Participants in the Trans-Antarctic Winter Traverse Expedition Showed Increased Bacterial Load and Diversity in Saliva but Maintained Individual Differences within Stool Microbiota and Across Metabolite Fingerprints.

Understanding the impact of long-term physiological and environmental stress on the human microbiota and metabolome may be important for the success of space flight. This work is logistically difficult and has a limited number of available participants. Terrestrial analogies present important opportunities to understand changes in the microbiota and metabolome and how this may impact participant health and fitness. Here, we present work from one such analogy: the Transarctic Winter Traverse expedition, which we believe is the first assessment of the microbiota and metabolome from different bodily locations during prolonged environmental and physiological stress. Bacterial load and diversity were significantly higher during the expedition when compared with baseline levels (p < 0.001) in saliva but not stool, and only a single operational taxonomic unit assigned to the Ruminococcaceae family shows significantly altered levels in stool (p < 0.001). Metabolite fingerprints show the maintenance of individual differences across saliva, stool, and plasma samples when analysed using flow infusion electrospray mass spectrometry and Fourier transform infrared spectroscopy. Significant activity-associated changes in terms of both bacterial diversity and load are seen in saliva but not in stool, and participant differences in metabolite fingerprints persist across all three sample types.

Human Milk from Tandem Feeding Dyads Does Not Differ in Metabolite and Metataxonomic Features When Compared to Single Nursling Dyads under Six Months of Age.

Given the long-term advantages of exclusive breastfeeding to infants and their mothers, there is both an individual and public health benefit to its promotion and support. Data on the composition of human milk over the course of a full period of lactation for a single nursling is sparse, but data on human milk composition during tandem feeding (feeding children of different ages from different pregnancies) is almost entirely absent. This leaves an important knowledge gap that potentially endangers the ability of parents to make a fully informed choice on infant feeding. We compared the metataxonomic and metabolite fingerprints of human milk samples from 15 tandem feeding dyads to that collected from ten exclusively breastfeeding single nursling dyads where the nursling is under six months of age. Uniquely, our cohort also included three tandem feeding nursling dyads where each child showed a preferential side for feeding-allowing a direct comparison between human milk compositions for different aged nurslings. Across our analysis of volume, total fat, estimation of total microbial load, metabolite fingerprinting, and metataxonomics, we showed no statistically significant differences between tandem feeding and single nursling dyads. This included comparisons of preferential side nurslings of different ages. Together, our findings support the practice of tandem feeding of nurslings, even when feeding an infant under six months.

Rapid ex vivo molecular fingerprinting of biofluids using laser-assisted rapid evaporative ionization mass spectrometry.

Of the many metabolites involved in any clinical condition, only a narrow range of biomarkers is currently being used in the clinical setting. A key to personalized medicine would be to extend this range. Metabolic fingerprinting provides a more comprehensive insight, but many methods used for metabolomics analysis are too complex and time-consuming to be diagnostically useful. Here, a rapid evaporative ionization mass spectrometry (REIMS) system for direct ex vivo real-time analysis of biofluids with minor sample pretreatment is detailed. The REIMS can be linked to various laser wavelength systems (such as optical parametric oscillator or CO2 laser) and with automation for high-throughput analysis. Laser-induced sample evaporation occurs within seconds through radiative heating with the plume guided to the MS instrument. The presented procedure includes (i) laser setup with automation, (ii) analysis of biofluids (blood/urine/stool/saliva/sputum/breast milk) and (iii) data analysis. We provide the optimal settings for biofluid analysis and quality control, enabling sensitive, precise and robust analysis. Using the automated setup, 96 samples can be analyzed in ~35-40 min per ionization mode, with no intervention required. Metabolic fingerprints are made up of 2,000-4,000 features, for which relative quantification can be achieved at high repeatability when total ion current normalization is applied. With saliva and feces as example matrices, >70% of features had a coefficient of variance ≤30%. However, to achieve acceptable long-term reproducibility, additional normalizations by, e.g., LOESS are recommended, especially for positive ionization.

Sample Preparation Free Mass Spectrometry Using Laser-Assisted Rapid Evaporative Ionization Mass Spectrometry: Applications to Microbiology, Metabolic Biofluid Phenotyping, and Food Authenticity.

