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It has long been thought that iminosugar antiviral activity is a function of inhibition of endoplasmic reticulum-resident α-glucosidases, and on this basis, many iminosugars have been investigated as therapeutic agents for treatment of infection by a diverse spectrum of viruses, including dengue virus (DENV). However, iminosugars are glycomimetics possessing a nitrogen atom in place of the endocyclic oxygen atom, and the ubiquity of glycans in host metabolism suggests that multiple pathways can be targeted via iminosugar treatment. Successful treatment of patients with glycolipid processing defects using iminosugars highlights the clinical exploitation of iminosugar inhibition of enzymes other than ER α-glucosidases. Evidence correlating antiviral activity with successful inhibition of ER glucosidases together with the exclusion of alternative mechanisms of action of iminosugars in the context of DENV infection is limited. Celgosivir, a bicyclic iminosugar evaluated in phase Ib clinical trials as a therapeutic for the treatment of DENV infection, was confirmed to be antiviral in a lethal mouse model of antibody-enhanced DENV infection. In this study we provide the first evidence of the antiviral activity of celgosivir in primary human macrophages in vitro, in which it inhibits DENV secretion with an EC50 of 5 μM. We further demonstrate that monocyclic glucose-mimicking iminosugars inhibit isolated glycoprotein and glycolipid processing enzymes and that this inhibition also occurs in primary cells treated with these drugs. By comparison to bicyclic glucose-mimicking iminosugars which inhibit glycoprotein processing but do not inhibit glycolipid processing and galactose-mimicking iminosugars which do not inhibit glycoprotein processing but do inhibit glycolipid processing, we demonstrate that inhibition of endoplasmic reticulum-resident α-glucosidases, not glycolipid processing, is responsible for iminosugar antiviral activity against DENV. Our data suggest that inhibition of ER α-glucosidases prevents release of virus and is the primary antiviral mechanism of action of iminosugars against DENV.

Digital pathology for the routine assessment of cases for primary diagnosis has been implemented by few laboratories worldwide. The Gravina Hospital in Caltagirone (Sicily, Italy), which collects cases from 7 different hospitals distributed in the Catania area, converted the entire workflow to digital starting from 2019. Before the transition, the Caltagirone pathology laboratory was characterized by a non-tracked workflow, based on paper requests, hand-written blocks and slides, as well as manual assembling and delivering of the cases and glass slides to the pathologists. Moreover, the arrangement of the spaces and offices in the department was illogical and under-productive for the linearity of the workflow. For these reasons, an adequate 2D barcode system for tracking purposes, the redistribution of the spaces inside the laboratory and the implementation of the whole-slide imaging (WSI) technology based on a laboratory information system (LIS)-centric approach were adopted as a needed prerequisite to switch to a digital workflow. The adoption of a dedicated connection for transfer of clinical and administrative data between different software and interfaces using an internationally recognised standard (Health Level 7, HL7) in the pathology department further facilitated the transition, helping in the integration of the LIS with WSI scanners. As per previous reports, the components and devices chosen for the pathologists’ workstations did not significantly impact on the WSI-based reporting phase in primary histological diagnosis. An analysis of all the steps of this transition has been made retrospectively to provide a useful “handy” guide to lead the digital transition of “analog”, non-tracked pathology laboratories following the experience of the Caltagirone pathology department. Following the step-by-step instructions, the implementation of a paperless routine with more standardized and safe processes, the possibility to manage the priority of the cases and to implement artificial intelligence (AI) tools are no more an utopia for every “analog” pathology department.

The COVID-19 pandemic has reached direct and indirect medical and social consequences with a subset of patients who rapidly worsen and die from severe-critical manifestations. As a result, there is still an urgent need to identify prognostic biomarkers and effective therapeutic approaches. Severe-critical manifestations of COVID-19 are caused by a dysregulated immune response. Immune checkpoint molecules such as Programmed death-1 (PD-1) and its ligand programmed death-ligand 1 (PD-L1) play an important role in regulating the host immune response and several lines of evidence underly the role of PD-1 modulation in COVID-19. Here, by analyzing blood sample collection from both hospitalized COVID-19 patients and healthy donors, as well as levels of PD-L1 RNA expression in a variety of model systems of SARS-CoV-2, including in vitro tissue cultures, ex-vivo infections of primary epithelial cells and biological samples obtained from tissue biopsies and blood sample collection of COVID-19 and healthy individuals, we demonstrate that serum levels of PD-L1 have a prognostic role in COVID-19 patients and that PD-L1 dysregulation is associated to COVID-19 pathogenesis. Specifically, PD-L1 upregulation is induced by SARS-CoV-2 in infected epithelial cells and is dysregulated in several types of immune cells of COVID-19 patients including monocytes, neutrophils, gamma delta T cells and CD4+ T cells. These results have clinical significance since highlighted the potential role of PD-1/PD-L1 axis in COVID-19, suggest a prognostic role of PD-L1 and provide a further rationale to implement novel clinical studies in COVID-19 patients with PD-1/PD-L1 inhibitors.

