Skip to main content

Alterations of oral microbiota and impact on the gut microbiome in type 1 diabetes mellitus revealed by integrated multi-omic analyses

Abstract

Background

Alterations to the gut microbiome have been linked to multiple chronic diseases. However, the drivers of such changes remain largely unknown. The oral cavity acts as a major route of exposure to exogenous factors including pathogens, and processes therein may affect the communities in the subsequent compartments of the gastrointestinal tract. Here, we perform strain-resolved, integrated meta-genomic, transcriptomic, and proteomic analyses of paired saliva and stool samples collected from 35 individuals from eight families with multiple cases of type 1 diabetes mellitus (T1DM).

Results

We identified distinct oral microbiota mostly reflecting competition between streptococcal species. More specifically, we found a decreased abundance of the commensal Streptococcus salivarius in the oral cavity of T1DM individuals, which is linked to its apparent competition with the pathobiont Streptococcus mutans. The decrease in S. salivarius in the oral cavity was also associated with its decrease in the gut as well as higher abundances in facultative anaerobes including Enterobacteria. In addition, we found evidence of gut inflammation in T1DM as reflected in the expression profiles of the Enterobacteria as well as in the human gut proteome. Finally, we were able to follow transmitted strain-variants from the oral cavity to the gut at the individual omic levels, highlighting not only the transfer, but also the activity of the transmitted taxa along the gastrointestinal tract.

Conclusions

Alterations of the oral microbiome in the context of T1DM impact the microbial communities in the lower gut, in particular through the reduction of “mouth-to-gut” transfer of Streptococcus salivarius. Our results indicate that the observed oral-cavity-driven gut microbiome changes may contribute towards the inflammatory processes involved in T1DM. Through the integration of multi-omic analyses, we resolve strain-variant “mouth-to-gut” transfer in a disease context.

Video Abstract

Introduction

Thousands of distinct microbial taxa colonise the different mucosal and skin habitats of the human body [1]. These communities and their functional gene complements directly interface with host physiology, most notably the immune system [2, 3]. Altered community compositions are thought to play crucial roles in triggering inflammatory processes which are most likely drivers of chronic diseases [1, 4, 5], including autoimmune diseases [6,7,8]. The human microbiome is influenced by biotic and abiotic factors specific to each body site, which leads to distinct microbial community compositions [9]. Although closely related taxa can be present at multiple sites, most species exhibit differentiation into locally adapted strains [10].

Bacterial species usually consist of an ensemble of strains which form coherent clades [11]. Thereby they are clearly distinguishable from the closest co-occurring related species based on their high genetic similarity [12, 13]. The classical metagenomic approach consists of assembling short DNA reads into contigs and to group them into different metagenome-assembled genomes (MAGs). However, the assembly produces a patchwork of consensus contigs corresponding to the most abundant genotypes in the sample and thus can lose strain variations. Multiple approaches exist to retrieve variant information which typically involves the mapping of the metagenomic reads against the assembled contigs or reference genomes. Variant calling is then performed to determine the alleles or haplotypes [14]. Despite the genetic similarity between strains of a single species, the individual strains can exhibit different phenotypes. Such cases are notably well documented in the context of pathogenicity where many species are known to have both pathogenic and commensal strains [11]. Therefore, strain-level resolution is highly relevant in the study of the human microbiome and its links to health and disease.

The gut microbiome has been extensively studied primarily in the context of chronic diseases including cardiovascular diseases [15], inflammatory bowel disease [16], obesity [17], cancers [18], neurodegenerative diseases [19] or autoimmune conditions such as rheumatoid arthritis [20] or type 1 [21], and type 2 diabetes [22]. Type 1 diabetes mellitus (T1DM) is a chronic disease characterised by insulin deficiency due to autoimmune destruction of insulin-producing β-cells within the pancreatic islets. T1DM often starts during the early years of life and is one of the most common chronic diseases in childhood [23]. Its incidence worldwide has reached 15 per 100,000 people and has been globally increasing in the last decades in most developed countries [24,25,26]. Despite a significant genetic influence, the rise in T1DM prevalence in individuals who are not genetically predisposed strongly suggests an interplay between genetic predisposition and environmental factors [27].

Among the possible different environmental factors, the gut microbiome modulates the function of the immune system via direct and indirect interactions with innate and adaptive immune cells [3, 28]. Several studies have shown alterations of the gut microbiome composition between individuals with T1DM compared to healthy controls [29,30,31,32]. However, contrasting findings between studies have not led to a generalisable microbiome signature for T1DM and it still remains unclear how microbiome changes affect the gastrointestinal tract and immune functions in T1DM.

The oral cavity and the colon sit at opposite sides of the gastrointestinal tract. The mouth is considered a gateway to different organs of the body, and therefore acts as a potential reservoir for different pathogens [33]. Poor dental health and dysfunctional periodontal immune-inflammatory reactions caused by bacterial pathogens may lead to periodontitis and are associated with increased risks of developing systemic inflammatory disorders [34]. The development of inflammation in the oral cavity has notably been found to be associated with systemic inflammation and cardiovascular disease [35], insulin resistance [36], and complications in type 1 and type 2 diabetes [37]. Despite the limited number of shared taxa between the oral cavity and the lower gut [38] due to the gastric bactericidal barrier, intestinal motility or bile and pancreatic secretions [39], a recent study has shown that the oral community type was predictive of the community recovered from stool [40]. Additionally, Schmidt, Hayward et al. recently found that a subset of 74 species were frequently transmitted from mouth to gut and formed coherent strain populations along the gastrointestinal tract [41]. Finally, it is known that the physiology of the oral cavity is altered in T1DM patients, notably with a decrease of salivary flow rate (dry-mouth symptom) and an increased concentration of glucose in the saliva and subsequent acidification of the oral cavity [42,43,44]. However, the effect of T1DM on the microbiome of the oral cavity, or the effect of the microbiome on T1DM in general is still poorly understood, with few, and regularly contradicting findings [45].

Here, we apply an integrated multi-omic approach, including matched meta- genomics, transcriptomics and proteomics together with available clinical data to characterise differences in the oral and gut microbiomes in the context of T1DM on 35 individuals from eight families with multiple case of T1DM per family. We identify distinct oral microbiota suggestive of competition between streptococcal species and an acidified oral cavity. We link these differences to alterations in the gut microbiome and the host’s inflammatory response. Finally, we explore the level of mouth-to-gut transmissions in T1DM, highlight transferred and active strains, and identify differences in strain-level transmission profiles in T1DM patients compared to healthy controls.

Methods

Ethics

Written informed consent was obtained from all subjects enrolled in the study. This study was approved by the Comité d'Ethique de Recherche (CNER; reference no. 201110/05) and the National Commission for Data Protection in Luxembourg.

Sample acquisition

The study design was an observational study of eight selected families (M01-M06, M08, M11) containing at least two members with T1DM and healthy individuals in two generations or more, from existing patient cohorts from the Centre Hospitalier du Luxembourg. Individual patients are annotated as a combination of their family and a number for each individual per family (e.g. M05.1). Recruited families were seen three times (V1, V2, V3) at intervals of between 4 and 8 weeks for data and samples collection. On enrolment, study participant pedigrees were drawn, medical history was collected and a ‘Food Frequency Questionnaire’ was completed. During every visit, anthropometric data were recorded as previously described [46] (Supplementary Data 1). Donors collected 2–3 ml of saliva at home before dental hygiene and breakfast in the early morning. Faecal samples were also self-collected and both samples were immediately frozen on dry-ice, transported to the laboratory and stored at – 80 °C until further processing. Part of the cohort’s raw data (families M01–04) [41, 46] as well as the oral and gut metagenomics (families M05–11) [41, 46] were previously studied and published. The following method sections describe the processing of the newly produced dataset.

Biomolecular extractions

For each individual and visit, faecal and saliva samples were subjected to comprehensive biomolecular isolations.

For the faecal samples, 150 mg of each snap-frozen sample was reduced to a fine powder and homogenised in a liquid nitrogen bath followed by the addition of 1.5 ml of cold RNAlater and brief vortexing prior to incubation overnight at − 20 °C. After incubation, the sample was re-homogenised by shaking for 2 min at 10 Hz in an oscillating Mill MM 400 (Retsch) and subsequently centrifuged at 700×g for 2 min at 4 °C. The supernatant was retrieved and the cells were pelleted by centrifugation at 14,000×g for 5 min. Cold stainless steel milling balls and 600 μl of RLT buffer (Qiagen) were added to the pellet and this was re-suspended via quick vortexing. Cells were disrupted by bead beating in an Oscillating Mill MM 400 (Retsch) for 30 s at 25 Hz and at 4 °C. Finally, the lysate was transferred onto a QIAshredder column and centrifuged at 14,000×g for 2 min and the eluate retrieved for multi-omics extraction. The subsequent biomacromolecular extractions were based on the Qiagen Allprep kit (Qiagen) using an automated robotic liquid handling system (Freedom Evo, Tecan) as described in Roume et al. and in accordance with the manufacturer’s instructions [47].

