Name: exploring the root microbiome: extracting bacterial community data from the soil, rhizosphere, and root endosphere
Uploaded: 2018-05-02T14:30:00
Description: Watch this Scientific Journal Video about Exploring the Root Microbiome: Extracting Bacterial Community Data from the Soil, Rhizosphere, and Root Endosphere at JoVE.com

This work was funded by the USDA-ARS (CRIS 2030-21430-008-00D). TS is supported by the NSF Graduate Research Fellowship Program.

1. Collection and Separation of Root Endosphere, Rhizosphere, and Soil Samples
<ol>
	<li>Prior to entering the field, autoclave ultrapure water (at least 90 mL of water per sample) to sterilize. Prepare epiphyte removal buffer (at least 25 mL per sample) by adding 6.75 g of KH2PO4, 8.75 g of K2HPO4, and 1 mL of Triton X-100, to 1 L of sterile water. Sterilize the buffer using a vacuum filter with 0.2 &#181;m pore size.
	<ol>
		<li>For steps 1.2 to 1.5, wear clean gloves steri...

This protocol demonstrates an established pipeline for exploring root endosphere, rhizosphere, and soil microbial community compositions, from field sampling to sample processing and downstream sequencing. Studying root-associated microbiomes presents unique challenges, due in part to the inherent difficulties in sampling from soil. Soils are highly variable in terms of physical and chemical properties, and different soil conditions can be separated by as little as a few millimeters28,<...

Plant-associated microbiomes consist of dynamic and complex microbial communities comprised of bacteria, archaea, viruses, fungi, and other eukaryotic microorganisms. In addition to their well-studied role in causing plant disease, plant-associated microbes can also positively influence plant health by improving tolerance to biotic and abiotic stresses, promoting nutrient availability, and enhancing plant growth through the production of phytohormones. For this reason, particular interest exists in characterizing the taxa that associate with plant root endospheres, rhizospheres, and the surrounding soil. While some microbes can be cultured in isolation on laboratory generated media, many cannot, in part because they may rely on symbiotic relationships with other microbes, grow very slowly, or require conditions that cannot be replicated in a lab environment. Because it circumvents the need for cultivation and is relatively inexpensive and high-throughput, sequence-based phylogenetic profiling of environmental and host-associated microbial samples has become a preferred method for assaying microbial community composition.
The selection of appropriate sequencing technologies provided by various next generation sequencing (NGS) platforms1 is dependent on the users&#39; needs, with important factors including: desired coverage, amplicon length, expected community diversity, as well as sequencing error-rate, read-length, and the cost-per-run/megabase. Another variable that needs to be considered in amplicon-based sequencing experiments is what gene will be amplified and what primers will be used. When designing or choosing primers, researchers are often forced to make tradeoffs between the universality of amplification and the taxonomic resolution achievable from the resulting amplicons. For this reason, these types of studies often chose primers and markers that selectively target specific subsets of the microbiome. Evaluating the composition of bacterial communities is commonly accomplished by sequencing one or more of the hypervariable regions of the bacterial 16S rRNA gene2,3. In this study, we describe an amplicon based sequencing protocol developed for a NGS platform that targets the 500 bp V3-V4 region of the bacterial 16S rRNA gene, which allows for broad amplification of bacterial taxa while also providing sufficient variability to distinguish between different taxa. Additionally, this protocol can easily be adapted for the use with other primer sets, such as those targeting the ITS2 marker of fungi or the 18S rRNA subunit of eukaryotes.
While other approaches such as shotgun metagenomics, metatranscriptomics, and single-cell sequencing, offer other advantages including resolved microbial genomes and more direct measurement of community function, these techniques are typically more expensive and computationally intensive than the phylogenetic profiling described here4. Additionally, performing shotgun metagenomics and metatranscriptomics on root samples yields a large percentage of reads belonging to the host plant genome, and methods to overcome this limitation are still being developed5,6.
As with any experimental platform, amplicon-based profiling can introduce a number of potential biases which should be considered during the experimental design and data analysis. These include the methods of sample collection, DNA extraction, selection of PCR primers, and how library preparation is performed. Different methods can significantly impact the amount of usable data generated, and can also hinder the efforts to compare results between studies. For example, the method of removing rhizosphere bacteria7 and the use of different extraction techniques or choice of DNA extraction kits8,9 have been shown to significantly impact downstream analysis, which leads to different conclusions regarding which microbes are present and their relative abundances. Since amplicon-based profiling can be customized, making comparisons across studies can be challenging. The Earth Microbiome Project has suggested that researchers studying complex systems such as the plant-associated microbiome would benefit from the development of standardized protocols as a means of minimizing the variability caused by the application of different methods between studies10,11. Here, we discuss many of the above topics and offer suggestions as to best practices where appropriate.
The protocol demonstrates the process of collecting soil, rhizosphere, and root samples from Sorghum bicolor and extracting DNA using a well-established DNA isolation kit11. Additionally, our protocol includes a detailed amplicon sequencing workflow, using a commonly utilized NGS platform, to determine the structure of the bacterial communities12,13,14. This protocol has been validated for the use in a wide range of plant hosts in a recent published study of the roots, rhizosphere, and associated-soils of 18 monocot species including Sorghum bicolor, Zea mays, and Triticum aestivum15. This method has also been validated for use with other marker genes, as demonstrated by its successful application to studying the fungal ITS2 marker gene in studies of the agave microbiome16,17 and strawberry microbiome18.

