BackgroundRecent innovations in sequencing technologies have provided researchers with the ability to rapidly characterize the microbial content of an environmental or clinical sample with unprecedented resolution. These approaches are producing a wealth of information that is providing novel insights into the microbial ecology of the environment and human health. However, these sequencing-based approaches produce large and complex datasets that require efficient and sensitive computational analysis workflows. Many recent tools for analyzing metagenomic-sequencing data have emerged, however, these approaches often suffer from issues of specificity, efficiency, and typically do not include a complete metagenomic analysis framework.ResultsWe present PathoScope 2.0, a complete bioinformatics framework for rapidly and accurately quantifying the proportions of reads from individual microbial strains present in metagenomic sequencing data from environmental or clinical samples. The pipeline performs all necessary computational analysis steps; including reference genome library extraction and indexing, read quality control and alignment, strain identification, and summarization and annotation of results. We rigorously evaluated PathoScope 2.0 using simulated data and data from the 2011 outbreak of Shiga-toxigenic Escherichia coli O104:H4.ConclusionsThe results show that PathoScope 2.0 is a complete, highly sensitive, and efficient approach for metagenomic analysis that outperforms alternative approaches in scope, speed, and accuracy. The PathoScope 2.0 pipeline software is freely available for download at: http://sourceforge.net/projects/pathoscope/.

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Emerging next-generation sequencing technologies have revolutionized the collection of genomic data for applications in bioforensics, biosurveillance, and for use in clinical settings. However, to make the most of these new data, new methodology needs to be developed that can accommodate large volumes of genetic data in a computationally efficient manner. We present a statistical framework to analyze raw next-generation sequence reads from purified or mixed environmental or targeted infected tissue samples for rapid species identification and strain attribution against a robust database of known biological agents. Our method, Pathoscope, capitalizes on a Bayesian statistical framework that accommodates information on sequence quality, mapping quality, and provides posterior probabilities of matches to a known database of target genomes. Importantly, our approach also incorporates the possibility that multiple species can be present in the sample and considers cases when the sample species/strain is not in the reference database. Furthermore, our approach can accurately discriminate between very closely related strains of the same species with very little coverage of the genome and without the need for multiple alignment steps, extensive homology searches, or genome assembly-which are time-consuming and labor-intensive steps. We demonstrate the utility of our approach on genomic data from purified and in silico ''environmental'' samples from known bacterial agents impacting human health for accuracy assessment and comparison with other approaches.

BackgroundThe relationships between infections in early life and asthma are not completely understood. Likewise, the clinical relevance of microbial communities present in the respiratory tract is only partially known. A number of microbiome studies analyzing respiratory tract samples have found increased proportions of gamma-Proteobacteria including Haemophilus influenzae, Moraxella catarrhalis, and Firmicutes such as Streptococcus pneumoniae. The aim of this study was to present a new approach that combines RNA microbial identification with host gene expression to characterize and validate metagenomic taxonomic profiling in individuals with asthma.MethodsUsing whole metagenomic shotgun RNA sequencing, we characterized and compared the microbial communities of individuals, children and adolescents, with asthma and controls. The resulting data were analyzed by partitioning human and microbial reads. Microbial reads were then used to characterize the microbial diversity of each patient, and potential differences between asthmatic and healthy groups. Human reads were used to assess the expression of known genes involved in the host immune response to specific pathogens and detect potential differences between those with asthma and controls.ResultsMicrobial communities in the nasal cavities of children differed significantly between asthmatics and controls. After read count normalization, some bacterial species were significantly overrepresented in asthma patients (Wald test, p-value < 0.05), including Escherichia coli and Psychrobacter. Among these, Moraxella catarrhalis exhibited ~14-fold over abundance in asthmatics versus controls. Differential host gene expression analysis confirms that the presence of Moraxella catarrhalis is associated to a specific M. catarrhalis core gene signature expressed by the host.ConclusionsFor the first time, we show the power of combining RNA taxonomic profiling and host gene expression signatures for microbial identification. Our approach not only identifies microbes from metagenomic data, but also adds support to these inferences by determining if the host is mounting a response against specific infectious agents. In particular, we show that M. catarrhalis is abundant in asthma patients but not in controls, and that its presence is associated with a specific host gene expression signature.Electronic supplementary materialThe online version of this article (doi:10.1186/s12920-015-0121-1) contains supplementary material, which is available to authorized users.

