The authors would like to acknowledge the funding sources for the projects that led to the development of these methods, with those sources being: the Howard Hughes Medical Institute (http://www.hhmi.org) through the Precollege and Undergraduate Science Education Program, as well as by the&#160;National Science Foundation (http://www.nsf.gov)&#160;through NSF awards DBI-1248096 and CBET-1805549.

1. Collection of sediment samples for nucleic acid extraction
<ol>
	<li>Submerge a sterile 50 mL conical tube into the stream water. Wear gloves during sample collection to avoid introducing unwanted human contamination. Perform this step either from the shore or facing upstream if in the water.</li>
	<li>While the conical tube is submerged, remove the cap, and use it to scoop approximately 3 mL of sediment from a depth of 1 to 3 cm into the conical tube.</li>
	<li>Remove the conical tube from the water and dump out all water, except for a thin layer covering the sediment sample (approximately 1 mL).</li>
	<li>Using a 1000 &#181;L pipette and appropriate pipette tips, add 3 mL of DNA/RNA preservative (see Table of Materials for the preservative specifications) to the collected sample. Keep the pipette tips in a sterile pipette tip box and only attach them immediately before use and discarded after use. Invert the capped conical tube 10 times to ensure the preservative and sample are thoroughly mixed. 
	NOTE Step 1.4 is not necessary, but it is strongly recommended if RNA is to be extracted from the sediments later.</li>
	<li>Place the samples on ice for the rest of sample collection. Upon returning from collection, store in a freezer at -20 &#176;C if the samples are to be used for 16S analysis (DNA), or -70 &#176;C, if they are to be used for metatranscriptomics analysis (RNA).</li>
</ol>
2. Filter collection for nucleic acid extraction
<ol>
	<li>Remove the cap of a sterile 1 L bottle. While facing upstream or from the shore, fill the bottle with stream water to the top and then dump it out. Repeat this process two more times to condition the bottle. Fill the entire bottle a fourth time and cap it. 
	NOTE: If reusing a 1 L bottle, it can be sterilized by rinsing with 10% bleach for 2 min, followed by rinsing three times with deionized water and then once with 70% ethanol, and finally autoclaving with settings: 30 min exposure time at 121.1 &#176;C and 15 min drying time. During autoclaving, the cap on the bottle should be very loose to avoid the bottle being compressed in the process.</li>
	<li>Once on a stable surface, use a sterile Luer lock syringe and draw up a full volume. Then connect the syringe to a sterile and DNA/RNA-free 1.7 cm diameter polyethersulfone filter with a pore size of 0.22 &#181;m and push the entire volume through the filter by pressing the plunger all the way down. Repeat this process until the total volume collected in the bottle (1 L) is pushed through the filter. 
	NOTE: The volume of the syringe can be variable, if, the total amount of water pushed through the filter is tracked. However, generally, 60 mL is preferred. While 1 L is ideal, anecdotally, a volume of at least 200 mL would likely still collect enough biomass (assuming ~20,000 cells per mL) for the extraction of DNA and RNA.</li>
	<li>Remove excess water from the filter by drawing up roughly 20 mL worth of air into the syringe and pushing it through the filter. 
	NOTE: This will help prevent loss of the preservative if step 2.4 is performed.</li>
	<li>Using a P1000 micropipette, add 2 mL of a DNA/RNA preservative by discharging it through the filter&#39;s larger opening (where it was attached to the syringe) while holding the filter horizontally. The tip of the pipette should be within the barrel of the filter when the pipette is depressed to ensure the preservative enters the filter. Change the tip after each use. 
	NOTE: As with the sediment collection, this step is not necessary, but it is strongly recommended for increased nucleic acid yield later, especially for RNA.</li>
	<li>Peel off one square of paraffin film and wrap it tightly around each opening/end of the filter to seal. Place the paraffin film wrapped filter into a sterile sample bag and then place the entire bag on ice during collection. 
	NOTE: Ensure that the side used to wrap the filter is sterile, i.e., not previously exposed to the environment.</li>
	<li>Upon return from sampling, store filters at -20 &#176;C for 16S or -70 &#176;C for meta-transcriptomics.</li>
</ol>
3. Nucleic acid extraction and quantification
<ol>
	<li>Clean the work area with 10% Bleach and 70% Ethanol before beginning sample transfer.</li>
	<li>For sediment (from step 1.5), generally, use ~0.25 g of sample. Flame sterilize a metal tool by dipping it in a beaker of 70% ethanol and burning the ethanol off between samples.</li>
	<li>For filters (from step 2.6), move the filter paper into a sterile tube for extraction. To do so follow the steps below.
	<ol>
		<li>Create a sterile, DNA and RNA free-surface by folding aluminum foil so that the inner part of the fold is not exposed to the outside environment and autoclaving the folded piece with the settings: 121.1 &#176;C and 5 min drying time.</li>
		<li>Sterilize a vise-grip with 70% ethanol and an open flame. Then use the vise-grip to break open the filter casing on the sterile surface and remove the core from the casing.</li>
		<li>Use a sterile scalpel to cut the filter paper away from the core by slicing at the top and bottom and then along the seam. Fold the filter paper using sterile tweezers and then cut the filter into small pieces using the scalpel.</li>
		<li>Place the filter pieces in a microcentrifuge tube for extraction. Make sure that the filter paper does not come into contact with any surfaces which are not sterilized or that could have nucleic acid present, as this would lead to unwanted contamination of the sample.</li>
	</ol>
	</li>
	<li>Perform DNA isolation as described previously13 or by using a commercially available column-based kit (see Table of Materials). The steps for the commercial kit listed are briefly described below.
	<ol>
		<li>Lyse the cells within the sample by transferring it to a bead tube and subjecting it to a cell disruptor at high speed for at least 5 min. Centrifuge and transfer the supernatant to a sterile microcentrifuge tube.</li>
		<li>Add lysis buffer to the supernatant (1:1 volume) and transfer to the provided filter (yellow). Centrifuge the filter.</li>
		<li>Transfer the filter to a new sterile microcentrifuge tube. Add the preparation buffer (400 &#181;L), centrifuge, and discard the flow through.</li>
		<li>Add wash buffer (700 &#181;L), centrifuge, and discard the flow through. Then add wash buffer (400 &#181;L), centrifuge, and discard the flow through again.</li>
		<li>Transfer the filter to a new sterile microcentrifuge tube. Elute with 50 &#181;L of DNase/RNase free water and let sit for 5 min at room temperature before centrifuging.</li>
		<li>During that in cubation period, prepare the III-HRC filter by placing it in a collection tube and adding the HRC prep solution (600 &#181;L) to it, followed by a centrifugation step of 3 min at 8,000 x g.</li>
		<li>Move the prepared filter onto a sterile microcentrifuge tube. Transfer the eluted DNA from step 3.4.5 to this filter and centrifuge at 16,000 x g for 3 min. The flow through contains the extracted DNA.</li>
	</ol>
	</li>
	<li>Store DNA extracts for both sediments and filters at -20 &#176;C. 
	NOTE: DNA extracts can be stored for around 8 years at -20 &#176;C assuming stable temperature, limited light exposure, and no harmful contaminants14.</li>
	<li>Perform RNA isolation as per the manufacturer&#39;s protocol. Store RNA extracts at -80 &#176;C.
	<ol>
		<li>Lyse the cells within the sample by transferring it to a bead tube and subjecting it to a cell disruptor at high speed for at least five minutes. Centrifuge and transfer the supernatant to a sterile microcentrifuge tube.</li>
		<li>Add lysis buffer to the supernatant (1:1 volume) and transfer to the provided column (yellow). Centrifuge the column.</li>
		<li>Add an equal volume of 95-100% ethanol to the flow through and mix by pipetting up and down five times.</li>
		<li>Place the IICG Column (green) on a sterile microcentrifuge tube. Transfer the mixed solution to the column and centrifuge.</li>
		<li>Add wash buffer (400 &#181;L), centrifuge, and discard the flow through.</li>