<table>
	<tbody>
		<tr>
			<td>0.1-10/20 &#181;L filtered micropipette tips</td>
			<td>USA Scientific</td>
			<td>1120-3810</td>
			<td>Can substitute with equivalent from other suppliers.</td>
		</tr>
		<tr>
			<td>1.5 mL microcentrifuge tubes</td>
			<td>USA Scientific</td>
			<td>1615-5510</td>
			<td>Can substitute with equivalent from other suppliers.</td>
		</tr>
		<tr>
			<td>10 &#181;L multi-channel pipette</td>
			<td>Eppendorf</td>
			<td>3122000027</td>
			<td>Can substitute with equivalent from other suppliers.</td>
		</tr>
		<tr>
			<td>10 &#181;L, 100 &#181;L, and 1000 &#181;L micropipettes</td>
			<td>Eppendorf</td>
			<td>3120000909</td>
			<td>Can substitute with equivalent from other suppliers.</td>
		</tr>
		<tr>
			<td>100 &#181;L multi-channel pipette</td>
			<td>Eppendorf</td>
			<td>3122000043</td>
			<td>Can substitute with equivalent from other suppliers.</td>
		</tr>
		<tr>
			<td>1000 &#181;L filtered micropipette tips</td>
			<td>USA Scientific</td>
			<td>1122-1830</td>
			<td>Can substitute with equivalent from other suppliers.</td>
		</tr>
		<tr>
			<td>2 mL microcentrifuge tubes</td>
			<td>USA Scientific</td>
			<td>1620-2700</td>
			<td>Can substitute with equivalent from other suppliers.</td>
		</tr>
		<tr>
			<td>2 mm soil sieve</td>
			<td>Forestry Suppliers</td>
			<td>60141009</td>
			<td>Can substitute with equivalent from other suppliers.</td>
		</tr>
		<tr>
			<td>200 &#181;L filtered micropipette tips</td>
			<td>USA Scientific</td>
			<td>1120-8810</td>
			<td>Can substitute with equivalent from other suppliers.</td>
		</tr>
		<tr>
			<td>25 mL reservoirs</td>
			<td>VWR International LLC</td>
			<td>89094-664</td>
			<td>Can substitute with equivalent from other suppliers.</td>
		</tr>
		<tr>
			<td>50 mL conical vials</td>
			<td>Thermo Fisher Scientific</td>
			<td>352098</td>
			<td>Can substitute with equivalent from other suppliers.</td>
		</tr>
		<tr>
			<td>500 mL vacuum filters (0.2 &#181;m pore size)</td>
			<td>VWR International LLC</td>
			<td>156-4020</td>
			<td></td>
		</tr>
		<tr>
			<td>96-well microplates</td>
			<td>USA Scientific</td>
			<td>655900</td>
			<td></td>
		</tr>
		<tr>
			<td>96-well PCR plates</td>
			<td>BioRad</td>
			<td>HSP9631</td>
			<td></td>
		</tr>
		<tr>
			<td>Agencourt AMPure XP beads</td>
			<td>Thermo Fisher Scientific</td>
			<td>NC9933872</td>
			<td>Instructions for use: 
			https://www.beckmancoulter.com/wsrportal/ajax/downloadDocument/B37419AA.pdf?autonomyId=TP_DOC_150180&#38;documentName=B37419AA.pdf</td>
		</tr>
		<tr>
			<td>Aluminum foil</td>
			<td>Boardwalk</td>
			<td>7124</td>
			<td>Can substitute with equivalent from other suppliers.</td>
		</tr>
		<tr>
			<td>Analytical scale with 0.001 g resolution</td>
			<td>Ohaus Pioneer</td>
			<td>PA323</td>
			<td>Can substitute with equivalent from other suppliers.</td>
		</tr>
		<tr>
			<td>Bioruptor Plus ultrasonicator</td>
			<td>Diagenode</td>
			<td>B01020001</td>
			<td></td>
		</tr>
		<tr>
			<td>Bovine Serum Albumin (BSA) 20 mg/mL</td>
			<td>New England Biolabs</td>
			<td>B9000S</td>
			<td></td>
		</tr>
		<tr>
			<td>Centrifuge</td>
			<td>Eppendorf</td>
			<td>5811000908</td>
			<td>Including 50mL and 96-well plate bucket adapters</td>
		</tr>
		<tr>
			<td>Cryogenic gloves</td>
			<td>Millipore Sigma</td>
			<td>Z183490</td>
			<td>Can substitute with equivalent from other suppliers.</td>
		</tr>
		<tr>
			<td>DNeasy PowerClean kit (optional)</td>
			<td>Qiagen Inc.</td>
			<td>12877-50</td>
			<td>Previously MoBio</td>
		</tr>
		<tr>
			<td>DNeasy PowerSoil kit</td>
			<td>Qiagen Inc.</td>
			<td>12888-100</td>
			<td>Previously MoBio</td>
		</tr>
		<tr>
			<td>Dry ice</td>
			<td>Any</td>
			<td>NA</td>
			<td></td>
		</tr>
		<tr>
			<td>DynaMag-2 magnet</td>
			<td>Thermo Fisher Scientific</td>
			<td>12321D</td>
			<td>Do not substitute</td>
		</tr>
		<tr>
			<td>Ethanol</td>
			<td>VWR International LLC</td>
			<td>89125-188</td>
			<td>Can substitute with equivalent from other suppliers.</td>
		</tr>
		<tr>
			<td>Gallon size freezer bags</td>
			<td>Ziploc</td>
			<td>NA</td>
			<td>Can substitute with equivalent from other suppliers.</td>
		</tr>
		<tr>
			<td>Gemini EM Microplate Reader</td>
			<td>Molecular Devices</td>
			<td>EM</td>
			<td>Can use another fluorometer that reads 96-well plates from the top.</td>
