
Sphingobacteriales 0.0103 [0.0082, 0.0230]0.0000 [0.0000, 0.0000]0.0074 [0.0060, 0.0156]<0.001
Streptomycetales 0.0247 [0.0167, 0.0394]0.0080 [0.0051, 0.0097]0.0311 [0.0091, 0.0478]0.044
Synergistales 0.0095 [0.0054, 0.0969]0.0000 [0.0000, 0.0000]0.0106 [0.0032, 0.0201]<0.001
Syntrophobacterales 0.0007 [0.0000, 0.0097]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.001
Thermoanaerobacterales 0.0060 [0.0028, 0.0126]0.0000 [0.0000, 0.0000]0.0041 [0.0028, 0.0314]<0.001
Tissierellales 0.0137 [0.0118, 0.0175]0.0000 [0.0000, 0.0000]0.0124 [0.0070, 0.0401]<0.001
Veillonellales 2.9426 [0.2835, 4.7890]1.8227 [0.1197, 5.6735]0.0389 [0.0156, 0.1560]0.033
Vibrionales 0.0026 [0.0020, 0.0073]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]<0.001
FamilyAcholeplasmataceae 0.0049 [0.0010, 0.0149]0.0000 [0.0000, 0.0000]0.0052 [0.0032, 0.1064]<0.001
Aeromonadaceae 0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.0134 [0.0048, 0.0156]<0.001
Anaeroplasmataceae 0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.0009 [0.0000, 0.0118]0.002
Atopobiaceae 0.0045 [0.0016, 0.0115]0.0029 [0.0006, 0.0207]0.0000 [0.0000, 0.0000]0.002
Bacillaceae 0.0171 [0.0072, 0.0244]0.0000 [0.0000, 0.0000]0.0186 [0.0069, 0.0220]<0.001
Catabacteriaceae 0.0327 [0.0234, 0.0440]0.0081 [0.0035, 0.0175]0.0126 [0.0037, 0.0343]0.035
Christensenellaceae 0.6797 [0.2907, 1.3801]0.1033 [0.0223, 0.2216]0.2123 [0.0957, 0.3820]0.035
Clostridiales Family XII. Incertae Sedis 0.0063 [0.0013, 0.0232]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]<0.001
Clostridiales Family XIII. Incertae Sedis 0.0000 [0.0000, 0.0000]0.0206 [0.0125, 0.0302]0.0092 [0.0060, 0.0419]<0.001
Cytophagaceae 0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.0028 [0.0023, 0.0371]<0.001
Desulfohalobiaceae 0.0036 [0.0028, 0.0167]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]<0.001
Desulfurellaceae 0.0023 [0.0004, 0.0127]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]<0.001
Enterococcaceae 0.0047 [0.0010, 0.0218]0.0000 [0.0000, 0.0000]0.0060 [0.0016, 0.0113]0.001
Erwiniaceae 0.0097 [0.0082, 0.0216]0.0000 [0.0000, 0.0000]0.0142 [0.0128, 0.0239]<0.001
Flavobacteriaceae 0.0107 [0.0070, 0.0145]0.0000 [0.0000, 0.0000]0.0093 [0.0050, 0.0150]<0.001
Nautiliaceae 0.0177 [0.0035, 0.0277]0.0000 [0.0000, 0.0000]0.0044 [0.0018, 0.0161]0.001
Nitrospiraceae 0.0029 [0.0017, 0.0058]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]<0.001
Oxalobacteraceae 0.0020 [0.0000, 0.0125]0.0000 [0.0000, 0.0000]0.0226 [0.0016, 0.0436]0.002
Paenibacillaceae 0.0161 [0.0085, 0.0247]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]<0.001
Peptococcaceae 0.0379 [0.0230, 0.0551]0.0035 [0.0026, 0.0224]0.0074 [0.0055, 0.0103]0.007
Peptostreptococcaceae 0.0331 [0.0256, 0.0396]0.0000 [0.0000, 0.0000]0.0418 [0.0194, 0.0707]<0.001
Polyangiaceae 0.0002 [0.0000, 0.0052]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.009
Propionibacteriaceae 0.1445 [0.0063, 0.2139]0.0026 [0.0014, 0.0043]0.2381 [0.0218, 0.6672]0.023
Pseudomonadaceae 0.0168 [0.0100, 0.0303]0.0000 [0.0000, 0.0000]0.0140 [0.0100, 0.0177]<0.001
Puniceicoccaceae 0.0000 [0.0000, 0.0045]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.023
Ruminococcaceae 15.3334 [9.8807, 21.7498]25.0671 [23.1509, 29.6044]19.1784 [11.2163, 24.2467]0.037