Mass spectrometry has established itself as a powerful tool in the chemical, biological, medical, environmental, and agricultural fields. However, experimental approaches and potential application areas have been limited by a traditional reliance on sample preparation, extraction, and chromatographic separation. Ambient ionization mass spectrometry methods have addressed this challenge but are still somewhat restricted in requirements for sample manipulation to make it suitable for analysis. These limitations are particularly restrictive in view of the move toward high-throughput and automated analytical workflows. To address this, we present what we consider to be the first automated sample-preparation-free mass spectrometry platform utilizing a carbon dioxide (CO2) laser for sample thermal desorption linked to the rapid evaporative ionization mass spectrometry (LA-REIMS) methodology. We show that the pulsatile operation of the CO2 laser is the primary factor in achieving high signal-to-noise ratios. We further show that the LA-REIMS automated platform is suited to the analysis of three diverse biological materials within different application areas. First, clinical microbiology isolates were classified to species level with an accuracy of 97.2%, the highest accuracy reported in current literature. Second, fecal samples from a type 2 diabetes mellitus cohort were analyzed with LA-REIMS, which allowed tentative identification of biomarkers which are potentially associated with disease pathogenesis and a disease classification accuracy of 94%. Finally, we showed the ability of the LA-REIMS system to detect instances of adulteration of cooking oil and determine the geographical area of production of three protected olive oil products with 100% classification accuracy.

Metabolomic and Metataxonomic Fingerprinting of Human Milk Suggests Compositional Stability over a Natural Term of Breastfeeding to 24 Months.

Sparse data exist regarding the normal range of composition of maternal milk beyond the first postnatal weeks. This single timepoint, observational study in collaboration with the 'Parenting Science Gang' citizen science group evaluated the metabolite and bacterial composition of human milk from 62 participants (infants aged 3-48 months), nearly 3 years longer than previous studies. We utilised rapid evaporative ionisation mass spectrometry (REIMS) for metabolic fingerprinting and 16S rRNA gene metataxonomics for microbiome composition analysis. Milk expression volumes were significantly lower beyond 24 months of lactation, but there were no corresponding changes in bacterial load, composition, or whole-scale metabolomic fingerprint. Some individual metabolite features (~14%) showed altered abundances in nursling age groups above 24 months. Neither milk expression method nor nursling sex affected metabolite and metataxonomic fingerprints. Self-reported lifestyle factors, including diet and physical traits, had minimal impact on metabolite and metataxonomic fingerprints. Our findings suggest remarkable consistency in human milk composition over natural-term lactation. The results add to previous studies suggesting that milk donation can continue up to 24 months postnatally. Future longitudinal studies will confirm the inter-individual and temporal nature of compositional variations and the use of donor milk as a personalised therapeutic.

Laser-assisted rapid evaporative ionisation mass spectrometry (LA-REIMS) as a metabolomics platform in cervical cancer screening.

BACKGROUND: The introduction of high-risk human papillomavirus (hrHPV) testing as part of primary cervical screening is anticipated to improve sensitivity, but also the number of women who will screen positive. Reflex cytology is the preferred triage test in most settings but has limitations including moderate diagnostic accuracy, lack of automation, inter-observer variability and the need for clinician-collected sample. Novel, objective and cost-effective approaches are needed. METHODS: In this study, we assessed the potential use of an automated metabolomic robotic platform, employing the principle of laser-assisted Rapid Evaporative Ionisation Mass Spectrometry (LA-REIMS) in cervical cancer screening. FINDINGS: In a population of 130 women, LA-REIMS achieved 94% sensitivity and 83% specificity (AUC: 91.6%) in distinguishing women testing positive (n = 65) or negative (n = 65) for hrHPV. We performed further analysis according to disease severity with LA-REIMS achieving sensitivity and specificity of 91% and 73% respectively (AUC: 86.7%) in discriminating normal from high-grade pre-invasive disease. INTERPRETATION: This automated high-throughput technology holds promise as a low-cost and rapid test for cervical cancer screening and triage. The use of platforms like LA-REIMS has the potential to further improve the accuracy and efficiency of the current national screening programme. FUNDING: Work was funded by the MRC Imperial Confidence in Concept Scheme, Imperial College Healthcare Charity, British Society for Colposcopy and Cervical Pathology, National Research Development and Innovation Office of Hungary, Waters corporation and NIHR BRC.

Off-Colony Screening of Biosynthetic Libraries by Rapid Laser-Enabled Mass Spectrometry.

