Ecological drivers of CRISPR immune systems.

CRISPR-Cas is the only known adaptive immune system of prokaryotes. It is a powerful defense system against mobile genetic elements such as bacteriophages. While CRISPR-Cas systems can be found throughout the prokaryotic tree of life, they are distributed unevenly across taxa and environments. Since adaptive immunity is more useful in environments where pathogens persist or reoccur, the density and/or diversity of the host/pathogen community may drive the uneven distribution of CRISPR system. We directly tested hypotheses connecting CRISPR incidence with prokaryotic density/diversity by analyzing 16S rRNA and metagenomic data from publicly available environmental sequencing projects. In terms of density, we found that CRISPR systems are significantly favored in lower abundance (less dense) taxa and disfavored in higher abundance taxa, at least in marine environments. When we extended this work to compare taxonomic diversity between samples, we found CRISPR system incidence strongly correlated with diversity in human oral environments. Together, these observations confirm that, at least in certain types of environments, the prokaryotic ecological context indeed plays a key role in selecting for CRISPR immunity. Importance: 2Microbes must constantly defend themselves against viral pathogens, and a large proportion of prokaryotes do so using the highly effective CRISPR-Cas adaptive immune system. However, many prokaryotes do not. We investigated the ecological factors behind this uneven distribution of CRISPR-Cas immune systems in natural microbial populations. We found strong patterns linking CRISPR-Cas systems to prokaryotic density within ocean environments and to prokaryotic diversity within human oral environments. Our study validates previous within-lab experimental results that suggested these factors might be important and confirms that local environment and ecological context interact to select for CRISPR immunity.

What is microbial dormancy?

Life can be stressful. One way to deal with stress is to simply wait it out. Microbes do this by entering a state of reduced activity and increased resistance commonly called 'dormancy'. But what is dormancy? Different scientific disciplines emphasize distinct traits and phenotypic ranges in defining dormancy for their microbial species and system-specific questions of interest. Here, we propose a unified definition of microbial dormancy, using a broad framework to place earlier discipline-specific definitions in a new context. We then discuss how this new definition and framework may improve our ability to investigate dormancy using multi-omics tools. Finally, we leverage our framework to discuss the diversity of genomic mechanisms for dormancy in an extreme environment that challenges easy definitions - the permafrost.

Building a queer- and trans-inclusive microbiology conference.

Microbiology conferences can be powerful places to build collaborations and exchange ideas, but for queer and transgender (trans) scientists, they can also become sources of alienation and isolation. Many conference organizers would like to create welcoming and inclusive events but feel ill-equipped to make this vision a reality, and a historical lack of representation of queer and trans folks in microbiology means we rarely occupy these key leadership roles ourselves. Looking more broadly, queer and trans scientists are systematically marginalized across scientific fields, leading to disparities in career outcomes, professional networks, and opportunities, as well as the loss of unique scientific perspectives at all levels. For queer and trans folks with multiple, intersecting, marginalized identities, these barriers often become even more severe. Here, we draw from our experiences as early-career microbiologists to provide concrete, practical advice to help conference organizers across research communities design inclusive, safe, and welcoming conferences, where queer and trans scientists can flourish.

Symbiotic UCYN-A strains co-occurred with El Niño, relaxed upwelling, and varied eukaryotes over 10 years off Southern California.

Biological nitrogen fixation, the conversion of N2 gas into a bioavailable form, is vital to sustaining marine primary production. Studies have shifted beyond traditionally studied tropical diazotrophs. Candidatus Atelocyanobacterium thalassa (or UCYN-A) has emerged as a focal point due to its streamlined metabolism, intimate partnership with a haptophyte host, and broad distribution. Here, we explore the environmental parameters that govern UCYN-A's presence at the San Pedro Ocean Time-series (SPOT), its host specificity, and statistically significant interactions with non-host eukaryotes from 2008-2018. 16S and 18S rRNA gene sequences were amplified by "universal primers" from monthly samples and resolved into Amplicon Sequence Variants, allowing us to observe multiple UCYN-A symbioses. UCYN-A1 relative abundances increased following the 2015-2016 El Niño event. This "open ocean ecotype" was present when coastal upwelling declined, and Ekman transport brought tropical waters into the region. Network analyses reveal all strains of UCYN-A co-occur with dinoflagellates including Lepidodinium, a potential predator, and parasitic Syndiniales. UCYN-A2 appeared to pair with multiple hosts and was not tightly coupled to its predominant host, while UCYN-A1 maintained a strong host-symbiont relationship. These biological relationships are particularly important to study in the context of climate change, which will alter UCYN-A distribution at regional and global scales.

