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SUMMARY: COBREXA.jl is a Julia package for scalable, high-performance constraint-based reconstruction and analysis of very large-scale biological models. Its primary purpose is to facilitate the integration of modern high performance computing environments with the processing and analysis of large-scale metabolic models of challenging complexity. We report the architecture of the package, and demonstrate how the design promotes analysis scalability on several use-cases with multi-organism community models. AVAILABILITY AND IMPLEMENTATION: https://doi.org/10.17881/ZKCR-BT30. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Metodologías Computacionales , Programas Informáticos , Modelos BiológicosRESUMEN
The novel betacoronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused a worldwide pandemic (COVID-19) after emerging in Wuhan, China. Here we analyzed public host and viral RNA sequencing data to better understand how SARS-CoV-2 interacts with human respiratory cells. We identified genes, isoforms and transposable element families that are specifically altered in SARS-CoV-2-infected respiratory cells. Well-known immunoregulatory genes including CSF2, IL32, IL-6 and SERPINA3 were differentially expressed, while immunoregulatory transposable element families were upregulated. We predicted conserved interactions between the SARS-CoV-2 genome and human RNA-binding proteins such as the heterogeneous nuclear ribonucleoprotein A1 (hnRNPA1) and eukaryotic initiation factor 4 (eIF4b). We also identified a viral sequence variant with a statistically significant skew associated with age of infection, that may contribute to intracellular host-pathogen interactions. These findings can help identify host mechanisms that can be targeted by prophylactics and/or therapeutics to reduce the severity of COVID-19.
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COVID-19/genética , Biología Computacional/métodos , Interacciones Huésped-Patógeno/genética , Pandemias , SARS-CoV-2/genética , Sitios de Unión , COVID-19/virología , Citocinas/genética , Bases de Datos Genéticas , Regulación de la Expresión Génica , Genoma Viral , Humanos , ARN Viral/genética , ARN Viral/metabolismo , Proteínas de Unión al ARN/genética , Proteínas de Unión al ARN/metabolismo , RNA-Seq , Serpinas/genética , Transducción de Señal/genética , Transcriptoma , Replicación Viral/genéticaRESUMEN
MOTIVATION: Analysis of differential expression of genes is often performed to understand how the metabolic activity of an organism is impacted by a perturbation. However, because the system of metabolic regulation is complex and all changes are not directly reflected in the expression levels, interpreting these data can be difficult. RESULTS: In this work, we present a new algorithm and computational tool that uses a genome-scale metabolic reconstruction to infer metabolic changes from differential expression data. Using the framework of constraint-based analysis, our method produces a qualitative hypothesis of a change in metabolic activity. In other words, each reaction of the network is inferred to have increased, decreased, or remained unchanged in flux. In contrast to similar previous approaches, our method does not require a biological objective function and does not assign on/off activity states to genes. An implementation is provided and it is available online. We apply the method to three published datasets to show that it successfully accomplishes its two main goals: confirming or rejecting metabolic changes suggested by differentially expressed genes based on how well they fit in as parts of a coordinated metabolic change, as well as inferring changes in reactions whose genes did not undergo differential expression. AVAILABILITY AND IMPLEMENTATION: github.com/htpusa/moomin. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Redes y Vías Metabólicas , Algoritmos , Biología Computacional , Genoma , Modelos BiológicosRESUMEN
Xylella fastidiosa is a notorious plant pathogenic bacterium that represents a threat to crops worldwide. Its subspecies, Xylella fastidiosa subsp. fastidiosa is the causal agent of Pierce's disease of grapevines. Pierce's disease has presented a serious challenge for the grapevine industry in the United States and turned into an epidemic in Southern California due to the invasion of the insect vector Homalodisca vitripennis. In an attempt to minimize the effects of Xylella fastidiosa subsp. fastidiosa in vineyards, various studies have been developing and testing strategies to prevent the occurrence of Pierce's disease, i.e., prophylactic strategies. Research has also been undertaken to investigate therapeutic strategies to cure vines infected by Xylella fastidiosa subsp. fastidiosa. This report explicitly reviews all the strategies published to date and specifies their current status. Furthermore, an epidemiological model of Xylella fastidiosa subsp. fastidiosa is proposed and key parameters for the spread of Pierce's disease deciphered in a sensitivity analysis of all model parameters. Based on these results, it is concluded that future studies should prioritize therapeutic strategies, while investments should only be made in prophylactic strategies that have demonstrated promising results in vineyards.
