People treat social robots as real social agents.

When people interact with social robots, they treat them as real social agents. How people depict robots is fun to consider, but when people are confronted with embodied entities that move and talk - whether humans or robots - they interact with them as authentic social agents with minds, and not as mere representations.

Adiabatic Trajectory Approximation: A New General Method in the Toolbox of Mixed Quantum/Classical Theory for Collisional Energy Transfer.

A new version of the MQCT program is presented, which includes an important addition, adiabatic trajectory approximation (AT-MQCT), in which the equations of motion for the classical and quantum parts of the system are decoupled. This method is much faster, which permits calculations for larger molecular systems and at higher collision energies than was possible before. AT-MQCT is general and can be applied to any molecule + molecule inelastic scattering problem. A benchmark study is presented for H2O + H2O rotational energy transfer, an important asymmetric-top rotor + asymmetric-top rotor collision process, a very difficult problem unamenable to the treatment by other codes that exist in the community. Our results indicate that AT-MQCT represents a reliable computational tool for prediction of collisional energy transfer between the individual rotational states of two molecules, and this is valid for all combinations of state symmetries (such as para and ortho states of each collision partner).

MIMOSA2: a metabolic network-based tool for inferring mechanism-supported relationships in microbiome-metabolome data.

MOTIVATION: Recent technological developments have facilitated an expansion of microbiome-metabolome studies, in which samples are assayed using both genomic and metabolomic technologies to characterize the abundances of microbial taxa and metabolites. A common goal of these studies is to identify microbial species or genes that contribute to differences in metabolite levels across samples. Previous work indicated that integrating these datasets with reference knowledge on microbial metabolic capacities may enable more precise and confident inference of microbe-metabolite links. RESULTS: We present MIMOSA2, an R package and web application for model-based integrative analysis of microbiome-metabolome datasets. MIMOSA2 uses genomic and metabolic reference databases to construct a community metabolic model based on microbiome data and uses this model to predict differences in metabolite levels across samples. These predictions are compared with metabolomics data to identify putative microbiome-governed metabolites and taxonomic contributors to metabolite variation. MIMOSA2 supports various input data types and customization with user-defined metabolic pathways. We establish MIMOSA2's ability to identify ground truth microbial mechanisms in simulation datasets, compare its results with experimentally inferred mechanisms in honeybee microbiota, and demonstrate its application in two human studies of inflammatory bowel disease. Overall, MIMOSA2 combines reference databases, a validated statistical framework, and a user-friendly interface to facilitate modeling and evaluating relationships between members of the microbiota and their metabolic products. AVAILABILITY AND IMPLEMENTATION: MIMOSA2 is implemented in R under the GNU General Public License v3.0 and is freely available as a web server at http://elbo-spice.cs.tau.ac.il/shiny/MIMOSA2shiny/ and as an R package from http://www.borensteinlab.com/software_MIMOSA2.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Infants with cystic fibrosis have altered fecal functional capacities with potential clinical and metabolic consequences.

BACKGROUND: Infants with cystic fibrosis (CF) suffer from gastrointestinal (GI) complications, including pancreatic insufficiency and intestinal inflammation, which have been associated with impaired nutrition and growth. Recent evidence identified altered fecal microbiota taxonomic compositions in infants with CF relative to healthy infants that were characterized by differences in the abundances of taxa associated with GI health and nutrition. Furthermore, these taxonomic differences were more pronounced in low length infants with CF, suggesting a potential link to linear growth failure. We hypothesized that these differences would entail shifts in the microbiome's functional capacities that could contribute to inflammation and nutritional failure in infants with CF. RESULTS: To test this hypothesis, we compared fecal microbial metagenomic content between healthy infants and infants with CF, supplemented with an analysis of fecal metabolomes in infants with CF. We identified notable differences in CF fecal microbial functional capacities, including metabolic and environmental response functions, compared to healthy infants that intensified during the first year of life. A machine learning-based longitudinal metagenomic age analysis of healthy and CF fecal metagenomic functional profiles further demonstrated that these differences are characterized by a CF-associated delay in the development of these functional capacities. Moreover, we found metagenomic differences in functions related to metabolism among infants with CF that were associated with diet and antibiotic exposure, and identified several taxa as potential drivers of these functional differences. An integrated metagenomic and metabolomic analysis further revealed that abundances of several fecal GI metabolites important for nutrient absorption, including three bile acids, correlated with specific microbes in infants with CF. CONCLUSIONS: Our results highlight several metagenomic and metabolomic factors, including bile acids and other microbial metabolites, that may impact nutrition, growth, and GI health in infants with CF. These factors could serve as promising avenues for novel microbiome-based therapeutics to improve health outcomes in these infants.