		</tr>
		<tr>
			<td>K2HPO4</td>
			<td>Sigma-Aldrich</td>
			<td>P3786</td>
			<td></td>
		</tr>
		<tr>
			<td>KH2PO4</td>
			<td>Sigma-Aldrich</td>
			<td>P5655</td>
			<td></td>
		</tr>
		<tr>
			<td>Lab coat</td>
			<td>Workrite</td>
			<td>J1367</td>
			<td>Can substitute with equivalent from other suppliers.</td>
		</tr>
		<tr>
			<td>Liquid N2</td>
			<td>Any</td>
			<td>NA</td>
			<td>Can substitute with equivalent from other suppliers.</td>
		</tr>
		<tr>
			<td>Liquid N2 dewar</td>
			<td>Thermo Fisher Scientific</td>
			<td>4150-9000</td>
			<td>Can substitute with equivalent from other suppliers.</td>
		</tr>
		<tr>
			<td>Milli-Q ultrapure water purification system</td>
			<td>Millipore Sigma</td>
			<td>SYNS0R0WW</td>
			<td></td>
		</tr>
		<tr>
			<td>Mini-centrifuge</td>
			<td>Eppendorf</td>
			<td>5404000014</td>
			<td></td>
		</tr>
		<tr>
			<td>Molecular grade water</td>
			<td>Thermo Fisher Scientific</td>
			<td>4387937</td>
			<td>Can substitute with equivalent from other suppliers.</td>
		</tr>
		<tr>
			<td>Mortars</td>
			<td>VWR International LLC</td>
			<td>89038-150</td>
			<td>Can substitute with equivalent from other suppliers.</td>
		</tr>
		<tr>
			<td>Nitrile gloves</td>
			<td>Thermo Fisher Scientific</td>
			<td>19167032B</td>
			<td>Can substitute with equivalent from other suppliers.</td>
		</tr>
		<tr>
			<td>Paper towels</td>
			<td>VWR International LLC</td>
			<td>BWK6212</td>
			<td>Can substitute with equivalent from other suppliers.</td>
		</tr>
		<tr>
			<td>PCR plate sealing film</td>
			<td>Thermo Fisher Scientific</td>
			<td>NC9684493</td>
			<td></td>
		</tr>
		<tr>
			<td>PCR strip tubes</td>
			<td>USA Scientific</td>
			<td>1402-2700</td>
			<td></td>
		</tr>
		<tr>
			<td>Pestles</td>
			<td>VWR International LLC</td>
			<td>89038-166</td>
			<td>Can substitute with equivalent from other suppliers.</td>
		</tr>
		<tr>
			<td>Plastic spatulas</td>
			<td>LevGo, Inc.</td>
			<td>17211</td>
			<td></td>
		</tr>
		<tr>
			<td>Platinum Hot Start PCR Master Mix (2x)</td>
			<td>Thermo Fisher Scientific</td>
			<td>13000014</td>
			<td></td>
		</tr>
		<tr>
			<td>PNAs - chloroplast and mitochondrial</td>
			<td>PNA Bio</td>
			<td>NA</td>
			<td>Make sure to verify sequence bioinformatically</td>
		</tr>
		<tr>
			<td>Protective eyewear</td>
			<td>Millipore Sigma</td>
			<td>Z759015</td>
			<td>Can substitute with equivalent from other suppliers.</td>
		</tr>
		<tr>
			<td>Qubit 3.0 Fluorometer</td>
			<td>Thermo Fisher Scientific</td>
			<td>Q33216</td>
			<td></td>
		</tr>
		<tr>
			<td>Qubit dsDNA HS assay kit</td>
			<td>Thermo Fisher Scientific</td>
			<td>Q32854</td>
			<td></td>
		</tr>
		<tr>
			<td>Rubber mallet (optional)</td>
			<td>Ace Hardware</td>
			<td>2258622</td>
			<td>Can substitute with equivalent from other suppliers.</td>
		</tr>
		<tr>
			<td>Shears or scissors</td>
			<td>VWR International LLC</td>
			<td>89259-936</td>
			<td>Can substitute with equivalent from other suppliers.</td>
		</tr>
		<tr>
			<td>Shovel</td>
			<td>Home Depot</td>
			<td>2597400</td>
			<td>Can substitute with equivalent from other suppliers.</td>
		</tr>
		<tr>
			<td>Soil core collector (small diameter: &#60;1 inch)</td>
			<td>Ben Meadows</td>
			<td>221700</td>
			<td>Can substitute with equivalent from other suppliers.</td>
		</tr>
		<tr>
			<td>Spray bottles</td>
			<td>Santa Cruz Biotechnology</td>
			<td>sc-395278</td>
			<td>Can substitute with equivalent from other suppliers.</td>
		</tr>
		<tr>
			<td>Standard desalted barcoded primers (10 &#181;M) (Table 1)</td>
			<td>IDT</td>
			<td>NA</td>
			<td>4 nmole Ultramer DNA Oligo with standard desalting. NGS adapter and sequencing primer (Table 1) are designed for use with Illumina MiSeq using v3 chemistry.</td>
		</tr>
		<tr>
			<td>Thermocycler</td>
			<td>Thermo Fisher Scientific</td>
			<td>E950040015</td>
			<td>Can substitute with equivalent from other suppliers.</td>
		</tr>
		<tr>
			<td>Triton X-100</td>
			<td>Sigma-Aldrich</td>
			<td>X100</td>
			<td>Can substitute with equivalent from other suppliers.</td>
		</tr>
		<tr>
			<td>Weigh boats</td>
			<td>Spectrum Chemicals</td>
			<td>B6001W</td>
			<td>Can substitute with equivalent from other suppliers.</td>
		</tr>
	</tbody>
</table>

