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Four endemic seasonal human coronaviruses causing common colds circulate worldwide: HKU1, 229E, NL63 and OC43 (ref. 1 ). After binding to cellular receptors, coronavirus spike proteins are primed for fusion by transmembrane serine protease 2 (TMPRSS2) or endosomal cathepsins 2 – 9 . NL63 uses angiotensin-converting enzyme 2 as a receptor 10 , whereas 229E uses human aminopeptidase-N 11 . HKU1 and OC43 spikes bind cells through 9-O-acetylated sialic acid, but their protein receptors remain unknown 12 . Here we show that TMPRSS2 is a functional receptor for HKU1. TMPRSS2 triggers HKU1 spike-mediated cell–cell fusion and pseudovirus infection. Catalytically inactive TMPRSS2 mutants do not cleave HKU1 spike but allow pseudovirus infection. Furthermore, TMPRSS2 binds with high affinity to the HKU1 receptor binding domain (Kd 334 and 137 nM for HKU1A and HKU1B genotypes) but not to SARS-CoV-2. Conserved amino acids in the HKU1 receptor binding domain are essential for binding to TMPRSS2 and pseudovirus infection. Newly designed anti-TMPRSS2 nanobodies potently inhibit HKU1 spike attachment to TMPRSS2, fusion and pseudovirus infection. The nanobodies also reduce infection of primary human bronchial cells by an authentic HKU1 virus. Our findings illustrate the various evolution strategies of coronaviruses, which use TMPRSS2 to either directly bind to target cells or prime their spike for membrane fusion and entry. We demonstrate that the transmembrane protease TMPRSS2 is a receptor for coronavirus HKU1; it triggers HKU1-mediated cell–cell fusion and viral entry by binding to both HKU1A and HKU1B spikes.

Dipeptidyl peptidases 8 and 9 are intracellular N-terminal dipeptidyl peptidases (preferentially postproline) associated with pathophysiological roles in immune response and cancer biology. While the DPP family member DPP4 is extensively characterized in molecular terms as a validated therapeutic target of type II diabetes, experimental 3D structures and ligand-/substrate-binding modes of DPP8 and DPP9 have not been reported. In this study we describe crystal and molecular structures of human DPP8 (2.5 Å) and DPP9 (3.0 Å) unliganded and complexed with a noncanonical substrate and a small molecule inhibitor, respectively. Similar to DPP4, DPP8 and DPP9 molecules consist of one β-propeller and α/β hydrolase domain, forming a functional homodimer. However, they differ extensively in the ligand binding site structure. In intriguing contrast to DPP4, where liganded and unliganded forms are closely similar, ligand binding to DPP8/9 induces an extensive rearrangement at the active site through a disorder-order transition of a 26-residue loop segment, which partially folds into an α-helix (R-helix), including R160/133, a key residue for substrate binding. As vestiges of this helix are also seen in one of the copies of the unliganded form, conformational selection may contributes to ligand binding. Molecular dynamics simulations support increased flexibility of the R-helix in the unliganded state. Consistently, enzyme kinetics assays reveal a cooperative allosteric mechanism. DPP8 and DPP9 are closely similar and display few opportunities for targeted ligand design. However, extensive differences from DPP4 provide multiple cues for specific inhibitor design and development of the DPP family members as therapeutic targets or antitargets.

The Covid-19 pandemic has not only outlined the importance of using evidence in the healthcare policy making process but also the complexity that exists between policymakers and the scientific community. As a matter of fact, scientific data is just one of many other concurrent factors, including economic, social and cultural, that may provide the rationale for policy making. The pandemic has also raised citizens’ awareness and represented an unprecedented moment of willingness to access and understand the evidence underpinning health policies. This commentary provides policy recommendations to improve evidence-based policy making in health, through the lens of a young generation of public policy students and future policymakers, enrolled in a 24-hour course at Sciences Po Paris entitled “Evidence-based policy-making in health: theory and practice(s)”. Four out of 11 recommendations were prioritised and presented in this commentary which target both policymakers and the scientific community to make better use of evidence-based policy making in health. First, policy makers and scientists should build trusting partnerships with citizens and engage them, especially those facing our target health care issues or systems. Second, while artificial intelligence raises new opportunities in healthcare, its use in contexts of uncertainty should be addressed by policymakers in terms of liability and ethics. Third, conflicts of interest must be disclosed as much as possible and effectively managed to (re) build a trust relationship between policymakers, the scientific community and citizens, implying the need for risk management tools and cross border disclosure mechanisms. Last, well-designed and secure health information systems need to be implemented, following the FAIR (findable, accessible, interoperable and reusable) principles for health data. This will take us a step further from data to ‘policy wisdom’. Overall, these recommendations identified and formulated by students highlight some key issues that need to be rethought in the health policy cycle through elements like institutional incentives, cultural changes and dialogue between policy makers and the scientific community. This input from a younger generation of students highlights the importance of making the conversation on evidence-based policy making in health accessible to all generations and backgrounds.

