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Spatio-Temporal Study of Microbiology in the Stratified Oxic-Hypoxic-Euxinic, Freshwater- To-Hypersaline Ursu Lake
Spatio-temporal insights into microbiology of the freshwater-to- hypersaline, oxic-hypoxic-euxinic waters of Ursu Lake Baricz, A., Chiriac, C. M., Andrei, A-., Bulzu, P-A., Levei, E. A., Cadar, O., Battes, K. P., Cîmpean, M., enila, M., Cristea, A., Muntean, V., Alexe, M., Coman, C., Szekeres, E. K., Sicora, C. I., Ionescu, A., Blain, D., O’Neill, W. K., Edwards, J., ... Banciu, H. L. (2020). Spatio-temporal insights into microbiology of the freshwater-to- hypersaline, oxic-hypoxic-euxinic waters of Ursu Lake. Environmental Microbiology. https://doi.org/10.1111/1462-2920.14909, https://doi.org/10.1111/1462-2920.14909 Published in: Environmental Microbiology Document Version: Peer reviewed version Queen's University Belfast - Research Portal: Link to publication record in Queen's University Belfast Research Portal Publisher rights Copyright 2019 Wiley. This work is made available online in accordance with the publisher’s policies. Please refer to any applicable terms of use of the publisher. General rights Copyright for the publications made accessible via the Queen's University Belfast Research Portal is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights. Take down policy The Research Portal is Queen's institutional repository that provides access to Queen's research output. Every effort has been made to ensure that content in the Research Portal does not infringe any person's rights, or applicable UK laws. If you discover content in the Research Portal that you believe breaches copyright or violates any law, please contact [email protected]. -
Annual Conference Abstracts
ANNUAL CONFERENCE 14-17 April 2014 Arena and Convention Centre, Liverpool ABSTRACTS SGM ANNUAL CONFERENCE APRIL 2014 ABSTRACTS (LI00Mo1210) – SGM Prize Medal Lecture (LI00Tu1210) – Marjory Stephenson Climate Change, Oceans, and Infectious Disease Prize Lecture Dr. Rita R. Colwell Understanding the basis of antibiotic resistance University of Maryland, College Park, MD, USA as a platform for early drug discovery During the mid-1980s, satellite sensors were developed to monitor Laura JV Piddock land and oceans for purposes of understanding climate, weather, School of Immunity & Infection and Institute of Microbiology and and vegetation distribution and seasonal variations. Subsequently Infection, University of Birmingham, UK inter-relationships of the environment and infectious diseases Antibiotic resistant bacteria are one of the greatest threats to human were investigated, both qualitatively and quantitatively, with health. Resistance can be mediated by numerous mechanisms documentation of the seasonality of diseases, notably malaria including mutations conferring changes to the genes encoding the and cholera by epidemiologists. The new research revealed a very target proteins as well as RND efflux pumps, which confer innate close interaction of the environment and many other infectious multi-drug resistance (MDR) to bacteria. The production of efflux diseases. With satellite sensors, these relationships were pumps can be increased, usually due to mutations in regulatory quantified and comparatively analyzed. More recent studies of genes, and this confers resistance to antibiotics that are often used epidemic diseases have provided models, both retrospective and to treat infections by Gram negative bacteria. RND MDR efflux prospective, for understanding and predicting disease epidemics, systems not only confer antibiotic resistance, but altered expression notably vector borne diseases. -
Corynebacterium Sp.|NML98-0116
