Search

In the Western United States, volcanic spring-fed rivers are anticipated to become increas-ingly more important for salmonids and other native fishes, as these rivers will retain coldwater habitats as the climate warms. Despite this, little is known about the hydro-biogeochemical interactions within these ecosystems. A review of existing literature on spring-fed rivers, coupled with a decade of research on volcanic spring-fed rivers of northern California, finds that these systems are exceptionally productive and exhibit stable environmental conditions. These unique conditions stem from hydrogeologic processes typical of young volcanic terrains. Aquatic macrophytes, com-mon to some nutrient-rich spring-fed systems, play a disproportionate role in hydrologic and geo-morphic processes by facilitating ecological interactions and velocity conditions that improve juvenile salmonid growth. We find that volcanic spring-fed rivers are also resilient to climate change, due not only to their ability to dampen water temperature changes through deep groundwater flow but also because of their nutrient-driven high ecosystem productivity, which may enable coldwater species to metabolically compensate for marginal increases in water temperature. Understanding the fundamental geomorphic and ecological differences between these rare ecosystems and their numerically dominant runoff rivers is essential for developing long-term conservation strategies for coldwater species under a rapidly changing climate.

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.

Background: As a nexus of routine antibiotic use and zoonotic pathogen presence, the livestock farming environment is a potential hotspot for the emergence of zoonotic diseases and antibiotic resistant bacteria. Livestock can further facilitate disease transmission by serving as intermediary hosts for pathogens before a spillover event. In light of this, we aimed to characterize the microbiomes and resistomes of dairy workers, whose exposure to the livestock farming environment places them at risk for facilitating community transmission of antibiotic resistant genes and emerging zoonotic diseases., Results: Using shotgun sequencing, we investigated differences in the taxonomy, diversity and gene presence of 10 dairy farm workers and 6 community controls' gut metagenomes, contextualizing these samples with additional publicly available gut metagenomes. We found no significant differences in the prevalence of resistance genes, virulence factors, or taxonomic composition between the two groups. The lack of statistical significance may be attributed, in part, to the limited sample size of our study or the potential similarities in exposures between the dairy workers and community controls. We did, however, observe patterns warranting further investigation including greater abundance of tetracycline resistance genes and prevalence of cephamycin resistance genes as well as lower average gene diversity (even after accounting for differential sequencing depth) in dairy workers' metagenomes. We also found evidence of commensal organism association with tetracycline resistance genes in both groups (including Faecalibacterium prausnitzii, Ligilactobacillus animalis, and Simiaoa sunii)., Conclusions: This study highlights the utility of shotgun metagenomics in examining the microbiomes and resistomes of livestock workers, focusing on a cohort of dairy workers in the United States. While our study revealed no statistically significant differences between groups in taxonomy, diversity and gene presence, we observed patterns in antibiotic resistance gene abundance and prevalence that align with findings from previous studies of livestock workers in China and Europe. Our results lay the groundwork for future research involving larger cohorts of dairy and non-dairy workers to better understand the impact of occupational exposure to livestock farming on the microbiomes and resistomes of workers., (© 2024. The Author(s).)

Microbiome scientists critically need modern tools to explore and analyze microbial evolution. Often this involves studying the evolution of microbial genomes as a whole. However, different genes in a single genome can be subject to different evolutionary pressures, which can result in distinct gene-level evolutionary histories. To address this challenge, we propose to treat estimated gene-level phylogenies as data objects, and present an interactive method for the analysis of a collection of gene phylogenies. We use a local linear approximation of phylogenetic tree space to visualize estimated gene trees as points in low-dimensional Euclidean space, and address important practical limitations of existing related approaches, allowing an intuitive visualization of complex data objects. We demonstrate the utility of our proposed approach through microbial data analyses, including by identifying outlying gene histories in strains of Prevotella, and by contrasting Streptococcus phylogenies estimated using different gene sets. Our method is available as an open-source R package, and assists with estimating, visualizing, and interacting with a collection of bacterial gene phylogenies., (© The Author 2023. Published by Oxford University Press.)