Size-fractionated microbiome observed during an eight-month long sampling in Jiaozhou Bay and the Yellow Sea.

Jiaozhou Bay is a typical semi-enclosed bay with a temperate climate imposed by strong anthropogenic influence. To investigate microbial biodiversity and ecosystem services in this highly dynamic coastal environment, we conducted a monthly microbial survey spanning eight months at two stations in the bay and the open Yellow Sea starting in April 2015. This report provides a comprehensive inventory of amplicon sequences and environmental microbial genomes from this survey. In total, 2,543 amplicon sequence variants were obtained with monthly relative abundance profiles in three size fractions (>2.7 µm, 2.7-0.7 µm, and 0.7-0.22 µm). Shotgun metagenomes yielded 915 high-quality metagenome-assembled genomes with ≥50% completeness and ≤5% contamination. These environmental genomes comprise 27 bacterial and 5 archaeal phyla. We expect this comprehensive dataset will facilitate a better understanding of coastal microbial ecology.

Benchmarking Community-Wide Estimates of Growth Potential from Metagenomes Using Codon Usage Statistics.

Trait inference from mixed-species assemblages is a central problem in microbial ecology. Frequently, sequencing information from an environment is available, but phenotypic measurements from individual community members are not. With the increasing availability of molecular data for microbial communities, bioinformatic approaches that map metagenome to (meta)phenotype are needed. Recently, we developed a tool, gRodon, that enables the prediction of the maximum growth rate of an organism from genomic data on the basis of codon usage patterns. Our work and that of other groups suggest that such predictors can be applied to mixed-species communities in order to derive estimates of the average community-wide maximum growth rate. Here, we present an improved maximum growth rate predictor designed for metagenomes that corrects a persistent GC bias in the original gRodon model for metagenomic prediction. We benchmark this predictor with simulated metagenomic data sets to show that it has superior performance on mixed-species communities relative to earlier models. We go on to provide guidance on data preprocessing and show that calling genes from assembled contigs rather than directly from reads dramatically improves performance. Finally, we apply our predictor to large-scale metagenomic data sets from marine and human microbiomes to illustrate how community-wide growth prediction can be a powerful approach for hypothesis generation. Altogether, we provide an updated tool with clear guidelines for users about the uses and pitfalls of metagenomic prediction of the average community-wide maximal growth rate. IMPORTANCE Microbes dominate nearly every known habitat, and therefore tools to survey the structure and function of natural microbial communities are much needed. Metagenomics, in which the DNA content of an entire community of organisms is sequenced all at once, allows us to probe the genetic diversity contained in a habitat. Yet, mapping metagenomic information to the actual traits of community members is a difficult and largely unsolved problem. Here, we present and validate a tool that allows users to predict the average maximum growth rate of a microbial community directly from metagenomic data. Maximum growth rate is a fundamental characteristic of microbial species that can give us a great deal of insight into their ecological role, and by applying our community-level predictor to large-scale metagenomic data sets from marine and human-associated microbiomes, we show how community-wide growth prediction can be a powerful approach for hypothesis generation.

NetGAM: Using generalized additive models to improve the predictive power of ecological network analyses constructed using time-series data.

Ecological network analyses are used to identify potential biotic interactions between microorganisms from species abundance data. These analyses are often carried out using time-series data; however, time-series networks have unique statistical challenges. Time-dependent species abundance data can lead to species co-occurrence patterns that are not a result of direct, biotic associations and may therefore result in inaccurate network predictions. Here, we describe a generalize additive model (GAM)-based data transformation that removes time-series signals from species abundance data prior to running network analyses. Validation of the transformation was carried out by generating mock, time-series datasets, with an underlying covariance structure, running network analyses on these datasets with and without our GAM transformation, and comparing the network outputs to the known covariance structure of the simulated data. The results revealed that seasonal abundance patterns substantially decreased the accuracy of the inferred networks. In addition, the GAM transformation increased the predictive power (F1 score) of inferred ecological networks on average and improved the ability of network inference methods to capture important features of network structure. This study underscores the importance of considering temporal features when carrying out network analyses and describes a simple, effective tool that can be used to improve results.