For the saliva samples, the individual snap-frozen sample was thawed on ice, and 1 ml was subsampled and centrifuged at 18,000×g for 15 min at 4 °C. The supernatant was discarded and the pellet directly refrozen in liquid nitrogen. Cold stainless steel milling balls were added to the frozen pellet for homogenisation by cryo-milling for 2 min at 25 Hz in an oscillating Mill MM 400 (Retsch). Subsequently, 300 μl of methanol and 300 μl of chloroform were added before a second passage through the Oscillating Mill at 20 Hz for 2 min. After centrifugation at 14,000×g for 5 min, two phases (polar and non-polar) and a solid interphase were visible. The two phases were discarded and the solid interphase kept for multi-omics extraction. Stainless steel milling balls and 600 μl of RLT buffer (Qiagen) were added to the pellet, re-suspended via quick vortexing and cells were disrupted by bead beating in an Oscillating Mill MM 400 (Retsch) for 30 s at 25 Hz at 4 °C. The lysate was transferred onto a QIAshredder column and centrifuged at 14,000×g for 2 min. The subsequent steps were performed as described for the faecal samples.

DNA sequencing

After extraction, the retrieved DNA was depleted of leftover RNA by RNAse A treatment at 65 °C for 45 min. After ethanol precipitation, the samples were re-suspended in 50 μl nuclease-free water. The quality and quantity of the retrieved DNA were assessed both before and after treatment via gel electrophoresis and Nanodrop analysis (ThermoFisher Scientific).

Sequencing libraries for salivary samples were prepared using the NEBNext Ultra DNA Library Prep kit (New England Biolabs, Ipswich) using a dual barcoding system, and sequenced at 150 bp paired-end on Illumina HiSeq 4000 and Illumina NextSeq 500 machines.

RNA sequencing

The extracted RNA was treated with DNase I at 37 °C for 30 min and purified using phenol-chloroform. From the aqueous phase, RNA was precipitated with isopropanol and re-suspended in 50 μl nuclease free water.

RNA integrity and quantity were assessed before and after treatment using the RNA LabChip GX II (Perkin Elmer). Subsequently, 1 μg of RNA sample was rRNA-depleted using the RiboZero kit (Illumina, MRZB12424). Further library preparation of rRNA-depleted samples was performed using TruSeq Stranded mRNA library preparation kit (Illumina, RS-122-2101) according to the manufacturer’s instructions apart from omitting the initial steps for mRNA pull-down. Prepared libraries were checked again using the RNA LabChip GX II (Perkin Elmer) and quantified using Qubit (Invitrogen). A 10-nM pool of the libraries was sent to the EMBL genomics platform for sequencing on a Illumina NextSeq 500 machine.

Protein processing and mass spectrometry

The following section describes the procedures for samples from families M05, M06, M08, and M11. For a description of the protein processing of samples from families M01–M04 see Heintz-Buschart et al. [46].

Extracted proteins were processed and digested using the S-TrapTM system (ProtiFi) following manufacturer's instructions. Briefly, protein suspensions were solubilised with SDS then reduced, alkylated and acidified for complete denaturation.

Approximately 200 μl of samples were transferred onto the S-Trap column and centrifuged until all of the sample volume was transferred. The columns were then washed twice with 180 μl S-Trap protein binding buffer. Protein digestion was performed by adding 20 μl of 0.04 μg/μl trypsin solution to each column, to achieve a trypsin to protein ratio of 1:50. Incubation was performed for three hours at 47 °C in a Thermomixer. Tryptic peptides were eluted with 40 μl 50 mM TEAB, 40 μl 0.1% acetic acid, and 35 μl 60% acetonitrile with 0.1% acetic acid at 4000×g for 1 min per elution. Samples were dried at 45 °C in a vacuum centrifuge and stored at − 20 °C.

Peptides were fractionated into eight fractions using the high pH reversed-phase peptide fractionation kit (PierceTM Thermo Fisher Scientific) according to the manufacturer’s instructions and using self-made columns as previously described [48]. Digested, dried peptides were resuspended in 300 μl of 0.1% trifluoroacetic acid and suspensions transferred onto the columns. After centrifugation at 3000×g for 2 min the eluate was retained as “flow-through”-fraction. Columns were then washed with 300 μl water (ASTM Type I) at 3000×g for 4 min. Separation of samples into eight fractions was performed using 300 μl of elution solutions with increasing concentrations of acetonitrile in 0.1% trifluoroacetic acid at 3000×g for 4 min. Each elution fraction was collected in a separate microcentrifuge tube, dried at 45 °C in a vacuum centrifuge and stored at − 20 °C.

Peptide concentrations were measured for fraction two of each sample using the Quantitative Fluorometric Peptide Assay kit (PierceTM Thermo Fisher Scientific) according to the manufacturer’s instructions.

Of each of the samples, for each fraction, the volume for 170 ng of peptides were loaded onto in-house built columns (100 μm × 20 cm), filled with 3 μm ReproSil-Pur material and separated using a non-linear 100 min gradient from 1 to 99% buffer B (99.9% acetonitrile, 0.1% acetic acid in water (ASTM Type I) at a flow rate of 300 nl/min operated on an EASY-nLC 1200. Measurements were performed on an Orbitrap Elite mass spectrometer performing one full MS scan in a range from 300 to 1700 m/z followed by a data-dependent MS/MS scan of the 20 most intense ions, a dynamic exclusion repeat count of 1, and repeat exclusion duration of 30 s.

Metagenomic and metatranscriptomic data analysis

For each individual time point, metagenomic (MG) and metatranscriptomic (MT) data were processed and co-assembled using the Integrated Meta-omic Pipeline (IMP) [49] which includes steps for the trimming and quality filtering of the reads, the filtering of rRNA from the MT data, and the removal of human reads after mapping against the human genome (hg38). Pre-processed DNA and RNA reads were co-assembled using the IMP-based iterative co-assembly using MEGAHIT 1.0.3 [50]. After co-assembly, prediction and annotation of open-reading frames (ORFs) were performed using IMP and followed by binning and then taxonomic annotation at both the contig and bin level. MG and MT read counts for the predicted genes obtained using featureCounts [51] were linked to the different annotation sources (KEGG [52], Pfam [53], Resfams [54], dbCAN [55], Cas [56], and DEG [57], as well as to taxonomy (mOTUs 2.5.1 [58] and Kraken2 using the maxikraken2_1903_140GB database [59]). Kraken2 annotations were used to generate read count matrices for each taxonomic rank (phylum, class, order, family, genus, and species) by summing up reads at the respective levels.

Identification of variants

IMP produced the mapping of the processed DNA and RNA reads against the final co-assembled contigs with the Burrows-Wheeler Aligner tool (BWA 0.7.17) [60] using the BWA-MEM algorithm with default parameters. Additionally for each individual, the oral DNA reads from all available visits were mapped against the gut contigs produced from all available visits with the same parameters.

All alignment files per sample were used to call variants using bcftools 1.9 [61, 62]. Bcftools mpileup was run on the gut contigs as reference FASTA file with default parameters except for the --max-depth being set to 1000 to increase variant calling certainty. Called variants were filtered based on their quality and read depth with minimum values set to 20 and 10, respectively and indels were excluded. Subsequently, in order to reinforce confidence in the variant calling, variants were kept for downstream analysis, only if they fitted the following criteria: (i) positive allelic depths on both the forward and reverse strands for the corresponding gut and oral DNA reads, and (ii) presence of an alternative allele (genotype = 1 in the vcf file) at the oral DNA reads and the gut RNA read levels. These criteria ensured that the variants were resolved in both the gut and oral samples at both the DNA and RNA levels.

Because we have different assemblies, we obtained different mappings and different variants. In order to perform a comparison between samples, the reads containing the variants were extracted from the mapping files and taxonomically annotated using Kraken2. For metaproteomics, missense variants (variant that leads to a different amino acid) were identified using an in-house script [46] and the generated ORFs containing variants were added to the metaproteomic database (see below).

Metaproteomic data analysis

As the mass spectrometry analysis of the protein fraction was performed at different facilities for families M01-04 and families M05, M06, M08, and M11, certain parts of the preprocessing workflow and analyses had to be tailored to the data, as mentioned below.

Raw files were converted to mzML format using ThermoRawFileParser [63] and to ms2 format using ProteoWizard’s msconvert [64]. The files for families M01–04 were filtered for the top 300 most intense spectra, the files for the other families for the top 150 most intense spectra to optimise protein identifications.

