dissertation topics in soil microbiology

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Dissertations / Theses on the topic 'Soil microbiology'

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Jeffery, Simon. "The microbiology of arable soil surfaces." Thesis, Cranfield University, 2007. http://dspace.lib.cranfield.ac.uk/handle/1826/2245.

Jones, Frances Patricia. "The microbiology of lean and obese soil." Thesis, University of Reading, 2017. http://centaur.reading.ac.uk/69408/.

Paulse, Arnelia N. (Arnelia Natalie). "Soil stabilization by microbial activity." Thesis, Stellenbosch : Stellenbosch University, 2003. http://hdl.handle.net/10019.1/53593.

Wagai, Rota. "Climatic and Lithogenic Controls on Soil Organic Matter-Mineral Associations." Fogler Library, University of Maine, 2005. http://www.library.umaine.edu/theses/pdf/WagaiR2005.pdf.

Marí, Marí Teresa. "Changes in soil biodiversity and activity along management and climatic gradients." Doctoral thesis, Universitat de Lleida, 2017. http://hdl.handle.net/10803/457976.

Rodriguez, Luis A. (Luis Antonio). "Adenylate Energy Charge Determinations of Soil Bacteria Grown in Soil Extract Medium." Thesis, University of North Texas, 1988. https://digital.library.unt.edu/ark:/67531/metadc500662/.

Bester, Reinhard. "Growth and survival of Saccharomyces cerevisiae in soil." Thesis, Stellenbosch : University of Stellenbosch, 2011. http://hdl.handle.net/10019.1/16597.

Coyle, Kieran. "An investigation of the role of soil micro-organisms in phosphorus mobilisation : a report submitted to fulfil the requrements of the degree of Doctor of Philosophy." Title page, table of contents and abstract only, 2001. http://web4.library.adelaide.edu.au/theses/09PH/09phc8814.pdf.

Hoyle, Frances Carmen. "The effect of soluble organic carbon substrates, and environmental modulators on soil microbial function and diversity /." Connect to this title, 2006. http://theses.library.uwa.edu.au/adt-WU2007.0050.

Jenkins, Anthony Blaine. "Organic carbon and fertility of forest soils on the Allegheny Plateau of West Virginia." Morgantown, W. Va. : [West Virginia University Libraries], 2002. http://etd.wvu.edu/templates/showETD.cfm?recnum=2486.

Menefee, Dorothy. "Anthropogenic influences on soil microbial properties." Thesis, Kansas State University, 2016. http://hdl.handle.net/2097/32657.

Sutanto, Yovita. "Manure from grazing cattle effects on soil microbial communities and soil quality in northern West Virginia pastures /." Morgantown, W. Va. : [West Virginia University Libraries], 2005. https://etd.wvu.edu/etd/controller.jsp?moduleName=documentdata&jsp%5FetdId=3933.

Sheremata, Tamara W. "The influence of soil organic matter on the fate of trichloroethylene in soil." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape11/PQDD_0017/NQ44582.pdf.

Blair, Jennifer M. "Mechanistic modelling of bioavailability : putting soil microbiology in its pore scale context." Thesis, Abertay University, 2007. https://rke.abertay.ac.uk/en/studentTheses/bf7beb4b-07fa-41a8-8eeb-25aabf2669a7.

Williams, David. "Ecophysiological studies of soil ammonia oxidising bacteria." Thesis, Available from the University of Aberdeen Library and Historic Collections Digital Resources, 2009. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?application=DIGITOOL-3&owner=resourcediscovery&custom_att_2=simple_viewer&pid=26464.

Watts, Dexter Brown. "Mineralization in soils amended with manure as affected by environmental conditions." Auburn, Ala. :, 2007. http://repo.lib.auburn.edu/2007%20Spring%20Dissertations/WATTS_DEXTER_20.pdf.

Jones, Hilary A. "The oxidation of methane in landfill soil cover." Thesis, University of Essex, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.306045.

Herron, Paul Robert. "Interactions between actinophage and streptomycetes in soil." Thesis, University of Warwick, 1991. http://wrap.warwick.ac.uk/79686/.

Trubl, Gareth. "Pioneering Soil Viromics to Elucidate Viral Impacts on Soil Ecosystem Services." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1543425468999981.

Padua, Roberto R. "Purification and characterization of an antimicrobial compound secreted by a soil bacterium /." Abstract Full Text (HTML) Full Text (PDF), 2008. http://eprints.ccsu.edu/archive/00000530/02/1979FT.htm.

Mansoor, E. Y. "Immunological approaches to the ecology of Arthrobacter in soil." Thesis, University of Essex, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.315755.

Lynch, Ryan P. "Controlling Soilborne Diseases of Potato and Influencing Soil Microbiology with Brassica Cover Crops." Fogler Library, University of Maine, 2008. http://www.library.umaine.edu/theses/pdf/LynchRP2008.pdf.

Kinneer, Krista L. "Size fractionation of bacterial functional diversity within soils." Morgantown, W. Va. : [West Virginia University Libraries], 1999. http://etd.wvu.edu/templates/showETD.cfm?recnum=1095.

Caleb, Oluwafemi James. "Microbial community structure as an indicator of soil health in apple orchards." Thesis, Stellenbosch : University of Stellenbosch, 2010. http://hdl.handle.net/10019.1/4133.

Moodley, Kamini. "Microbial diversity of Antarctic Dry Valley mineral soil." Thesis, University of the Western Cape, 2004. http://etd.uwc.ac.za/index.php?module=etd&amp.

Rhode, Owen H. J. "Intraspecies diversity of Cryptococcus laurentii (Kufferath) C.E. Skinner and Cryptococcus podzolicus (Bab’eva & Reshetova) originating from a single soil sample." Thesis, Stellenbosch : University of Stellenbosch, 2005. http://hdl.handle.net/10019.1/1812.

Kumaresa, Deepak. "Molecular ecology of methanotrophs in a landfill cover soil." Thesis, University of Warwick, 2009. http://wrap.warwick.ac.uk/2771/.

Pino, Vanessa. "Soil Microbial Diversity Across Different Agroecological Zones in New South Wales." Thesis, The University of Sydney, 2016. http://hdl.handle.net/2123/16705.

AraÃjo, Jackson de Lima. "Arthropods and attributes soil microbiology in fruit trees en Vale do Curu-CE, Brasil." Universidade Federal do CearÃ, 2014. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=13481.

Cartwright, Colin. "Biodegradation and impact of phthalate plasticisers on a soil microbial community." Thesis, University of Kent, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.264768.

Burden, John P. "The survival, activity and distribution of gentamicin-producing Micromonospora in soil." Thesis, University of Warwick, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.302692.

Kurtboke, Dilber Ipek. "New approaches to the isolation of non-streptomycete actinomycetes from soil." Thesis, University of Liverpool, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.314514.

Leech, Fiona. "Influence of alternative fertilizers on pasture production, soil properties and soil microbial community structure." Thesis, The University of Sydney, 2019. https://hdl.handle.net/2123/21460.

Wang, Jing, and 王静. "Culture-independent analysis of anammox, AOA and AOB in paddy soil of Sanjiang Plain in Northeast China." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2011. http://hub.hku.hk/bib/B4622158X.

Walter, Diana Joyce, and dianawalter@internode on net. "The Environmental Impact of Genetically Modified Crop Plants on the Microbiology of the Rhizosphere." Flinders University. Biotechnology, 2005. http://catalogue.flinders.edu.au./local/adt/public/adt-SFU20070301.161014.

Quinones, Casilda. "Influence of three organic solvents on soil microbial activity." Thesis, The University of Arizona, 1985. http://etd.library.arizona.edu/etd/GetFileServlet?file=file:///data1/pdf/etd/azu_e9791_1985_420_sip1_w.pdf&type=application/pdf.

Barakat, Mohammad 1962. "Seasonal fluctuation in soil and thatch microbial populations in an 80%:20% sand:peat creeping bentgrass putting green." Thesis, The University of Arizona, 1991. http://hdl.handle.net/10150/277909.

Chapman, Joshua A. "Soil microbial communities from the alimentary canal of the earthworm Lumbricus terrestris (Oligochaeta: lumbricidae)." Morgantown, W. Va. : [West Virginia University Libraries], 2006. https://eidr.wvu.edu/etd/documentdata.eTD?documentid=4756.

Millar, Neville. "The effect of improved fallow residue quality on nitrous oxide emissions from tropical soils." Thesis, Imperial College London, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.268667.

Woodward, Rebecca Stanton Wain. "Analysis of tetracycline resistance in compost bacilli." Thesis, University of Liverpool, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.259753.

Edvantoro, Bagus Bina. "Bioavailability, toxicity and microbial volatilisation of arsenic in soils from cattle dip sites." Title page, Contents and Abstract only, 2000. http://web4.library.adelaide.edu.au/theses/09A/09ae24.pdf.