		<li>Add 5 &#181;L of DNase I and 75 &#181;L of DNA digestion buffer to the column and incubate at room temperature for 15 minutes.</li>
		<li>Add prep buffer (400 &#181;L), centrifuge, and discard the flow through.</li>
		<li>Add wash buffer (700 &#181;L), centrifuge, and discard the flow through. Then add wash buffer (400 &#181;L), centrifuge, and discard the flow through again.</li>
		<li>Transfer the column to a new sterile microcentrifuge tube. Elute with 50 &#181;L of DNase/RNase free water and let sit for 5 min before centrifuging.</li>
		<li>During that incubation period, prepare the III-HRC filter by placing it in a collection tube and adding the HRC prep solution (600 &#181;L) to it, followed by a centrifugation step of 3 min at 8,000 x g.</li>
		<li>Move the prepared filter onto a sterile microcentrifuge tube. Transfer the eluted RNA from step 3.6.9 to this filter and centrifuge at 16,000 x g for 3 min. The flow through contains the extracted RNA. 
		NOTE: RNA extracts can only be stored for one year before they start to degrade15. Both DNA and RNA extracts are degraded by repeated freeze-thawing. Some protocols allow for the extraction of both DNA and RNA from the same sample16,17.</li>
	</ol>
	</li>
	<li>Quantify the extracted DNA and RNA samples using a fluorometer or a spectrophotometer. See Table 1 for example fluorometer DNA concentration values. For an example spectrophotometer quantification protocol, see reference18. Sediment DNA concentration values with the kit listed in Table of Materials generally range from 1 to 40 ng/&#181;L, while filter DNA concentration values tend to range from 0.5 to 10 ng/&#181;L. Sediment RNA concentration values with the kit listed in Table of Materials generally range from around 1 to 20 ng/&#181;L, while filter RNA concentration values tend to be lower, typically ranging from 0.5 to 5 ng/&#181;L.</li>
</ol>
4.&#160;16S rRNA library creation
<ol>
	<li>Clean the work area with 10% Bleach and 70% Ethanol. The work area should be an enclosed space capable of producing laminar flow conditions (laminar flow hood).</li>
	<li>Use the DNA extracts (from step 3.5) and prepare samples for 16S rRNA amplicon sequencing with a standard PCR protocol, such as the one described on the Earth Microbiome&#39;s website that amplifies the V4 hypervariable region of 16S rRNA19 under laminar flow conditions.</li>
	<li>Prepare a 2% agarose gel as described previously and let it solidify17. Mix 7 &#181;L of PCR product and 13 &#181;L of DNase free water. Add a gel loading dye to a final concentration of 1x. Once agarose is solidified, load this PCR products mix on a 2% agarose gel. 
	NOTE: Alternatively, a pre-cast gel can be used instead, as these gels run faster and come pre-made.</li>
	<li>Run the gel at 90 V for 60-90 min to check for the band size of 386 as successful amplification for 16S rRNA V4 amplicons, using the Earth Microbiome&#39;s protocol.</li>
</ol>
5. DNA 16S rRNA library purification
<ol>
	<li>Pool 10 &#181;L of PCR products for the samples that yielded bright bands and 13 &#181;L for the samples that yielded faint bands in an appropriately sized sterile microcentrifuge tube.</li>
	<li>Check the concentration of the resulting pool using a fluorometer or spectrophotometer and prepare a 2% agarose gel as before. Ideally, the pool should have a concentration of at least 10 ng/&#181;L, and most samples should have had a concentration of around 25 ng/&#181;L.</li>
	<li>Concentration and volume permitting, load around 150-200 ng in a well of 2% agarose gel.</li>
	<li>Run the gel for 60-90 min at 90 volts.</li>
	<li>Purify the pooled library by running a 2% agarose gel.
	<ol>
		<li>Excise the 386 bp DNA band from the gel and purify the pooled library using a commercially available kit as described previously20. Elute the purified DNA with 30 &#181;L of 10 mM Tris-Cl (pH 8.5). Perform this step in a different area than DNA or RNA extraction to prevent future contamination, as cutting the gel will spread PCR amplicons onto both the experimenter and the surrounding area.</li>
	</ol>
	</li>
	<li>Check the concentration of the purified pool using a fluorometer or spectrophotometer. If purification went well, its concentration should be at least half of the unpurified pool&#39;s. Generally, the final concentration should range from 5 to 20 ng/&#181;L.</li>
	<li>Send the purified libraries for next generation sequencing. Ensure that they are kept cold during transport by including dry ice in the shipping container.</li>
</ol>
6. RNA library creation and purification
<ol>
	<li>Several commercial kits can be utilized to create RNA libraries. For whichever one is used, follow the manufacturer&#39;s protocol as written while working in a sterile laminar flow environment. A very summarized version of the protocol for kit in the Table of Materials is presented below21.
	<ol>
		<li>Make the first strand cDNA synthesis master mix (8 &#181;L of nuclease-free water and 2 &#181;L of First Strand Synthesis Enyzme Mix) and add it to the sample. Place the sample in the thermocycler with the conditions specified in the protocol.</li>
		<li>Make the second strand cDNA synthesis master mix (8 &#181;L of Second Strand Synthesis Reaction Buffer, 4 &#181;L Second Strand Synthesis Enzyme Mix, and 48 &#181;L of nuclease-free water) on ice and add it to the sample. Place in a thermocycler set to 16 &#176;C for one hour.</li>
		<li>Purify the reaction by adding the provided beads (144 &#181;L) and performing two 80% ethanol washes (200 &#181;L).</li>
		<li>Elute with the provided TE buffer (53 &#181;L) and transfer 50 &#181;L of the supernatant to a clean PCR tube. Place the PCR tube on ice.</li>
		<li>Make the end prep master mix (7 &#181;L of End Prep Reaction Buffer and 3 &#181;L of End Prep Enzyme Mix) on ice and add it to the PCR tube. Place the PCR tube in a thermocycler with the conditions specified in the protocol.</li>
		<li>Mix the Diluted Adaptor (2.5 &#181;L), Ligation Master Mix (30 &#181;L) and Ligation Enhancer (1 &#181;L) solutions on ice. Add the mixed solutions to the sample and place in a thermocycler for 15 min at 20 &#176;C.</li>
		<li>Purify the reaction by adding the provided beads (87 &#181;L) and performing ethanol washes (200 &#181;L) and elution as before, except only add 17 &#181;L of TE.</li>
		<li>Add indices (10 &#181;L) and the Q5 Master Mix (25 &#181;L) solution and place in a thermocycler with the conditions described in the protocol.</li>
		<li>Purify the reaction by adding the provided beads (45 &#181;L) and performing an addition two ethanol washes (200 &#181;L) and elute with 23 &#181;L of TE. Transfer 20 &#181;L to a clean PCR tube.</li>
	</ol>
	</li>
	<li>Check the libraries for detectable concentrations of RNA using a Bioanalyzer, fluorometer, or spectrophotometer.</li>
	<li>Pool the metatranscriptomic libraries in a roughly equimolar ratio.</li>
	<li>Purify the library following the same protocol for the 16S library purification, except excise fragments between 250 and 400 bp. Whereas the 16S library had a distinct band representing the amplified region, the result here is a smear.</li>
	<li>Check the concentration of the purified library as before.</li>
	<li>Ship the purified library with dry ice to a sequencing facility. 
	&#8203;NOTE: Alternatively, RNA extracts can be sent to a university or private company for library preparation and sequencing.</li>
</ol>
7. Microbial community analysis
<ol>
	<li>Once sequencing is complete, access the sample data. Download it to a usable computer. 
	NOTE: Ideally, the device should have at least 16 gigabytes of RAM. For a discussion of computing requirements (for Qiime2), see https://forum.qiime2.org/t/recommended-specifications-to-run-qiime2/9808.</li>
	<li>Use software, such as mothur, QIIME2, and R, to analyze 16S rRNA data. See here https://docs.qiime2.org/2020.11/tutorials/moving-pictures/ for an example QIIME2 16S analysis tutorial.</li>
	<li>For metatranscriptomics (RNA) data, use HUMAnN2 and ATLAS to determine which genes and pathways are present in the samples. 
	NOTE: An example metatranscriptomics pipeline culminating in diversity and random forest analysis is presented in the Supplemental Information file. All commands are run through command line, e.g., Terminal for Mac users.</li>
</ol>

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The authors have nothing to disclose.

