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Community Analysis of Microbial Sharing and Specialization in A
Downloaded from http://rspb.royalsocietypublishing.org/ on March 15, 2017 Community analysis of microbial sharing rspb.royalsocietypublishing.org and specialization in a Costa Rican ant–plant–hemipteran symbiosis Elizabeth G. Pringle1,2 and Corrie S. Moreau3 Research 1Department of Biology, Program in Ecology, Evolution, and Conservation Biology, University of Nevada, Cite this article: Pringle EG, Moreau CS. 2017 Reno, NV 89557, USA 2Michigan Society of Fellows, University of Michigan, Ann Arbor, MI 48109, USA Community analysis of microbial sharing and 3Department of Science and Education, Field Museum of Natural History, 1400 South Lake Shore Drive, specialization in a Costa Rican ant–plant– Chicago, IL 60605, USA hemipteran symbiosis. Proc. R. Soc. B 284: EGP, 0000-0002-4398-9272 20162770. http://dx.doi.org/10.1098/rspb.2016.2770 Ants have long been renowned for their intimate mutualisms with tropho- bionts and plants and more recently appreciated for their widespread and diverse interactions with microbes. An open question in symbiosis research is the extent to which environmental influence, including the exchange of Received: 14 December 2016 microbes between interacting macroorganisms, affects the composition and Accepted: 17 January 2017 function of symbiotic microbial communities. Here we approached this ques- tion by investigating symbiosis within symbiosis. Ant–plant–hemipteran symbioses are hallmarks of tropical ecosystems that produce persistent close contact among the macroorganism partners, which then have substantial opportunity to exchange symbiotic microbes. We used metabarcoding and Subject Category: quantitative PCR to examine community structure of both bacteria and Ecology fungi in a Neotropical ant–plant–scale-insect symbiosis. Both phloem-feed- ing scale insects and honeydew-feeding ants make use of microbial Subject Areas: symbionts to subsist on phloem-derived diets of suboptimal nutritional qual- ecology, evolution, microbiology ity. -
Table S1. Bacterial Otus from 16S Rrna
Table S1. Bacterial OTUs from 16S rRNA sequencing analysis including only taxa which were identified to genus level (those OTUs identified as Ambiguous taxa, uncultured bacteria or without genus-level identifications were omitted). OTUs with only a single representative across all samples were also omitted. Taxa are listed from most to least abundant. Pitcher Plant Sample Class Order Family Genus CB1p1 CB1p2 CB1p3 CB1p4 CB5p234 Sp3p2 Sp3p4 Sp3p5 Sp5p23 Sp9p234 sum Gammaproteobacteria Legionellales Coxiellaceae Rickettsiella 1 2 0 1 2 3 60194 497 1038 2 61740 Alphaproteobacteria Rhodospirillales Rhodospirillaceae Azospirillum 686 527 10513 485 11 3 2 7 16494 8201 36929 Sphingobacteriia Sphingobacteriales Sphingobacteriaceae Pedobacter 455 302 873 103 16 19242 279 55 760 1077 23162 Betaproteobacteria Burkholderiales Oxalobacteraceae Duganella 9060 5734 2660 40 1357 280 117 29 129 35 19441 Gammaproteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas 3336 1991 3475 1309 2819 233 1335 1666 3046 218 19428 Betaproteobacteria Burkholderiales Burkholderiaceae Paraburkholderia 0 1 0 1 16051 98 41 140 23 17 16372 Sphingobacteriia Sphingobacteriales Sphingobacteriaceae Mucilaginibacter 77 39 3123 20 2006 324 982 5764 408 21 12764 Gammaproteobacteria Pseudomonadales Moraxellaceae Alkanindiges 9 10 14 7 9632 6 79 518 1183 65 11523 Betaproteobacteria Neisseriales Neisseriaceae Aquitalea 0 0 0 0 1 1577 5715 1471 2141 177 11082 Flavobacteriia Flavobacteriales Flavobacteriaceae Flavobacterium 324 219 8432 533 24 123 7 15 111 324 10112 Alphaproteobacteria -
Leadbetterella Byssophila Type Strain (4M15)