By leveraging advances in DNA synthesis and molecular cloning techniques, synthetic biology increasingly makes use of large construct libraries to explore large design spaces. For biosynthetic pathway engineering, the ability to screen these libraries for a variety of metabolites of interest is essential. If the metabolite of interest or the metabolic phenotype is not easily measurable, screening soon becomes a major bottleneck involving time-consuming culturing, sample preparation, and extraction. To address this, we demonstrate the use of automated laser-assisted rapid evaporative ionization mass spectrometry (LA-REIMS)-a form of ambient laser desorption ionization mass spectrometry-to perform rapid mass spectrometry analysis direct from agar plate yeast colonies without sample preparation or extraction. We use LA-REIMS to assess production levels of violacein and betulinic acid directly from yeast colonies at a rate of 6 colonies per minute. We then demonstrate the throughput enabled by LA-REIMS by screening over 450 yeast colonies within <4 h, while simultaneously generating recoverable glycerol stocks of each colony in real time. This showcases LA-REIMS as a prescreening tool to complement downstream quantification methods such as liquid chromatography-mass spectroscopy (LCMS). By prescreening several hundred colonies with LA-REIMS, we successfully isolate and verify a strain with a 2.5-fold improvement in betulinic acid production. Finally, we show that LA-REIMS can detect 20 out of a panel of 27 diverse biological molecules, demonstrating the broad applicability of LA-REIMS to metabolite detection. The rapid and automated nature of LA-REIMS makes this a valuable new technology to complement existing screening technologies currently employed in academic and industrial workflows.

Evaluation of Direct from Sample Metabolomics of Human Feces Using Rapid Evaporative Ionization Mass Spectrometry.

Mass spectrometry is a powerful tool in the investigation of the human fecal metabolome. However, current approaches require time-consuming sample preparation, chromatographic separations, and consequently long analytical run times. Rapid evaporative ionization mass spectrometry (REIMS) is a method of ambient ionization mass spectrometry and has been utilized in the metabolic profiling of a diverse range of biological materials, including human tissue, cell culture lines, and microorganisms. Here, we describe the use of an automated, high-throughput REIMS robotic platform for direct analysis of human feces. Through the analysis of fecal samples from five healthy male participants, REIMS analytical parameters were optimized and used to assess the chemical information obtainable using REIMS. Within the fecal samples analyzed, bile acids, including primary, secondary, and conjugate species, were identified, and phospholipids of possible bacterial origin were detected. In addition, the effect of storage conditions and consecutive freeze/thaw cycles was determined. Within the REIMS mass spectra, the lower molecular weight metabolites, such as fatty acids, were shown to be significantly affected by storage conditions for prolonged periods at temperatures above -80 °C and consecutive freeze/thaw cycles. However, the complex lipid region was shown to be unaffected by these conditions. A further cohort of 50 fecal samples, collected from patients undergoing bariatric surgery, were analyzed using the optimized REIMS parameters and the complex lipid region mass spectra used for multivariate modeling. This analysis showed a predicted separation between pre- and post-surgery specimens, suggesting that REIMS analysis can detect biological differences, such as microbiome-level differences, which have traditionally been reliant upon methods utilizing extensive sample preparations and chromatographic separations and/or DNA sequencing.

Utilisation of Ambient Laser Desorption Ionisation Mass Spectrometry (ALDI-MS) Improves Lipid-Based Microbial Species Level Identification.

The accurate and timely identification of the causative organism of infection is important in ensuring the optimum treatment regimen is prescribed for a patient. Rapid evaporative ionisation mass spectrometry (REIMS), using electrical diathermy for the thermal disruption of a sample, has been shown to provide fast and accurate identification of microorganisms directly from culture. However, this method requires contact to be made between the REIMS probe and microbial biomass; resulting in the necessity to clean or replace the probes between analyses. Here, optimisation and utilisation of ambient laser desorption ionisation (ALDI) for improved speciation accuracy and analytical throughput is shown. Optimisation was completed on 15 isolates of Escherichia coli, showing 5 W in pulsatile mode produced the highest signal-to-noise ratio. These parameters were used in the analysis of 150 clinical isolates from ten microbial species, resulting in a speciation accuracy of 99.4% - higher than all previously reported REIMS modalities. Comparison of spectral data showed high levels of similarity between previously published electrical diathermy REIMS data. ALDI does not require contact to be made with the sample during analysis, meaning analytical throughput can be substantially improved, and further, increases the range of sample types which can be analysed in potential direct-from-sample pathogen detection.