Johnson-Rollings, Ashley S. "A polyphasic approach to the study of chitinolytic bacteria in soil." Thesis, University of Warwick, 2012. http://wrap.warwick.ac.uk/51637/.

Mueller, Sabrina R. "Chromium, DNA, and Soil Microbial Communities." Cincinnati, Ohio : University of Cincinnati, 2006. http://rave.ohiolink.edu/etdc/view.cgi?acc_num=ucin1141334651.

Nielsen, Uffe Nygaard. "Influences on species richness and composition of belowground communities at multiple spatial scales." Thesis, Available from the University of Aberdeen Library and Historic Collections Digital Resources, 2008. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?application=DIGITOOL-3&owner=resourcediscovery&custom_att_2=simple_viewer&pid=24811.

Iker, Brandon Charles. "Application of Advanced Molecular Techniques in Applied Environmental Microbiology." Diss., The University of Arizona, 2013. http://hdl.handle.net/10150/301699.

Cogram, Kirstie J. "The effects of seaweed extracts on soilborne diseases, soil microbiology and the growth of wheat." Thesis, University of Bristol, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.336244.

Parekh, Nisha Rajnikant Parekh. "New approaches to the selective isolation of wall chemotype IV actinomycestes from soil." Thesis, University of Liverpool, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.333669.

Cundiff, Gary Thomas. "Using Arbuscular Mycorrhizae to Influence Yield, Available Soil Nutrients and Soil Quality in Conventional VS. Organic Vegetable Production." TopSCHOLAR®, 2012. http://digitalcommons.wku.edu/theses/1155.

Davies, Nicholas Julian. "Microbial response to simulated climate change in Antarctic fellfield soil." Thesis, University of Kent, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.242859.

Chattopadhyay, Suhana. "Ecosystem Controls on Soil Microbial Guilds: dynamics and carbon sequestration." Kent State University / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=kent1397749555.

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• (137); 2018

Isolation and Analysis of Microbial Communities in Soil, Rhizosphere, and Roots in Perennial Grass Experiments

Morgan r. mcpherson.

1 Department of Agronomy and Horticulture and Center for Plant Science Innovation, University of Nebraska-Lincoln

Ellen L. Marsh

Robert b. mitchell.

2 USDA-ARS, Wheat, Sorghum and Forage Research Unit, University of Nebraska-Lincoln East Campus

Daniel P. Schachtman

Plant and soil microbiome studies are becoming increasingly important for understanding the roles microorganisms play in agricultural productivity. The purpose of this manuscript is to provide detail on how to rapidly sample soil, rhizosphere, and endosphere of replicated field trials and analyze changes that may occur in the microbial communities due to sample type, treatment, and plant genotype. The experiment used to demonstrate these methods consists of replicated field plots containing two, pure, warm-season grasses ( Panicum virgatum and Andropogon gerardii) and a low-diversity grass mixture ( A. gerardii, Sorghastrum nutans, and Bouteloua curtipendula) . Briefly, plants are excavated, a variety of roots are cut and placed in phosphate buffer, and then shaken to collect the rhizosphere. Roots are brought to the laboratory on ice and surface sterilized with bleach and ethanol (EtOH). The rhizosphere is filtered and concentrated by centrifugation. Excavated soil from around the root ball is placed into plastic bags and brought to the lab where a small amount of soil is taken for DNA extractions. DNA is extracted from roots, soil, and rhizosphere and then amplified with primers for the V4 region of the 16S rRNA gene. Amplicons are sequenced, then analyzed with open access bioinformatics tools. These methods allow researchers to test how the microbial community diversity and composition varies due to sample type, treatment, and plant genotype. Using these methods along with statistical models, the representative results demonstrate there are significant differences in microbial communities of roots, rhizosphere, and soil. Methods presented here provide a complete set of steps for how to collect field samples, isolate, extract, quantify, amplify, and sequence DNA, and analyze microbial community diversity and composition in replicated field trials.

Introduction

Microbiome research has important implications for understanding and manipulating ecosystem processes such as nutrient cycling, organic matter turnover, and the development or inhibition of soil pathogens 1 , 2 . This area of research also holds great potential for understanding the impacts of soil microbes on the productivity of natural plant communities and agroecosystems. While there are many studies that have focused on the soil microbiome in natural ecosystems, fewer have focused on plant rhizosphere and endosphere microbes in agroecosystems 3 . In Nebraska, agriculture dominates the landscape across large parts of the state, making the studies of these soils where agriculturally important crops are grown a vital topic for research. The aim of this methods paper is to provide researchers with a standard set of protocols to describe the microbes present in agroecosystems, to determine how plant roots modify the microbial communities in the rhizosphere and endosphere, and to eventually understand the functions these microbes play in soil health and plant productivity.

The method presented here differs slightly from methods used by others 4 , 5 in that this paper is aimed at learning which microbes are exclusively inside the root and how they differ from microbes immediately outside the root in the rhizosphere. The amplicon sequencing used in this study identifies the microbial taxa found in the DNA sample and allows investigators to determine how the communities change depending on sample type or treatment. One of the key differences between this protocol and a very similar protocol used by Lundberg et al. 6 is that instead of sonication, this protocol uses surface sterilization with bleach and ethanol to remove the rhizosphere from the roots. Others have also used surface sterilization effectively 7 , 8 , 9 , 10 . These methods are not more advantageous than other methods, but slightly different. These methods are particularly well suited for large field experiments because with enough people it is possible to process over 150 field plots per day, which adds up to approximately 450 samples when partitioned into endosphere, rhizosphere, and soil. This manuscript describes in detail the methods used to sample in the field, process the material in the lab, extract and sequence the DNA, and provides a brief overview of the steps to analyze the resulting sequencing data.

1. Field Site Description

• Describe experimental field sites during collection periods. Determine the location of field (latitude, longitude, and altitude) using a GPS.
• Describe the sampling depth, sampling time, and soil texture.
• Environmental factors play important roles in shaping microbial communities. Record climate information such as annual mean temperature, annual precipitation, prior years’ crop rotation, tillage practices, method of fertilization, and history of the field site. Automated weather stations or other devices are useful to record the daily rainfall and temperature over a growing season.

2. Collection and Processing of Soil, Rhizosphere, and Root Field Samples

• Label a wash pan and bucket with a sticky note containing information about the plant material to be sampled. Include information such as plot number, plant genotype, and plant species. Carry the labeled bucket to the plot and leave the wash pan behind at the established workstation in the field.
• Pierce soil with a shovel to a depth of 30 cm to cut any of the lateral roots holding the plant in the soil. The approximate volume is 18 cm 3 . Randomly choose and collect two plants per plot from different areas within the plot.
• Excavate the plant roots by leveraging the shovel and place the root ball in the labeled bucket. Bring the labeled bucket with the excavated root ball back to the workstation in the field. Cut off and discard the aboveground plant biomass.
• Shake roots to manually remove soil or use a spade or a hand-held tiller to remove soil from the roots. Shaking the roots is sufficient to remove soil in very sandy soils. Wear gloves and place roots near the processing station.
• After shaking the roots, the bulk of the soil will be in the wash pan. Mix the soil in the wash pan and break up any soil clods with a hand-held tiller. Place a sample of soil that is free of debris into a labeled, 17.7 x 19.5 cm zipper storage bag and place in a cool place or on ice.
• Using pruning scissors sterilized in 70% EtOH, excise a variety of roots, approximately 4 to 6 roots per plant and each root around 9 - 12 cm in length. Place the excised roots (cutting as needed to fit) in a labeled 50 mL tube containing 35 mL of autoclaved, phosphate buffer (6.33 g/L NaH 2 PO 4 , 8.5 g/L Na 2 HPO 4 anhydrous, pH = 6.5, 200 µl/L surfactant). Note: The surfactant (see Table of Materials ) was added after autoclaving the phosphate buffer. The volume of the root ball and root lengths vary depending on plant age and plant species.
• Shake tubes for 2 min to release the rhizosphere from the surface of the roots. With forceps sterilized in 70% EtOH, remove the roots from the tube, blot briefly on paper towels, and place in a new, labeled 50 mL tube. Place both the tube containing the rhizosphere and the one containing roots on ice.