Lawrence Berkeley National Laboratory Recent Work Title Complete genome sequence of Leadbetterella byssophila type strain (4M15). Permalink https://escholarship.org/uc/item/907989cw Journal Standards in genomic sciences, 4(1) ISSN 1944-3277 Authors Abt, Birte Teshima, Hazuki Lucas, Susan et al. Publication Date 2011-03-04 DOI 10.4056/sigs.1413518 Peer reviewed eScholarship.org Powered by the California Digital Library University of California Standards in Genomic Sciences (2011) 4:2-12 DOI:10.4056/sigs.1413518 Complete genome sequence of Leadbetterella byssophila type strain (4M15T) Birte Abt1, Hazuki Teshima2,3, Susan Lucas2, Alla Lapidus2, Tijana Glavina Del Rio2, Matt Nolan2, Hope Tice2, Jan-Fang Cheng2, Sam Pitluck2, Konstantinos Liolios2, Ioanna Pagani2, Natalia Ivanova2, Konstantinos Mavromatis2, Amrita Pati2, Roxane Tapia2,3, Cliff Han2,3, Lynne Goodwin2,3, Amy Chen4, Krishna Palaniappan4, Miriam Land2,5, Loren Hauser2,5, Yun-Juan Chang2,5, Cynthia D. Jeffries2,5, Manfred Rohde6, Markus Göker1, Brian J. Tindall1, John C. Detter2,3, Tanja Woyke2, James Bristow2, Jonathan A. Eisen2,7, Victor Markowitz4, Philip Hugenholtz2,8, Hans-Peter Klenk1, and Nikos C. Kyrpides2* 1 DSMZ - German Collection of Microorganisms and Cell Cultures GmbH, Braunschweig, Germany 2 DOE Joint Genome Institute, Walnut Creek, California, USA 3 Los Alamos National Laboratory, Bioscience Division, Los Alamos, New Mexico USA 4 Biological Data Management and Technology Center, Lawrence Berkeley National Laboratory, Berkeley, California, USA 5 Lawrence Livermore National Laboratory, Livermore, California, USA 6 HZI – Helmholtz Centre for Infection Research, Braunschweig, Germany 7 University of California Davis Genome Center, Davis, California, USA 8 Australian Centre for Ecogenomics, School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Australia *Corresponding author: Nikos C. -
Targeted Manipulation of Abundant and Rare Taxa in the Daphnia Magna Microbiota With
bioRxiv preprint doi: https://doi.org/10.1101/2020.09.07.286427; this version posted September 7, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 1 Targeted manipulation of abundant and rare taxa in the Daphnia magna microbiota with 2 antibiotics impacts host fitness differentially 3 4 Running title: Targeted microbiota manipulation impacts fitness 5 6 Reilly O. Coopera#, Janna M. Vavraa, Clayton E. Cresslera 7 1: School of Biological Sciences, University of Nebraska-Lincoln, Lincoln, NE, USA 8 9 Corresponding Author Information: 10 Reilly O. Cooper 11 [email protected] 12 13 14 Competing Interests: The authors have no competing interests to declare. 15 16 17 18 19 20 21 22 23 bioRxiv preprint doi: https://doi.org/10.1101/2020.09.07.286427; this version posted September 7, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 24 Abstract 25 Host-associated microbes contribute to host fitness, but it is unclear whether these contributions 26 are from rare keystone taxa, numerically abundant taxa, or interactions among community 27 members. Experimental perturbation of the microbiota can highlight functionally important taxa; 28 however, this approach is primarily applied in systems with complex communities where the 29 perturbation affects hundreds of taxa, making it difficult to pinpoint contributions of key 30 community members. Here, we use the ecological model organism Daphnia magna to examine 31 the importance of rare and abundant taxa by perturbing its relatively simple microbiota with 32 targeted antibiotics. -
Supplementary Information for Microbial Electrochemical Systems Outperform Fixed-Bed Biofilters for Cleaning-Up Urban Wastewater