Selenomonadaceae 0.0512 [0.0298, 0.1030]0.0070 [0.0052, 0.0103]0.0043 [0.0026, 0.0086]0.023
Sphingobacteriaceae 0.0088 [0.0071, 0.0220]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]<0.001
Streptomycetaceae 0.0206 [0.0123, 0.0365]0.0078 [0.0042, 0.0096]0.0280 [0.0091, 0.0473]0.046
Succinivibrionaceae 0.0010 [0.0000, 0.0019]0.0007 [0.0006, 0.0010]0.0000 [0.0000, 0.0000]0.009
Synergistaceae 0.0067 [0.0036, 0.0856]0.0000 [0.0000, 0.0000]0.0097 [0.0026, 0.0183]<0.001
Syntrophorhabdaceae 0.0003 [0.0000, 0.0085]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.009
Thermoanaerobacterales Family III Incertae Sedis 0.0008 [0.0000, 0.0014]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.001
Tissierellaceae 0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.0060 [0.0029, 0.0097]<0.001
Veillonellaceae 2.9400 [0.2817, 4.7843]1.8205 [0.1195, 5.6608]0.0389 [0.0156, 0.1558]0.025
Vibrionaceae 0.0026 [0.0020, 0.0073]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]<0.001
GenusAcetanaerobacterium0.0192 [0.0064, 0.0330]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]<0.001
Acetivibrio0.0144 [0.0117, 0.0210]0.0000 [0.0000, 0.0000]0.0112 [0.0075, 0.0275]<0.001
Acetobacteroides0.0002 [0.0000, 0.0032]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.009
Aeromonas0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.0107 [0.0032, 0.0149]<0.001
Alkaliphilus0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.0086 [0.0057, 0.0126]<0.001
Alloprevotella0.0000 [0.0000, 0.0000]0.0007 [0.0003, 0.0014]0.0000 [0.0000, 0.0000]<0.001
Anaerofilum0.0068 [0.0044, 0.0207]0.0086 [0.0044, 0.0173]0.0000 [0.0000, 0.0000]<0.001
Anaeroplasma0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0091]0.012
Anaerostipes0.0234 [0.0089, 0.0611]0.0174 [0.0121, 0.0206]0.1156 [0.0316, 0.5372]0.024
Bacillus0.0097 [0.0049, 0.0155]0.0000 [0.0000, 0.0000]0.0065 [0.0031, 0.0124]<0.001
Candidatus Phytoplasma0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.0021 [0.0016, 0.0113]<0.001
Candidatus Soleaferrea0.0122 [0.0083, 0.0572]0.0091 [0.0075, 0.0119]0.0000 [0.0000, 0.0000]0.001
Catabacter0.0289 [0.0200, 0.0339]0.0075 [0.0030, 0.0153]0.0103 [0.0036, 0.0280]0.036
Christensenella0.4438 [0.1379, 0.7502]0.0479 [0.0142, 0.1074]0.1088 [0.0463, 0.1956]0.017
Coprobacillus0.0000 [0.0000, 0.0047]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.024
Dakarella0.1114 [0.0015, 0.7843]0.0002 [0.0000, 0.0004]0.0000 [0.0000, 0.0000]0.002
Defluviitalea0.0151 [0.0062, 0.0247]0.0000 [0.0000, 0.0000]0.0191 [0.0062, 0.0218]<0.001
Denitrobacterium0.0000 [0.0000, 0.0000]0.0036 [0.0000, 0.0198]0.0020 [0.0005, 0.0075]0.005
Desulfohalobium0.0009 [0.0003, 0.0049]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]<0.001
Desulfotomaculum0.0077 [0.0036, 0.0154]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]<0.001
Dialister1.7288 [0.0421, 4.6387]1.7564 [0.0939, 5.6073]0.0041 [0.0027, 0.0080]0.016
Dysgonomonas0.0106 [0.0040, 0.0272]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]<0.001
Eggerthella0.0069 [0.0026, 0.0144]0.0000 [0.0000, 0.0000]0.0195 [0.0145, 0.0349]<0.001
Escherichia0.0029 [0.0009, 0.0262]0.0000 [0.0000, 0.0000]0.0011 [0.0000, 0.0089]0.001
Eubacterium1.8114 [1.2982, 3.1914]1.0085 [0.7616, 3.2710]4.5088 [2.7865, 6.5613]0.046
Faecalibacterium6.7009 [5.1594, 8.8515]18.9963 [17.4550, 19.9556]9.0591 [6.4928, 10.7255]0.003