G protein-coupled receptors (GPCR) are involved in the regulation of numerous physiological functions. Therefore, GPCR variants may have conferred important selective advantages during periods of human evolution. Indeed, several genomic loci with signatures of recent selection in humans contain GPCR genes among them the X-chromosomally located gene for GPR82. This gene encodes a so-called orphan GPCR with unknown function. To address the functional relevance of GPR82 gene-deficient mice were characterized. GPR82-deficient mice were viable, reproduced normally, and showed no gross anatomical abnormalities. However, GPR82-deficient mice have a reduced body weight and body fat content associated with a lower food intake. Moreover, GPR82-deficient mice showed decreased serum triacylglyceride levels, increased insulin sensitivity and glucose tolerance, most pronounced under Western diet. Because there were no differences in respiratory and metabolic rates between wild-type and GPR82-deficient mice our data suggest that GPR82 function influences food intake and, therefore, energy and body weight balance. GPR82 may represent a thrifty gene most probably representing an advantage during human expansion into new environments.

Defolliculated (Dfl) is a spontaneous mouse mutant with a hair-loss phenotype that includes altered sebaceous gland differentiation, short hair shafts, aberrant catagen stage of the hair cycle, and eventual loss of the hair follicle. Recently a similar mutant, finnegan (Fgn), with an identical phenotype was discovered during a phenotypic screen for mutations induced by chemical mutagenesis. The gene underlying the phenotype of both finnegan and defolliculated has been mapped to chromosome 11 and here we show that both mice harbor mutations in gasdermin 3 (Gsdm3), a gene of unknown function. Gsdm3Dfl is a B2 insertion near the 3′ splice site of exon 7 and Gsdm3Fgn is a point mutation T278P. To investigate the role of the gasdermin gene family an antiserum was raised to a peptide highly homologous to all three mouse gasdermins and human gasdermin. Immunohistochemical analysis revealed that gasdermins are expressed specifically in cells at advanced stages of differentiation in the upper epidermis, the differentiating inner root sheath and hair shaft and in the most mature sebocytes of the sebaceous gland and preputial, meibomium, ceruminous gland, and anal glands. This expression pattern suggests a role for gasdermins in differentiation of the epidermis and its appendages.

Primases are essential components of the DNA replication apparatus in every organism. They catalyze the synthesis of oligoribonucleotides on single-stranded DNA, which subsequently serve as primers for the replicative DNA polymerases. In contrast to bacterial primases, the archaeal enzymes are closely related to their eukaryotic counterparts. We have solved the crystal structure of the catalytic primase subunit from the hyperthermophilic archaeon Pyrococcus furiosus at 2.3 Å resolution by multiwavelength anomalous dispersion methods. The structure shows a two-domain arrangement with a novel zinc knuckle motif located in the primase (prim) domain. In this first structure of a complete protein of the archaeal/eukaryotic primase family, the arrangement of the catalytically active residues resembles the active sites of various DNA polymerases that are unrelated in fold.