3. Processing of Field Samples in the Laboratory

• Add approximately 35 mL of 50% bleach + 0.01% Tween 20 to 50 mL root tubes collected in the field. Shake 50 mL tubes for 30 to 60 seconds. Pour off 50% bleach and add 35 mL of 70% EtOH. Shake for another 30 to 60 seconds.
• Pour off 70% EtOH and add 35 mL of sterile, ultrapure water. Shake for 1 minute. Repeat the water wash two more times. Note: To ensure our surface sterilization treatment is sufficient, we plated samples of water from the last rinse and observed no growth of bacteria (P. Wang, unpublished data, 2014). Other investigators have tested for root sterilization efficiency using similar methods 9 , 10 .
• Blot roots dry on clean paper towels. Use a clean paper towel for each sample. Note: Paper towels can be sterilized before use. We do not sterilize our paper towels. However, we use 1 paper towel per sample and keep towels wrapped until used.
• Using sterile forceps and pruning scissors, cut the roots into approximately 5 mm pieces and place cut roots in a clean, labeled 15 mL conical tube. Store samples at -80 °C until further processed.
• Shake 50 mL tubes containing rhizosphere samples from the field to resuspend the entire sample. Using a sterile, 100-µm-mesh cell strainer (see Table of Materials ), filter the resuspended sample into a new 50 mL tube.
• Centrifuge the tubes at 3000 x g for 5 min at room temperature. Immediately pour off and discard the supernatant.
• Place rhizosphere pellets in the 50 mL tubes on ice. Add 1.5 mL of sterile phosphate buffer (without surfactant) to rhizosphere pellets and vortex to suspend.
• Pipet the suspended liquid into a clean, labeled, 2 mL microfuge tube. Spin tubes at 15,871 x g for 2 min at room temperature. Immediately pour off the supernatant and drain the tubes on clean paper towels.
• Store the pellets at -20 °C until further processed. Note: Steps 3.2.2 - 3.2.4 are done to reduce the sample tube size for storage. It is more space efficient to store the smaller 2 mL tubes compared to the 50 mL tubes.
• Using a sterile metal spatula, fill a clean, labeled 2 mL tube with approximately 3 g of soil for DNA extraction. Avoid any small root pieces and debris. Store this soil sample at -20 °C. Rinse metal spatula in 70% EtOH between each sample.
• In a clean wash pan, empty the bag of soil into stacked sieves (see Table of Materials ), larger sieve on top of the smaller sieve, and manually sieve the soil through both sieves. Use a brush to carefully clean the sieves between samples.
• Set aside 100 to 125 g of sieved soil in a 17.7 x 19.5 cm zippered bag for future soil physicochemical and texture analysis. Place the bags of soil at 4 °C for short-term storage.
• Tare a labeled brown paper bag on a scale. Measure 40 to 45 g of sieved soil into the brown paper bag. Record the weight of the brown paper bag and soil on a data sheet and place bags in a drying oven set to 55 - 60 °C.
• After 72 hours, remove the bags from the drying oven. Allow the soil bags to cool for at least 30 min and then weigh.

4. Preparation of Processed Root Samples for DNA Extractions.

• Pour liquid nitrogen in a plastic beaker with clean spatulas and in a clean mortar and pestle to keep samples frozen throughout the grinding process.
• Place the frozen tissue in the mortar and grind with the pestle into a fine powder. Continually add liquid nitrogen throughout grinding to keep samples frozen.
• Use a spatula to place ground tissue in a clean, labeled 2 mL tube. Store at -80 °C.

5. Extraction of DNA from Soil and Rhizosphere Samples in 96-well Format

• Wipe down the work area with 70% EtOH and 1% bleach. Wear lab gloves during these steps. Remove the soil samples from -20 °C storage and allow to thaw in an ice bucket.
• Remove the sealing mat cover from a 96-well extraction plate that is provided with the DNA extraction kit. Place the sealing mat cover between 2 paper wipes to keep clean while not in use. To avoid contamination, use adhesive 8-well PCR strips to cover the 12 columns of the 96-well extraction plate.
• Tare a sterile weigh-funnel (size SM) on a scale and weigh out 200 to 250 mg of soil. Note: These sterile funnels are flattened on one side, so the funnel lays flat on a scale. The funnel is filled with soil and placed directly into a well. This technique avoids the loss of samples, minimizes spillage, and prevents cross-contamination.
• To uncover the first well of the extraction plate, carefully lift the adhesive strip up, place the neck of the filled weigh-funnel into the appropriate well, and gently guide the soil sample into the appropriate well. Replace the adhesive strip to cover the well.
• Repeat this process for every well of the plate, using a new, sterile funnel for each sample until the plate is filled. Leave one well empty as an extraction blank control. Note: One well is left blank on every extraction plate to serve as a negative (blank) control. This controls for contaminants that may be present in kit reagents 11 .
• Replace the sealing mat cover on the extraction plate and store the plate at -20 °C until ready for DNA extraction.
• Remove the rhizosphere samples from -20 °C storage and allow to thaw in an ice bucket.
• Follow step 5.1.2 above to prepare the DNA extraction plate.
• Place a clean paper wipe on a scale, then tare a sterile metal spatula on the scale. Use the spatula to carefully scoop out some of the rhizosphere pellet from a sample tube. Return the spatula to the scale and weigh between 200 and 250 mg of the rhizosphere sample.
• Carefully lift the adhesive strip to uncover the first well of the extraction plate, angle the filled spatula into the well, and scrape off the rhizosphere material into the appropriate well with a sterile toothpick.
• Rinse the metal spatula in water, followed by 70% EtOH between samples. Repeat this process for every well of the plate until the plate is filled, leaving one well empty as an extraction blank control.
• Extract soil and rhizosphere DNA using a kit optimized for soil (see Table of Materials ), following manufacturer’s protocol. Note: We use this specific kit to isolate soil and rhizosphere DNA because of the ability of the proprietary reagents to remove humic acid and other powerful PCR inhibitors found in soil.
• Quantify 92 samples and 4 concentrations of standards using a kit (see Table of Materials ; standards are included), according to manufacturer’s protocol.
• Quantify the remaining 4 samples that were removed from the plate to accommodate wells for the four standards using a kit (see Table of Materials ), according to manufacturer’s protocol.

6. Extraction of DNA from Root Samples in 96-well Format.

• Wipe down the work area with 70% EtOH and 1% bleach. Wear gloves during these steps.
• Keep ground root samples frozen at all times by placing samples in a bucket with dry ice.
• Fill a plastic beaker with liquid nitrogen and place antistatic microspatulas and sterile weigh-funnels (size XSM) in the plastic beaker to cool down.
• Remove the sealing mat cover from the 96-well extraction bead plate that is provided with kit and place it between 2 paper wipes to keep clean while not in use. Note: The newer version of this kit, released subsequent to the time we performed these DNA extractions, requires the user to supply the extraction bead plates. In the Table of Materials , we have listed the vendor catalog information needed to order these items.
• To avoid contamination, use adhesive 8-well PCR strips to cover the 12 columns of the 96-well extraction plate.
• Place the extraction bead plate on dry ice to keep samples in the wells frozen.
• Carefully lift the adhesive strip up to uncover the first well of the extraction plate, place the neck of the filled weigh-funnel into the appropriate well, and add 3 spatula scoops of ground root tissue. Replace the adhesive strip to cover the well. Note: The soil and rhizosphere are thawed on ice prior to weighing, whereas plant material is weighed out frozen. Frozen plant tissue, especially small amounts, is difficult to weigh on a scale without thawing. Weigh tests were done on ground root tissue to determine how many spatula scoops were sufficient. Note that the manufacturer of the kit does not require an exact amount of tissue but recommends around 50 mg. Different plant sample types will vary and the user will need to determine the appropriate amount.
• Repeat this process for every well of the plate until the plate is filled.
• Store plate at -20 °C until ready for DNA extraction.
• Extract DNA using a kit optimized for plants (see Table of Materials ) following the manufacturer's protocol. Note: We use this kit that is designed specifically for plant tissue to achieve maximum yields from the root samples. Unlike soil and rhizosphere, the humic acid and other contaminants are less of a problem for the root tissue.
• Quantify the DNA as in step 5.4.