1 Limnochorda_pilosa~GCF_001544015.1@NZ_AP014924=Bacteria-Firmicutes-Limnochordia-Limnochordales-Limnochordaceae-Limnochorda-Limnochorda_pilosa 0,9635 Ammonifex_degensii|KC4~GCF_000024605.1@NC_013385=Bacteria-Firmicutes-Clostridia-Thermoanaerobacterales-Thermoanaerobacteraceae-Ammonifex-Ammonifex_degensii 0,985 Symbiobacterium_thermophilum|IAM14863~GCF_000009905.1@NC_006177=Bacteria-Firmicutes-Clostridia-Clostridiales-Symbiobacteriaceae-Symbiobacterium-Symbiobacterium_thermophilum Varibaculum_timonense~GCF_900169515.1@NZ_LT827020=Bacteria-Actinobacteria-Actinobacteria-Actinomycetales-Actinomycetaceae-Varibaculum-Varibaculum_timonense 1 Rubrobacter_aplysinae~GCF_001029505.1@NZ_LEKH01000003=Bacteria-Actinobacteria-Rubrobacteria-Rubrobacterales-Rubrobacteraceae-Rubrobacter-Rubrobacter_aplysinae 0,975 Rubrobacter_xylanophilus|DSM9941~GCF_000014185.1@NC_008148=Bacteria-Actinobacteria-Rubrobacteria-Rubrobacterales-Rubrobacteraceae-Rubrobacter-Rubrobacter_xylanophilus 1 Rubrobacter_radiotolerans~GCF_000661895.1@NZ_CP007514=Bacteria-Actinobacteria-Rubrobacteria-Rubrobacterales-Rubrobacteraceae-Rubrobacter-Rubrobacter_radiotolerans Actinobacteria_bacterium_rbg_16_64_13~GCA_001768675.1@MELN01000053=Bacteria-Actinobacteria-unknown_class-unknown_order-unknown_family-unknown_genus-Actinobacteria_bacterium_rbg_16_64_13 1 Actinobacteria_bacterium_13_2_20cm_68_14~GCA_001914705.1@MNDB01000040=Bacteria-Actinobacteria-unknown_class-unknown_order-unknown_family-unknown_genus-Actinobacteria_bacterium_13_2_20cm_68_14 1 0,9803 Thermoleophilum_album~GCF_900108055.1@NZ_FNWJ01000001=Bacteria-Actinobacteria-Thermoleophilia-Thermoleophilales-Thermoleophilaceae-Thermoleophilum-Thermoleophilum_album -
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 -
Drylands Soil Bacterial Community Is Affected by Land Use Change and Different Irrigation Practices in the Mezquital Valley
www.nature.com/scientificreports OPEN Drylands soil bacterial community is afected by land use change and diferent irrigation practices in the Received: 23 June 2017 Accepted: 3 January 2018 Mezquital Valley, Mexico Published: xx xx xxxx Kathia Lüneberg1, Dominik Schneider2, Christina Siebe1 & Rolf Daniel 2 Dryland agriculture nourishes one third of global population, although crop irrigation is often mandatory. As freshwater sources are scarce, treated and untreated wastewater is increasingly used for irrigation. Here, we investigated how the transformation of semiarid shrubland into rainfed farming or irrigated agriculture with freshwater, dam-stored or untreated wastewater afects the total (DNA-based) and active (RNA-based) soil bacterial community composition, diversity, and functionality. To do this we collected soil samples during the dry and rainy seasons and isolated DNA and RNA. Soil moisture, sodium content and pH were the strongest drivers of the bacterial community composition. We found lineage-specifc adaptations to drought and sodium content in specifc land use systems. Predicted functionality profles revealed gene abundances involved in nitrogen, carbon and phosphorous cycles difered among land use systems and season. Freshwater irrigated bacterial community is taxonomically and functionally susceptible to seasonal environmental changes, while wastewater irrigated ones are taxonomically susceptible but functionally resistant to them. Additionally, we identifed potentially harmful human and phytopathogens. The analyses of 16 S rRNA genes, its transcripts and deduced functional profles provided extensive understanding of the short- term and long-term responses of bacterial communities associated to land use, seasonality, and water quality used for irrigation in drylands. Drylands are defned as regions with arid, semi-arid, and dry sub humid climate with an annual precipitation/ evapotranspiration potential ratio (P/PET)1 ranging from 0.05 to 0.652. -
Table S5. the Information of the Bacteria Annotated in the Soil Community at Species Level
