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      Dissemination and Mechanism for the MCR-1 Colistin Resistance

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          Abstract

          Polymyxins are the last line of defense against lethal infections caused by multidrug resistant Gram-negative pathogens. Very recently, the use of polymyxins has been greatly challenged by the emergence of the plasmid-borne mobile colistin resistance gene ( mcr-1). However, the mechanistic aspects of the MCR-1 colistin resistance are still poorly understood. Here we report the comparative genomics of two new mcr- 1-harbouring plasmids isolated from the human gut microbiota, highlighting the diversity in plasmid transfer of the mcr- 1 gene. Further genetic dissection delineated that both the trans-membrane region and a substrate-binding motif are required for the MCR-1-mediated colistin resistance. The soluble form of the membrane protein MCR-1 was successfully prepared and verified. Phylogenetic analyses revealed that MCR-1 is highly homologous to its counterpart PEA lipid A transferase in Paenibacili, a known producer of polymyxins. The fact that the plasmid-borne MCR-1 is placed in a subclade neighboring the chromosome-encoded colistin-resistant Neisseria LptA (EptA) potentially implies parallel evolutionary paths for the two genes. In conclusion, our finding provids a first glimpse of mechanism for the MCR-1-mediated colistin resistance.

          Author Summary

          Colistin is an ultimate line of refuge against fatal infections by multidrug-resistant Gram-negative pathogens. The plasmid-mediated transfer of the mobile colistin resistance gene ( mcr-1) represents a novel mechanism for antibacterial drug resistance, and also poses new threats to public health. However, the mechanistic aspects of the MCR-1 colistin resistance are not fully understood. Here we report comparative genomics of two new mcr-1-harbouring plasmids isolated from the human gut microbiota. Genetic studies determined that both the transmembrane region and a substrate-binding motif are essential for its function. Phylogenetic analyses revealed that MCR-1 is highly homologous to the PEA lipid A transferase in Paenibacillus, a known producer of polymyxins. The fact that the plasmid-borne MCR-1 is placed in a subclade neighboring the chromosome-encoded colistin-resistant Neisseria LptA potentially implies parallel evolutionary paths for the two genes. Our results reveal mechanistic insights into the MCR-1-mediated colistin resistance.

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          Protein structure prediction and structural genomics.

          Genome sequencing projects are producing linear amino acid sequences, but full understanding of the biological role of these proteins will require knowledge of their structure and function. Although experimental structure determination methods are providing high-resolution structure information about a subset of the proteins, computational structure prediction methods will provide valuable information for the large fraction of sequences whose structures will not be determined experimentally. The first class of protein structure prediction methods, including threading and comparative modeling, rely on detectable similarity spanning most of the modeled sequence and at least one known structure. The second class of methods, de novo or ab initio methods, predict the structure from sequence alone, without relying on similarity at the fold level between the modeled sequence and any of the known structures. In this Viewpoint, we begin by describing the essential features of the methods, the accuracy of the models, and their application to the prediction and understanding of protein function, both for single proteins and on the scale of whole genomes. We then discuss the important role that protein structure prediction methods play in the growing worldwide effort in structural genomics.
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            GUIDANCE2: accurate detection of unreliable alignment regions accounting for the uncertainty of multiple parameters

            Inference of multiple sequence alignments (MSAs) is a critical part of phylogenetic and comparative genomics studies. However, from the same set of sequences different MSAs are often inferred, depending on the methodologies used and the assumed parameters. Much effort has recently been devoted to improving the ability to identify unreliable alignment regions. Detecting such unreliable regions was previously shown to be important for downstream analyses relying on MSAs, such as the detection of positive selection. Here we developed GUIDANCE2, a new integrative methodology that accounts for: (i) uncertainty in the process of indel formation, (ii) uncertainty in the assumed guide tree and (iii) co-optimal solutions in the pairwise alignments, used as building blocks in progressive alignment algorithms. We compared GUIDANCE2 with seven methodologies to detect unreliable MSA regions using extensive simulations and empirical benchmarks. We show that GUIDANCE2 outperforms all previously developed methodologies. Furthermore, GUIDANCE2 also provides a set of alternative MSAs which can be useful for downstream analyses. The novel algorithm is implemented as a web-server, available at: http://guidance.tau.ac.il.
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              PyEvolve: a toolkit for statistical modelling of molecular evolution