Fusobacterium nucleatum (Fn), a bacterium present in the human oral cavity and rarely found in the lower gastrointestinal tract of healthy individuals 1 , is enriched in human colorectal cancer (CRC) tumours 2-5 . High intratumoural Fn loads are associated with recurrence, metastases and poorer patient prognosis 5-8 . Here, to delineate Fn genetic factors facilitating tumour colonization, we generated closed genomes for 135 Fn strains; 80 oral strains from individuals without cancer and 55 unique cancer strains cultured from tumours from 51 patients with CRC. Pangenomic analyses identified 483 CRC-enriched genetic factors. Tumour-isolated strains predominantly belong to Fn subspecies animalis (Fna). However, genomic analyses reveal that Fna, considered a single subspecies, is instead composed of two distinct clades (Fna C1 and Fna C2). Of these, only Fna C2 dominates the CRC tumour niche. Inter-Fna analyses identified 195 Fna C2-associated genetic factors consistent with increased metabolic potential and colonization of the gastrointestinal tract. In support of this, Fna C2-treated mice had an increased number of intestinal adenomas and altered metabolites. Microbiome analysis of human tumour tissue from 116 patients with CRC demonstrated Fna C2 enrichment. Comparison of 62 paired specimens showed that only Fna C2 is tumour enriched compared to normal adjacent tissue. This was further supported by metagenomic analysis of stool samples from 627 patients with CRC and 619 healthy individuals. Collectively, our results identify the Fna clade bifurcation, show that specifically Fna C2 drives the reported Fn enrichment in human CRC and reveal the genetic underpinnings of pathoadaptation of Fna C2 to the CRC niche., (© 2024. The Author(s).)

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., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Recovering metagenome-assembled genomes (MAGs) from shotgun sequencing data is an increasingly common task in microbiome studies, as MAGs provide deeper insight into the functional potential of both culturable and non-culturable microorganisms. However, metagenome-assembled genomes vary in quality and may contain omissions and contamination. These errors present challenges for detecting genes and comparing gene enrichment across sample types. To address this, we propose happi, an approach to testing hypotheses about gene enrichment that accounts for genome quality. We illustrate the advantages of happi over existing approaches using published Saccharibacteria MAGs, Streptococcus thermophilus MAGs, and via simulation., (© 2023. BioMed Central Ltd., part of Springer Nature.)

Comprehensive sampling of natural genetic diversity with metagenomics enables highly resolved insights into the interplay between ecology and evolution. However, resolving adaptive, neutral, or purifying processes of evolution from intrapopulation genomic variation remains a challenge, partly due to the sole reliance on gene sequences to interpret variants. Here, we describe an approach to analyze genetic variation in the context of predicted protein structures and apply it to a marine microbial population within the SAR11 subclade 1a.3.V, which dominates low-latitude surface oceans. Our analyses reveal a tight association between genetic variation and protein structure. In a central gene in nitrogen metabolism, we observe decreased occurrence of nonsynonymous variants from ligand-binding sites as a function of nitrate concentrations, revealing genetic targets of distinct evolutionary pressures maintained by nutrient availability. Our work yields insights into the governing principles of evolution and enables structure-aware investigations of microbial population genetics.

High-throughput sequencing is widely used to study microbial communities. However, choice of laboratory protocol is known to affect the resulting microbiome data, which has an unquantified impact on many comparisons between communities of scientific interest. We propose a novel approach to evaluating replicability in high-dimensional data and apply it to assess the cross-laboratory replicability of signals in microbiome data using the Microbiome Quality Control Project data set. We learn distinctions between samples as measured by a single laboratory and evaluate whether the same distinctions hold in data produced by other laboratories. While most sequencing laboratories can consistently distinguish between samples (median correct classification 87% on genus-level proportion data), these distinctions frequently fail to hold in data from other laboratories (median correct classification 55% across laboratory on genus-level proportion data). As identical samples processed by different laboratories generate substantively different quantitative results, we conclude that 16S sequencing does not reliably resolve differences in human microbiome samples. However, because we observe greater replicability under certain data transformations, our results inform the analysis of microbiome data., (© The Author 2021. Published by Oxford University Press.)