Falcatimonas0.0000 [0.0000, 0.0000]0.0037 [0.0022, 0.0086]0.0097 [0.0009, 0.0456]<0.001
Fastidiosipila0.0041 [0.0017, 0.0068]0.0000 [0.0000, 0.0000]0.0011 [0.0002, 0.0022]0.001
Fenollaria0.0136 [0.0057, 0.0178]0.0000 [0.0000, 0.0000]0.0203 [0.0124, 0.0289]<0.001
Gabonibacter0.0000 [0.0000, 0.0000]0.0003 [0.0000, 0.0009]0.0000 [0.0000, 0.0000]0.007
Gorbachella0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.0172 [0.0093, 0.0202]<0.001
Gordonibacter0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.0186 [0.0021, 0.0473]<0.001
Haemophilus0.0014 [0.0004, 0.0213]0.0000 [0.0000, 0.0000]0.0093 [0.0028, 0.0252]0.001
Harryflintia0.0005 [0.0000, 0.0022]0.0006 [0.0004, 0.0094]0.0000 [0.0000, 0.0000]0.005
Hespellia0.0075 [0.0027, 0.0117]0.0161 [0.0109, 0.0229]0.0000 [0.0000, 0.0000]<0.001
Hippea0.0023 [0.0000, 0.0118]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.001
Howardella0.0005 [0.0003, 0.0113]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]<0.001
Hungatella0.0000 [0.0000, 0.0000]0.0131 [0.0025, 0.0207]0.0000 [0.0000, 0.0000]<0.001
Hydrogenoanaerobacterium0.0177 [0.0089, 0.0243]0.0000 [0.0000, 0.0000]0.0158 [0.0052, 0.0193]<0.001
Ihubacter0.0000 [0.0000, 0.0000]0.0096 [0.0066, 0.0205]0.0057 [0.0025, 0.0247]<0.001
Intestinibacter0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.0037 [0.0005, 0.0382]<0.001
Lachnobacterium0.0000 [0.0000, 0.0000]0.0118 [0.0080, 0.0242]0.0187 [0.0104, 0.0365]<0.001
Lachnotalea0.0084 [0.0070, 0.0119]0.0000 [0.0000, 0.0000]0.0156 [0.0050, 0.0294]<0.001
Libanicoccus0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0004]0.0000 [0.0000, 0.0000]0.023
Lutispora0.0051 [0.0036, 0.0229]0.0000 [0.0000, 0.0000]0.0054 [0.0028, 0.0214]0.001
Mediterranea0.0012 [0.0003, 0.0024]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]<0.001
Megamonas0.0010 [0.0000, 0.0042]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.001
Megasphaera0.0343 [0.0003, 0.5532]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]<0.001
Mitsuokella0.0010 [0.0000, 0.0094]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.001
Mucilaginibacter0.0000 [0.0000, 0.0019]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.024
Natranaerovirga0.0177 [0.0100, 0.0249]0.0000 [0.0000, 0.0000]0.0208 [0.0183, 0.0279]<0.001
Nautilia0.0129 [0.0029, 0.0231]0.0000 [0.0000, 0.0000]0.0034 [0.0014, 0.0054]0.001
Nitrospira0.0013 [0.0000, 0.0029]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.001
Olsenella0.0033 [0.0000, 0.0109]0.0009 [0.0000, 0.0203]0.0000 [0.0000, 0.0000]0.024
Oribacterium0.0069 [0.0037, 0.0124]0.0000 [0.0000, 0.0000]0.0062 [0.0032, 0.0073]<0.001
Oxalobacter0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.0204 [0.0005, 0.0343]<0.001
Paenibacillus0.0143 [0.0068, 0.0230]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]<0.001
Paludibacter0.0054 [0.0025, 0.0116]0.0000 [0.0000, 0.0000]0.0046 [0.0023, 0.0048]<0.001
Pantoea0.0016 [0.0007, 0.0103]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]<0.001
Parasporobacterium0.0000 [0.0000, 0.0000]0.0023 [0.0000, 0.0075]0.0000 [0.0000, 0.0000]0.002
Peptococcus0.0093 [0.0034, 0.0207]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]<0.001
Porphyromonas0.0246 [0.0185, 0.0293]0.0000 [0.0000, 0.0000]0.0138 [0.0112, 0.0254]<0.001