              Background Examining the distribution of variation has proven an extremely profitable technique in the effort to identify sequences of biological significance. Most approaches in the field, however, evaluate only the conserved portions of sequences – ignoring the biological significance of sequence differences. A suite of sophisticated likelihood based statistical models from the field of molecular evolution provides the basis for extracting the information from the full distribution of sequence variation. The number of different problems to which phylogeny-based maximum likelihood calculations can be applied is extensive. Available software packages that can perform likelihood calculations suffer from a lack of flexibility and scalability, or employ error-prone approaches to model parameterisation. Results Here we describe the implementation of PyEvolve, a toolkit for the application of existing, and development of new, statistical methods for molecular evolution. We present the object architecture and design schema of PyEvolve, which includes an adaptable multi-level parallelisation schema. The approach for defining new methods is illustrated by implementing a novel dinucleotide model of substitution that includes a parameter for mutation of methylated CpG's, which required 8 lines of standard Python code to define. Benchmarking was performed using either a dinucleotide or codon substitution model applied to an alignment of BRCA1 sequences from 20 mammals, or a 10 species subset. Up to five-fold parallel performance gains over serial were recorded. Compared to leading alternative software, PyEvolve exhibited significantly better real world performance for parameter rich models with a large data set, reducing the time required for optimisation from ~10 days to ~6 hours. Conclusion PyEvolve provides flexible functionality that can be used either for statistical modelling of molecular evolution, or the development of new methods in the field. The toolkit can be used interactively or by writing and executing scripts. The toolkit uses efficient processes for specifying the parameterisation of statistical models, and implements numerous optimisations that make highly parameter rich likelihood functions solvable within hours on multi-cpu hardware. PyEvolve can be readily adapted in response to changing computational demands and hardware configurations to maximise performance. PyEvolve is released under the GPL and can be downloaded from .
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Pathog
                PLoS Pathog
                plos
                plospath
                PLoS Pathogens
                Public Library of Science (San Francisco, CA USA )
                1553-7366
                1553-7374
                28 November 2016
                November 2016
                : 12
                : 11
                Affiliations
                [1 ]Department of Medical Microbiology and Parasitology, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
                [2 ]CAS Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China
                [3 ]National Risk Assessment Laboratory for Antimicrobial Resistance of Animal Original Bacteria, South China Agricultural University, Guangzhou, China
                [4 ]Department of Biochemistry, University of Illinois, Urbana, Illinois, United States of America
                [5 ]Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
                [6 ]Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
                National Jewish Health, UNITED STATES
                Author notes

                The authors have declared that no competing interests exist.

                • Conceived and designed the experiments: YF.

                • Performed the experiments: YF RG QW YH HY ZL JL SS.

                • Analyzed the data: YF RG QW YH HY ZL JL SS.

                • Contributed reagents/materials/analysis tools: FL YH DL BZ JS YL GT YF.

                • Wrote the paper: YF SS YH.

                Article
                PPATHOGENS-D-16-01520
                10.1371/journal.ppat.1005957
                5125707
                27893854
                43cf3a7f-d259-4175-afd2-e8a181f9ee97
                © 2016 Gao et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                Page count
                Figures: 5, Tables: 2, Pages: 19
                Product
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: 31570027
                Award Recipient :
                Funded by: Natural Science Foundation of Zhejiang Province, China
                Award ID: LR15H190001
                Award Recipient :
                Funded by: The National Basic Research Program of China
                Award ID: 2016YFC1200100
                Award Recipient :
                Funded by: The National Basic Research Program of China
                Award ID: 2015CB554200
                Award Recipient :
                Funded by: The “Young 1000 Talents” Award of China
                Award Recipient :
                Funded by: The Youth Innovation Promotion Association of Chinese Academy of Sciences
                Award Recipient :
                This work was supported by Zhejiang Provincial Natural Science Foundation for Distinguished Young Scholars (LR15H190001 to YF), the National Natural Science Foundation of China (31570027 to YF), and the National Key Basic Research Program of China (2016YFC1200100 to YF; 2015CB554200 to BZ). YF is a recipient of the “Young 1000 Talents” Award. YH is a member of the Youth Innovation Promotion Association of Chinese Academy of Sciences(2015069). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Molecular Biology
                Molecular Biology Techniques
                Sequencing Techniques
                Sequence Analysis
                Sequence Alignment
                Research and Analysis Methods
                Molecular Biology Techniques
                Sequencing Techniques
                Sequence Analysis
                Sequence Alignment
                Research and Analysis Methods
                Computational Techniques
                Split-Decomposition Method
                Multiple Alignment Calculation
                Biology and Life Sciences
                Biochemistry
                Enzymology
                Enzymes
                Transferases
                Biology and Life Sciences
                Biochemistry
                Proteins
                Enzymes
                Transferases
                Biology and Life Sciences
                Organisms
                Plants
                Legumes
                Peas
                Biology and Life Sciences
                Biochemistry
                Lipids
                Biology and Life Sciences
                Organisms
                Bacteria
                Neisseria
                Medicine and Health Sciences
                Pharmacology
                Drugs
                Antimicrobials
                Antibiotics
                Polymyxins
                Biology and Life Sciences
                Microbiology
                Microbial Control
                Antimicrobials
                Antibiotics
                Polymyxins
                Biology and Life Sciences
                Microbiology
                Microbial Control
                Antimicrobial Resistance
                Medicine and Health Sciences
                Pharmacology
                Antimicrobial Resistance
                Custom metadata
                All relevant data are within the paper and its Supporting Information files except for the sequences of the two plasmids (pE15004 & pE15017) deposited into the GenBank database with the accession numbers KX772777 and KX772778, respectively.

                Infectious disease & Microbiology
                Infectious disease & Microbiology

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