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      Sequence-specific capture and concentration of viral RNA by type III CRISPR system enhances diagnostic

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          Abstract

          Type-III CRISPR-Cas systems have recently been adopted for sequence-specific detection of SARS-CoV-2. Here, we make two major advances that simultaneously limit sample handling and significantly enhance the sensitivity of SARS-CoV-2 RNA detection directly from patient samples. First, we repurpose the type III-A CRISPR complex from Thermus thermophilus (TtCsm) for programmable capture and concentration of specific RNAs from complex mixtures. The target bound TtCsm complex primarily generates two cyclic oligoadenylates (i.e., cA 3 and cA 4) that allosterically activate ancillary nucleases. To improve sensitivity of the diagnostic, we identify and test several ancillary nucleases (i.e., Can1, Can2, and NucC). We show that Can1 and Can2 are activated by both cA 3 and cA 4, and that different activators trigger changes in the substrate specificity of these nucleases. Finally, we integrate the type III-A CRISPR RNA-guided capture technique with the Can2 nuclease for 90 fM (5×10 4 copies/ul) detection of SARS-CoV-2 RNA directly from nasopharyngeal swab samples.

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          MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability

          We report a major update of the MAFFT multiple sequence alignment program. This version has several new features, including options for adding unaligned sequences into an existing alignment, adjustment of direction in nucleotide alignment, constrained alignment and parallel processing, which were implemented after the previous major update. This report shows actual examples to explain how these features work, alone and in combination. Some examples incorrectly aligned by MAFFT are also shown to clarify its limitations. We discuss how to avoid misalignments, and our ongoing efforts to overcome such limitations.
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            FastTree 2 – Approximately Maximum-Likelihood Trees for Large Alignments

            Background We recently described FastTree, a tool for inferring phylogenies for alignments with up to hundreds of thousands of sequences. Here, we describe improvements to FastTree that improve its accuracy without sacrificing scalability. Methodology/Principal Findings Where FastTree 1 used nearest-neighbor interchanges (NNIs) and the minimum-evolution criterion to improve the tree, FastTree 2 adds minimum-evolution subtree-pruning-regrafting (SPRs) and maximum-likelihood NNIs. FastTree 2 uses heuristics to restrict the search for better trees and estimates a rate of evolution for each site (the “CAT” approximation). Nevertheless, for both simulated and genuine alignments, FastTree 2 is slightly more accurate than a standard implementation of maximum-likelihood NNIs (PhyML 3 with default settings). Although FastTree 2 is not quite as accurate as methods that use maximum-likelihood SPRs, most of the splits that disagree are poorly supported, and for large alignments, FastTree 2 is 100–1,000 times faster. FastTree 2 inferred a topology and likelihood-based local support values for 237,882 distinct 16S ribosomal RNAs on a desktop computer in 22 hours and 5.8 gigabytes of memory. Conclusions/Significance FastTree 2 allows the inference of maximum-likelihood phylogenies for huge alignments. FastTree 2 is freely available at http://www.microbesonline.org/fasttree.
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              IQ-TREE 2: New Models and Efficient Methods for Phylogenetic Inference in the Genomic Era

              Abstract IQ-TREE (http://www.iqtree.org, last accessed February 6, 2020) is a user-friendly and widely used software package for phylogenetic inference using maximum likelihood. Since the release of version 1 in 2014, we have continuously expanded IQ-TREE to integrate a plethora of new models of sequence evolution and efficient computational approaches of phylogenetic inference to deal with genomic data. Here, we describe notable features of IQ-TREE version 2 and highlight the key advantages over other software.
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                Author and article information

                Journal
                Res Sq
                ResearchSquare
                Research Square
                American Journal Experts
                19 April 2022
                : rs.3.rs-1466718
                Affiliations
                [1 ]Department of Microbiology and Cell Biology, Montana State University, Bozeman, MT 59717, USA
                [2 ]These authors contributed equally
                [3 ]These authors contributed equally
                [4 ]Department of Chemistry, University of Michigan, Ann Arbor, MI 48105, USA
                [5 ]Lead contact
                Author notes

                Author contributions

                B.W., A. Nemudraia, A. Nemudryi, and A.S.-F. conceived the experimental plans. A. Nemudraia, A. Nemudryi and R.W. developed and performed Type III Csm-based RNA concentration method. A.M.S., T.Z., R.W., M.B. and A.S.-F. purified the proteins. A. Nemudraia, A.S.-F., S.P., J.N., and R.W. performed biochemical characterization of the ancillary nucleases. A. Nemudraia performed RNA reporter’s screen. A. Nemudryi performed statistical analyses and analyzed sequencing data. L.R., J.J., and K.K. contributed to the initial design of TLC assays. L.H. and A. Nemudryi performed TLC; M.B., S.P., and T.W. performed the bioinformatic analyses and phylogenetics. H.L. and A.M. performed RNA extractions and RT-qPCR of patient nasopharyngeal swab samples. A. Nemudraia and A. Nemudryi performed RT-qPCR and Csm-based detection assay. A. Nemudraia, A. Nemudryi, and B.W. wrote the manuscript. All authors edited and approved the manuscript.

                Author information
                http://orcid.org/0000-0002-0528-268X
                http://orcid.org/0000-0001-8831-2081
                http://orcid.org/0000-0002-7763-9262
                http://orcid.org/0000-0001-9615-065X
                http://orcid.org/0000-0001-9297-5304
                Article
                10.21203/rs.3.rs-1466718
                10.21203/rs.3.rs-1466718/v1
                9040678
                35475170
                19b0e53c-8c6e-4a85-aacb-dd39ed7af808

                This work is licensed under a Creative Commons Attribution 4.0 International License, which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use.

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