exploring the root microbiome: extracting bacterial community data from the soil, rhizosphere, and root endosphere

The intimate interaction between plant host and associated microorganisms is crucial in determining plant fitness, and can foster improved tolerance to abiotic stresses and diseases. As the plant microbiome can be highly complex, low-cost, high-throughput methods such as amplicon-based sequencing of the 16S rRNA gene are often preferred for characterizing its microbial composition and diversity. However, the selection of appropriate methodology when conducting such experiments is critical for reducing biases that can make analysis and comparisons between samples and studies difficult. This protocol describes in detail a standardized methodology for the collection and extraction of DNA from soil, rhizosphere, and root samples. Additionally, we highlight a well-established 16S rRNA amplicon sequencing pipeline that allows for the exploration of the composition of bacterial communities in these samples, and can easily be adapted for other marker genes. This pipeline has been validated for a variety of plant species, including sorghum, maize, wheat, strawberry, and agave, and can help overcome issues associated with the contamination from plant organelles.

Performing the recommended protocol should result in a dataset of indexed paired-end reads that can be matched back to each sample and assigned to either a bacterial operational taxonomic units (OTU) or exact sequence variant (ESV, also referred to as amplicon sequence variant (ASV) and sub-operational taxonomic unit (sOTU)), depending on downstream analysis. In order to obtain high-quality sequence data, care must be taken at each step to maintain consistency between samples and minimize...

Watch this Scientific Journal Video about Exploring the Root Microbiome: Extracting Bacterial Community Data from the Soil, Rhizosphere, and Root Endosphere at JoVE.com

Exploring the Root Microbiome: Extracting Bacterial Community Data from the Soil, Rhizosphere, and Root Endosphere

Here, we describe a protocol to obtain amplicon sequence data of soil, rhizosphere, and root endosphere microbiomes. This information can be used to investigate the composition and diversity of plant-associated microbial communities, and is suitable for the use with a wide range of plant species.

The intimate interaction between plant host and associated microorganisms is crucial in determining plant fitness, and can foster improved tolerance to ...