Prevotellamassilia0.0003 [0.0000, 0.0016]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.009
Propionibacterium0.1439 [0.0041, 0.2118]0.0000 [0.0000, 0.0000]0.1904 [0.0156, 0.6649]<0.001
Provencibacterium0.0000 [0.0000, 0.0006]0.0000 [0.0000, 0.0000]0.0006 [0.0000, 0.0011]0.030
Pseudomonas0.0156 [0.0094, 0.0245]0.0000 [0.0000, 0.0000]0.0118 [0.0092, 0.0138]<0.001
Raoultibacter0.0052 [0.0016, 0.0131]0.0000 [0.0000, 0.0000]0.0161 [0.0097, 0.0218]<0.001
Robinsoniella0.0179 [0.0128, 0.0248]0.0122 [0.0065, 0.0148]0.0481 [0.0374, 0.1007]0.016
Saccharofermentans0.0380 [0.0251, 0.0634]0.0000 [0.0000, 0.0000]0.0121 [0.0065, 0.0403]<0.001
Selenomonas0.0061 [0.0027, 0.0218]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]<0.001
Succinivibrio0.0008 [0.0000, 0.0017]0.0007 [0.0006, 0.0010]0.0000 [0.0000, 0.0000]0.009
Synergistes0.0000 [0.0000, 0.0147]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.024
Syntrophorhabdus0.0000 [0.0000, 0.0048]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.024
Wigglesworthia0.0069 [0.0043, 0.0088]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]<0.001
SpeciesClostridium colinum0.0000 [0.0000, 0.0054]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.026
Clostridium glycyrrhizinilyticum0.0000 [0.0000, 0.0028]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.026
Clostridium polysaccharolyticum0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.0073 [0.0038, 0.0114]<0.001
Clostridium symbiosum0.0021 [0.0016, 0.0049]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]<0.001
Clostridium xylanolyticum0.0000 [0.0000, 0.0000]0.0099 [0.0006, 0.0226]0.0000 [0.0000, 0.0000]<0.001
Eubacterium hallii0.0161 [0.0060, 0.0439]0.0000 [0.0000, 0.0000]0.0651 [0.0207, 0.0704]<0.001
Adlercreutzia equolifaciens0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.0011 [0.0005, 0.0086]<0.001
Alistipes finegoldii 0.0011 [0.0006, 0.0128]0.1265 [0.1095, 0.6407]0.0419 [0.0031, 0.0692]0.003
Alistipes sp. S457 0.0030 [0.0000, 0.1588]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.1262]0.027
Alloprevotella rava0.0000 [0.0000, 0.0000]0.0002 [0.0000, 0.0007]0.0000 [0.0000, 0.0000]0.007
Anaerostipes butyraticus0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.0220 [0.0031, 0.0334]<0.001
Anaerostipes hadrus0.0023 [0.0003, 0.0039]0.0000 [0.0000, 0.0000]0.0089 [0.0041, 0.0289]0.001
Anaerotruncus colihominis0.0054 [0.0023, 0.0089]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]<0.001
Anaerotruncus rubiinfantis 0.0117 [0.0069, 0.0252]0.0000 [0.0000, 0.0000]0.0064 [0.0025, 0.0170]<0.001
Angelakisella massiliensis0.0212 [0.0065, 0.0532]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]<0.001
Bacillus nealsonii0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.0019 [0.0000, 0.0038]0.003
Bacteroides acidifaciens 0.0158 [0.0116, 0.0317]0.0000 [0.0000, 0.0000]0.0265 [0.0254, 0.0307]<0.001
Bacteroides barnesiae0.0040 [0.0020, 0.0105]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]<0.001
Bacteroides caecicola0.0000 [0.0000, 0.0007]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.026
Bacteroides clarus 0.0066 [0.0032, 0.0233]0.0000 [0.0000, 0.0000]0.0050 [0.0032, 0.4975]<0.001
Bacteroides faecichinchillae0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.0094 [0.0031, 0.0176]<0.001
