Microbial functional pathways based on metatranscriptomic profiling enable effective saliva-based health assessments for precision wellness.

It is increasingly recognized that an important step towards improving overall health is to accurately measure biomarkers of health from the molecular activities prevalent in the oral cavity. We present a general methodology for computationally quantifying the activity of microbial functional pathways using metatranscriptomic data. We describe their implementation as a collection of eight oral pathway scores using a large salivary sample dataset (n = 9350), and we evaluate score associations with oropharyngeal disease phenotypes within an unseen independent cohort (n = 14,129). Through this validation, we show that the relevant oral pathway scores are significantly worse in individuals with periodontal disease, acid reflux, and nicotine addiction, compared with controls. Given these associations, we make the case to use these oral pathway scores to provide molecular health insights from simple, non-invasive saliva samples, and as molecular endpoints for actionable interventions to address the associated conditions.

A novel method for sampling subgingival microbiome: a comparative metatranscriptomic study.

The subgingival microbiome has been implicated in oral and systemic diseases such as periodontitis and Alzheimer's disease. However, subgingival sampling is challenging. We developed a novel method of sampling the subgingival microbiome by rotationally swabbing the supragingival area, named subgingival-P (for proxy) samples. We sampled and metatranscriptomically analyzed subgingival and subgingival-P samples of three different teeth in 20 individuals. The subgingival-P samples were comparable to the subgingival samples in the relative abundances of microorganisms and microbial gene expression levels. Our data demonstrate that the novel method of collecting and analyzing the subgingival-P samples can act as a proxy for the subgingiva, paving the way for large and diverse studies investigating the role of the subgingival microbiome in health and disease.

An accurate aging clock developed from large-scale gut microbiome and human gene expression data.

Accurate measurement of the biological markers of the aging process could provide an "aging clock" measuring predicted longevity and enable the quantification of the effects of specific lifestyle choices on healthy aging. Using machine learning techniques, we demonstrate that chronological age can be predicted accurately from (1) the expression level of human genes in capillary blood and (2) the expression level of microbial genes in stool samples. The latter uses a very large metatranscriptomic dataset, stool samples from 90,303 individuals, which arguably results in a higher quality microbiome-aging model than prior work. Our analysis suggests associations between biological age and lifestyle/health factors, e.g., people on a paleo diet or with IBS tend to have higher model-predicted ages and people on a vegetarian diet tend to have lower model-predicted ages. We delineate the key pathways of systems-level biological decline based on the age-specific features of our model.

Comparison of the gut microbiome composition between men with erectile dysfunction and a matched cohort: a pilot study.

BACKGROUND: To-date there have been minimal studies to investigate an association between the gut microbiome and erectile dysfunction. There have been many inflammatory diseases linked to gut microbiome dysbiosis; such as cardiovascular disease and metabolic syndrome. These same inflammatory diseases have been heavily linked to erectile dysfunction. Given the correlations between both conditions and cardiovascular disease and the metabolic syndrome, we believe that it is worthwhile to investigate a link between the two. OBJECTIVE: To investigate the potential association between the gut microbiome and erectile dysfunction. METHODS: Stool samples were collected from 28 participants with erectile dysfunction and 32 age-matched controls. Metatranscriptome sequencing was used to analyze the samples. RESULTS: No significant differences were found in the gut microbiome characteristics, including Kyoto Encyclopedia of Genes and Genomes richness (p = 0.117), Kyoto Encyclopedia of Genes and Genomes diversity (p = 0.323), species richness (p = 0.364), and species diversity (p = 0.300), between the erectile dysfunction and control groups. DISCUSSION: The association of gut microbiome dysbiosis and pro-inflammatory conditions has been well studied and further literature continues to add to this evidence. Our main limitation for this study was our small-sample size due to recruitment issues. We believe that a study with a larger population size may find an association between the gut microbiome and erectile dysfunction. CONCLUSIONS: The results of this study do not support a significant association between the gut microbiome and erectile dysfunction. Further research is needed to fully understand the relationship between these two conditions.

Detecting salivary host and microbiome RNA signature for aiding diagnosis of oral and throat cancer.

