Understanding population dynamics from the analysis of molecular and spatial data requires sound statistical modeling. Current approaches assume that populations are naturally partitioned into discrete demes, thereby failing to be relevant in cases where individuals are scattered on a spatial continuum. Other models predict the formation of increasingly tight clusters of individuals in space, which, again, conflicts with biological evidence. Building on recent theoretical work, we introduce a new genealogy-based inference framework that alleviates these issues. This approach effectively implements a stochastic model in which the distribution of individuals is homogeneous and stationary, thereby providing a relevant null model for the fluctuation of genetic diversity in time and space. Importantly, the spatial density of individuals in a population and their range of dispersal during the course of evolution are two parameters that can be inferred separately with this method. The validity of the new inference framework is confirmed with extensive simulations and the analysis of influenza sequences collected over five seasons in the USA.
In eukaryotes, MAPK scaffold proteins are crucial for regulating the function of MAPK cascades. However, only a few MAPK scaffold proteins have been reported in plants, and the molecular mechanism through which scaffold proteins regulate the function of the MAPK cascade remains poorly understood. Here, we identified GhMORG1, a GhMKK6-GhMPK4 cascade scaffold protein that positively regulates the resistance of cotton to Fusarium oxysporum. GhMORG1 interacted with GhMKK6 and GhMPK4, and the overexpression of GhMORG1 in cotton protoplasts dramatically increased the activity of the GhMKK6-GhMPK4 cascade. Quantitative phosphoproteomics was used to clarify the mechanism of GhMORG1 in regulating disease resistance, and thirty-two proteins were considered as the putative substrates of the GhMORG1dependent GhMKK6-GhMPK4 cascade. These putative substrates were involved in multiple disease resistance processes, such as cellular amino acid metabolic processes, calcium ion binding and RNA binding. The kinase assays verified that most of the putative substrates were phosphorylated by the GhMKK6-GhMPK4 cascade. For functional analysis, nine putative substrates were silenced in cotton, respectively. The resistance of cotton to F. oxysporum was decreased in the substrate-silenced cottons. These results suggest that GhMORG1 regulates several different disease resistance processes by facilitating the phosphorylation of GhMKK6-GhMPK4 cascade substrates. Taken together, these findings reveal a new plant MAPK scaffold protein and provide insights into the mechanism of plant resistance to pathogens.
Understanding population dynamics from the analysis of molecular and spatial data requires sound statistical modeling. Current approaches assume that populations are naturally partitioned into discrete demes, thereby failing to be relevant in cases where individuals are scattered on a spatial continuum. Other models predict the formation of increasingly tight clusters of individuals in space, which, again, conflicts with biological evidence. Building on recent theoretical work, we introduce a new genealogy-based inference framework that alleviates these issues. This approach effectively implements a stochastic model in which the distribution of individuals is homogeneous and stationary, thereby providing a relevant null model for the fluctuation of genetic diversity in time and space. Importantly, the spatial density of individuals in a population and their range of dispersal during the course of evolution are two parameters that can be inferred separately with this method. The validity of the new inference framework is confirmed with extensive simulations and the analysis of influenza sequences collected over five seasons in the USA.
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