The methods described in this paper have been developed and refined over the course of several studies published by our group between 2014 and 20187,8,10 and have been employed successfully in a collaborative project to investigate the impacts of fracking on aquatic communities in a three year project that will soon submit a paper for publication. These methods will continue to be utilized over the course of the remainder of the project. Additionally, other current literature investigating the impact of fracking on streams and ecosystems describe similar methods for sample collection, processing, and analysis7,8,10,11. However, none of those papers utilized metatranscriptomic analysis, making this paper the first to describe how those analyses can be used to elucidate fracking&#39;s impact on nearby streams. Furthermore, the methods presented here for sample collection are more detailed, as are the steps taken to avoid contamination.
One of the most important steps of our protocol is initial sample collection and preservation. Field sampling and collection comes with certain challenges, as maintaining an aseptic or sterile environment during collection can be difficult. During this step, it is vital to avoid contaminating samples. To do this, gloves should be worn, and only sterile containers and tools should be allowed to come into contact with samples. Samples should also be immediately placed on ice after collection to mitigate nucleic acid degradation. Adding a commercial nucleic acid preservative upon collection can also increase nucleic acid yield and allow samples to be stored for longer periods of time after collection. Whenever nucleic acid extraction is performed, it is important to use the appropriate amount of sample, too much can clog spin filters used for extraction (for those protocols that make use of them) but too little can result in low yields. Be sure to follow the instructions for whichever kit is used.
Similar to field collection, avoiding or minimizing contamination is also important during nucleic acid extraction and sample preparation, especially when working with low nucleic acid yield samples, such as suboptimal sediment samples (samples containing a large amount of gravel or rocks) or water samples. Therefore, as with sample collection, gloves should be worn during all these steps to reduce contamination. Additionally, all work surfaces used during lab procedures should be sterilized beforehand by wiping with a 10% bleach solution, followed by a 70% ethanol solution. For pipetting steps (3-6), filter tips should be used to avoid contamination due to the pipette itself, with tips being changed every time they touch a non-sterile surface. All tools used for lab work, including pipettes, should be wiped down before and after with the bleach and ethanol solutions. To evaluate contamination, extraction blanks and negatives (sterile liquid) should be included during every set of nucleic acid extractions and PCR reactions. If quantification after extractions reveals a detectable amount of DNA/RNA in the negatives, extractions can be repeated if there is sufficient sample left. If negative samples for PCR show amplification, troubleshooting should be performed to determine the source and then the samples should be rerun. To account for low levels of contamination, it is recommended that extraction blanks and PCR negatives be sequenced so that the contaminants can be identified and removed, if necessary, during computational analysis. Conversely, PCR amplification could also fail due to a variety of causes. For environmental samples, inhibition of the PCR reaction is often the culprit, which can be due to a variety of substances interfering with Taq polymerase23. If inhibition is suspected, PCR grade water (see Table of Materials) can be used to dilute the DNA extracts.
This protocol has a few notable limitations and potential difficulties. Sample collection can be challenging for both water and sediment samples. In order to get enough biomass, ideally 1 L of stream water needs to be pushed through a filter. The pores of the filter need to be small to capture microbes but can also trap sediment. If a lot of sediment is in the water due to recent rainfall, the filter can clog making it difficult to push the entire volume through the filter. For sediment collection, it can be challenging to estimate the depth of sediment during collection. Furthermore, it is important to ensure that the sediment collected is predominantly soil, as pebbles and rocks will lead to lower nucleic acid yield and may not be an accurate representation of the microbial community. Lastly, it is vital as well that samples are kept on ice after collection, especially if a preservative is not used.
Though this protocol covers both metatranscriptomics and 16S lab protocols, it should be emphasized that these two methods are very different in both process and in the type of data they provide. The 16S rRNA gene is a commonly targeted region, highly conserved in bacteria and archaea, and useful for characterizing the bacterial community in a sample. Although a targeted and specific approach, species level resolution is often unattainable, and characterizing newly diverged species or strains is difficult. Contrarily, metatranscriptomics is a broader approach that captures all the active genes and microbes present within a sample. Whereas 16S provides only data for identification, metatranscriptomics can provide functional data such as expressed genes and metabolic pathways. Both are valuable and when combined, they can reveal which bacteria are present and which genes they are expressing.
This paper describes methods for field collection and sample processing for both 16S rRNA and metatranscriptomic analyses in the context of studying fracking. Additionally, it details collection methods for high quality DNA/RNA from low biomass samples and for long-term storage. The methods described here are the culmination of our experiences with sample collection and processing in our efforts to learn how fracking impacts nearby streams through examining the structure and function of their microbial communities. Microbes respond quickly to disturbances, and consequently, which microbes are present and the genes they express can provide information about the effects of fracking on ecosystems. Overall, these methods could be invaluable in our understanding of how fracking impacts these important ecosystems.

Hydraulic fracturing (HF), or &#34;fracking&#34;, is a method of natural gas extraction, which has become increasingly prevalent as the demand for fossil fuels continues to rise. This technique consists of using high-powered drilling equipment to inject a blend of water, sand, and chemicals into methane-rich shale deposits, usually to release trapped gasses1.
Because these unconventional harvesting techniques are relatively new, it is important to investigate the effects of such practices on nearby waterways. Fracking activities mandate the clearing of large swaths of land for equipment transportation and well pad construction. Approximately 1.2-1.7 hectares of land must be cleared for each well pad2, potentially impacting runoff and water quality of the system3. There is a lack of transparency surrounding the exact chemical composition of fracking fluid, including what biocides are used. Additionally, fracking wastewater tends to be highly saline2. Furthermore, the wastewater may contain metals and naturally occurring radioactive substances2. Therefore, the possibility of leaks and spills of fracking fluid due to human error or equipment malfunction is concerning.
Stream ecosystems are known to be very sensitive to changes in surrounding landscapes4 and are important for maintaining biodiversity5 and proper nutrient cycling6 within the entire watershed. Microbes are the most abundant organisms in freshwater streams and thus, are essential to nutrient cycling, biodegradation, and primary production. Microbial community composition and function serve as great tools to gain information on the ecosystem due to their sensitivity to perturbance, and recent research has shown distinct shifts in observed bacterial assemblages based on proximity to fracking activity7,8. For example, Beijerinckia, Burkholderia, and Methanobacterium were identified as enriched in streams near fracking while Pseudonocardia, Nitrospira, and Rhodobacter were enriched in the streams not near fracking7.
Next generation sequencing of the 16S ribosomal RNA (rRNA) gene is an affordable method of determining bacterial community composition that is faster and cheaper than whole genome sequencing approaches9. A common practice within the field of molecular ecology is to use the highly variable V4 region of the 16S rRNA gene for sequencing resolution, often down to the genus level with a wide scope of identification9, as it is ideal for unpredictable environmental samples. This technique has been implemented widely in published studies and has been successfully utilized to identify the impact of fracking operations on aquatic environments7,8. However, it is worth noting that bacteria have varying copy numbers of the 16S rRNA gene, which affects their detected abundances10. There are a few tools to account for this, but their efficacy is questionable10. Another practice that is quickly growing in prevalence and lacks this weakness is metatranscriptomic sequencing, in which all RNA is sequenced, allowing researchers to identify both active bacteria and their genes expression.
Therefore, in contrast to methods in previously published studies7,8,11,12, this protocol also covers sample collection, preservation, processing, and analysis for investigating microbial community function (metatranscriptomics). The steps detailed herein allow researchers to see what impact, if any, fracking has had on the genes and pathways expressed by microbes in their streams, including antimicrobial resistance genes. Moreover, the level of detail presented for sample collection is improved.&#160;Although several of the steps and notes may seem obvious to experienced researchers, they could be invaluable to those just starting research.
Herein, we describe methods for sample collection and processing to generate bacterial genetic data as a means to investigate the impact of fracking on nearby streams based on our labs&#39; several years of experience. These data can be used in downstream applications to identify differences corresponding to fracking status.

