Edited by: Gabriele Berg, Graz University of Technology, Austria
Reviewed by: Irene Wagner-Doebler, Helmholtz Centre for Infection Research, Germany; Catherine Maree Burke, University of Technology, Sydney, Australia
*Correspondence: Lisa Lindheim
This article was submitted to Microbial Symbioses, a section of the journal Frontiers in Microbiology
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Polycystic ovary syndrome (PCOS) is a common female endocrine condition affecting 6–18% of reproductive-age women and comprising the three primary symptoms hyperandrogenism, oligo/amenorrhoea, and polycystic ovarian morphology (Diamanti-Kandarakis et al.,
The etiology of PCOS is still unclear, although a multifactorial pathogenesis including genetic, lifestyle, and intrauterine factors has been suggested (Dumesic et al.,
We have investigated the microbiome of the proximal digestive tract as a possible indicator of disease in PCOS. Saliva offers several advantages over stool as a sample material for microbiome studies. These are the non-invasive on-site collection with little or no discomfort to the patient, the possibility for immediate processing and/or freezing following collection to conserve bacterial community structures, and the use of defined, reproducible sample volumes for DNA extraction. It has recently been shown that saliva microbiome profiles correlate with those in the stool, despite the fact that the bacterial communities in the two locations differ greatly (Ding and Schloss,
To our knowledge, there are no published studies of either the fecal or saliva microbiome in patients with PCOS using a next-generation sequencing approach. PCOS patients exhibit an increased prevalence of gingivitis, which was found to be accompanied by changes in certain oral bacterial species, assessed by qPCR (Akcali et al.,
We performed a pilot study to describe the salivary microbiome in PCOS and to investigate the potential of specific taxa and measures of bacterial diversity to distinguish between women with PCOS and healthy women. Additionally, we investigated the association of diagnostic (serum testosterone, oligo/amenorrhoea) and common co-occurring (overweight, insulin resistance, inflammation) features of PCOS with saliva microbiome parameters. Finally, we addressed the role of age and diet as possible confounding factors in saliva microbiome studies.
Twenty-five women with PCOS and 25 hormonally healthy controls were recruited from the endocrinological outpatient clinic at the University Hospital Graz. PCOS was diagnosed according to the Rotterdam Criteria, requiring the presence of two out of three of the following criteria: clinical/biochemical hyperandrogenism, oligo-/anovulation, and polycystic ovaries (Rotterdam ESHRE/ASRM-Sponsored PCOS consensus Workshop Group,
Study visits took place in the morning after an overnight fast. Study subjects were instructed not to brush their teeth and to drink only water prior to saliva sampling. Saliva was collected in the mouth for several minutes and then voided into Sali-Tubes (DRG Diagnostics, Marburg, Germany). This process was repeated until the desired volume of 1–2 ml was reached. Saliva samples were immediately cooled on ice, flash-frozen in liquid nitrogen, and stored at −70°C until further processing.
Anthropometric data were recorded and a baseline hormonal and metabolic assessment performed. Following the baseline blood sampling, a 2-h, 75 g oral glucose tolerance test (oGTT; Glucoral 75 Citron, Germania Pharmazeutika, Vienna, Austria) was performed and glucose and insulin were measured after 30, 60, and 120 min.
Estrone (E1), 17-estradiol (E2), total testosterone, androstenedione, dehydroepiandrosterone (DHEA), DHEAS, and dihydrotestosterone (DHT) were measured by liquid chromatography-tandem mass spectrometry at the Department of Clinical Chemistry at the University Hospital of South Manchester, Manchester, United Kingdom, as described by Keevil et al. (Chadwick et al.,
Insulin was measured by chemiluminescence immunoassay on the ADVIA Centaur XP (Roche, Rotkreuz, Switzerland). Anti-Muellerian hormone (AMH) was measured by chemiluminescence immunoassay on the Access2 (Beckman Coulter, Brea, USA). Luteinizing hormone (LH) and follicle-stimulating hormone (FSH) were measured by ELISA (both DiaSource, Louvain-la-Neuve, Belgium). Sex hormone-binding globulin (SHBG) was measured by chemiluminescence immunoassay on the Cobas e411 (Roche). Total cholesterol, high-density lipoprotein-cholesterol (HDL), triglycerides, and glucose were measured by enzymatic colorimetric assay on the Cobas c module (Roche). Serum high-sensitivity C-reactive protein (hs-CRP) was measured by ELISA (BioVendor, Brno, Czech Republic). A total and differential blood count was performed on the XE-5000 Hematology Analyzer (Sysmex, Vienna, Austria).