Exploring the functional composition of the human microbiome using a hand-curated microbial trait database.

BACKGROUND: Even when microbial communities vary wildly in their taxonomic composition, their functional composition is often surprisingly stable. This suggests that a functional perspective could provide much deeper insight into the principles governing microbiome assembly. Much work to date analyzing the functional composition of microbial communities, however, relies heavily on inference from genomic features. Unfortunately, output from these methods can be hard to interpret and often suffers from relatively high error rates. RESULTS: We built and analyzed a domain-specific microbial trait database from known microbe-trait pairs recorded in the literature to better understand the functional composition of the human microbiome. Using a combination of phylogentically conscious machine learning tools and a network science approach, we were able to link particular traits to areas of the human body, discover traits that determine the range of body areas a microbe can inhabit, and uncover drivers of metabolic breadth. CONCLUSIONS: Domain-specific trait databases are an effective compromise between noisy methods to infer complex traits from genomic data and exhaustive, expensive attempts at database curation from the literature that do not focus on any one subset of taxa. They provide an accurate account of microbial traits and, by limiting the number of taxa considered, are feasible to build within a reasonable time-frame. We present a database specific for the human microbiome, in the hopes that this will prove useful for research into the functional composition of human-associated microbial communities.

Estimating maximal microbial growth rates from cultures, metagenomes, and single cells via codon usage patterns.

Maximal growth rate is a basic parameter of microbial lifestyle that varies over several orders of magnitude, with doubling times ranging from a matter of minutes to multiple days. Growth rates are typically measured using laboratory culture experiments. Yet, we lack sufficient understanding of the physiology of most microbes to design appropriate culture conditions for them, severely limiting our ability to assess the global diversity of microbial growth rates. Genomic estimators of maximal growth rate provide a practical solution to survey the distribution of microbial growth potential, regardless of cultivation status. We developed an improved maximal growth rate estimator and predicted maximal growth rates from over 200,000 genomes, metagenome-assembled genomes, and single-cell amplified genomes to survey growth potential across the range of prokaryotic diversity; extensions allow estimates from 16S rRNA sequences alone as well as weighted community estimates from metagenomes. We compared the growth rates of cultivated and uncultivated organisms to illustrate how culture collections are strongly biased toward organisms capable of rapid growth. Finally, we found that organisms naturally group into two growth classes and observed a bias in growth predictions for extremely slow-growing organisms. These observations ultimately led us to suggest evolutionary definitions of oligotrophy and copiotrophy based on the selective regime an organism occupies. We found that these growth classes are associated with distinct selective regimes and genomic functional potentials.

NCBI's Virus Discovery Codeathon: Building "FIVE" -The Federated Index of Viral Experiments API Index.

Viruses represent important test cases for data federation due to their genome size and the rapid increase in sequence data in publicly available databases. However, some consequences of previously decentralized (unfederated) data are lack of consensus or comparisons between feature annotations. Unifying or displaying alternative annotations should be a priority both for communities with robust entry representation and for nascent communities with burgeoning data sources. To this end, during this three-day continuation of the Virus Hunting Toolkit codeathon series (VHT-2), a new integrated and federated viral index was elaborated. This Federated Index of Viral Experiments (FIVE) integrates pre-existing and novel functional and taxonomy annotations and virus-host pairings. Variability in the context of viral genomic diversity is often overlooked in virus databases. As a proof-of-concept, FIVE was the first attempt to include viral genome variation for HIV, the most well-studied human pathogen, through viral genome diversity graphs. As per the publication of this manuscript, FIVE is the first implementation of a virus-specific federated index of such scope. FIVE is coded in BigQuery for optimal access of large quantities of data and is publicly accessible. Many projects of database or index federation fail to provide easier alternatives to access or query information. To this end, a Python API query system was developed to enhance the accessibility of FIVE.

Avoidance of Self during CRISPR Immunization.

