Multiple Functions for ORF75c in Murid Herpesvirus-4 Infection

All gamma-herpesviruses encode at least one homolog of the cellular enzyme formyl-glycineamide-phosphoribosyl-amidotransferase. Murid herpesvirus-4 (MuHV-4) encodes 3 (ORFs 75a, 75b and 75c), suggesting that at least some copies have acquired new functions. Here we show that the corresponding proteins are all present in virions and localize to infected cell nuclei. Despite these common features, ORFs 75a and 75b did not substitute functionally for a lack of ORF75c, as ORF75c virus knockouts were severely impaired for lytic replication in vitro and for host colonization in vivo. They showed 2 defects: incoming capsids failed to migrate to the nuclear margin following membrane fusion, and genomes that did reach the nucleus failed to initiate normal gene expression. The latter defect was associated with a failure of in-coming virions to disassemble PML bodies. The capsid transport deficit seemed to be functionally more important, since ORF75c− MuHV-4 infected both PML+ and PML− cells poorly. The original host enzyme has therefore evolved into a set of distinct and multi-functional viral tegument proteins. One important function is moving incoming capsids to the nuclear margin for viral genome delivery

Virulence of Hymenoscyphus albidus and H. fraxineus on Fraxinus excelsior and F. pennsylvanica

European ash (Fraxinus excelsior) is currently battling an onslaught of ash dieback, a disease emerging in the greater part of its native area, brought about by the introduction of the ascomycete Hymenoscyphus fraxineus (= Hymenoscyphus pseudoalbidus). The closely-related fungus Hymenoscyphus albidus, which is indigenous to Europe, is non-pathogenic when in contact with F. excelsior, but could pose a potential risk to exotic Fraxinus species. The North American green ash (Fraxinus pennsylvanica) is planted widely throughout Europe and regenerates naturally within this environment but little is known about the susceptibility of this species to ash dieback. We performed wound inoculations with both fungi (nine strains of H. fraxineus and three strains of H. albidus) on rachises and stems of F. excelsior and F. pennsylvanica under field conditions in Southern Poland. Necrosis formation was evaluated after two months on the rachises and after 12 months on the stems. After inoculation of H. albidus, only small lesions (of up to 1.3 cm in length) developed on the F. excelsior and F. pennsylvanica rachises, but with no significant distinction from the controls. Hymenoscyphus albidus did not cause necrotic lesions on the stems of either Fraxinus species. In contrast, H. fraxineus induced necroses on all inoculated rachises of both ash species with mean lengths of 8.4 cm (F. excelsior) and 1.9 cm (F. pennsylvanica). Necroses also developed on all of the inoculated F. excelsior stems (mean length 18.0 cm), whereas on F. pennsylvanica such lesions only occurred on about 5% of the stems (mean length 1.9 cm). The differences between strains were negligible. No necroses were observed on the control plants. Reisolations of H. albidus were only successful in around 8–11% of the cases, while H. fraxineus was reisolated from 50–70% of the inoculated organs showing necrotic lesions. None of the Hymenoscyphus species were isolated from the control plants. Our data confirm H. fraxineus’ high virulence with regards to F. excelsior and demonstrate a low virulence in relation to F. pennsylvanica under field conditions in Poland. Hymenoscyphus albidus did not express any perceivable pathogenicity on both host species

Predicting Geothermal Heat Flow in Antarctica With a Machine Learning Approach

We present a machine learning approach to statistically derive geothermal heat flow (GHF) for Antarctica. The adopted approach estimates GHF from multiple geophysical and geological data sets, assuming that GHF is substantially related to the geodynamic setting of the plates. We apply a Gradient Boosted Regression Tree algorithm to find an optimal prediction model relating GHF to the observables. The geophysical and geological features are primarily global data sets, which are often unreliable in polar regions due to limited data coverage. Quality and reliability of the data sets are reviewed and discussed in line with the estimated GHF model. Predictions for Australia, where an extensive database of GHF measurements exists, demonstrate the validity of the approach. In Antarctica, only a sparse number of direct GHF measurements are available. Therefore, we explore the use of regional data sets of Antarctica and its tectonic Gondwana neighbors to refine the predictions. With this, we demonstrate the need for adding reliable data to the machine learning approach. Finally, we present a new geothermal heat flow map, which exhibits intermediate values compared to previous models, ranging from 35 to 156 mW/m2, and visible connections to the conjugate margins in Australia, Africa, and India.Plain Language Summary: The heat energy transferred from the Earth's interior to the surface (geothermal heat flow) can substantially affect the dynamics of an overlying ice sheet. It can lead to melting at the base and hence, decouple the ice sheet from the bedrock. In Antarctica, this parameter is poorly constrained, and only a sparse number of thermal gradient measurements exist. Indirect methods, therefore, try to estimate the continental Antarctic heat flow. Here, we use a machine learning approach to combine multiple information on geology, tectonic setting, and heat flow measurements from all continents to predict Antarctic values. We further show that using reliable data is crucial for the resulting prediction and a mindful choice of features is recommendable. The final result exhibits values within the range of previously proposed heat flow maps and shows local similarities to the continents once connected to East Antarctica within the supercontinent Gondwana. We suggest a minimum and maximum heat flow map, which can be used as input for ice sheet modeling and sea level rise predictions.Key Points: A new geothermal heat flow map of Antarctica is established by adopting a machine learning approach. Input features include both global and regional geological and tectonic information, and heat flow observations. A Gondwana reconstruction shows connections of heat flow at the conjugate margins of East Antarctica.Deutsche Forschungsgemeinschaft (DFG) http://dx.doi.org/10.13039/50110000165