7. Amplification and Sequencing the Isolated DNA.

• Amplify the V4 region of the 16S gene with a proof reading polymerase (see Table of Materials ) as described in Gohl et al. 12 Barcode samples with different indexing primers and pool before sequencing. Sequence using the methods described by Gohl et al 12 using a two step PCR process with V4 primers ( Supplemental File 1 ). Note: Some of the methods for these steps are described in detail elsewhere 12 , 13 , 14 and therefore will not be described here. For the root samples, PNA blockers were added to reduce the amount of plastid DNA amplified from the plant tissue, which has previously been fully described 15 . Two controls were used in sequencing, a negative control which included the extraction plate blank controls (see note after step 5.1.5), and a mock community of a known population of bacterial DNA (see Table of Materials ) which served as a positive control. Note: In most cases, the MiSeq Reagent Kits v3 are used in the 2 X 300 base paired end mode. For the samples in this manuscript, the Illumina HiSeq 2500 was used in rapid mode with 250 Paired-end (2 x 250) mode. All the sample were sequenced in the same lane.
• Prepare the sequencing data using USEARCH 17 . Note: USEARCH is available online with full instructions (https://www.drive5.com/usearch/).
• Demultiplex the sequencing data using index reads or barcodes to assign Illumina reads to samples.
• Merge the paired-end reads to get consensus sequences. Use the command: usearch -fastq_mergepairs *R1*.fastq -relabel @ -fastq_maxdiffs 10 -fastq_minmergelen 230 -fastq_maxmergelen 320 -fastq_pctid 80 -fastqout merged.fq. Note: The parameters are set through referencing the USEARCH instruction manual.
• Remove the primers from the sequencing data to avoid substitutions in the primer sequences, which may be caused by the PCR reaction. Use the command: usearch -fastx_truncate merge.fq -stripleft 19 -stripright 20 -fastqout stripped.fq.
• Filter sequencing data to remove the low-quality reads and keep high quality operational taxonomic unit (OTU) sequences. Use the command: usearch -fastq_filter stripped.fq -fastq_maxee 1.0 -fastaout filtered.fa.
• Perform the dereplication to identify the set of unique OTU sequences. Use the command: usearch -fastx_uniques filtered.fa -fastaout uniques.fa -sizeout -relabel Uniq.
• Cluster OTUs with 97 – 100% sequence similarity to designate the unique OTUs. Use the command: usearch -cluster_otus uniques.fa -minsize 2 -otus otus.fa -relabel Otu. Note: This step also incorporates the removal of singletons from the clustered OTUs and removal of chimeras from sequencing data.
• Create an OTU table in USEARCH. Use the command: usearch -usearch_global stripped.fq -db otus.fa -strand plus -id 0.97 -otutabout otutable.txt. Note: This command generates a table with the number of reads (counts) of all OTUs for each sample. The OTU table is used for downstream steps including differential abundance analyses and microbial diversity analyses. Example of OTU table is shown in supplemental figure 2.
• Conduct a rarefaction analysis in QIIME v1.9.1 18 . Note: The rarefaction curve is calculated using the OTU table to determine whether the depth of the sequencing properly samples the microbial community. Example of rarefaction curves are shown in Supplemental Figure 3 .
• Conduct an alpha diversity analysis 18 . Use alpha_rarefaction.py in QIIME v1.9.1 to calculate the diversity of the microbial community within each sample. Note: This analysis calculates diversity indices such as Shannon 19 , Simpson 20 , and Chao1 21 .
• Conduct a beta diversity analysis 18 , 22 . Use the Python script: beta_diversity_through_plots.py in QIIME v1.9.1 Bray–Curtis dissimilarity matrix. Note: This analysis compares the microbial community composition between samples.
• Conduct statistical analyses between groups. Use the distance matrices calculated for PERMONOVA using the adonis and anova function in the vegan package 23 v2.4.5 in RStudio 16 . Conduct canonical analysis of principal coordinates (CAP) analysis using the capscale function in the vegan package. Visualize data using ggplot2 package 24 v2.2.1 in RStudio.

Representative Results

The representative results presented in this manuscript come from a field site established in 2012 at the University of Nebraska-Lincoln Agriculture Research Division Farm near Mead, NE. Prior to the experiment, the site had been managed as a corn-soybean rotation. The study site was located on three different types of soils, but the data was analyzed as if all changes in measured soil properties were due to the treatments imposed.

The field site contained two, pure, stands of switchgrass ( P. virgatum cv Liberty) and big bluestem ( A. gerardii ) as well as a low-diversity grass mixture containing big bluestem, indiangrass ( S. nutans ), and 'Butte' sideoats grama ( B. curtipendula ). The three warm-season grass plots were in a randomized complete block design that was replicated three times. Nested into the three different grass plots were two nitrogen (N) fertilization treatments, which were 56 (N1) and 112 (N2) kg N ha -1 of applied urea. At the time of microbiome sampling at the end of the growing season, the soil contained 8.0 ± 1.1 (mean ± SD) ppm nitrate in the plots fertilized with 112 kg N ha -1 and 6.8 ± 0.7 (mean + SD) ppm nitrate in the plots fertilized with 56 kg N ha -1 . The plots had been fertilized once a year. The warm-season grass plots were designated as the main plots (8000 m 2 ) and N treatments were the split plots (4000 m 2 ). Big bluestem was seeded as a 50:50 blend of 'Bonanza' and 'Goldmine' and Indiangrass was seeded as a 50:50 blend of 'Scout' and 'Warrior'. The plots were planted in 2012, and the first N application occurred in the spring of 2013.

The soil and root sampling was conducted on September 15, 2014. The work described below was conducted on a field that was set up as a split-plot randomized design with three replicates ( Figure 1 ). The average sequencing depth of all the samples for endosphere were as follows: 4871 ± 5711 (mean ± SD), rhizosphere: 40726 ± 14684, soil: 38184 ± 9043. One of the largest sources of variation in these experiments, using the methods described, is the difference in microbial communities found between sample types ( Figure 2 ). In this representative data set, the rhizosphere and soil appear to be more similar in composition to each other than the endosphere ( Figure 2A ). However, there were also highly significant ( p = 0.001) differences in microbial community composition between rhizosphere and soil ( Figure 2B ). The total variation accounted for in these experiments analyzed by sample type was 26%.

Alpha diversity analysis showed that the microbial communities in the endosphere were lower in sample diversity as compared to soil and rhizosphere ( Figure 3 ). The only significant differences in diversity between the grass species in any compartment were between the endosphere samples of big bluestem and switchgrass, with switchgrass having significantly higher microbial species diversity ( Figure 3 ). The relative abundance analysis ( Figure 4 ) highlights the dominance of Proteobacteria followed by Actinobacteria in all sample types. Soil and rhizosphere are also dominated by Acidobacteria and Chloroflexi whereas the endosphere had a larger relative abundance of Bacteriodetes .

In this experiment, plants were grown with two different amounts of N fertilizer and therefore we analyzed the data to determine whether there were treatment effects. Treatment effects accounted for 12% of the total variation but were not significantly different although in the ordination the two treatments look different ( Figure 5 ). This highlights the importance of statistical analyses for these datasets rather than visual inspection or qualitative judgments.

Plant-influenced differences in the microbiome of plant tissues and soil were visualized using a constrained method of ordination. Statistical differences were determined using a PERMANOVA analysis to test whether specific variables, such as species, result in significantly different microbial community composition between samples. When all the sample types were analyzed together, a highly significant difference was found in microbial community composition due to plant species ( Figure 6 ). In this experiment, the amount of variation accounted for by plant species was 6.7%. Finally, each sample type was analyzed individually to determine which of the sample types might be driving the significant plant species effect. Only in the endosphere was there a highly significant difference ( p = 0.001) between the microbial community compositions of the different plant species ( Figure 7 ). In the other sample types, the species effect was not significant when analyzed individually. In the endosphere, the percent variation due to species was 27%, whereas it was lower in rhizosphere (18%) and soil (15%). This further highlights the importance of analyzing each tissue type individually.

Figure 1: Example of the experimental field design. Experimental field design illustrating a randomized complete block design in triplicate of the field site located at the University of Nebraska-Lincoln Eastern Nebraska Research and Extension Center near Mead, NE. For full site description see the Results section. N1 is the low (56 kg N ha -1 urea) and N2 (112 kg N ha -1 urea) is the higher nitrogen rate that was applied. Please click here to view a larger version of this figure.

Figure 2: Beta diversity analysis comparing the microbial composition in the different sample types including endosphere, rhizosphere, and soil from the perennial grass sampling in 2014. The analysis was carried out using a Python script in QIIME1.9.1 to produce the Bray-Curtis dissimilarity matrix. Principal coordinates analysis (PCoA) based on the Bray-Curtis dissimilarity matrix was visualized in RStudio. PCoA1 and PCoA2 indicate the first and second largest variance explained by the PCoA analysis. PERMANOVA statistical analysis was performed to determine the significance between sample types, and the p value is shown on the top right corner. Each symbol in the figures represent the entire microbial community for each sample. (A) Endosphere, rhizosphere and soil sample types were analyzed together. All 87 samples were rarefied to 486 sequences per sample. (B) Rhizosphere and soil samples were analyzed together. All 59 samples were rarefied with 8231 sequences. Please click here to view a larger version of this figure.

Figure 3: Alpha diversity analysis using Shannon index for each species in the endosphere, rhizosphere and soil. The analysis was carried out using a Python script in the QIIME1.9.1. Rarefaction was done for the endosphere, rhizosphere, and soil sample types respectively with 486, 17154 and 8231 sequences per sample. Boxes indicate the 25th and 75th percentiles (first and third quartiles). The horizontal line within the box denotes the median and the red plus shows the mean. Whiskers show the range of the data excluding outliers (which are shown as black dots) that fell more than 1.5 times the interquartile range ( n = 6 for each sample except for sideoats grama mix where n = 5). The Shannon index of all five species in the endosphere were lower than both rhizosphere and soil. Non-parametric Wilcoxon rank sum test was used to determine the significance between the species and only significant differences between species were shown on top of the boxes. Please click here to view a larger version of this figure.