BackgroundThe use of sequencing technologies to investigate the microbiome of a sample can positively impact patient healthcare by providing therapeutic targets for personalized disease treatment. However, these samples contain genomic sequences from various sources that complicate the identification of pathogens.ResultsHere we present Clinical PathoScope, a pipeline to rapidly and accurately remove host contamination, isolate microbial reads, and identify potential disease-causing pathogens. We have accomplished three essential tasks in the development of Clinical PathoScope. First, we developed an optimized framework for pathogen identification using a computational subtraction methodology in concordance with read trimming and ambiguous read reassignment. Second, we have demonstrated the ability of our approach to identify multiple pathogens in a single clinical sample, accurately identify pathogens at the subspecies level, and determine the nearest phylogenetic neighbor of novel or highly mutated pathogens using real clinical sequencing data. Finally, we have shown that Clinical PathoScope outperforms previously published pathogen identification methods with regard to computational speed, sensitivity, and specificity.ConclusionsClinical PathoScope is the only pathogen identification method currently available that can identify multiple pathogens from mixed samples and distinguish between very closely related species and strains in samples with very few reads per pathogen. Furthermore, Clinical PathoScope does not rely on genome assembly and thus can more rapidly complete the analysis of a clinical sample when compared with current assembly-based methods. Clinical PathoScope is freely available at: http://sourceforge.net/projects/pathoscope/.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2105-15-262) contains supplementary material, which is available to authorized users.

Maternal immune activation (MIA) disrupts the central innate immune system during a critical neurodevelopmental period. Microglia are primary innate immune cells in the brain although their direct influence on the MIA phenotype is largely unknown. Here we show that MIA alters microglial gene expression with upregulation of cellular protrusion/neuritogenic pathways, concurrently causing repetitive behavior, social deficits, and synaptic dysfunction to layer V intrinsically bursting pyramidal neurons in the prefrontal cortex of mice. MIA increases plastic dendritic spines of the intrinsically bursting neurons and their interaction with hyper-ramified microglia. Treating MIA offspring by colony stimulating factor 1 receptor inhibitors induces depletion and repopulation of microglia, and corrects protein expression of the newly identified MIAassociated neuritogenic molecules in microglia, which coalesces with correction of MIA-associated synaptic, neurophysiological, and behavioral abnormalities. Our study demonstrates that maternal immune insults perturb microglial phenotypes and influence neuronal functions throughout adulthood, and reveals a potent effect of colony stimulating factor 1 receptor inhibitors on the correction of MIA-associated microglial, synaptic, and neurobehavioral dysfunctions.

BackgroundIn participants with pulmonary arterial hypertension, 24 weeks of sotatercept resulted in a significantly greater reduction from baseline in pulmonary vascular resistance than placebo. This report characterises the longer-term safety and efficacy of sotatercept in the PULSAR open-label extension. We report cumulative safety, and efficacy at months 18–24, for all participants treated with sotatercept.MethodsPULSAR was a phase 2, randomised, double-blind, placebo-controlled study followed by an open-label extension, which evaluated sotatercept on top of background pulmonary arterial hypertension therapy in adults. Participants originally randomised to placebo were re-randomised 1:1 to sotatercept 0.3 or 0.7 mg·kg−1 (placebo-crossed group); those initially randomised to sotatercept continued the same sotatercept dose (continued-sotatercept group). Safety was evaluated in all participants who received ≥1 dose of sotatercept. The primary efficacy endpoint was change from baseline to months 18–24 in pulmonary vascular resistance. Secondary endpoints included 6-min walk distance and functional class. Two prespecified analyses, placebo-crossed and delayed-start, evaluated efficacy irrespective of dose.ResultsOf 106 participants enrolled in the PULSAR study, 97 continued into the extension period. Serious treatment-emergent adverse events were reported in 32 (30.8%) participants; 10 (9.6%) reported treatment-emergent adverse events leading to study discontinuation. Three (2.9%) participants died, none considered related to study drug. The placebo-crossed group demonstrated significant improvement across primary and secondary endpoints and clinical efficacy was maintained in the continued-sotatercept group.ConclusionThese results support the longer-term safety and durability of clinical benefit of sotatercept for pulmonary arterial hypertension.

BackgroundCombining genomic data sets from multiple studies is advantageous to increase statistical power in studies where logistical considerations restrict sample size or require the sequential generation of data. However, significant technical heterogeneity is commonly observed across multiple batches of data that are generated from different processing or reagent batches, experimenters, protocols, or profiling platforms. These so-called batch effects often confound true biological relationships in the data, reducing the power benefits of combining multiple batches, and may even lead to spurious results in some combined studies. Therefore there is significant need for effective methods and software tools that account for batch effects in high-throughput genomic studies.ResultsHere we contribute multiple methods and software tools for improved combination and analysis of data from multiple batches. In particular, we provide batch effect solutions for cases where the severity of the batch effects is not extreme, and for cases where one high-quality batch can serve as a reference, such as the training set in a biomarker study. We illustrate our approaches and software in both simulated and real data scenarios.ConclusionsWe demonstrate the value of these new contributions compared to currently established approaches in the specified batch correction situations.Electronic supplementary materialThe online version of this article (10.1186/s12859-018-2263-6) contains supplementary material, which is available to authorized users.