Reference-guided metagenomics reveals genome-level evidence of potential microbial transmission from the ISS environment to an astronaut's microbiome

AMAnD: an automated metagenome anomaly detection methodology utilizing DeepSVDD neural networks

The composition of metagenomic communities within the human body often reflects localized medical conditions such as upper respiratory diseases and gastrointestinal diseases. Fast and accurate computational tools to flag anomalous metagenomic samples from typical samples are desirable to understand different phenotypes, especially in contexts where repeated, long-duration temporal sampling is done. Here, we present Automated Metagenome Anomaly Detection (AMAnD), which utilizes two types of Deep Support Vector Data Description (DeepSVDD) models; one trained on taxonomic feature space output by the Pan-Genomics for Infectious Agents (PanGIA) taxonomy classifier and one trained on kmer frequency counts. AMAnD's semi-supervised one-class approach makes no assumptions about what an anomaly may look like, allowing the flagging of potentially novel anomaly types. Three diverse datasets are profiled. The first dataset is hosted on the National Center for Biotechnology Information's (NCBI) Sequence Read Archive (SRA) and contains nasopharyngeal swabs from healthy and COVID-19-positive patients. The second dataset is also hosted on SRA and contains gut microbiome samples from normal controls and from patients with slow transit constipation (STC). AMAnD can learn a typical healthy nasopharyngeal or gut microbiome profile and reliably flag the anomalous COVID+ or STC samples in both feature spaces. The final dataset is a synthetic metagenome created by the Critical Assessment of Metagenome Annotation Simulator (CAMISIM). A control dataset of 50 well-characterized organisms was submitted to CAMISIM to generate 100 synthetic control class samples. The experimental conditions included 12 different spiked-in contaminants that are taxonomically similar to organisms present in the laboratory blank sample ranging from one strain tree branch taxonomic distance away to one family tree branch taxonomic distance away. This experiment was repeated in triplicate at three different coverage levels to probe the dependence on sample coverage. AMAnD was again able to flag the contaminant inserts as anomalous. AMAnD's assumption-free flagging of metagenomic anomalies, the real-time model training update potential of the deep learning approach, and the strong performance even with lightweight models of low sample cardinality would make AMAnD well-suited to a wide array of applied metagenomics biosurveillance use-cases, from environmental to clinical utility.

Understanding the Complexities and Changes of the Astronaut Microbiome for Successful Long-Duration Space Missions

During space missions, astronauts are faced with a variety of challenges that are unique to spaceflight and that have been known to cause physiological changes in humans over a period of time. Several of these changes occur at the microbiome level, a complex ensemble of microbial communities residing in various anatomic sites of the human body, with a pivotal role in regulating the health and behavior of the host. The microbiome is essential for day-to-day physiological activities, and alterations in microbiome composition and function have been linked to various human diseases. For these reasons, understanding the impact of spaceflight and space conditions on the microbiome of astronauts is important to assess significant health risks that can emerge during long-term missions and to develop countermeasures. Here, we review various conditions that are caused by long-term space exploration and discuss the role of the microbiome in promoting or ameliorating these conditions, as well as space-related factors that impact microbiome composition. The topics explored pertain to microgravity, radiation, immunity, bone health, cognitive function, gender differences and pharmacomicrobiomics. Connections are made between the trifecta of spaceflight, the host and the microbiome, and the significance of these interactions for successful long-term space missions.

Microbiome Studies from Saudi Arabia over the Last 10 Years: Achievements, Gaps, and Future Directions

In the past ten years, microbiome studies have shown tremendous potentiality for implementation of understanding microbiome structures and functions of various biomes and application of this knowledge for human betterment. Saudi Arabia is full of geographical, ecological, ethnical, and industrial diversities and scientific capacities. Therefore, there is a great potential in Saudi Arabia to conduct and implement microbiome-based research and applications. However, there is no review available on where Saudi Arabia stands with respect to global microbiome research trends. This review highlights the metagenome-assisted microbiome research from Saudi Arabia compared to the global focuses on microbiome research. Further, it also highlights the gaps and areas that should be focused on by Saudi microbiome researchers and the possible initiatives to be taken by Saudi government and universities. This literature review shows that the global trends of microbiome research cover a broad spectrum of human and animal health conditions and diseases, environmental and antimicrobial resistance surveillance, surveillance of food and food processing, production of novel industrial enzymes and bioactive pharmaceutical products, and space applications. However, Saudi microbiome studies are mostly confined to very few aspects of health (human and animal) and environment/ecology in last ten years, without much application. Therefore, Saudi Arabia should focus more on applied microbiome research through government, academic, and industry initiatives and global cooperation to match the global trends.