The battle between microbes and their viruses is ancient and ongoing. Clustered regularly interspaced short palindromic repeat (CRISPR) immunity, the first and, to date, only form of adaptive immunity found in prokaryotes, represents a flexible mechanism to recall past infections while also adapting to a changing pathogenic environment. Critical to the role of CRISPR as an adaptive immune mechanism is its capacity for self versus non-self recognition when acquiring novel immune memories. Yet, CRISPR systems vary widely in both how and to what degree they can distinguish foreign from self-derived genetic material. We document known and hypothesized mechanisms that bias the acquisition of immune memory towards non-self targets. We demonstrate that diversity is the rule, with many widespread but no universal mechanisms for self versus non-self recognition.

Network-Based Prediction of Novel CRISPR-Associated Genes in Metagenomes.

A diversity of clustered regularly interspaced short palindromic repeat (CRISPR)-Cas systems provide adaptive immunity to bacteria and archaea through recording "memories" of past viral infections. Recently, many novel CRISPR-associated proteins have been discovered via computational studies, but those studies relied on biased and incomplete databases of assembled genomes. We avoided these biases and applied a network theory approach to search for novel CRISPR-associated genes by leveraging subtle ecological cooccurrence patterns identified from environmental metagenomes. We validated our method using existing annotations and discovered 32 novel CRISPR-associated gene families. These genes span a range of putative functions, with many potentially regulating the response to infection.IMPORTANCE Every branch on the tree of life, including microbial life, faces the threat of viral pathogens. Over the course of billions of years of coevolution, prokaryotes have evolved a great diversity of strategies to defend against viral infections. One of these is the CRISPR adaptive immune system, which allows microbes to "remember" past infections in order to better fight them in the future. There has been much interest among molecular biologists in CRISPR immunity because this system can be repurposed as a tool for precise genome editing. Recently, a number of comparative genomics approaches have been used to detect novel CRISPR-associated genes in databases of genomes with great success, potentially leading to the development of new genome-editing tools. Here, we developed novel methods to search for these distinct classes of genes directly in environmental samples ("metagenomes"), thus capturing a more complete picture of the natural diversity of CRISPR-associated genes.

Variation in expenditure for common, high cost surgical procedures in a working age population: implications for reimbursement reform.

BACKGROUND: In the move toward value-based care, bundled payments are believed to reduce waste and improve coordination. Some commercial insurers have addressed this through the use of bundled payment, the provision of one fee for all care associated with a given index procedure. This system was pioneered by Medicare, using a population generally over 65 years of age, and despite its adoption by mainstream insurers, little is known of bundled payments' ability to reduce variation or cost in a working-age population. This study uses a universally-insured, nationally-representative population of adults aged 18-65 to examine the effect of bundled payments for five high-cost surgical procedures which are known to vary widely in Medicare reimbursement: hip replacement, knee replacement, coronary artery bypass grafting (CABG), lumbar spinal fusion, and colectomy. METHODS: Five procedures conducted on adults aged 18-65 were identified from the TRICARE database from 2011 to 2014. A 90-day period from index procedure was used to determine episodes of associated post-acute care. Data was sorted by Zip code into hospital referral regions (HRR). Payments were determined from TRICARE reimbursement records, they were subsequently price standardized and adjusted for patient and surgical characteristics. Variation was assessed by stratifying the HRR into quintiles by spending for each index procedure. RESULTS: After adjusting for case mix, significant inter-quintile variation was observed for all procedures, with knee replacement showing the greatest variation in both index surgery (107%) and total cost of care (75%). Readmission was a driver of variation for colectomy and CABG, with absolute cost variation of $17,257 and $13,289 respectively. Other post-acute care spending was low overall (≤$1606, for CABG). CONCLUSIONS: This study demonstrates significant regional variation in total spending for these procedures, but much lower spending for post-acute care than previously demonstrated by similar procedures in Medicare. Targeting post-acute care spending, a common approach taken by providers in bundled payment arrangements with Medicare, may be less fruitful in working aged populations.

Linking high GC content to the repair of double strand breaks in prokaryotic genomes.

