Geochemistry of cave pools in the Guadalupe Mountains, NM : implications for geomicrobiology

The study of the chemistry of waters in cave pools in the Guadalupe Mountains, NM was conducted to determine the variation in cave pool water geochemistry, explain hydrogeologic variations in a geologic context, identify the processes that control cave pool hydrologic variations and determine geochemical controls on modern and paleo-bacterial communities. Structural studies indicate cave pools align along NE and NW structures in the karst system. The intersection of Permian structures and Cenozoic related structures provide pathways for infiltrating water. Water samples were collected from 19 new cave pools; the results were integrated with published geochemistry from 192 cave pools, aquifer samples, and surface sites. The waters were analyzed for major and minor ions, modeled to explain flow paths, connected with structural data, examined for thermodynamic potential to support metabolic reactions, and to elucidate the relationship between current geochemistry and presence of biothems (biogenically mediated speleothems, including pool fingers). Infiltrating waters dominant the character of the cave pool waters. Variations in geochemistry of the cave pools can be explained by several geochemical processes: 1) infiltrating water-rock interactions 2) outgassing of CO2 3) precipitation of minerals and 4) evaporation. The thermodynamic data of available energy for use by microbial communities predicted the potential for nitrate, nitrite, oxygen, and sulfate to be used as terminal electron acceptors. The geochemical trends of pools containing biothems showed no connection between pool geochemistry and the presence of biothems. The small number of processes governing the variation in geochemistry give rise to a complex, unique geochemical signature and history for each cave pool thus unique microbial communities

Actinobacterial Diversity in Volcanic Caves and Associated Geomicrobiological Interactions

16 páginas.-- 8 figuras.-- 2 tablas.-- 66 referencias.-- Material suplementario http://dx.doi.org/10.3389/fmicb.2015.01342Volcanic caves are filled with colorful microbial mats on the walls and ceilings. These volcanic caves are found worldwide, and studies are finding vast bacteria diversity within these caves. One group of bacteria that can be abundant in volcanic caves, as well as other caves, is Actinobacteria. As Actinobacteria are valued for their ability to produce a variety of secondary metabolites, rare and novel Actinobacteria are being sought in underexplored environments. The abundance of novel Actinobacteria in volcanic caves makes this environment an excellent location to study these bacteria. Scanning electron microscopy (SEM) from several volcanic caves worldwide revealed diversity in the morphologies present. Spores, coccoid, and filamentous cells, many with hair-like or knobby extensions, were some of the microbial structures observed within the microbial mat samples. In addition, the SEM study pointed out that these features figure prominently in both constructive and destructive mineral processes. To further investigate this diversity, we conducted both Sanger sequencing and 454 pyrosequencing of the Actinobacteria in volcanic caves from four locations, two islands in the Azores, Portugal, and Hawai'i and New Mexico, USA. This comparison represents one of the largest sequencing efforts of Actinobacteria in volcanic caves to date. The diversity was shown to be dominated by Actinomycetales, but also included several newly described orders, such as Euzebyales, and Gaiellales. Sixty-two percent of the clones from the four locations shared less than 97% similarity to known sequences, and nearly 71% of the clones were singletons, supporting the commonly held belief that volcanic caves are an untapped resource for novel and rare Actinobacteria. The amplicon libraries depicted a wider view of the microbial diversity in Azorean volcanic caves revealing three additional orders, Rubrobacterales, Solirubrobacterales, and Coriobacteriales. Studies of microbial ecology in volcanic caves are still very limited. To rectify this deficiency, the results from our study help fill in the gaps in our knowledge of actinobacterial diversity and their potential roles in the volcanic cave ecosystems.The authors acknowledge the Spanish Ministry of Economy and Competitiveness (project CGL2013-41674-P) and FEDER Funds for financial support. AM acknowledges the support from the Marie Curie Intra-European Fellowship of the European Commission's 7th Framework Programme (PIEF-GA-2012-328689). CR was funded by the Regional Fund for Science and Technology and Pro-Emprego program of the Regional Government of the Azores, Portugal [M3.1.7/F/013/2011, M3.1.7/F/030/2011]. Her work was partly supported by National funds from the Foundation for Science and Technology of the Portuguese Government, [Understanding Underground Biodiversity: Studies in Azorean Lava Tubes (reference PTDC/AMB/70801/2006]. The authors would like to thank the TRU Innovation in Research Grant, TRU UREAP Fund, Western Economic Diversification Canada Fund, Kent Watson (assisted with the Helmcken Falls Cave sample collection), Derrick Horne (UBC BioImaging Facility for the SEM work). We acknowledged the Canadian Ministry of Forests, Lands, and Natural Resource Operations for Park Use Permit#102172. This work was also supported by the Cave Conservancy of the Virginias, the Graduate Research Allocation Committee at UNM Biology, UNM Biology Grove Scholarship, the Student Research Allocation Committee at UNM, the National Speleological Society, the New Mexico Space Grant Consortium, the New Mexico Alliance for Minority Participation Program, the New Mexico Geological Society, and Kenneth Ingham Consulting. We acknowledge support from the UNM Molecular Biology Facility, which is supported by NIH grant number P20GM103452. The authors also wish to thank Fernando Pereira, Ana Rita Varela, Pedro Correia, Berta Borges, and Guida Pires for help during field and lab work in the Azores. The authors gratefully acknowledge the photographic contributions of Kenneth Ingham and Pedro Cardoso and Michael Spilde (SEM images). The authors would like to thank Dr. Steven Van Wagoner (TRU) and Drs. Julian Davies and Vivian Miao (UBC) for their invaluable comments in manuscript preparation. We gratefully acknowledge the help and collecting permits granted by the staff of El Malpais National Monument and Hawai'i Volcanoes National Park (USA).Peer reviewe