Electronic Supplementary Material (ESI) for Environmental Science: Water Research & Technology. This journal is © The Royal Society of Chemistry 2016 Supplementary information for Microbial Electrochemical Systems outperform fixed-bed biofilters for cleaning-up urban wastewater AUTHORS: Arantxa Aguirre-Sierraa, Tristano Bacchetti De Gregorisb, Antonio Berná, Juan José Salasc, Carlos Aragónc, Abraham Esteve-Núñezab* Fig.1S Total nitrogen (A), ammonia (B) and nitrate (C) influent and effluent average values of the coke and the gravel biofilters. Error bars represent 95% confidence interval. Fig. 2S Influent and effluent COD (A) and BOD5 (B) average values of the hybrid biofilter and the hybrid polarized biofilter. Error bars represent 95% confidence interval. Fig. 3S Redox potential measured in the coke and the gravel biofilters Fig. 4S Rarefaction curves calculated for each sample based on the OTU computations. Fig. 5S Correspondence analysis biplot of classes’ distribution from pyrosequencing analysis. Fig. 6S. Relative abundance of classes of the category ‘other’ at class level. Table 1S Influent pre-treated wastewater and effluents characteristics. Averages ± SD HRT (d) 4.0 3.4 1.7 0.8 0.5 Influent COD (mg L-1) 246 ± 114 330 ± 107 457 ± 92 318 ± 143 393 ± 101 -1 BOD5 (mg L ) 136 ± 86 235 ± 36 268 ± 81 176 ± 127 213 ± 112 TN (mg L-1) 45.0 ± 17.4 60.6 ± 7.5 57.7 ± 3.9 43.7 ± 16.5 54.8 ± 10.1 -1 NH4-N (mg L ) 32.7 ± 18.7 51.6 ± 6.5 49.0 ± 2.3 36.6 ± 15.9 47.0 ± 8.8 -1 NO3-N (mg L ) 2.3 ± 3.6 1.0 ± 1.6 0.8 ± 0.6 1.5 ± 2.0 0.9 ± 0.6 TP (mg -
Multilevel Social Structure and Diet Shape the Gut Microbiota of the Gelada Monkey, the Only Grazing Primate Pål Trosvik 1*, Eric J
Multilevel social structure and diet shape the gut microbiota of the gelada monkey, the only grazing primate Pål Trosvik 1*, Eric J. de Muinck 1, Eli K. Rueness 1, Peter J. Fashing 2, Evan C. Beierschmitt 3, Kadie R. Callingham 4, Jacob B. Kraus 5, Thomas H. Trew 6, Amera Moges 7, Addisu Mekonnen 1,8 , Vivek V. Venkataraman 9, Nga Nguyen 2 Supplementary information: Supplementary Figures 1-17, Supplementary Tables 1-10. Figure S1. Relative abundances of the eight most prevalent phyla in the gelada samples. Data are shown for all samples combined, as well as split into samples collected during the dry or wet season. The category “Other” includes OTUs that could not be classified to the phylum level with a probability higher than 0.5. Figure S2. Between-sample weighted (a) and unweighted (b) UniFrac distances in gelada samples collected during the dry (n=142) or the wet (n=174) season. Each box represents the interquartile range, with the horizontal lines representing the medians and the whiskers representing 1.5 times the interquartile range. Points outside the whiskers represent outliers. For both comparisons the difference in mean distance was highly significant (t<<0.001 for both comparisons, unpaired t-tests). Figure S3. Non-metric multidimensional scaling of all primate samples based on weighted (a) and unweighted (b) UniFrac distances. The plot shows the two main dimensions of variation, with plotted characters color coded according to sample type. Clustering according to samples type was highly significant, explaining 46.2% and 63.1% of between-sample variation, respectively (p<<0.001 for both tests, PERMANOVA). -
Which Organisms Are Used for Anti-Biofouling Studies
Table S1. Semi-systematic review raw data answering: Which organisms are used for anti-biofouling studies? Antifoulant Method Organism(s) Model Bacteria Type of Biofilm Source (Y if mentioned) Detection Method composite membranes E. coli ATCC25922 Y LIVE/DEAD baclight [1] stain S. aureus ATCC255923 composite membranes E. coli ATCC25922 Y colony counting [2] S. aureus RSKK 1009 graphene oxide Saccharomycetes colony counting [3] methyl p-hydroxybenzoate L. monocytogenes [4] potassium sorbate P. putida Y. enterocolitica A. hydrophila composite membranes E. coli Y FESEM [5] (unspecified/unique sample type) S. aureus (unspecified/unique sample type) K. pneumonia ATCC13883 P. aeruginosa BAA-1744 composite membranes E. coli Y SEM [6] (unspecified/unique sample type) S. aureus (unspecified/unique sample type) graphene oxide E. coli ATCC25922 Y colony counting [7] S. aureus ATCC9144 P. aeruginosa ATCCPAO1 composite membranes E. coli Y measuring flux [8] (unspecified/unique sample type) graphene oxide E. coli Y colony counting [9] (unspecified/unique SEM sample type) LIVE/DEAD baclight S. aureus stain (unspecified/unique sample type) modified membrane P. aeruginosa