Table S5. The information of the bacteria annotated in the soil community at species level No. Phylum Class Order Family Genus Species The number of contigs Abundance(%) 1 Firmicutes Bacilli Bacillales Bacillaceae Bacillus Bacillus cereus 1749 5.145782459 2 Bacteroidetes Cytophagia Cytophagales Hymenobacteraceae Hymenobacter Hymenobacter sedentarius 1538 4.52499338 3 Gemmatimonadetes Gemmatimonadetes Gemmatimonadales Gemmatimonadaceae Gemmatirosa Gemmatirosa kalamazoonesis 1020 3.000970902 4 Proteobacteria Alphaproteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Sphingomonas indica 797 2.344876284 5 Firmicutes Bacilli Lactobacillales Streptococcaceae Lactococcus Lactococcus piscium 542 1.594633558 6 Actinobacteria Thermoleophilia Solirubrobacterales Conexibacteraceae Conexibacter Conexibacter woesei 471 1.385742446 7 Proteobacteria Alphaproteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Sphingomonas taxi 430 1.265115184 8 Proteobacteria Alphaproteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Sphingomonas wittichii 388 1.141545794 9 Proteobacteria Alphaproteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Sphingomonas sp. FARSPH 298 0.876754244 10 Proteobacteria Alphaproteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Sorangium cellulosum 260 0.764953367 11 Proteobacteria Deltaproteobacteria Myxococcales Polyangiaceae Sorangium Sphingomonas sp. Cra20 260 0.764953367 12 Proteobacteria Alphaproteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Sphingomonas panacis 252 0.741416341 -
A Noval Investigation of Microbiome from Vermicomposting Liquid Produced by Thai Earthworm, Perionyx Sp
International Journal of Agricultural Technology 2021Vol. 17(4):1363-1372 Available online http://www.ijat-aatsea.com ISSN 2630-0192 (Online) A novel investigation of microbiome from vermicomposting liquid produced by Thai earthworm, Perionyx sp. 1 Kraisittipanit, R.1,2, Tancho, A.2,3, Aumtong, S.3 and Charerntantanakul, W.1* 1Program of Biotechnology, Faculty of Science, Maejo University, Chiang Mai, Thailand; 2Natural Farming Research and Development Center, Maejo University, Chiang Mai, Thailand; 3Faculty of Agricultural Production, Maejo University, Thailand. Kraisittipanit, R., Tancho, A., Aumtong, S. and Charerntantanakul, W. (2021). A noval investigation of microbiome from vermicomposting liquid produced by Thai earthworm, Perionyx sp. 1. International Journal of Agricultural Technology 17(4):1363-1372. Abstract The whole microbiota structure in vermicomposting liquid derived from Thai earthworm, Perionyx sp. 1 was estimated. It showed high richness microbial species and belongs to 127 species, separated in 3 fungal phyla (Ascomycota, Basidiomycota, Mucoromycota), 1 Actinomycetes and 16 bacterial phyla (Acidobacteria, Armatimonadetes, Bacteroidetes, Balneolaeota, Candidatus, Chloroflexi, Deinococcus, Fibrobacteres, Firmicutes, Gemmatimonadates, Ignavibacteriae, Nitrospirae, Planctomycetes, Proteobacteria, Tenericutes and Verrucomicrobia). The OTUs data analysis revealed the highest taxonomic abundant ratio in bacteria and fungi belong to Proteobacteria (70.20 %) and Ascomycota (5.96 %). The result confirmed that Perionyx sp. 1 -
Wedding Higher Taxonomic Ranks with Metabolic Signatures Coded in Prokaryotic Genomes