The absolute abundance of bacterial taxa in human host-associated environments plays a critical role in reproductive and gastrointestinal health. However, obtaining the absolute abundance of many bacterial species is typically prohibitively expensive. In contrast, relative abundance data for many species are comparatively cheap and easy to collect (e.g., with universal primers for the 16S rRNA gene). In this paper, we propose a method to jointly model relative abundance data for many taxa and absolute abundance data for a subset of taxa. Our method provides point and interval estimates for the absolute abundance of all taxa. Crucially, our proposal accounts for differences in the efficiency of taxon detection in the relative and absolute abundance data. We show that modeling taxon-specific efficiencies substantially reduces the estimation error for absolute abundance, and controls the coverage of interval estimators. We demonstrate the performance of our proposed method via a simulation study, a study of the effect of HIV acquisition on microbial abundances, and a sensitivity study where we jackknife the taxa with observed absolute abundances., (© 2021 The Authors. Biometrics published by Wiley Periodicals, Inc. on behalf of International Biometric Society.)

Development of sexual characters and generation of gametes are tightly coupled with growth. Platynereis dumerilii is a marine annelid that has been used to study germline development and gametogenesis. P. dumerilii has germ cell clusters found across the body in the juvenile worms, and the clusters eventually form the gametes. Like other segmented worms, P. dumerilii grows by adding new segments at its posterior end. The number of segments reflect the growth state of the worms and therefore is a useful and measurable growth state metric to study the growth-reproduction crosstalk. To understand how growth correlates with progression of gametogenesis, we investigated germline development across several developmental stages. We discovered a distinct transition period when worms increase the number of germline clusters at a particular segment number threshold. Additionally, we found that keeping worms short in segment number, by manipulating environmental conditions or via amputations, supported a segment number threshold requirement for germline development. Finally, we asked if these clusters in P. dumerilii play a role in regeneration (as similar free-roaming cells are observed in Hydra and planarian regeneration) and found that the clusters were not required for regeneration in P. dumerilii, suggesting a strictly germline nature. Overall, these molecular analyses suggest a previously unidentified developmental transition dependent on the growth state of juvenile P. dumerilii leading to substantially increased germline expansion., (© 2021 The Authors. Journal of Experimental Zoology Part B: Molecular and Developmental Evolution published by Wiley Periodicals LLC.)

Comparing ecological communities across environmental gradients can be challenging, especially when the number of different taxonomic groups in the communities is large. In this setting, community-level summaries called diversity indices are widely used to detect changes in the community ecology. However, estimation of diversity indices has received relatively little attention from the statistical community. The most common estimates of diversity are the maximum likelihood estimates of the parameters of a multinomial model, even though the multinomial model implies strict assumptions about the sampling mechanism. In particular, the multinomial model prohibits ecological networks, where taxa positively and negatively co-occur. In this article, we leverage models from the compositional data literature that explicitly account for co-occurrence networks and use them to estimate diversity. Instead of proposing new diversity indices, we estimate popular diversity indices under these models. While the methodology is general, we illustrate the approach for the estimation of the Shannon, Simpson, Bray-Curtis, and Euclidean diversity indices. We contrast our method to multinomial, low-rank, and nonparametric methods for estimating diversity indices. Under simulation, we find that the greatest gains of the method are in strongly networked communities with many taxa. Therefore, to illustrate the method, we analyze the microbiome of seafloor basalts based on a 16S amplicon sequencing dataset with 1425 taxa and 12 communities., (© The Author 2020. Published by Oxford University Press.)

