SPECTRA Phase 2b Study: Impact of Sotatercept on Exercise Tolerance and Right Ventricular Function in Pulmonary Arterial Hypertension.

BACKGROUND: This study aims to assess the impact of sotatercept on exercise tolerance, exercise capacity, and right ventricular function in pulmonary arterial hypertension. METHODS: SPECTRA (Sotatercept Phase 2 Exploratory Clinical Trial in PAH) was a phase 2a, single-arm, open-label, multicenter exploratory study that evaluated the effects of sotatercept by invasive cardiopulmonary exercise testing in participants with pulmonary arterial hypertension and World Health Organization functional class III on combination background therapy. The primary end point was the change in peak oxygen uptake from baseline to week 24. Cardiac magnetic resonance imaging was performed to assess right ventricular function. RESULTS: Among the 21 participants completing 24 weeks of treatment, there was a significant improvement from baseline in peak oxygen uptake, with a mean change of 102.74 mL/min ([95% CIs, 27.72-177.76]; P=0.0097). Sotatercept demonstrated improvements in secondary end points, including resting and peak exercise hemodynamics, and 6-minute walk distance versus baseline measures. Cardiac magnetic resonance imaging showed improvements from baseline at week 24 in right ventricular function. CONCLUSIONS: The clinical efficacy and safety of sotatercept demonstrated in the SPECTRA study emphasize the potential of this therapy as a new treatment option for patients with pulmonary arterial hypertension. Improvements in right ventricular structure and function underscore the potential for sotatercept as a disease-modifying agent with reverse-remodeling capabilities. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT03738150.

The Impact of Immunogenicity on the Pharmacokinetics, Efficacy, and Safety of Sotatercept in a Phase III Study of Pulmonary Arterial Hypertension.

Sotatercept, a soluble fusion protein comprising the extracellular domain of activin receptor type IIA linked to the Fc portion of human IgG1, is a first-in-class activin signaling inhibitor under development for the treatment of pulmonary arterial hypertension (PAH). We evaluated antidrug antibody (ADA) development and determined the effects of immunogenicity on the pharmacokinetics (PKs), efficacy, and safety of sotatercept in STELLAR, a multicenter, double-blind phase III trial (NCT04576988) wherein participants with PAH were randomized 1:1 to receive sotatercept (starting dose 0.3; target dose 0.7 mg/kg) or placebo subcutaneously every 3 weeks in combination with background therapies for ≤ 72 weeks. ADA-positive (ADA-POS) participants were identified and characterized for neutralizing antibodies (NAbs). PKs, efficacy, and safety were evaluated by ADA and NAb status. Of 162 evaluable participants, 42 (25.9%) were ADA-POS through week 24, of whom 11 (6.8%) were also NAb-POS. Median onset of ADAs was 3.29 weeks (interquartile range (IQR): 3.14-6.14), and median duration was 6 weeks (IQR: 3.14-17.86). No clinically meaningful differences were found across subgroups that were ADA-NEG, ADA-POS/NAb-NEG, and ADA-POS/NAb-POS, in terms of PKs (sotatercept trough concentration over time, mean postdose trough concentration at the end of treatment, and clearance), efficacy (changes from baseline in 6-minute walk distance, pulmonary vascular resistance, and N-terminal pro-B-type natriuretic peptide levels), and safety (incidence of hypersensitivity, anaphylactic reactions, and administration site reactions). We conclude that ADA incidence from sotatercept treatment was 25.9% and did not meaningfully affect the PKs, efficacy, or safety of sotatercept in participants with PAH.

Sotatercept for the treatment of pulmonary arterial hypertension: PULSAR open-label extension.

BACKGROUND: In 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. METHODS: PULSAR 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. RESULTS: Of 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. CONCLUSION: These results support the longer-term safety and durability of clinical benefit of sotatercept for pulmonary arterial hypertension.

animalcules: interactive microbiome analytics and visualization in R.

BACKGROUND: Microbial communities that live in and on the human body play a vital role in health and disease. Recent advances in sequencing technologies have enabled the study of microbial communities at unprecedented resolution. However, these advances in data generation have presented novel challenges to researchers attempting to analyze and visualize these data. RESULTS: To address some of these challenges, we have developed animalcules, an easy-to-use interactive microbiome analysis toolkit for 16S rRNA sequencing data, shotgun DNA metagenomics data, and RNA-based metatranscriptomics profiling data. This toolkit combines novel and existing analytics, visualization methods, and machine learning models. For example, the toolkit features traditional microbiome analyses such as alpha/beta diversity and differential abundance analysis, combined with new methods for biomarker identification are. In addition, animalcules provides interactive and dynamic figures that enable users to understand their data and discover new insights. animalcules can be used as a standalone command-line R package or users can explore their data with the accompanying interactive R Shiny interface. CONCLUSIONS: We present animalcules, an R package for interactive microbiome analysis through either an interactive interface facilitated by R Shiny or various command-line functions. It is the first microbiome analysis toolkit that supports the analysis of all 16S rRNA, DNA-based shotgun metagenomics, and RNA-sequencing based metatranscriptomics datasets. animalcules can be freely downloaded from GitHub at https://github.com/compbiomed/animalcules or installed through Bioconductor at https://www.bioconductor.org/packages/release/bioc/html/animalcules.html . Video abstract.

