Biology subjects

Allen, T. W.

Publications and source records attributed to Allen, T. W..

5 recordsLinked to original sources

bioRxiv2023

Potassium dependent structural changes in the selectivity filter of HERG potassium channels

The fine tuning of biological electrical signaling is mediated by variations in the rates of opening and closing of gates that control ion flux through different ion channels. Human ether-a-go-go related gene (HERG) potassium channels have uniquely rapid inactivation kinetics which are critical to the role they play in regulating cardiac electrical activity. Here, we have exploited the K+ sensitivity of HERG inactivation to determine structures of both a conductive and non-conductive selectivity filter structure of HERG. We propose that inactivation is the result of a high propensity for flipping of the selectivity filter valine carbonyl oxygens. Molecular dynamics simulations point to a low energy barrier, and hence rapid kinetics, for flipping of the valine 625 carbonyl oxygens facilitated by a previously unrecognized interaction between S620 and Y616 that stabilizes the transition state between conducting and non-conducting structures. Our model represents a new mechanism by which ion channels fine tune their activity that explains the uniquely rapid inactivation kinetics of HERG. HighlightsStructures of a conductive and non-conductive HERG selectivity filter have been determined. Reduced potassium causes flipping of selectivity filter valine carbonyl oxygens. The sidechain of S620 on the pore helix coordinates distinct sets of interactions between conductive, non-conductive, and transition states.

biophysics↗

bioRxiv2023

Uncovering the Environmental Conditions Required for Phyllachora maydis Infection and Tar Spot Development on Corn in the United States for Use as Predictive Models for Future Epidemics

Phyllachora maydis is a fungal pathogen causing tar spot of corn (Zea mays L.), a new and emerging, yield-limiting disease in the United States. Since being first reported in Illinois and Indiana in 2015, P. maydis can now be found across much of the corn growing of the United States. Knowledge of the epidemiology of P. maydis is limited but could be useful in developing tar spot prediction tools. The research presented here aims to elucidate the environmental conditions necessary for the development of tar spot in the field and the creation of predictive models to anticipate future tar spot epidemics. Extended periods (30-day windowpanes) of moderate ambient temperature were most significant for explaining the development of tar spot. Shorter periods (14- to 21-day windowpanes) of moisture (relative humidity, dew point, number of hours with predicted leaf wetness) were negatively correlated with tar spot development. These weather variables were used to develop multiple logistic regression models, an ensembled model, and two machine learning models for the prediction of tar spot development. This work has improved the understanding of P. maydis epidemiology and provided the foundation for the development of a predictive tool for anticipating future tar spot epidemics.

plant biology↗

bioRxiv2019

Modeling the relationship between estimated foliar fungicide use and soybean yield losses due to foliar fungal diseases in the United States

Fungicide use in the United States to manage soybean diseases has increased in recent years. The ability of fungicides to reduce disease-associated yield losses varies greatly depending on multiple factors. Nonetheless, historical data are useful to understand the broad sense and long-term trends related to fungicide use practices. In the current study, the relationship between estimated soybean yield losses due to selected foliar diseases and foliar fungicide use was investigated using annual data from 28 soybean growing states over the period of 2005 to 2015. At a national scale, a significant quadratic relationship was observed between total estimated yield losses and total fungicide use (R2 = 0.123, P < 0.0001) where yield losses initially increased, reached a plateau, and subsequently decreased with increasing fungicide use. The positive phase of the quadratic curve could be associated with insufficient amount of fungicides being used to manage targeted diseases, application of more-than-recommended prophylactic fungicides under no/low disease pressure, application of curative fungicides after economic injury level, and reduced fungicide efficacy due to a variety of factors such as unfavorable environmental conditions and resistance of targeted pathogen populations to the specific active ingredient applied. Interestingly, a significant quadratic relationship was also observed between total soybean production and total foliar fungicide use (R2= 0.36, P < 0.0001). The positive phase of the quadratic curve may suggest that factors like plant physiological changes, including increased chlorophyll content, photosynthetic rates, water use efficiency, and delayed senescence that have been widely reported to occur after application of certain foliar fungicides could have potentially contributed to enhanced yield. Therefore, the current study provides evidence of the potential usefulness of foliar fungicide applications to mitigate soybean yield losses associated with foliar diseases and their potential to positively impact soybean production/yield at national and regional scales although discrepancies to the general trends observed at national and regional scales do prevail at the local (state) level.