Gastrointestinal Factors Associated With Hospitalization in Infants With Cystic Fibrosis: Results From the Baby Observational and Nutrition Study.

OBJECTIVES: To identify factors that increase the risk of gastrointestinal-related (GI-related) hospitalization of infants with cystic fibrosis (CF) during the first year of life. METHODS: The Baby Observational and Nutrition Study was a longitudinal, observational cohort of 231 infants diagnosed with CF by newborn screening. We performed a post-hoc assessment of the frequency and indications for GI-related admissions during the first year of life. RESULTS: Sixty-five participants had at least one admission in the first 12 months of life. High pancreatic enzyme replacement therapy (PERT) dosing (>2000 lipase units/kg per meal; hazard ratio [HR] = 14.75, P = 0.0005) and use of acid suppressive medications (HR = 4.94, P = 0.01) during the study period were positively associated with subsequent GI-related admissions. High levels of fecal calprotectin (fCP) (>200 µg/g) and higher relative abundance of fecal Klebsiella pneumoniae were also positively associated with subsequent GI-related admissions (HR = 2.64, P = 0.033 and HR = 4.49, P = 0.002, respectively). During the first 12 months of life, participants with any admission had lower weight-for-length z scores (WLZ) (P = 0.01). The impact of admission on WLZ was particularly evident in participants with a GI-related admission (P < 0.0001). CONCLUSIONS: Factors associated with a higher risk for GI-related admission during the first 12 months include high PERT dosing, exposure to acid suppressive medications, higher fCP levels, and/or relative abundance of fecal K pneumoniae early in life. Infants with CF requiring GI-related hospitalization had lower WLZ at 12 months of age than those not admitted as well as those admitted for non-GI-related indications.

MetaLAFFA: a flexible, end-to-end, distributed computing-compatible metagenomic functional annotation pipeline.

BACKGROUND: Microbial communities have become an important subject of research across multiple disciplines in recent years. These communities are often examined via shotgun metagenomic sequencing, a technology which can offer unique insights into the genomic content of a microbial community. Functional annotation of shotgun metagenomic data has become an increasingly popular method for identifying the aggregate functional capacities encoded by the community's constituent microbes. Currently available metagenomic functional annotation pipelines, however, suffer from several shortcomings, including limited pipeline customization options, lack of standard raw sequence data pre-processing, and insufficient capabilities for integration with distributed computing systems. RESULTS: Here we introduce MetaLAFFA, a functional annotation pipeline designed to take unfiltered shotgun metagenomic data as input and generate functional profiles. MetaLAFFA is implemented as a Snakemake pipeline, which enables convenient integration with distributed computing clusters, allowing users to take full advantage of available computing resources. Default pipeline settings allow new users to run MetaLAFFA according to common practices while a Python module-based configuration system provides advanced users with a flexible interface for pipeline customization. MetaLAFFA also generates summary statistics for each step in the pipeline so that users can better understand pre-processing and annotation quality. CONCLUSIONS: MetaLAFFA is a new end-to-end metagenomic functional annotation pipeline with distributed computing compatibility and flexible customization options. MetaLAFFA source code is available at https://github.com/borenstein-lab/MetaLAFFA and can be installed via Conda as described in the accompanying documentation.

Maintenance tobramycin primarily affects untargeted bacteria in the CF sputum microbiome.