The LINE-1 (L1) retrotransposon is an ancient genetic parasite that has written around one-third of the human genome through a ‘copy and paste’ mechanism catalysed by its multifunctional enzyme, open reading frame 2 protein (ORF2p) 1 . ORF2p reverse transcriptase (RT) and endonuclease activities have been implicated in the pathophysiology of cancer 2 , 3 , autoimmunity 4 , 5 and ageing 6 , 7 , making ORF2p a potential therapeutic target. However, a lack of structural and mechanistic knowledge has hampered efforts to rationally exploit it. We report structures of the human ORF2p ‘core’ (residues 238–1061, including the RT domain) by X-ray crystallography and cryo-electron microscopy in several conformational states. Our analyses identified two previously undescribed folded domains, extensive contacts to RNA templates and associated adaptations that contribute to unique aspects of the L1 replication cycle. Computed integrative structural models of full-length ORF2p show a dynamic closed-ring conformation that appears to open during retrotransposition. We characterize ORF2p RT inhibition and reveal its underlying structural basis. Imaging and biochemistry show that non-canonical cytosolic ORF2p RT activity can produce RNA:DNA hybrids, activating innate immune signalling through cGAS/STING and resulting in interferon production 6 – 8 . In contrast to retroviral RTs, L1 RT is efficiently primed by short RNAs and hairpins, which probably explains cytosolic priming. Other biochemical activities including processivity, DNA-directed polymerization, non-templated base addition and template switching together allow us to propose a revised L1 insertion model. Finally, our evolutionary analysis demonstrates structural conservation between ORF2p and other RNA- and DNA-dependent polymerases. We therefore provide key mechanistic insights into L1 polymerization and insertion, shed light on the evolutionary history of L1 and enable rational drug development targeting L1. X-ray crystallography, cryo-electron microscopy, structural modelling, biochemistry, cell biology, and evolutionary analysis enable characterization of ORF2p, the reverse transcriptase of the ancient ‘parasitic’ LINE-1 retrotransposon that has written around one-third of the human genome.

Degenerative disorders of motor neurons include a range of progressive fatal diseases such as amyotrophic lateral sclerosis (ALS), spinal-bulbar muscular atrophy (SBMA), and spinal muscular atrophy (SMA). Although the causative genetic alterations are known for some cases, the molecular basis of many SMA and SBMA-like syndromes and most ALS cases is unknown. Here we show that missense point mutations in the cytoplasmic dynein heavy chain result in progressive motor neuron degeneration in heterozygous mice, and in homozygotes this is accompanied by the formation of Lewy-like inclusion bodies, thus resembling key features of human pathology. These mutations exclusively perturb neuron-specific functions of dynein.

Background ‘Phylogenetic trees’ are commonly used for the analysis of chemogenomics datasets and to relate protein targets to each other, based on the (shared) bioactivities of their ligands. However, no real assessment as to the suitability of this representation has been performed yet in this area. We aimed to address this shortcoming in the current work, as exemplified by a kinase data set, given the importance of kinases in many diseases as well as the availability of large-scale datasets for analysis. In this work, we analyzed a dataset comprising 157 compounds, which have been tested at concentrations of 1 μM and 10 μM against a panel of 225 human protein kinases in full-matrix experiments, aiming to explain kinase promiscuity and selectivity against inhibitors. Compounds were described by chemical features, which were used to represent kinases ( i.e. each kinase had an active set of features and an inactive set). Results Using this representation, a bioactivity-based classification was made of the kinome, which partially resembles previous sequence-based classifications, where particularly kinases from the TK, CDK, CLK and AGC branches cluster together. However, we were also able to show that in approximately 57% of cases, on average 6 kinase inhibitors exhibit activity against kinases which are located at a large distance in the sequence-based classification (at a relative distance of 0.6 – 0.8 on a scale from 0 to 1), but are correctly located closer to each other in our bioactivity-based tree (distance 0 – 0.4). Despite this improvement on sequence-based classification, also the bioactivity-based classification needed further attention: for approximately 80% of all analyzed kinases, kinases classified as neighbors according to the bioactivity-based classification also show high SAR similarity ( i.e. a high fraction of shared active compounds and therefore, interaction with similar inhibitors). However, in the remaining ~20% of cases a clear relationship between kinase bioactivity profile similarity and shared active compounds could not be established, which is in agreement with previously published atypical SAR (such as for LCK, FGFR1, AKT2, DAPK1, TGFR1, MK12 and AKT1). Conclusions In this work we were hence able to show that (1) targets (here kinases) with few shared activities are difficult to establish neighborhood relationships for, and (2) phylogenetic tree representations make implicit assumptions ( i.e. that neighboring kinases exhibit similar interaction profiles with inhibitors) that are not always suitable for analyses of bioactivity space. While both points have been implicitly alluded to before, this is to the information of the authors the first study that explores both points on a comprehensive basis. Excluding kinases with few shared activities improved the situation greatly (the percentage of kinases for which no neighborhood relationship could be established dropped from 20% to only 4%). We can conclude that all of the above findings need to be taken into account when performing chemogenomics analyses, also for other target classes.