DOCSLIB.ORG

Miniontm for Dog Microbiome Analyses

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Miniontm for Dog Microbiome Analyses NANOPORE SEQUENCING FOR ONE-HEALTH: genome and plasmid characterization of a methicillin-resistant Staphylococcus pseudintermedius strain isolated from canine otitis and metagenomic samples from horse rectum Joaquim Viñes a,b, Olga Francino a, Anna Cuscó b a SVGM, Servei Veterinari de Genètica Molecular, Universitat Autònoma de Barcelona b Vetgenomics, Edifici EUREKA, Parc de Recerca de la UAB • One-Health studies rely on the interdisciplinary collaboration in all aspects of health care for humans, animals and the environment. • Staphylococcus pseudintermedius is prone to cause opportunistic infections in skin of dogs and cats, with zoonotic potential. • Methicillin-Resistant S. pseudintermedius (MRSP) is gaining ground, so rapid characterization may improve treatment. • Clinical metagenomics samples are still challenging. The application of new sequencing approaches may help in rapid diagnostics. Introduction For the run of 10th April 2018 Strain characterization >BLAST 2 different >BANDAGE >ResFinder (DTU) runs >CARD >BIBLIOGRAPHY Rapid >NCTC REFERENCE GENOMES Sequencing SQK-RBK004 DNA extraction ZymoBIOMICS MicroPrep Kit methods For the run of 8th February 2018 and Phenotypic approach Pcr & electrophoresis Material Material mecA/mecC and nuc genes: methicillin resistant? Which Staphylococcus species? Strain characterization >BLAST >BANDAGE Disk diffusion test >ResFinder (DTU) This strain is resistant to... >CARD >BIBLIOGRAPHY Figure 1 – Disk diffusion results: S. pseudintermedius strain causative of otitis was resistant to the following antibiotics. Aminogl. = Aminoglycosides Methicillin Aminogl. Quinolones Tetracyclines Macrolides resistance Others markers quinolones Gentamicin Ciprofloxacin Tetracycline Erythromycin Oxacillin Clindamycin Resistance to quinolones are due to mutations in Tobramycin Marbofloxacin Doxycycline Cefoxitin Co-trimoxazole gyrA gene. Using MinION we found point Pradofloxacin Minocycline mutations described in the bibliography to confer Orbifloxacin resistance to that kind of antibiotics! Figure 3 – Assembled genome and plasmid (Canu) from S. pseudintermedius. Resistance genes can be visualized in Bandage through BLAST search 8 7 (%) 6 5 4 abundance abundance 3 Results 2 dfrG 1 Relative erm(B) 0 aad(6) sat4 aph(3’)-IIIa Bacteria Nostocsp. Firmicutes Bacteroides Clostridiales Opisthokonta Bacteroidales Escherichiacoli Proteobacteria Parabacteroides Escherichiaphage cellularorganisms Xanthomonascitri Streptococcusequi Bacteroidesovatus Bacteroidesfragilis [Eubacterium]hallii Parabacteroidessp. Terrabacteriagroup Enterobacteriaceae Ruminococcusalbus [Clostridium]bolteae Butyrivibriohungatei Prevotellaruminicola Lachnoclostridiumsp. Prevotellaintermedia Staphylococcusaureus Bacteroidescaecimuris Bacteroideshelcogenes Xanthomonascampestris Fibrobactersuccinogenes Akkermansiamuciniphila Bacteroidescellulosilyticus Figure 2 – PCR of mecA Parabacteroidesdistasonis Faecalibacteriumprausnitzii and nuc gene. A band of Bacteroidesthetaiotaomicron First sampling Second sampling approximately 900 bp Figure 4 – Relative abundances (%) of the microbial species from the horse rectal swabs. While the first determines the presence point of sampling corresponds to the admission of a horse with left colon displacement, the second point is of S. pseudintermedius after surgery, which incision got infected and followed with endotoxemia MinION sequencing using Rapid Barcoding Kit The microbiota taxonomy of horse rectal samples obtained from (Oxford Nanopore) allowed us to describe all metagenomics analyses with WIMP is consistent with the resistances found with disk diffusion test, even bibliography Conclusions resistances caused by point mutations The change of microbiota profile in the second point of sampling We could found other resistance gene: sat4 may be indicative of the infection (e.g. increase in S. aureus or E. coli) Dougal, K. et al. Identification of a Core Bacterial Community within the Large Intestine of the Horse. PLoS One 8, 1–12 (2013) Costa, M. C. & Weese, J. S. Understanding the Intestinal Microbiome in Health and Disease. Vet. Clin. North Am. - Equine Pract. 34, 25–38 (2018) Rodriguez, C. et al. Faecal microbiota characterisation of horses using 16 rDNA barcoded pyrosequencing, and carriage rate of clostridium difficile at hospital admission. BMC Microbiol. 15, (2015) One Health Initiative. (2011). Available at: http://www.onehealthinitiative.com/about.php%0A. Wick R.R., Schultz M.B., Zobel J. & Holt K.E. (2015). Bandage: interactive visualisation of de novogenome assemblies. Bioinformatics, 31(20), 3350-3352. Zankari E, Hasman H, Cosentino S, Vestergaard M, Rasmussen S, Lund O, Aarestrup FM, Larsen MV. Identification of acquired antimicrobial resistance genes. J Antimicrob Chemother. 2012 Jul 10. PMID: 22782487 doi: 10.1093/jac/dks261 eferences Jia et al. 2017. CARD 2017: expansion and model-centric curation of the Comprehensive Antibiotic Resistance Database. Nucleic Acids Research, 45, D566-573 R Isgren, C. M., Salem, S. E., Archer, D. C., Worsman, F. C. F. & Townsend, N. B. Risk factors for surgical site infection following laparotomy: Effect of season and perioperative variables and reporting of bacterial isolates in 287 horses. Equine Vet. J. 49, 39–44 (2017)..
Load more

Recommended publications
  • Battistuzzi2009chap07.Pdf
    Eubacteria Fabia U. Battistuzzia,b,* and S. Blair Hedgesa shown increasing support for lower-level phylogenetic Department of Biology, 208 Mueller Laboratory, The Pennsylvania clusters (e.g., classes and below), they have also shown the State University, University Park, PA 16802-5301, USA; bCurrent susceptibility of eubacterial phylogeny to biases such as address: Center for Evolutionary Functional Genomics, The Biodesign horizontal gene transfer (HGT) (20, 21). Institute, Arizona State University, Tempe, AZ 85287-5301, USA In recent years, three major approaches have been used *To whom correspondence should be addressed (Fabia.Battistuzzi@ asu.edu) for studying prokaryote phylogeny with data from com- plete genomes: (i) combining gene sequences in a single analysis of multiple genes (e.g., 7, 9, 10), (ii) combining Abstract trees from individual gene analyses into a single “super- tree” (e.g., 22, 23), and (iii) using the presence or absence The ~9400 recognized species of prokaryotes in the of genes (“gene content”) as the raw data to investigate Superkingdom Eubacteria are placed in 25 phyla. Their relationships (e.g., 17, 18). While the results of these dif- relationships have been diffi cult to establish, although ferent approaches have not agreed on many details of some major groups are emerging from genome analyses. relationships, there have been some points of agreement, A molecular timetree, estimated here, indicates that most such as support for the monophyly of all major classes (85%) of the phyla and classes arose in the Archean Eon and some phyla (e.g., Proteobacteria and Firmicutes). (4000−2500 million years ago, Ma) whereas most (95%) of 7 ese A ndings, although criticized by some (e.g., 24, 25), the families arose in the Proterozoic Eon (2500−542 Ma).