Genomic GC content varies widely among microbes for reasons unknown. While mutation bias partially explains this variation, prokaryotes near-universally have a higher GC content than predicted solely by this bias. Debate surrounds the relative importance of the remaining explanations of selection versus biased gene conversion favoring GC alleles. Some environments (e.g. soils) are associated with a high genomic GC content of their inhabitants, which implies that either high GC content is a selective adaptation to particular habitats, or that certain habitats favor increased rates of gene conversion. Here, we report a novel association between the presence of the non-homologous end joining DNA double-strand break repair pathway and GC content; this observation suggests that DNA damage may be a fundamental driver of GC content, leading in part to the many environmental patterns observed to-date. We discuss potential mechanisms accounting for the observed association, and provide preliminary evidence that sites experiencing higher rates of double-strand breaks are under selection for increased GC content relative to the genomic background.

Trait-based analysis of the human skin microbiome.

BACKGROUND: The past decade of microbiome research has concentrated on cataloging the diversity of taxa in different environments. The next decade is poised to focus on microbial traits and function. Most existing methods for doing this perform pathway analysis using reference databases. This has both benefits and drawbacks. Function can go undetected if reference databases are coarse-grained or incomplete. Likewise, detection of a pathway does not guarantee expression of the associated function. Finally, function cannot be connected to specific microbial constituents, making it difficult to ascertain the types of organisms exhibiting particular traits-something that is important for understanding microbial success in specific environments. A complementary approach to pathway analysis is to use the wealth of microbial trait information collected over years of lab-based, culture experiments. METHODS: Here, we use journal articles and Bergey's Manual of Systematic Bacteriology to develop a trait-based database for 971 human skin bacterial taxa. We then use this database to examine functional traits that are over/underrepresented among skin taxa. Specifically, we focus on three trait classes-binary, categorical, and quantitative-and compare trait values among skin taxa and microbial taxa more broadly. We compare binary traits using a Chi-square test, categorical traits using randomization trials, and quantitative traits using a nonparametric relative effects test based on global rankings using Tukey contrasts. RESULTS: We find a number of traits that are over/underrepresented within the human skin microbiome. For example, spore formation, acid phosphatase, alkaline phosphatase, pigment production, catalase, and oxidase are all less common among skin taxa. As well, skin bacteria are less likely to be aerobic, favoring, instead, a facultative strategy. They are also less likely to exhibit gliding motility, less likely to be spirillum or rod-shaped, and less likely to grow in chains. Finally, skin bacteria have more difficulty at high pH, prefer warmer temperatures, and are much less resilient to hypotonic conditions. CONCLUSIONS: Our analysis shows how an approach that relies on information from culture experiments can both support findings from pathway analysis, and also generate new insights into the structuring principles of microbial communities.

Visualization and prediction of CRISPR incidence in microbial trait-space to identify drivers of antiviral immune strategy.

Bacteria and archaea are locked in a near-constant battle with their viral pathogens. Despite previous mechanistic characterization of numerous prokaryotic defense strategies, the underlying ecological drivers of different strategies remain largely unknown and predicting which species will take which strategies remains a challenge. Here, we focus on the CRISPR immune strategy and develop a phylogenetically-corrected machine learning approach to build a predictive model of CRISPR incidence using data on over 100 traits across over 2600 species. We discover a strong but hitherto-unknown negative interaction between CRISPR and aerobicity, which we hypothesize may result from interference between CRISPR-associated proteins and non-homologous end-joining DNA repair due to oxidative stress. Our predictive model also quantitatively confirms previous observations of an association between CRISPR and temperature. Finally, we contrast the environmental associations of different CRISPR system types (I, II, III) and restriction modification systems, all of which act as intracellular immune systems.

Immune loss as a driver of coexistence during host-phage coevolution.

Bacteria and their viral pathogens face constant pressure for augmented immune and infective capabilities, respectively. Under this reciprocally imposed selective regime, we expect to see a runaway evolutionary arms race, ultimately leading to the extinction of one species. Despite this prediction, in many systems host and pathogen coexist with minimal coevolution even when well-mixed. Previous work explained this puzzling phenomenon by invoking fitness tradeoffs, which can diminish an arms race dynamic. Here we propose that the regular loss of immunity by the bacterial host can also produce host-phage coexistence. We pair a general model of immunity with an experimental and theoretical case study of the CRISPR-Cas immune system to contrast the behavior of tradeoff and loss mechanisms in well-mixed systems. We find that, while both mechanisms can produce stable coexistence, only immune loss does so robustly within realistic parameter ranges.