<table><tbody><tr><td>200 Proof Ethanol</td><td>Thermo Fisher Scientific</td><td>A4094</td><td>400 mL need to be added to Buffer PE (see Qiagen QIAQuck Gel Extraction kit protocol) and 96 mL needs to be added to the DNA/RNA Wash Buffer (see ZymoBIOMICS DNA/RNA Miniprep kit protocol).&lt;br/&gt; Additional ethanol is needed for the ZymoBIOMICS DNA/RNA Miniprep and NEBNext&amp;reg; Ultra&amp;trade; II RNA Library Prep with Sample Purification Beads kits.</td></tr><tr><td>Agarose</td><td>Thermo Fisher Scientific</td><td>BP1356-100</td><td>100 g per bottle. 0.6 g of agarose would be needed to make one 2% 30 mL gel.</td></tr><tr><td>Disinfecting Bleach</td><td>Walmart (Clorox)</td><td>No catalog number</td><td>Use a 10% bleach solution for cleaning the work area before and after lab procedures</td></tr><tr><td>DNA gel loading dye</td><td>Thermo Fisher Scientific</td><td>R0611</td><td>Each user-made (i.e. non-e-gel) should include loading dye with all of the samples in the ratio of 1 &amp;micro;L dye to 5 &amp;micro;L sample</td></tr><tr><td>DNA ladder</td><td>MilliporeSigma</td><td>D3937-1VL</td><td>A ladder should be run on every gel/e-gel</td></tr><tr><td>DNA/RNA Shield (2x)</td><td>Zymo Research</td><td>R1200-125</td><td>3 mL per sediment sample (50 mL conical) and 2 mL per water sample (filter)</td></tr><tr><td>Ethidium bromide</td><td>Thermo Fisher Scientific</td><td>BP1302-10</td><td>Used for staining user-made e-gels</td></tr><tr><td>Forward Primer</td><td>Integrated DNA Technologies (IDT)</td><td>51-01-19-06</td><td>0.5 &amp;micro;L per PCR reaction</td></tr><tr><td>Isopropanol</td><td>MilliporeSigma</td><td>563935-1L</td><td>Generally less than 2 mL per library. Volume needed varies by mass of excised gel fragment (see Qiagen QIAQuick Gel Extraction kit protocol).&amp;nbsp;</td></tr><tr><td>PCR-grade water</td><td>MilliporeSigma</td><td>3315932001</td><td>13 &amp;micro;L per PCR reaction (assuming 1 &amp;micro;L of sample DNA template is used)</td></tr><tr><td>Platinum Hot Start PCR Master Mix (2x)</td><td>Thermo Fisher Scientific</td><td>13000012</td><td>10 &amp;micro;L per PCR reaction</td></tr><tr><td>Reverse Primer</td><td>Integrated DNA Technologies (IDT)</td><td>51-01-19-07</td><td>0.5 &amp;micro;L per PCR reaction</td></tr><tr><td>TBE Buffer (Tris-borate-EDTA)</td><td>Thermo Fisher Scientific</td><td>B52</td><td>1 L of 10x TBE buffer (30 mL of 1x TBE buffer would be needed to make one 30 mL gel)</td></tr><tr><td>1 L bottle</td><td>Thermo Fisher Scientific</td><td>02-893-4E</td><td>One needed per stream (the same bottle can be used for multiple streams if it is sterilized between uses)</td></tr><tr><td>1.5 mL Microcentrifuge tubes</td><td>MilliporeSigma</td><td>BR780400-450EA</td><td>5 microcentrifuge tubes are needed per DNA extraction and an additional 3 are needed to purify RNA (see ZymoBIOMICS DNA/RNA Miniprep kit protocol)</td></tr><tr><td>2% Agarose e-gel</td><td>Thermo Fisher Scientific</td><td>G401002</td><td>Each gel can run 10 samples (so 9 with a PCR negative and 8 if the extraction negative is run on the same gel)</td></tr><tr><td>50 mL Conicals</td><td>CellTreat</td><td>229421</td><td>1 50 mL conical needed per sediment samples</td></tr><tr><td>500 mL Beaker</td><td>MilliporeSigma</td><td>Z740580</td><td>Only 1 needed (for flame sterilization)</td></tr><tr><td>Aluminum foil</td><td>Walmart (Reynolds KITCHEN)</td><td>No number</td><td>Aluminum foil can be folded and autoclaved. The part not exposed to the environment can then be used as a sterile, DNA and RNA free surface for processing filters&lt;br/&gt; (one folded piece per filter to avoid cross-contamination)</td></tr><tr><td>Autoclave</td><td>Gettinge</td><td>LSS 130</td><td>Only one needed</td></tr><tr><td>Centrifuge</td><td>MilliporeSigma</td><td>EP5404000138-1EA</td><td>Only 1 needed</td></tr><tr><td>Cooler</td><td>ULINE</td><td>S-22567</td><td>Just about any cooler can be used. This one is listed due to being made of foam, making it lighter and thus easier to take along for field sampling.</td></tr><tr><td>Disruptor Genie</td><td>Bio-Rad</td><td>3591456</td><td>Only one needed</td></tr><tr><td>Electrophoresis chamber</td><td>Bio-Rad</td><td>1664000EDU</td><td>Only 1 needed</td></tr><tr><td>Electrophoresis power supply</td><td>Bio-Rad</td><td>1645050</td><td>Only 1 needed</td></tr><tr><td>Freezer (-20 C)</td><td>K2 SCIENTIFIC</td><td>K204SDF</td><td>One needed to store DNA extracts</td></tr><tr><td>Freezer (-80 C)</td><td>K2 SCIENTIFIC</td><td>K205ULT</td><td>One needed to store RNA extracts</td></tr><tr><td>Gloves</td><td>Thermo Fisher Scientific</td><td>19-020-352</td><td>The catalog number is for Medium gloves.</td></tr><tr><td>Heat block</td><td>MilliporeSigma</td><td>Z741333-1EA</td><td>Only one needed</td></tr><tr><td>Lab burner</td><td>Sterlitech</td><td>177200-00</td><td>Only one needed</td></tr><tr><td>Laminar Flow Hood</td><td>AirClean Systems</td><td>AC624LFUV</td><td>Only 1 needed</td></tr><tr><td>Library purification kit</td><td>Qiagen</td><td>28704</td><td>One kit has enough for 50 reactions</td></tr><tr><td>Magnet Plate</td><td>Alpaqua</td><td>A001219</td><td>Only one needed</td></tr><tr><td>Microcentrifuge</td><td>Thermo Fisher Scientific</td><td>75004061</td><td>Only one needed</td></tr><tr><td>Micropipette (1000 &amp;micro;L volume)</td><td>Pipette.com</td><td>L-1000</td><td>Only 1 needed</td></tr><tr><td>Micropipette (2 &amp;micro;L volume)</td><td>Pipette.com</td><td>L-2</td><td>Only 1 needed</td></tr><tr><td>Micropipette (20 &amp;micro;L volume)</td><td>Pipette.com</td><td>L-20</td><td>Only 1 needed</td></tr><tr><td>Micropipette (200 &amp;micro;L volume)</td><td>Pipette.com</td><td>L-200R</td><td>Only 1 needed</td></tr><tr><td>NEBNext Ultra II RNA Library Prep with Sample Purification Beads</td><td>New England BioLabs Inc.</td><td>E7775S</td><td>One kit has enough reagents for 24 samples.</td></tr><tr><td>Parafilm</td><td>MilliporeSigma</td><td>P7793-1EA</td><td>2 1&quot; x 1&quot; squares are needed per filter</td></tr><tr><td>PCR Tubes</td><td>Thermo Fisher Scientific</td><td>AM12230</td><td>One tube needed per reaction</td></tr><tr><td>Pipette tips (for 1000 &amp;micro;L volume)</td><td>Pipette.com</td><td>LF-1000</td><td>Pack of 576 tips</td></tr><tr><td>Pipette tips (for 20 &amp;micro;L volume)</td><td>Pipette.com</td><td>LF-20</td><td>Pack of 960 tips</td></tr><tr><td>Pipette tips (for 200 &amp;micro;L volume)</td><td>Pipette.com</td><td>LF-250</td><td>Pack of 960 tips</td></tr><tr><td>PowerWulf ZXR1+ computer cluster</td><td>PSSC Labs</td><td>No number</td><td>This is just an example of a supercomputer powerful enough to perform metatranscriptomics analysis in a timely manner. Only one needed.&amp;nbsp;</td></tr><tr><td>Qubit fluorometer starter kit</td><td>Thermo Fisher Scientific</td><td>Q33239</td><td>Comes with a Qubit 4 fluorometer, enough reagent for 100 DNA assays, and 500 Qubit tubes</td></tr><tr><td>Scoopula</td><td>Thermo Fisher Scientific</td><td>14-357Q</td><td>Only one needed</td></tr><tr><td>Sterile blades</td><td>AD Surgical</td><td>A600-P10-0</td><td>One needed per filter</td></tr><tr><td>Sterivex-GP Pressure Filter Unit</td><td>MilliporeSigma</td><td>SVGP01050</td><td>1 filter needed per water sample</td></tr><tr><td>Thermocycler</td><td>Bio-Rad</td><td>1861096</td><td>Only one needed</td></tr><tr><td>Vise-grip</td><td>Irwin</td><td>2078500</td><td>Only one needed (for cracking open the filters)</td></tr><tr><td>Vortex-Genie 2</td><td>MilliporeSigma</td><td>Z258415-1EA</td><td>Only 1 needed</td></tr><tr><td>WHIRL-PAK bags</td><td>ULINE</td><td>S-22729</td><td>1 needed per filter</td></tr><tr><td>ZymoBIOMICS DNA/RNA Miniprep kit</td><td>Zymo Research</td><td>R2002</td><td>One kit has enough reagents for 50 samples.&amp;nbsp;</td></tr></tbody></table>