Colorectal cancer is a major health concern worldwide. Growing evidence for the role of the gut microbiota in the initiation of CRC has sparked interest in approaches that target these microorganisms. However, little is known about the composition and role of the microbiota associated with precancerous polyps. Here, we found distinct microbial signatures between patients with and without polyps and between polyp subtypes using sequencing and culturing techniques. We found a correlation between Bacteroides fragilis recovered and the level of inflammatory cytokines in the mucosa adjacent to the polyp. Additional analysis revealed that B. fragilis from patients with polyps are bft-negative, activate NF-κB through Toll-like receptor 4, induce a pro-inflammatory response, and are enriched in genes associated with LPS biosynthesis. This study provides fundamental insight into the microbial microenvironment of the pre-neoplastic polyp by highlighting strain-specific genomic and proteomic differences, as well as more broad compositional differences in the microbiome., Competing Interests: Declaration of interests The authors declare the following competing interests. R.W.D. is on the advisory board of MicrobiomX. W.M.G. is on the advisory board of Guardant Health, Freenome, SEngine, and consults for Diacarta and receives research support from Jannsen and Tempus. . C.W.K. receives research support from Freenome., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Stream temperature science and management is rapidly shifting from single-metric driven approaches to multi-metric, thermal regime characterizations of streamscapes. Given considerable investments in recovery of cold-water fisheries (e.g., Pacific salmon and other declining native species), understanding where cold water is likely to persist, and how cold-water thermal regimes vary, is critical for conservation. California's unique position at the southern end of cold-water ecosystems in the northern hemisphere, variable geography and hydrology, and extensive flow regulation requires a systematic approach to thermal regime classification. We used publicly available, long-term (> 8 years) stream temperature data from 77 sites across California to model their thermal regimes, calculate three temperature metrics, and use the metrics to classify each regime with an agglomerative nesting algorithm. Then, we assessed the variation in each class and considered underlying physical or anthropogenic factors that could explain differences between classes. Finally, we considered how different classes might fit existing criteria for cool- or cold-water thermal regimes, and how those differences complicate efforts to manage stream temperature through regulation. Our results demonstrate that cool- and cold-water thermal regimes vary spatially across California. Several salient findings emerge from this study. Groundwater-dominated streams are a ubiquitous, but as yet, poorly explored class of thermal regimes. Further, flow regulation below dams imposes serial discontinuities, including artificial thermal regimes on downstream ecosystems. Finally, and contrary to what is often assumed, California reservoirs do not contain sufficient cold-water storage to replicate desirable, reach-scale thermal regimes. While barriers to cold-water conservation are considerable and the trajectory of cold-water species towards extinction is dire, protecting reaches that demonstrate resilience to climate warming remains worthwhile., Competing Interests: The authors have declared that no competing interests exist.

Little is known about woolly mammoth ( Mammuthus primigenius ) mobility and range. Here we use high temporal resolution sequential analyses of strontium isotope ratios along an entire 1.7-meter-long tusk to reconstruct the movements of an Arctic woolly mammoth that lived 17,100 years ago, during the last ice age. We use an isotope-guided random walk approach to compare the tusk's strontium and oxygen isotope profiles to isotopic maps. Our modeling reveals patterns of movement across a geographically extensive range during the animal's ~28-year life span that varied with life stages. Maintenance of this level of mobility by megafaunal species such as mammoth would have been increasingly difficult as the ice age ended and the environment changed at high latitudes., (Copyright © 2021 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)

Researchers must be able to generate experimentally testable hypotheses from sequencing-based observational microbiome experiments to discover the mechanisms underlying the influence of gut microbes on human health. We describe geneshot, a novel bioinformatics tool for identifying testable hypotheses based on gene-level metagenomic analysis of WGS microbiome data. By applying geneshot to two independent previously published cohorts, we identify microbial genomic islands consistently associated with response to immune checkpoint inhibitor (ICI)-based cancer treatment in culturable type strains. The identified genomic islands are within operons involved in type II secretion, TonB-dependent transport, and bacteriophage growth.

CRISPR technology has enabled cell lineage tracing for complex multicellular organisms through insertion-deletion mutations of synthetic genomic barcodes during organismal development. To reconstruct the cell lineage tree from the mutated barcodes, current approaches apply general-purpose computational tools that are agnostic to the mutation process and are unable to take full advantage of the data's structure. We propose a statistical model for the CRISPR mutation process and develop a procedure to estimate the resulting tree topology, branch lengths, and mutation parameters by iteratively applying penalized maximum likelihood estimation. By assuming the barcode evolves according to a molecular clock, our method infers relative ordering across parallel lineages, whereas existing techniques only infer ordering for nodes along the same lineage. When analyzing transgenic zebrafish data from McKenna, Findlay and Gagnon et al. (2016), we find that our method recapitulates known aspects of zebrafish development and the results are consistent across samples.