Figure 4: Relative abundance on the phylum level in the endosphere, rhizosphere, and soil. Samples were analyzed to compare the abundance of microbial phyla among different samples types ( n = 29 for each sample type). The analysis was carried out using a Python script in QIIME1.9.1 from the OTU table. The different colors inside the pie chart denote the phyla. The percentage indicates the relative abundance of each phylum in each sample type. The phylum information was annotated using the Ribosomal Database Project classifier (RDP) 25 . Please click here to view a larger version of this figure.

Figure 5: Analysis using treatment as constraining factor between all sample types. Canonical analysis of principal coordinates (CAP) analysis was performed to determine whether there were differences in microbial community composition between treatments. For each N treatment, n = 42 for N1 (56 kg N ha -1 ) and n = 45 for N2 (112 kg N ha -1 ). The Bray-Curtis dissimilarity matrix was generated using a python script in the QIIME1.9.1. CAP analysis based on the Bray-Curtis dissimilarity matrix was done by constraining the treatment as the factor in RStudio. PERMANOVA analysis was performed to determine whether treatment differences were significant, and the p value is shown on the top right corner. Please click here to view a larger version of this figure.

Figure 6: Analysis using plant species as constraining factor between all sample types. Analysis was conducted to determine whether there were differences in the microbial community composition between plant species in all sample types. Principal coordinates ordination and CAP analysis of all sample types (endosphere, rhizosphere, and soil) were done using a Bray-Curtis dissimilarity matrix. The Bray-Curtis dissimilarity matrix was generated using the Python script in QIIME1.9.1. CAP analysis based on the Bray-Curtis dissimilarity matrix was done by constraining the plant species as the factor in RStudio. PERMANOVA statistical analysis was performed to determine the significance between plant species, and the P value is shown on the top right corner. Each symbol in the figures represents the entire microbial community for that sample. n = 18 for each species in all sample types except n = 15 for the sideoats grama mix. Please click here to view a larger version of this figure.

Figure 7: Example of CAP analysis using species as constraining factor for each sample type individually. Principal coordinate ordination and CAP analysis of each sample type (endosphere, rhizosphere, and soil) using Bray-Curtis dissimilarity matrix. Each sample type was rarefied to 486, 17154, and 8231 reads per sample respectively in endosphere, rhizosphere and soil. Species was used as the factor to constrain the ordination. PERMANOVA statistical analysis was performed to determine the significance between plant species in each sample type, and the p value is shown on the top right corner. Each symbol in the figure represents the entire microbial community for each sample. Sample size is n = 29 for each sample type, n = 6 for each plant species in each sample type except for the sideoats grama mix ( n = 5). Please click here to view a larger version of this figure.

The methods described in this manuscript should enable scientists to easily enter the field of soil and plant metagenomics. Over the years, we have refined our methods since conducting the experiment described in this manuscript. One change is that we now pre-label tubes before going out to the field to sample. Our lab uses a barcoding system and a label printer. The label printer not only saves time when labeling tubes, but also makes everything easier to track and to correctly identify samples without the vagaries of human hand writing. Another critical point is that we try to process the material after bringing it back from the field as soon as possible. We aim to freeze the soil used for DNA analysis, sterilize and freeze the roots, and filter and freeze the rhizosphere within 12 to 36 hours after returning from the field. The DNA extraction procedures are lengthy with many steps, particularly for soil and rhizosphere, so we purchased a robot (Kingfisher Flex, ThermoFisher) that minimizes the hands on time for the DNA extraction protocols, reduces human error that may be introduced, and improves the consistency in the way different batches of soil, roots, or rhizosphere are processed. When working with plant material it is important to decide on the root type to be studied or to take a variety of root types to get a "representative sample". Maintaining roots and leaves in a frozen state when conducting the DNA extractions is important, as is ensuring there is no cross-contamination between samples when filling 96-well DNA extraction plates. Another important factor to consider is the number of replicates to be used when designing field experiments and using a complete randomized design where possible 26 . Due to high field variability it may be necessary to have a large number of replicates to detect small differences. Finally, from our experience it is essential to make sure soils are not too wet when excavating the roots. If the soils are saturated with water it is not only messy to work with, but it is also very difficult to define the rhizosphere and to remove the soil from the roots.

One modification that was made early on during the development of these methods was instead of shaking the tubes by hand to release the rhizosphere we upgraded to vortexers powered by a gas generator to make the work easier in the field and more standardized in terms of the time and manner that each tube was agitated. One limitation of the amplicon sequencing approach is that the taxonomic resolution of the results is often limited and many OTUs are unknown or only known at the family or genus level. This field of research is rapidly evolving so it is important to be aware of new and developing approaches, particularly for data analysis that may enhance the resolution of the results.

These protocols are only for studying bacteria and archaea, not fungi. The use of different primers for amplification will allow for the study of fungal communities using the same DNA samples 27 , 28 . These methods do not require the purchase of large amounts of equipment because the methods can be simplified. The methods we describe here are mainly for determining "who is there", but the field is quickly evolving into asking important questions about function, which may be addressed by using shotgun sequencing methods, isolation and testing the functionality of microbes, or sequencing whole microbial genomes.

The representative results highlight the differences in microbial communities that may be identified using the methods described. Using a beta-diversity approach to the data analysis 22 , compositional differences were shown between sample types. These difference have been clearly observed in most other studies where endosphere, rhizosphere, and soil contain unique microbial communities 3 . The Shannon diversity index was calculated to determine the abundance and evenness of the microbial species present within each plant species in the endosphere, rhizosphere, and soil. As shown in this study and in many others, alpha diversity is highest in the soil, decreasing slightly in the rhizosphere and then decreasing significantly in the endosphere 3 , 5 , 29 . These results indicate that the methods described here are suitable for identifying compositional changes in the endosphere, rhizosphere, and soil.

The dominance of the Proteobacteria is a common finding in studies on endosphere and soil 30 , 31 , 32 . Endosphere generally has a lower diversity of microbial species with a higher relative abundance of the Proteobacteria . This again highlights that the results here are representative of other findings in the literature. The treatment effects in this study were not significantly different and two major reasons for that may be that the differences imposed by the treatments were not large enough to generate sufficient variation to detect and that this sampling was done at the end of the growing season, when the fields may have had sufficient time to draw down the nitrogen to similar levels, which is what was measured at the end of the season. In another study using similar fertilization rates over a longer period of time, only relatively small changes in the composition of the microbiome were measured 33 . Other studies have shown changes in both fungal and bacterial communities due to nitrogen fertilizer 34 , 35 .

Plant species are known to play roles in determining their microbiomes 3 , 32 , 36 and even small differences in microbial community variation have been demonstrated between different plant genotypes within a single species 37 . In this study, a significant difference in microbial community composition was found between plant species. In all the sample types it appeared that switchgrass had the most distinct microbial composition, but differences between species were only statistically significant in the endosphere. Rhizosphere community composition may have become significant if more replicates were available for analysis.

The combined field, lab, and analytical protocols described here provide a powerful method for studying how different factors influence the composition of microbial communities in soils, rhizosphere, and the endosphere of roots 36 . There is a great deal of work to be done in the area of studying microbiomes, particularly in agricultural fields. Important questions about how yields are altered by the soil microbiome have yet to be fully elucidated. Even the most basic questions regarding how crop rotations influence the soil microbiome, how timing alters the microbiome, how abiotic stress alters the microbiome, how soil type interacts with these factors to alter the microbiome, and whether there are universal microbes in certain crops or regions of the USA are all open questions. These methods will also be useful for epidemiological studies to identify the presence and persistence of pathogenic and beneficial bacteria. Another future horizon for these methods will be to start integrating the DNA methods described here with plant and microbe RNA and metabolite data. Additional improvement and testing of more variables will be important for further optimization of these protocols.

Disclosures

The authors have nothing to disclose.

Acknowledgments

The development of this manuscript is supported by the National Science Foundation EPSCoR Center for Root and Rhizobiome Innovation Award OIA-1557417. The data collection was supported by funds from University of Nebraska-Lincoln, Agricultural Research and Development and by a Hatch Grant from USDA. We also acknowledge support from the USDA-ARS and support was provided by the Agriculture and Food Research Initiative Competitive Grant no. 2011-68005-30411 from the USDA National Institute of Food and Agriculture to establish and manage these fields.

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Dissertations from 2024 2024.

