A cryptic plasmid is among the most numerous genetic elements in the human gut.

Plasmids are extrachromosomal genetic elements that often encode fitness-enhancing features. However, many bacteria carry "cryptic" plasmids that do not confer clear beneficial functions. We identified one such cryptic plasmid, pBI143, which is ubiquitous across industrialized gut microbiomes and is 14 times as numerous as crAssphage, currently established as the most abundant extrachromosomal genetic element in the human gut. The majority of mutations in pBI143 accumulate in specific positions across thousands of metagenomes, indicating strong purifying selection. pBI143 is monoclonal in most individuals, likely due to the priority effect of the version first acquired, often from one's mother. pBI143 can transfer between Bacteroidales, and although it does not appear to impact bacterial host fitness in vivo, it can transiently acquire additional genetic content. We identified important practical applications of pBI143, including its use in identifying human fecal contamination and its potential as an alternative approach to track human colonic inflammatory states.

Diverse plasmid systems and their ecology across human gut metagenomes revealed by PlasX and MobMess.

Plasmids alter microbial evolution and lifestyles by mobilizing genes that often confer fitness in changing environments across clades. Yet our ecological and evolutionary understanding of naturally occurring plasmids is far from complete. Here we developed a machine-learning model, PlasX, which identified 68,350 non-redundant plasmids across human gut metagenomes and organized them into 1,169 evolutionarily cohesive 'plasmid systems' using our sequence containment-aware network-partitioning algorithm, MobMess. Individual plasmids were often country specific, yet most plasmid systems spanned across geographically distinct human populations. Cargo genes in plasmid systems included well-known determinants of fitness, such as antibiotic resistance, but also many others including enzymes involved in the biosynthesis of essential nutrients and modification of transfer RNAs, revealing a wide repertoire of likely fitness determinants in complex environments. Our study introduces computational tools to recognize and organize plasmids, and uncovers the ecological and evolutionary patterns of diverse plasmids in naturally occurring habitats through plasmid systems.

Microbes with higher metabolic independence are enriched in human gut microbiomes under stress.

A wide variety of human diseases are associated with loss of microbial diversity in the human gut, inspiring a great interest in the diagnostic or therapeutic potential of the microbiota. However, the ecological forces that drive diversity reduction in disease states remain unclear, rendering it difficult to ascertain the role of the microbiota in disease emergence or severity. One hypothesis to explain this phenomenon is that microbial diversity is diminished as disease states select for microbial populations that are more fit to survive environmental stress caused by inflammation or other host factors. Here, we tested this hypothesis on a large scale, by developing a software framework to quantify the enrichment of microbial metabolisms in complex metagenomes as a function of microbial diversity. We applied this framework to over 400 gut metagenomes from individuals who are healthy or diagnosed with inflammatory bowel disease (IBD). We found that high metabolic independence (HMI) is a distinguishing characteristic of microbial communities associated with individuals diagnosed with IBD. A classifier we trained using the normalized copy numbers of 33 HMI-associated metabolic modules not only distinguished states of health versus IBD, but also tracked the recovery of the gut microbiome following antibiotic treatment, suggesting that HMI is a hallmark of microbial communities in stressed gut environments.

Metabolic independence drives gut microbial colonization and resilience in health and disease.

BACKGROUND: Changes in microbial community composition as a function of human health and disease states have sparked remarkable interest in the human gut microbiome. However, establishing reproducible insights into the determinants of microbial succession in disease has been a formidable challenge. RESULTS: Here we use fecal microbiota transplantation (FMT) as an in natura experimental model to investigate the association between metabolic independence and resilience in stressed gut environments. Our genome-resolved metagenomics survey suggests that FMT serves as an environmental filter that favors populations with higher metabolic independence, the genomes of which encode complete metabolic modules to synthesize critical metabolites, including amino acids, nucleotides, and vitamins. Interestingly, we observe higher completion of the same biosynthetic pathways in microbes enriched in IBD patients. CONCLUSIONS: These observations suggest a general mechanism that underlies changes in diversity in perturbed gut environments and reveal taxon-independent markers of "dysbiosis" that may explain why widespread yet typically low-abundance members of healthy gut microbiomes can dominate under inflammatory conditions without any causal association with disease.

A highly conserved and globally prevalent cryptic plasmid is among the most numerous mobile genetic elements in the human gut.

Plasmids are extrachromosomal genetic elements that often encode fitness enhancing features. However, many bacteria carry 'cryptic' plasmids that do not confer clear beneficial functions. We identified one such cryptic plasmid, pBI143, which is ubiquitous across industrialized gut microbiomes, and is 14 times as numerous as crAssphage, currently established as the most abundant genetic element in the human gut. The majority of mutations in pBI143 accumulate in specific positions across thousands of metagenomes, indicating strong purifying selection. pBI143 is monoclonal in most individuals, likely due to the priority effect of the version first acquired, often from one's mother. pBI143 can transfer between Bacteroidales and although it does not appear to impact bacterial host fitness in vivo, can transiently acquire additional genetic content. We identified important practical applications of pBI143, including its use in identifying human fecal contamination and its potential as an inexpensive alternative for detecting human colonic inflammatory states.