BMI was calculated as
A food frequency questionnaire designed by dieticians of the Clinical Medical Nutrition Therapy Unit, University Clinic Graz, was administered to assess the intake of major food groups. Based on the results of the questionnaire, study participants were categorized as consuming a high carbohydrate or high animal protein diet.
Total DNA was extracted from saliva samples using the MagNAPure LC DNA Isolation Kit III (Bacteria, Fungi) on the MagNA Pure Instrument (Roche, Rotkreuz, Switzerland). Saliva was thawed, vortexed, and 250 μl saliva was added to 250 μl bacteria lysis buffer in a sample tube containing MagNALyser Green Beads (1.4 mm diameter ceramic beads, Roche). Samples were homogenized in a MagNALyser Instrument (2 × 6000 rpm for 30 s, separated by 1 min cooling), treated with 25 μl lysozyme (Roth, Karlsruhe, Germany) at 37°C for 30 min, and then with 43.3 μl proteinase K (Roche) at 60°C for 1 h. Lysates were incubated at 95°C for 10 min, cooled on ice for 5 min, and centrifuged for 5 min at full speed. DNA was isolated from 200 μl lysate supernatant by the MagNAPure Instrument using the manufacturer's software and eluted in 100 μl elution buffer. A PCR reaction was performed to amplify the V1-2 region of the bacterial 16S rRNA gene using the primers F27 (AGAGTTTGATCCTGGCTCAG) and R357 (CTGCTGCCTYCCGTA; Eurofins Genomics, Ebersberg, Germany) and the FastStart High Fidelity PCR System, dNTPack (Roche) with initial denaturation at 95°C for 3 min followed by 28 cycles of denaturation at 95°C for 45 s, annealing at 55°C for 45 s, and extension at 72°C for 1 min, one cycle of final extension at 72°C for 7 min, and a final cooling step to 10°C. Triplicates were pooled, checked on a 1% agarose gel, and 15 μl of pooled PCR product were normalized according to manufacturer's instructions on a SequalPrep Normalization Plate (Life Technologies, Vienna, Austria). Fifteen microliters of the normalized PCR product were used as template for indexing PCR in a 50 μl single reaction to introduce barcode sequences to each sample according to Kozich et al. (
Raw reads were processed using the open-source software mothur V1.35.0 according to the protocol by Kozich et al. (April 2015), with the following adaptations: no maxlength was defined during the screening step, start (1046) and end (6426) positions were adapted to the V1-V2 region, and a difference of 3 bases was permitted during the precluster step (based on the recommendation by the authors to allow one mismatch per 100 bp; Kozich et al.,
Raw sequencing data are available in NCBI's short read archive (SRA) under the accession number
Nonparametric student's
All remaining statistical calculations were performed in IBM SPSS Statistics Version 22. Depending on the statistical distribution of the variable, unpaired
All 50 subjects included in the study provided saliva samples. Three subjects were excluded from the control group due to previously undetected hyperandrogenemia (elevation of two or more androgens in fasted blood sample), two subjects were excluded due to smoking prior to sampling, and one subject was excluded due to a BMI < 18. The final analyses were performed with 20 healthy controls and 24 PCOS patients.