Bacteroides fluxus0.0035 [0.0013, 0.0093]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]<0.001
Bacteroides intestinalis0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.0018 [0.0005, 0.0046]<0.001
Bacteroides mediterraneensis0.0032 [0.0010, 0.1966]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]<0.001
Bacteroides paurosaccharolyticus0.0016 [0.0013, 0.0092]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]<0.001
Bacteroides rodentium0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.0005 [0.0003, 0.0010]<0.001
Bacteroides salanitronis0.0009 [0.0004, 0.0032]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]<0.001
Bacteroides salyersiae0.0000 [0.0000, 0.0000]0.0018 [0.0006, 0.0033]0.0031 [0.0016, 0.2846]<0.001
Bacteroides sp. 35AE37 0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.0022 [0.0011, 0.0031]<0.001
Bacteroides sp. ANH 2438 0.0002 [0.0000, 0.0013]0.0000 [0.0000, 0.0000]0.0011 [0.0005, 0.0023]0.010
Bacteroides sp. HGA0138 0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.0045 [0.0000, 0.0086]0.003
Bacteroides sp. Marseille P3108 0.0190 [0.0046, 0.0212]0.0000 [0.0000, 0.0000]0.0108 [0.0031, 0.0225]<0.001
Bacteroides stercorirosoris0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.0041 [0.0000, 0.0354]0.001
Bacteroides vulgatus0.7618 [0.0572, 1.2351]1.4728 [0.9327, 2.1484]1.9609 [1.7974, 3.7528]0.032
Bifidobacterium adolescentis0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.0011 [0.0000, 0.0099]0.003
Bifidobacterium sp. 113 0.0000 [0.0000, 0.0000]0.0012 [0.0004, 0.0197]0.0000 [0.0000, 0.0000]<0.001
Bifidobacterium sp. TM 7 0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0005]0.0000 [0.0000, 0.0000]0.024
Blautia luti 0.0000 [0.0000, 0.0000]0.0236 [0.0004, 0.1324]0.0000 [0.0000, 0.0000]<0.001
Blautia obeum0.1137 [0.0579, 0.2103]0.0627 [0.0455, 0.1708]0.5450 [0.2173, 0.6162]0.019
Blautia sp. Canine oral taxon 337 0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.0134 [0.0083, 0.0258]<0.001
Butyricicoccus desmolans0.0000 [0.0000, 0.0000]0.0108 [0.0024, 0.0188]0.0103 [0.0005, 0.0296]<0.001
Butyricimonas faecihominis0.0000 [0.0000, 0.0000]0.0021 [0.0000, 0.0132]0.0000 [0.0000, 0.0000]0.007
Butyricimonas sp. 180 3 0.0000 [0.0000, 0.0000]0.0035 [0.0002, 0.0134]0.0000 [0.0000, 0.0000]0.001
Butyricimonas sp. AT11 0.0168 [0.0013, 0.0422]0.0000 [0.0000, 0.0000]0.0025 [0.0010, 0.0106]0.002
Butyricimonas sp. S479 0.0000 [0.0000, 0.0000]0.0053 [0.0004, 0.0200]0.0000 [0.0000, 0.0000]0.001
Butyrivibrio crossotus 0.0000 [0.0000, 0.0000]0.0023 [0.0014, 0.0029]0.0000 [0.0000, 0.0000]<0.001
Candidatus Dorea massiliensis0.0008 [0.0000, 0.0030]0.0015 [0.0006, 0.0019]0.0000 [0.0000, 0.0000]0.022
Candidatus Soleaferrea massiliensis0.0019 [0.0010, 0.0069]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]<0.001
Catabacter hongkongensis0.0097 [0.0068, 0.0142]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]<0.001
Christensenella timonensis0.0177 [0.0047, 0.0223]0.0000 [0.0000, 0.0000]0.0037 [0.0023, 0.0124]<0.001
Clostridium carnis0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0009]0.044
Clostridium phoceensis0.0000 [0.0000, 0.0000]0.0011 [0.0001, 0.0059]0.0023 [0.0003, 0.0074]0.003
Clostridium sp. 14,505 0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0006]0.0000 [0.0000, 0.0000]0.024
