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      Powassan Virus Neuropathology and Genomic Diversity in Patients With Fatal Encephalitis

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          Abstract

          Background

          Powassan virus (POWV) is an emerging cause of severe encephalitis; very little is known about human pathogenicity due to challenges in diagnosis and viral RNA recovery. We present 3 patients with fatal encephalitis due to POWV lineage II (deer tick virus).

          Methods

          We obtained 27 unique samples, including from brain biopsy and autopsy, and used metagenomic sequencing, quantitative reverse transcriptase polymerase chain reaction, and a newly developed CRISPR-based diagnostic assay to perform the first detailed characterization of POWV compartmentalization and genomics between and within human subjects.

          Results

          In all 3 patients, imaging and histopathology findings were notable for profound cerebellar involvement. All patients were initially diagnosed with POWV by metagenomic sequencing, and 2 of the 3 had negative clinical testing by serology. We detected POWV RNA in 13 clinical samples; levels were highest in the cerebellum, and there was very little involvement of peripheral tissue. We assembled complete POWV genomes from 8 samples, providing unique information about the strains of POWV lineage II (deer tick virus) that infect humans.

          Conclusions

          We demonstrate the utility of molecular assays for detecting POWV infection, including in seronegative patients, and nominate viral genomic features that may relate to human infection and neuropathogenicity. The cerebellum was identified as a key target POWV in fatal infection, by radiological and histopathological findings as well as molecular testing.

          Abstract

          In three patients with fatal encephalitis, Powassan virus was diagnosed by metagenomic sequencing. Imaging, histopathology, and molecular testing of 27 unique samples showed profound cerebellar involvement, and little peripherally. Powassan virus genome sequencing offered insight into strains that infect humans.

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          Most cited references43

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          The Sequence Alignment/Map format and SAMtools

          Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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            IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies

            Large phylogenomics data sets require fast tree inference methods, especially for maximum-likelihood (ML) phylogenies. Fast programs exist, but due to inherent heuristics to find optimal trees, it is not clear whether the best tree is found. Thus, there is need for additional approaches that employ different search strategies to find ML trees and that are at the same time as fast as currently available ML programs. We show that a combination of hill-climbing approaches and a stochastic perturbation method can be time-efficiently implemented. If we allow the same CPU time as RAxML and PhyML, then our software IQ-TREE found higher likelihoods between 62.2% and 87.1% of the studied alignments, thus efficiently exploring the tree-space. If we use the IQ-TREE stopping rule, RAxML and PhyML are faster in 75.7% and 47.1% of the DNA alignments and 42.2% and 100% of the protein alignments, respectively. However, the range of obtaining higher likelihoods with IQ-TREE improves to 73.3-97.1%.
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              MUSCLE: multiple sequence alignment with high accuracy and high throughput.

              We describe MUSCLE, a new computer program for creating multiple alignments of protein sequences. Elements of the algorithm include fast distance estimation using kmer counting, progressive alignment using a new profile function we call the log-expectation score, and refinement using tree-dependent restricted partitioning. The speed and accuracy of MUSCLE are compared with T-Coffee, MAFFT and CLUSTALW on four test sets of reference alignments: BAliBASE, SABmark, SMART and a new benchmark, PREFAB. MUSCLE achieves the highest, or joint highest, rank in accuracy on each of these sets. Without refinement, MUSCLE achieves average accuracy statistically indistinguishable from T-Coffee and MAFFT, and is the fastest of the tested methods for large numbers of sequences, aligning 5000 sequences of average length 350 in 7 min on a current desktop computer. The MUSCLE program, source code and PREFAB test data are freely available at http://www.drive5. com/muscle.
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                Author and article information

                Journal
                Open Forum Infect Dis
                Open Forum Infect Dis
                ofid
                Open Forum Infectious Diseases
                Oxford University Press (US )
                2328-8957
                October 2020
                29 August 2020
                29 August 2020
                : 7
                : 10
                : ofaa392
                Affiliations
                [1 ] Broad Institute of MIT and Harvard , Cambridge, Massachusetts, USA
                [2 ] Systems Biology, Harvard Medical School , Boston, Massachusetts, USA
                [3 ] Department of Pathology, Brigham and Women’s Hospital , Boston, Massachusetts, USA
                [4 ] Harvard College , Cambridge, Massachusetts, USA
                [5 ] Division of Infectious Diseases, Massachusetts General Hospital , Boston, Massachusetts, USA
                [6 ] PhD Program in Virology, Division of Medical Sciences, Harvard University , Boston, Massachusetts, USA
                [7 ] Department of Neurology, Massachusetts General Hospital , Boston, Massachusetts, USA
                [8 ] Department of Organismic and Evolutionary Biology, Harvard University , Cambridge, Massachusetts, USA
                [9 ] Department of Immunology and Infectious Disease , Harvard School of Public Health, Boston, Massachusetts, USA
                [10 ] Howard Hughes Medical Institute , Chevy Chase, Maryland, USA
                [11 ] Emory University School of Medicine , Atlanta, Georgia, USA
                Author notes
                Correspondence: Anne Piantadosi, MD, PhD, Emory University School of Medicine, 101 Woodruff Circle, Room 7207A, Atlanta, GA 30322 ( anne.piantadosi@ 123456emory.edu ).

                Equal contribution

                Equal contribution

                Author information
                http://orcid.org/0000-0002-5942-1534
                Article
                ofaa392
                10.1093/ofid/ofaa392
                7566439
                33094116
                f55fe994-0fb0-4052-a23b-694f49ae2ae4
                © The Author(s) 2020. Published by Oxford University Press on behalf of Infectious Diseases Society of America.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence ( http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

                History
                : 22 July 2020
                : 19 August 2020
                : 27 August 2020
                : 15 October 2020
                Page count
                Pages: 10
                Funding
                Funded by: National Institutes of Health, DOI 10.13039/100000002;
                Award ID: R01AI137424
                Award ID: K08AI139348
                Funded by: Howard Hughes Medical Institute, DOI 10.13039/100000011;
                Funded by: Defense Advanced Research Projects Agency, DOI 10.13039/100000185;
                Award ID: DC18AC00006
                Categories
                Major Articles
                AcademicSubjects/MED00290

                flavivirus,molecular diagnostics,neuropathology,viral genomics

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