For each sample, microbial protein sequence databases were constructed from the Prokka [65] predicted protein sequences of the IMP co-assemblies and supplemented with variant protein sequences (missense variants) identified in both the oral cavity and the gut, during the variant calling step. This was done in order to consider only the variant sequences originating from the oral cavity that could also be found in the gut. If no database was available for a single sample, all databases available from the individual were concatenated. If an individual had no database, all databases from the individual’s family were concatenated. In addition, the human RefSeq protein sequences (release 92), a collection of plant storage proteins that might be present due to food intake as well as the cRAP contaminant database (release 04/03/2019) were added. The databases were then filtered according to size (60–40,000 residues) to eliminate noise from very large or small proteins that can be erroneously produced during the ORF prediction step. Duplicate sequences were removed by sequence using SeqKit [66].

Concatenated target-decoy databases were built using Sipros Ensembles sipros_prepare_protein_database.py. Using Sipros Ensemble [67], each sample was searched against the prepared database for that sample. Identifications were filtered to a protein FDR of 1%.

After the search, human and microbial protein identifications were treated separately. Human proteins/protein groups that ended up having identical protein identifiers after processing the database identifiers in the output were collapsed and their spectral counts summed up. The same was done for the microbial proteins but gene identifiers were replaced by the corresponding annotation identifiers from the respective source (e.g. KEGG, Pfam. (see above)).

Diversity analysis

Raw read counts per taxon for each sample were transformed from absolute counts to relative abundances by dividing each value by samples total taxon read counts. The richness as a total number of detected species after filtering was recorded as well as alpha diversity using the Simpson index [68]. Beta diversity was analysed using Bray-Curtis as a distance measure with hierarchical clustering, distance-based redundancy analysis (dbRDA), and nonmetric multi-dimensional scaling (NMDS). Significance tests between groups were carried out using the Mann–Whitney–Wilcoxon test (MWW) or analysis of variance (ANOVA, dbRDA formula: species~condition+family). Analyses were performed in R using the picante [69] and vegan [70] packages.

Statistical analyses

An initial screening was performed based on MG and MT sequencing and assembly statistics, principal component analysis and hierarchical clustering on gene abundances to highlight potential outliers. Samples whose sequencing and assembly statistics consistently appeared outside ± 1.5× the interquartile range and clustered substantially differently compared to other samples from the same individual with hierarchical clustering were considered as outliers and removed from the dataset. Similarly, filtering was performed for the MP data with MS raw data quality and protein identification rate. After quality control, several individuals were removed because of their high variability due to either a very young age (age under 4 years old for M08–04 and M11–03) or a comorbidity that was not present in the rest of the dataset (T2DM for M11–05 and M11–06).

After taxonomic and functional analysis, gene/taxa read count and protein spectral count matrices were generated for differential abundance and expression analysis using the DESeq2 R package [71]. As the sampling visits for each individual are not independent, the median value for each gene/protein of the available visits for each individual was computed to obtain a matrix with one representative value per gene/protein per individual. Additionally, genes in read count matrices were removed if they did not have at least 20 reads in 25% of all the individuals, ensuring sufficient representation of the gene in the sample set for downstream statistical analyses. Proteins in the spectral count matrices were removed if they did not have at least 10 spectra in 25% of all the individuals. Finally, family membership was set as confounder for the DESeq2 the differential analyses.

Correlation analyses were performed on the same filtered matrices and combined depending on what correlations were tested. Spearman’s rank correlation coefficients were calculated with two-sided significance tests corrected for multiple testing using the Benjamini-Hochberg method. For the correlations between transcripts and differentially active taxa in the oral cavity a significance threshold of 0.001 and a correlation threshold of 0.7 were applied and the analysis was performed with the rcorr function of the Hmisc R package (https://github.com/harrelfe/Hmisc). All other correlation analyses were performed with a significance threshold of 0.05 using the rstatix R package (https://github.com/kassambara/rstatix).

Results and discussions

Study description

In this study, we performed a multi-omic oral and gut microbiome study of eight families with at least two T1DM cases per family (Fig. 1A). This expanded on previous studies focusing on a subset of the data [41, 46]. The present work additionally includes metagenomic (MG) and metatranscriptomic (MT) analyses of the oral cavity for all participants. In total, we analysed 84 stool and 76 saliva samples from 35 individuals coming from multiple visits. We generated MG data for 84 stool and 74 saliva, MT data for 64 stool and 71 saliva, and MP data for 71 stool sample (Table 1). Of the 35 individuals, 17 were T1DM patients and 18 were healthy family members (Fig. 1A). In total, 653.4 Gbp of DNA sequencing data, 870.6 Gbps RNA sequencing data, and 13,833,325 fragment ion spectra were acquired.

Fig. 1
figure 1

Description of the cohort and overview of the study workflow. The upper panel (A) shows the different individuals with family membership as well as disease status in the cohort. The lower panel (B) describes the integrated multi-omics analysis workflow to process, integrate and analyse metagenomic (MG), metatranscriptomic (MT), and metaproteomic (MP) data from saliva and stool samples

Table 1 Overview of the multi-omics study data

Over all samples, the DNA and RNA sequencing data per sample amounted to on average 4.2 ± 0.9 Gbp for MG and 6.3 ± 1.6 Gbp for MT. While the gut data consisted of 4.2 ± 0.8 Gbp of MG and 5.6 ± 1.1 Gbp MT sequencing data, the oral data represented 4.2 ± 0.9 Gbp of MG and 7.0 ± 1.6 Gbp MT sequencing data. For the stool samples, on average 95,000 ± 59,000 MS2 scans were performed and 4500 ± 3400 proteins identified. For samples from families 01–04 on average 63,000 ± 4700 fragment ion scans were obtained. The database searches resulted in 1500 ± 300 proteins on average. A mean of 203,000 ± 11,800 fragment ion scans were obtained for samples from families 05, 06, 08, and 11 and 8000 ± 1600 proteins could be identified. For detailed statistics see Supplementary Table 1. In the present study, we combined information from three omes in order to identify and follow strain-variants across the two body sites. To be able to do so, the overlap among the different omes had to be maximised to preserve all their sample specificity. Thus, the complete set of contigs from sample-specific assemblies were used rather than metagenome-assembled genomes that would have only covered a subset of all the multi-omic data (Fig. 1B).

Overall microbial community structure does not differ significantly between T1DM and healthy controls

We compared the community structures of both body sites between T1DM patients and controls using the MG data. Overall, the number of total species detected in the gut varied more in healthy individuals, but no significant differences in richness (MWW: p val 0.72, Supplementary Fig. 1A) nor in Simpson’s index of diversity were observed (MWW: p val 0.53, Supplementary Fig. 1B). Beta diversity differed significantly according to family membership but not between T1DM patients and controls (ANOVA on dbRDA; p vals 0.001 (family), 0.11 (condition); R2 0.49; Supplementary Fig. 1C).

The oral microbiota did not differ significantly in species richness (MWW: p val 0.48, Supplementary Fig. 1A) nor in their Simpson’s Index of Diversity (MWW: p val 0.90, Supplementary Fig. 1B). The beta diversity, as in the gut, showed no significant difference for T1DM but for family membership (ANOVA on dbRDA, p vals 0.5 (condition), 0.003 (family); R2 0.37; Supplementary Fig. 1C). Thereby, for both body sites, no evidence was found that suggested a significant effect of T1DM on the overall microbiota community diversity. As shown before, observable differences in oral community composition may instead be related to family membership [46].

The acidification of the oral cavity in T1DM impacts specific taxa and destabilises the equilibrium between Streptococcus species

Streptococcus species are the primary colonisers of the oral cavity and are key players in oral homeostasis and disease [72]. In healthy subjects, there is a balance between the abundance of opportunistic pathogens (e.g. S. mutans or S. pneumoniae) and non-pathogenic commensal species (e.g. S. salivarius, S. parasanguinis, or S. mitis) which compete with each other via different mechanisms such as acid or base production, or secretion of bacteriocins [72,73,74,75].

In our study, the abundance of several members of the genus Streptococcus varied in the oral cavity of T1DM patients compared to controls. In particular at the MG level, we observed high variability among Streptococcus species (Fig. 2). Such variability is in agreement with previous findings whereby the numbers of different Streptococcus species were found to be increased or decreased in T1DM depending on the study [76, 77]. For example, a 16S rRNA gene-based study of both body sites observed an increase in the abundance of the genus Streptococcus in the mouth but a decrease in the gut of T1DM patients [45].

