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      Genome‐scale metabolic modelling of SARS‐CoV‐2 in cancer cells reveals an increased shift to glycolytic energy production

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          Abstract

          Cancer is considered a high‐risk condition for severe illness resulting from COVID‐19. The interaction between severe acute respiratory syndrome coronavirus‐2 (SARS‐CoV‐2) and human metabolism is key to elucidating the risk posed by COVID‐19 for cancer patients and identifying effective treatments, yet it is largely uncharacterised on a mechanistic level. We present a genome‐scale map of short‐term metabolic alterations triggered by SARS‐CoV‐2 infection of cancer cells. Through transcriptomic‐ and proteomic‐informed genome‐scale metabolic modelling, we characterise the role of RNA and fatty acid biosynthesis in conjunction with a rewiring in energy production pathways and enhanced cytokine secretion. These findings link together complementary aspects of viral invasion of cancer cells, while providing mechanistic insights that can inform the development of treatment strategies.

          Abstract

          Summary of main metabolic changes in SARS‐CoV‐2‐infected Huh7 cells at 72 hpi. The mitochondrial ETC and oxidative phosphorylation are downregulated, reducing ATP production from the TCA cycle but potentially providing protection from apoptosis. The components of the TCA producing intermediates for viral amino acid synthesis are upregulated. While the production of ATP is reduced in the TCA, the energy‐producing reactions of glycolysis are upregulated, producing ATP for the viral production of amino acids and RNA. Fatty acid synthesis is also upregulated, producing fatty acids for the synthesis of the viral envelope. The production of anti‐inflammatory polyunsaturated fatty acids and the electron transport chain are both downregulated. Upregulation of inflammatory proteins IP10, eotaxin, MIP‐1β and IL‐6 has been linked with the cytokine storm. AGP is an acute‐phase protein involved in the inflammatory response. Green upward arrows represent upregulation. Red downward arrows represent downregulation. (A) Upregulated transcriptomic‐informed flux reactions involved in RNA production. (B) Dysregulated transcriptomic‐informed flux reactions involved in oxidative phosphorylation and glycolysis. (C) Visual overview of main metabolic changes. (D) Dysregulated transcriptomic‐informed flux reactions involved in fatty acid metabolism; RE3243R, production of polyunsaturated fatty acids, for example oleic acid; C183806m, final step of β‐oxidation producing octanyl‐CoA; C163C143m, first step of β‐oxidation; ETF/QO, electron transfer from octanoyl‐CoA to ubiquinone. (E) Upregulated transcriptomic‐informed secretory pathways involved in the immune response. PPP, pentose phosphate pathway; FAS, fatty acid synthesis; FA, fatty acid, TCA, citric acid cycle; AA, amino acid; FA‐β‐oxidation, fatty acid‐β‐oxidation; ETC, electron transport chain; PUFA, polyunsaturated fatty acid; IP10, interferon γ‐induced protein‐10; AGP, alpha‐1 acid glycoprotein; IL‐6, interleukin 6; MIP‐1β, macrophage inflammatory protein‐1β; TDK, deoxyuridine kinase; TK, thymidine kinase; NDPK, NDP kinase; C183806m, FAOXC183806m; C163C143m, FAOXC163C143m; ETF/QO, electron transfer flavoprotein/electron transfer flavoprotein–ubiquinone oxidoreductase, CI, NADH: ubiquinone oxidoreductase; CII, succinate dehydrogenase; CIII, cytochrome reductase; CIV, cytochrome c oxidase; CV, ATP synthase; PFK, phosphofructokinase; ALDD2, aldehyde dehydrogenase.

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          Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China