ROLE OF GLUTATHIONE DEGRADATION PATHWAY GENES FOR GLUTATHIONE HOMEOSTASIS AND TOXIC METAL TOLERANCE IN PLANTS , Gurpal Singh, Plant, Soil & Insect Sciences

Dissertations from 2023 2023

Using Cover Crops and Arbuscular Mycorrhizal (AM) Fungi to Enhance the Sustainability of Hardneck Garlic Production in the Northeast , Alexandra Smychkovich, Plant, Soil & Insect Sciences

THE ROLE OF NATURAL ORGANIC MATTER IN THE FORMATION OF SILVER NANOPARTICLES, AND AGGREGATION AND BIOLOGICAL RESPONSE OF NANOPLASTICS , Sicheng Xiong, Plant, Soil & Insect Sciences

Dissertations from 2019 2019

SMALL FRUIT PHENOLICS: PHENOLIC VARIATIONS AND RELATED HEALTH RELEVANCE , Susan Cheplick, Plant, Soil & Insect Sciences

Introducing faba bean as a new multi-purpose crop for Northeast U.S.A. , Fatemeh Etemadi, Plant, Soil & Insect Sciences

EFFECTS OF FERTILIZATION AND DRYING CONDITIONS ON THE QUALITY OF SELECTED CHINESE MEDICINAL PLANTS , Zoe Gardner, Plant, Soil & Insect Sciences

FACTORS INFLUENCING SPARTINA ALTERNIFLORA PRODUCTIVITY IN RELATIONSHIP TO ESTUARY INLET OPENING ELLISVILLE MARSH, PLYMOUTH, MA , Ellen K. Russell, Plant, Soil & Insect Sciences

Dissertations from 2018 2018

Integrated Transcriptomic and Metabolomic Approaches to Identify Genes and Gene Networks Involved in Lipid Biosynthesis in Camelina sativa (L.) Crantz Seeds , Hesham Abdullah, Plant, Soil & Insect Sciences

Dissertations from 2017 2017

Adsorption of Biomolecules on Carbon-Based Nanomaterial as Affected by Surface Chemistry and Ionic Strength , Peng Du, Plant, Soil & Insect Sciences

Investigation of Fungicide Resistance Mechanisms and Dynamics of the Multiple Fungicide Resistant Population in Sclerotinia homoeocarpa , Hyunkyu Sang, Plant, Soil & Insect Sciences

Dissertations from 2016 2016

Uptake and Accumulation of Engineered Nanomaterials by Agricultural Crops and Associated Risks in the Environment and Food Safety , Yingqing Deng, Plant, Soil & Insect Sciences

Evaluating the Role of Glutathione in Detoxification of Metal-Based Nanoparticles in Plants , Chuanxin Ma, Plant, Soil & Insect Sciences

Aggregation and Coagulation of C60 Fullerene as Affected by Natural Organic Matter and Ionic Strength , Hamid Mashayekhi, Plant, Soil & Insect Sciences

Dissertations from 2015 2015

Assessing Kiln-Produced Hardwood Biochar for Improving Soil Health in a Temperate Climate Agricultural Soil , Emily J. Cole, Plant, Soil & Insect Sciences

Cover Crop and Nitrogen Fertilizer Management for Potato Production in the Northeast , Emad Jahanzad, Plant, Soil & Insect Sciences

Evaluation of Leafy Green Species Popular Among Ethnic Groups for Production and Markets in the Northeastern USA , Ricardo A. Orellana, Plant, Soil & Insect Sciences

Effects of Overexpression of SAP12 and SAP13 in Providing Tolerance to Multiple Abiotic Stresses in Plants , Parul R. Tomar, Plant, Soil & Insect Sciences

Dissertations from 2014 2014

PRODUCTION, MARKETING, AND HANDLING PRACTICES TO EXPORT MCINTOSH APPLES TO CENTRAL AMERICAN MARKETS , Mildred L. Alvarado Herrera, Plant, Soil & Insect Sciences

Identification and Epidemiological Features of Important Fungal Species Causing Sooty Blotch on Apples in the Northeastern United States , Angela Marie Madeiras, Plant, Soil & Insect Sciences

Increasing Nutrient Density of Food Crops through Soil Fertility Management and Cultivar Selection , Md. J. Meagy, Plant, Soil & Insect Sciences

ASSESSING BEST MANAGEMENT PRACTICES FOR IMPROVING SWITCHGRASS ESTABLISHMENT AND PRODUCTION , Amir Sadeghpour, Plant, Soil & Insect Sciences

Dissertations from 2013 2013

Evaluation of a Split-Root Nutrition System to Optimize Nutrition of Basil , Ganisher Djurakulovich Abbasov, Plant, Soil & Insect Sciences

Understanding the Links Between Human Health and Climate Change: Agricultural Productivity and Allergenic Pollen Production of Timothy Grass(Phleum pratense L.) Under Future Predicted Levels of Carbon Dioxide and Ozone , Jennifer M. Albertine, Plant, Soil & Insect Sciences

Use of Flame Cultivation as a Nonchemical Weed Control In Cranberry Cultivation , Katherine M. Ghantous, Plant, Soil & Insect Sciences

Dissertations from 2012 2012

Management of Switchgrass for the Production Of Biofuel , Leryn Elise Gorlitsky, Plant, Soil & Insect Sciences

Functional characterization of members of plasma membrane intrinsic proteins subfamily and their involvement in metalloids transport in plants , Kareem A Mosa

Influence of Phosphate on the Adsorption/Desorption of Bovine Serum Albumin on Nano and Bulk Oxide Particles , Lei Song, Plant, Soil & Insect Sciences

Biological control of the ambermarked birch leafminer (Profenusa thomsoni) in Alaska , Anna L Soper

Dissertations from 2011 2011

Armillaria in Massachusetts Forests: Ecology, Species Distribution, and Population Structure, with an Emphasis on Mixed Oak Forests , Nicholas Justin Brazee, Plant, Soil & Insect Sciences

Functional characterization of stress associated proteins (SAPS) from arabidopsis , Anirudha R Dixit

Developing an Efficient Cover Cropping System for Maximum Nitrogen Recovery in Massachusetts , Ali Farsad, Plant, Soil & Insect Sciences

Bacterial Toxicity of Oxide Nanoparticles and Their Effects on Bacterial Surface Biomolecules , Wei Jiang, Plant, Soil & Insect Sciences

Population dynamics of the Hemlock Woolly Adelgid (Hemiptera: Adelgidae) , Annie F Paradis

Dissertations from 2010 2010

Influence of natural organic matter (NOM) and synthetic polyelectrolytes on colloidal behavior of metal oxide nanoparticles , Saikat Ghosh

Dissertations from 2009 2009

Proline-associated antioxidant enzyme response in cool-season turfgrasses under abiotic stress , Dipayan Sarkar

Dissertations from 2008 2008

Characterization of adsorbed organic matter on mineral surfaces , Seunghun Kang

Effects of fire mitigation on post-settlement ponderosa pine non-structural carbohydrate root reserves , Jonathan Thomas Parrott

Dissertations from 2007 2007

Soilless culture of moringa (Moringa oleifera Lam.) for the production of fresh biomass , George William Crosby

Effects of reducing phosphorus nutrition on plant growth and phosphorus leaching of containerized greenhouse crops , Roger A Gagne

Performance and microbial evaluation of an artificial wetland treatment system for simulation model development , Lesley A Spokas

Dissertations from 2006 2006

Structures and phenanthrene sorption behavior of plant cuticles and soil humic substances , Elizabeth Joy Johnson

Activity of primisulfuron and Alternaria helianthi as affected by leaf surface micro-morphology and surfactants , Debanjan Sanyal

Dissertations from 2005 2005

Patterns of predation by natural enemies of the banana weevil (Coleoptera: Curculionidae) in Indonesia and Uganda , Agnes Matilda Abera-Kanyamuhungu

Upright dieback disease of cranberry, Vaccinium macrocarpon Ait.: Causal agents and infection courts , Nora J Catlin

Myotropic peptide hormones and serotonin in the regulation of feeding in the adult blow fly Phormia regina, and the adult horse fly Tabanus nigrovittatus , Aaron T Haselton

Evaluation of organic turfgrass management and its environmental impact by dissolved organic matter , Kun Li

Dynamics of plum curculio, Conotrachelus nenuphar (Herbst.) (Coleoptera: Curculionidae), immigration into apple orchards , Jaime Cesar Pinero

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Evaluation of paper mill sludge as a soil amendment and as a component of topsoil mixtures , Tara A O'Brien

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• Review Article
• Published: September 2007

Visualization, modelling and prediction in soil microbiology

• Anthony G. O'Donnell 1 ,
• Iain M. Young 2 ,
• Steven P. Rushton 1 ,
• Mark D. Shirley 1 &
• John W. Crawford 2  

Nature Reviews Microbiology volume  5 ,  pages 689–699 ( 2007 ) Cite this article

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Soil microbiology is enjoying a period of challenge and discovery made possible by the availability of new approaches for characterizing microbial communities and for imaging the soil environment.

Soils are highly complex and the development of soil microbiology as a systems science requires that microbial ecologists take full account of the spatio–temporal heterogeneity of microbial communities and their physical environments. The evolution of microbial communities (diversity) and their response in space and time (function) can be seen as emergent properties of this physicochemical environment.