P60 Y DAPI [10] Bacillus sp. G-84 LIVE/DEAD baclight stain bacteriophages E. coli (K12) Y measuring flux [11] ATCC11303-B4 quorum quenching P. aeruginosa KCTC LIVE/DEAD baclight [12] 2513 stain modified membrane E. coli colony counting [13] (unspecified/unique colony counting sample type) measuring flux S. aureus (unspecified/unique sample type) modified membrane E. coli BW26437 Y measuring flux [14] graphene oxide Klebsiella colony counting [15] (unspecified/unique sample type) P. aeruginosa (unspecified/unique sample type) graphene oxide P. aeruginosa measuring flux [16] (unspecified/unique sample type) composite membranes E. -
Taxonomy JN869023
Species that differentiate periods of high vs. low species richness in unattached communities Species Taxonomy JN869023 Bacteria; Actinobacteria; Actinobacteria; Actinomycetales; ACK-M1 JN674641 Bacteria; Bacteroidetes; [Saprospirae]; [Saprospirales]; Chitinophagaceae; Sediminibacterium JN869030 Bacteria; Actinobacteria; Actinobacteria; Actinomycetales; ACK-M1 U51104 Bacteria; Proteobacteria; Betaproteobacteria; Burkholderiales; Comamonadaceae; Limnohabitans JN868812 Bacteria; Proteobacteria; Betaproteobacteria; Burkholderiales; Comamonadaceae JN391888 Bacteria; Planctomycetes; Planctomycetia; Planctomycetales; Planctomycetaceae; Planctomyces HM856408 Bacteria; Planctomycetes; Phycisphaerae; Phycisphaerales GQ347385 Bacteria; Verrucomicrobia; [Methylacidiphilae]; Methylacidiphilales; LD19 GU305856 Bacteria; Proteobacteria; Alphaproteobacteria; Rickettsiales; Pelagibacteraceae GQ340302 Bacteria; Actinobacteria; Actinobacteria; Actinomycetales JN869125 Bacteria; Proteobacteria; Betaproteobacteria; Burkholderiales; Comamonadaceae New.ReferenceOTU470 Bacteria; Cyanobacteria; ML635J-21 JN679119 Bacteria; Proteobacteria; Betaproteobacteria; Burkholderiales; Comamonadaceae HM141858 Bacteria; Acidobacteria; Holophagae; Holophagales; Holophagaceae; Geothrix FQ659340 Bacteria; Verrucomicrobia; [Pedosphaerae]; [Pedosphaerales]; auto67_4W AY133074 Bacteria; Elusimicrobia; Elusimicrobia; Elusimicrobiales FJ800541 Bacteria; Verrucomicrobia; [Pedosphaerae]; [Pedosphaerales]; R4-41B JQ346769 Bacteria; Acidobacteria; [Chloracidobacteria]; RB41; Ellin6075 -
Pedobacter Ghigonii Sp. Nov., Isolated from the Microbiota of the Planarian Schmidtea Mediterranea
Article Pedobacter ghigonii sp. nov., Isolated from the Microbiota of the Planarian Schmidtea mediterranea Luis Johnson Kangale 1,2 , Didier Raoult 2,3,4 and Fournier Pierre-Edouard 1,2,* 1 UMR VITROME, SSA, Aix-Marseille University, IRD, AP-HM, IHU-Méditerranée-Infection, 13385 Marseille, France; [email protected] 2 IHU-Méditerranée-Infection, 13385 Marseille, France; [email protected] 3 Department of Epidemiology of Parasitic Diseases, Aix Marseille University, IRD, AP-HM, MEPHI, 13385 Marseille, France 4 Special Infectious Agents Unit, King Fahd Medical Research Center, King Abdulaziz University, Jeddah 21589, Saudi Arabia * Correspondence: [email protected]; Tel.: +33-0413732401; Fax: +33-0413732402 Abstract: The planarian S. mediterranea is a platyhelminth with worldwide distribution that can regenerate any part of its body after amputation and has the capacity to eliminate a large spectrum of human bacterial pathogens. Surprisingly, the microbiota of S. mediterranea remains poorly investi- gated. Using the culturomics strategy to study the bacterial component of planarians, we isolated a new bacterial strain, Marseille-Q2390, which we characterized with the taxono-genomic approach that associates phenotypic assays and genome sequencing and analysis. Strain Marseille-Q2390 exhibited a 16S rRNA sequence similarity of 99.36% with Pedobacter kyungheensis strain THG-T17T, the closest phylogenetic neighbor. It is a white-pigmented, Gram-negative, and rod-shaped bacterium. It grows in aerobic conditions and belongs to the family Sphingobacteriaceae. The genome of strain Marseille-Q2390 is 5,919,359 bp-long, with a G + C content of 40.3%. By comparing its genome with Citation: Kangale, L.J.; Raoult, D.; other closely related strains, the highest Orthologous Average Nucleotide Identity (Ortho-ANI) and Pierre-Edouard, F. -