RATIONALE: The most common antibiotic used to treat people with cystic fibrosis (PWCF) is inhaled tobramycin, administered as maintenance therapy for chronic Pseudomonas aeruginosa lung infections. While the effects of inhaled tobramycin on P. aeruginosa abundance and lung function diminish with continued therapy, this maintenance treatment is known to improve long-term outcomes, underscoring how little is known about why antibiotics work in CF infections, what their effects are on complex CF sputum microbiomes and how to improve these treatments. OBJECTIVES: To rigorously define the effect of maintenance tobramycin on CF sputum microbiome characteristics. METHODS AND MEASUREMENTS: We collected sputum from 30 PWCF at standardised times before, during and after a single month-long course of maintenance inhaled tobramycin. We used traditional culture, quantitative PCR and metagenomic sequencing to define the dynamic effects of this treatment on sputum microbiomes, including abundance changes in both clinically targeted and untargeted bacteria, as well as functional gene categories. MAIN RESULTS: CF sputum microbiota changed most markedly by 1 week of antibiotic therapy and plateaued thereafter, and this shift was largely driven by changes in non-dominant taxa. The genetically conferred functional capacities (ie, metagenomes) of subjects' sputum communities changed little with antibiotic perturbation, despite taxonomic shifts, suggesting functional redundancy within the CF sputum microbiome. CONCLUSIONS: Maintenance treatment with inhaled tobramycin, an antibiotic with demonstrated long-term mortality benefit, primarily impacted clinically untargeted bacteria in CF sputum, highlighting the importance of monitoring the non-canonical effects of antibiotics and other treatments to accurately define and improve their clinical impact.

Fecal dysbiosis in infants with cystic fibrosis is associated with early linear growth failure.

Most infants with cystic fibrosis (CF) have pancreatic exocrine insufficiency that results in nutrient malabsorption and requires oral pancreatic enzyme replacement. Newborn screening for CF has enabled earlier diagnosis, nutritional intervention and enzyme replacement for these infants, allowing most infants with CF to achieve their weight goals by 12 months of age1. Nevertheless, most infants with CF continue to have poor linear growth during their first year of life1. Although this early linear growth failure is associated with worse long-term respiratory function and survival2,3, the determinants of body length in infants with CF have not been defined. Several characteristics of the CF gastrointestinal (GI) tract, including inflammation, maldigestion and malabsorption, may promote intestinal dysbiosis4,5. As GI microbiome activities are known to affect endocrine functions6,7, the intestinal microbiome of infants with CF may also impact growth. We identified an early, progressive fecal dysbiosis that distinguished infants with CF and low length from infants with CF and normal length. This dysbiosis included altered abundances of taxa that perform functions that are important for GI health, nutrient harvest and growth hormone signaling, including decreased abundance of Bacteroidetes and increased abundance of Proteobacteria. Thus, the GI microbiota represent a potential therapeutic target for the correction of low linear growth in infants with CF.

Microbial community design: methods, applications, and opportunities.

Microbial communities can perform a variety of behaviors that are useful in both therapeutic and industrial settings. Engineered communities that differ in composition from naturally occurring communities offer a unique opportunity for improving upon existing community functions and expanding the range of microbial community applications. This has prompted recent advances in various community design approaches including artificial selection procedures, reduction from existing communities, combinatorial evaluation of potential microbial combinations, and model-based in silico community optimization. Computational methods in particular offer a likely avenue toward improved synthetic community development going forward. This review introduces each class of design approach and surveys their recent applications and notable innovations, closing with a discussion of existing design challenges and potential opportunities for advancement.

Human and Extracellular DNA Depletion for Metagenomic Analysis of Complex Clinical Infection Samples Yields Optimized Viable Microbiome Profiles.

Metagenomic sequencing is a promising approach for identifying and characterizing organisms and their functional characteristics in complex, polymicrobial infections, such as airway infections in people with cystic fibrosis. These analyses are often hampered, however, by overwhelming quantities of human DNA, yielding only a small proportion of microbial reads for analysis. In addition, many abundant microbes in respiratory samples can produce large quantities of extracellular bacterial DNA originating either from biofilms or dead cells. We describe a method for simultaneously depleting DNA from intact human cells and extracellular DNA (human and bacterial) in sputum, using selective lysis of eukaryotic cells and endonuclease digestion. We show that this method increases microbial sequencing depth and, consequently, both the number of taxa detected and coverage of individual genes such as those involved in antibiotic resistance. This finding underscores the substantial impact of DNA from sources other than live bacteria in microbiological analyses of complex, chronic infection specimens.

