Metabolic Flexibility of Enigmatic SAR324 Revealed Through
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Some English Terms Used in Microbiology 1
Some English terms used in Microbiology 1 Shapes & Structures General terms Antibiotics and related Bacillus (pl. bacilli) Acid fast (acid fastness) ácido-alcohol resistente Acetylases Capsule Bacterial (adj.) Ampicillin Cell wall pared celular Bacterium (pl., bacteria): Beta-lactamase Coccus (cocci; and hence Staphylococcus, Bench: poyata Beta-lactamic Staphylococci) Biofilm Cephalosporin Core oligosaccharide núcleo oligosacarídico Burner (Bunsen Burner): mechero (Bunsen) Chloramphenicol Cortex Colony: colonia Colistin Fimbria (pl. fimbriae) Coverslip: (vidrio) cubreobjetos D-Cycloserine Flagellum (pl. flagella) Dye colorante DNA-gyrase Glycocalix Eukaryote or eucaryote Erythromycin Lipid A Incubator: estufa de incubación Ethambuthol Lipopolysaccharide Inoculating loop asa de siembra Fluoroquinolone Murein mureína Inoculum (inocula): Gentamicin (formerly gentamycin) Omp: outer membrane major protein proteína To flame: flamear Isoniazide de membrane externa Flask (Erlenmeyer flask): matraz Methicillin Outer membrane membrana externa Volumetric flask: matraz aforado Methylases PAMP (pathogen associated molecular pattern): Microorganism Nalidixic acid patrón molecular asociado a patógeno Motility movilidad Penicillin Peptidoglycan peptidoglucano Mycoplasm Penicillin binding protein (PBP) Periplasm periplasma Negative staining tinción negativa Phosphonomycin Periplasmic space espacio periplásmico Petri dish: placa de Petri Phosphorylases Permeability barrier barrera de permeabilidad Prokaryote or procaryote Polymyxin Pilus (pl. pili) Rack: -
The Regulation of Arsenic Metabolism in Rhizobium Sp. NT-26
The regulation of arsenic metabolism in Rhizobium sp. NT-26 Paula Corsini Madeira University College London Thesis submitted for the degree of Doctor of Philosophy 2016 I, Paula Corsini Madeira, confirm that the work presented in this thesis is my own. Where information has been derived from other sources, I confirm that this has been indicated in the thesis. Date: Signed: II Abstract Arsenic (As) is a toxic metalloid and a major contaminant in terrestrial and aquatic environments. The two soluble forms, arsenite (AsIII) and arsenate (AsV) are toxic to most organisms. A range of phylogenetically distant bacteria are able to oxidize AsIII to the less toxic form, arsenate AsV using the periplasmic arsenite oxidase (AioBA). The two-component signal transduction system AioS/AioR and the AsIII-binding periplasmic protein AioX are required for AsIII oxidation and are involved in the transcriptional regulation of the aioBA operon. Most AsIII oxidisers can also reduce AsV to AsIII via the As (Ars) resistance system. The focus of this work was to understand the regulation of genes involved in AsIII oxidation and As resistance together with those involved in phosphate metabolism in the facultative chemolithoautotrophic AsIII oxidiser NT-26 grown under different conditions. Gene expression was studied by quantitative PCR in cells grown heterotrophically with and without AsIII or AsV in late-log and stationary phases. qPCR was optimised and suitable reference genes were chosen. The expression of genes involved in phosphate transport, sensing As and the genes aioX, aioS, aioR (AsIII-sensing and regulation) and aioB, aioA (AsIII oxidation) and cytC (cytochrome c) were also analysed in NT-26 grown heterotrophically in the presence or absence of AsIII or AsV at different growth stages (i.e., late-log and stationary phases). -
Evolution of Bacterial Communities in the Wheat Crop Rhizosphere