The overall goal of this experiment is to characterize three compartments of the root microbiome, soil, rhizosphere, and root endosphere, using community profiling of the 16S ribosomal RNA gene. This method can help answer key questions in microbial ecology, such as the biotic and abiotic factors that are the significant drivers of microbiome structure. The main advantage of this technique is that it standardizes each step, from the separation of root and rhizosphere to DNA's extraction to preparing a sequencing library.Demonstrating this procedure will be Tuesday Simmons, a graduate student in the Coleman-Derr Laboratory. To begin the protocol, collect bulk soil samples with an ethanol-sterilized soil core collector to obtain soil that is free of plant roots. Collect a core approximately 23 to 30 centimeters from the base of the plant.Next, transfer the soil to a plastic bag, homogenize the soil by gentle shaking the bag, transfer a 600-milligram aliquot of the soil sample into one two-milliliter tube, and immediately place the two-milliliter tube on dry ice until DNA extraction. To collect the root and rhizosphere, use an ethanol-sterilized shovel to dig up the plant, taking care to obtain as much of the root as possible. Gently shake off excess soil from the roots until there is approximately two millimeters of soil adhering to the root surface.For large plants, use sterile scissors to cut a representative subsection of roots, and place a minimum of 500 milligrams of root tissue into a 50-milliliter conical vial. Add enough epiphyte removal buffer to cover the roots, then immediately place the sample on dry ice. To separate the rhizosphere from the roots, thaw the root sample on ice, then sonicate the root samples.Transfer the roots into a chilled, clean 50-milliliter tube using sterile forceps. Do not dispose of the original tube with buffer and soil. This contains the rhizosphere fraction.Next, centrifuge the tube containing the buffer and rhizosphere. Decant the supernatant, and place the rhizosphere fraction in dry ice. To wash the roots, add approximately 20 milliliters of four degrees Celsius sterile water to the root fraction.Wash the root by shaking vigorously for 15 to 30 seconds, then drain the water. Use a sterile spatula to quickly transfer 250 milligrams of soil and rhizosphere into separate collection tubes provided in a commercial DNA isolation kit for soil extraction, then proceed using the kit supplier's protocol. After elution, store the DNA at minus 20 degrees Celsius until ready to proceed to the amplicon library preparation, then extract DNA from the root samples.Chill a sterilized mortar and pestle using liquid nitrogen. Measure out 600 to 700 milligrams of root tissue, and place the tissue into the mortar and carefully add enough liquid nitrogen to cover the roots. Grind the roots into small pieces.Continue the process of adding liquid nitrogen and grinding at least two times, being consistent between samples, until the roots are a fine powder. Quickly, before the root powder begins to thaw, use a sterile spatula to transfer the root powder into pre-weighed 1.5-milliliter tubes on ice. Record the weight of the tube and powder.Use a sterile spatula to quickly transfer 150 milligrams of root powder to the collection tube provided in the commercial DNA isolation kit designed for extraction from soil, then proceed with DNA isolation using the kit supplier's protocol. Prepare sufficient PCR master mix to amplify each DNA sample in triplicate. Pour the master mix into a sterile, 25-milliliter multichannel pipette reservoir, and distribute 66 microliters of master mix into each well of a new 96-well PCR plate.Next, add six microliters of five nanograms per microliter DNA from the normalized DNA plate to the master mix plate. Then, add to the master plate 1.5 microliters of 10 micromolar forward primer, such that each column has a different forward barcode, and 1.5 microliters of 10 micromolar reverse primer, such that each row has a different reverse barcode. Cover the plate with film, and spin down the plate briefly at 3, 000 RCF.Use a multichannel pipette to gently mix the contents, then divide them into three plates with 25 microliters of reaction mixture, and cover the plates with PCR film. Amplify the DNA in each plate using a thermocycler. After the amplification, pool the three replicate plates into a single 96-well plate.Using a computer spreadsheet, calculate the volume of 100 nanograms of each sample, as well as the average volume for all samples. For each blank PCR product, add the calculated average volume into a single 1.5-milliliter tube. For the successfully amplified samples, pool 100 nanograms of each sample into the same tube, then measure the concentration of the pooled product using a benchtop fluorometer, and elute 600 nanograms of DNA in molecular-grade water to a final volume of 100 microliters in a 1.5-milliliter tube.Store the remaining pooled product at minus 20 degrees Celsius. Prior to purifying the 600-nanogram DNA aliquot, prepare 600 microliters of fresh 70%ethanol. Follow the established PCR purification process using paramagnetic beads.Shake the tube of magnetic beads to resuspend the beads that settle to the bottom. Add 1x volume, 100 microliters, of bead solution to the 600-nanogram aliquot of DNA. Mix the solution and beads thoroughly by pipetting 10 times.After thorough mixing, incubate the mixture for five minutes at room temperature. Place the tube onto the magnetic stand for two minutes or until the solution is clear to separate the beads from the solution. Keep the tube in the stand, aspirate the clear supernatant carefully without touching the magnetic beads, and discard it.Leave the tube in the stand, add 300 microliters of 70%ethanol to the tube, and incubate the beads at room temperature for 30 seconds. Aspirate out the ethanol, and discard it. Repeat this process, and remove all ethanol after the second wash.Remove the tube from the magnetic stand, and air-dry the contents for five minutes. Add 30 microliters of molecular-grade water to the dried beads, and mix by pipetting 10 times. Incubate at room temperature for two minutes.Return the tube to the magnetic stand for one minute to separate the beads from solution. Transfer the eluate to a new tube. Measure the final concentration of the cleaned, pooled DNA using a benchtop fluorometer.Dilute an aliquot to 10 nanomolar in a final volume of 30 microliters or to the concentration and the volume preferred by the sequencing facility. Following the amplification step, sequencing was performed to determine the bacterial community composition of each sample. An alignment of the PNA sequence to each chloroplast and mitochondrial 16S ribosomal RNA gene for the plant host investigated should not reveal any mismatches.A single mismatch to the 13 base pair PNA sequence can drastically reduce the effectiveness, as in the case of the provided chloroplast PNA sequence and the chloroplast 16S ribosomal RNA gene of Lactuca sativa, or lettuce. Since an equal amount of amplified DNA was pooled per sample, an even number of reads, which match to bacterial taxa, were obtained per sample after sequencing, sorted based on their barcoded index. Once mastered, this technique can be done for eight plants in approximately eight hours.While attempting this procedure, it is important to be consistent between experiments. Small details such as changes in DNA extraction method and PCR master mix can introduce bias. Following this procedure, other methods such as metatranscriptomics can be used to answer additional questions, such as which microbes are active, and what genes are they expressing?Don't forget that working with liquid nitrogen can be extremely hazardous and precautions such as wearing appropriate PPE should always be taken while performing this procedure.