evaluating the impact of hydraulic fracturing on streams using microbial molecular signatures

Hydraulic fracturing (HF), commonly called &#34;fracking&#34;, uses a mixture of high-pressure water, sand, and chemicals to fracture rocks, releasing oil and gas. This process revolutionized the U.S. energy industry, as it gives access to resources that were previously unobtainable and now produces two-thirds of the total natural gas in the United States. Although fracking has had a positive impact on the U.S. economy, several studies have highlighted its detrimental environmental effects. Of particular concern is the effect of fracking on headwater streams, which are especially important due to their disproportionately large impact on the health of the entire watershed. The bacteria within those streams can be used as indicators of stream health, as the bacteria present and their abundance in a disturbed stream would be expected to differ from those in an otherwise comparable but undisturbed stream. Therefore, this protocol aims to use the bacterial community to determine if streams have been impacted by fracking. To this end, sediment, and water samples, from streams near fracking (potentially impacted) and upstream or in a different watershed of fracking activity (unimpacted) must be collected. Those samples are then subjected to nucleic acid extraction, library preparation, and sequencing to investigate microbial community composition. Correlational analysis and machine learning models can subsequently be employed to identify which features are explanative of variation in the community, as well as identification of predictive biomarkers for fracking&#39;s impact. These methods can reveal a variety of differences in the microbial communities among headwater streams, based on the proximity to fracking, and serve as a foundation for future investigations on the environmental impact of fracking activities.

