<jats:title>ABSTRACT</jats:title><jats:p>As a recycling center, lysosomes are filled with numerous acid hydrolase enzymes that break down waste materials and invading pathogens. Recently, lysosomal cell death has been defined as \u201clysosomal membrane permeabilization and the consequent leakage of lysosome contents into cytosol.\u201d Here, we show that the neuraminidase (NA) of H5N1 influenza A virus markedly deglycosylates and degrades lysosome-associated membrane proteins (LAMPs; the most abundant membrane proteins of lysosome), which induces lysosomal rupture, and finally leads to cell death of alveolar epithelial carcinoma A549 cells and human tracheal epithelial cells. The NA inhibitors peramivir and zanamivir could effectively block the deglycosylation of LAMPs, inhibit the virus cell entry, and prevent cell death induced by the H5N1 influenza virus. The NA of seasonal H1N1 virus, however, does not share these characteristics. Our findings not only reveal a novel role of NA in the early stage of the H5N1 influenza virus life cycle but also elucidate the molecular mechanism of lysosomal rupture crucial for influenza virus induced cell death.</jats:p><jats:p><jats:bold>IMPORTANCE</jats:bold>The integrity of lysosomes is vital for maintaining cell homeostasis, cellular defense and clearance of invading pathogens. This study shows that the H5N1 influenza virus could induce lysosomal rupture through deglycosylating lysosome-associated membrane proteins (LAMPs) mediated by the neuraminidase activity of NA protein. NA inhibitors such as peramivir and zanamivir could inhibit the deglycosylation of LAMPs and protect lysosomes, which also further interferes with the H5N1 influenza virus infection at early stage of life cycle. This work is significant because it presents new concepts for NA's function, as well as for influenza inhibitors' mechanism of action, and could partially explain the high mortality and high viral load after H5N1 virus infection in human beings and why NA inhibitors have more potent therapeutic effects for lethal avian influenza virus infections at early stage.</jats:p>
\n \n\n \n \n\u00a9 2020, The Author(s). The availability of high-quality RNA-sequencing and genotyping data of post-mortem brain collections from consortia such as CommonMind Consortium (CMC) and the Accelerating Medicines Partnership for Alzheimer\u2019s Disease (AMP-AD) Consortium enable the generation of a large-scale brain cis-eQTL meta-analysis. Here we generate cerebral cortical eQTL from 1433 samples available from four cohorts (identifying >4.1 million significant eQTL for >18,000 genes), as well as cerebellar eQTL from 261 samples (identifying 874,836 significant eQTL for >10,000 genes). We find substantially improved power in the meta-analysis over individual cohort analyses, particularly in comparison to the Genotype-Tissue Expression (GTEx) Project eQTL. Additionally, we observed differences in eQTL patterns between cerebral and cerebellar brain regions. We provide these brain eQTL as a resource for use by the research community. As a proof of principle for their utility, we apply a colocalization analysis to identify genes underlying the GWAS association peaks for schizophrenia and identify a potentially novel gene colocalization with lncRNA RP11-677M14.2 (posterior probability of colocalization 0.975).
\n \n\n \n \nThe role of host genetics in influenza infection is unclear despite decades of interest. Confounding factors such as age, sex, ethnicity and environmental factors have made it difficult to assess the role of genetics without influence. In recent years a single nucleotide polymorphism, interferon-induced transmembrane protein 3 (IFITM3) rs12252, has been shown to alter the severity of influenza infection in Asian populations. In this review we investigate this polymorphism as well as several others suggested to alter the host's defence against influenza infection. In addition, we highlight the open questions surrounding the viral restriction protein IFITM3 with the hope that by answering some of these questions we can elucidate the mechanism of IFITM3 viral restriction and therefore how this restriction is altered due to the rs12252 polymorphism.