Laboratory, anthropometric, and patient history data are summarized in Table
Age | 32 | 12.0 | 27 | 5.9 | 0.003 |
|
Body mass index | 18.5–25.0 |
22.3 | 4.10 | 24.9 | 11.75 | 0.147 |
Waist to hip ratio | <0.85 |
0.80 | 0.063 | 0.82 | 0.077 | 0.439 |
Fasting glucose (mmol/l) | <7.0 |
4.5 | 0.50 | 4.7 | 0.59 | 0.209 |
2h glucose (mmol/l) | <11.1 |
4.3 | 1.09 | 4.8 | 1.15 | 0.296 |
AUC glucose (mmolh/l) | 10.2 | 4.52 | 10.9 | 3.61 | 0.273 | |
Fasting insulin (pmol/l) | 20.9–173.8 | 41.4 | 51.08 | 84.4 | 55.25 | 0.022 |
2h insulin (pmol/l) | 129 | 140.0 | 188 | 336.7 | 0.371 | |
AUC insulin (mmolh/l) | 353 | 427.3 | 691 | 562.0 | 0.009 |
|
HOMA2-IR | <2 | 0.8 | 1.05 | 1.7 | 1.20 | 0.027 |
Total cholesterol (mmol/l) | <5.2 | 4.6 | 0.64 | 4.5 | 1.13 | 0.699 |
HDL-cholesterol (mmol/l) | >1.0 | 2.0 | 0.42 | 1.7 | 0.49 | 0.006 |
Triglycerides (mmol/l) | <1.65 | 0.59 | 0.248 | 0.74 | 0.242 | 0.010 |
Follicle-stimulating hormone (IU/l) | 0.5–61.2 |
9.2 | 8.11 | 7.5 | 2.73 | 0.178 |
Luteinizing hormone (IU/l) | 2.0–22.0 |
5.8 | 9.34 | 9.3 | 8.60 | 0.042 |
LH:FSH ratio | 1.2 | 1.19 | 1.5 | 1.06 | 0.035 |
|
Anti-Muellerian hormone (pmol/l) | 1.4–65.2 | 26.8 | 22.42 | 61.1 | 52.59 | <0.001 |
Total testosterone (nmol/l) | 0.37–2.12 | 1.1 | 0.56 | 1.3 | 0.77 | 0.002 |
Dihydrotestosterone (nmol/l) | 0.34 | 0.241 | 0.46 | 0.528 | 0.096 | |
Androstenedione (nmol/l) | 0.89–7.46 | 2.6 | 1.61 | 4.2 | 2.69 | <0.001 |
Dehydroepiandrosterone (nmol/l) | 13.7 | 11.37 | 21.4 | 12.40 | 0.015 |
|
Dehydroepiandrosterone sulfate (μmol/l) | 3.3 | 3.74 | 4.9 | 2.35 | 0.073 | |
Estrone (pmol/l) | 274 | 184.8 | 195 | 118.9 | 0.138 | |
17-Estradiol (pmol/l) | 436 | 285.8 | 163 | 181.1 | <0.001 |
|
Free androgen index | 1.3 | 0.68 | 3.1 | 2.75 | <0.001 |
|
Free testosterone (pmol/l) | 10.6 | 5.86 | 20.9 | 13.00 | <0.001 |
|
Free dihydrotestosterone (pmol/l) | 1.3 | 1.03 | 3.0 | 2.19 | <0.001 |
|
Total blood leukocytes (G/l) | 4.4–11.3 | 4.7 | 1.47 | 5.5 | 1.78 | 0.040 |
hsCRP (mg/l) | 0.5 | 0.70 | 0.8 | 3.97 | 0.078 | |
Polycystic ovarian morphology | 0 | 0 | 22 | 96 | <0.001 |
|
Hirsutism | 1 | 5 | 11 | 46 | 0.003 |
|
Oligo-/Amenorrhoea | 1 | 5 | 17 | 71 | <0.001 |
|
High carbohydrate diet | 8 | 40 | 9 | 38 | 0.555 | |
High animal protein diet | 12 | 60 | 15 | 63 | 0.555 |
A mock community containing genomic DNA from twenty bacterial species, representing 17 genera, was included in the 16S rRNA PCR and sequencing to estimate OTU inflation and classification bias due to sequencing errors. After removal of singleton OTUs, we detected 214 OTUs from 29 genera in the mock community sample, indicating an overestimation of the number of OTUs due to sequencing errors (Table
0.05 | 0.02 | −2.4 | |
0.05 | 0.03 | −1.6 | |
0.05 | 0.04 | −1.2 | |
0.05 | 0.12 | 2.4 | |
0.05 | 0.06 | 1.2 | |
0.05 | 0.04 | −1.2 | |
0.05 | 0.04 | −1.4 | |
0.05 | 0.02 | −2.2 | |
0.05 | 0.11 | 2.2 | |
0.05 | 0.06 | 1.3 | |
0.05 | 0.05 | 1.0 | |
0.05 | 0.06 | 1.3 | |
0.05 | 0.06 | 1.1 | |
0.05 | 0.02 | −2.7 | |
0.05 | 0.04 | −1.2 | |
0.10 | 0.09 | −1.2 | |
0.15 | 0.13 | −1.1 |
Using the 0.1% cutoff, all bacteria in the mock community were correctly classified at the family level, 15/17 at the genus level, and 7/20 at the species level (Supplementary Data Sheet
16S rRNA amplicon-based microbiome analysis was performed on saliva samples from 20 healthy controls and 24 PCOS patients, using an OTU relative abundance cutoff of 0.1%. A median of 80,555 (IQR 18,509) and 72,284 (IQR 20,330) paired-end Illumina reads were analyzed per sample in the control and PCOS groups, respectively (