    [Show full text]
  • Fatty Acid Diets: Regulation of Gut Microbiota Composition and Obesity and Its Related Metabolic Dysbiosis
    International Journal of Molecular Sciences Review Fatty Acid Diets: Regulation of Gut Microbiota Composition and Obesity and Its Related Metabolic Dysbiosis David Johane Machate 1, Priscila Silva Figueiredo 2 , Gabriela Marcelino 2 , Rita de Cássia Avellaneda Guimarães 2,*, Priscila Aiko Hiane 2 , Danielle Bogo 2, Verônica Assalin Zorgetto Pinheiro 2, Lincoln Carlos Silva de Oliveira 3 and Arnildo Pott 1 1 Graduate Program in Biotechnology and Biodiversity in the Central-West Region of Brazil, Federal University of Mato Grosso do Sul, Campo Grande 79079-900, Brazil; [email protected] (D.J.M.); [email protected] (A.P.) 2 Graduate Program in Health and Development in the Central-West Region of Brazil, Federal University of Mato Grosso do Sul, Campo Grande 79079-900, Brazil; pri.fi[email protected] (P.S.F.); [email protected] (G.M.); [email protected] (P.A.H.); [email protected] (D.B.); [email protected] (V.A.Z.P.) 3 Chemistry Institute, Federal University of Mato Grosso do Sul, Campo Grande 79079-900, Brazil; [email protected] * Correspondence: [email protected]; Tel.: +55-67-3345-7416 Received: 9 March 2020; Accepted: 27 March 2020; Published: 8 June 2020 Abstract: Long-term high-fat dietary intake plays a crucial role in the composition of gut microbiota in animal models and human subjects, which affect directly short-chain fatty acid (SCFA) production and host health. This review aims to highlight the interplay of fatty acid (FA) intake and gut microbiota composition and its interaction with hosts in health promotion and obesity prevention and its related metabolic dysbiosis.
    [Show full text]
  • World Journal of Clinical Cases
    World Journal of W J C C Clinical Cases Submit a Manuscript: http://www.f6publishing.com World J Clin Cases 2018 April 16; 6(4): 54-63 DOI: 10.12998/wjcc.v6.i4.54 ISSN 2307-8960 (online) ORIGINAL ARTICLE Observational Study Correlations between microbial communities in stool and clinical indicators in patients with metabolic syndrome Lang Lin, Zai-Bo Wen, Dong-Jiao Lin, Jiang-Ting Dong, Jie Jin, Fei Meng Lang Lin, Zai-Bo Wen, Dong-Jiao Lin, Jiang-Ting Dong, the use is non-commercial. See: http://creativecommons.org/ Department of Gastroenterology, Cangnan People’s Hospital, licenses/by-nc/4.0/ Cangnan 325800, Zhejiang Province, China Manuscript source: Unsolicited manuscript Jie Jin, Fei Meng, Department of Research Service, Zhiyuan Medical Inspection Institute CO., LTD, Hangzhou 310030, Correspondence to: Lang Lin, MSc, Chief Doctor, Department Zhejiang Province, China of Gastroenterology, Cangnan People’s Hospital, Lingxi Town, Yucang Road No.195, Cangnan 325800, Zhejiang Province, ORCID number: Lang Lin (0000-0001-5879-7487); Zai-Bo Wen China. [email protected] (0000-0003-1290-0404); Dong-Jiao Lin (0000-0001-5186-8182); Jiang- Telephone: +86-577-64767351 Ting Dong (0000-0002-6433-0143); Jie Jin (0000-0003-1481-5107); Fei Fax: +86-577-64767351 Meng (0000-0003-1233-4270). Received: January 2, 2018 Author contributions: Lin L formulated the problem; Wen ZB, Peer-review started: January 2, 2018 Lin DJ and Dong JT collected samples; Meng F performed 16S First decision: January 18, 2018 rDNA sequencing; Jin J analyzed the data; Lin L and Jin J wrote Revised: February 2, 2018 the paper.
