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.

Multi-omics profiles of the intestinal microbiome in irritable bowel syndrome and its bowel habit subtypes.

BACKGROUND: Irritable bowel syndrome (IBS) is a common gastrointestinal disorder that is thought to involve alterations in the gut microbiome, but robust microbial signatures have been challenging to identify. As prior studies have primarily focused on composition, we hypothesized that multi-omics assessment of microbial function incorporating both metatranscriptomics and metabolomics would further delineate microbial profiles of IBS and its subtypes. METHODS: Fecal samples were collected from a racially/ethnically diverse cohort of 495 subjects, including 318 IBS patients and 177 healthy controls, for analysis by 16S rRNA gene sequencing (n = 486), metatranscriptomics (n = 327), and untargeted metabolomics (n = 368). Differentially abundant microbes, predicted genes, transcripts, and metabolites in IBS were identified by multivariate models incorporating age, sex, race/ethnicity, BMI, diet, and HAD-Anxiety. Inter-omic functional relationships were assessed by transcript/gene ratios and microbial metabolic modeling. Differential features were used to construct random forests classifiers. RESULTS: IBS was associated with global alterations in microbiome composition by 16S rRNA sequencing and metatranscriptomics, and in microbiome function by predicted metagenomics, metatranscriptomics, and metabolomics. After adjusting for age, sex, race/ethnicity, BMI, diet, and anxiety, IBS was associated with differential abundance of bacterial taxa such as Bacteroides dorei; metabolites including increased tyramine and decreased gentisate and hydrocinnamate; and transcripts related to fructooligosaccharide and polyol utilization. IBS further showed transcriptional upregulation of enzymes involved in fructose and glucan metabolism as well as the succinate pathway of carbohydrate fermentation. A multi-omics classifier for IBS had significantly higher accuracy (AUC 0.82) than classifiers using individual datasets. Diarrhea-predominant IBS (IBS-D) demonstrated shifts in the metatranscriptome and metabolome including increased bile acids, polyamines, succinate pathway intermediates (malate, fumarate), and transcripts involved in fructose, mannose, and polyol metabolism compared to constipation-predominant IBS (IBS-C). A classifier incorporating metabolites and gene-normalized transcripts differentiated IBS-D from IBS-C with high accuracy (AUC 0.86). CONCLUSIONS: IBS is characterized by a multi-omics microbial signature indicating increased capacity to utilize fermentable carbohydrates-consistent with the clinical benefit of diets restricting this energy source-that also includes multiple previously unrecognized metabolites and metabolic pathways. These findings support the need for integrative assessment of microbial function to investigate the microbiome in IBS and identify novel microbiome-related therapeutic targets. Video Abstract.

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.

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.

Optogenetic activation of short neuropeptide F (sNPF) neurons induces sleep in Drosophila melanogaster.

Sleep abnormalities have widespread and costly public health consequences, yet we have only a rudimentary understanding of the events occurring at the cellular level in the brain that regulate sleep. Several key signaling molecules that regulate sleep across taxa come from the family of neuropeptide transmitters. For example, in Drosophila melanogaster, the neuropeptide Y (NPY)-related transmitter short neuropeptide F (sNPF) appears to promote sleep. In this study, we utilized optogenetic activation of neuronal populations expressing sNPF to determine the causal effects of precisely timed activity in these cells on sleep behavior. Combining sNPF-GAL4 and UAS-Chrimson transgenes allowed us to activate sNPF neurons using red light. We found that activating sNPF neurons for as little as 3 s at a time of day when most flies were awake caused a rapid transition to sleep that persisted for another 2+ hours following the stimulation. Changing the timing of red light stimulation to times of day when flies were already asleep caused the control flies to wake up (due to the pulse of light), but the flies in which sNPF neurons were activated stayed asleep through the light pulse, and then showed further increases in sleep at later points when they would have normally been waking up. Video recording of individual fly responses to short-term (0.5-20 s) activation of sNPF neurons demonstrated a clear light duration-dependent decrease in movement during the subsequent 4-min period. These results provide supportive evidence that sNPF-producing neurons promote long-lasting increases in sleep, and show for the first time that even brief periods of activation of these neurons can cause changes in behavior that persist after cessation of activation. We have also presented evidence that sNPF neuron activation produces a homeostatic sleep drive that can be dissipated at times long after the neurons were stimulated. Future studies will determine the specific roles of sub-populations of sNPF-producing neurons, and will also assess how sNPF neurons act in concert with other neuronal circuits to control sleep.