Entry of the bat influenza H17N10 virus into mammalian cells is enabled by the MHC class II HLA-DR receptor

Haemagglutinin and neuraminidase surface glycoproteins of the bat influenza H17N10 virus neither bind to nor cleave sialic acid receptors, indicating that this virus employs cell entry mechanisms distinct from those of classical influenza A viruses. We observed that certain human haematopoietic cancer cell lines and canine MDCK II cells are susceptible to H17-pseudotyped viruses. We identified the human HLA-DR receptor as an entry mediator for H17 pseudotypes, suggesting that H17N10 possesses zoonotic potential

Planarian cell number depends on Blitzschnell, a novel gene family that balances cell proliferation and cell death

Control of cell number is crucial to define body size during animal development and to restrict tumoral transformation. The cell number is determined by the balance between cell proliferation and cell death. Although many genes are known to regulate those processes, the molecular mechanisms underlying the relationship between cell number and body size remain poorly understood. This relationship can be better understood by studying planarians, flatworms that continuously change their body size according to nutrient availability. We identified a novel gene family, blitzschnell (bls), which consists of de novo and taxonomically restricted genes that control cell proliferation:cell death ratio. Their silencing promotes faster regeneration and increases cell number during homeostasis. Importantly, this increase in cell number only leads to an increase in body size in a nutrient-rich environment; in starved planarians silencing results in a decrease in cell size and cell accumulation that ultimately produces overgrowths. bls expression is down-regulated after feeding and related with the Insulin/Akt/mTOR network activity, suggesting that the bls family evolved in planarians as an additional mechanism by which to restrict cell number in nutrient-fluctuating environments

DeepLoc 2.0: multi-label subcellular localization prediction using protein language models

The prediction of protein subcellular localization is of great relevance for proteomics research. Here, we propose an update to the popular tool DeepLoc with multi-localization prediction and improvements in both performance and interpretability. For training and validation, we curate eukaryotic and human multi-location protein datasets with stringent homology partitioning and enriched with sorting signal information compiled from the literature. We achieve state-of-the-art performance in DeepLoc 2.0 by using a pre-trained protein language model. It has the further advantage that it uses sequence input rather than relying on slower protein profiles. We provide two means of better interpretability: an attention output along the sequence and highly accurate prediction of nine different types of protein sorting signals. We find that the attention output correlates well with the position of sorting signals. The webserver is available at services.healthtech.dtu.dk/service.php?DeepLoc-2.0

Spectrum of Protein Location in Proteomes Captures Evolutionary Relationship Between Species

The native subcellular location (also referred to as localization or cellular compartment) of a protein is the one in which it acts most frequently; it is one aspect of protein function. Do ten eukaryotic model organisms differ in their location spectrum, i.e., the fraction of its proteome in each of seven major cellular compartments? As experimental annotations of locations remain biased and incomplete, we need prediction methods to answer this question. After systematic bias corrections, the complete but faulty prediction methods appeared to be more appropriate to compare location spectra between species than the incomplete more accurate experimental data. This work compared the location spectra for ten eukaryotes: Homo sapiens (human), Gorilla gorilla (gorilla), Pan troglodytes (chimpanzee), Mus musculus (mouse), Rattus norvegicus (rat), Drosophila melanogaster (fruit/vinegar fly), Anopheles gambiae (African malaria mosquito), Caenorhabitis elegans (nematode), Saccharomyces cerevisiae (baker’s yeast), and Schizosaccharomyces pombe (fission yeast). The two largest classes were predicted to be the nucleus and the cytoplasm together accounting for 47–62% of all proteins, while 7–21% of the proteins were predicted in the plasma membrane and 4–15% to be secreted. Overall, the predicted location spectra were largely similar. However, in detail, the differences sufficed to plot trees (UPGMA) and 2D (PCA) maps relating the ten organisms using a simple Euclidean distance in seven states (location classes). The relations based on the simple predicted location spectra captured aspects of cross-species comparisons usually revealed only by much more detailed evolutionary comparisons. Most interestingly, known phylogenetic relations were reproduced better by paralog-only than by ortholog-only trees. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00239-021-10022-4