bs_bs_banner Environmental Microbiology (2014) doi:10.1111/1462-2920.12452 Evolution of bacterial communities in the wheat crop rhizosphere Suzanne Donn, John A. Kirkegaard, Geetha Perera, per year, yet need to lift to at least 1.5% per year by 2050 Alan E. Richardson and Michelle Watt* to avoid food price increases (Fischer and Edmeades, CSIRO Plant Industry, GPO Box 1600, Canberra, ACT 2010). Intensive cereal systems, including wheat following 2601, Australia. wheat rotations, dominate many global food production systems and yields have been declining in these systems, often due to undiagnosed soil biological causes (Lobell Summary et al., 2009). Developing a predictive understanding of the The gap between current average global wheat yields links between soil biology, agronomy and crop perfor- and that achievable through best agronomic manage- mance would be a first step towards improving productivity ment and crop genetics is large. This is notable in of intensive cereals, and would have a major impact on intensive wheat rotations which are widely used. global food production (Cassman, 1999). Expectations are that this gap can be reduced by Globally, expectations are high that soil biology can be manipulating soil processes, especially those that manipulated through crop management and genetics involve microbial ecology. Cross-year analysis of the to increase yields in agricultural production systems soil microbiome in an intensive wheat cropping (Morrissey et al., 2004; Welbaum et al., 2004). Rhizobial system revealed that rhizosphere bacteria changed associations with legumes for N2-fixation, crop species much more than the bulk soil community. Dominant rotation for pathogen control and mycorrhizal associations factors influencing populations included binding to are obvious examples that have had clear productivity roots, plant age, site and planting sequence. -
Diversity of Biodeteriorative Bacterial and Fungal Consortia in Winter and Summer on Historical Sandstone of the Northern Pergol
applied sciences Article Diversity of Biodeteriorative Bacterial and Fungal Consortia in Winter and Summer on Historical Sandstone of the Northern Pergola, Museum of King John III’s Palace at Wilanow, Poland Magdalena Dyda 1,2,* , Agnieszka Laudy 3, Przemyslaw Decewicz 4 , Krzysztof Romaniuk 4, Martyna Ciezkowska 4, Anna Szajewska 5 , Danuta Solecka 6, Lukasz Dziewit 4 , Lukasz Drewniak 4 and Aleksandra Skłodowska 1 1 Department of Geomicrobiology, Institute of Microbiology, Faculty of Biology, University of Warsaw, Miecznikowa 1, 02-096 Warsaw, Poland; [email protected] 2 Research and Development for Life Sciences Ltd. (RDLS Ltd.), Miecznikowa 1/5a, 02-096 Warsaw, Poland 3 Laboratory of Environmental Analysis, Museum of King John III’s Palace at Wilanow, Stanislawa Kostki Potockiego 10/16, 02-958 Warsaw, Poland; [email protected] 4 Department of Environmental Microbiology and Biotechnology, Institute of Microbiology, Faculty of Biology, University of Warsaw, Miecznikowa 1, 02-096 Warsaw, Poland; [email protected] (P.D.); [email protected] (K.R.); [email protected] (M.C.); [email protected] (L.D.); [email protected] (L.D.) 5 The Main School of Fire Service, Slowackiego 52/54, 01-629 Warsaw, Poland; [email protected] 6 Department of Plant Molecular Ecophysiology, Institute of Experimental Plant Biology and Biotechnology, Faculty of Biology, University of Warsaw, Miecznikowa 1, 02-096 Warsaw, Poland; [email protected] * Correspondence: [email protected] or [email protected]; Tel.: +48-786-28-44-96 Citation: Dyda, M.; Laudy, A.; Abstract: The aim of the presented investigation was to describe seasonal changes of microbial com- Decewicz, P.; Romaniuk, K.; munity composition in situ in different biocenoses on historical sandstone of the Northern Pergola in Ciezkowska, M.; Szajewska, A.; the Museum of King John III’s Palace at Wilanow (Poland). -
Microbial Diversity of Drilling Fluids from 3000 M Deep Koyna Pilot
Science Reports Sci. Dril., 27, 1–23, 2020 https://doi.org/10.5194/sd-27-1-2020 © Author(s) 2020. This work is distributed under the Creative Commons Attribution 4.0 License. Microbial diversity of drilling fluids from 3000 m deep Koyna pilot borehole provides insights into the deep biosphere of continental earth crust Himadri Bose1, Avishek Dutta1, Ajoy Roy2, Abhishek Gupta1, Sourav Mukhopadhyay1, Balaram Mohapatra1, Jayeeta Sarkar1, Sukanta Roy3, Sufia K. Kazy2, and Pinaki Sar1 1Environmental Microbiology and Genomics Laboratory, Department of Biotechnology, Indian Institute of Technology Kharagpur, Kharagpur, 721302, West Bengal, India 2Department of Biotechnology, National Institute of Technology Durgapur, Durgapur, 713209, West Bengal, India 3Ministry of Earth Sciences, Borehole Geophysics Research