Wedding higher taxonomic ranks with metabolic signatures coded in prokaryotic genomes Gregorio Iraola*, Hugo Naya* Corresponding authors: E-mail: [email protected], [email protected] This PDF file includes: Supplementary Table 1 Supplementary Figures 1 to 4 Supplementary Methods SUPPLEMENTARY TABLES Supplementary Tab. 1 Supplementary Tab. 1. Full prediction for the set of 108 external genomes used as test. genome domain phylum class order family genus prediction alphaproteobacterium_LFTY0 Bacteria Proteobacteria Alphaproteobacteria Rhodobacterales Rhodobacteraceae Unknown candidatus_nasuia_deltocephalinicola_PUNC_CP013211 Bacteria Proteobacteria Gammaproteobacteria Unknown Unknown Unknown candidatus_sulcia_muelleri_PUNC_CP013212 Bacteria Bacteroidetes Flavobacteriia Flavobacteriales NA Candidatus Sulcia deinococcus_grandis_ATCC43672_BCMS0 Bacteria Deinococcus-Thermus Deinococci Deinococcales Deinococcaceae Deinococcus devosia_sp_H5989_CP011300 Bacteria Proteobacteria Unknown Unknown Unknown Unknown micromonospora_RV43_LEKG0 Bacteria Actinobacteria Actinobacteria Micromonosporales Micromonosporaceae Micromonospora nitrosomonas_communis_Nm2_CP011451 Bacteria Proteobacteria Betaproteobacteria Nitrosomonadales Nitrosomonadaceae Unknown nocardia_seriolae_U1_BBYQ0 Bacteria Actinobacteria Actinobacteria Corynebacteriales Nocardiaceae Nocardia nocardiopsis_RV163_LEKI01 Bacteria Actinobacteria Actinobacteria Streptosporangiales Nocardiopsaceae Nocardiopsis oscillatoriales_cyanobacterium_MTP1_LNAA0 Bacteria Cyanobacteria NA Oscillatoriales -
Data of Read Analyses for All 20 Fecal Samples of the Egyptian Mongoose
Supplementary Table S1 – Data of read analyses for all 20 fecal samples of the Egyptian mongoose Number of Good's No-target Chimeric reads ID at ID Total reads Low-quality amplicons Min length Average length Max length Valid reads coverage of amplicons amplicons the species library (%) level 383 2083 33 0 281 1302 1407.0 1442 1769 1722 99.72 466 2373 50 1 212 1310 1409.2 1478 2110 1882 99.53 467 1856 53 3 187 1308 1404.2 1453 1613 1555 99.19 516 2397 36 0 147 1316 1412.2 1476 2214 2161 99.10 460 2657 297 0 246 1302 1416.4 1485 2114 1169 98.77 463 2023 34 0 189 1339 1411.4 1561 1800 1677 99.44 471 2290 41 0 359 1325 1430.1 1490 1890 1833 97.57 502 2565 31 0 227 1315 1411.4 1481 2307 2240 99.31 509 2664 62 0 325 1316 1414.5 1463 2277 2073 99.56 674 2130 34 0 197 1311 1436.3 1463 1899 1095 99.21 396 2246 38 0 106 1332 1407.0 1462 2102 1953 99.05 399 2317 45 1 47 1323 1420.0 1465 2224 2120 98.65 462 2349 47 0 394 1312 1417.5 1478 1908 1794 99.27 501 2246 22 0 253 1328 1442.9 1491 1971 1949 99.04 519 2062 51 0 297 1323 1414.5 1534 1714 1632 99.71 636 2402 35 0 100 1313 1409.7 1478 2267 2206 99.07 388 2454 78 1 78 1326 1406.6 1464 2297 1929 99.26 504 2312 29 0 284 1335 1409.3 1446 1999 1945 99.60 505 2702 45 0 48 1331 1415.2 1475 2609 2497 99.46 508 2380 30 1 210 1329 1436.5 1478 2139 2133 99.02 1 Supplementary Table S2 – PERMANOVA test results of the microbial community of Egyptian mongoose comparison between female and male and between non-adult and adult. -
Induction of Secondary Metabolism Across Actinobacterial Genera
Induction of secondary metabolism across actinobacterial genera A thesis submitted for the award Doctor of Philosophy at Flinders University of South Australia Rio Risandiansyah Department of Medical Biotechnology Faculty of Medicine, Nursing and Health Sciences Flinders University 2016 TABLE OF CONTENTS TABLE OF CONTENTS ............................................................................................ ii TABLE OF FIGURES ............................................................................................. viii LIST OF TABLES .................................................................................................... xii SUMMARY ......................................................................................................... xiii DECLARATION ...................................................................................................... xv ACKNOWLEDGEMENTS ...................................................................................... xvi Chapter 1. Literature review ................................................................................. 1 1.1 Actinobacteria as a source of novel bioactive compounds ......................... 1 1.1.1 Natural product discovery from actinobacteria .................................... 1 1.1.2 The need for new antibiotics ............................................................... 3 1.1.3 Secondary metabolite biosynthetic pathways in actinobacteria ........... 4 1.1.4 Streptomyces genetic potential: cryptic/silent genes .......................... -