    [Show full text]
  • Table S4. Phylogenetic Distribution of Bacterial and Archaea Genomes in Groups A, B, C, D, and X
    Table S4. Phylogenetic distribution of bacterial and archaea genomes in groups A, B, C, D, and X. Group A a: Total number of genomes in the taxon b: Number of group A genomes in the taxon c: Percentage of group A genomes in the taxon a b c cellular organisms 5007 2974 59.4 |__ Bacteria 4769 2935 61.5 | |__ Proteobacteria 1854 1570 84.7 | | |__ Gammaproteobacteria 711 631 88.7 | | | |__ Enterobacterales 112 97 86.6 | | | | |__ Enterobacteriaceae 41 32 78.0 | | | | | |__ unclassified Enterobacteriaceae 13 7 53.8 | | | | |__ Erwiniaceae 30 28 93.3 | | | | | |__ Erwinia 10 10 100.0 | | | | | |__ Buchnera 8 8 100.0 | | | | | | |__ Buchnera aphidicola 8 8 100.0 | | | | | |__ Pantoea 8 8 100.0 | | | | |__ Yersiniaceae 14 14 100.0 | | | | | |__ Serratia 8 8 100.0 | | | | |__ Morganellaceae 13 10 76.9 | | | | |__ Pectobacteriaceae 8 8 100.0 | | | |__ Alteromonadales 94 94 100.0 | | | | |__ Alteromonadaceae 34 34 100.0 | | | | | |__ Marinobacter 12 12 100.0 | | | | |__ Shewanellaceae 17 17 100.0 | | | | | |__ Shewanella 17 17 100.0 | | | | |__ Pseudoalteromonadaceae 16 16 100.0 | | | | | |__ Pseudoalteromonas 15 15 100.0 | | | | |__ Idiomarinaceae 9 9 100.0 | | | | | |__ Idiomarina 9 9 100.0 | | | | |__ Colwelliaceae 6 6 100.0 | | | |__ Pseudomonadales 81 81 100.0 | | | | |__ Moraxellaceae 41 41 100.0 | | | | | |__ Acinetobacter 25 25 100.0 | | | | | |__ Psychrobacter 8 8 100.0 | | | | | |__ Moraxella 6 6 100.0 | | | | |__ Pseudomonadaceae 40 40 100.0 | | | | | |__ Pseudomonas 38 38 100.0 | | | |__ Oceanospirillales 73 72 98.6 | | | | |__ Oceanospirillaceae
    [Show full text]
  • A Genomic Timescale of Prokaryote Evolution: Insights Into the Origin of Methanogenesis, Phototrophy, and the Colonization of Land
    BMC Evolutionary Biology BioMed Central Research article Open Access A genomic timescale of prokaryote evolution: insights into the origin of methanogenesis, phototrophy, and the colonization of land Fabia U Battistuzzi1, Andreia Feijao2 and S Blair Hedges*1 Address: 1NASA Astrobiology Institute and Department of Biology, 208 Mueller Laboratory, The Pennsylvania State University, University Park, PA 16802, USA and 2European Molecular Biology Laboratory, Meyerhofstrasse 1, 69117 Heidelberg, Germany Email: Fabia U Battistuzzi - [email protected]; Andreia Feijao - [email protected]; S Blair Hedges* - [email protected] * Corresponding author Published: 09 November 2004 Received: 07 August 2004 Accepted: 09 November 2004 BMC Evolutionary Biology 2004, 4:44 doi:10.1186/1471-2148-4-44 This article is available from: http://www.biomedcentral.com/1471-2148/4/44 © 2004 Battistuzzi et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract Background: The timescale of prokaryote evolution has been difficult to reconstruct because of a limited fossil record and complexities associated with molecular clocks and deep divergences. However, the relatively large number of genome sequences currently available has provided a better opportunity to control for potential biases such as horizontal gene transfer and rate differences among lineages. We assembled a data set of sequences from 32 proteins (~7600 amino acids) common to 72 species and estimated phylogenetic relationships and divergence times with a local clock method. Results: Our phylogenetic results support most of the currently recognized higher-level groupings of prokaryotes.