Fig. 2
figure 2

Taxon-resolved differential abundance and gene expression in the oral microbiome in T1DM. The differences in abundance (triangles) and expression (circle) in T1DM versus healthy individuals using metagenomic and metatranscriptomic data, respectively, are shown on the volcano plot. A minimum log2 fold change of 5 (dashed vertical lines) and an adjusted p value of 0.01 (dashed horizontal line) were required (red dots). Taxa that satisfy the fold-change threshold but not the adjusted p value threshold are displayed in green. A subset of Supplementary Fig. 2 is shown in the insert in the upper-right and highlights the correlation between S. mutans activity and the expression of a target-specific bacteriocin

We observed an increased abundance of the acid-tolerant but non-pathogenic Streptococcus parasanguinis and closely related Streptococcus HMSC073D05 (log2 fold changes 3.5 and 3.4, respectively; adj. p val < 0.05). In contrast, the abundance of the commensal and acid-intolerant Streptococcus salivarius was found to be decreased in T1DM (log2 fold change − 3.5; adj. p val < 0.05) [78]. Additionally, we observed a decreased abundance of Porphyromonas gingivalis in the cavity of T1DM patients. P. gingivalis is usually associated with a dysbiotic state but is also known to be unable to grow in acidic conditions [79]. Taken together, these results indicate a microbial profile corresponding to an acidified cavity in the case of T1DM patients [42,43,44, 80].

Further evidence was provided by the metatranscriptomic data, which showed a significantly increased activity of the pathogenic Streptococcus mutans [81] (log2 fold change 11.3; adj. p val < 0.05), while other Streptococci, notably S. salivarius/S. sp. CCH8-H5 (log2 fold change − 13.3 at adj. p val < 0.05) were less active (Fig. 2). S. mutans is a common pathogen of the oral cavity associated with periodontal diseases and known for its acid-tolerance and acidogenicity, which leads to further microbial acidification of the oral cavity in T1D patients [82, 83].

In order to better understand the underlying patterns in the oral microbiomes, we looked at correlations of the expressed genes with the taxa that were found to be differentially active. We observed significant positive correlations (rho > 0.7 at p value < 0.001) between S. mutans and two specific expressed transcripts related to bacterial competition among closely related species, namely bacteriocin IIc and pre-toxin TG, which are the constituent domains of uberolysin (Fig. 2—network analysis and Supplementary Fig. 2). This peptidic toxin is a circular bacteriocin characterised in the genus Streptococcus and has a broad spectrum of inhibitory activity, which includes most streptococci with the notable exception of S. rattus and S. mutans [84, 85]. The corresponding gene expression was not found to be linked with a particular species. However, the fact that S. mutans is resistant to the toxin and the observation that S. mutans is strongly correlated with both transcripts for this toxin, supports our hypothesis that S. mutans is responsible for the expression of the bacteriocin. The acidified oral cavity of T1DM patients, originally due to the host pathophysiology [42,43,44], according to our data, leads to the decreased abundance of acid-intolerant bacteria and favours the growth of acid-tolerant pathogenic S. mutans, which then further acidifies the environment and outcompetes the commensal S. salivarius by expressing a target-specific bacteriocin.

Streptococcus salivarius’ abundance decreases in the gut favouring an inflamed environment and an enterobacterial bloom

The differential abundance analysis of the gut-derived multi-omic data showed few differences between conditions. The lower abundance of S. salivarius in the gut follows the trend we observed in the oral cavity (Supplementary Table 2). S. salivarius colonises the intestine of adults and contributes to gut homeostasis by anti-inflammatory effects as well as by preventing the bloom of pathogens [86,87,88]. Previous studies have shown that a S. salivarius strain isolated from the oral cavity was able to prevent inflammatory responses both in vitro and in vivo by significantly reducing the activation of NF-κB and IL-8 secretion in intestinal epithelial and immune cell lines [86, 89, 90]. Therefore, a decrease of S. salivarius abundance may culminate in a more inflamed gut environment.

We also observed an increased abundance in the Escherichia coli (Enterobacteria) in the gut (Supplementary Table 2). Enterobacteria are among the most commonly overgrowing potential pathobionts whose expansion is associated with many diseases and, in particular, inflammation [91].

By investigating gene expression in the gut, we found multiple differentially expressed genes in T1DM in comparison to healthy controls (Fig. 3). Strikingly, a majority of the overexpressed genes are associated with Enterobacteria indicating a strong activity of this group in T1DM patients. They are usually found in low abundance in the gut in close proximity to the mucosal epithelium due to their facultative anaerobic metabolism [92]. Enterobacteria are also well known to have their growth favoured in many conditions involving inflammation [93]. The identified overexpressed genes contribute to bacterial virulence, oxidative stress response, cell motility and biofilm formation, and general replication and growth. Notably, an upregulation of a catalase-peroxidase was identified, an enzyme that detoxifies reactive oxygen intermediates such as H2O2 and, thus, is involved in protection against oxidative stress produced by the host. Enzymes associated with biofilm formation (YliH) were also overexpressed. Finally, OmpA-like transmembrane domain was identified as well the protein HokC/D, which corresponds to the E. coli toxin-antitoxin system that ensures the transmission of the associated plasmid.

Fig. 3
figure 3

Differential gene expression analysis within the gut in T1DM. Difference in expression using metatranscriptomic data is shown on the volcano plot. A minimum log2 fold change of 2 (dashed vertical lines) and adjusted p value of 0.05 (dashed horizontal line) were required (red dot). Functions that satisfy only the fold change or the adjusted p value threshold are displayed in green and blue, respectively. Diamonds and circles respectively indicate complementary annotations from both the Pfam and KEGG databases. Genes associated with Enterobacteria are marked in pink

There are multiple possible mechanisms of inflammation-driven blooms of Enterobacteria in the gut. One of them relies on the inflammatory host response that produces a potent antimicrobial agent (peroxynitrite) which is quickly converted to nitrate and can then be used for bacterial growth through nitrate respiration [93]. Since the genes encoding nitrate reductase in the gut are mostly encoded by Enterobacteria, this nitrate-rich environment provides a growth advantage for Enterobacteria such as E. coli. In addition to the genes involved in oxidative stress, we also found the molybdopterin oxidoreductase 4Fe-4S domain to be overexpressed in T1DM (Fig. 3 and Supplementary Table 3). This domain is found in a number of reductase/dehydrogenase families and notably the respiratory nitrate reductase in E. coli which further supports our hypothesis of inflamed gut in the context of T1DM. Increased abundance of Enterobacteria in T1DM has been partially observed before but the signal was not necessarily clear [94] or was associated with confounding factors like antibiotic-induced acceleration of T1DM [95] and no functional evidence were found.

Additionally, we looked at the effect of T1DM on the abundance of human proteins in the gut. We hypothesised that inflammation of the gut would lead to higher abundances of proteins involved in the host immune response. Interestingly, we mostly found evidence of exocrine pancreatic insufficiency with several types of proteases, such as pancreatic carboxypeptidases, elastases or trypsin-related enzymes, being less abundant in T1DM (Fig. 4 and Supplementary Table 4) which can be associated with T1DM [96]. One protein involved in the host immune response, the polymeric immunoglobulin receptor (pIgR), was found at elevated levels in T1DM (log2 fold change 0.42 at p val < 0.05) (Fig. 4 and Supplementary Table 4). pIgR is a transmembrane protein expressed by epithelial cells and responsible for the transcytosis of the secreted polymeric IgA produced in the mucosa by plasma cells to the gut lumen [97, 98]. Binding of polymeric IgA to the microbial surface protects the intestinal mucosa by preventing attachment to the epithelial cells, thus inhibiting infection and colonisation. When looking at differentially expressed proteins taking all visits as independent samples into account (see “Methods” section), we found similar proteins as when using median information but also several additional proteins associated with the host immune response and inflammation to be more expressed in T1DM (Supplementary Fig. 3 and Supplementary Table 5). While that approach is statically less robust (see “Methods” section), it allows to observe additional trends in the dataset. Notably, we found higher levels of the lipocalin 2 enzyme (LCN2) (log2 fold change 0.37 at p val < 0.05) which is a typical biomarker in human inflammatory disease [99] and has been associated with metabolic disorders such as obesity and diabetes [100,101,102]. The analysis also confirmed the higher expression of the lactotransferrin (LTF) (log2 fold change 0.76 at p val < 0.05), which was already found in our previous study [46]. LTF plays a role in innate immunity and insulin function [103, 104] and its antimicrobial activity can influence the gastrointestinal microbiota [105].