Laboratory, Karad, 415114, Maharashtra, India Correspondence: Pinaki Sar ([email protected], [email protected]) Received: 24 June 2019 – Revised: 25 September 2019 – Accepted: 16 December 2019 – Published: 27 May 2020 Abstract. Scientific deep drilling of the Koyna pilot borehole into the continental crust up to a depth of 3000 m below the surface at the Deccan Traps, India, provided a unique opportunity to explore microbial life within the deep granitic bedrock of the Archaean Eon. Microbial communities of the returned drilling fluid (fluid re- turned to the mud tank from the underground during the drilling operation; designated here as DF) sampled during the drilling operation of the Koyna pilot borehole at a depth range of 1681–2908 metres below the surface (m b.s.) were explored to gain a glimpse of the deep biosphere underneath the continental crust. Change of pH 2− to alkalinity, reduced abundance of Si and Al, but enrichment of Fe, Ca and SO4 in the samples from deeper horizons suggested a gradual infusion of elements or ions from the crystalline bedrock, leading to an observed geochemical shift in the DF. -
Correlation of the Lung Microbiota with Metabolic Profiles in Bronchoalveolar Lavage Fluid in HIV Infection Sushma K
Cribbs et al. Microbiome (2016) 4:3 DOI 10.1186/s40168-016-0147-4 RESEARCH Open Access Correlation of the lung microbiota with metabolic profiles in bronchoalveolar lavage fluid in HIV infection Sushma K. Cribbs1,2*, Karan Uppal2, Shuzhao Li2, Dean P. Jones2, Laurence Huang3, Laura Tipton4,5, Adam Fitch6, Ruth M. Greenblatt7, Lawrence Kingsley8, David M. Guidot1,2, Elodie Ghedin5 and Alison Morris6 Abstract Background: While 16S ribosomal RNA (rRNA) sequencing has been used to characterize the lung’s bacterial microbiota in human immunodeficiency virus (HIV)-infected individuals, taxonomic studies provide limited information on bacterial function and impact on the host. Metabolic profiles can provide functional information on host-microbe interactions in the lungs. We investigated the relationship between the respiratory microbiota and metabolic profiles in the bronchoalveolar lavage fluid of HIV-infected and HIV-uninfected outpatients. Results: Targeted sequencing of the 16S rRNA gene was used to analyze the bacterial community structure and liquid chromatography-high-resolution mass spectrometry was used to detect features in bronchoalveolar lavage fluid. Global integration of all metabolic features with microbial species was done using sparse partial least squares regression. Thirty-nine HIV-infected subjects and 20 HIV-uninfected controls without acute respiratory symptoms were enrolled. Twelve mass-to-charge ratio (m/z) features from C18 analysis were significantly different between HIV-infected individuals and controls (false discovery rate (FDR) = 0.2); another 79 features were identified by network analysis. Further metabolite analysis demonstrated that four features were significantly overrepresented in the bronchoalveolar lavage (BAL) fluid of HIV-infected individuals compared to HIV-uninfected, including cystine, two complex carbohydrates, and 3,5-dibromo-L-tyrosine. -
Phylogenetic Conservation of Bacterial Responses to Soil Nitrogen Addition Across Continents
ARTICLE https://doi.org/10.1038/s41467-019-10390-y OPEN Phylogenetic conservation of bacterial responses to soil nitrogen addition across continents Kazuo Isobe1,2, Steven D. Allison 1,3, Banafshe Khalili1, Adam C. Martiny 1,3 & Jennifer B.H. Martiny1 Soil microbial communities are intricately linked to ecosystem functioning such as nutrient cycling; therefore, a predictive understanding of how these communities respond to envir- onmental changes is of great interest. Here, we test whether phylogenetic information can 1234567890():,; predict the response of bacterial taxa to nitrogen (N) addition. We analyze the composition of soil bacterial communities in 13 field experiments across 5 continents and find that the N response of bacteria is phylogenetically conserved at each location. Remarkably, the phylo- genetic pattern of N responses is similar when merging data across locations. Thus, we can identify bacterial clades – the size of which are highly variable across the bacterial tree – that respond consistently to N addition across locations. Our findings suggest that a phylogenetic approach may be useful in predicting shifts in microbial community composition in the face of other environmental changes. 