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Bartels, D.

Publications and source records attributed to Bartels, D..

2 recordsLinked to original sources

The complex octoploid Craterostigma genome and tissue-specific mechanisms underlying desiccation tolerance

Resurrection plants can survive prolonged anhydrobiosis, and desiccation tolerance has evolved recurrently across land plants as a common adaptation for survival in regions with seasonal drying. Craterostigma plantagineum was among the first model resurrection plants, and many of the genetic mechanisms underlying desiccation tolerance were discovered in this important system. Here, we analyzed the complex, octoploid Craterostigma (C. plantagineum) genome and surveyed spatial and temporal expression dynamics to identify genetic elements underlying desiccation tolerance. Homeologous genes within the Craterostigma genome have divergent expression profiles, suggesting the subgenomes contribute differently to desiccation tolerance traits. The Craterostigma genome contains almost 200 tandemly duplicated early light induced proteins (ELIPs), a hallmark trait of desiccation tolerance, with massive upregulation under water deficit. We identified a core network of desiccation responsive genes across all tissues but observed almost entirely unique expression dynamics in each tissue during recovery. Roots and leaves have differential responses related to light and photoprotection, autophagy, and nutrient transport, reflecting their divergent functions. Our findings highlight a universal set of likely ancestral desiccation tolerance mechanisms to protect cellular macromolecules under anhydrobiosis, with secondary adaptations related to tissue function.

plant biology↗

Modelling the spread and mitigation of an emerging vector-borne pathogen:citrus greening in the U.S.

Predictive models, based upon epidemiological principles and fitted to surveillance data, play an increasingly important role in shaping regulatory and operational policies for emerging outbreaks. Data for parameterising these strategically important models are often scarce when rapid actions are required to change the course of an epidemic invading a new region. We provide a flexible toolkit for landscape-scale disease management, which is applicable to a range of emerging pathogens including vector-borne pathogens for both endemic and invading epidemic vectors. We use the toolkit to analyse and predict the spread of Huanglongbing disease or citrus greening in the U.S. We estimate epidemiological parameters using survey data from one region (Texas) and show how to transfer and test parameters to construct predictive spatio-temporal models for another region (California). The models are used to screen effective coordinated and reactive management strategies for different regions.

ecology↗