Efficient predictive biomarkers are needed for immune checkpoint inhibitor (ICI)-based immunotherapy in non-small cell lung cancer (NSCLC). Testing the predictive value of single nucleotide polymorphisms (SNPs) in programmed cell death 1 ( PD-1 ) or its ligand 1 ( PD-L1 ) has shown contrasting results. Here, we aim to validate the predictive value of PD-L1 SNPs in advanced NSCLC patients treated with ICIs as well as to define the molecular mechanisms underlying the role of the identified SNP candidate. rs822336 efficiently predicted response to anti-PD-1/PD-L1 immunotherapy in advanced non-oncogene addicted NSCLC patients as compared to rs2282055 and rs4143815. rs822336 mapped to the promoter/enhancer region of PD-L1 , differentially affecting the induction of PD-L1 expression in human NSCLC cell lines as well as their susceptibility to HLA class I antigen matched PBMCs incubated with anti-PD-1 monoclonal antibody nivolumab. The induction of PD-L1 expression by rs822336 was mediated by a competitive allele-specificity binding of two identified transcription factors: C/EBPβ and NFIC. As a result, silencing of C/EBPβ and NFIC differentially regulated the induction of PD-L1 expression in human NSCLC cell lines carrying different rs822336 genotypes. Analysis by binding microarray further validated the competitive allele-specificity binding of C/EBPβ and NFIC to PD-L1 promoter/enhancer region based on rs822336 genotype in human NSCLC cell lines. These findings have high clinical relevance since identify rs822336 and induction of PD-L1 expression as novel biomarkers for predicting anti-PD-1/PD-L1-based immunotherapy in advanced NSCLC patients.

Glycoproteins traversing the eukaryotic secretory pathway begin life in the endoplasmic reticulum (ER), where their folding is surveyed by the 170-kDa UDP-glucose:glycoprotein glucosyltransferase (UGGT). The enzyme acts as the single glycoprotein folding quality control checkpoint: it selectively reglucosylates misfolded glycoproteins, promotes their association with ER lectins and associated chaperones, and prevents premature secretion from the ER. UGGT has long resisted structural determination and sequence-based domain boundary prediction. Questions remain on how this single enzyme can flag misfolded glycoproteins of different sizes and shapes for ER retention and how it can span variable distances between the site of misfold and a glucose-accepting N-linked glycan on the same glycoprotein. Here, crystal structures of a full-length eukaryotic UGGT reveal four thioredoxin-like (TRXL) domains arranged in a long arc that terminates in two β-sandwiches tightly clasping the glucosyltransferase domain. The fold of the molecule is topologically complex, with the first β-sandwich and the fourth TRXL domain being encoded by nonconsecutive stretches of sequence. In addition to the crystal structures, a 15-Å cryo-EM reconstruction reveals interdomain flexibility of the TRXL domains. Double cysteine point mutants that engineer extra interdomain disulfide bridges rigidify the UGGT structure and exhibit impaired activity. The intrinsic flexibility of the TRXL domains of UGGT may therefore endow the enzyme with the promiscuity needed to recognize and reglucosylate its many different substrates and/or enable reglucosylation of N-linked glycans situated at variable distances from the site of misfold.