    [Show full text]
  • Genes Preferring Non-AUG Start Codons in Bacteria Abstract
    Genes Preferring Non-AUG Start Codons in Bacteria 1,2 2,3,4 Anne Gvozdjak ​ and Manoj P. Samanta ​ ​ 1. Bellevue High School, 10416 SE Wolverine Way, Bellevue, WA 98004 2. Coding for Life Science, Redmond, WA 98053 3. Systemix Institute, Redmond, WA 98053 4. [email protected] Abstract Here we investigate translational regulation in bacteria by analyzing the distribution of start codons in fully assembled genomes. We report 36 genes (infC, rpoC, rnpA, etc.) showing a preference for non-AUG start codons in evolutionarily diverse phyla (“non-AUG genes”). Most of the non-AUG genes are functionally associated with translation, transcription or replication. In E. ​ coli, the percentage of essential genes among these 36 is significantly higher than among all ​ genes. Furthermore, the functional distribution of these genes suggests that non-AUG start codons may be used to reduce gene expression during starvation conditions, possibly through translational autoregulation or IF3-mediated regulation. Introduction Understanding the regulation of gene expression at the transcriptional and translational levels is a key step in deciphering the information contained by genomes. Whereas inexpensive, high-throughput technologies (ChIP-seq, RNAseq) are helping in extensive experimental investigation of transcriptional regulation [1,2], the measurement of translational regulation using ​ ​ high-throughput mass spectroscopy [3] is less widely adopted. Bioinformatics provides another ​ avenue to leverage the rapidly falling cost of DNA sequencing to explore translational regulation. Prior to 2000, computational researchers identified a number of relevant patterns through the comparative analysis of gene sequences [4,5] . With the availability of ~250,000 ​ prokaryotic genomes, thousands of eukaryotic genomes, and additional metagenomic sequences, those patterns can now be studied at a significantly larger scale, and new patterns may also be identified.
    [Show full text]
  • Prokaryotic Gene Start Prediction: Algorithms for Genomes and Metagenomes
    PROKARYOTIC GENE START PREDICTION: ALGORITHMS FOR GENOMES AND METAGENOMES A Dissertation Presented to The Academic Faculty By Karl Gemayel In Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the School of Computational Science and Engineering Georgia Institute of Technology December 2020 © Karl Gemayel 2020 PROKARYOTIC GENE START PREDICTION: ALGORITHMS FOR GENOMES AND METAGENOMES Thesis committee: Dr. Mark Borodovsky Dr. Polo Chau School of Computational Science and En- School of Computational Science and En- gineering and Department of Biomedical gineering Engineering Georgia Institute of Technology Georgia Institute of Technology Dr. Umit¨ C¸atalyurek¨ Dr. King Jordan School of Computational Science and En- School of Biological Sciences gineering Georgia Institute of Technology Georgia Institute of Technology Dr. Pen Qui Department of Biomedical Engineering Georgia Institute of Technology Date approved: October 31, 2020 Wooster: “There are moments, Jeeves, when one asks oneself, ‘Do trousers matter?’” Jeeves: “The mood will pass, sir.” P.G. Wodehouse, The Code Of The Woosters To Mom and Dad, all my ancestors, and the first self-replicating molecule. Without you, this work would literally not have been possible. ACKNOWLEDGMENTS I am bound to forget someone or something and so, in fairness to all, I will forget most things and keep this vague and terse, though not necessarily short. I was very much at the right place at the right time to do this work, a time where these problems had not yet been solved. To the driven students who graduated early enough before such ideas came to them, thank you for being considerate. To my advisor Mark Borodovsky, who insisted that a lack of community funding for prokaryotic gene finding does not mean that the problem has actually been solved, thank you for continuously pushing for rigorous science that questions accepted beliefs.