          Summary Background A recent cluster of pneumonia cases in Wuhan, China, was caused by a novel betacoronavirus, the 2019 novel coronavirus (2019-nCoV). We report the epidemiological, clinical, laboratory, and radiological characteristics and treatment and clinical outcomes of these patients. Methods All patients with suspected 2019-nCoV were admitted to a designated hospital in Wuhan. We prospectively collected and analysed data on patients with laboratory-confirmed 2019-nCoV infection by real-time RT-PCR and next-generation sequencing. Data were obtained with standardised data collection forms shared by WHO and the International Severe Acute Respiratory and Emerging Infection Consortium from electronic medical records. Researchers also directly communicated with patients or their families to ascertain epidemiological and symptom data. Outcomes were also compared between patients who had been admitted to the intensive care unit (ICU) and those who had not. Findings By Jan 2, 2020, 41 admitted hospital patients had been identified as having laboratory-confirmed 2019-nCoV infection. Most of the infected patients were men (30 [73%] of 41); less than half had underlying diseases (13 [32%]), including diabetes (eight [20%]), hypertension (six [15%]), and cardiovascular disease (six [15%]). Median age was 49·0 years (IQR 41·0–58·0). 27 (66%) of 41 patients had been exposed to Huanan seafood market. One family cluster was found. Common symptoms at onset of illness were fever (40 [98%] of 41 patients), cough (31 [76%]), and myalgia or fatigue (18 [44%]); less common symptoms were sputum production (11 [28%] of 39), headache (three [8%] of 38), haemoptysis (two [5%] of 39), and diarrhoea (one [3%] of 38). Dyspnoea developed in 22 (55%) of 40 patients (median time from illness onset to dyspnoea 8·0 days [IQR 5·0–13·0]). 26 (63%) of 41 patients had lymphopenia. All 41 patients had pneumonia with abnormal findings on chest CT. Complications included acute respiratory distress syndrome (12 [29%]), RNAaemia (six [15%]), acute cardiac injury (five [12%]) and secondary infection (four [10%]). 13 (32%) patients were admitted to an ICU and six (15%) died. Compared with non-ICU patients, ICU patients had higher plasma levels of IL2, IL7, IL10, GSCF, IP10, MCP1, MIP1A, and TNFα. Interpretation The 2019-nCoV infection caused clusters of severe respiratory illness similar to severe acute respiratory syndrome coronavirus and was associated with ICU admission and high mortality. Major gaps in our knowledge of the origin, epidemiology, duration of human transmission, and clinical spectrum of disease need fulfilment by future studies. Funding Ministry of Science and Technology, Chinese Academy of Medical Sciences, National Natural Science Foundation of China, and Beijing Municipal Science and Technology Commission.
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            STAR: ultrafast universal RNA-seq aligner.

            Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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              limma powers differential expression analyses for RNA-sequencing and microarray studies

              limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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                Author and article information

                Contributors
                c.angione@tees.ac.uk
                Journal
                FEBS Lett
                FEBS Lett
                10.1002/(ISSN)1873-3468
                FEB2
                Febs Letters
                John Wiley and Sons Inc. (Hoboken )
                0014-5793
                1873-3468
                05 September 2021
                05 September 2021
                : 10.1002/1873-3468.14180
                Affiliations
                [ 1 ] School of Computing, Engineering and Digital Technologies Teesside University Middlesbrough UK
                [ 2 ] Department of Biology University of Padua Italy
                [ 3 ] BMR Genomics Padua Italy
                [ 4 ] Healthcare Innovation Centre Teesside University Middlesbrough UK
                [ 5 ] Centre for Digital Innovation Teesside University Middlesbrough UK
                Author notes
                [*] [* ] Correspondence

                C. Angione, School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough TS1 3BX, UK

                Tel: +44 1642 335030

                E‐mail: c.angione@ 123456tees.ac.uk

                Author information
                https://orcid.org/0000-0001-6230-4016
                https://orcid.org/0000-0002-4518-5913
                https://orcid.org/0000-0002-8516-7680
                https://orcid.org/0000-0001-9147-4946
                https://orcid.org/0000-0002-3140-7909
                Article
                FEB214180
                10.1002/1873-3468.14180
                8427129
                34409594
                298047dd-12ff-41c9-9cbe-7203a90ca41b
                © 2021 The Authors. FEBS Letters published by John Wiley & Sons Ltd on behalf of Federation of European Biochemical Societies

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 02 August 2021
                : 18 May 2021
                : 15 August 2021
                Page count
                Figures: 5, Tables: 1, Pages: 16, Words: 28650
                Funding
                Funded by: Children's Liver Disease Foundation , doi 10.13039/501100000290;
                Funded by: UK Research and Innovation , doi 10.13039/100014013;
                Categories
                Research Letter
                Research Letters
                Custom metadata
                2.0
                corrected-proof
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.0.7 mode:remove_FC converted:09.09.2021

                Molecular biology
                cancer,covid‐19,flux balance analysis,genome‐scale metabolic modelling,multi‐omics,sars‐cov‐2

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