\n \n\n \n \n<jats:title>Abstract</jats:title><jats:p>COVID-19 is an ongoing global crisis in which the development of effective vaccines and therapeutics will depend critically on understanding the natural immunity to the virus, including the role of SARS-CoV-2-specific T cells. We have conducted a study of 42 patients following recovery from COVID-19, including 28 mild and 14 severe cases, comparing their T cell responses to those of 16 control donors. We assessed the immune memory of T cell responses using IFN\u03b3 based assays with overlapping peptides spanning SARS-CoV-2 apart from ORF1. We found the breadth, magnitude and frequency of memory T cell responses from COVID-19 were significantly higher in severe compared to mild COVID-19 cases, and this effect was most marked in response to spike, membrane, and ORF3a proteins. Total and spike-specific T cell responses correlated with the anti-Spike, anti-Receptor Binding Domain (RBD) as well as anti-Nucleoprotein (NP) endpoint antibody titre (p<0.001, <0.001 and =0.002). We identified 39 separate peptides containing CD4<jats:sup>+</jats:sup> and/or CD8<jats:sup>+</jats:sup> epitopes, which strikingly included six immunodominant epitope clusters targeted by T cells in many donors, including 3 clusters in spike (recognised by 29%, 24%, 18% donors), two in the membrane protein (M, 32%, 47%) and one in the nucleoprotein (Np, 35%). CD8+ responses were further defined for their HLA restriction, including B*4001-restricted T cells showing central memory and effector memory phenotype. In mild cases, higher frequencies of multi-cytokine producing M- and NP-specific CD8<jats:sup>+</jats:sup> T cells than spike-specific CD8<jats:sup>+</jats:sup> T cells were observed. They furthermore showed a higher ratio of SARS-CoV-2-specific CD8<jats:sup>+</jats:sup> to CD4<jats:sup>+</jats:sup> T cell responses. Immunodominant epitope clusters and peptides containing T cell epitopes identified in this study will provide critical tools to study the role of virus-specific T cells in control and resolution of SARS-CoV-2 infections. The identification of T cell specificity and functionality associated with milder disease, highlights the potential importance of including non-spike proteins within future COVID-19 vaccine design.</jats:p>
\n \n\n \n \nChanges in tissue architecture and multicellular organisation contribute to many diseases, including cancer and cardiovascular diseases. Scratch wound assay is a commonly used tool that assesses cells' migratory ability based on the area of a wound they cover over a certain time. However, analysis of changes in the organisational patterns formed by migrating cells following genetic or pharmacological perturbations are not well explored in these assays, in part because analysing the resulting imaging data is challenging. Here we present DeepScratch, a neural network that accurately detects the cells in scratch assays based on a heterogeneous set of markers. We demonstrate the utility of DeepScratch by analysing images of more than 232,000 lymphatic endothelial cells. In addition, we propose various topological measures of cell connectivity and local cell density (LCD) to characterise tissue remodelling during wound healing. We show that LCD-based metrics allow classification of CDH5 and CDC42 genetic perturbations that are known to affect cell migration through different biological mechanisms. Such differences cannot be captured when considering only the wound area. Taken together, single-cell detection using DeepScratch allows more detailed investigation of the roles of various genetic components in tissue topology and the biological mechanisms underlying their effects on collective cell migration.
\n \n\n \n \nMycobacterium tuberculosis is the leading cause of death from bacterial infection. Improved rapid diagnosis and antimicrobial resistance determination, such as by whole-genome sequencing, are required. Our aim was to develop a simple, low-cost method of preparing DNA for sequencing direct from M. tuberculosis-positive clinical samples (without culture). Simultaneous sputum liquefaction, bacteria heat inactivation (99\u00b0C/30\u2009min), and enrichment for mycobacteria DNA were achieved using an equal volume of thermo-protection buffer (4 M KCl, 0.05 M HEPES buffer, pH 7.5, 0.1% dithiothreitol [DTT]). The buffer emulated intracellular conditions found in hyperthermophiles, thus protecting DNA from rapid thermodegradation, which renders it a poor template for sequencing. Initial validation experiments employed mycobacteria DNA, either extracted or intracellular. Next, mock clinical samples (infection-negative human sputum spiked with 0 to 105 Mycobacterium bovis BCG cells/ml) underwent liquefaction in thermo-protection buffer and heat inactivation. DNA was extracted and sequenced. Human DNA degraded faster than mycobacteria DNA, resulting in target enrichment. Four replicate experiments achieved M. tuberculosis detection at 101 BCG cells/ml, with 31 to 59 M. tuberculosis complex reads. Maximal genome coverage (>97% at 5\u00d7 depth) occurred at 104 BCG cells/ml; >91% coverage (1\u00d7 depth) occurred at 103 BCG cells/ml. Final validation employed M. tuberculosis-positive clinical samples (n\u2009=\u200920), revealing that initial sample volumes of \u22651 ml typically yielded higher mean depths of M. tuberculosis genome coverage, with an overall range of 0.55 to 81.02. A mean depth of 3 gave >96% 1-fold tuberculosis (TB) genome coverage (in 15/20 clinical samples). A mean depth of 15 achieved >99% 5-fold genome coverage (in 9/20 clinical samples). In summary, direct-from-sample sequencing of M. tuberculosis genomes was facilitated by a low-cost thermo-protection buffer.