Our goal is to estimate the true number of classes in a population, called the species richness. We consider the case where multiple frequency count tables have been collected from a homogeneous population, and investigate a penalized maximum likelihood estimator under a negative binomial model. Because high probabilities of unobserved classes increase the variance of species richness estimates, our method penalizes the probability of a class being unobserved. Tuning the penalization parameter is challenging because the true species richness is never known, and so we propose and validate four novel methods for tuning the penalization parameter. We illustrate and contrast the performance of the proposed methods by estimating the strain-level microbial diversity of Lake Champlain over 3 consecutive years, and global human host-associated species-level microbial richness., Competing Interests: Conflicts of Interest: None to declare.

Introduction: Microbial residents of the human oral cavity have long been a major focus of microbiology due to their influence on host health and intriguing patterns of site specificity amidst the lack of dispersal limitation. However, the determinants of niche partitioning in this habitat are yet to be fully understood, especially among taxa that belong to recently discovered branches of microbial life., Results: Here, we assemble metagenomes from tongue and dental plaque samples from multiple individuals and reconstruct 790 non-redundant genomes, 43 of which resolve to TM7, a member of the Candidate Phyla Radiation, forming six monophyletic clades that distinctly associate with either plaque or tongue. Both pangenomic and phylogenomic analyses group tongue-specific clades with other host-associated TM7 genomes. In contrast, plaque-specific TM7 group with environmental TM7 genomes. Besides offering deeper insights into the ecology, evolution, and mobilome of cryptic members of the oral microbiome, our study reveals an intriguing resemblance between dental plaque and non-host environments indicated by the TM7 evolution, suggesting that plaque may have served as a stepping stone for environmental microbes to adapt to host environments for some clades of microbes. Additionally, we report that prophages are widespread among oral-associated TM7, while absent from environmental TM7, suggesting that prophages may have played a role in adaptation of TM7 to the host environment., Conclusions: Our data illuminate niche partitioning of enigmatic members of the oral cavity, including TM7, SR1, and GN02, and provide genomes for poorly characterized yet prevalent members of this biome, such as uncultivated Flavobacteriaceae.

Using a sample from a population to estimate the proportion of the population with a certain category label is a broadly important problem. In the context of microbiome studies, this problem arises when researchers wish to use a sample from a population of microbes to estimate the population proportion of a particular taxon, known as the taxon's relative abundance. In this paper, we propose a beta-binomial model for this task. Like existing models, our model allows for a taxon's relative abundance to be associated with covariates of interest. However, unlike existing models, our proposal also allows for the overdispersion in the taxon's counts to be associated with covariates of interest. We exploit this model in order to propose tests not only for differential relative abundance, but also for differential variability. The latter is particularly valuable in light of speculation that dysbiosis, the perturbation from a normal microbiome that can occur in certain disease conditions, may manifest as a loss of stability, or increase in variability, of the counts associated with each taxon. We demonstrate the performance of our proposed model using a simulation study and an application to soil microbial data.

Studies using the Mouse Grimace Scale have shown that for many NSAID, including meloxicam, minimal doses of at least 20 mg/kg may be necessary to achieve adequate peri- and post-operative analgesia in mice. However, more data are needed to determine whether such NSAID doses exceed the threshold for gastrointestinal ulceration or induce other relevant pathology. We administered equal volumes of saline or injectable meloxicam (1 or 5 mg/mL) at a dose of 20 mg/kg SC to 20 young adult male and female C57BL/6N mice daily for 6 d and performed necropsies on all mice on the seventh day. Mice given 5 mg/mL meloxicam subcutaneously developed significantly more severe pathology at the injection site than saline controls. Pathology was characterized by full-thickness epidermal necrosis; cavitary lesions within subcutis, muscle, or fat; steatitis; and myositis. Mice that received 1 mg/mL meloxicam subcutaneously developed lesions that were qualitatively similar but far less severe than those after 5 mg/mL. However, no pathologic lesions typically associated with NSAID toxicity, such as gastric ulceration and liver and kidney lesions, were seen. These results demonstrate that although meloxicam injected subcutaneously causes concentration-dependent skin pathology at the injection site, a dose of 20 mg/kg can be safely administered subcutaneously at a concentration of 1 mg/mL for as long as 6 d.