Significant healthcare burden and life cost of spinal muscular atrophy: real-world data.

OBJECTIVES: The aim of this study is to quantify the mortality rate, direct healthcare costs, and cumulative life costs of pediatric patients with spinal muscular atrophy (SMA) type 1, type 2, and type 3 born in Hong Kong. METHODS: Data were collected from genetically confirmed SMA patients born in or after 2000 from the Hospital Authority medical database. Patients were followed up from birth until they died, left Hong Kong, reached 18 years, or initiated disease-modifying treatment. Study outcomes included incidence risks of mortality, cumulative direct medical costs-attendances of special outpatient clinics, emergency department, allied health services, and mean length of stay in hospitals over time. Total direct medical costs were calculated as unit costs multiplied by utilization frequencies of corresponding healthcare services at each age. RESULTS: Seventy-one patients with SMA were included. Over a median follow-up period of 6 years, the overall incidence rate of death was 5.422/100 person-years (95%CI 3.542-7.945/100 person-years). 67.7% and 11% of deaths occurred in SMA1 and SMA2 groups, respectively. The median age of death was 0.8 years in SMA1 and 10.9 years in SMA2. The mean cumulative direct medical costs in overall SMA, SMA1, SMA2 and SMA3 groups per patient were US$935,570, US$2,393,250, US$413,165, and US$40,735, respectively. INTERPRETATION: Our results confirmed a significantly raised mortality and extremely high healthcare burden for patients with SMA especially SMA type 1 and 2 without disease-modifying treatment. Study evaluating health and economic impact of newborn screening and early treatment is needed.

Interpretation of cancer mutations using a multiscale map of protein systems.

A major goal of cancer research is to understand how mutations distributed across diverse genes affect common cellular systems, including multiprotein complexes and assemblies. Two challenges­how to comprehensively map such systems and how to identify which are under mutational selection­have hindered this understanding. Accordingly, we created a comprehensive map of cancer protein systems integrating both new and published multi-omic interaction data at multiple scales of analysis. We then developed a unified statistical model that pinpoints 395 specific systems under mutational selection across 13 cancer types. This map, called NeST (Nested Systems in Tumors), incorporates canonical processes and notable discoveries, including a PIK3CA-actomyosin complex that inhibits phosphatidylinositol 3-kinase signaling and recurrent mutations in collagen complexes that promote tumor proliferation. These systems can be used as clinical biomarkers and implicate a total of 548 genes in cancer evolution and progression. This work shows how disparate tumor mutations converge on protein assemblies at different scales.

Identifying Epistasis in Cancer Genomes: A Delicate Affair.

Recent studies of the tumor genome seek to identify cancer pathways as groups of genes in which mutations are epistatic with one another or, specifically, "mutually exclusive." Here, we show that most mutations are mutually exclusive not due to pathway structure but to interactions with disease subtype and tumor mutation load. In particular, many cancer driver genes are mutated preferentially in tumors with few mutations overall, causing mutations in these cancer genes to appear mutually exclusive with numerous others. Researchers should view current epistasis maps with caution until we better understand the multiple cause-and-effect relationships among factors such as tumor subtype, positive selection for mutations, and gross tumor characteristics including mutational signatures and load.

Visible Machine Learning for Biomedicine.

A major ambition of artificial intelligence lies in translating patient data to successful therapies. Machine learning models face particular challenges in biomedicine, however, including handling of extreme data heterogeneity and lack of mechanistic insight into predictions. Here, we argue for "visible" approaches that guide model structure with experimental biology.

A new patient safety smartphone application for prevention of "forgotten" ureteral stents: results from a clinical pilot study in 194 patients.

BACKGROUND: Approximately 12% of all ureteral stents placed are retained or "forgotten." Forgotten stents are associated with significant safety concerns as well as increased costs and legal issues. Retained ureteral stents (RUS) often occur due to lack of clinical follow-up, communication or language barriers, and economic concerns. METHODS: We describe a multiplatform application that facilitates data collection to prevent RUS. The "Stent Tracker" application can be installed on mobile devices and computers. The encrypted and password-protected information is accessible from any device and provides information about each procedure, stent placement and removal dates, as well as product description. This multicenter retrospective study included 194 patients who underwent stent placement between July and October 2015. Nominal data was tallied and ordinal data was divided into quartiles of 25, 50, and 75%. RESULTS: A total of 194 patients from three institutions underwent ureteral stent placement. Reasons for stent placement include 122 cases post ureteroscopy (63%), 8 cases post percutaneous nephrolithotomy (PCNL) (4%), 14 cases post extracorporeal shock wave lithotripsy (SWL) (7%), 18 cases of cancer-related ureteral obstruction (9%), 21 cases of hydronephrosis (11%), and 11 for other reasons (6%). Of these patients, only one patient was lost to follow-up (0.5%). On average, ureteral stents were removed within 14 days of placement (IQR: 8-26 days). CONCLUSIONS: The "Stent Tracker" is a patient safety application that provides a secure and simplified interface, which can significantly reduce the incidence of RUS. Further developments could include automated notifications to patients and staff, color-coding, and integrated information with electronic patient charts.