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Summary This work revisits a publication by Beanet al.(2018) that reports seven amino acid substitutions are essential for the evolution ofl‐DOPA 4,5‐dioxygenase (DODA) activity in Caryophyllales. In this study, we explore several concerns which led us to replicate the analyses of Beanet al.(2018).Our comparative analyses, with structural modelling, implicate numerous residues additional to those identified by Beanet al.(2018), with many of these additional residues occurring around the active site of BvDODAα1. We therefore replicated the analyses of Beanet al.(2018) to re‐observe the effect of their original seven residue substitutions in a BvDODAα2 background, that is the BvDODAα2‐mut3 variant.Multiplein vivoassays, in bothSaccharomyces cerevisiaeandNicotiana benthamiana, did not result in visible DODA activity in BvDODAα2‐mut3, with betalain production always 10‐fold below BvDODAα1.In vitroassays also revealed substantial differences in both catalytic activity and pH optima between BvDODAα1, BvDODAα2 and BvDODAα2‐mut3 proteins, explaining their differing performancein vivo.In summary, we were unable to replicate thein vivoanalyses of Beanet al.(2018), and our quantitativein vivoandin vitroanalyses suggest a minimal effect of these seven residues in altering catalytic activity of BvDODAα2. We conclude that the evolutionary pathway to high DODA activity is substantially more complex than implied by Beanet al.(2018).
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Reconstructing bird trajectories from pressure and wind data using a highly optimized hidden Markov model
Abstract Tracking technologies have widely expanded our understanding of bird migration routes, destinations and underlying strategies. However, determining the entire trajectory of small birds equipped with lightweight geolocators remains a challenge.We develop a highly optimized hidden Markov model (HMM) for reconstructing bird trajectories. The observation model is defined by pressure and, optionally, light measurements, while the movement model incorporates wind data to constrain consecutive positions based on realistic airspeeds. To reduce the computational costs associated with a large state space, we prune the HMM states and transitions based on flight and observation constraints to efficiently model the entire trajectory.The approach presented is based on a mathematically exact procedure and is fast to compute. We demonstrate how to compute (1) the most likely trajectory, (2) the marginal probability map of each stationary period, (3) simulated trajectories and (4) the wind conditions (wind support/drift) encountered by the bird during each migratory flight.We construct a version of an HMM optimized for reconstructing a bird's migration trajectory based on lightweight geolocator data. To render this approach easily accessible to researchers, we designed a dedicated R packageGeoPressureR(https://raphaelnussbaumer.com/GeoPressureR/).
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- PAR ID:
- 10473117
- Publisher / Repository:
- British Ecological Society
- Date Published:
- Journal Name:
- Methods in Ecology and Evolution
- Volume:
- 14
- Issue:
- 4
- ISSN:
- 2041-210X
- Page Range / eLocation ID:
- 1118 to 1129
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1111/2041-210X.14082