Clostridium sp. 37hoe 0.0034 [0.0000, 0.0135]0.0000 [0.0000, 0.0000]0.0114 [0.0062, 0.0294]0.003
Clostridium sp. ACB 29 0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.0011 [0.0005, 0.0035]<0.001
Clostridium sp. Clone 17 0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.0240 [0.0184, 0.0255]<0.001
Clostridium sp. Culture Jar 19 0.0076 [0.0059, 0.0221]0.0000 [0.0000, 0.0000]0.0092 [0.0056, 0.0146]<0.001
Clostridium sp. Culture 41 0.0056 [0.0044, 0.0072]0.0000 [0.0000, 0.0000]0.0062 [0.0022, 0.0092]<0.001
Clostridium sp. Culture 46 0.0007 [0.0000, 0.0011]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.001
Clostridium sp. Enrichment culture clone 06 1235251 89 0.0118 [0.0087, 0.0296]0.0000 [0.0000, 0.0000]0.0080 [0.0047, 0.0149]<0.001
Clostridium sp. enrichment culture clone 7 14 0.0007 [0.0000, 0.0088]0.0000 [0.0000, 0.0000]0.0043 [0.0023, 0.0097]0.005
Clostridium sp. Enrichment culture clone VanCtr97 0.0049 [0.0028, 0.0131]0.0000 [0.0000, 0.0000]0.0374 [0.0032, 0.0521]0.001
Clostridium sp. Enrichment culture clone Y234 0.0000 [0.0000, 0.0000]0.0040 [0.0002, 0.0072]0.0002 [0.0000, 0.0290]0.009
Clostridium sp. ID6 0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.0054 [0.0000, 0.0073]0.003
Clostridium sp. Marseille P3122 0.0080 [0.0038, 0.0166]0.0000 [0.0000, 0.0000]0.0067 [0.0031, 0.0126]<0.001
Clostridium sp. Marseille P3244 0.0012 [0.0000, 0.0094]0.0000 [0.0000, 0.0000]0.0038 [0.0028, 0.0095]0.001
Clostridium sp. MLG856.1 0.0000 [0.0000, 0.0089]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.026
Clostridium sp. PI S10 B5A 0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0003]0.044
Clostridium sp. TM 40 0.0003 [0.0000, 0.0010]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.010
Clostridium sp. VKM B 2202 0.0082 [0.0061, 0.0092]0.0000 [0.0000, 0.0000]0.0114 [0.0083, 0.0186]<0.001
Coprobacter secundus0.0082 [0.0010, 0.0222]0.0023 [0.0002, 0.0207]0.0000 [0.0000, 0.0000]0.010
Coprococcus eutactus0.0029 [0.0013, 0.0098]0.0000 [0.0000, 0.0000]0.0062 [0.0005, 0.0225]0.003
Dakarella massiliensis0.1038 [0.0008, 0.7340]0.0000 [0.0000, 0.0004]0.0000 [0.0000, 0.0000]0.002
Dialister invisus0.0032 [0.0000, 0.0207]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0005]0.015
Dialister sp. GBA27 0.0000 [0.0000, 0.0000]0.0008 [0.0000, 0.0090]0.0000 [0.0000, 0.0000]0.007
Dialister sp. Oral clone MCE7_134 0.0008 [0.0000, 0.0028]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.010
Dialister sp. oral taxon 119 0.0029 [0.0005, 0.0186]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]<0.001
Dialister sp. Oral taxon A97 0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0005]0.0000 [0.0000, 0.0000]0.024
Dialister succinatiphilus0.0024 [0.0000, 0.2609]0.0038 [0.0015, 0.0074]0.0000 [0.0000, 0.0000]0.004
Eisenbergiella massiliensis0.0000 [0.0000, 0.0014]0.0061 [0.0013, 0.0322]0.0000 [0.0000, 0.0000]0.007
Escherichia coli0.0019 [0.0009, 0.0237]0.0000 [0.0000, 0.0000]0.0011 [0.0000, 0.0073]0.002
Eubacterium coprostanoligenes 0.0264 [0.0088, 0.0484]0.0000 [0.0000, 0.0000]0.1952 [0.0921, 0.6125]<0.001
Faecalibacterium CM04 06 0.0082 [0.0034, 0.0594]1.4640 [0.6198, 3.3800]0.0250 [0.0063, 0.0389]0.004
Faecalibacterium prausnitzii 3.7151 [2.5044, 5.1455]12.7134 [10.3494, 14.3182]5.5894 [3.2108, 7.2897]<0.001