The introduction of imaging techniques such as fluorescence in situ hybridization (FISH), FISH-microautoradiography (MAR), nano-secondary ion mass spectrometry (Nano-SIMS) and X-ray tomography offer new opportunities for locating microorganisms in their three-dimensional (3D) environment and for relating this to selected functions. However, none of the current analytical approaches offer an ideal solution for quantitatively imaging microorganisms in their physical environment, and further developments are needed.

Innovations in modelling are providing the tools needed to tackle the twin problems of integrating the physical heterogeneity of the soil environment with the dynamics of microbial communities. These methods have all been derived from developments in physics and have the advantage of enabling the stochastic behaviour of individual components of these complex, spatially segregated systems to be modelled.

Three modelling approaches are described. The first of these, individual based (IB) models, makes it possible to deal with individual particles and simulate their behaviours stochastically. General IB models are used when variability in individual components are deemed important in governing system processes. However, IB models are limited in their application to soils as they cannot model the dynamic impacts of microbial activity on the physical environment.

The lattice Boltzmann (LBM) method can be used to describe the multiphase transport processes typical of soil environments and is itself an IB model that simulates the 3D environment by tracking individual particles. It has the advantage over general IB models in that it uses interaction rules to reproduce surface tension and viscosity effects and can accommodate how these are modified by microbial growth and activity.

Network models are based on graph theory and, like IB and LBM, can be used to model individual components such as colonies of individual cells in a biological system. They differ in their application from IB and LBM in that the interactions between individual components are modelled as individual entities rather than as emergent properties. An important limitation of network models is that they currently do not explicitly address space.

The introduction of new approaches for characterizing microbial communities and imaging soil environments has benefited soil microbiology by providing new ways of detecting and locating microorganisms. Consequently, soil microbiology is poised to progress from simply cataloguing microbial complexity to becoming a systems science. A systems approach will enable the structures of microbial communities to be characterized and will inform how microbial communities affect soil function. Systems approaches require accurate analyses of the spatio–temporal properties of the different microenvironments present in soil. In this Review we advocate the need for the convergence of the experimental and theoretical approaches that are used to characterize and model the development of microbial communities in soils.

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Acknowledgements

We are grateful to the anonymous reviewers for helpful and constructive comments on the manuscript. Our work in this area is supported by a range of sponsors, but in particular by the Biotechnology and Biological Sciences Research Council (BBSRC), the Natural Environment Research Council (NERC) and the Engineering and Physical Sciences Research Council (EPSRC), UK.

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Anthony G. O'Donnell, Steven P. Rushton & Mark D. Shirley

SIMBIOS Centre, University of Abertay, Bell Street, Dundee, DD1 1HG, Dundee, UK

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Although there is no sharp demarcation, macropores allow the movement of air and percolating water, whereas micropores, under normal field conditions, are generally filled with water and limit the movement of air. Water movement in micropores is usually restricted to slow capillary movement. A good illustration of the importance of pore-size distribution is a sandy soil, where despite a low total porosity the movement of air and water is rapid because macropores dominate.

As used in this Review, a capillary force enables water to move against gravity and is the net effect of the attractive force of water for the solid matrix through which it moves (adhesion) and the surface tension of water. Surface tension of water is largely a consequence of the attraction of polar water molecules for each other (cohesion).

Refers to the movement of soil water under the force of gravity. Gravitational water drains easily from soils and is not influenced by interactions with the solid matrix.

The soil microbial biomass concept assumes that the entire soil microbial population (bacteria, fungi, protozoa and so on) can be treated as a single entity. It is easy to measure and has been used extensively in soil science to assess and predict the impact of management, climate, pollution and other factors on the soil biota.

Refers to the adaptation of microorganisms to growth in natural environments. In soils, the physiology of individual members of a population can change according to the individual biotic and abiotic environments. It is the distribution of these physiologies in space and time that delivers soil functions.

Kriging is a set of geostatistical methods that are used to interpolate values of spatial patterns at unsampled points. Kriging recognizes that in any set of samples or measurements there may be underlying and systematic spatial patterning of the data.

Mean field theory deals with multiple system components by replacing the complexity of interactions with an average interaction. The accuracy of mean field analyses is dependent on the number of interacting systems. High dimension systems are more accurate.

Graph theory is the study of graphs, which in mathematics and computer sciences are used to model pair-wise relationships between objects. The interactions (edges) between each pair of objects (vertices) can be directed (for example, vertex A predates vertex B), or undirected (for example, vertices A and B are in competition).

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O'Donnell, A., Young, I., Rushton, S. et al. Visualization, modelling and prediction in soil microbiology. Nat Rev Microbiol 5 , 689–699 (2007). https://doi.org/10.1038/nrmicro1714

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Soil microbial ecology.

Plants, Soils & Climate

The study of biota that inhabit the soil, their functions, the processes that they mediate, and the effect of their activities on the character of the soil and the growth and health of plant life. Soil microbiology focuses on the soil viruses, bacteria, actinomycetes, fungi and protozoa. Soil microbiology also has tradionally included investigations of the soil animals such as neatods, mites, and other microarthrowpods. Modern soil microbiology represents an integration of microbiology with the conepts of soil science, chemistry, and ecology to understand the functions of microorganisms in the soil environment.

Professor Jeanette M. Norton  (Jenny) is Professor of Soil Microbiology in the Department of Plants, Soils and Climate, an Ecology Center Associate and an Adjunct Professor in Biology at Utah State University. Dr. Norton’s research focuses on understanding key organisms in the nitrogen and carbon cycles of terrestrial systems on many different levels -  from individual genes to ecosystem processes. Dr. Norton is on the steering committee for the Nitrification Network ( nitrificationnetwork.org ). Dr. Norton has served on the editorial boards of Applied Environmental Microbiology and Soil Science Society America Journal and as panelist for NSF and USDA. She is dedicated to student research mentoring on the graduate and undergraduate levels. Dr. Norton graduated with a Bachelor of Science in Forest Biology from the State University of New College of Environmental Science and Forestry in 1980, received her PhD. in Soil Science (Microbiology) under the mentoring of Prof. Mary Firestone at University of California, Berkeley in 1991 and gained further experience as a post-doctoral researcher at the NSF Center for Microbial Ecology, Michigan State University with Professor Eldor Paul and Professor James Tiedje. Dr. Norton is a member of the American Society of Microbiology, Soil Science Society of America, International Society of Microbial Ecology and the Society for the Advancement of Chicanos and Native Americans in Science (SACNAS). Curriculum Vita

Brittany Johnson

Research Assistant

Wayne Breon

Undergraduate Research Assistant

Dr. Jenny Norton’s research focuses on understanding key organisms in the nitrogen and carbon cycles of terrestrial systems on many different levels - from individual genes to ecosystem processes. Key areas of research interest include:

• Ecology of nitrification and genomics of ammonia oxidizing bacteria.
• Microbial carbon and nitrogen cycling in soils.
• DNA probes for enzymatic functions.
• Microbial / plant interactions in waste management.
• Role of microbial community structure in biogeochemistry and bioremediation.

Currently funded projects in these areas include (click on links below for proposal abstracts):

• Improving the understanding of the genomics of nitrifiers, characterizing the processes in agricultural and wildland systems, and delineation of limiting factors for nitrification in water delivery/wastewater treatment systems. (UAES)
• Development of a Research Coordination Network to organize activities of researchers in this field and to disseminate results. (NSF)
• Genome sequencing for Nitrosomonas sp. AL212 and Isolate IS-79, ammonia-oxidizing bacteria adapted for growth at low ammonia concentrations. (JGI)
• Describing functional diversity in the root-zone of microbial communities responsible for selected N transformations in agroecosystems under contrasting nitrogen management.
• Recovering and characterizing novel bacterial and fungal genes encoding key enzymatic functions in N transformations.
• Delineation of the role of bacterial and archaeal ammonia oxidizers in soil nitrification.
• Delineating positive feedbacks from cheatgrass-induced changes in nutrient cycling, and developing management strategies to speed reestablishment of perennial plant species on cheatgrass-dominated rangelands (USDA).
• Determining plant-soil feedbacks in the shrub steppe ecosystem - collaboration with investigators to characterize microbial communities using next-generation sequencing of phylogenetic and functional genes extracted from soils (see A. Kumultiski ).