Chemiresistor microsensors for in-situ monitoring of volatile organic compounds : final LDRD report.

This report provides a summary of the three-year LDRD (Laboratory Directed Research and Development) project aimed at developing microchemical sensors for continuous, in-situ monitoring of volatile organic compounds. A chemiresistor sensor array was integrated with a unique, waterproof housing that allows the sensors to be operated in a variety of media including air, soil, and water. Numerous tests were performed to evaluate and improve the sensitivity, stability, and discriminatory capabilities of the chemiresistors. Field tests were conducted in California, Nevada, and New Mexico to further test and develop the sensors in actual environments within integrated monitoring systems. The field tests addressed issues regarding data acquisition, telemetry, power requirements, data processing, and other engineering requirements. Significant advances were made in the areas of polymer optimization, packaging, data analysis, discrimination, design, and information dissemination (e.g., real-time web posting of data; see www.sandia.gov/sensor). This project has stimulated significant interest among commercial and academic institutions. A CRADA (Cooperative Research and Development Agreement) was initiated in FY03 to investigate manufacturing methods, and a Work for Others contract was established between Sandia and Edwards Air Force Base for FY02-FY04. Funding was also obtained from DOE as part of their Advanced Monitoring Systems Initiative program from FY01 to FY03, and a DOE EMSP contract was awarded jointly to Sandia and INEEL for FY04-FY06. Contracts were also established for collaborative research with Brigham Young University to further evaluate, understand, and improve the performance of the chemiresistor sensors

Distribution of GC Content by Ecosystem − Bacterial and Archaeal Genomes from JGI