Understanding the drivers of diversity is a fundamental question in ecology. Extensive literature discusses different methods for describing diversity and documenting its effects on ecosystem health and function. However, it is widely believed that diversity depends on the intensity of sampling. I discuss a statistical perspective on diversity, framing the diversity of an environment as an unknown parameter, and discussing the bias and variance of plug-in and rarefied estimates. I describe the state of the statistical literature for addressing these problems, focusing on the analysis of microbial diversity. I argue that latent variable models can address issues with variance, but bias corrections need to be utilized as well. I encourage ecologists to use estimates of diversity that account for unobserved species, and to use measurement error models to compare diversity across ecosystems., (Copyright © 2019 Willis.)

Marker-gene and metagenomic sequencing have profoundly expanded our ability to measure biological communities. But the measurements they provide differ from the truth, often dramatically, because these experiments are biased toward detecting some taxa over others. This experimental bias makes the taxon or gene abundances measured by different protocols quantitatively incomparable and can lead to spurious biological conclusions. We propose a mathematical model for how bias distorts community measurements based on the properties of real experiments. We validate this model with 16S rRNA gene and shotgun metagenomics data from defined bacterial communities. Our model better fits the experimental data despite being simpler than previous models. We illustrate how our model can be used to evaluate protocols, to understand the effect of bias on downstream statistical analyses, and to measure and correct bias given suitable calibration controls. These results illuminate new avenues toward truly quantitative and reproducible metagenomics measurements., Competing Interests: MM, AW, BC No competing interests declared, (© 2019, McLaren et al.)

Background: Whole-genome "shotgun" (WGS) metagenomic sequencing is an increasingly widely used tool for analyzing the metagenomic content of microbiome samples. While WGS data contains gene-level information, it can be challenging to analyze the millions of microbial genes which are typically found in microbiome experiments. To mitigate the ultrahigh dimensionality challenge of gene-level metagenomics, it has been proposed to cluster genes by co-abundance to form Co-Abundant Gene groups (CAGs). However, exhaustive co-abundance clustering of millions of microbial genes across thousands of biological samples has previously been intractable purely due to the computational challenge of performing trillions of pairwise comparisons., Results: Here we present a novel computational approach to the analysis of WGS datasets in which microbial gene groups are the fundamental unit of analysis. We use the Approximate Nearest Neighbor heuristic for near-exhaustive average linkage clustering to group millions of genes by co-abundance. This results in thousands of high-quality CAGs representing complete and partial microbial genomes. We applied this method to publicly available WGS microbiome surveys and found that the resulting microbial CAGs associated with inflammatory bowel disease (IBD) and colorectal cancer (CRC) were highly reproducible and could be validated independently using multiple independent cohorts., Conclusions: This powerful approach to gene-level metagenomics provides a powerful path forward for identifying the biological links between the microbiome and human health. By proposing a new computational approach for handling high dimensional metagenomics data, we identified specific microbial gene groups that are associated with disease that can be used to identify strains of interest for further preclinical and mechanistic experimentation.

High-throughput sequencing has facilitated discovery in microbiome science, but distinguishing true discoveries from spurious signals can be challenging. The Statistical Diversity Lab develops rigorous statistical methods and statistical software for the analysis of microbiome and biodiversity data. Developing statistical methods that produce valid P values requires thoughtful modeling and careful validation, but careful statistical analysis reduces the risk of false discoveries and increases scientific understanding., Competing Interests: Conflict of Interest Disclosures: A.D.W. has nothing to disclose., (Copyright © 2019 Willis.)