pathology↗

bioRxiv2019

Dissecting the economic impact of soybean diseases in the United States over two decades

Soybean (Glycine max L. Merrill) is a key commodity for United States agriculture. Here we analyze the economic impacts of 23 common soybean diseases in 28 soybean-producing states in the U.S., from 1996 to 2016. From 1996 to 2016, the total estimated economic loss due to soybean diseases in the U.S. was $81.39 billion, with $68.98 billion and $12.41 billion accounting for the northern and southern U.S. losses, respectively. Across states and years, soybean cyst nematode, charcoal rot, and seedling diseases were the most economically damaging pathogens/diseases while soybean rust, bacterial blight, and southern blight were the least economically damaging. Significantly positive linear correlation of mean soybean yield loss with the mean state-wide soybean production (MT) and mean soybean yield (kg ha-1) indicated that high production zones are more vulnerable to soybean diseases-associated yield losses. Our findings provide useful insights into how research, policy, and educational efforts should be prioritized in soybean disease management.

epidemiology↗

bioRxiv2017

Genomics-enabled analysis of the emergent disease cotton bacterial blight

Cotton bacterial blight (CBB), an important disease of (Gossypium hirsutum) in the early 20th century, had been controlled by resistant germplasm for over half a century. Recently, CBB re-emerged as an agronomic problem in the United States. Here, we report analysis of cotton variety planting statistics that indicate a steady increase in the percentage of susceptible cotton varieties grown each year since 2009. Phylogenetic analysis revealed that strains from the current outbreak cluster with race 18 Xanthomonas citri pv. malvacearum (Xcm) strains.\n\nIllumina based draft genomes were generated for thirteen Xcm isolates and analyzed along with 4 previously published Xcm genomes. These genomes encode 24 conserved and nine variable type three effectors. Strains in the race 18 clade contain 3 to 5 more effectors than other Xcm strains. SMRT sequencing of two geographically and temporally diverse strains of Xcm yielded circular chromosomes and accompanying plasmids. These genomes encode eight and thirteen distinct transcription activator-like effector genes. RNA-sequencing revealed 52 genes induced within two cotton cultivars by both tested Xcm strains. This gene list includes a homeologous pair of genes, with homology to the known susceptibility gene, MLO. In contrast, the two strains of Xcm induce different clade III SWEET sugar transporters. Subsequent genome wide analysis revealed patterns in the overall expression of homeologous gene pairs in cotton after inoculation by Xcm. These data reveal host-pathogen specificity at the genetic level and strategies for future development of resistant cultivars.\n\nAuthor SummaryCotton bacterial blight (CBB), caused by Xanthomonas citri pv. malvacearum (Xcm), significantly limited cotton yields in the early 20th century but has been controlled by classical resistance genes for more than 50 years. In 2011, the pathogen re-emerged with a vengeance. In this study, we compare diverse pathogen isolates and cotton varieties to further understand the virulence mechanisms employed by Xcm and to identify promising resistance strategies. We generate fully contiguous genome assemblies for two diverse Xcm strains and identify pathogen proteins used to modulate host transcription and promote susceptibility. RNA-Sequencing of infected cotton reveals novel putative gene targets for the development of durable Xcm resistance. Together, the data presented reveal contributing factors for CBB re-emergence in the U.S. and highlight several promising routes towards the development of durable resistance including classical resistance genes and potential manipulation of susceptibility targets.

plant biology↗