Fig. 4
figure 4

Human proteome differences in T1DM. Heatmap displaying the relative abundances of human proteins with the highest significance in a differential analysis of T1DM versus healthy individuals (unadjusted p value < 0.05). The samples are ordered by conditions. Healthy individuals and T1DM patients are respectively shown in orange and blue boxes

Multi-omics integration highlights the transfer and the activity of bacteria from the oral cavity to the gut

Since a lower abundance of S. salivarius was found in both the oral cavity and the gut, we sought to explore the transmission between both extremities of the gastrointestinal tract and assess the levels of transfer in our cohort. To do so, we identified and followed genomic variations with read support from both the oral cavity and the gut (see “Methods”). In contrast to a previous study that only looked at the transmission using MG-based strain-variants [41], we additionally took advantage of the MT and/or MP data to identify not only transferred but also functionally active strain-variants. Furthermore, while MG and MT analyses are based on sequencing, metaproteomics provides an independent layer of information based on peptides and mass-spectrometry analyses. This provides the opportunity to strongly validate identified transferred missense variants by identifying the translated protein with the variant amino acid sequence. Using first all genomic variants (synonymous and missense) with read support from both the oral cavity and the gut, we identified the genera Prevotella and Bacteroides to be transferred and active at the MT level in the gut in the majority of our cohort (Fig. 5A). The genus Prevotella is relatively common and abundant in the oral cavity but less prevalent in the gut. Finding it to be transferred and active is thus not surprising. In contrast, while the genus Bacteroides is strongly abundant in the gut, it is rarely identified in the oral cavity [9, 106]. Indeed, in our study, the signal observed from Bacteroides mostly came from a few particular individuals and was not representative of the entire cohort.

Fig. 5
figure 5

Identified variants of genera across multiple omes. The figure indicates the distribution of reads for metagenomic (MG) and metatranscriptomic (MT) abundance, and spectra for metaproteomic (MP) abundance for each set of variants associated with a taxa. The numbers on top of each box indicate the number of identified variants, the number of samples in which variants have been identified and the median number of variants per sample. A and B correspond to the MG-MT supported variants while C and D show the MG-only supported variants. Comparisons of distributions were also performed and are represented by a light orange (healthy controls) and a light blue box (T1DM patients)

Remarkably, we identified several peptides supporting strain-variants at the MP level (Fig. 5B), showing that we could follow, and thus validate, variants across all three omic layers. Whilst the number of variant-supporting peptides is relatively low (due notably to the typical lower depth of MP or expected lower abundance of variant peptides), their identification confirms that the taxa we find to be transferred from the oral cavity are also active in the gut. Strain-variants belonging to the genus Bacteroides is not identified anymore at the variant peptide level, which can be explained by the low number of samples in which Bacteroides was identified. More surprising is the absence of the Streptococcus genus using MT-supported variants but its presence at the MP level. This indicates that the representation of strain-variants belonging to the genus Streptococcus was too low at the MT but not at the MP level to be detected over their respective threshold (see “Methods” section and Supplementary Table 1). Additionally, Streptococci are known to inhabit the upper part (small intestine) of the gut rather than the lower part (colon) [107, 108]. As RNA transcripts are less stable than proteins, it is not surprising that only peptides are identifiable from taxa active in the upper gut. We thus hypothesised that the applied strict MT read abundance threshold might be too stringent to identify transferred bacteria active in the upper part of the gut and that MP support would be more appropriate. To test this, we used missense variants with only MG read support and performed the metaproteomic search including the new protein variants. We distinguish variants supported by MG from the oral cavity and MG and MT from the gut (referred to as MG-MT supported variants) and variants supported only by MG from the oral cavity and MG from the gut (referred to as MG-only supported variants). Both types of variants can be further supported at the MP level (Fig. 5B, D).

By applying only the MG support criterion, around 10 times more variants across 81 samples were found and additional genera including Alistipes, Bifidobacterium, and Faecalibacterium were identified as transferred. With the exception of Faecalibacterium, all those taxa are commonly found at both body sites [9]. As hypothesised, strain-variants belonging to the genus Streptococcus were now found at the MG level (Fig. 5C). Adding the MP layer notably confirmed the presence and the activity of the Streptococcus strain-variants while those from the genera Alistipes and Faecalibacterium (initially not found by MG-MT variants) are not found (Fig. 5D). Metaproteomics thus essentially supports and validates the variants detected via the others omes, either due to the higher stability of proteins or to metaproteomics’ different and independent technology (e.g. it does not suffer from sequencing errors). Furthermore, as proteins are immunogenic, using metaproteomics to detect strain-variant peptides adds a valuable layer of information as proteins from the oral cavity may fuel inflammation in the large intestine.

Streptococcus is less transmitted in T1DM in comparison to healthy controls

Being able to identify and follow variants across all omic layers and both body sites allowed us to assess the level of transfer of the different identified taxa. Streptococcus salivarius was found to be less abundant and less active in both the oral cavity and the gut in TIDM. While the difference is not significant, a similar trend can be observed at the transfer level for the Streptococcus genus. Not only does Streptococcus seem less transferred at the MG-only level (Fig. 5C), this trend seems to be further supported by lower amount of peptides, and thus a lower activity, associated to Streptococcus at the metaproteomic level using both the MG-MT supported variants and the MG-only supported variants (Fig. 5B, D). This suggests that the lower abundance of S. salivarius in the oral cavity and in the gut may indeed be connected. However, the lack of taxonomic resolution due to the method employed prevents strong conclusions. Further analyses should use a common assembly for all samples and be fully resolved at the species level to validate our findings.

Transmission levels strongly correlate with taxa abundances in the gut but not in the oral cavity

Correlation analyses between the MG and MT levels of the transferred bacteria and their abundance in the oral cavity and in the gut were performed in order to verify if the taxa abundances at both extremities of the gastrointestinal tract were associated. Strong positive correlations (rs = 0.6–0.7 at p value < 0.05) were found between the abundance (MG and MG_Only) of the transferred bacteria and their abundance in the gut, which indicates that the levels of transfer indeed influences the final abundance of the taxa in the gut (Fig. 6). The activities (MT) were also positively correlated but at lower values (rs = 0.4–0.5 at p value < 0.05). Interestingly, no correlations were found between the oral MG abundance of the taxa and their level of transfer (Supplementary Fig. 4), which is consistent with the correlations found in our previous study [41]. This would suggest that the transfer rate does not simply depend on the original abundance of the taxa in the oral cavity, but rather is driven by other parameters. For example, the host physiology of the oral cavity (saliva flow-rate, glucose concentration, pH) might affect the levels of transmission along the gastrointestinal tract as well as the microbial physiology (e.g. low pH and bile acids tolerance). We therefore looked at correlations between the level of transfer and the available metadata but no strong significant correlations were found (Supplementary Fig. 5, Supplementary Data 1).

Fig. 6
figure 6

Correlations between the abundances of transferred taxa in comparison to the abundance in the gut. The figure shows the correlation between the transfer and the gut abundances. Abundances of taxa with either MG or MT labels correspond to the abundances of supported variants at the metagenomic and metatranscriptomic levels. MGonly is used if variants were supported with MG reads only and not on the MT level. Colored values indicate positive (blue) or negative (red) significant correlations (adj. p value < 0.05). Values with white background indicate non-significant correlations

Conclusions

In this study, we looked at the microbiota of two important body sites at both extremes of the gastrointestinal tract, the oral cavity and the gut, and identified differences in composition, function, and transfer of bacterial taxa in a case study of familial T1DM.

In the oral cavity of T1DM patients, the abundances of different taxa strongly resembled an acidified cavity. Notably, we found a lower abundance and activity of the commensal acid-intolerant S. salivarius and a higher activity of the acid-tolerant pathogenic S. mutans, which additionally correlated with the expression of a bacteriocin, highlighting competition between the two Streptococci species (Fig. 2).

In the gut, we observed lower abundance of S. salivarius and higher abundance of E. coli as well as an overall increased expression of genes involved in bacterial virulence and oxidative stress response related to the Enterobacteriaceae family (Fig. 3). Besides the increased abundance and activity of Enterobacteria, we found further evidence of gut inflammation in T1DM through the overexpression of several human proteins involved either in the host immune response or inflammation (Fig. 4 and Supplementary Fig. 3).

The multi-omic data for both body sites enabled us for the first time to trace the variants and taxa across all three omic layers and thus to identify specific taxa that were both transmitted along the gastrointestinal tract, and active in the gut. This strengthened the identification of transmitted variants and brought additional evidence on actual gut colonisation by oral bacteria. We found multiple genera to be transmitted and we have highlighted the importance of using functional omic support to identify taxa active in the gut (Fig. 5). We also discussed the limitations inherent to metatranscriptomics and highlighted how metaproteomics can be advantageously used to validate identified variants and explore the upper part of the gut.