OBJECTIVE: Oral squamous cell carcinoma (OSCC) and oropharyngeal squamous cell carcinoma (OPSCC) can go undetected resulting in late detection and poor outcomes. We describe the development and validation of CancerDetect for Oral & Throat cancer™ (CDOT), to detect markers of OSCC and/or OPSCC within a high-risk population. MATERIAL AND METHODS: We collected saliva samples from 1,175 individuals who were 50 years or older, or adults with a tobacco use history. 945 of those were used to train a classifier using machine learning methods, resulting in a salivary microbial and human metatranscriptomic signature. The classifier was then independently validated on the 230 remaining samples prospectively collected and unseen by the classifier, consisting of 20 OSCC (all stages), 76 OPSCC (all stages), and 134 negatives (including 14 pre-malignant). RESULTS: On the validation cohort, the specificity of the CDOT test was 94 %, sensitivity was 90 % for participants with OSCC, and 84.2 % for participants with OPSCC. Similar classification results were observed among people in early stage (stages I & II) vs late stage (stages III & IV). CONCLUSIONS: CDOT is a non-invasive test that can be easily administered in dentist offices, primary care centres and specialised cancer clinics for early detection of OPSCC and OSCC. This test, having received FDA's breakthrough designation for accelerated review, has the potential to enable early diagnosis, saving lives and significantly reducing healthcare expenditure.

A clinically validated human saliva metatranscriptomic test for global systems biology studies.

The authors report here the development of a high-throughput, automated, inexpensive and clinically validated saliva metatranscriptome test that requires less than 100 µl of saliva. RNA is preserved at the time of sample collection, allowing for ambient-temperature transportation and storage for up to 28 days. Critically, the RNA preservative is also able to inactivate pathogenic microorganisms, rendering the samples noninfectious and allowing for safe and easy shipping. Given the unique set of convenience, low cost, safety and technical performance, this saliva metatranscriptomic test can be integrated into longitudinal, global-scale systems biology studies that will lead to an accelerated development of precision medicine, diagnostic and therapeutic tools.

Pathogen detection and characterization from throat swabs using unbiased metatranscriptomic analyses.

OBJECTIVES: Infectious diseases are common but are not easily or readily diagnosed with current methodologies. This problem is further exacerbated by the constant presence of mutated, emerging, and novel pathogens. One of the most common sites of infection by many pathogens is the human throat. However, there is no universal diagnostic test that can distinguish these pathogens. Metatranscriptomic (MT) analysis of the throat represents an important and novel development in infectious disease detection and characterization, because it is able to identify all pathogens using a fully unbiased approach. METHODS: To test the utility of an MT approach to pathogen detection, throat samples were collected from participants before, during, and after an acute sickness. RESULTS: Clear sickness-associated shifts in pathogenic microorganisms were detected in the patients. Important insights into microbial functions and antimicrobial resistance genes were obtained. CONCLUSION: MT analysis of the throat represents an effective method for the unbiased identification and characterization of pathogens. Because MT data include all microorganisms in the sample, this approach should not only allow the identification of pathogens, but provide an understanding of the effects of the resident throat microbiome in the context of human health and disease.

Gut Microbiome Activity Contributes to Prediction of Individual Variation in Glycemic Response in Adults.

Limiting postprandial glycemic response (PPGR) is an important intervention in reducing the risk of chronic metabolic diseases and has been shown to impart significant health benefits in people with elevated levels of blood sugar. In this study, we collected gut microbiome activity data by assessing the metatranscriptome, and we measured the glycemic responses of 550 adults who consumed more than 30,000 meals, collectively, from omnivore or vegetarian/gluten-free diets. We demonstrate that gut microbiome activity, anthropometric factors, and food macronutrients modulate individual variation in glycemic response. We employ two predictive models, including a mixed-effects linear regression model (R = 0.77) and a gradient boosting machine model (Rtrain = 0.80/R2train = 0.64; Rtest = 0.64/R2test = 0.40), which demonstrate variation in PPGR between individuals when ingesting the same foods. All features in the final mixed-effects linear regression model were significant (p < 0.05) except for two features which were retained as suggestive: glutamine production pathways (p = 0.08) and the interaction between tyrosine metabolizers and carbs (p = 0.06). We introduce molecular functions as features in these two models, aggregated from microbial activity data, and show their statistically significant contributions to glycemic control. In summary, we demonstrate for the first time that metatranscriptomic activity of the gut microbiome is correlated with PPGR among adults.