<p>This is a plot of the raw data pulled down from the JGI websites on bacterial and archaeal genomes. The overall hypothesis is that there should be patterns in the GC content and genome size that relate to phylogenetics, ecosystem, energy sources and use, biosynthetic clusters, secondary metabolite gene clusters, and other factors.</p> <p>R software<br>R Core Team (2014). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/</p> <p>H. Wickham. ggplot2: elegant graphics for data analysis. Springer New York, 2009.</p> <p>Genome data provided by:<br>The genome portal of the Department of Energy Joint Genome Institute: 2014 updates.<br>Nordberg H, Cantor M, Dusheyko S, Hua S, Poliakov A, Shabalov I, Smirnova T, Grigoriev IV, Dubchak I. Nucleic Acids Res. 2014,42(1):D26-31.</p> <p>The Genome Portal of the Department of Energy Joint Genome Institute<br>Grigoriev IV, Nordberg H, Shabalov I, Aerts A, Cantor M, Goodstein D, Kuo A, Minovitsky S, Nikitin R, Ohm RA, Otillar R, Poliakov A, Ratnere I, Riley R, Smirnova T, Rokhsar D, Dubchak I.Nucleic Acids Res. 2012 Jan;40(Database issue):D26-32.</p> <p> </p> <p>IMG: the integrated microbial genomes database and comparative analysis system Victor M. Markowitz1, I-Min A. Chen, Krishna Palaniappan, Ken Chu, Ernest Szeto, Yuri Grechkin, Anna Ratner, Biju Jacob, Jinghua Huang, Peter Williams, Marcel Huntemann, Iain Anderson, Konstantinos Mavromatis, Natalia N. Ivanova and Nikos C. Kyrpides</p> <p> "These sequence data were produced by the US Department of Energy Joint Genome Institute http://www.jgi.doe.gov/ in collaboration with the user community." We request that you notify us upon publication so that this information can be included in the final annotation.</p

Bacteria and Euryarchaeota Genome Size and GC Content

<p>April 2014, NCBI Bacterial and Euryarchaeota completed genomes. GC content plotted against genome size. </p> <p>n = 2969</p> <p><strong>Bayesian Correlation on the same data is here:</strong></p> <p>http://figshare.com/articles/Bayesian_First_Aid_Pearson_s_Correlation_Coefficient_Test_on_Completed_Bacterial_and_Euryarchaeota_Genomes_April_2014_data_set/1022802</p> <p><strong>The R script that generated this analysis is available here:</strong></p> <p>https://github.com/bioinfonm/2ndmeta/blob/master/R%20Scripts/gc%20content%20genome%20size/gc%20content%20genome%20size%20bacteria.R</p> <p><strong>The raw data is available from NCBI here:</strong></p> <p>ftp://ftp.ncbi.nlm.nih.gov/genomes/GENOME_REPORTS/prokaryotes.txt</p

GC content and genome size - Completed Bacteria and Euryarchaeota Genomes

<p>The complete fileset for analysis of GC content and genome size in completed Bacteria and Euryarchaeota genomes from the NCBI database. </p

Density Plot of CAI of KS Domains From AB, AZ, UT, Marine Sponge, and NM

<p>Data set from Reddy et al 2012, SRR342214 and Trindade-Silva et al 2012. Marine sponge and desert soil KS domains. CodonW was used to calculate all codon bias indices. AB - California, AZ - Arizona, UT - Utah, KS_MarSpg- microbiom from Arenosclera brasiliensis, NM - New Mexico</p> <p>Condonw command: ./codonw yourfile.fasta -all_indices -c_type 2 -f_type 4 -nomenu -silent</p

Density Plot of GC Content of KS Domains From AB, AZ, UT, Marine Sponge, and NM

<p>Data set from Reddy et al 2012, SRR342214, Trindade-Silva et al 2012, Owen et al 2012. Marine sponge and desert soil KS domains. CodonW was used to calculate all codon bias indices. AB - California, AZ - Arizona, UT - Utah, KS_MarSpg - microbiom from Arenosclera brasiliensis, KS_NM - New Mexico</p> <p>Condonw command: ./codonw yourfile.fasta -all_indices -c_type 2 -f_type 4 -nomenu -silent</p

Explanation of the box plot (pdf)

<p>This is the pdf version of the explanation of the box plot. </p

Oxic gene count against W for 59 prokaryotes

<p>Oxic gene count and W for 59 prokaryotes from the paper: <em>The proportion of genes in a functional category is linked to mass-specific metabolic rate and lifespan,</em> Takemoto and Kawakami, 2015/05/06/, Nature.</p> <p>This shows positive relationship between oxic gene count and repsiration rate for 59 prokaryotes. I suspect this is due to bacteria with larger genomes being able to utilize a greater variatey of substrates. The "larger genomes, large toolkits" approach. </p> <p>Takemoto and Kawakami noted that prokaryotes follow different trends from mammals, birds, and insects.</p