The biosynthesis of enveloped viruses depends heavily on the host cell endoplasmic reticulum (ER) glycoprotein quality control (QC) machinery. This dependency exceeds the dependency of host glycoproteins, offering a window for the targeting of ERQC for the development of broad-spectrum antivirals. We determined small-angle X-ray scattering (SAXS) and crystal structures of the main ERQC enzyme, ER α-glucosidase II (α-GluII; from mouse), alone and in complex with key ligands of its catalytic cycle and antiviral iminosugars, including two that are in clinical trials for the treatment of dengue fever. The SAXS data capture the enzyme’s quaternary structure and suggest a conformational rearrangement is needed for the simultaneous binding of a monoglucosylated glycan to both subunits. The X-ray structures with key catalytic cycle intermediates highlight that an insertion between the +1 and +2 subsites contributes to the enzyme’s activity and substrate specificity, and reveal that the presence of D-mannose at the +1 subsite renders the acid catalyst less efficient during the cleavage of the monoglucosylated substrate. The complexes with iminosugar antivirals suggest that inhibitors targeting a conserved ring of aromatic residues between the α-GluII +1 and +2 subsites would have increased potency and selectivity, thus providing a template for further rational drug design.

Background Digital pathology (DP) has revolutionized cancer diagnostics and enabled the development of deep-learning (DL) models aimed at supporting pathologists in their daily work and improving patient care. However, the clinical adoption of such models remains challenging. Here, we describe a proof-of-concept framework that, leveraging Health Level 7 (HL7) standard and open-source DP resources, allows a seamless integration of both publicly available and custom developed DL models in the clinical workflow. Methods Development and testing of the framework were carried out in a fully digitized Italian pathology department. A Python-based server-client architecture was implemented to interconnect through HL7 messaging the anatomic pathology laboratory information system (AP-LIS) with an external artificial intelligence-based decision support system (AI-DSS) containing 16 pre-trained DL models. Open-source toolboxes for DL model deployment were used to run DL model inference, and QuPath was used to provide an intuitive visualization of model predictions as colored heatmaps. Results A default deployment mode runs continuously in the background as each new slide is digitized, choosing the correct DL model(s) on the basis of the tissue type and staining. In addition, pathologists can initiate the analysis on-demand by selecting a specific DL model from the virtual slide tray. In both cases, the AP-LIS transmits an HL7 message to the AI-DSS, which processes the message, runs DL model inference, and creates the appropriate visualization style for the employed classification model. The AI-DSS transmits model inference results to the AP-LIS, where pathologists can visualize the output in QuPath and/or directly as slide description in the virtual slide tray. Conclusions Taken together, the developed integration framework through the use of the HL7 standard and freely available DP resources offers a standardized, portable, and open-source solution that lays the groundwork for the future widespread adoption of DL models in pathology diagnostics.

Puromycin and the Streptomyces alboniger -derived puromycin N -acetyltransferase (PAC) enzyme form a commonly used system for selecting stably transfected cultured cells. The crystal structure of PAC has been solved using X-ray crystallography, revealing it to be a member of the GCN5-related N -acetyltransferase (GNAT) family of acetyltransferases. Based on structures in complex with acetyl-CoA or the reaction products CoA and acetylated puromycin, four classes of mutations in and around the catalytic site were designed and tested for activity. Single-residue mutations were identified that displayed a range of enzymatic activities, from complete ablation to enhanced activity relative to wild-type (WT) PAC. Cell pools of stably transfected HEK293 cells derived using two PAC mutants with attenuated activity, Y30F and A142D, were found to secrete up to three-fold higher levels of a soluble, recombinant target protein than corresponding pools derived with the WT enzyme. A third mutant, Y171F, appeared to stabilise the intracellular turnover of PAC, resulting in an apparent loss of selection stringency. Our results indicate that the structure-guided manipulation of PAC function can be utilised to enhance selection stringency for the derivation of mammalian cell lines secreting elevated levels of recombinant proteins.

Objective Digital pathology (DP) is currently in the spotlight and is rapidly gaining ground, even though the history of this field spans decades. Despite great technological progress, the adoption of DP for routine clinical diagnostic use remains limited. Methods A systematic search was conducted in the electronic databases Pubmed-MEDLINE and Embase. Inclusion criteria were all published studies that encompassed any application of DP. Results Of 4888 articles retrieved, 4041 were included. Relevant articles were categorized as “diagnostic” (147/4041, 4%) where DP was utilized for routine diagnostic workflow and “non-diagnostic” (3894/4041, 96%) for all other applications. The “non-diagnostic” articles were further categorized according to DP application including “artificial intelligence” (33%), “education” (5%), “narrative” (17%) for reviews and editorials, and “technical” (45%) for pure research publications. Conclusion This manuscript provided temporal and geographical insight into the global adoption of DP by analyzing the published scientific literature.