Blood sugar dysregulation is caused by various underlying conditions, including type 2 diabetes, and this may lead to extended periods of hypoglycemia or hyperglycemia, which can be harmful or deadly. Clinically, glycemic control is a primary therapeutic target for dysglycemia, and food and nutrition are frequent interventions used to reduce postprandial blood glucose excursions. Primary determinants of postprandial glycemic response (PPGR) include dietary carbohydrates, individual phenotypes, and individual molecular characteristics which include the gut microbiome. Typical investigations of gut microbiomes depend on analysis methods which have poor taxonomic resolution, cannot identify certain microorganisms, and are prone to errors. In this study, each RNA molecule was identified and counted, allowing quantitative strain-level taxonomic classification and molecular pathway analysis. The primary goal of the study was to assess the impact of microbial functional activity on PPGR. The study was conducted in the USA and involved a multiethnic population of healthy adults with HbA1c levels below 6.5. All participants received 14-day omnivore diets or vegetarian/gluten-free diets, depending on nutritional requirements (omnivore diets include meat while vegetarian/gluten-free diets exclude both gluten and meat). Over this timeframe, blood glucose levels were measured in 15-min intervals, 24 h per day, capturing postprandial responses for more than 27,000 meals, including more than 18,000 provided meals which spanned a wide range of foods and macronutrient characteristics. Computational modeling demonstrated the statistical significance of all features and identified new features which may be relevant to glycemic control. These results show, for the first time, that a person's glycemic response depends on individual traits, including both their anthropometrics and their gut metatranscriptome, representing the activity of gut microbiomes.

The salivary metatranscriptome as an accurate diagnostic indicator of oral cancer.

Despite advances in cancer treatment, the 5-year mortality rate for oral cancers (OC) is 40%, mainly due to the lack of early diagnostics. To advance early diagnostics for high-risk and average-risk populations, we developed and evaluated machine-learning (ML) classifiers using metatranscriptomic data from saliva samples (n = 433) collected from oral premalignant disorders (OPMD), OC patients (n = 71) and normal controls (n = 171). Our diagnostic classifiers yielded a receiver operating characteristics (ROC) area under the curve (AUC) up to 0.9, sensitivity up to 83% (92.3% for stage 1 cancer) and specificity up to 97.9%. Our metatranscriptomic signature incorporates both taxonomic and functional microbiome features, and reveals a number of taxa and functional pathways associated with OC. We demonstrate the potential clinical utility of an AI/ML model for diagnosing OC early, opening a new era of non-invasive diagnostics, enabling early intervention and improved patient outcomes.

A clinically validated human capillary blood transcriptome test for global systems biology studies.

To prevent and treat chronic diseases, including cancer, a global application of systems biology is needed. We report here a whole blood transcriptome test that needs only 50 µl of capillary (fingerprick) blood. This test is suitable for global applications because the samples are preserved at ambient temperature for up to 4 weeks and the RNA preservative inactivates all pathogens, enabling safe transportation. Both the laboratory and bioinformatic steps are automated and performed in a clinical lab, which minimizes batch effects and creates unbiased datasets. Given its clinical testing performance and accessibility to traditionally underrepresented and diverse populations, this test offers a unique ability to reveal molecular mechanisms of disease and enable longitudinal, population-scale studies.

A Robust Metatranscriptomic Technology for Population-Scale Studies of Diet, Gut Microbiome, and Human Health.

A functional readout of the gut microbiome is necessary to enable precise control of the gut microbiome's functions, which support human health and prevent or minimize a wide range of chronic diseases. Stool metatranscriptomic analysis offers a comprehensive functional view of the gut microbiome, but despite its usefulness, it has rarely been used in clinical studies due to its complexity, cost, and bioinformatic challenges. This method has also received criticism due to potential intrasample variability, rapid changes, and RNA degradation. Here, we describe a robust and automated stool metatranscriptomic method, called Viomega, which was specifically developed for population-scale studies. Viomega includes sample collection, ambient temperature sample preservation, total RNA extraction, physical removal of ribosomal RNAs (rRNAs), preparation of directional Illumina libraries, Illumina sequencing, taxonomic classification based on a database of >110,000 microbial genomes, and quantitative microbial gene expression analysis using a database of ~100 million microbial genes. We applied this method to 10,000 human stool samples and performed several small-scale studies to demonstrate sample stability and consistency. In summary, Viomega is an inexpensive, high-throughput, automated, and accurate sample-to-result stool metatranscriptomic technology platform for large-scale studies and a wide range of applications.