Compile.Xlsx
Silva OTU GS1A % PS1B % Taxonomy_Silva_132 otu0001 0 0 2 0.05 Bacteria;Acidobacteria;Acidobacteria_un;Acidobacteria_un;Acidobacteria_un;Acidobacteria_un; otu0002 0 0 1 0.02 Bacteria;Acidobacteria;Acidobacteriia;Solibacterales;Solibacteraceae_(Subgroup_3);PAUC26f; otu0003 49 0.82 5 0.12 Bacteria;Acidobacteria;Aminicenantia;Aminicenantales;Aminicenantales_fa;Aminicenantales_ge; otu0004 1 0.02 7 0.17 Bacteria;Acidobacteria;AT-s3-28;AT-s3-28_or;AT-s3-28_fa;AT-s3-28_ge; otu0005 1 0.02 0 0 Bacteria;Acidobacteria;Blastocatellia_(Subgroup_4);Blastocatellales;Blastocatellaceae;Blastocatella; otu0006 0 0 2 0.05 Bacteria;Acidobacteria;Holophagae;Subgroup_7;Subgroup_7_fa;Subgroup_7_ge; otu0007 1 0.02 0 0 Bacteria;Acidobacteria;ODP1230B23.02;ODP1230B23.02_or;ODP1230B23.02_fa;ODP1230B23.02_ge; otu0008 1 0.02 15 0.36 Bacteria;Acidobacteria;Subgroup_17;Subgroup_17_or;Subgroup_17_fa;Subgroup_17_ge; otu0009 9 0.15 41 0.99 Bacteria;Acidobacteria;Subgroup_21;Subgroup_21_or;Subgroup_21_fa;Subgroup_21_ge; otu0010 5 0.08 50 1.21 Bacteria;Acidobacteria;Subgroup_22;Subgroup_22_or;Subgroup_22_fa;Subgroup_22_ge; otu0011 2 0.03 11 0.27 Bacteria;Acidobacteria;Subgroup_26;Subgroup_26_or;Subgroup_26_fa;Subgroup_26_ge; otu0012 0 0 1 0.02 Bacteria;Acidobacteria;Subgroup_5;Subgroup_5_or;Subgroup_5_fa;Subgroup_5_ge; otu0013 1 0.02 13 0.32 Bacteria;Acidobacteria;Subgroup_6;Subgroup_6_or;Subgroup_6_fa;Subgroup_6_ge; otu0014 0 0 1 0.02 Bacteria;Acidobacteria;Subgroup_6;Subgroup_6_un;Subgroup_6_un;Subgroup_6_un; otu0015 8 0.13 30 0.73 Bacteria;Acidobacteria;Subgroup_9;Subgroup_9_or;Subgroup_9_fa;Subgroup_9_ge; -
Supplement of Biogeographical Distribution of Microbial Communities Along the Rajang River–South China Sea Continuum
Supplement of Biogeosciences, 16, 4243–4260, 2019 https://doi.org/10.5194/bg-16-4243-2019-supplement © Author(s) 2019. This work is distributed under the Creative Commons Attribution 4.0 License. Supplement of Biogeographical distribution of microbial communities along the Rajang River–South China Sea continuum Edwin Sien Aun Sia et al. Correspondence to: Moritz Müller ([email protected]) The copyright of individual parts of the supplement might differ from the CC BY 4.0 License. Supp. Fig. S1: Monthly Mean Precipitation (mm) from Jan 2016 to Sep 2017. Relevant months are highlighted red (Aug 2016), blue (Mar 2017) and dark blue (Sep 2017). Monthly precipitation for the period in between the cruises (August 2016 to September 2017) were obtained from the Tropical Rainfall Measuring Mission website (NASA 2019) in order to gauge the seasonality (wet or dry). As the rainfall data do not correlate with the monsoonal periods, the seasons in which the sampling cruises coincide with were classified based on the mean rainfall that occurred for each month. The August 2016 cruise (colored red) is classified as the dry season based on the lower mean rainfall value as compared to the other two (March 2017 and September 2017), in which the both are classified as the wet season. Supp. Fig. S2: Non-metric Multi-dimensional Scaling (NMDS) diagram of seasonal (August 2016, March 2017 and September 2017) and particle association (particle-attached or free-living) Seasonality was observed within the three cruises irrespective of the particle association (Supp. Fig. 3). The August 2016 cruise was found to cluster with the September 2017 whereas the March 2017 cruise clustered separately from the other two cruises.