Metabolic Phenotyping and Strain Characterisation of Pseudomonas aeruginosa Isolates from Cystic Fibrosis Patients Using Rapid Evaporative Ionisation Mass Spectrometry.

Rapid evaporative ionisation mass spectrometry (REIMS) is a novel technique for the real-time analysis of biological material. It works by conducting an electrical current through a sample, causing it to rapidly heat and evaporate, with the analyte containing vapour channelled to a mass spectrometer. It was used to characterise the metabolome of 45 Pseudomonas aeruginosa (P. aeruginosa) isolates from cystic fibrosis (CF) patients and compared to 80 non-CF P. aeruginosa. Phospholipids gave the highest signal intensity; 17 rhamnolipids and 18 quorum sensing molecules were detected, demonstrating that REIMS has potential for the study of virulence-related metabolites. P. aeruginosa isolates obtained from respiratory samples showed a higher diversity, which was attributed to the chronic nature of most respiratory infections. The analytical sensitivity of REIMS allowed the detection of a metabolome that could be used to classify individual P. aeruginosa isolates after repeated culturing with 81% accuracy, and an average 83% concordance with multilocus sequence typing. This study underpins the capacities of REIMS as a tool with clinical applications, such as metabolic phenotyping of the important CF pathogen P. aeruginosa, and highlights the potential of metabolic fingerprinting for fine scale characterisation at a sub-species level.

Mass spectrometry approaches to metabolic profiling of microbial communities within the human gastrointestinal tract.

The interaction between microbial communities and their environment, such as the human gastrointestinal tract, has been an area of microbiology rapidly advanced, by developments in sequencing technology. However, these techniques are largely limited to the detection of the taxonomic composition of a microbial community and/or its genetic functional capacity. Here, we discuss a range of mass spectrometry-based approaches which researchers can employ to explore the host-microbiome interactions at the metabolic level. Traditional approaches to mass spectrometry are detailed, alongside new developments in the field, namely ambient ionisation mass spectrometry and imaging mass spectrometry, which we believe will prove to be important to future work in this field. We further discuss considerations for experimental workflows, data analysis options and propose a methodology for the establishment of causal relationships between functional host-microbiome interactions with regards to health and disease in the human gastrointestinal tract.

Visible-Light-Mediated Nitration of Protected Anilines.

The photocatalytic nitration of protected anilines proceeds with riboflavin tetraacetate as an organic photoredox catalyst. Sodium nitrite serves as the NO2 source in this visible-light-driven room temperature reaction. Various nitroanilines are obtained in moderate to good yields without the addition of acid or stoichiometric oxidation agents. The catalytic cycle is closed by aerial oxygen as the terminal oxidant.

A pilot study using metagenomic sequencing of the sputum microbiome suggests potential bacterial biomarkers for lung cancer.

Lung cancer (LC) is the most prevalent cancer worldwide, and responsible for over 1.3 million deaths each year. Currently, LC has a low five year survival rates relative to other cancers, and thus, novel methods to screen for and diagnose malignancies are necessary to improve patient outcomes. Here, we report on a pilot-sized study to evaluate the potential of the sputum microbiome as a source of non-invasive bacterial biomarkers for lung cancer status and stage. Spontaneous sputum samples were collected from ten patients referred with possible LC, of which four were eventually diagnosed with LC (LC+), and six had no LC after one year (LC-). Of the seven bacterial species found in all samples, Streptococcus viridans was significantly higher in LC+ samples. Seven further bacterial species were found only in LC-, and 16 were found only in samples from LC+. Additional taxonomic differences were identified in regards to significant fold changes between LC+ and LC-cases, with five species having significantly higher abundances in LC+, with Granulicatella adiacens showing the highest level of abundance change. Functional differences, evident through significant fold changes, included polyamine metabolism and iron siderophore receptors. G. adiacens abundance was correlated with six other bacterial species, namely Enterococcus sp. 130, Streptococcus intermedius, Escherichia coli, S. viridans, Acinetobacter junii, and Streptococcus sp. 6, in LC+ samples only, which could also be related to LC stage. Spontaneous sputum appears to be a viable source of bacterial biomarkers which may have utility as biomarkers for LC status and stage.