    [Show full text]
  • 1 Supplementary Material a Major Clade of Prokaryotes with Ancient
    Supplementary Material A major clade of prokaryotes with ancient adaptations to life on land Fabia U. Battistuzzi and S. Blair Hedges Data assembly and phylogenetic analyses Protein data set: Amino acid sequences of 25 protein-coding genes (“proteins”) were concatenated in an alignment of 18,586 amino acid sites and 283 species. These proteins included: 15 ribosomal proteins (RPL1, 2, 3, 5, 6, 11, 13, 16; RPS2, 3, 4, 5, 7, 9, 11), four genes (RNA polymerase alpha, beta, and gamma subunits, Transcription antitermination factor NusG) from the functional category of Transcription, three proteins (Elongation factor G, Elongation factor Tu, Translation initiation factor IF2) of the Translation, Ribosomal Structure and Biogenesis functional category, one protein (DNA polymerase III, beta subunit) of the DNA Replication, Recombination and repair category, one protein (Preprotein translocase SecY) of the Cell Motility and Secretion category, and one protein (O-sialoglycoprotein endopeptidase) of the Posttranslational Modification, Protein Turnover, Chaperones category, as annotated in the Cluster of Orthologous Groups (COG) (Tatusov et al. 2001). After removal of multiple strains of the same species, GBlocks 0.91b (Castresana 2000) was applied to each protein in the concatenation to delete poorly aligned sites (i.e., sites with gaps in more than 50% of the species and conserved in less than 50% of the species) with the following parameters: minimum number of sequences for a conserved position: 110, minimum number of sequences for a flank position: 110, maximum number of contiguous non-conserved positions: 32000, allowed gap positions: with half. The signal-to-noise ratio was determined by altering the “minimum length of a block” parameter.
    [Show full text]
  • Whole-Proteome Tree of Life Suggests a Deep Burst of Organism Diversity
    Whole-proteome tree of life suggests a deep burst of organism diversity JaeJin Choia,b,c and Sung-Hou Kima,b,c,1 aDepartment of Chemistry, University of California, Berkeley, CA 94720; bCenter for Computational Biology, University of California, Berkeley, CA 94720; and cMolecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720 Contributed by Sung-Hou Kim, December 11, 2019 (sent for review September 12, 2019; reviewed by Se-Ran Jun and Charles G. Kurland) An organism tree of life (organism ToL) is a conceptual and addition, an important issue of rooting gene ToLs has not been well metaphorical tree to capture a simplified narrative of the evolution- resolved and still is being debated (ref. 13 and references within). ary course and kinship among the extant organisms. Such a tree These and other issues of gene ToLs highlight the need for cannot be experimentally validated but may be reconstructed based alternative surrogates for the organism ToL built based on as on characteristics associated with the organisms. Since the whole- completely different assumptions as possible from those of gene genome sequence of an organism is, at present, the most compre- ToLs. A “genome ToL” (see below) constructed based on in- hensive descriptor of the organism, a whole-genome sequence-based formation theory (14) may provide an independent and alter- ToL can be an empirically derivable surrogate for the organism ToL. native view of the organism ToL. However, experimentally determining the whole-genome sequences of many diverse organisms was practically impossible until recently. Genome ToL We have constructed three types of ToLs for diversely sampled Following the commonly used definition of gene ToL (see organisms using the sequences of whole genome, of whole tran- above), the term genome ToL is used in this study for the ToLs scriptome, and of whole proteome.