Functional And Molecular Diversity In Nitrogen Cycle Enzymes Under Contrasting Agricultural Management Systems

Non-Technical Summary: Human activities have dramatically altered the global nitrogen cycle by increasing the amount of reactive nitrogen in the environment; human associated inputs of industrially produced N fertilizers and N fixation by crops now exceed the natural N inputs to terrestrial systems. Nitrifying microorganisms play critical roles in the movement of this reactive nitrogen through ecosystems and in the availability of nitrogen for plant growth. In many agricultural soils when available nitrogen supply exceeds plant demand, nitrification increases leading to accumulation of nitrate that is reactive and mobile in the environment. The nitrogen use efficiency of our N fertilizers in agricultural systems remains quite low, typically only around 30% in cereal crops. Nitrification may lead to losses of nitrogen by leaching and denitrification. Nitrate leaching from agricultural systems is a significant contribution to the contamination of surface and groundwater. Nitrification therefore needs to be managed to protect the quality of surface waters and soils. Information is needed on the physiology and ecology of the bacteria and archaea responsible for cycling nitrate in ecosystems. We will improve understanding of the genomics of nitrifiers, characterize the processes in agricultural and wildland systems and examine links between nitrification and plants in soils. Improved understanding of nitrifying bacteria and archaea in soils and in wastewater systems may suggest management options for particular environments. Delineation of the limiting factors for nitrification in water delivery and wastewater treatment systems will help municipal and other government entities in preventing water pollution and planning for water reuse in the semi-arid and arid Intermountain West region.

Utah Agricultural Experiment Station Project Objectives Objective 1. Functional genomics of nitrifying bacteria. Supply genomic DNA of selected nitrifying bacteria for draft-level and complete genome sequencing. Work with the Joint Genome Institute and the DOE to facilitate genome sequencing and analysis. Annotate and check functional designations for selected genes especially those encoding enzymatic functions. Perform comparative genome analysis on nitrifying bacteria sequenced. Write collaborative papers Objective 2. Ecology of nitrification in agricultural systems in the Intermountain West. Collaborate in ongoing agricultural systems experiments comparing conventional and organic fertility management for effects on nitrification. Examine the role of tannins in the regulation of nitrification rates and changes in nitrifier communities in soil environments in pasture systems. Objective 3. Ecology of nitrification in wildland systems in the Intermountain West. Collaborate in ongoing experiments in wildland systems examining plant species diversity and function to assess nitrifier functional diversity and ecology of nitrification. This area of research will require establishment of collaborative relationships, site assessment and selection and the collection of baseline data on nitrification in a variety of ecosystems in Colorado and Utah. Objective 4. Characterization of N cycling in wastewater treatment systems. Explore wastewater N cycle in selected wastewater treatment systems in Utah. Assess the key microbial communities, specifically ammonia oxidizing bacteria and archaea by the application of molecular tools to waste treatment systems. Determine rate parameters for nitrification in lagoon, artificial wetland, and storage pond environments. Identify key vulnerability points of the N cycling processes needed to maintain effluent water quality.

Research Coordination Network: Nitrification, a bacterial process at the interface of the carbon and nitrogen cycles Funded NSF 2006-2011 Investigator Institution Disciplines Dan Arp* Oregon State University Biochemistry, Microbiology, Genomics Bill Hickey University of Wisconsin Soil Science, Proteomics Martin Klotz University of Louisville Physiology, Molecular Evol. & Gen., Genomics Jenny Norton Utah State University Soil Science, Microbial Ecology Bess Ward Princeton University Microbial Ecology, Oceanography, Geochemistry

Project Summary Nitrification, the oxidation of ammonia to nitrate, is a bacterially mediated process that is an essential part of the biogeochemical N cycle. By controlling available N for growth, nitrification also influences the C cycle. Despite extensive studies of the N-cycle for over 100 years and the heightened awareness due to concerns about its balance, the anthropogenic influence on the Ncycle is at present greater than on any of the other biogeochemical cycles. Anthropogenic input has changed from about 40% to nearly 200% of the natural contribution in just the last 50 years. This relatively recently elevated input to the cycle has a myriad of effects on waters, soils, and the atmosphere. There is a continuing and urgently growing need to understand the details of the nitrification process in order to provide the tools to manage nitrification at local levels for a global impact. The study of nitrification has been dramatically altered in the last few years due to recent advances in the areas of genomics, structural biology, physiology, and ecology. As the study of nitrification becomes ever more cross-disciplinary and collaborative, there is a need to coordinate the activities of those studying nitrification. A Research Coordination Network would provide the appropriate structure to organize activities of researchers in this field as well as to disseminate results within and outside the field. Current Genome Project Funded Dept. of Energy, Joint Genome Institute, genomes completed 2011, analysis in progress Genome Sequencing for Nitrosomonas sp. AL212 and Isolate IS-79; Ammonia-oxidizing Bacteria Adapted for Growth at Low Ammonia Concentrations Abstract As candidates for draft sequencing, we recommend two ammonia-oxidizing bacteria that mediate the first step in the process of nitrification. These bacteria are classified in the genus Nitrosomonas but have contrasting physiology to N. europaea and N. eutropha. Nitrosomonas sp.(strain AL212) and Isolate IS-79 belong to a cluster of the Nitrosomonas with higher substrate affinity (low Km), lower growth rates and increased sensitivity to high ammonia concentrations compared to N. europaea and N. eutropha. These unique physiological attributes improve their ability to grow at low concentrations of their substrate, ammonia. Similar oligotrophic ammonia-oxidizing bacteria have been found to be widely distributed in the environment although their importance was often overlooked because they were rarely isolated in the typical high nutrient media.

PI John Stark, USU Biology, Norton Co-PI, Funded USDA 2007-2011

Cheatgrass establishment results in stable exotic plant communities that resist re-invasion by native perennials. We hypothesize that positive feedbacks resulting from shifts in nutrient cycling and microbial activity are responsible for the long-term persistence of cheatgrass. To test this hypothesis we will revisit a set of restoration plots set up by John Stark (PI) in 1984 in Western Colorado. We will examine the soil microbial structure and nutrient cycling characteristics in these well-replicated experimental plots to determine whether during the past 26 years, cheatgrass-dominated or native perennial-dominated plant communities have 'cultured' different soil characteristics.The study will provide new information on the mechanisms by which cheatgrass establishes stable, persistent plant communities. Through these experiments we will address the CSREES priority of establishing 'mechanisms determining the abundance and distribution of weedy and invasive species on western rangelands. In addition, our results will provide information essential for developing management strategies that will speed the reestablishment of perennial plant species on cheatgrass-dominated rangelands.

Plant-Soil Feedbacks in the Shrub Steppe Ecosystem

Norton Cooperator with Andrew Kulmatiski and Karen Beard

J. Norton will be working with these investigators to characterize microbial communities using next-generation sequencing of phylogenetic and functional genes extracted from the soils from the Washington State manipulative plots. See Kulmatiski A., Beard, K.H. 2011. Long-term plant growth legacies overwhelm short-term plant growth effects on soil microbial community structure. Soil Biology and Biochemistry .

Professor of Soil Microbiology Dept. of Plants, Soils and Climate Utah State University, Logan, UT 84322-4820 Email: [email protected]

Professional preparation:

BS., 1980. State University of New York College of Environmental Science and Forestry. Forest Biology. Concentrations in Forest Botany and Pathology and Biochemistry. Ph.D., 1991. University of California, Berkeley. Soil Science (Soil Microbiology).Dissertation: Carbon and Nitrogen Dynamics in the Rhizosphere of Pinus ponderosa seedlings. Post-doctoral Research Associate, Michigan State Univ. NSF Center for Microbial Ecology 1991-1992

On This Page

• Norton Lab Group
• Nitrogen Cycles Abstract
• Genomics and Ecology Abstract
• Below Ground Feedbacks Abstract
• Cirriculum Vita

A Comprehensive Insight of Current and Future Challenges in Large-Scale Soil Microbiome Analyses

• Soil Microbiology
• Published: 23 June 2022
• Volume 86 , pages 75–85, ( 2023 )

Cite this article

• Jean Legeay   ORCID: orcid.org/0000-0002-6895-0569 1 &
• Mohamed Hijri 1 , 2  

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In the last decade, various large-scale projects describing soil microbial diversity across large geographical gradients have been undertaken. However, many questions remain unanswered about the best ways to conduct these studies. In this review, we present an overview of the experience gathered during these projects, and of the challenges that future projects will face, such as standardization of protocols and results, considering the temporal variation of microbiomes, and the legal constraints limiting such studies. We also present the arguments for and against the exhaustive description of soil microbiomes. Finally, we look at future developments of soil microbiome studies, notably emphasizing the important role of cultivation techniques.

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Acknowledgements

We thank the OCP Africa team for their helpful comments, and Andrew Blakney for the English editing and commenting to this manuscript. We also thank Stéphane Daigle for his advice on statistical mapping.

This study received funding from the OCP Innovation (Grant Number: AS-85).

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Both authors contributed to the writing of the review. The first draft of the manuscript was written by Jean Legeay, and Mohamed Hijri commented on and modified previous versions of the manuscript. Both authors read and approved the final manuscript.

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Legeay, J., Hijri, M. A Comprehensive Insight of Current and Future Challenges in Large-Scale Soil Microbiome Analyses. Microb Ecol 86 , 75–85 (2023). https://doi.org/10.1007/s00248-022-02060-2

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Received : 31 January 2022

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Published : 23 June 2022

Issue Date : July 2023

DOI : https://doi.org/10.1007/s00248-022-02060-2

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