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SCIENTISTS IN ACTION

Conjugative Mating Assays for Sequence-specific Analysis of Transfer Proteins Involved in Bacterial Conjugation

The transfer of genetic material by bacterial conjugation is a process that takes place via complexes formed by specific transfer proteins. In Escherichia coli, these transfer proteins make up a DNA transfer machinery known as the mating pair formation, or DNA transfer complex, which facilitates conjugative plasmid transfer. The objective of this paper is to provide a method that can be used to determine the role of a specific transfer protein that is involved in conjugation using a series of deletions and/or point mutations in combination with mating assays. The target gene is knocked out on the conjugative plasmid and is then provided in trans through the use of a small recovery plasmid harboring the target gene. Mutations affecting the target gene on the recovery plasmid can reveal information about functional aspects of the target protein that result in the alteration of mating efficiency of donor cells harboring the mutated gene. Alterations in mating efficiency provide insight into the role and importance of the particular transfer protein, or a region therein, in facilitating conjugative DNA transfer. Coupling this mating assay with detailed three-dimensional structural studies will provide a comprehensive understanding of the function of the conjugative transfer protein as well as provide a means for identifying and characterizing regions of protein-protein interaction.

Here, we present a protocol to knockout a gene of interest involved in plasmid conjugation and subsequently analyze the impact of its absence using mating assays. The function of the gene is further explored to a specific region of its sequence using deletion or point mutations.

The transfer of genetic material by bacterial conjugation is a process that takes place via complexes formed by specific transfer proteins. In Escherichia ...

Transcriptomic Analysis of C. elegans RNA Sequencing Data Through the Tuxedo Suite on the Galaxy Project

Next generation sequencing (NGS) technologies have revolutionized the nature of biological investigation. Of these, RNA Sequencing (RNA-Seq) has emerged as a powerful tool for gene-expression analysis and transcriptome mapping. However, handling RNA-Seq datasets requires sophisticated computational expertise and poses inherent challenges for biology researchers. This bottleneck has been mitigated by the open access Galaxy project that allows users without bioinformatics skills to analyze RNA-Seq data, and the Database for Annotation, Visualization, and Integrated Discovery (DAVID), a Gene Ontology (GO) term analysis suite that helps derive biological meaning from large data sets. However, for first-time users and bioinformatics' amateurs, self-learning and familiarization with these platforms can be time-consuming and daunting. We describe a straightforward workflow that will help C. elegans researchers to isolate worm RNA, conduct an RNA-Seq experiment and analyze the data using Galaxy and DAVID platforms. This protocol provides stepwise instructions for using the various Galaxy modules for accessing raw NGS data, quality-control checks, alignment, and differential gene expression analysis, guiding the user with parameters at every step to generate a gene list that can be screened for enrichment of gene classes or biological processes using DAVID. Overall, we anticipate that this article will provide information to C. elegans researchers undertaking RNA-Seq experiments for the first time as well as frequent users running a small number of samples.

Transcriptomic Analysis of C. elegans RNA Sequencing Data Through the Tuxedo Suite on the Galaxy Project

Galaxy and DAVID have emerged as popular tools that allow investigators without bioinformatics training to analyze and interpret RNA-Seq data. We describe a protocol for C. elegans researchers to perform RNA-Seq experiments, access and process the dataset using Galaxy and obtain meaningful biological information from the gene lists using DAVID.

Next generation sequencing (NGS) technologies have revolutionized the nature of biological investigation. Of these, RNA Sequencing (RNA-Seq) has emerged ...

Bidirectional Retroviral Integration Site PCR Methodology and Quantitative Data Analysis Workflow

Integration Site (IS) assays are a critical component of the study of retroviral integration sites and their biological significance. In recent retroviral gene therapy studies, IS assays, in combination with next-generation sequencing, have been used as a cell-tracking tool to characterize clonal stem cell populations sharing the same IS. For the accurate comparison of repopulating stem cell clones within and across different samples, the detection sensitivity, data reproducibility, and high-throughput capacity of the assay are among the most important assay qualities. This work provides a detailed protocol and data analysis workflow for bidirectional IS analysis. The bidirectional assay can simultaneously sequence both upstream and downstream vector-host junctions. Compared to conventional unidirectional IS sequencing approaches, the bidirectional approach significantly improves IS detection rates and the characterization of integration events at both ends of the target DNA. The data analysis pipeline described here accurately identifies and enumerates identical IS sequences through multiple steps of comparison that map IS sequences onto the reference genome and determine sequencing errors. Using an optimized assay procedure, we have recently published the detailed repopulation patterns of thousands of Hematopoietic Stem Cell (HSC) clones following transplant in rhesus macaques, demonstrating for the first time the precise time point of HSC repopulation and the functional heterogeneity of HSCs in the primate system. The following protocol describes the step-by-step experimental procedure and data analysis workflow that accurately identifies and quantifies identical IS sequences.

This manuscript describes the experimental procedure and software analysis for a bidirectional integration site assay that can simultaneously analyze upstream and downstream vector-host junction DNA. Bidirectional PCR products can be used for any downstream sequencing platform. The resulting data are useful for a high-throughput, quantitative comparison of integrated DNA targets.