1 Department of Ecology and Evolutionary Biology, University of California, Irvine, CA 92697, USA. 2 Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo 113-8657, Japan. 3 Department of Earth System Science, University of California, Irvine, CA 92697, USA. Correspondence and requests for materials should be addressed to K.I. (email: [email protected]) or to J.B.H.M. (email: [email protected]) NATURE COMMUNICATIONS | (2019) 10:2499 | https://doi.org/10.1038/s41467-019-10390-y | www.nature.com/naturecommunications 1 ARTICLE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-019-10390-y t is well established that environmental changes such as soil up the signal of vertical inherence and result in a random dis- warming and nutrient addition alter the composition of bac- tribution of response across the phylogeny6. -
Cell Structure and Function in the Bacteria and Archaea
4 Chapter Preview and Key Concepts 4.1 1.1 DiversityThe Beginnings among theof Microbiology Bacteria and Archaea 1.1. •The BacteriaThe are discovery classified of microorganismsinto several Cell Structure wasmajor dependent phyla. on observations made with 2. theThe microscope Archaea are currently classified into two 2. •major phyla.The emergence of experimental 4.2 Cellscience Shapes provided and Arrangements a means to test long held and Function beliefs and resolve controversies 3. Many bacterial cells have a rod, spherical, or 3. MicroInquiryspiral shape and1: Experimentation are organized into and a specific Scientificellular c arrangement. Inquiry in the Bacteria 4.31.2 AnMicroorganisms Overview to Bacterialand Disease and Transmission Archaeal 4.Cell • StructureEarly epidemiology studies suggested how diseases could be spread and 4. Bacterial and archaeal cells are organized at be controlled the cellular and molecular levels. 5. • Resistance to a disease can come and Archaea 4.4 External Cell Structures from exposure to and recovery from a mild 5.form Pili allowof (or cells a very to attach similar) to surfacesdisease or other cells. 1.3 The Classical Golden Age of Microbiology 6. Flagella provide motility. Our planet has always been in the “Age of Bacteria,” ever since the first 6. (1854-1914) 7. A glycocalyx protects against desiccation, fossils—bacteria of course—were entombed in rocks more than 3 billion 7. • The germ theory was based on the attaches cells to surfaces, and helps observations that different microorganisms years ago. On any possible, reasonable criterion, bacteria are—and always pathogens evade the immune system. have been—the dominant forms of life on Earth. -
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. -
Gram Staining Staining & Acid Fast & Acid Fast Staining
GRAM STAINING && AACCIIDD FFAASSTT SSTTAAIINNIINNGG Dr. R. Haritha Lecturer in Biotechnology Visakha Government Degree College for Women Visakhapatnam Staining Stains Principle Types Staining methods Smear Preparation Smear- Distribution of Bacterial cells on a slide Objective-To kill the microorganism & fix bacteria Method- Air Dry, Heat Fixation GRAM STAINING Gram staining is most widely Hans Christian Joachim Gram used differential staining in Microbiology. It classifies bacteria into two major groups: Gram positive Gram negative Appears violet Appears red after Gram’s stain after Gram’s stain REAGENTS 1. CRYSTAL VIOLET Primary stain Violet colored, stains all micro-organism 2. GRAM IODINE Mordant Forms Crystal violet iodine complexes 3. DECOLORIZER Acetone + Methanol Removes Crystal violet iodine complex from thin peptidoglycan layers 4. GRAM SAFRANINE Counter stain Red colored Step 1: Crystal Violet Step 2: Gram’’s Iodine Step 3: Decolorization (Aceton-Alcohol) Step 4: Safranin Red 7 PRINCIPLE : Composition of the cell wall Gram-positive bacteria Cell wall has a thick peptidoglycan layer The Crystal Violet stain gets trapped into this layer and the bacteria turned purple. Retains the color of the primary stain (crystal violet) after decolorization with alcohol. Gram-negative bacteria Cell wall has a thin peptidoglycan layer that does not retain crystal violet stain. Cell wall has a thick lipid layer which dissolves easily upon decoulorization with Aceton-Alcohol. Therefore, cells will be counterstained with safranin and turned red. Gram’s +ve Bacteria Gram’s -ve Bacteria 9 RESULT Bacteria that manage to keep the original purple are called Gram positive. Bacteria that lose the original purple dye and can therefore take up the second red dye are called Gram negative APPLICATIONS 1.Rapid preliminary diagnosis of diseases such as Bacterial meningitis. -