Rapid Evaporative Ionisation Mass Spectrometry (REIMS) Provides Accurate Direct from Culture Species Identification within the Genus Candida.

Members of the genus Candida, such as C. albicans and C. parapsilosis, are important human pathogens. Other members of this genus, previously believed to carry minimal disease risk, are increasingly recognised as important human pathogens, particularly because of variations in susceptibilities to widely used anti-fungal agents. Thus, rapid and accurate identification of clinical Candida isolates is fundamental in ensuring timely and effective treatments are delivered. Rapid Evaporative Ionisation Mass Spectrometry (REIMS) has previously been shown to provide a high-throughput platform for the rapid and accurate identification of bacterial and fungal isolates. In comparison to commercially available matrix assisted laser desorption ionisation time of flight mass spectrometry (MALDI-ToF), REIMS based methods require no preparative steps nor time-consuming cell extractions. Here, we report on the ability of REIMS-based analysis to rapidly and accurately identify 153 clinical Candida isolates to species level. Both handheld bipolar REIMS and high-throughput REIMS platforms showed high levels of species classification accuracy, with 96% and 100% of isolates classified correctly to species level respectively. In addition, significantly different (FDR corrected P value < 0.05) lipids within the 600 to 1000 m/z mass range were identified, which could act as species-specific biomarkers in complex microbial communities.

Automated High-Throughput Identification and Characterization of Clinically Important Bacteria and Fungi using Rapid Evaporative Ionization Mass Spectrometry.

Rapid evaporative ionization mass spectrometry (REIMS) has been shown to quickly and accurately speciate microorganisms based upon their species-specific lipid profile. Previous work by members of this group showed that the use of a hand-held bipolar probe allowed REIMS to analyze microbial cultures directly from culture plates without any prior preparation. However, this method of analysis would likely be unsuitable for a high-throughput clinical microbiology laboratory. Here, we report the creation of a customized platform that enables automated, high-throughput REIMS analysis that requires minimal user input and operation and is suitable for use in clinical microbiology laboratories. The ability of this high-throughput platform to speciate clinically important microorganisms was tested through the analysis of 375 different clinical isolates collected from distinct patient samples from 25 microbial species. After optimization of our data analysis approach, we achieved substantially similar results between the two REIMS approaches. For hand-held bipolar probe REIMS, a speciation accuracy of 96.3% was achieved, whereas for high-throughput REIMS, an accuracy of 93.9% was achieved. Thus, high-throughput REIMS offers an alternative mass spectrometry based method for the rapid and accurate identification of clinically important microorganisms in clinical laboratories without any preanalysis preparative steps.

Metabolomic-based biomarker discovery for non-invasive lung cancer screening: A case study.

BACKGROUND: Lung cancer (LC) is one of the leading lethal cancers worldwide, with an estimated 18.4% of all cancer deaths being attributed to the disease. Despite developments in cancer diagnosis and treatment over the previous thirty years, LC has seen little to no improvement in the overall five year survival rate after initial diagnosis. METHODS: In this paper, we extended a recent study which profiled the metabolites in sputum from patients with lung cancer and age-matched volunteers smoking controls using flow infusion electrospray ion mass spectrometry. We selected key metabolites for distinguishing between different classes of lung cancer, and employed artificial neural networks and leave-one-out cross-validation to evaluate the predictive power of the identified biomarkers. RESULTS: The neural network model showed excellent performance in classification between lung cancer and control groups with the area under the receiver operating characteristic curve of 0.99. The sensitivity and specificity of for detecting cancer from controls were 96% and 94% respectively. Furthermore, we have identified six putative metabolites that were able to discriminate between sputum samples derived from patients suffering small cell lung cancer (SCLC) and non-small cell lung cancer. These metabolites achieved excellent cross validation performance with a sensitivity of 80% and specificity of 100% for predicting SCLC. CONCLUSIONS: These results indicate that sputum metabolic profiling may have potential for screening of lung cancer and lung cancer recurrence, and may greatly improve effectiveness of clinical intervention. This article is part of a Special Issue entitled "System Genetics" Guest Editor: Dr. Yudong Cai and Dr. Tao Huang.

The metabolomic detection of lung cancer biomarkers in sputum.