The saliva microbiome was dominated by bacteria from the phylum Bacteroidetes (median relative abundance 45%) and Firmicutes (26%), while bacteria from the phyla Proteobacteria, Fusobacteria, Actinobacteria, and TM7 contributed <10% each to total bacterial content (Table
32.5 | 8.63 | 30.8 | 4.17 | 0.981 | |
10.3 | 2.69 | 12.2 | 8.29 | 0.740 | |
8.4 | 3.65 | 8.2 | 5.42 | 0.847 | |
7.1 | 7.67 | 6.5 | 5.75 | 0.740 | |
6.9 | 5.74 | 5.5 | 7.63 | 0.740 | |
3.8 | 3.44 | 5.4 | 4.11 | 0.749 | |
3.0 | 5.63 | 5.5 | 4.60 | 0.749 | |
4.5 | 3.31 | 2.7 | 2.33 | 0.726 | |
3.0 | 2.06 | 2.3 | 1.81 | 0.910 | |
2.1 | 4.00 | 1.5 | 2.25 | 0.740 | |
2.4 | 0.93 | 2.3 | 1.30 | 0.749 | |
1.5 | 1.04 | 1.4 | 0.83 | 0.740 | |
1.0 | 1.11 | 0.7 | 0.61 | 0.740 | |
0.9 | 0.76 | 0.7 | 0.69 | 0.941 | |
0.8 | 0.50 | 0.7 | 0.53 | 0.749 | |
0.8 | 0.78 | 0.6 | 0.75 | 0.740 | |
43.9 | 6.70 | 46.4 | 6.88 | 0.706 | |
24.8 | 6.21 | 27.1 | 7.98 | 0.706 | |
9.5 | 8.94 | 10.1 | 8.21 | 0.706 | |
7.2 | 4.58 | 7.3 | 5.37 | 0.706 | |
8.2 | 2.19 | 6.1 | 2.82 | 0.024 |
|
1.3 | 1.38 | 1.1 | 1.11 | 0.492 |
The cumulative curve of observed genus level abundances followed a long-tailed distribution, with the ten most abundant genera accounting for 86% of all identified bacteria (Figure
Faith's phylogenetic diversity and the number of observed OTUs did not differ between PCOS patients and controls (Figure
In beta diversity analyses, saliva samples showed a tendency toward a statistically significant clustering in unweighted UniFrac analysis (Figure
Grouping samples based on overweight, insulin resistance, hsCRP, blood leukocytes, age, and diet did not affect alpha diversity, beta diversity, or taxonomic composition (data not shown).
To our knowledge, this is the first study reporting a next-generation sequencing-based profile of the saliva microbiome in PCOS patients. The phyla and genera that were found to dominate saliva microbiome profiles in our study cohort correspond to those previously reported for healthy adults (Keijser et al.,
Actinobacteria, a phylum of gram-positive bacteria, are common members of the skin and oral microbiota and have been reported to be reduced in periodontal disease (Liu et al.,
Salivary microbiome profiles of PCOS patients showed a borderline significant clustering in unweighted UniFrac analysis, while weighted UniFrac distance matrices and alpha diversity metrics were not significantly different to controls. Several explanations exist for this apparent lack of pronounced differences. On average, the patients in our study displayed mild phenotypes of PCOS, in that serum androgens were only slightly elevated compared to controls, parameters related to glucose and lipid metabolism were within the normal range for most patients, and BMI was not significantly different from controls. We did not specifically recruit only lean or obese PCOS patients, as we were aiming for a broadly representative cohort of PCOS phenotypes. Shifts in the saliva microbiome may parallel the clinical phenotype, becoming more pronounced in the presence of severe hyperandrogenism and anovulation, either alone or in combination with obesity and/or manifest type 2 diabetes. Furthermore, the approach of this study provides only information on the presence of bacterial DNA, but not on bacterial function. Salivary bacteria may have altered gene expression patterns either in response or as a contributing factor to the biochemical changes observed in PCOS.