By contextualising the information concerning oral to gut transfer in T1DM, we notably found a trend of lower levels of transmission of Streptococcus in T1DM patients, thereby reinforcing the notion that the lower abundance of S. salivarius in the oral cavity and the gut are indeed connected and both in relation to T1DM (Fig. 5B, D). However, correlations between the levels of transmission of taxa and their abundance at both body sites showed strong correlations with the gut but not with the oral cavity (Fig. 6 and Supplementary Fig. 4). As the physiology of the oral cavity is altered in T1DM patients [42,43,44], we would hypothesise that some of those factors (e.g. saliva flow-rate, glucose concentration, pH) might have a stronger influence on the transmission rate of oral microbes along the gastrointestinal tract than just their initial abundances. A follow-up study could combine different metadata measurements of the oral cavity together with the newly developed strain-variant methodology and assess if any physiological parameter influences the abundance of particular variants and their transmission rate along the gastrointestinal tract.

Availability of data and materials

Metagenomic and metatranscriptomic sequencing reads can be accessed from NCBI BioProject PRJNA289586. All mass spectrometry proteomics data and results were deposited to the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) via the PRIDE [109] partner repository with the data set identifier PXD031579. All custom scripts are available at https://git-r3lab.uni.lu/ESB/must2.

References

  1. Gilbert JA, et al. Current understanding of the human microbiome. Nat Med. 2018;24:392–400.

    Article  CAS  Google Scholar 

  2. Karczewski J, Poniedziałek B, Adamski Z, Rzymski P. The effects of the microbiota on the host immune system. Autoimmunity. 2014;47:494–504.

    Article  CAS  Google Scholar 

  3. Zheng D, Liwinski T, Elinav E. Interaction between microbiota and immunity in health and disease. Cell Res. 2020;30:492–506.

    Article  Google Scholar 

  4. Duvallet C, Gibbons SM, Gurry T, Irizarry RA, Alm EJ. Meta-analysis of gut microbiome studies identifies disease-specific and shared responses. Nat Commun. 2017;8:1784.

    Article  Google Scholar 

  5. Gilbert JA, et al. Microbiome-wide association studies link dynamic microbial consortia to disease. Nature. 2016;535:94–103.

    Article  CAS  Google Scholar 

  6. Wen L, et al. Innate immunity and intestinal microbiota in the development of Type 1 diabetes. Nature. 2008;455:1109–13.

    Article  CAS  Google Scholar 

  7. Hooper LV, Littman DR, Macpherson AJ. Interactions between the microbiota and the immune system. Science. 2012;336:1268–73.

    Article  CAS  Google Scholar 

  8. Honda K, Littman DR. The microbiota in adaptive immune homeostasis and disease. Nature. 2016;535:75–84.

    Article  CAS  Google Scholar 

  9. Human Microbiome Project Consortium. Structure, function and diversity of the healthy human microbiome. Nature. 2012;486:207–14.

    Article  Google Scholar 

  10. Lloyd-Price J, et al. Strains, functions and dynamics in the expanded Human Microbiome Project. Nature. 2017;550:61–6.

    Article  CAS  Google Scholar 

  11. Van Rossum T, Ferretti P, Maistrenko OM, Bork P. Diversity within species: interpreting strains in microbiomes. Nat Rev Microbiol. 2020;18:491–506.

    Article  Google Scholar 

  12. Caro-Quintero A, Konstantinidis KT. Bacterial species may exist, metagenomics reveal. Environ Microbiol. 2012;14:347–55.

    Article  CAS  Google Scholar 

  13. Denef VJ. Peering into the genetic makeup of natural microbial populations using metagenomics. In: Polz MF, Rajora OP, editors. Population genomics: microorganisms. New York City: Springer; 2019. p. 49–75.

  14. Zojer M, et al. Variant profiling of evolving prokaryotic populations. PeerJ. 2017;5:e2997.

    Article  Google Scholar 

  15. Wang Z, et al. Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease. Nature. 2011;472:57–63.

    Article  CAS  Google Scholar 

  16. Frank DN, et al. Molecular-phylogenetic characterization of microbial community imbalances in human inflammatory bowel diseases. Proc Natl Acad Sci U S A. 2007;104:13780–5.

    Article  CAS  Google Scholar 

  17. Turnbaugh PJ, et al. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature. 2006;444:1027–31.

    Article  Google Scholar 

  18. Garrett WS. Cancer and the microbiota. Science. 2015;348:80–6.

    Article  CAS  Google Scholar 

  19. Spielman LJ, Gibson DL, Klegeris A. Unhealthy gut, unhealthy brain: the role of the intestinal microbiota in neurodegenerative diseases. Neurochem Int. 2018;120:149–63.

    Article  CAS  Google Scholar 

  20. Zhang X, et al. The oral and gut microbiomes are perturbed in rheumatoid arthritis and partly normalized after treatment. Nat Med. 2015;21:895–905.

    Article  CAS  Google Scholar 

  21. Paun A, Yau C, Danska JS. The influence of the microbiome on type 1 diabetes. J Immunol. 2017;198:590–5.

    Article  CAS  Google Scholar 

  22. Sharma S, Tripathi P. Gut microbiome and type 2 diabetes: where we are and where to go? J Nutr Biochem. 2019;63:101–8.

    Article  CAS  Google Scholar 

  23. Diaz-Valencia PA, Bougnères P, Valleron A-J. Global epidemiology of type 1 diabetes in young adults and adults: a systematic review. BMC Public Health. 2015;15:255.

    Article  Google Scholar 

  24. Dabelea D. The accelerating epidemic of childhood diabetes. Lancet. 2009;373:1999–2000.

    Article  Google Scholar 

  25. Patterson CC, et al. Incidence trends for childhood type 1 diabetes in Europe during 1989-2003 and predicted new cases 2005-20: a multicentre prospective registration study. Lancet. 2009;373:2027–33.

    Article  Google Scholar 

  26. Mobasseri M, et al. Prevalence and incidence of type 1 diabetes in the world: a systematic review and meta-analysis. Health Promot Perspect. 2020;10:98–115.

    Article  Google Scholar 

  27. Rewers M, Ludvigsson J. Environmental risk factors for type 1 diabetes. Lancet. 2016;387:2340–8.

    Article  CAS  Google Scholar 

  28. Rooks MG, Garrett WS. Gut microbiota, metabolites and host immunity. Nat Rev Immunol. 2016;16:341–52.

    Article  CAS  Google Scholar 

  29. Brown CT, et al. Gut microbiome metagenomics analysis suggests a functional model for the development of autoimmunity for type 1 diabetes. PLoS ONE. 2011;6:e25792.

    Article  CAS  Google Scholar 

  30. Alkanani AK, et al. Alterations in intestinal microbiota correlate with susceptibility to type 1 diabetes. Diabetes. 2015;64:3510–20.

    Article  CAS  Google Scholar 

  31. Giongo A, et al. Toward defining the autoimmune microbiome for type 1 diabetes. ISME J. 2011;5:82–91.

    Article  CAS  Google Scholar 

  32. Jamshidi P, et al. Is there any association between gut microbiota and type 1 diabetes? A systematic review. Gut Pathog. 2019;11:49.

    Article  Google Scholar 

  33. Xiao J, Fiscella KA, Gill SR. Oral microbiome: possible harbinger for children’s health. Int J Oral Sci. 2020;12:12.

    Article  Google Scholar 

  34. Hajishengallis G. Periodontitis: from microbial immune subversion to systemic inflammation. Nat Rev Immunol. 2015;15:30–44.

    Article  CAS  Google Scholar 

  35. Cullinan MP, Seymour GJ. Periodontal disease and systemic illness: will the evidence ever be enough? Periodontol 2000. 2013;62:271–86.

    Article  Google Scholar 

  36. Song I-S, et al. Severe periodontitis is associated with insulin resistance in non-abdominal obese adults. J Clin Endocrinol Metab. 2016;101:4251–9.

    Article  CAS  Google Scholar 

  37. Borgnakke WS, Ylöstalo PV, Taylor GW, Genco RJ. Effect of periodontal disease on diabetes: systematic review of epidemiologic observational evidence. J Periodontol. 2013;84:S135–52.

    Article  Google Scholar 

  38. Segata N, et al. Composition of the adult digestive tract bacterial microbiome based on seven mouth surfaces, tonsils, throat and stool samples. Genome Biol. 2012;13:R42.

    Article  CAS  Google Scholar 

  39. Martinsen TC, Bergh K, Waldum HL. Gastric juice: a barrier against infectious diseases. Basic Clin Pharmacol Toxicol. 2005;96:94–102.

    Article  CAS  Google Scholar 

  40. Ding T, Schloss PD. Dynamics and associations of microbial community types across the human body. Nature. 2014;509:357–60.

