Biology subjects

Robertson, A. E.

Publications and source records attributed to Robertson, A. E..

3 recordsLinked to original sources

bioRxiv2025

Yellow Fever Virus Interactomes Reveal Common and Divergent Strategies of Replication and Evolution for Mosquito-borne Flaviviruses

Pathogenic mosquito-borne flaviviruses infect mosquito and human hosts, relying on host protein interactions to replicate, evade immunity, and mediate pathogenesis. Prior proteomic studies mapped such interactions for some flaviviruses, but yellow fever virus (YFV)--a pathogen of resurgent concern--remains understudied. Here, we map YFV interactomes in human and mosquito cells to identify interactions common among divergent flaviviruses or unique to YFV. Functional assays reveal a previously unrecognized YFV restriction factor: RBBP6 inhibits YFV genome replication by interacting with the viral polymerase NS5. We enhance the identification of dual-host interactions using structural modeling and holistic network integration. Extending our holistic approach to other flavivirus interactomes, we distinguish conserved mechanisms of host targeting from those unique to YFV. Contrary to expectations that conserved viral proteins lead to conserved protein interactions, we find that Capsid, a divergent structural protein, shares more host interactions than NS5, a conserved enzyme. Integrating proteomics with complementary analyses defines new principles of host-targeting strategies across flavivirus and host evolution, offering a versatile resource for navigating the complex landscape of flavivirus biology.

systems biology↗

bioRxiv2023

Small but significant genetic differentiation among populations of Phyllachora maydis in the midwestern United States revealed by microsatellite (SSR) markers.

Phyllachora maydis Maubl, the causal pathogen of tar spot of corn (Zea mays L.), has emerged recently in the United States and Canada. Studies related to its genetic diversity and population structure are limited and are necessary to improve our understanding of this pathogens biology, ecology, epidemiology, and evolutionary potential within this region. This study developed and used 13 microsatellites (SSR markers) to assess the genetic population structure, diversity, gene flow and reproductive mode of 181 P. maydis samples across five states in the Midwest U.S. The polymorphic information content (PIC) of loci ranged from 0.32 to 0.72 per locus, indicating their high utility for assessing the dynamics of P. maydis populations. Analysis of molecular variance (AMOVA) detected a significantly low, but statistically significant genetic differentiation (FST = 0.15) among populations, where 85% of the variance resided within populations. P. maydis populations were highly diverse (He = 0.55), with moderate gene flow (Nm = 2.80), and showed evidence of sexual recombination ([r]d; p = > 0.001). Structure analysis showed the samples were not geographically structured but rather grouped into two genetic clusters (k =2) of severe genetic admixture suggesting possible long-distance dispersal of aerial spores or infected corn materials among the five Midwest states. Both principal coordinate analysis (PCoA) and discriminate analysis of principal component (DAPC) supported the STRUCTURE analysis of the two clusters. These 13 highly polymorphic molecular markers could be used for future investigations of this pathogens population dynamics within the U.S., and possibly populations outside.

pathology↗

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↗