The success of DNA and RNA extractions can be evaluated using a variety of equipment and protocols. Generally, any detectable concentration of either is considered sufficient to conclude that the extraction was successful. Examining Table 1 then, all extractions, except for one, would be dubbed successful. Failure at this step is often due to low initial biomass, poor sample preservation, or human error during extraction. In the case of filters, extraction may have been successful even if the concentration is below detection. If those extracts do not yield bands for PCR (if doing 16S) or a detectable concentration after library preparation (metatranscriptomics), then they likely did truly fail.
If the 16S protocol is followed, bright bands following PCR amplification, as seen in wells 4 and 6 in Figure 1, indicate success, while a lack of bands, as seen in the other wells in the top row, indicates failure. Moreover, a bright band in the gel lane that contains a negative PCR control would also indicate a failure since it would be risky to assume that the contamination impacting the negative control(s) did not affect the samples.
For both 16S and metatranscriptomics, the success of sequencing can be evaluated by looking at the number of sequences obtained (Figure 2). 16S samples should have a minimum of 1,000 sequences, with at least 5,000 being ideal (Figure 2A). Likewise, metatranscriptomics samples should have a minimum of 500,000 sequences, with at least 2,000,000 being ideal (Figure 2B). Samples with fewer sequences than those minimums should not be used for analyses, as they may not accurately represent their bacterial community. However, samples that fall between the minimum and ideal can still be used though results should be interpreted more cautiously if many samples fall in that range.
The success of subsequent downstream analysis can be determined simply on the basis of whether the expected output files were obtained or not. At any rate, programs, such as QIIME2 and R (Figure 3), should allow for the evaluation of potential significant differences among the bacterial communities based on fracking. The data for Figure 3 was obtained by collecting sediment samples from twenty-one different sites at thirteen different streams for 16S and metatranscriptomics analysis. Of those twenty-one sites, twelve of them were downstream of fracking activity and classified as HF+, and nine of them were either upstream of fracking activity or in a watershed where fracking was not occurring; these streams were classified as HF-. Besides the presence of fracking activity, the streams were otherwise comparable.
Those differences could take the form of consistent compositional shifts based on fracking status. If that were the case, HF+ and HF- samples would be expected to cluster apart from each other in a PCoA plot, as is the case in Figure 3A and Figure 3B. To confirm that those apparent shifts are not just an artifact of the ordination method, further statistical analysis is needed. For example, a PERMANOVA22 test on the distance matrix that Figure 3A and Figure 3B are based on revealed significant clustering based on fracking status, meaning that the separation observed in the plot is consistent with differences among the samples&#39; bacterial communities, instead of an artifact of ordination. A significant PERMANOVA or ANOSIM result is a strong indication of consistent differences between HF+ and HF- samples, which would indicate that the HF+ samples were impacted by fracking, while a high p-value would indicate that the samples were not impacted. Metatranscriptomic data can likewise be visualized and evaluated using the same methods.
Examining differential features (microbes or functions) can reveal evidence that samples have been impacted too. One method of determining differential features is to create a random forest model. The random forest model can be used to see how well the samples&#39; fracking status can be correctly classified. If the model performs better than expected by chance, that would be additional evidence of differences dependent on fracking status. Moreover, the most important predictors would reveal which features were most important for correctly differentiating samples (Figure 3C). Those features also then would have had consistently different values based on fracking status. Once those differential features are determined, the literature can be reviewed to see if they have been previously associated with fracking. However, it may be challenging to find studies that determined differential functions, as most have only used 16S rRNA compositional data. Therefore, for evaluating the implications of differential functions, one possible method would be to see if they have been previously associated with potential resistance to biocides commonly used in fracking fluid or if they could aid in tolerating highly saline conditions. Furthermore, examining the functional profile of a taxon of interest could reveal evidence of fracking&#39;s impact (Figure 3D). For example, if a taxon is identified as differential by the random forest model, its antimicrobial resistance profile in HF+ samples could be compared to its profile in HF- samples and if they differ greatly, that could suggest that fracking fluid containing biocides entered the stream.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>SampleID</td>
			<td>Concentration (ng/&#181;L)</td>
		</tr>
		<tr>
			<td>1</td>
			<td>1.5</td>
		</tr>
		<tr>
			<td>2</td>
			<td>1.55</td>
		</tr>
		<tr>
			<td>3</td>
			<td>0.745</td>
		</tr>
		<tr>
			<td>4</td>
			<td>0.805</td>
		</tr>
		<tr>
			<td>5</td>
			<td>7.82</td>
		</tr>
		<tr>
			<td>6</td>
			<td>0.053</td>
		</tr>
		<tr>
			<td>7</td>
			<td>0.248</td>
		</tr>
		<tr>
			<td>8</td>
			<td>0.945</td>
		</tr>
		<tr>
			<td>9</td>
			<td>1.82</td>
		</tr>
		<tr>
			<td>10</td>
			<td>0.804</td>
		</tr>
		<tr>
			<td>11</td>
			<td>0.551</td>
		</tr>
		<tr>
			<td>12</td>
			<td>1.69</td>
		</tr>
		<tr>
			<td>13</td>
			<td>4.08</td>
		</tr>
		<tr>
			<td>14</td>
			<td>Below_Detection</td>
		</tr>
		<tr>
			<td>15</td>
			<td>7.87</td>
		</tr>
		<tr>
			<td>16</td>
			<td>0.346</td>
		</tr>
		<tr>
			<td>17</td>
			<td>2.64</td>
		</tr>
		<tr>
			<td>18</td>
			<td>1.15</td>
		</tr>
		<tr>
			<td>19</td>
			<td>0.951</td>
		</tr>
	</tbody>
</table>
Table 1: Example DNA concentrations based on Fluorometer 1x DS DNA high sensitivity assay. Extractions for all these samples, except for 14, would be considered successful due to having detectable amounts of DNA.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/61904/61904fig01.jpg" /> 
Figure 1: Example e-gel with PCR products. The gel was pre-stained and visualized under a UV light, causing any DNA present on it to glow. PCR worked for the samples in wells 4 and 6 in the first row, as they both had one single bright band of the expected size (based on the ladder). PCR for the samples in the other six wells failed, as they did not produce any bands. The positive control (first well, second row) had a bright band, indicating that PCR was performed properly, and the negative controls (wells 6 and 7, second row) did not have any bands, indicating that samples were not contaminated. If a negative had a band as bright as the samples, PCR would have been considered a failure since it would be risky to assume that the samples had amplicons that were not just the result of contamination. <a href="https://www.jove.com/files/ftp_upload/61904/61904fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/61904/61904fig02.jpg" /> 
Figure 2: Example sequence counts. (A) 16S example sequence counts. Nearly all these 16S samples had over 1,000 sequences. The very few that had less than 1,000 sequences should be excluded from downstream analyses, as they had insufficient sequences to accurately represent their bacterial communities. Several sequences had between 1,000 and 5,000 sequences; while not ideal, they would still be usable since they exceed the bare minimum, and the majority of samples exceed the ideal minimum of 5,000 as well. (B) Metatranscriptomics example counts. All samples exceeded both the minimum (500,000) and ideal minimum (2,000,000) number of sequences. Therefore, sequencing was successful for all of them, and they could all be used in downstream analysis. <a href="https://www.jove.com/files/ftp_upload/61904/61904fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/61904/61904fig03.jpg" /> 
Figure 3: Example analysis. (A) PCoA plot based on coordinates calculated with a Weighted Unifrac distance matrix created and visualized through QIIME2. (B) PCoA plot based on coordinates calculated with the Weighted Unifrac distance matrix exported from QIIME2. The coordinates were visualized using the Phyloseq and ggplot2 packages in R. Metadata vectors were fitted to the plot using the Vegan package. Each point represents a sample&#39;s bacterial community, with closer points indicating more similar community compositions. Clustering based on fracking status for these 16S sediment samples was observed (PERMANOVA, p=0.001). Furthermore, the vectors reveal that the HF+ samples tended to have higher levels of Barium, Bromide, Nickel, and Zinc, which corresponded to different bacterial community composition compared to the HF- samples. (C) Plot of best predictors for a random forest model that tested where bacterial abundances could be used to predict fracking status among the samples. The random forest model was created through R using the randomForest package. The top 20 predictors are shown as well as the resulting decreases in impurity (measure of the number of HF+ and HF- samples grouped together) in the form of Mean Decrease in Gini Index when they are utilized to separate samples. (D) Pie chart showing the antimicrobial resistance profile of the Burkholderiales profile based on metatranscriptomic data. Sequences were first annotated with Kraken2 to determine which taxa they belonged to. BLAST was then used with those annotated sequences and the MEGARes 2.0 database to determine which antimicrobial resistance genes (in the form of &#34;MEG_#&#34;) were being actively expressed. Antimicrobial resistance genes expressed by members of Burkholderiales were then extracted to see which ones were most prevalent among that taxa. While more costly and time-consuming, metatranscriptomics does allow for functional analyses, such as this which cannot be done with 16S data. Notably, Kraken2 was used for this example analysis, instead of HUMAnN2. Kraken2 is faster than HUMAnN2; however, it only outputs compositional information, instead of composition, contribution, and functions (genes) and pathways like HUMAnN2 does. <a href="https://www.jove.com/files/ftp_upload/61904/61904fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Supplementary File: An example metatranscriptomics pipeline.&#160;<a href="https://www.jove.com/files/ftp_upload/61904/Supplemental File_61904_R2_RE.docx" target="_blank">Please click here to download&#160;this file.</a>

Watch this Scientific Journal Video about Evaluating the Impact of Hydraulic Fracturing on Streams using Microbial Molecular Signatures at JoVE.com

Evaluating the Impact of Hydraulic Fracturing on Streams using Microbial Molecular Signatures

Here, we present a protocol to investigate the impacts of hydraulic fracturing on nearby streams by analyzing their water and sediment microbial communities.

Hydraulic fracturing (HF), commonly called &#34;fracking&#34;, uses a mixture of high-pressure water, sand, and chemicals to fracture rocks, releasing oil ...

evaluating-impact-hydraulic-fracturing-on-streams-using-microbial

Research

JoVE Journal

Environment

3.1K Views.  Juniata College. Here, we present a protocol to investigate the impacts of hydraulic fracturing on nearby streams by analyzing their water and sediment microbial communities. 

Video: Evaluating the Impact of Hydraulic Fracturing on Streams using Microbial Molecular Signatures

Determination of Microbial Extracellular Enzyme Activity in Waters, Soils, and Sediments using High Throughput Microplate Assays

Much of the nutrient cycling and carbon processing in natural environments occurs through the activity of extracellular enzymes released by microorganisms. Thus, measurement of the activity of these extracellular enzymes can give insights into the rates of ecosystem level processes, such as organic matter decomposition or nitrogen and phosphorus mineralization. Assays of extracellular enzyme activity in environmental samples typically involve exposing the samples to artificial colorimetric or fluorometric substrates and tracking the rate of substrate hydrolysis. Here we describe microplate based methods for these procedures that allow the analysis of large numbers of samples within a short time frame. Samples are allowed to react with artificial substrates within 96-well microplates or deep well microplate blocks, and enzyme activity is subsequently determined by absorption or fluorescence of the resulting end product using a typical microplate reader or fluorometer. Such high throughput procedures not only facilitate comparisons between spatially separate sites or ecosystems, but also substantially reduce the cost of such assays by reducing overall reagent volumes needed per sample.

Microplate based procedures are described for the colorimetric or fluorometric analysis of extracellular enzyme activity. These procedures allow for the rapid assay of such activity in large numbers of environmental samples within a manageable time frame.

Much of the nutrient cycling and carbon  processing in natural environments occurs through the activity of extracellular  enzymes released by ...

