A meta-analysis of the gut microbiome in inflammatory bowel disease patients identifies disease-associated small molecules.

Changes in the gut microbiome have been associated with several human diseases, but the molecular and functional details underlying these associations remain largely unknown. Here, we performed a multi-cohort analysis of small molecule biosynthetic gene clusters (BGCs) in 5,306 metagenomic samples of the gut microbiome from 2,033 Inflammatory Bowel Disease (IBD) patients and 833 matched healthy subjects and identified a group of Clostridia-derived BGCs that are significantly associated with IBD. Using synthetic biology, we discovered and solved the structures of six fatty acid amides as the products of the IBD-enriched BGCs. Using two mouse models of colitis, we show that the discovered small molecules disrupt gut permeability and exacerbate inflammation in chemically and genetically susceptible mice. These findings suggest that microbiome-derived small molecules may play a role in the etiology of IBD and represent a generalizable approach for discovering molecular mediators of microbiome-host interactions in the context of microbiome-associated diseases.

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.

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.

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 metagenomic strategy for harnessing the chemical repertoire of the human microbiome.

Extensive progress has been made in determining the effects of the microbiome on human physiology and disease, but the underlying molecules and mechanisms governing these effects remain largely unexplored. Here, we combine a new computational algorithm with synthetic biology to access biologically active small molecules encoded directly in human microbiome-derived metagenomic sequencing data. We discover that members of a clinically used class of molecules are widely encoded in the human microbiome and that they exert potent antibacterial activities against neighboring microbes, implying a possible role in niche competition and host defense. Our approach paves the way toward a systematic unveiling of the chemical repertoire encoded by the human microbiome and provides a generalizable platform for discovering molecular mediators of microbiome-host and microbiome-microbiome interactions.