Perchlorate Bioremediation: Controlling Media Loss in Ex-Situ
UNLV Theses, Dissertations, Professional Papers, and Capstones 5-1-2016 Perchlorate Bioremediation: Controlling Media Loss in Ex-Situ Fluidized Bed Reactors and In-Situ Biological Reduction by Slow-Release Electron Donor Sichu Shrestha University of Nevada, Las Vegas, [email protected] Follow this and additional works at: https://digitalscholarship.unlv.edu/thesesdissertations Part of the Civil Engineering Commons, and the Environmental Engineering Commons Repository Citation Shrestha, Sichu, "Perchlorate Bioremediation: Controlling Media Loss in Ex-Situ Fluidized Bed Reactors and In-Situ Biological Reduction by Slow-Release Electron Donor" (2016). UNLV Theses, Dissertations, Professional Papers, and Capstones. 2744. https://digitalscholarship.unlv.edu/thesesdissertations/2744 This Dissertation is brought to you for free and open access by Digital Scholarship@UNLV. It has been accepted for inclusion in UNLV Theses, Dissertations, Professional Papers, and Capstones by an authorized administrator of Digital Scholarship@UNLV. For more information, please contact [email protected]. PERCHLORATE BIOREMEDIATION: CONTROLLING MEDIA LOSS IN EX-SITU FLUIDIZED BED REACTORS AND IN-SITU BIOLOGICAL REDUCTION BY SLOW- RELEASE ELECTRON DONOR By Sichu Shrestha Bachelor of Engineering in Civil Engineering, Institute of Engineering, Tribhuvan University, Nepal 2004 Master of Science in Civil and Environmental Engineering Carnegie Mellon University 2011 A dissertation submitted in partial fulfillment of the requirements for the Doctor of Philosophy- -
Supplementary Information the Biodiversity and Geochemistry Of
Supplementary Information The Biodiversity and Geochemistry of Cryoconite Holes in Queen Maud Land, East Antarctica Figure S1. Principal component analysis of the bacterial OTUs. Samples cluster according to habitats. Figure S2. Principal component analysis of the eukaryotic OTUs. Samples cluster according to habitats. Figure S3. Principal component analysis of selected trace elements that cause the separation (primarily Zr, Ba and Sr). Figure S4. Partial canonical correspondence analysis of the bacterial abundances and all non-collinear environmental variables (i.e., after identification and exclusion of redundant predictor variables) and without spatial effects. Samples from Lake 3 in Utsteinen clustered with higher nitrate concentration and samples from Dubois with a higher TC abundance. Otherwise no clear trends could be observed. Table S1. Number of sequences before and after quality control for bacterial and eukaryotic sequences, respectively. 16S 18S Sample ID Before quality After quality Before quality After quality filtering filtering filtering filtering PES17_36 79285 71418 112519 112201 PES17_38 115832 111434 44238 44166 PES17_39 128336 123761 31865 31789 PES17_40 107580 104609 27128 27074 PES17_42 225182 218495 103515 103323 PES17_43 219156 213095 67378 67199 PES17_47 82531 79949 60130 59998 PES17_48 123666 120275 64459 64306 PES17_49 163446 158674 126366 126115 PES17_50 107304 104667 158362 158063 PES17_51 95033 93296 - - PES17_52 113682 110463 119486 119205 PES17_53 126238 122760 72656 72461 PES17_54 120805 117807 181725 181281 PES17_55 112134 108809 146821 146408 PES17_56 193142 187986 154063 153724 PES17_59 226518 220298 32560 32444 PES17_60 186567 182136 213031 212325 PES17_61 143702 140104 155784 155222 PES17_62 104661 102291 - - PES17_63 114068 111261 101205 100998 PES17_64 101054 98423 70930 70674 PES17_65 117504 113810 192746 192282 Total 3107426 3015821 2236967 2231258 Table S2.