    [Show full text]
  • Diderm Firmicutes Challenge the Gram-Positive/Gram-Negative Divide Daniela Megrian, Najwa Taib, Jerzy Witwinowski, Christophe Beloin, Simonetta Gribaldo
    One or two membranes? Diderm Firmicutes challenge the Gram-positive/Gram-negative divide Daniela Megrian, Najwa Taib, Jerzy Witwinowski, Christophe Beloin, Simonetta Gribaldo To cite this version: Daniela Megrian, Najwa Taib, Jerzy Witwinowski, Christophe Beloin, Simonetta Gribaldo. One or two membranes? Diderm Firmicutes challenge the Gram-positive/Gram-negative divide. Molecular Microbiology, Wiley, 2020, 10.1111/MMI.14469. pasteur-02505848 HAL Id: pasteur-02505848 https://hal-pasteur.archives-ouvertes.fr/pasteur-02505848 Submitted on 11 Mar 2020 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Distributed under a Creative Commons Attribution - NonCommercial| 4.0 International License DR. SIMONETTA GRIBALDO (Orcid ID : 0000-0002-7662-021X) Article type : MicroReview One or two membranes? Diderm Firmicutes challenge the Gram-positive/Gram-negative divide Daniela Megrian1,2, Najwa Taib1,3, Jerzy Witwinowski1, Christophe Beloin4, and Simonetta Gribaldo1* 1 Institut Pasteur, Department of Microbiology, Unit Evolutionary Biology of the Microbial Cell,
    [Show full text]
  • A Metagenomics Roadmap to the Uncultured Genome Diversity in Hypersaline Soda Lake Sediments Charlotte D
    Vavourakis et al. Microbiome (2018) 6:168 https://doi.org/10.1186/s40168-018-0548-7 RESEARCH Open Access A metagenomics roadmap to the uncultured genome diversity in hypersaline soda lake sediments Charlotte D. Vavourakis1 , Adrian-Stefan Andrei2†, Maliheh Mehrshad2†, Rohit Ghai2, Dimitry Y. Sorokin3,4 and Gerard Muyzer1* Abstract Background: Hypersaline soda lakes are characterized by extreme high soluble carbonate alkalinity. Despite the high pH and salt content, highly diverse microbial communities are known to be present in soda lake brines but the microbiome of soda lake sediments received much less attention of microbiologists. Here, we performed metagenomic sequencing on soda lake sediments to give the first extensive overview of the taxonomic diversity found in these complex, extreme environments and to gain novel physiological insights into the most abundant, uncultured prokaryote lineages. Results: We sequenced five metagenomes obtained from four surface sediments of Siberian soda lakes with a pH 10 and a salt content between 70 and 400 g L−1. The recovered 16S rRNA gene sequences were mostly from Bacteria,evenin the salt-saturated lakes. Most OTUs were assigned to uncultured families. We reconstructed 871 metagenome-assembled genomes (MAGs) spanning more than 45 phyla and discovered the first extremophilic members of the Candidate Phyla Radiation (CPR). Five new species of CPR were among the most dominant community members. Novel dominant lineages were found within previously well-characterized functional groups involved in carbon, sulfur, and nitrogen cycling. Moreover, key enzymes of the Wood-Ljungdahl pathway were encoded within at least four bacterial phyla never previously associated with this ancient anaerobic pathway for carbon fixation and dissimilation, including the Actinobacteria.
    [Show full text]
  • Characterising Antibiotic Susceptibility and Resistance in Human Commensal Gut Bacteria
    Characterising antibiotic susceptibility and resistance in human commensal gut bacteria Lindsay Jacqueline Pike Gonville and Caius College, University of Cambridge Wellcome Sanger Institute August 2019 This dissertation is submitted for the degree of Doctor of Philosophy Supervised by Dr Trevor Lawley, Host-Microbiota Interactions Laboratory Funded by the Medical Research Council and the Wellcome Sanger Institute i ii Declaration This dissertation is the result of my own work and includes nothing that is the outcome of work done in collaboration except as declared in the Preface and specified in the text. It is not substantially the same as any that I have submitted, or, is being concurrently submitted for a degree or diploma or other qualification at the University of Cambridge or any other University or similar institution except as declared in the Preface and specified in the text. I further state that no substantial part of my dissertation has already been submitted, or, is being concurrently submitted for any such degree, diploma or other qualification at the University of Cambridge or any other University or similar institution except as declared in the Preface and specified in the text. It does not exceed the word limit of 60,000 words (excluding bibliography, figures, and appendixes) as prescribed by the Degree Committee for the Faculty of Biology at the University of Cambridge. Mr Mark Stares assisted with phenotyping gut bacteria against antibiotics and extracting DNA for whole genome sequencing. Mr Matthew Dorman assisted with cloning of candidate novel antibiotic resistance genes. Dr Simon Clare and members of his team looked after the mice used in this study and collected mouse faecal pellets.
    [Show full text]