Unraveling the Unseen Players in the Ocean - A Field Guide to Water Chemistry and Marine Microbiology

Here we introduce a series of thoroughly tested and well standardized research protocols adapted for use in remote marine environments. The sampling protocols include the assessment of resources available to the microbial community (dissolved organic carbon, particulate organic matter, inorganic nutrients), and a comprehensive description of the viral and bacterial communities (via direct viral and microbial counts, enumeration of autofluorescent microbes, and construction of viral and microbial metagenomes). We use a combination of methods, which represent a dispersed field of scientific disciplines comprising already established protocols and some of the most recent techniques developed. Especially metagenomic sequencing techniques used for viral and bacterial community characterization, have been established only in recent years, and are thus still subjected to constant improvement. This has led to a variety of sampling and sample processing procedures currently in use. The set of methods presented here provides an up to date approach to collect and process environmental samples. Parameters addressed with these protocols yield the minimum on information essential to characterize and understand the underlying mechanisms of viral and microbial community dynamics. It gives easy to follow guidelines to conduct comprehensive surveys and discusses critical steps and potential caveats pertinent to each technique.

Here, we present a comprehensive protocol to assess the organic and inorganic nutrient availability and the abundance and structure of microbial and viral communities in remote marine environments.

Here we introduce a series of thoroughly tested and well standardized research protocols adapted for use in remote marine environments. The sampling ...

A Whole Cell Bioreporter Approach to Assess Transport and Bioavailability of Organic Contaminants in Water Unsaturated Systems

Bioavailability of contaminants is a prerequisite for their effective biodegradation in soil. The average bulk concentration of a contaminant, however, is not an appropriate measure for its availability; bioavailability rather depends on the dynamic interplay of potential mass transfer (flux) of a compound to a microbial cell and the capacity of the latter to degrade the compound. In water-unsaturated parts of the soil, mycelia have been shown to overcome bioavailability limitations by actively transporting and mobilizing organic compounds over the range of centimeters. Whereas the extent of mycelia-based transport can be quantified easily by chemical means, verification of the contaminant-bioavailability to bacterial cells requires a biological method. Addressing this constraint, we chose the PAH fluorene (FLU) as a model compound and developed a water unsaturated model microcosm linking a spatially separated FLU point source and the FLU degrading bioreporter bacterium Burkholderia sartisoli RP037-mChe by a mycelial network of Pythium ultimum. Since the bioreporter expresses eGFP in response of the PAH flux to the cell, bacterial FLU exposure and degradation could be monitored directly in the microcosms via confocal laser scanning microscopy (CLSM). CLSM and image analyses revealed a significant increase of the eGFP expression in the presence of P. ultimum compared to controls without mycelia or FLU thus indicating FLU bioavailability to bacteria after mycelia-mediated transport. CLSM results were supported by chemical analyses in identical microcosms. The developed microcosm proved suitable to investigate contaminant bioavailability and to concomitantly visualize the involved bacteria-mycelial interactions.

A whole cell bioreporter assay with Burkholderia sartisoli RP037-mChe was developed to detect fractions of an organic contaminant (i.e., fluorene) available for bacterial degradation after active transport by mycelia bridging air-filled pores in a water unsaturated model system.

Bioavailability of contaminants is a prerequisite for their effective biodegradation in soil. The average bulk concentration of a contaminant, however, is ...

A Duplex Digital PCR Assay for Simultaneous Quantification of the Enterococcus spp. and the Human Fecal-associated HF183 Marker in Waters

This manuscript describes a duplex digital PCR assay (EntHF183 dPCR) for simultaneous quantification of Enterococcus spp. and the human fecal-associated HF183 marker. The EntHF183 duplex dPCR (referred as EntHF183 dPCR hereon) assay uses the same primer and probe sequences as its published individual quantitative PCR (qPCR) counterparts. Likewise, the same water filtration and DNA extraction procedures as performed prior to qPCR are followed prior to running dPCR. However, the duplex dPCR assay has several advantages over the qPCR assays. Most important, the dPCR assay eliminates the need for running a standard curve and hence, the associated bias and variability, by direct quantification of its targets. In addition, while duplexing (i.e. simultaneous quantification) Enterococcus and HF183 in qPCR often leads to severe underestimation of the less abundant target in a sample, dPCR provides consistent quantification of both targets, whether quantified individually or simultaneously in the same reaction. The dPCR assay is also able to tolerate PCR inhibitor concentrations that are one to two orders of magnitude higher than those tolerated by qPCR. These advantages make the EntHF183 dPCR assay particularly attractive because it simultaneously provides accurate and repeatable information on both general and human-associated fecal contamination in environmental waters without the need to run two separate qPCR assays. Despite its advantages over qPCR, the upper quantification limit of the dPCR assay with currently available instrumentation is approximately four orders of magnitude lower than that achievable by qPCR. Consequently, dilution is needed for measurement of high concentrations of target organisms such as those typically observed following sewage spills.

A Duplex Digital PCR Assay for Simultaneous Quantification of the Enterococcus spp. and the Human Fecal-associated HF183 Marker in Waters

This manuscript describes a duplex digital PCR assay that can be used to simultaneously quantify Enterococcus spp. and the HF183 genetic markers as indicators of general and human-associated fecal contamination in recreational waters.

This manuscript describes a duplex digital PCR assay (EntHF183 dPCR) for simultaneous quantification of Enterococcus spp. and the human fecal-associated ...

Extraction and Analysis of Microbial Phospholipid Fatty Acids in Soils

Phospholipid fatty acids (PLFAs) are key components of microbial cell membranes. The analysis of PLFAs extracted from soils can provide information about the overall structure of terrestrial microbial communities. PLFA profiling has been extensively used in a range of ecosystems as a biological index of overall soil quality, and as a quantitative indicator of soil response to land management and other environmental stressors.
The standard method presented here outlines four key steps: 1. lipid extraction from soil samples with a single-phase chloroform mixture, 2. fractionation using solid phase extraction columns to isolate phospholipids from other extracted lipids, 3. methanolysis of phospholipids to produce fatty acid methyl esters (FAMEs), and 4. FAME analysis by capillary gas chromatography using a flame ionization detector (GC-FID). Two standards are used, including 1,2-dinonadecanoyl-sn-glycero-3-phosphocholine (PC(19:0/19:0)) to assess the overall recovery of the extraction method, and methyl decanoate (MeC10:0) as an internal standard (ISTD) for the GC analysis.

Phospholipid fatty acids provide information about the structure of soil microbial communities. We present methods for extraction from soil samples with a single-phase chloroform mixture, fractionation of extracted lipids using solid phase extraction columns, and methanolysis to produce fatty acid methyl esters, which are analyzed by capillary gas chromatography.

Phospholipid fatty acids (PLFAs) are key components of microbial cell membranes. The analysis of PLFAs extracted from soils can provide information about ...

A Lipid Extraction and Analysis Method for Characterizing Soil Microbes in Experiments with Many Samples

Microbial communities are important drivers and regulators of ecosystem processes. To understand how management of ecosystems may affect microbial communities, a relatively precise but effort-intensive technique to assay microbial community composition is phospholipid fatty acid (PLFA) analysis. PLFA was developed to analyze phospholipid biomarkers, which can be used as indicators of microbial biomass and the composition of broad functional groups of fungi and bacteria. It has commonly been used to compare soils under alternative plant communities, ecology, and management regimes. The PLFA method has been shown to be sensitive to detecting shifts in microbial community composition.
An alternative method, fatty acid methyl ester extraction and analysis (MIDI-FA) was developed for rapid extraction of total lipids, without separation of the phospholipid fraction, from pure cultures as a microbial identification technique. This method is rapid but is less suited for soil samples because it lacks an initial step separating soil particles and begins instead with a saponification reaction that likely produces artifacts from the background organic matter in the soil. 
This article describes a method that increases throughput while balancing effort and accuracy for extraction of lipids from the cell membranes of microorganisms for use in characterizing both total lipids and the relative abundance of indicator lipids to determine soil microbial community structure in studies with many samples. The method combines the accuracy achieved through PLFA profiling by extracting and concentrating soil lipids as a first step, and a reduction in effort by saponifying the organic material extracted and processing with the MIDI-FA method as a second step.