Falcatimonas natans0.0000 [0.0000, 0.0000]0.0021 [0.0001, 0.0042]0.0086 [0.0009, 0.0434]0.002
Fenollaria timonensis 0.0126 [0.0048, 0.0157]0.0000 [0.0000, 0.0000]0.0181 [0.0107, 0.0238]<0.001
Flintibacter butyricus 0.0127 [0.0104, 0.0169]0.0000 [0.0000, 0.0000]0.0140 [0.0043, 0.0311]<0.001
Holdemania massiliensis0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0091]0.044
Howardella ureilytica0.0000 [0.0000, 0.0106]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.026
Hungatella hathewayi0.0000 [0.0000, 0.0000]0.0047 [0.0011, 0.0163]0.0000 [0.0000, 0.0000]<0.001
Intestinibacter bartlettii0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.0037 [0.0005, 0.0382]<0.001
Intestinimonas butyriciproducens0.0000 [0.0000, 0.0000]0.0021 [0.0010, 0.0056]0.0000 [0.0000, 0.0000]<0.001
Intestinimonas gabonensis0.0108 [0.0077, 0.0270]0.0925 [0.0581, 0.1601]0.0067 [0.0023, 0.0165]<0.001
Intestinimonas timonensis0.0031 [0.0017, 0.0180]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]<0.001
Lachnobacterium bovis0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.0152 [0.0011, 0.0187]<0.001
Mediterranea massiliensis0.0009 [0.0003, 0.0014]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]<0.001
Megasphaera elsdenii0.0004 [0.0000, 0.0077]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.010
Merdibacter massiliensis0.0026 [0.0009, 0.0055]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]<0.001
Methanobrevibacter smithii0.0061 [0.0000, 0.0322]0.0019 [0.0000, 0.0329]0.0000 [0.0000, 0.0000]0.031
Oribacterium sp. NK2B42 0.0033 [0.0011, 0.0091]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]<0.001
Oxalobacter formigenes0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.0172 [0.0005, 0.0280]<0.001
Pantoea dispersa0.0013 [0.0004, 0.0102]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]<0.001
Papillibacter cinnamivorans0.0000 [0.0000, 0.0000]0.0083 [0.0024, 0.0115]0.0057 [0.0002, 0.0086]<0.001
Parabacteroides merdae0.0007 [0.0000, 0.0013]0.0004 [0.0000, 0.0056]0.0000 [0.0000, 0.0000]0.025
Parabacteroides sp. SN4 0.0000 [0.0000, 0.0034]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.026
Phascolarctobacterium sp. 377 0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0034]0.012
Phascolarctobacterium succinatutens0.0000 [0.0000, 0.0000]0.0002 [0.0000, 1.0265]0.0000 [0.0000, 0.0023]0.050
Phocea massiliensis0.0031 [0.0020, 0.0116]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]<0.001
Prevotella copri0.0009 [0.0000, 0.0112]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.001
Prevotella sp. 109 0.0048 [0.0003, 1.4759]0.0000 [0.0000, 0.0000]0.0034 [0.0022, 0.0074]0.005
Prevotella sp. Canine oral taxon 282 0.0008 [0.0004, 0.0011]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]<0.001
Prevotella sp. DJF_RP53 0.0000 [0.0000, 0.0003]0.0029 [0.0022, 0.0957]0.0000 [0.0000, 0.0000]<0.001
Prevotella stercorea0.0002 [0.0000, 0.0064]0.0006 [0.0001, 0.0034]0.0000 [0.0000, 0.0000]0.034
Propionibacterium sp. S342 0.1322 [0.0024, 0.1969]0.0000 [0.0000, 0.0000]0.1538 [0.0062, 0.6465]<0.001
Pseudoflavonifractor sp. Marseille P3106 0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.0023 [0.0012, 0.0050]<0.001
Robinsoniella peoriensis0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.0046 [0.0032, 0.0138]<0.001
Robinsoniella sp. MCWD5 0.0110 [0.0042, 0.0156]0.0000 [0.0000, 0.0000]0.0298 [0.0276, 0.0405]<0.001