The ketogenic diet influences taxonomic and functional composition of the gut microbiota in children with severe epilepsy.

The gut microbiota has been linked to various neurological disorders via the gut-brain axis. Diet influences the composition of the gut microbiota. The ketogenic diet (KD) is a high-fat, adequate-protein, low-carbohydrate diet established for treatment of therapy-resistant epilepsy in children. Its efficacy in reducing seizures has been confirmed, but the mechanisms remain elusive. The diet has also shown positive effects in a wide range of other diseases, including Alzheimer's, depression, autism, cancer, and type 2 diabetes. We collected fecal samples from 12 children with therapy-resistant epilepsy before starting KD and after 3 months on the diet. Parents did not start KD and served as diet controls. Applying shotgun metagenomic DNA sequencing, both taxonomic and functional profiles were established. Here we report that alpha diversity is not changed significantly during the diet, but differences in both taxonomic and functional composition are detected. Relative abundance of bifidobacteria as well as E. rectale and Dialister is significantly diminished during the intervention. An increase in relative abundance of E. coli is observed on KD. Functional analysis revealed changes in 29 SEED subsystems including the reduction of seven pathways involved in carbohydrate metabolism. Decomposition of these shifts indicates that bifidobacteria and Escherichia are important contributors to the observed functional shifts. As relative abundance of health-promoting, fiber-consuming bacteria becomes less abundant during KD, we raise concern about the effects of the diet on the gut microbiota and overall health. Further studies need to investigate whether these changes are necessary for the therapeutic effect of KD.

The Skin Microbiome of the Neotropical Frog Craugastor fitzingeri: Inferring Potential Bacterial-Host-Pathogen Interactions From Metagenomic Data.

Skin symbiotic bacteria on amphibians can play a role in protecting their host against pathogens. Chytridiomycosis, the disease caused by Batrachochytrium dendrobatidis, Bd, has caused dramatic population declines and extinctions of amphibians worldwide. Anti-Bd bacteria from amphibian skin have been cultured, and skin bacterial communities have been described through 16S rRNA gene amplicon sequencing. Here, we present a shotgun metagenomic analysis of skin bacterial communities from a Neotropical frog, Craugastor fitzingeri. We sequenced the metagenome of six frogs from two different sites in Panamá: three frogs from Soberanía (Sob), a Bd-endemic site, and three frogs from Serranía del Sapo (Sapo), a Bd-naïve site. We described the taxonomic composition of skin microbiomes and found that Pseudomonas was a major component of these communities. We also identified that Sob communities were enriched in Actinobacteria while Sapo communities were enriched in Gammaproteobacteria. We described gene abundances within the main functional classes and found genes enriched either in Sapo or Sob. We then focused our study on five functional classes of genes: biosynthesis of secondary metabolites, metabolism of terpenoids and polyketides, membrane transport, cellular communication and antimicrobial drug resistance. These gene classes are potentially involved in bacterial communication, bacterial-host and bacterial-pathogen interactions among other functions. We found that C. fitzingeri metagenomes have a wide array of genes that code for secondary metabolites, including antibiotics and bacterial toxins, which may be involved in bacterial communication, but could also have a defensive role against pathogens. Several genes involved in bacterial communication and bacterial-host interactions, such as biofilm formation and bacterial secretion systems were found. We identified specific genes and pathways enriched at the different sites and determined that gene co-occurrence networks differed between sites. Our results suggest that skin microbiomes are composed of distinct bacterial taxa with a wide range of metabolic capabilities involved in bacterial defense and communication. Differences in taxonomic composition and pathway enrichments suggest that skin microbiomes from different sites have unique functional properties. This study strongly supports the need for shotgun metagenomic analyses to describe the functional capacities of skin microbiomes and to tease apart their role in host defense against pathogens.

BURRITO: An Interactive Multi-Omic Tool for Visualizing Taxa-Function Relationships in Microbiome Data.