The article describes a method that increases throughput while balancing effort and accuracy for extraction of lipids from the cell membranes of microorganisms for use in characterizing both total lipids and the relative abundance of indicator lipids to determine soil microbial community structure in studies with many samples.

Microbial communities are important drivers and regulators of ecosystem processes. To understand how management of ecosystems may affect microbial ...

Characterization of Aquatic Biofilms with Flow Cytometry

Biofilms are dynamic consortia of microorganism that play a key role in freshwater ecosystems. By changing their community structure, biofilms respond quickly to environmental changes and can be thus used as indicators of water quality. Currently, biofilm assessment is mostly based on integrative and functional endpoints, such as photosynthetic or respiratory activity, which do not provide information on the biofilm community structure. Flow cytometry and computational visualization offer an alternative, sensitive, and easy-to-use method for assessment of the community composition, particularly of the photoautotrophic part of freshwater biofilms. It requires only basic sample preparation, after which the entire sample is run through the flow cytometer. The single-cell optical and fluorescent information is used for computational visualization and biological interpretation. Its main advantages over other methods are the speed of analysis and the high-information-content nature. Flow cytometry provides information on several cellular and biofilm traits in a single measurement: particle size, density, pigment content, abiotic content in the biofilm, and coarse taxonomic information. However, it does not provide information on biofilm composition on the species level. We see high potential in the use of the method for environmental monitoring of aquatic ecosystems and as an initial biofilm evaluation step that informs downstream detailed investigations by complementary and more detailed methods.

Flow cytometry in combination with visual clustering offers an easy-to-use and fast method for studying aquatic biofilms. It can be used for biofilm characterization, detection of changes in biofilm community structure, and detection of abiotic particles embedded in the biofilm.

Biofilms are dynamic consortia of microorganism that play a key role in freshwater ecosystems. By changing their community structure, biofilms respond ...

Hellgrammite as a Non-Conventional Biomonitor for Surface Water and Environmental Health Assessment

The larvae of dobsonflies from the genus Corydalus, commonly known as Hellgrammites, are characterized by their notable size, extensive range of occurrence, and extended period of immaturity, which can last up to one year. Hellgrammites are clearly known to exhibit sensitivity to pollution and habitat structure impacts. Given these unique features, using Corydalus texanus larvae is highly suitable as reliable biomonitoring agents to assess the ecological integrity of aquatic ecosystems. This protocol aims to provide the necessary tools for C. texanus assessment and demonstrate their efficacy through a case study. Research findings have practical implications, indicating that C. texanus larvae exhibit early-warning responses to mining pollution, bioaccumulating high amounts of heavy metals such as Zn, Fe, and Al. The presence or absence of C. texanus populations may serve as a helpful indicator for identifying potential issues related to ecosystem health. The unconventional approach has shown early warnings of pollution in mining-impacted sites, highlighting the need for timely action to protect the environment. Given their unique traits, the use of C. texanus larvae is highly suggested as a reliable non-conventional bioindicator.

This protocol describes the use of biomarkers for the early detection of deleterious impacts in aquatic ecosystems. The biomarkers are closely related to sentinel traits, and their changes aid in detecting early-warning damages.

The larvae of dobsonflies from the genus Corydalus, commonly known as Hellgrammites, are characterized by their notable size, extensive range of ...

Visualizing Methane-Cycling Microbial Dynamics in Coastal Wetlands

Coastal wetlands are the largest biotic source of methane, where methanogens convert organic matter into methane and methanotrophs oxidize methane, thus playing a critical role in regulating the methane cycle. The wetlands in South Texas, which are subject to frequent weather events, fluctuating salinity levels, and anthropogenic activities due to climate change, influence methane cycling. Despite the ecological importance of these processes, methane cycling in South Texas coastal wetlands remains insufficiently explored. To address this gap, we developed and optimized a method for detecting genes related to methanogens and methanotrophs, including mcrA as a biomarker for methanogens and pmoA1, pmoA2, and mmoX as biomarkers for methanotrophs. Additionally, this study aimed to visualize the spatial and temporal distribution patterns of methanogen and methanotroph abundance utilizing the geographic information system (GIS) software ArcGIS Pro. The integration of these molecular techniques with advanced geospatial visualization provided critical insights into the spatial and temporal distribution of methanogen and methanotroph communities across South Texas wetlands. Thus, the methodology established in this study offers a robust framework for mapping microbial dynamics in wetlands, enhancing our understanding of methane cycling under varying environmental conditions, and supporting broader ecological and environmental change studies.

The protocol detects key methane-cycling genes in South Texas coastal wetlands and visualizes their spatial distribution to enhance understanding of methane regulation and its environmental impacts in these dynamic ecosystems.

Coastal wetlands are the largest biotic source of methane, where methanogens convert organic matter into methane and methanotrophs oxidize methane, thus ...

Next-generation Sequencing of 16S Ribosomal RNA Gene Amplicons

One of the major questions in microbial ecology is &#8220;who is there?&#8221; This question can be answered using various tools, but one of the long-lasting gold standards is to sequence 16S ribosomal RNA (rRNA) gene amplicons generated by domain-level PCR reactions amplifying from genomic DNA. Traditionally, this was performed by cloning and Sanger (capillary electrophoresis) sequencing of PCR amplicons. The advent of next-generation sequencing has tremendously simplified and increased the sequencing depth for 16S rRNA gene sequencing. The introduction of benchtop sequencers now allows small labs to perform their 16S rRNA sequencing in-house in a matter of days. Here, an approach for 16S rRNA gene amplicon sequencing using a benchtop next-generation sequencer is detailed. The environmental DNA is first amplified by PCR using primers that contain sequencing adapters and barcodes. They are then coupled to spherical particles via emulsion PCR. The particles are loaded on a disposable chip and the chip is inserted in the sequencing machine after which the sequencing is performed. The sequences are retrieved in fastq format, filtered and the barcodes are used to establish the sample membership of the reads. The filtered and binned reads are then further analyzed using publically available tools. An example analysis where the reads were classified with a taxonomy-finding algorithm within the software package Mothur is given. The method outlined here is simple, inexpensive and straightforward and should help smaller labs to take advantage from the ongoing genomic revolution.

Characterizing microbial community has been a longstanding goal in environmental microbiology. Next-generation sequencing methods now allow for the characterization of microbial communities at an unprecedented depth with minimal cost and labor. We detail here our approach to sequence bacterial 16S ribosomal RNA genes using a benchtop sequencer.

Evaluating the Impact of Hydraulic Fracturing on Streams using Microbial Molecular Signatures

Summary

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Determination of Microbial Extracellular Enzyme Activity in Waters, Soils, and Sediments using High Throughput Microplate Assays

Unraveling the Unseen Players in the Ocean - A Field Guide to Water Chemistry and Marine Microbiology

A Whole Cell Bioreporter Approach to Assess Transport and Bioavailability of Organic Contaminants in Water Unsaturated Systems

A Duplex Digital PCR Assay for Simultaneous Quantification of the Enterococcus spp. and the Human Fecal-associated HF183 Marker in Waters

Extraction and Analysis of Microbial Phospholipid Fatty Acids in Soils

A Lipid Extraction and Analysis Method for Characterizing Soil Microbes in Experiments with Many Samples

Characterization of Aquatic Biofilms with Flow Cytometry

Hellgrammite as a Non-Conventional Biomonitor for Surface Water and Environmental Health Assessment

Visualizing Methane-Cycling Microbial Dynamics in Coastal Wetlands

Next-generation Sequencing of 16S Ribosomal RNA Gene Amplicons