Roseburia inulinivorans0.0639 [0.0282, 0.1109]0.5866 [0.1126, 1.7230]0.0204 [0.0002, 0.0274]0.006
Roseburia sp. 831b 0.0672 [0.0000, 0.4704]0.9504 [0.2563, 1.5792]0.0000 [0.0000, 0.0003]0.030
Roseburia sp. DJF_VR77 0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.0026 [0.0000, 0.0345]0.003
Ruminiclostridium thermocellum0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.0021 [0.0014, 0.0045]<0.001
Ruminococcus flavefaciens0.0090 [0.0010, 0.0314]0.0063 [0.0025, 0.0110]0.0000 [0.0000, 0.0000]0.001
Ruminococcus sp. 16442 0.0068 [0.0053, 0.0099]0.0000 [0.0000, 0.0000]0.0113 [0.0002, 0.0147]0.001
Ruminococcus sp. 653 0.0000 [0.0000, 0.0000]0.0103 [0.0074, 0.0152]0.0140 [0.0070, 0.0260]<0.001
Ruminococcus sp. AT10 0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.0092 [0.0037, 0.1247]<0.001
Ruminococcus sp. DJF_VR52 0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.0005 [0.0000, 0.0034]0.003
Ruminococcus sp. DJF_VR70k1 0.0000 [0.0000, 0.0000]0.0040 [0.0008, 0.0102]0.0000 [0.0000, 0.0000]<0.001
Ruminococcus sp. ID1 0.0235 [0.0164, 0.0259]0.0000 [0.0000, 0.0000]0.0368 [0.0172, 0.0697]<0.001
Ruminococcus sp. ZS2 15 0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.0011 [0.0005, 0.0063]<0.001
Saccharofermentans acetigenes0.0099 [0.0047, 0.0158]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]<0.001
Streptococcus agalactiae0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0005]0.012
Streptococcus equinus0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.0035 [0.0013, 0.0161]<0.001
Streptococcus mitis0.0000 [0.0000, 0.0009]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.026
Succinivibrio dextrinosolvens0.0008 [0.0000, 0.0012]0.0003 [0.0000, 0.0004]0.0000 [0.0000, 0.0000]0.030
Sutterella sp. 252 0.0000 [0.0000, 0.0843]0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.026
Tyzzerella sp. Marseille P3062 0.0000 [0.0000, 0.0000]0.0000 [0.0000, 0.0000]0.0002 [0.0000, 0.0057]0.003
# KW: p-value from Kruskal-Wallis test.

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Fontana, A.; Panebianco, C.; Picchianti-Diamanti, A.; Laganà, B.; Cavalieri, D.; Potenza, A.; Pracella, R.; Binda, E.; Copetti, M.; Pazienza, V. Gut Microbiota Profiles Differ among Individuals Depending on Their Region of Origin: An Italian Pilot Study. Int. J. Environ. Res. Public Health 2019, 16, 4065. https://doi.org/10.3390/ijerph16214065

AMA Style

Fontana A, Panebianco C, Picchianti-Diamanti A, Laganà B, Cavalieri D, Potenza A, Pracella R, Binda E, Copetti M, Pazienza V. Gut Microbiota Profiles Differ among Individuals Depending on Their Region of Origin: An Italian Pilot Study. International Journal of Environmental Research and Public Health. 2019; 16(21):4065. https://doi.org/10.3390/ijerph16214065

Chicago/Turabian Style

Fontana, Andrea, Concetta Panebianco, Andrea Picchianti-Diamanti, Bruno Laganà, Duccio Cavalieri, Adele Potenza, Riccardo Pracella, Elena Binda, Massimiliano Copetti, and Valerio Pazienza. 2019. "Gut Microbiota Profiles Differ among Individuals Depending on Their Region of Origin: An Italian Pilot Study" International Journal of Environmental Research and Public Health 16, no. 21: 4065. https://doi.org/10.3390/ijerph16214065

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