Ice-Nucleating Particles Impact the Severity of Precipitations in West Texas
Ice-nucleating particles impact the severity of precipitations in West Texas Hemanth S. K. Vepuri1,*, Cheyanne A. Rodriguez1, Dimitri G. Georgakopoulos4, Dustin Hume2, James Webb2, Greg D. Mayer3, and Naruki Hiranuma1,* 5 1Department of Life, Earth and Environmental Sciences, West Texas A&M University, Canyon, TX, USA 2Office of Information Technology, West Texas A&M University, Canyon, TX, USA 3Department of Environmental Toxicology, Texas Tech University, Lubbock, TX, USA 4Department of Crop Science, Agricultural University of Athens, Athens, Greece 10 *Corresponding authors: [email protected] and [email protected] Supplemental Information 15 S1. Precipitation and Particulate Matter Properties S1.1 Precipitation Categorization In this study, we have segregated our precipitation samples into four different categories, such as (1) snows, (2) hails/thunderstorms, (3) long-lasted rains, and (4) weak rains. For this categorization, we have considered both our observation-based as well as the disdrometer-assigned National Weather Service (NWS) 20 code. Initially, the precipitation samples had been assigned one of the four categories based on our manual observation. In the next step, we have used each NWS code and its occurrence in each precipitation sample to finalize the precipitation category. During this step, a precipitation sample was categorized into snow, only when we identified a snow type NWS code (Snow: S-, S, S+ and/or Snow Grains: SG). Likewise, a precipitation sample was categorized into hail/thunderstorm, only when the cumulative sum of NWS codes for hail was 25 counted more than five times (i.e., A + SP ≥ 5; where A and SP are the codes for soft hail and hail, respectively). -
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 -
Variability in Snake Skin Microbial Assemblages Across Spatial Scales and Disease States
The ISME Journal (2019) 13:2209–2222 https://doi.org/10.1038/s41396-019-0416-x ARTICLE Variability in snake skin microbial assemblages across spatial scales and disease states 1 2 1 1 2 Donald M. Walker ● Jacob E. Leys ● Matthew Grisnik ● Alejandro Grajal-Puche ● Christopher M. Murray ● Matthew C. Allender3 Received: 24 August 2018 / Revised: 10 April 2019 / Accepted: 12 April 2019 / Published online: 7 May 2019 © The Author(s) 2019. This article is published with open access Abstract Understanding how biological patterns translate into functional processes across different scales is a central question in ecology. Within a spatial context, extent is used to describe the overall geographic area of a study, whereas grain describes the overall unit of observation. This study aimed to characterize the snake skin microbiota (grain) and to determine host–microbial assemblage–pathogen effects across spatial extents within the Southern United States. The causative agent of snake fungal disease, Ophidiomyces ophiodiicola, is a fungal pathogen threatening snake populations. We hypothesized that the skin microbial assemblage of snakes differs from its surrounding environment, by host species, spatial scale, season, and 1234567890();,: 1234567890();,: in the presence of O. ophiodiicola. We collected snake skin swabs, soil samples, and water samples across six states in the Southern United States (macroscale extent), four Tennessee ecoregions (mesoscale extent), and at multiple sites within each Tennessee ecoregion (microscale extent). These samples were subjected to DNA extraction and quantitative PCR to determine the presence/absence of O. ophiodiicola. High-throughput sequencing was also utilized to characterize the microbial communities. We concluded that the snake skin microbial assemblage was partially distinct from environmental microbial communities.