Sotatercept for the Treatment of Pulmonary Arterial Hypertension.

BACKGROUND: Pulmonary arterial hypertension is characterized by pulmonary vascular remodeling, cellular proliferation, and poor long-term outcomes. Dysfunctional bone morphogenetic protein pathway signaling is associated with both hereditary and idiopathic subtypes. Sotatercept, a novel fusion protein, binds activins and growth differentiation factors in the attempt to restore balance between growth-promoting and growth-inhibiting signaling pathways. METHODS: In this 24-week multicenter trial, we randomly assigned 106 adults who were receiving background therapy for pulmonary arterial hypertension to receive subcutaneous sotatercept at a dose of 0.3 mg per kilogram of body weight every 3 weeks or 0.7 mg per kilogram every 3 weeks or placebo. The primary end point was the change from baseline to week 24 in pulmonary vascular resistance. RESULTS: Baseline characteristics were similar among the three groups. The least-squares mean difference between the sotatercept 0.3-mg group and the placebo group in the change from baseline to week 24 in pulmonary vascular resistance was -145.8 dyn · sec · cm-5 (95% confidence interval [CI], -241.0 to -50.6; P = 0.003). The least-squares mean difference between the sotatercept 0.7-mg group and the placebo group was -239.5 dyn · sec · cm-5 (95% CI, -329.3 to -149.7; P<0.001). At 24 weeks, the least-squares mean difference between the sotatercept 0.3-mg group and the placebo group in the change from baseline in 6-minute walk distance was 29.4 m (95% CI, 3.8 to 55.0). The least-squares mean difference between the sotatercept 0.7-mg group and the placebo group was 21.4 m (95% CI, -2.8 to 45.7). Sotatercept was also associated with a decrease in N-terminal pro-B-type natriuretic peptide levels. Thrombocytopenia and an increased hemoglobin level were the most common hematologic adverse events. One patient in the sotatercept 0.7-mg group died from cardiac arrest. CONCLUSIONS: Treatment with sotatercept resulted in a reduction in pulmonary vascular resistance in patients receiving background therapy for pulmonary arterial hypertension. (Funded by Acceleron Pharma; PULSAR ClinicalTrials.gov number, NCT03496207.).

Inhibition of colony stimulating factor 1 receptor corrects maternal inflammation-induced microglial and synaptic dysfunction and behavioral abnormalities.

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 MIA-associated 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.

Alternative empirical Bayes models for adjusting for batch effects in genomic studies.

BACKGROUND: Combining 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. RESULTS: Here 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. CONCLUSIONS: We demonstrate the value of these new contributions compared to currently established approaches in the specified batch correction situations.

BatchQC: interactive software for evaluating sample and batch effects in genomic data.

Sequencing and microarray samples often are collected or processed in multiple batches or at different times. This often produces technical biases that can lead to incorrect results in the downstream analysis. There are several existing batch adjustment tools for '-omics' data, but they do not indicate a priori whether adjustment needs to be conducted or how correction should be applied. We present a software pipeline, BatchQC, which addresses these issues using interactive visualizations and statistics that evaluate the impact of batch effects in a genomic dataset. BatchQC can also apply existing adjustment tools and allow users to evaluate their benefits interactively. We used the BatchQC pipeline on both simulated and real data to demonstrate the effectiveness of this software toolkit. AVAILABILITY AND IMPLEMENTATION: BatchQC is available through Bioconductor: http://bioconductor.org/packages/BatchQC and GitHub: https://github.com/mani2012/BatchQC CONTACT: wej@bu.eduSupplementary information: Supplementary data are available at Bioinformatics online.

Integrating microbial and host transcriptomics to characterize asthma-associated microbial communities.

BACKGROUND: The 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. METHODS: Using 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. RESULTS: Microbial 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. CONCLUSIONS: For 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.

PathoScope 2.0: a complete computational framework for strain identification in environmental or clinical sequencing samples.

BACKGROUND: Recent 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. RESULTS: We 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. CONCLUSIONS: The 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/.

Clinical PathoScope: rapid alignment and filtration for accurate pathogen identification in clinical samples using unassembled sequencing data.

BACKGROUND: The 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. RESULTS: Here 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. CONCLUSIONS: Clinical 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/.

PathoQC: Computationally Efficient Read Preprocessing and Quality Control for High-Throughput Sequencing Data Sets.

Quality control and read preprocessing are critical steps in the analysis of data sets generated from high-throughput genomic screens. In the most extreme cases, improper preprocessing can negatively affect downstream analyses and may lead to incorrect biological conclusions. Here, we present PathoQC, a streamlined toolkit that seamlessly combines the benefits of several popular quality control software approaches for preprocessing next-generation sequencing data. PathoQC provides a variety of quality control options appropriate for most high-throughput sequencing applications. PathoQC is primarily developed as a module in the PathoScope software suite for metagenomic analysis. However, PathoQC is also available as an open-source Python module that can run as a stand-alone application or can be easily integrated into any bioinformatics workflow. PathoQC achieves high performance by supporting parallel computation and is an effective tool that removes technical sequencing artifacts and facilitates robust downstream analysis. The PathoQC software package is available at http://sourceforge.net/projects/PathoScope/.

Pathoscope: species identification and strain attribution with unassembled sequencing data.

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.