The abundance of both taxonomic groups and gene categories in microbiome samples can now be easily assayed via various sequencing technologies, and visualized using a variety of software tools. However, the assemblage of taxa in the microbiome and its gene content are clearly linked, and tools for visualizing the relationship between these two facets of microbiome composition and for facilitating exploratory analysis of their co-variation are lacking. Here we introduce BURRITO, a web tool for interactive visualization of microbiome multi-omic data with paired taxonomic and functional information. BURRITO simultaneously visualizes the taxonomic and functional compositions of multiple samples and dynamically highlights relationships between taxa and functions to capture the underlying structure of these data. Users can browse for taxa and functions of interest and interactively explore the share of each function attributed to each taxon across samples. BURRITO supports multiple input formats for taxonomic and metagenomic data, allows adjustment of data granularity, and can export generated visualizations as static publication-ready formatted figures. In this paper, we describe the functionality of BURRITO, and provide illustrative examples of its utility for visualizing various trends in the relationship between the composition of taxa and functions in complex microbiomes.

Taxa-function robustness in microbial communities.

BACKGROUND: The species composition of a microbial community is rarely fixed and often experiences fluctuations of varying degrees and at varying frequencies. These perturbations to a community's taxonomic profile naturally also alter the community's functional profile-the aggregate set of genes encoded by community members-ultimately altering the community's overall functional capacities. The magnitude of such functional changes and the specific shift that will occur in each function, however, are strongly dependent on how genes are distributed across community members' genomes. This gene distribution, in turn, is determined by the taxonomic composition of the community and would markedly differ, for example, between communities composed of species with similar genomic content vs. communities composed of species whose genomes encode relatively distinct gene sets. Combined, these observations suggest that community functional robustness to taxonomic perturbations could vary widely across communities with different compositions, yet, to date, a systematic study of the inherent link between community composition and robustness is lacking. RESULTS: In this study, we examined how a community's taxonomic composition influences the robustness of that community's functional profile to taxonomic perturbation (here termed taxa-function robustness) across a wide array of environments. Using a novel simulation-based computational model to quantify this taxa-function robustness in host-associated and non-host-associated communities, we find notable differences in robustness between communities inhabiting different body sites, including significantly higher robustness in gut communities compared to vaginal communities that cannot be attributed solely to differences in species richness. We additionally find between-site differences in the robustness of specific functions, some of which are potentially related to site-specific environmental conditions. These taxa-function robustness differences are most strongly associated with differences in overall functional redundancy, though other aspects of gene distribution also influence taxa-function robustness in certain body environments, and are sufficient to cluster communities by environment. Further analysis revealed a correspondence between our robustness estimates and taxonomic and functional shifts observed across human-associated communities. CONCLUSIONS: Our analysis approach revealed intriguing taxa-function robustness variation across environments and identified features of community and gene distribution that impact robustness. This approach could be further applied for estimating taxa-function robustness in novel communities and for informing the design of synthetic communities with specific robustness requirements.

xenoGI: reconstructing the history of genomic island insertions in clades of closely related bacteria.

BACKGROUND: Genomic islands play an important role in microbial genome evolution, providing a mechanism for strains to adapt to new ecological conditions. A variety of computational methods, both genome-composition based and comparative, have been developed to identify them. Some of these methods are explicitly designed to work in single strains, while others make use of multiple strains. In general, existing methods do not identify islands in the context of the phylogeny in which they evolved. Even multiple strain approaches are best suited to identifying genomic islands that are present in one strain but absent in others. They do not automatically recognize islands which are shared between some strains in the clade or determine the branch on which these islands inserted within the phylogenetic tree. RESULTS: We have developed a software package, xenoGI, that identifies genomic islands and maps their origin within a clade of closely related bacteria, determining which branch they inserted on. It takes as input a set of sequenced genomes and a tree specifying their phylogenetic relationships. Making heavy use of synteny information, the package builds gene families in a species-tree-aware way, and then attempts to combine into islands those families whose members are adjacent and whose most recent common ancestor is shared. The package provides a variety of text-based analysis functions, as well as the ability to export genomic islands into formats suitable for viewing in a genome browser. We demonstrate the capabilities of the package with several examples from enteric bacteria, including an examination of the evolution of the acid fitness island in the genus Escherichia. In addition we use output from simulations and a set of known genomic islands from the literature to show that xenoGI can accurately identify genomic islands and place them on a phylogenetic tree. CONCLUSIONS: xenoGI is an effective tool for studying the history of genomic island insertions in a clade of microbes. It identifies genomic islands, and determines which branch they inserted on within the phylogenetic tree for the clade. Such information is valuable because it helps us understand the adaptive path that has produced living species.