    Article  CAS  Google Scholar 

  41. Schmidt TS, et al. Extensive transmission of microbes along the gastrointestinal tract. Elife. 2019;8:e42693.

    Article  Google Scholar 

  42. Naing C, Mak JW. Salivary glucose in monitoring glycaemia in patients with type 1 diabetes mellitus: a systematic review. J Diabetes Metab Disord. 2017;16:2.

    Article  Google Scholar 

  43. Seethalakshmi C, Reddy RCJ, Asifa N, Prabhu S. Correlation of salivary pH, incidence of dental caries and periodontal status in diabetes mellitus patients: a cross-sectional study. J Clin Diagn Res. 2016;10:ZC12–4.

    CAS  Google Scholar 

  44. Gandara BK, Morton TH. Non-periodontal oral manifestations of diabetes: a framework for medical care providers. Diabetes Spectr. 2011;24:199–205.

    Article  Google Scholar 

  45. de Groot PF, et al. Distinct fecal and oral microbiota composition in human type 1 diabetes, an observational study. PLoS ONE. 2017;12:e0188475.

    Article  Google Scholar 

  46. Heintz-Buschart A, et al. Integrated multi-omics of the human gut microbiome in a case study of familial type 1 diabetes. Nat Microbiol. 2016;2:16180.

    Article  CAS  Google Scholar 

  47. Roume H, et al. A biomolecular isolation framework for eco-systems biology. ISME J. 2013;7:110–21.

    Article  CAS  Google Scholar 

  48. Kroniger T, et al. Proteome analysis of the Gram-positive fish pathogen Renibacterium salmoninarum reveals putative role of membrane vesicles in virulence. Res Square. 2021. https://doi.org/10.21203/rs.3.rs-744942/v1.

  49. Narayanasamy S, et al. IMP: a pipeline for reproducible reference-independent integrated metagenomic and metatranscriptomic analyses. Genome Biol. 2016;17:260.

    Article  Google Scholar 

  50. Li D, Liu C-M, Luo R, Sadakane K, Lam T-W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics. 2015;31:1674–6.

    Article  CAS  Google Scholar 

  51. Liao Y, Smyth GK, Shi W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics. 2014;30:923–30.

    Article  CAS  Google Scholar 

  52. Kanehisa M, Sato Y, Kawashima M, Furumichi M, Tanabe M. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. 2016;44:D457–62.

    Article  CAS  Google Scholar 

  53. El-Gebali S, et al. The Pfam protein families database in 2019. Nucleic Acids Res. 2019;47:D427–32.

    Article  CAS  Google Scholar 

  54. Gibson MK, Forsberg KJ, Dantas G. Improved annotation of antibiotic resistance determinants reveals microbial resistomes cluster by ecology. ISME J. 2015;9:207–16.

    Article  CAS  Google Scholar 

  55. Zhang H, et al. dbCAN2: a meta server for automated carbohydrate-active enzyme annotation. Nucleic Acids Res. 2018;46:W95–101.

    Article  CAS  Google Scholar 

  56. Burstein D, et al. New CRISPR-Cas systems from uncultivated microbes. Nature. 2017;542:237–41.

    Article  CAS  Google Scholar 

  57. Luo H, et al. DEG 15, an update of the Database of Essential Genes that includes built-in analysis tools. Nucleic Acids Res. 2021;49:D677–86.

    Article  CAS  Google Scholar 

  58. Milanese A, et al. Microbial abundance, activity and population genomic profiling with mOTUs2. Nat Commun. 2019;10:1014.

    Article  Google Scholar 

  59. Wood DE, Lu J, Langmead B. Improved metagenomic analysis with Kraken 2. Genome Biol. 2019;20:257.

    Article  CAS  Google Scholar 

  60. Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009;25:1754–60.

    Article  CAS  Google Scholar 

  61. Li H. Improving SNP discovery by base alignment quality. Bioinformatics. 2011;27:1157–8.

    Article  CAS  Google Scholar 

  62. Li H, et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009;25:2078–9.

    Article  Google Scholar 

  63. Hulstaert N, et al. ThermoRawFileParser: modular, scalable, and cross-platform RAW file conversion. J Proteome Res. 2020;19:537–42.

    Article  CAS  Google Scholar 

  64. Chambers MC, et al. A cross-platform toolkit for mass spectrometry and proteomics. Nat Biotechnol. 2012;30:918–20.

    Article  CAS  Google Scholar 

  65. Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics. 2014;30:2068–9.

    Article  CAS  Google Scholar 

  66. Shen W, Le S, Li Y, Hu F. SeqKit: a cross-platform and ultrafast toolkit for FASTA/Q file manipulation. PLoS ONE. 2016;11:e0163962.

    Article  Google Scholar 

  67. Guo X, et al. Sipros Ensemble improves database searching and filtering for complex metaproteomics. Bioinformatics. 2018;34:795–802.

    Article  CAS  Google Scholar 

  68. Simpson EH. Measurement of diversity. Nature. 1949;163:688.

    Article  Google Scholar 

  69. Kembel SW, et al. Picante: R tools for integrating phylogenies and ecology. Bioinformatics. 2010;26:1463–4.

    Article  CAS  Google Scholar 

  70. Dixon P. VEGAN, a package of R functions for community ecology. J Veg Sci. 2003;14:927–30.

    Article  Google Scholar 

  71. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550.

    Article  Google Scholar 

  72. Abranches J, et al. Biology of oral Streptococci. Microbiol Spectr. 2018;6(5):6–5.

    Article  Google Scholar 

  73. Nes IF, Diep DB, Holo H. Bacteriocin diversity in Streptococcus and Enterococcus. J Bacteriol. 2007;189:1189–98.

    Article  CAS  Google Scholar 

  74. Mignolet J, et al. Circuitry rewiring directly couples competence to predation in the gut dweller Streptococcus salivarius. Cell Rep. 2018;22:1627–38.

    Article  CAS  Google Scholar 

  75. Hibbing ME, Fuqua C, Parsek MR, Peterson SB. Bacterial competition: surviving and thriving in the microbial jungle. Nat Rev Microbiol. 2010;8:15–25.

    Article  CAS  Google Scholar 

  76. Dedrick S, et al. The role of gut microbiota and environmental factors in type 1 diabetes pathogenesis. Front Endocrinol. 2020;11:78.

    Article  Google Scholar 

  77. Babatzia A, et al. Clinical and microbial oral health status in children and adolescents with type 1 diabetes mellitus. Int Dent J. 2020;70:136–44.

    Article  Google Scholar 

  78. Garnett JA, et al. Structural insight into the role of Streptococcus parasanguinis Fap1 within oral biofilm formation. Biochem Biophys Res Commun. 2012;417:421–6.

    Article  CAS  Google Scholar 

  79. Takahashi N, Saito K, Schachtele CF, Yamada T. Acid tolerance and acid-neutralizing activity of Porphyromonas gingivalis, Prevotella intermedia and Fusobacterium nucleatum. Oral Microbiol Immunol. 1997;12:323–8.

    Article  CAS  Google Scholar 

  80. Takahashi N. Oral microbiome metabolism: from ‘who are they?’ to ‘what are they doing?’ J Dent Res. 2015;94:1628–37.

    Article  CAS  Google Scholar 

  81. Lemos JA, et al. The biology of Streptococcus mutans. Microbiol Spectr. 2019;7. https://doi.org/10.1128/microbiolspec.GPP3-0051-2018.

  82. Matsui R, Cvitkovitch D. Acid tolerance mechanisms utilized by Streptococcus mutans. Future Microbiol. 2010;5:403–17.

    Article  CAS  Google Scholar 

  83. Liu Y-L, Nascimento M, Burne RA. Progress toward understanding the contribution of alkali generation in dental biofilms to inhibition of dental caries. Int J Oral Sci. 2012;4:135–40.

    Article  Google Scholar 

  84. Wirawan RE, Swanson KM, Kleffmann T, Jack RW, Tagg JR. Uberolysin: a novel cyclic bacteriocin produced by Streptococcus uberis. Microbiology. 2007;153:1619–30.

    Article  CAS  Google Scholar 

  85. Gabrielsen C, Brede DA, Nes IF, Diep DB. Circular bacteriocins: biosynthesis and mode of action. Appl Environ Microbiol. 2014;80:6854–62.

    Article  Google Scholar 

  86. Kaci G, et al. Anti-inflammatory properties of Streptococcus salivarius, a commensal bacterium of the oral cavity and digestive tract. Appl Environ Microbiol. 2014;80:928–34.

    Article  Google Scholar 

  87. Villmones HC, et al. Species level description of the human ileal bacterial microbiota. Sci Rep. 2018;8:4736.

    Article  Google Scholar 

  88. Couvigny B, et al. Commensal Streptococcus salivarius modulates PPARγ transcriptional activity in human intestinal epithelial cells. PLoS ONE. 2015;10:e0125371.

