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Sieve analysis using the number of infecting pathogens.

ABSTRACT: Assessment of vaccine efficacy as a function of the similarity of the infecting pathogen to the vaccine is an important scientific goal. Characterization of pathogen strains for which vaccine efficacy is low can increase understanding of the vaccine's mechanism of action and offer targets for vaccine improvement. Traditional sieve analysis estimates differential vaccine efficacy using a single identifiable pathogen for each subject. The similarity between this single entity and the vaccine immunogen is quantified, for example, by exact match or number of mismatched amino acids. With new technology, we can now obtain the actual count of genetically distinct pathogens that infect an individual. Let F be the number of distinct features of a species of pathogen. We assume a log-linear model for the expected number of infecting pathogens with feature "f," f = 1 , … , F . The model can be used directly in studies with passive surveillance of infections where the count of each type of pathogen is recorded at the end of some interval, or active surveillance where the time of infection is known. For active surveillance, we additionally assume that a proportional intensity model applies to the time of potentially infectious exposures and derive product and weighted estimating equation (WEE) estimators for the regression parameters in the log-linear model. The WEE estimator explicitly allows for waning vaccine efficacy and time-varying distributions of pathogens. We give conditions where sieve parameters have a per-exposure interpretation under passive surveillance. We evaluate the methods by simulation and analyze a phase III trial of a malaria vaccine.

SUBMITTER: Follmann D

PROVIDER: S-EPMC6004265 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

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Sieve analysis using the number of infecting pathogens.

Follmann Dean D Huang Chiung-Yu CY

Biometrics 20171214 3

Assessment of vaccine efficacy as a function of the similarity of the infecting pathogen to the vaccine is an important scientific goal. Characterization of pathogen strains for which vaccine efficacy is low can increase understanding of the vaccine's mechanism of action and offer targets for vaccine improvement. Traditional sieve analysis estimates differential vaccine efficacy using a single identifiable pathogen for each subject. The similarity between this single entity and the vaccine immun ...[more]

PMID: 29238965

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Project description:The plant microbiome is shaped by plant development and microbial interaction. Fungal pathogens infecting bell pepper plants may fluctuate across the growing seasons. Dynamic fluctuation of the microbiome and fungal pathogens in bell pepper plants is poorly understood, and the origin of fungal pathogens causing fruit rot and leaf wilt has been barely investigated. In this study, we used amplicon sequencing (i.e., 16S rRNA and internal transcribed spacer [ITS] sequencing) to explore the compositional variations of the microbiome in bell pepper plants and studied the fluctuation of fungal pathogens across the growing seasons. Co-occurrence network analysis was applied to track the origin and dissemination route of fungal pathogens that infected bell pepper plants. ITS and 16S rRNA sequencing analyses demonstrated that fungal pathogens infecting fruits and leaves probably belonged to the Penicillium, Cladosporium, Fusarium, and unclassified_Sclerotiniaceae genera rather than one specific genus. The dominant fungal pathogens were different, along with the development of bell pepper plants. Both plant development and fungal pathogens shaped microbial communities in bell pepper plants across the growing seasons. Fungal pathogens decreased species richness and diversity of fungal communities in fungus-infected fruit and leaf tissues but not the uninfected stem tissues. Bacterial metabolic functions of xenobiotics increased in fungus-infected leaves at a mature developmental stage. Competitive interaction was present between fungal and bacterial communities in leaves. Co-occurrence network analysis revealed that the origins of fungal pathogens included the greenhouse, packing house, and storage room. Niche differentiation of microbes was discovered among these locations. IMPORTANCE Bell peppers are widely consumed worldwide. Fungal pathogen infections of bell peppers lead to enormous economic loss. To control fungal pathogens and increase economic benefit, it is essential to investigate the shifting patterns of the microbiome and fungal pathogens in bell pepper plants across the growing seasons. In this study, bell pepper plant diseases observed in fruits and leaves were caused by different fungal pathogens. Fungal pathogens originated from the greenhouse, packing house, and storage room, and niche differentiation existed among microbes. This study improves the understanding of dynamic fluctuation and source of fungal pathogens infecting bell pepper plants in the farming system. It also facilitates precise management of fungal pathogens in the greenhouse.

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Sieve analysis using the number of infecting pathogens.

Publications

Sieve analysis using the number of infecting pathogens.

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