Microbiome sharing between children, livestock and household surfaces in western Kenya.

The gut microbiome community structure and development are associated with several health outcomes in young children. To determine the household influences of gut microbiome structure, we assessed microbial sharing within households in western Kenya by sequencing 16S rRNA libraries of fecal samples from children and cattle, cloacal swabs from chickens, and swabs of household surfaces. Among the 156 households studied, children within the same household significantly shared their gut microbiome with each other, although we did not find significant sharing of gut microbiome across host species or household surfaces. Higher gut microbiome diversity among children was associated with lower wealth status and involvement in livestock feeding chores. Although more research is necessary to identify further drivers of microbiota development, these results suggest that the household should be considered as a unit. Livestock activities, health and microbiome perturbations among an individual child may have implications for other children in the household.

High-resolution characterization of the human microbiome.

The human microbiome plays an important and increasingly recognized role in human health. Studies of the microbiome typically use targeted sequencing of the 16S rRNA gene, whole metagenome shotgun sequencing, or other meta-omic technologies to characterize the microbiome's composition, activity, and dynamics. Processing, analyzing, and interpreting these data involve numerous computational tools that aim to filter, cluster, annotate, and quantify the obtained data and ultimately provide an accurate and interpretable profile of the microbiome's taxonomy, functional capacity, and behavior. These tools, however, are often limited in resolution and accuracy and may fail to capture many biologically and clinically relevant microbiome features, such as strain-level variation or nuanced functional response to perturbation. Over the past few years, extensive efforts have been invested toward addressing these challenges and developing novel computational methods for accurate and high-resolution characterization of microbiome data. These methods aim to quantify strain-level composition and variation, detect and characterize rare microbiome species, link specific genes to individual taxa, and more accurately characterize the functional capacity and dynamics of the microbiome. These methods and the ability to produce detailed and precise microbiome information are clearly essential for informing microbiome-based personalized therapies. In this review, we survey these methods, highlighting the challenges each method sets out to address and briefly describing methodological approaches.

An algorithm for designing minimal microbial communities with desired metabolic capacities.

MOTIVATION: Recent efforts to manipulate various microbial communities, such as fecal microbiota transplant and bioreactor systems' optimization, suggest a promising route for microbial community engineering with numerous medical, environmental and industrial applications. However, such applications are currently restricted in scale and often rely on mimicking or enhancing natural communities, calling for the development of tools for designing synthetic communities with specific, tailored, desired metabolic capacities. RESULTS: Here, we present a first step toward this goal, introducing a novel algorithm for identifying minimal sets of microbial species that collectively provide the enzymatic capacity required to synthesize a set of desired target product metabolites from a predefined set of available substrates. Our method integrates a graph theoretic representation of network flow with the set cover problem in an integer linear programming (ILP) framework to simultaneously identify possible metabolic paths from substrates to products while minimizing the number of species required to catalyze these metabolic reactions. We apply our algorithm to successfully identify minimal communities both in a set of simple toy problems and in more complex, realistic settings, and to investigate metabolic capacities in the gut microbiome. Our framework adds to the growing toolset for supporting informed microbial community engineering and for ultimately realizing the full potential of such engineering efforts. AVAILABILITY AND IMPLEMENTATION: The algorithm source code, compilation, usage instructions and examples are available under a non-commercial research use only license at https://github.com/borenstein-lab/CoMiDA CONTACT: elbo@uw.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Metabolic Model-Based Integration of Microbiome Taxonomic and Metabolomic Profiles Elucidates Mechanistic Links between Ecological and Metabolic Variation.

