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The Gillenia trifoliata genome reveals dynamics correlated with growth and reproduction in Rosaceae.

ABSTRACT: The Rosaceae family has striking phenotypic diversity and high syntenic conservation. Gillenia trifoliata is sister species to the Maleae tribe of apple and ~1000 other species. Gillenia has many putative ancestral features, such as herb/sub-shrub habit, dry fruit-bearing and nine base chromosomes. This coalescence of ancestral characters in a phylogenetically important species, positions Gillenia as a 'rosetta stone' for translational science within Rosaceae. We present genomic and phenological resources to facilitate the use of Gillenia for this purpose. The Gillenia genome is the first fully annotated chromosome-level assembly with an ancestral genome complement (x = 9), and with it we developed an improved model of the Rosaceae ancestral genome. MADS and NAC gene family analyses revealed genome dynamics correlated with growth and reproduction and we demonstrate how Gillenia can be a negative control for studying fleshy fruit development in Rosaceae.

SUBMITTER: Ireland HS

PROVIDER: S-EPMC8558331 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature

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The Gillenia trifoliata genome reveals dynamics correlated with growth and reproduction in Rosaceae.

Ireland Hilary S HS Wu Chen C Deng Cecilia H CH Hilario Elena E Saei Ali A Erasmuson Sylvia S Crowhurst Ross N RN David Karine M KM Schaffer Robert J RJ Chagné David D

Horticulture research 20211101 1

The Rosaceae family has striking phenotypic diversity and high syntenic conservation. Gillenia trifoliata is sister species to the Maleae tribe of apple and ~1000 other species. Gillenia has many putative ancestral features, such as herb/sub-shrub habit, dry fruit-bearing and nine base chromosomes. This coalescence of ancestral characters in a phylogenetically important species, positions Gillenia as a 'rosetta stone' for translational science within Rosaceae. We present genomic and phenological ...[more]

PMID: 34719690

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Project description:Early estimates of the transmission potential of emerging and re-emerging infections are increasingly used to inform public health authorities on the level of risk posed by outbreaks. Existing methods to estimate the reproduction number generally assume exponential growth in case incidence in the first few disease generations, before susceptible depletion sets in. In reality, outbreaks can display subexponential (i.e. polynomial) growth in the first few disease generations, owing to clustering in contact patterns, spatial effects, inhomogeneous mixing, reactive behaviour changes or other mechanisms. Here, we introduce the generalized growth model to characterize the early growth profile of outbreaks and estimate the effective reproduction number, with no need for explicit assumptions about the shape of epidemic growth. We demonstrate this phenomenological approach using analytical results and simulations from mechanistic models, and provide validation against a range of empirical disease datasets. Our results suggest that subexponential growth in the early phase of an epidemic is the rule rather the exception. Mechanistic simulations show that slight modifications to the classical susceptible-infectious-removed model result in subexponential growth, and in turn a rapid decline in the reproduction number within three to five disease generations. For empirical outbreaks, the generalized-growth model consistently outperforms the exponential model for a variety of directly and indirectly transmitted diseases datasets (pandemic influenza, measles, smallpox, bubonic plague, cholera, foot-and-mouth disease, HIV/AIDS and Ebola) with model estimates supporting subexponential growth dynamics. The rapid decline in effective reproduction number predicted by analytical results and observed in real and synthetic datasets within three to five disease generations contrasts with the expectation of invariant reproduction number in epidemics obeying exponential growth. The generalized-growth concept also provides us a compelling argument for the unexpected extinction of certain emerging disease outbreaks during the early ascending phase. Overall, our approach promotes a more reliable and data-driven characterization of the early epidemic phase, which is important for accurate estimation of the reproduction number and prediction of disease impact.

Dataset Information

The Gillenia trifoliata genome reveals dynamics correlated with growth and reproduction in Rosaceae.

Publications

The Gillenia trifoliata genome reveals dynamics correlated with growth and reproduction in Rosaceae.

Similar Datasets

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