    Article  Google Scholar 

  89. Cosseau C, et al. The commensal Streptococcus salivarius K12 downregulates the innate immune responses of human epithelial cells and promotes host-microbe homeostasis. Infect Immun. 2008;76:4163–75.

    Article  CAS  Google Scholar 

  90. Kaci G, et al. Inhibition of the NF-kappaB pathway in human intestinal epithelial cells by commensal Streptococcus salivarius. Appl Environ Microbiol. 2011;77:4681–4.

    Article  CAS  Google Scholar 

  91. Winter SE, Bäumler AJ. Dysbiosis in the inflamed intestine: chance favors the prepared microbe. Gut Microbes. 2014;5:71–3.

    Article  Google Scholar 

  92. Brenner DJ, Farmer JJ III. Enterobacteriaceae. In: Bergey’s manual of systematics of archaea and bacteria. 2015. p. 1–24. https://doi.org/10.1002/9781118960608.fbm00222.

    Chapter  Google Scholar 

  93. Zeng MY, Inohara N, Nuñez G. Mechanisms of inflammation-driven bacterial dysbiosis in the gut. Mucosal Immunol. 2017;10:18–26.

    Article  CAS  Google Scholar 

  94. Soyucen E, et al. Differences in the gut microbiota of healthy children and those with type 1 diabetes. Pediatr Int. 2014;56:336–43.

    Article  Google Scholar 

  95. Zhang X-S, et al. Antibiotic-induced acceleration of type 1 diabetes alters maturation of innate intestinal immunity. Elife. 2018;7:e37816.

    Article  Google Scholar 

  96. Campbell-Thompson M, Rodriguez-Calvo T, Battaglia M. Abnormalities of the exocrine pancreas in type 1 diabetes. Curr Diab Rep. 2015;15:79.

    Article  Google Scholar 

  97. Kaetzel CS. The polymeric immunoglobulin receptor: bridging innate and adaptive immune responses at mucosal surfaces. Immunol Rev. 2005;206:83–99.

    Article  CAS  Google Scholar 

  98. Kaetzel CS, Robinson JK, Chintalacharuvu KR, Vaerman JP, Lamm ME. The polymeric immunoglobulin receptor (secretory component) mediates transport of immune complexes across epithelial cells: a local defense function for IgA. Proc Natl Acad Sci U S A. 1991;88:8796–800.

    Article  CAS  Google Scholar 

  99. Moschen AR, Adolph TE, Gerner RR, Wieser V, Tilg H. Lipocalin-2: a master mediator of intestinal and metabolic inflammation. Trends Endocrinol Metab. 2017;28:388–97.

    Article  CAS  Google Scholar 

  100. Guo H, et al. Lipocalin 2, a regulator of retinoid homeostasis and retinoid-mediated thermogenic activation in adipose tissue. J Biol Chem. 2016;291:11216–29.

    Article  CAS  Google Scholar 

  101. Bhusal A, Rahman MH, Lee I-K, Suk K. Role of hippocampal lipocalin-2 in experimental diabetic encephalopathy. Front Endocrinol. 2019;10:25.

    Article  Google Scholar 

  102. Arellano-Buendía AS, et al. Urinary excretion of neutrophil gelatinase-associated lipocalin in diabetic rats. Oxid Med Cell Longev. 2014;2014:961326.

    Article  Google Scholar 

  103. Legrand D, et al. Lactoferrin structure and functions. Adv Exp Med Biol. 2008;606:163–94.

    Article  CAS  Google Scholar 

  104. Akiyama Y, et al. A lactoferrin-receptor, intelectin 1, affects uptake, sub-cellular localization and release of immunochemically detectable lactoferrin by intestinal epithelial Caco-2 cells. J Biochem. 2013;154:437–48.

    Article  CAS  Google Scholar 

  105. Bertuccini L, et al. Lactoferrin prevents invasion and inflammatory response following E. coli strain LF82 infection in experimental model of Crohn’s disease. Dig Liver Dis. 2014;46:496–504.

    Article  CAS  Google Scholar 

  106. Dewhirst FE, et al. The human oral microbiome. J Bacteriol. 2010;192:5002–17.

    Article  CAS  Google Scholar 

  107. Zoetendal EG, et al. The human small intestinal microbiota is driven by rapid uptake and conversion of simple carbohydrates. ISME J. 2012;6:1415–26.

    Article  CAS  Google Scholar 

  108. Friedman ES, et al. Microbes vs. chemistry in the origin of the anaerobic gut lumen. Proc Natl Acad Sci U S A. 2018;115:4170–5.

    Article  CAS  Google Scholar 

  109. Perez-Riverol Y, et al. The PRIDE database and related tools and resources in 2019: improving support for quantification data. Nucleic Acids Res. 2019;47:D442–50.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

We would like to thank the European Molecular Biology Laboratory (EMBL) for its support. This work was supported by the Luxembourg National Research Fund (FNR) under grants CORE/15/BM/10404093, CORE/19/BM/13684739, PRIDE/11823097, and the European Research Council (ERC-CoG 863664) to P.W.

Author information

Authors and Affiliations

Authors

Contributions

B.J.K. and O.H contributed equally as the main authors of the manuscript; doing the data curation, analysis and visualisation, and writing the manuscript. L.A.L. and A.H-B. carried out the sample collection and the biomolecular extractions. R.H. performed the sequencing. D.B. and O.H. provided the metaproteomic data. P.Q. and P.M participated in the data processing and analysis. C.M-G., A.H-B, T.S.B.D.S., and P.M. helped with the data interpretation. T.S.B.D.S., M.R.H. C.d.B., and P.B. P.W. conceived the study and participated in its design. PM and P.W. coordinated the study. B.J.K, O.H., A.H-B., P.M., and P.W. wrote the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to B. J. Kunath or P. Wilmes.

Ethics declarations

Ethics approval and consent to participate

This study was approved by the Comité d'Ethique de Recherche (CNER; reference no. 201110/05) and the National Commission for Data Protection in Luxembourg. Written informed consent was obtained from all subjects enrolled in the study.

Consent for publication

Not applicable.

Competing interests

All authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1: Supplementary Data 1.

Metadata.

Additional file 2: Supplementary Table 1.

Summary statistics of metrics extracted from IMP.

Additional file 3: Supplementary Figure 1.

Oral and gut community structure analysis. Box plots of species richness (a) and alpha diversity (b) between controls and T1DM patients. Hierarchical clustering based on Bray-Curtis distance (c). NMDS ordination based on Bray-Curtis distance (d). All analyses are based on metagenomic data. Supplementary Figure 2. Correlation network of gene transcripts from all samples and differentially active taxa in the oral cavity. The figure corresponds to a subset of the correlation analysis where only the differentially abundant taxa and correlation over 0.7 are plotted. Green and red nodes indicate if the taxon is up- or down-regulated in the oral cavity. Annotations are based on the Pfam database. Supplementary Figure 3. Metaproteomic differences in T1DM at the visit level. Heatmap displaying the relative abundances of human proteins at the visit level with the highest significance in a differential analysis of T1DM. The samples are ordered by conditions. Healthy individuals and T1DM patients are respectively shown in orange and blue boxes. Supplementary Figure 4. Correlations between the abundances of transferred taxa in comparison to their abundance in the oral cavity. The figure shows the correlation between the transfer and the oral cavity. The labels with MG and MT correspond to the abundances at the metagenomic and metatranscriptomic levels for the MG-MT supported variants. MG_only is used for the MG abundances of variants supported by MG reads only and not on the MT level. Colored squares indicate a positive (blue) or negative (red) significant correlations (pval < 0.05). White squares indicate non-significant correlations. Supplementary Figure 5. Correlation analyses of transferred taxa and metadata. The figure shows the correlation between the transfer and the available metadata. The labels with MG and MT correspond to the abundances at the metagenomic and metatranscriptomic levels for the MG-MT supported variants. MG_only is used for the MG abundances of variants supported by MG reads only and not on the MT level. Colored squares indicate a positive (blue) or negative (red) significant correlations (p-val < 0.05). White squares indicate non-significant correlations.

Additional file 4: Supplementary Table 2.

Eubacterium siraeum DSM 15702.

Additional file 5: Supplementary Table 3.

Genera.

Additional file 6: Supplementary Table 4.

Differentially expressed human proteins using median data.

Additional file 7: Supplementary Table 5.

Differentially expressed human proteins using all individuals and visits.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Kunath, B.J., Hickl, O., Queirós, P. et al. Alterations of oral microbiota and impact on the gut microbiome in type 1 diabetes mellitus revealed by integrated multi-omic analyses. Microbiome 10, 243 (2022). https://doi.org/10.1186/s40168-022-01435-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s40168-022-01435-4