Show simple item record Lakhani, Vinal Li, Tan Mukherjee, Sayak Stewart, William C.L. Sword, W. Edward Das, Jayajit 2018-11-08T14:26:11Z 2018-11-08T14:26:11Z 2018-11-14
dc.identifier doi:10.5061/dryad.j89d064
dc.identifier.citation Lakhani V, Tan L, Mukherjee S, Stewart WCL, Swords WE, Das J (2018) Mutations in bacterial genes induce unanticipated changes in the relationship between bacterial pathogens in experimental otitis media. Royal Society Open Science 5(11): 180810.
dc.description Otitis media (OM) is a common polymicrobial infection of the middle ear in children under the age of fifteen years. A widely used experimental strategy to analyze roles of specific phenotypes of bacterial pathogens of OM is to study changes in co-infection kinetics of bacterial populations in animal models when a wild type bacterial strain is replaced by a specific isogenic mutant strain in the co-inoculating mixtures. Since relationships between the OM bacterial pathogens within the host are regulated by many interlinked processes, connecting the changes in the co-infection kinetics to a bacterial phenotype can be challenging. We investigated middle ear co-infections in adult chinchillas (Chinchilla lanigera) by two major OM pathogens: nontypeable Haemophilus influenzae (NTHI) and Moraxella catarrhalis (Mcat), as well as isogenic mutant strains in each bacterial species. We analyzed the infection kinetic data using Lotka-Volterra population dynamics, Maximum Entropy inference, and Akaike Information Criteria (AIC) based model selection. We found that changes in relationships between the bacterial pathogens that were not anticipated in the design of the co-infection experiments involving mutant strains are common and were strong regulators of the co-infecting bacterial populations. The framework developed here allows for a systematic analysis of host-host variations of bacterial populations and small sizes of animal cohorts in co-infection experiments to quantify the role of specific mutant strains in changing the infection kinetics. Our combined approach can be used to analyze the functional footprint of mutant strains in regulating co-infection kinetics in models of experimental OM and other polymicrobial diseases.
dc.relation.haspart doi:10.5061/dryad.j89d064/1
dc.relation.haspart doi:10.5061/dryad.j89d064/2
dc.relation.isreferencedby doi:10.1098/rsos.180810
dc.subject polymicrobial infection
dc.subject otitis media
dc.subject maximum entropy estimation
dc.subject lotka-volterra
dc.subject condorcet winner
dc.subject akkaike information criterion
dc.title Data from: Mutations in bacterial genes induce unanticipated changes in the relationship between bacterial pathogens in experimental otitis media
dc.type Article
dc.contributor.correspondingAuthor Das, Jayajit
prism.publicationName Royal Society Open Science
dryad.dansTransferDate 2018-12-10T20:38:22.485+0000
dryad.dansArchiveDate 2018-12-10T22:01:26.559+0000

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Title in vivo RawData
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Description Contains the data for bacterial counts in individual chinchilla ears
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Title MATLAB code used for analysis
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Description Set22A.mat This set of data provides the mapping from discretized alpha-space to a discretized N-space, as well as all the necessary information used to generate this particular mapping. This mapping was used throughout the data analysis in this manuscript (except as noted in Fig S5). Qa2Xmap – This matrix variable has 17,645,368 rows and 6 columns. This matrix is a map from alpha-space to N-space. For a given row, the first four columns are the alpha-matrix values {a11, a12, a21, a22}; the last two columns indicate the corresponding steady state N-values (in 106 CFU) {NTHi, Mcat}. See Eqn 3 in Methods B.1. X1, X2 – This column vector has the discretized values of N1, and N2 respectively (in 106 CFU). a11, a12, a21, a22 – These column vectors have the discretized values of a11, a12, a21, and a22 respectively. X1range, X2range – These variables contain the minimum and maximum of N1, and N2 respectively. Xbns – N1, and N2 were each discretized into this many number of bins. abns_diag – a11, and a22 were each discretized into this many number of bins. abns_offdiag – a12, and a21 were each discretized into this many number of bins. counts – this square matrix records the number of alpha matrices that correspond to steady state values in each bin of N-space. kappa – this square matrix is equal to “counts” / 17,645,368.
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