Integration Site (IS) assays are a critical component of the study of retroviral integration sites and their biological significance. In recent retroviral ...

Determination of the Optimal Chromosomal Location(s) for a DNA Element in Escherichia coli Using a Novel Transposon-mediated Approach

The optimal chromosomal position(s) of a given DNA element was/were determined by transposon-mediated random insertion followed by fitness selection. In bacteria, the impact of the genetic context on the function of a genetic element can be difficult to assess. Several mechanisms, including topological effects, transcriptional interference from neighboring genes, and/or replication-associated gene dosage, may affect the function of a given genetic element. Here, we describe a method that permits the random integration of a DNA element into the chromosome of Escherichia coli and select the most favorable locations using a simple growth competition experiment. The method takes advantage of a well-described transposon-based system of random insertion, coupled with a selection of the fittest clone(s) by growth advantage, a procedure that is easily adjustable to experimental needs. The nature of the fittest clone(s) can be determined by whole-genome sequencing on a complex multi-clonal population or by easy gene walking for the rapid identification of selected clones. Here, the non-coding DNA region DARS2, which controls the initiation of chromosome replication in E. coli, was used as an example. The function of DARS2 is known to be affected by replication-associated gene dosage; the closer DARS2 gets to the origin of DNA replication, the more active it becomes. DARS2 was randomly inserted into the chromosome of a DARS2-deleted strain. The resultant clones containing individual insertions were pooled and competed against one another for hundreds of generations. Finally, the fittest clones were characterized and found to contain DARS2 inserted in close proximity to the original DARS2 location.

Determination of the Optimal Chromosomal Locations for a DNA Element in Escherichia coli Using a Novel Transposon-mediated Approach

Here, the power of a transposon-mediated random insertion of a non-coding DNA element was used to resolve its optimal chromosomal position.

The optimal chromosomal position(s) of a given DNA element was/were determined by transposon-mediated random insertion followed by fitness selection. In ...

Sample Preparation and Analysis of RNASeq-based Gene Expression Data from Zebrafish

The analysis of global gene expression changes is a valuable tool for identifying novel pathways underlying observed phenotypes. The zebrafish is an excellent model for rapid assessment of whole transcriptome from whole animal or individual cell populations due to the ease of isolation of RNA from large numbers of animals. Here a protocol for global gene expression analysis in zebrafish embryos using RNA sequencing (RNASeq) is presented. We describe preparation of RNA from whole embryos or from cell populations obtained using cell sorting in transgenic animals. We also describe an approach for analysis of RNASeq data to identify enriched pathways and Gene Ontology (GO) terms in global gene expression data sets. Finally, we provide a protocol for validation of gene expression changes using quantitative reverse transcriptase PCR (qRT-PCR). These protocols can be used for comparative analysis of control and experimental sets of zebrafish to identify novel gene expression changes, and provide molecular insight into phenotypes of interest.

This protocol presents an approach for whole transcriptome analysis from zebrafish embryos, larvae, or sorted cells. We include isolation of RNA, pathway analysis of RNASeq data, and qRT-PCR-based validation of gene expression changes.

The analysis of global gene expression changes is a valuable tool for identifying novel pathways underlying observed phenotypes. The zebrafish is an ...

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

We have adapted the yeast 2-hybrid assay to simultaneously uncover dozens of transient and static protein interactions within a single screen utilizing high-throughput short-read DNA sequencing. The resulting sequence datasets can not only track what genes in a population that are enriched during selection for positive yeast 2-hybrid interactions, but also give detailed information about the relevant subdomains of proteins sufficient for interaction. Here, we describe a full suite of stand-alone software programs that allow non-experts to perform all the bioinformatics and statistical steps to process and analyze DNA sequence fastq files from a batch yeast 2-hybrid assay. The processing steps covered by these software include: 1) mapping and counting sequence reads corresponding to each candidate protein encoded within a yeast 2-hybrid prey library; 2) a statistical analysis program that evaluates the enrichment profiles; and 3) tools to examine the translational frame and position within the coding region of each enriched plasmid that encodes the interacting proteins of interest.

Deep sequencing of yeast populations selected for positive yeast 2-hybrid interactions potentially yields a wealth of information about interacting partner proteins. Here, we describe the operation of specific bioinformatics tools and customized updated software to analyze sequence data from such screens.

We have adapted the yeast 2-hybrid assay to simultaneously uncover dozens of transient and static protein interactions within a single screen utilizing ...

Exploring the Effects of Spaceflight on Mouse Physiology using the Open Access NASA GeneLab Platform

Performing biological experiments in space requires special accommodations and procedures to ensure that these investigations are performed effectively and efficiently. Moreover, given the infrequency of these experiments it is imperative that their impacts be maximized. The rapid advancement of omics technologies offers an opportunity to dramatically increase the volume of data produced from precious spaceflight specimens. To capitalize on this, NASA has developed the GeneLab platform to provide unrestricted access to spaceflight omics data and encourage its widespread analysis. Rodents (both rats and mice) are common model organisms used by scientists to investigate space-related biological impacts. The enclosure that house rodents during spaceflight are called Rodent Habitats (formerly Animal Enclosure Modules), and are substantially different from standard vivarium cages in their dimensions, air flow, and access to water and food. In addition, due to environmental and atmospheric conditions on the International Space Station (ISS), animals are exposed to a higher CO2 concentration. We recently reported that mice in the Rodent Habitats experience large changes in their transcriptome irrespective of whether animals were on the ground or in space. Furthermore, these changes were consistent with a hypoxic response, potentially driven by higher CO2 concentrations. Here we describe how a typical rodent experiment is performed in space, how omics data from these experiments can be accessed through the GeneLab platform, and how to identify key factors in this data. Using this process, any individual can make critical discoveries that could change the design of future space missions and activities.

