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      Diversity of Culturable Bacteria Isolated From the Feces of Wild Anopheles darlingi (Diptera: Culicidae) Mosquitoes From the Brazilian Amazon

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          Abstract

          Microorganisms living in the midgut of Anopheles mosquitoes have been studied to fight vector-borne diseases, such as malaria. Studies on the microbiota of the Neotropical Anopheles darlingi, the most important Brazilian vector for malaria, have been reported for the same purpose. Our aims were to isolate and identify culturable bacteria from An. darlingi mosquito guts through their feces and to estimate the species richness and the frequency distribution of the sampled bacteria. Sixty wild females of An. darlingi mosquitoes were captured at two rural locations, near Porto Velho, Rondônia, Brazil. Bacteria were isolated from mosquito feces, which were collected using cages which permit the collection of feces on LB nutrient agar plates. Sixty bacterial colonies were isolated and stored in glycerol at −80°C. Bacteria were identified by sequencing their 16S rRNA gene obtained using PCR and Sanger sequencing. To aid in species identification, MALDI-TOF, VITEK2, and BBL Crystal were used as complementary protocols. The sequences obtained from the 60 bacterial isolates were compared to sequences deposited in GenBank (NCBI) using BLAST. Homology greater than 97% between the query and the subject was used as the criteria for assigning the identity of each isolate. Fourteen species from eight different genera were identified among the 60 isolates. The most frequent species were Serratia liquefaciens (20%) and Serratia marcescens (15%). Due to their established apathogenicity and according to previous studies, we suggest Serratia and Pantoea species as suitable for paratransgenesis development to fight malaria in Brazilian Amazon.

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          Most cited references59

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          MEGA7: Molecular Evolutionary Genetics Analysis Version 7.0 for Bigger Datasets.

          We present the latest version of the Molecular Evolutionary Genetics Analysis (Mega) software, which contains many sophisticated methods and tools for phylogenomics and phylomedicine. In this major upgrade, Mega has been optimized for use on 64-bit computing systems for analyzing larger datasets. Researchers can now explore and analyze tens of thousands of sequences in Mega The new version also provides an advanced wizard for building timetrees and includes a new functionality to automatically predict gene duplication events in gene family trees. The 64-bit Mega is made available in two interfaces: graphical and command line. The graphical user interface (GUI) is a native Microsoft Windows application that can also be used on Mac OS X. The command line Mega is available as native applications for Windows, Linux, and Mac OS X. They are intended for use in high-throughput and scripted analysis. Both versions are available from www.megasoftware.net free of charge.
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            Is Open Access

            The SILVA ribosomal RNA gene database project: improved data processing and web-based tools

            SILVA (from Latin silva, forest, http://www.arb-silva.de) is a comprehensive web resource for up to date, quality-controlled databases of aligned ribosomal RNA (rRNA) gene sequences from the Bacteria, Archaea and Eukaryota domains and supplementary online services. The referred database release 111 (July 2012) contains 3 194 778 small subunit and 288 717 large subunit rRNA gene sequences. Since the initial description of the project, substantial new features have been introduced, including advanced quality control procedures, an improved rRNA gene aligner, online tools for probe and primer evaluation and optimized browsing, searching and downloading on the website. Furthermore, the extensively curated SILVA taxonomy and the new non-redundant SILVA datasets provide an ideal reference for high-throughput classification of data from next-generation sequencing approaches.
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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.

                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                Journal of Medical Entomology
                Oxford University Press (OUP)
                0022-2585
                1938-2928
                July 01 2021
                July 16 2021
                March 11 2021
                July 01 2021
                July 16 2021
                March 11 2021
                : 58
                : 4
                : 1900-1907
                Affiliations
                [1 ]Fundação Oswaldo Cruz, FIOCRUZ RONDÔNIA, Porto Velho, RO, Brazil
                [2 ]Instituto Federal de Rondônia - IFRO, Porto Velho, RO, Brazil
                [3 ]Laboratório Central de Saúde Pública de Rondônia, Porto Velho, RO, Brazil
                [4 ]Virginia Commonwealth University, CSBC, Life Sciences, Richmond – Virginia, USA
                [5 ]Fundação Oswaldo Cruz, Plataforma Bi-institucional de Medicina Translacional, Ribeirão Preto, Brazil
                [6 ]Departamento de Biologia, Universidade Federal de Rondônia, Porto Velho, RO, Brazil
                Article
                10.1093/jme/tjab028
                33704463
                ecd8e8c6-a8de-4370-8474-c945340a46e2
                © 2021

                https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model

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