Minimally invasive surgery and its impact on 30-day postoperative complications, unplanned readmissions and mortality.

BACKGROUND: A critical appraisal of the benefits of minimally invasive surgery (MIS) is needed, but is lacking. This study examined the associations between MIS and 30-day postoperative outcomes including complications graded according to the Clavien-Dindo classification, unplanned readmissions, hospital stay and mortality for five common surgical procedures. METHODS: Patients undergoing appendicectomy, colectomy, inguinal hernia repair, hysterectomy and prostatectomy were identified in the American College of Surgeons National Surgical Quality Improvement Program database. Non-parsimonious propensity score methods were used to construct procedure-specific matched-pair cohorts that reduced baseline differences between patients who underwent MIS and those who did not. Bonferroni correction for multiple comparisons was applied and P < 0·006 was considered significant. RESULTS: Of the 532 287 patients identified, 53·8 per cent underwent MIS. Propensity score matching yielded an overall sample of 327 736 patients (appendicectomy 46 688, colectomy 152 114, inguinal hernia repair 59 066, hysterectomy 59 066, prostatectomy 10 802). Within the procedure-specific matched pairs, MIS was associated with significantly lower odds of Clavien-Dindo grade I-II, III and IV complications (P ≤ 0·004), unplanned readmissions (P < 0·001) and reduced hospital stay (P < 0·001) in four of the five procedures studied, with the exception of inguinal hernia repair. The odds of death were lower in patients undergoing MIS colectomy (P < 0·001), hysterectomy (P = 0·002) and appendicectomy (P = 0·002). CONCLUSION: MIS was associated with significantly fewer 30-day postoperative complications, unplanned readmissions and deaths, as well as shorter hospital stay, in patients undergoing colectomy, prostatectomy, hysterectomy or appendicectomy. No benefits were noted for inguinal hernia repair.

Oncogenic activation of the PI3-kinase p110ß isoform via the tumor-derived PIK3Cß(D1067V) kinase domain mutation.

Activation of the phosphoinositide 3-kinase (PI3K) pathway occurs widely in human cancers. Although somatic mutations in the PI3K pathway genes PIK3CA and PTEN are known to drive PI3K pathway activation and cancer growth, the significance of somatic mutations in other PI3K pathway genes is less clear. Here, we establish the signaling and oncogenic properties of a recurrent somatic mutation in the PI3K p110ß isoform that resides within its kinase domain (PIK3Cß(D1067V)). We initially observed PIK3Cß(D1067V) by exome sequencing analysis of an EGFR-mutant non-small cell lung cancer (NSCLC) tumor biopsy from a patient with acquired erlotinib resistance. On the basis of this finding, we hypothesized that PIK3Cß(D1067V) might function as a novel tumor-promoting genetic alteration, and potentially an oncogene, in certain cancers. Consistent with this hypothesis, analysis of additional tumor exome data sets revealed the presence of PIK3Cß(D1067V) at low frequency in other patient tumor samples (including renal cell carcinoma, glioblastoma multiforme, head and neck squamous cell carcinoma, melanoma, thyroid carcinoma and endometrial carcinoma). Functional studies revealed that PIK3Cß(D1067V) promoted PI3K pathway signaling, enhanced cell growth in vitro, and was sufficient for tumor formation in vivo. Pharmacologic inhibition of PIK3Cß with TGX-221 (isoform-selective p110ß inhibitor) specifically suppressed growth in patient-derived renal-cell carcinoma cells with endogenous PIK3Cß(D1067V) and in NIH-3T3 and human EGFR-mutant lung adenocarcinoma cells engineered to express this mutant PI3K. In the EGFR-mutant lung adenocarcinoma cells, expression of PIK3Cß(D1067V) also promoted erlotinib resistance. Our data establish a novel oncogenic form of PI3K, revealing the signaling and oncogenic properties of PIK3Cß(D1067V) and its potential therapeutic relevance in cancer. Our findings provide new insight into the genetic mechanisms underlying PI3K pathway activation in human tumors and indicate that PIK3Cß(D1067V) is a rational therapeutic target in certain cancers.