Multiple molecular assays now enable high-throughput profiling of the ecology, metabolic capacity, and activity of the human microbiome. However, to date, analyses of such multi-omic data typically focus on statistical associations, often ignoring extensive prior knowledge of the mechanisms linking these various facets of the microbiome. Here, we introduce a comprehensive framework to systematically link variation in metabolomic data with community composition by utilizing taxonomic, genomic, and metabolic information. Specifically, we integrate available and inferred genomic data, metabolic network modeling, and a method for predicting community-wide metabolite turnover to estimate the biosynthetic and degradation potential of a given community. Our framework then compares variation in predicted metabolic potential with variation in measured metabolites' abundances to evaluate whether community composition can explain observed shifts in the community metabolome, and to identify key taxa and genes contributing to the shifts. Focusing on two independent vaginal microbiome data sets, each pairing 16S community profiling with large-scale metabolomics, we demonstrate that our framework successfully recapitulates observed variation in 37% of metabolites. Well-predicted metabolite variation tends to result from disease-associated metabolism. We further identify several disease-enriched species that contribute significantly to these predictions. Interestingly, our analysis also detects metabolites for which the predicted variation negatively correlates with the measured variation, suggesting environmental control points of community metabolism. Applying this framework to gut microbiome data sets reveals similar trends, including prediction of bile acid metabolite shifts. This framework is an important first step toward a system-level multi-omic integration and an improved mechanistic understanding of the microbiome activity and dynamics in health and disease. IMPORTANCE: Studies characterizing both the taxonomic composition and metabolic profile of various microbial communities are becoming increasingly common, yet new computational methods are needed to integrate and interpret these data in terms of known biological mechanisms. Here, we introduce an analytical framework to link species composition and metabolite measurements, using a simple model to predict the effects of community ecology on metabolite concentrations and evaluating whether these predictions agree with measured metabolomic profiles. We find that a surprisingly large proportion of metabolite variation in the vaginal microbiome can be predicted based on species composition (including dramatic shifts associated with disease), identify putative mechanisms underlying these predictions, and evaluate the roles of individual bacterial species and genes. Analysis of gut microbiome data using this framework recovers similar community metabolic trends. This framework lays the foundation for model-based multi-omic integrative studies, ultimately improving our understanding of microbial community metabolism.

PK10453, a nonselective platelet-derived growth factor receptor inhibitor, prevents the progression of pulmonary arterial hypertension.

The platelet-derived growth factor (PDGF) signaling pathway has been found to be activated in human pulmonary arterial hypertension (PAH) and in animal models of the disease. Our study tested the hypothesis that a novel, nonselective inhaled PDGF receptor inhibitor, PK10453, would decrease pulmonary hypertension both in the rat monocrotaline (MCT) model and the rat MCT plus pneumonectomy (MCT+PN) model of PAH. PK10453, delivered by inhalation for 4 (D4)- and 8 (D8)-minute exposures 3 times a day for 2 weeks, decreased right ventricular systolic pressure (RVSP) in both the rat MCT and rat MCT+PN models: RVSP was 80.4 ± 2.6 mmHg in the vehicle MCT group (n = 6), 44.4 ± 5.8 mmHg in the D4 MCT group (n = 6), and 37.1 ± 4.5 mmHg in the D8 MCT group (n = 5; P < 0.001 vs. vehicle); RVSP was 75.7 ± 7.1 mmHg in the vehicle MCT+PN group (n = 9), 40.4 ± 2.7 mmHg in the D4 MCT+PN group (n = 10), and 43.0 ± 3.0 mmHg in the D8 MCT+PN group (n = 8; P < 0.001). In the rat MCT+PN model, continuous telemetry monitoring of pulmonary artery pressures also demonstrated that PK10453 prevented the progression of PAH. Imatinib given by inhalation was equally effective in the MCT model but was not effective in the MCT+PN model. Immunohistochemistry demonstrated increased activation of the PDGFß receptor compared to the PDGFα receptor in neointimal and perivascular lesions found in the MCT+PN model. We show that imatinib is selective for the PDGFα receptor, whereas PK10453 has a lower half-maximal inhibitor concentration (IC50) for inhibition of kinase activity of both the PDGFα and PDGFß receptors compared to imatinib. In conclusion, PK10453, when delivered by inhalation, significantly decreased the progression of PAH in the rat MCT and MCT+PN models. Nonselective inhibition of both the PDGFα and PDGFß receptors may have a therapeutic advantage over selective PDGFα receptor inhibition in PAH.