Computer Science Research Works

Permanent URI for this collectionhttp://hdl.handle.net/1903/1593

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Author: search.filters.author.Corrada Bravo, Hector

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    Simultaneous transcriptional profiling of Leishmania major and its murine macrophage host cell reveals insights into host-pathogen interactions
    (Springer Nature, 2015-12-29) Dillon, Laura A. L.; Suresh, Rahul; Okrah, Kwame; Corrada Bravo, Hector; Mosser, David M.; El-Sayed, Najib M.
    Parasites of the genus Leishmania are the causative agents of leishmaniasis, a group of diseases that range in manifestations from skin lesions to fatal visceral disease. The life cycle of Leishmania parasites is split between its insect vector and its mammalian host, where it resides primarily inside of macrophages. Once intracellular, Leishmania parasites must evade or deactivate the host's innate and adaptive immune responses in order to survive and replicate. We performed transcriptome profiling using RNA-seq to simultaneously identify global changes in murine macrophage and L. major gene expression as the parasite entered and persisted within murine macrophages during the first 72 h of an infection. Differential gene expression, pathway, and gene ontology analyses enabled us to identify modulations in host and parasite responses during an infection. The most substantial and dynamic gene expression responses by both macrophage and parasite were observed during early infection. Murine genes related to both pro- and anti-inflammatory immune responses and glycolysis were substantially upregulated and genes related to lipid metabolism, biogenesis, and Fc gamma receptor-mediated phagocytosis were downregulated. Upregulated parasite genes included those aimed at mitigating the effects of an oxidative response by the host immune system while downregulated genes were related to translation, cell signaling, fatty acid biosynthesis, and flagellum structure. The gene expression patterns identified in this work yield signatures that characterize multiple developmental stages of L. major parasites and the coordinated response of Leishmania-infected macrophages in the real-time setting of a dual biological system. This comprehensive dataset offers a clearer and more sensitive picture of the interplay between host and parasite during intracellular infection, providing additional insights into how pathogens are able to evade host defenses and modulate the biological functions of the cell in order to survive in the mammalian environment.
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    A framework for assessing 16S rRNA marker-gene survey data analysis methods using mixtures.
    (Springer Nature, 2020-03-13) Olson, Nathan D.; Kumar, M. Senthil; Li, Shan; Braccia, Domenick J.; Hao, Stephanie; Timp, Winston; Salit, Marc L.; Stine, O. Colin; Corrada Bravo, Hector
    There are a variety of bioinformatic pipelines and downstream analysis methods for analyzing 16S rRNA marker-gene surveys. However, appropriate assessment datasets and metrics are needed as there is limited guidance to decide between available analysis methods. Mixtures of environmental samples are useful for assessing analysis methods as one can evaluate methods based on calculated expected values using unmixed sample measurements and the mixture design. Previous studies have used mixtures of environmental samples to assess other sequencing methods such as RNAseq. But no studies have used mixtures of environmental to assess 16S rRNA sequencing. We developed a framework for assessing 16S rRNA sequencing analysis methods which utilizes a novel two-sample titration mixture dataset and metrics to evaluate qualitative and quantitative characteristics of count tables. Our qualitative assessment evaluates feature presence/absence exploiting features only present in unmixed samples or titrations by testing if random sampling can account for their observed relative abundance. Our quantitative assessment evaluates feature relative and differential abundance by comparing observed and expected values. We demonstrated the framework by evaluating count tables generated with three commonly used bioinformatic pipelines: (i) DADA2 a sequence inference method, (ii) Mothur a de novo clustering method, and (iii) QIIME an open-reference clustering method. The qualitative assessment results indicated that the majority of Mothur and QIIME features only present in unmixed samples or titrations were accounted for by random sampling alone, but this was not the case for DADA2 features. Combined with count table sparsity (proportion of zero-valued cells in a count table), these results indicate DADA2 has a higher false-negative rate whereas Mothur and QIIME have higher false-positive rates. The quantitative assessment results indicated the observed relative abundance and differential abundance values were consistent with expected values for all three pipelines. We developed a novel framework for assessing 16S rRNA marker-gene survey methods and demonstrated the framework by evaluating count tables generated with three bioinformatic pipelines. This framework is a valuable community resource for assessing 16S rRNA marker-gene survey bioinformatic methods and will help scientists identify appropriate analysis methods for their marker-gene surveys.