The NASA GeneLab platform provides unfettered access to precious omics data from biological spaceflight experiments. We describe how a typical mouse experiment is conducted in space and how data from such experiments can be accessed and analyzed.

Performing biological experiments in space requires special accommodations and procedures to ensure that these investigations are performed effectively ...

Isolating, Sequencing and Analyzing Extracellular MicroRNAs from Human Mesenchymal Stem Cells

Extracellular and circulating RNAs (exRNA) are produced by many cell types of the body and exist in numerous bodily fluids such as saliva, plasma, serum, milk and urine. One subset of these RNAs are the posttranscriptional regulators &#8211; microRNAs (miRNAs). To delineate the miRNAs produced by specific cell types, in vitro culture systems can be used to harvest and profile exRNAs derived from one subset of cells. The secreted factors of mesenchymal stem cells are implicated in alleviating numerous diseases and is used as the in vitro model system here. This paper describes the process of collection, purification of small RNA and library generation to sequence extracellular miRNAs. ExRNAs from culture media differ from cellular RNA by being low RNA input samples, which calls for optimized procedures. This protocol provides a comprehensive guide to small exRNA sequencing from culture media, showing quality control checkpoints at each step during exRNA purification and sequencing.

This protocol demonstrates how to purify extracellular microRNAs from cell culture media for small RNA library construction and next generation sequencing. Various quality control checkpoints are described to allow readers to understand what to expect when working with low input samples like exRNAs.

Extracellular and circulating RNAs (exRNA) are produced by many cell types of the body and exist in numerous bodily fluids such as saliva, plasma, serum, ...

Tomato Root Transformation Followed by Inoculation with Ralstonia Solanacearum for Straightforward Genetic Analysis of Bacterial Wilt Disease

Ralstonia solanacearum is a devastating soil borne vascular pathogen that can infect a large range of plant species, causing an important threat to agriculture. However, the Ralstonia model is considerably underexplored in comparison to other models involving bacterial plant pathogens, such as Pseudomonas syringae in Arabidopsis. Research targeted to understanding the interaction between Ralstonia and crop plants is essential to develop sustainable solutions to fight against bacterial wilt disease but is currently hindered by the lack of straightforward experimental assays to characterize the different components of the interaction in native host plants. In this scenario, we have developed a method to perform genetic analysis of Ralstonia infection of tomato, a natural host of Ralstonia. This method is based on Agrobacterium rhizogenes-mediated transformation of tomato roots, followed by Ralstonia soil-drenching inoculation of the resulting plants, containing transformed roots expressing the construct of interest. The versatility of the root transformation assay allows performing either gene overexpression or gene silencing mediated by RNAi. As a proof of concept, we used this method to show that RNAi-mediated silencing of SlCESA6 in tomato roots conferred resistance to Ralstonia. Here, we describe this method in detail, enabling genetic approaches to understand bacterial wilt disease in a relatively short time and with small requirements of equipment and plant growth space.

Tomato Root Transformation Followed by Inoculation with Ralstonia Solanacearum for Straightforward Genetic Analysis of Bacterial Wilt Disease

Here, we present a versatile method for tomato root transformation followed by inoculation with Ralstonia solanacearum to perform straightforward genetic analysis for the study of bacterial wilt disease.

Ralstonia solanacearum is a devastating soil borne vascular pathogen that can infect a large range of plant species, causing an important threat to ...

Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size (LEfSe) in Microbiome Data

There is growing attention toward closed biological genomes in the environment and in health. To explore and reveal the intergroup differences among different samples or environments, it is crucial to discover biomarkers with statistical differences among groups. The application of Linear discriminant analysis Effect Size (LEfSe) can help find good biomarkers. Based on the original genome data, quality control, and quantification of different sequences based on taxa or genes are carried out. First, the Kruskal-Wallis rank test was used to distinguish between specific differences among statistical and biological groups. Then, the Wilcoxon rank test was performed between the two groups obtained in the previous step to assess whether the differences were consistent. Finally, a linear discriminant analysis (LDA) was conducted to evaluate the influence of biomarkers on significantly different groups based on LDA scores. To sum up, LEfSe provided the convenience for identifying genomic biomarkers that characterize statistical differences among biological groups.

Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data

LEfSe (LDA Effect Size) is a tool for high-dimensional biomarker mining to identify genomic features (such as genes, pathways, and taxonomies) that significantly characterize two or more groups in microbiome data.

There is growing attention toward closed biological genomes in the environment and in health. To explore and reveal the intergroup differences among ...