\n \n\n \n \nCombined photochemical arylation, \"nuisance effect\" (S N Ar) reaction sequences have been employed in the design of small arrays for immediate deployment in medium throughput X-ray protein-ligand structure determination. Reactions have been deliberately let \"out of control,\" in terms of selectivity; for example the ortho-arylation of 2-phenylpyridine gave five products resulting from mono-, bis- arylations combined with S N Ar processes.\u00a0 As a result, a number of crystallographic hits against NUDT7, a key peroxisomal CoA ester hydrolase, have been identified.
\n \n\n \n \n<jats:p>We conducted voluntary Covid-19 testing programmes for symptomatic and asymptomatic staff at a UK teaching hospital using naso-/oro-pharyngeal PCR testing and immunoassays for IgG antibodies. 1128/10,034 (11.2%) staff had evidence of Covid-19 at some time. Using questionnaire data provided on potential risk-factors, staff with a confirmed household contact were at greatest risk (adjusted odds ratio [aOR] 4.82 [95%CI 3.45\u20136.72]). Higher rates of Covid-19 were seen in staff working in Covid-19-facing areas (22.6% vs. 8.6% elsewhere) (aOR 2.47 [1.99\u20133.08]). Controlling for Covid-19-facing status, risks were heterogenous across the hospital, with higher rates in acute medicine (1.52 [1.07\u20132.16]) and sporadic outbreaks in areas with few or no Covid-19 patients. Covid-19 intensive care unit staff were relatively protected (0.44 [0.28\u20130.69]), likely by a bundle of PPE-related measures. Positive results were more likely in Black (1.66 [1.25\u20132.21]) and Asian (1.51 [1.28\u20131.77]) staff, independent of role or working location, and in porters and cleaners (2.06 [1.34\u20133.15]).</jats:p>
\n \n\n \n \n<jats:title>Abstract</jats:title><jats:p>Transcription is necessary for the synthesis of new proteins, often leading to the assumption that changes in transcript levels lead to changes in protein levels which directly impact a cell\u2019s phenotype. Using a synchronized biological rhythm, we show that despite genome-wide partitioning of transcription, transcripts and translation levels into two phase-shifted expression clusters related to metabolism, detectable protein levels remain constant over time. This disconnect between cycling translation and constant protein levels can be explained by slow protein turnover rates, with overall protein levels maintained by low level pulses of new protein synthesis. Instead, rhythmic post-translational regulation of the activities of different proteins, influenced by the metabolic state of the cells, appears to be key to coordinating the physiology of the biological rhythm with cycling transcription. Thus, transcriptional and translational cycling reflects, rather than drives, metabolic and biosynthetic changes during biological rhythms. We propose that transcriptional changes are often the consequence, rather than the cause, of changes in cellular physiology and that caution is needed when inferring the activity of biological processes from transcript data.\n<jats:list list-type=\"bullet\"><jats:list-item><jats:p>Changes in protein levels do not explain the changing states of a biological rhythm</jats:p></jats:list-item><jats:list-item><jats:p>Slow protein turnover rates decouple proteins levels from a rhythmic transcriptome</jats:p></jats:list-item><jats:list-item><jats:p>Metabolites determine protein activity via rhythmic post-translational modifications</jats:p></jats:list-item><jats:list-item><jats:p>Cycling protein activity explains rhythmic transcription and ribosome biogenesis</jats:p></jats:list-item><jats:list-item><jats:p>A cycling transcriptome is a consequence, not a cause, of physiological changes</jats:p></jats:list-item></jats:list></jats:p><jats:p><jats:fig id=\"ufig1\" position=\"float\" orientation=\"portrait\" fig-type=\"figure\"><jats:graphic xmlns:xlink=\"http://www.w3.org/1999/xlink\" xlink:href=\"833921v2_ufig1\" position=\"float\" orientation=\"portrait\" /></jats:fig></jats:p>
\n \n\n \n \nFine-grained classification of microscopic image data with limited samples is\nan open problem in computer vision and biomedical imaging. Deep learning based\nvision systems mostly deal with high number of low-resolution images, whereas\nsubtle detail in biomedical images require higher resolution. To bridge this\ngap, we propose a simple yet effective deep network that performs two tasks\nsimultaneously in an end-to-end manner. First, it utilises a gated attention\nmodule that can focus on multiple key instances at high resolution without\nextra annotations or region proposals. Second, the global structural features\nand local instance features are fused for final image level classification. The\nresult is a robust but lightweight end-to-end trainable deep network that\nyields state-of-the-art results in two separate fine-grained multi-instance\nbiomedical image classification tasks: a benchmark breast cancer histology\ndataset and our new fungi species mycology dataset. In addition, we demonstrate\nthe interpretability of the proposed model by visualising the concordance of\nthe learned features with clinically relevant features.
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