OBJECTIVES: Developing screening and diagnosis methodologies based on novel biomarkers should allow for the detection of the lung cancer (LC) and possibly at an earlier stage and thereby increase the effectiveness of clinical interventions. Here, our primary objective was to evaluate the potential of spontaneous sputum as a source of non-invasive metabolomic biomarkers for LC status. MATERIALS AND METHODS: Spontaneous sputum was collected and processed from 34 patients with suspected LC, alongside 33 healthy controls. Of the 34 patients, 23 were subsequently diagnosed with LC (LC(+), 16 NSCLC, six SCLC, and one radiological diagnosis), at various stages of disease progression. The 67 samples were analysed using flow infusion electrospray ion mass spectrometry (FIE-MS) and gas-chromatography mass spectrometry (GC-MS). RESULTS: Principal component analysis identified negative mode FIE-MS as having the main separating power between samples from healthy and LC. Discriminatory metabolites were identified using ANOVA and Random Forest. Indications of potential diagnostic accuracy involved the use of receiver operating characteristic/area under the curve (ROC/AUC) analyses. This approach identified metabolites changes that were only observed with LC. Metabolites with AUC values of greater than 0.8 which distinguished between LC(+)/LC(-) binary classifications where identified and included Ganglioside GM1 which has previously been linked to LC. CONCLUSION: This study indicates that metabolomics based on sputum can yield metabolites that can be used as a diagnostic and/or discriminator tool. These could aid clinical intervention and targeted diagnosis of LC within an 'at risk' LC(-) population group. The use of sputum as a non-invasive source of metabolite biomarkers may aid in the development of an at-risk population screening programme for lung cancer or enhanced clinical diagnostic pathways.

Metagenomic Sequencing of the Chronic Obstructive Pulmonary Disease Upper Bronchial Tract Microbiome Reveals Functional Changes Associated with Disease Severity.

Chronic Obstructive Pulmonary Disease (COPD) is a major source of mortality and morbidity worldwide. The microbiome associated with this disease may be an important component of the disease, though studies to date have been based on sequencing of the 16S rRNA gene, and have revealed unequivocal results. Here, we employed metagenomic sequencing of the upper bronchial tract (UBT) microbiome to allow for greater elucidation of its taxonomic composition, and revealing functional changes associated with the disease. The bacterial metagenomes within sputum samples from eight COPD patients and ten 'healthy' smokers (Controls) were sequenced, and suggested significant changes in the abundance of bacterial species, particularly within the Streptococcus genus. The functional capacity of the COPD UBT microbiome indicated an increased capacity for bacterial growth, which could be an important feature in bacterial-associated acute exacerbations. Regression analyses correlated COPD severity (FEV1% of predicted) with differences in the abundance of Streptococcus pneumoniae and functional classifications related to a reduced capacity for bacterial sialic acid metabolism. This study suggests that the COPD UBT microbiome could be used in patient risk stratification and in identifying novel monitoring and treatment methods, but study of a longitudinal cohort will be required to unequivocally relate these features of the microbiome with COPD severity.

The human salivary microbiome exhibits temporal stability in bacterial diversity.

The temporal variability of the human microbiome may be an important factor in determining its relationship with health and disease. In this study, the saliva of 40 participants was collected every 2 months over a one-year period to determine the temporal variability of the human salivary microbiome. Salivary pH and 16S rRNA gene copy number were measured for all participants, with the microbiome of 10 participants assessed through 16S rRNA amplicon sequencing. In February 2013, 16S rRNA gene copy number was significantly (P < 0.001) higher, with individual changes between time points significant (P = 0.003). Salivary pH levels were significantly (P < 0.001) higher in December 2012 than in October 2012 and February 2013, with significant (P < 0.001) individual variations seen throughout. Bacterial α-diversity showed significant differences between participants (P < 0.001), but not sampling periods (P = 0.801), and a significant positive correlation with salivary pH (R(2) = 7.8%; P = 0.019). At the phylum level, significant differences were evident between participants in the Actinobacteria (P < 0.001), Bacteroidetes (P < 0.001), Firmicutes (P = 0.008), Fusobacteria (P < 0.001), Proteobacteria (P < 0.001), Synergistetes (P < 0.001) and Spirochaetes (P = 0.003) phyla. This study charted the temporal variability of the salivary microbiome, suggesting that bacterial diversity is stable, but that 16S rRNA gene copy number may be subject to seasonal flux.