Browsing UMB Open Access Articles by Author "D'Mello, A."
An in vivo atlas of host-pathogen transcriptomes during Streptococcus pneumoniae colonization and diseaseD'Mello, A.; Riegler, A.N.; Martinez, E.; Beno, S.M.; Ricketts, T.D.; Foxman, E.F.; Orihuela, C.J.; Tettelin, H. (Proceedings of the National Academy of Sciences of the United States of America, 2020-12-14)Streptococcus pneumoniae (Spn) colonizes the nasopharynx and can cause pneumonia. From the lungs it spreads to the bloodstream and causes organ damage. We characterized the in vivo Spn and mouse transcriptomes within the nasopharynx, lungs, blood, heart, and kidneys using three Spn strains. We identified Spn genes highly expressed at all anatomical sites and in an organ-specific manner; highly expressed genes were shown to have vital roles with knockout mutants. The in vivo bacterial transcriptome during colonization/disease was distinct from previously reported in vitro transcriptomes. Distinct Spn and host gene-expression profiles were observed during colonization and disease states, revealing specific genes/operons whereby Spn adapts to and influences host sites in vivo. We identified and experimentally verified host-defense pathways induced by Spn during invasive disease, including proinflammatory responses and the interferon response. These results shed light on the pathogenesis of Spn and identify therapeutic targets. Copyright Copyright 2020 the Author(s). Published by PNAS.
ReVac: a reverse vaccinology computational pipeline for prioritization of prokaryotic protein vaccine candidatesD'Mello, A.; Ahearn, C.P.; Tettelin, H. (BioMed Central Ltd., 2019)BACKGROUND: Reverse vaccinology accelerates the discovery of potential vaccine candidates (PVCs) prior to experimental validation. Current programs typically use one bacterial proteome to identify PVCs through a filtering architecture using feature prediction programs or a machine learning approach. Filtering approaches may eliminate potential antigens based on limitations in the accuracy of prediction tools used. Machine learning approaches are heavily dependent on the selection of training datasets with experimentally validated antigens (positive control) and non-protective-antigens (negative control). The use of one or few bacterial proteomes does not assess PVC conservation among strains, an important feature of vaccine antigens. RESULTS: We present ReVac, which implements both a panoply of feature prediction programs without filtering out proteins, and scoring of candidates based on predictions made on curated positive and negative control PVCs datasets. ReVac surveys several genomes assessing protein conservation, as well as DNA and protein repeats, which may result in variable expression of PVCs. ReVac's orthologous clustering of conserved genes, identifies core and dispensable genome components. This is useful for determining the degree of conservation of PVCs among the population of isolates for a given pathogen. Potential vaccine candidates are then prioritized based on conservation and overall feature-based scoring. We present the application of ReVac, applied to 69 Moraxella catarrhalis and 270 non-typeable Haemophilus influenzae genomes, prioritizing 64 and 29 proteins as PVCs, respectively. CONCLUSION: ReVac's use of a scoring scheme ranks PVCs for subsequent experimental testing. It employs a redundancy-based approach in its predictions of features using several prediction tools. The protein's features are collated, and each protein is ranked based on the scoring scheme. Multi-genome analyses performed in ReVac allow for a comprehensive overview of PVCs from a pan-genome perspective, as an essential pre-requisite for any bacterial subunit vaccine design. ReVac prioritized PVCs of two human respiratory pathogens, identifying both novel and previously validated PVCs.