Objective: To determine whether sonographic markers of ovarian morphology or male pattern hair growth scores predict androgen levels in women with regular or irregular menstrual cycles., Design: Cross-sectional observational study., Setting: Clinical research unit., Patient(s): Seventy-six women of reproductive age (18-39 years) were evaluated for male-pattern hair growth (using a modified Ferriman-Gallwey scoring system), ovarian morphology (by transvaginal ultrasonography), and total serum testosterone (T) (by liquid chromatography tandem mass spectrometry)., Intervention(s): Not applicable., Main Outcome Measure(s): Regional and total modified Ferriman-Gallwey scores, number of follicles per follicle size category, follicle number per ovary, ovarian volume, ovarian area, stromal to ovarian area ratio, stromal echogenicity index, total testosterone (total T), and menstrual cycle length., Result(s): Neither regional nor total modified Ferriman-Gallwey scores correlated with total T concentrations in women with regular or irregular menstrual cycles, as judged by the Least Absolute Shrinkage and Selection Operator technique. By contrast, a sonographic marker (follicle number per ovary 6-9 mm) significantly predicted total T concentrations in women with regular menstrual cycles but not in women with irregular menstrual cycles., Conclusion(s): Sonographic markers of ovarian morphology, but not hirsutism scores, predicted total T levels. However, the predictive value of ovarian morphology for total T differed by menstrual cycle status. That sonographic markers did not predict androgen levels in a diverse cohort of women with cycle irregularity suggests the potential for distinct variations in ovarian morphology for androgenic and nonandrogenic types of cycle irregularity. Overall, our findings support that an assessment of ovarian morphology may be helpful in reflecting total T levels., (Copyright © 2016 American Society for Reproductive Medicine. Published by Elsevier Inc. All rights reserved.)

Objective: To compare the diagnostic potential of ultrasonographic markers of ovarian morphology, used alone or in combination, to predict polycystic ovary syndrome (PCOS)., Design: A diagnostic test study using cross-sectional data collected from 2006-2011., Setting: Academic hospital and clinical research unit., Patient(s): Eighty-two women with PCOS and 60 healthy female volunteers., Intervention(s): None., Main Outcome Measure(s): Follicle number per ovary (FNPO), ovarian volume (OV), follicle number per single cross-section (FNPS), follicle distribution pattern, stromal area, ovarian area, stromal-to-ovarian area ratio (S:A), and stromal index (SI)., Result(s): Follicle number per ovary best predicted PCOS (R(2) = 67%) with 85% sensitivity and 98% specificity, followed by OV (R(2) = 44%), and FNPS (R(2) = 36%). Neither S:A nor SI had predictive power for PCOS. In combination, FNPO+S:A and FNPO+SI most significantly predicted PCOS (R(2) = 74% vs. 73%, respectively). The diagnostic potentials of OV and FNPS were substantially improved when used in combination (OV+FNPO, R(2) = 55%)., Conclusion(s): As a single metric, FNPO best predicted PCOS. Although the addition of S:A or SI improved the predictive power of FNPO, gains were marginal, suggesting limited use in clinical practice. When image quality precludes a reliable estimation of FNPO, measurements of OV+FNPS provide the next closest level of diagnostic potential., (Copyright © 2014 American Society for Reproductive Medicine. Published by Elsevier Inc. All rights reserved.)

Background: Hepatopulmonary syndrome (HPS) is the association of liver disease, hypoxemia, and intrapulmonary vascular dilatations. There are little data on the management of HPS in children other than conventional orthotopic liver transplantation (OLT)., Aims: To describe the patient characteristics, mode of diagnosis, treatment, and outcomes of children with HPS at our center., Methods: Retrospective review of patients diagnosed with HPS between 1997 and 2007 after IRB approval., Results: There were 10 patients, six females; median age at diagnosis of HPS was 12 yr. Six with cirrhosis underwent OLT and had subsequent resolution of HPS and are stable at last follow-up. Of the remaining four, two had cirrhosis. HPS resolved without conventional OLT in the following four patients: hepatitis C after antiviral treatment, biliary atresia with portal hypertension after transjugular intrahepatic portosystemic shunting, Abernethy syndrome after auxiliary partial OLT, and in a child with splenic vein thrombosis after splenectomy., Conclusions: Our series shows resolution of HPS in all patients and 100% survival after conventional OLT. Four children had resolution of HPS after surgical or medical treatments other than conventional OLT. Careful review of clinical status and underlying pathophysiology and anatomy at diagnosis of HPS should inform treatment decisions., (© 2010 John Wiley & Sons A/S.)