As it is known that the microbiome can be affected by many exogenous and endogenous factors (Goodrich et al.,
Recent research has indicated that the effect of microbiome “confounders” may be less significant than previously assumed. Studies by De Filippis et al. and Belstrøm et al. have reported no effect of age and diet on saliva microbiome profiles (Belstrøm et al.,
The Illumina approach which we selected is among those with the highest sequencing depth (Sims et al.,
The main strength of our study is the thorough characterization of our study cohort, which included an assessment of ovarian and adrenal androgens, lipid metabolism, and glucose tolerance. Furthermore, we applied strict exclusion criteria to ensure that no subjects with an undiagnosed mild form of PCOS or other hormonal imbalance were included in the control group and to eliminate factors which may influence the saliva microbiome, such as smoking, the use of antibiotics, and periodontal disease. A second strength is our sampling approach. We collected saliva samples after an overnight fast, avoiding a disturbance of the oral microbiome due to brushing teeth or using mouthwash. Samples were immediately frozen in liquid nitrogen to optimally conserve the bacterial community structure at the time of sampling. Finally, we used a mock community to evaluate the quality of the sequencing methodology and found that a large percentage of OTUs were most likely the result of sequencing errors. By removing these OTUs, we greatly improved the validity of our results. Since we thereby also removed a proportion of true sequences, we applied several filters at different relative abundance levels, as well as performing an analysis on unfiltered data, to attain a balanced interpretation of the bacterial composition of our samples. The significant result was obtained only when using the 0.1% filter, therefore it should be interpreted with caution until it can be replicated in a larger cohort of patients.
Weaknesses of our study are the small sample size, which precluded stratification of PCOS subtypes, and the paucity of extreme phenotypes. However, this pilot study was designed to represent a spectrum of typical Austrian PCOS phenotypes, allowing a first glimpse at the saliva microbiome in this common condition. Future studies should aim to recruit large patient and control groups and stratify based on different PCOS phenotypes, either based on Rotterdam vs. NIH diagnostic criteria or as described by Jamil et al. (Dumesic et al.,
While the saliva microbiome appears to be only minorly changed in PCOS, the microbiome of other body areas may play a more significant role in this pathology. Two research groups have recently shown an alteration in the fecal microbiome of two different rodent models of PCOS (Guo et al.,
In conclusion, we present a first report of the saliva microbiome composition in PCOS. In our cohort, PCOS patients showed a reduced relative abundance of bacteria from the Actinobacteria phylum, while bacterial community composition and diversity seems to be independent of the reproductive and metabolic abnormalities observed in these patients. Larger studies with stratification of PCOS phenotypes are needed to clarify the presence or absence of microbiome changes due to different components of the syndrome. Bacterial functionality, assessed by metagenomics and metatranscriptomics, can provide further insights into the role of salivary bacteria in this condition.
LL: study design, patient recruitment, data collection, laboratory analyses, data analysis, interpretation of results, drafting of manuscript; MB: sequencing data analysis, interpretation of results; JM: coordination of steroid hormone measurements, laboratory analyses, interpretation of results; CT: patient recruitment; VZ: laboratory analyses; TP: interpretation of results; GG: study design, interpretation of results; BO: study design, interpretation of results, study supervision. All authors were involved in the revision and approved the final version of the manuscript.
This work was funded by the DK-MOLIN (Austrian Science Fund (FWF) W1241) and the Medical University of Graz. The funding source was not involved in the study design, data collection, analysis, or interpretation, drafting of, and decision to publish the manuscript.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
The authors would like to thank Roswitha Gumpold, Cornelia Missbrenner, and Hannelore Pock for contributions to sample collection and management, Ingeborg Klymiuk and the CF-MB at the ZMF Graz for advice on molecular biology techniques, Slave Trajanoski and Andrea Groselj-Strele for bioinformatics and statistics, Daniela Hofer, Matthias Ulbing, Olivia Trummer, and Christine Moissl-Eichinger for scientific discussion, and all study participants for their generous donations. The following reagent was obtained through BEI Resources, NIAID, NIH as part of the Human Microbiome Project: Genomic DNA from Microbial Mock Community B (Even, Low Concentration), v5.1L, for 16S rRNA Gene Sequencing, HM-782D.
The Supplementary Material for this article can be found online at: