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 border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<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). 
			Additional ethanol is needed for the ZymoBIOMICS DNA/RNA Miniprep and NEBNext&#174; Ultra&#8482; 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 &#181;L dye to 5 &#181;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 &#181;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).&#160;</td>
		</tr>
		<tr>
			<td>PCR-grade water</td>
			<td>MilliporeSigma</td>
			<td>3315932001</td>
			<td>13 &#181;L per PCR reaction (assuming 1 &#181;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 &#181;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 &#181;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 
			(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 &#181;L volume)</td>
			<td>Pipette.com</td>
			<td>L-1000</td>
			<td>Only 1 needed</td>
		</tr>
		<tr>
			<td>Micropipette (2 &#181;L volume)</td>
			<td>Pipette.com</td>
			<td>L-2</td>
			<td>Only 1 needed</td>
		</tr>
		<tr>
			<td>Micropipette (20 &#181;L volume)</td>
			<td>Pipette.com</td>
			<td>L-20</td>
			<td>Only 1 needed</td>
		</tr>
		<tr>
			<td>Micropipette (200 &#181;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&#34; x 1&#34; 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 &#181;L volume)</td>
			<td>Pipette.com</td>
			<td>LF-1000</td>
			<td>Pack of 576 tips</td>
		</tr>
		<tr>
			<td>Pipette tips (for 20 &#181;L volume)</td>
			<td>Pipette.com</td>
			<td>LF-20</td>
			<td>Pack of 960 tips</td>
		</tr>
		<tr>
			<td>Pipette tips (for 200 &#181;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.&#160;</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.&#160;</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 ...

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3.0K 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 ...

Methods for Facilitating Microbial Growth on Pulp Mill Waste Streams and Characterization of the Biodegradation Potential of Cultured Microbes

The kraft process is applied to wood chips for separation of lignin from the polysaccharides within lignocellulose for pulp that will produce a high quality paper. Black liquor is a pulping waste generated by the kraft process that has potential for downstream bioconversion. However, the recalcitrant nature of the lignocellulose resources, its chemical derivatives that constitute the majority of available organic carbon within black liquor, and its basic pH present challenges to microbial biodegradation of this waste material. Methods for the collection and modification of black liquor for microbial growth are aimed at utilization of this pulp waste to convert the lignin, organic acids, and polysaccharide degradation byproducts into valuable chemicals. The lignocellulose extraction techniques presented provide a reproducible method for preparation of lignocellulose growth substrates for understanding metabolic capacities of cultured microorganisms. Use of gas chromatography-mass spectrometry enables the identification and quantification of the fermentation products resulting from the growth of microorganisms on pulping waste. These methods when used together can facilitate the determination of the metabolic activity of microorganisms with potential to produce fermentation products that would provide greater value to the pulping system and reduce effluent waste, thereby increasing potential paper milling profits and offering additional uses for black liquor.

Industrial wastes can be collected and modified to analyze microbial growth. Lignocellulose extraction techniques provide components to analyze specific biodegradation ability. Gas chromatography-mass spectrometry identifies fermentation products of microorganisms grown on pulping waste. These methods determine the metabolic capacity of microorganisms to degrade pulping waste.

The kraft process is applied to wood chips for separation of lignin from the polysaccharides within lignocellulose for pulp that will produce a high ...

A Flow-through Exposure System for Evaluating Suspended Sediments Effects on Aquatic Life

This paper describes the Fish Larvae and Egg Exposure System (FLEES). The flow-through exposure system is used to investigate the effects of suspended sediment on various aquatic species and life stages in the laboratory by using pumps and automating delivery of sediment and water to simulate suspension of sediment. FLEES data are used to develop exposure-response curves between the effects on aquatic organisms and suspended sediment concentrations at the desired exposure duration. The effects data are used to evaluate management practices used to reduce the interactions between aquatic organisms and anthropogenic causes of suspended sediments. The FLEES is capable of generating total suspended solids (TSS) concentrations as low as 30 to as high as 800 mg/L, making this system an ideal choice for evaluating the effects of TSS resulting from many activities including simulating low ambient levels of TSS to evaluating sources of suspended sediments from dredging operations, vessel traffic, freshets, and storms.

A robust and flexible flow-through exposure system designed to maintain sediment in suspension is presented. The system is used to investigate the effects of suspended sediment on various aquatic species and life stages in the laboratory.

This paper describes the Fish Larvae and Egg Exposure System (FLEES). The flow-through exposure system is used to investigate the effects of suspended ...

Evaluating the Effect of Environmental Chemicals on Honey Bee Development from the Individual to Colony Level

The presence of pesticides in the beekeeping environment is one of the most serious problems that impacts the life of a honey bee. Pesticides can be brought back to the beehive after the bees have foraged on flowers that have been sprayed with pesticides. Pesticide contaminated food can be exchanged between workers which then feed larvae and therefore can potentially affect the development of honey bees. Thus, residual pesticides in the environment can become a chronic damaging factor to honey bee populations and gradually lead to colony collapse. In the presented protocol, honey bee feeding methods are described and applied to either an individual honey bee or to a colony. Here, the insect growth regulator (IGR) pyriproxyfen (PPN), which is widely used to control pest insects and is harmful to the development of honey bee larvae and pupae, is used as the pesticide. The presenting procedure can be applied to other potentially harmful chemicals or honeybee pathogens for further studies.

Herein we present a method to feed pesticide contaminated food to both an individual honey bee and a beehive colony. The procedure evaluates the pesticide effect on individual honey bees by in vivo feeding of basic larval diet and also on the natural condition of beehive colony.

The presence of pesticides in the beekeeping environment is one of the most serious problems that impacts the life of a honey bee. Pesticides can be ...

Preparation of DMMTAV and DMDTAV Using DMAV for Environmental Applications: Synthesis, Purification, and Confirmation

Dimethylated thioarsenicals such as dimethylmonothioarsinic acid (DMMTAV) and dimethyldithioarsinic acid (DMDTAV), which are produced by the metabolic pathway of dimethylarsinic acid (DMAV) thiolation, have been recently found in the environment as well as human organs. DMMTAV and DMDTAV can be quantified to determine the ecological effects of dimethylated thioarsenicals and their stability in environmental media. The synthesis method for these compounds is unstandardized, making replicating previous studies challenging. Furthermore, there is a lack of information about storage techniques, including storage of compounds without species transformation. Moreover, because only limited information about synthesis methods is available, there may be experimental difficulties in synthesizing standard chemicals and performing quantitative analysis. The protocol presented herein provides a practically modified synthesis method for the dimethylated thioarsenicals, DMMTAV and DMDTAV, and will help in the quantification of species separation analysis using high performance liquid chromatography in conjunction with inductively coupled plasma mass spectrometry (HPLC-ICP-MS). The experimental steps of this procedure were modified by focusing on the preparation of chemical reagents, filtration methods, and storage.

Preparation of DMMTAV and DMDTAV Using DMAV for Environmental Applications: Synthesis, Purification, and Confirmation

This article presents modified experimental protocols for dimethylmonothioarsinic acid (DMMTAV) and dimethyldithioarsinic acid (DMDTAV) synthesis, inducing dimethylarsinic acid (DMAV) thiolation through mixing of DMAV, Na2S, and H2SO4. The modified protocol provides an experimental guideline, thereby overcoming limitations of the synthesis steps that could have caused experimental failures in quantitative analysis.

Dimethylated thioarsenicals such as dimethylmonothioarsinic acid (DMMTAV) and dimethyldithioarsinic acid (DMDTAV), which are produced by the metabolic ...

On-Site Molecular Detection of Soil-Borne Phytopathogens Using a Portable Real-Time PCR System

On-site diagnosis of plant diseases can be a useful tool for growers for timely decisions enabling the earlier implementation of disease management strategies that reduce the impact of the disease. Presently in many diagnostic laboratories, the polymerase chain reaction (PCR), particularly real-time PCR, is considered the most sensitive and accurate method for plant pathogen detection. However, laboratory-based PCRs typically require expensive laboratory equipment and skilled personnel. In this study, soil-borne pathogens of potato are used to demonstrate the potential for on-site molecular detection. This was achieved using a rapid and simple protocol comprising of magnetic bead-based nucleic acid extraction, portable real-time PCR (fluorogenic probe-based assay). The portable real-time PCR approach compared favorably with a laboratory-based system, detecting as few as 100 copies of DNA from Spongospora subterranea. The portable real-time PCR method developed here can serve as an alternative to laboratory-based approaches and a useful on-site tool for pathogen diagnosis.

Rapid and accurate detection of plant pathogens on-site, especially soil-borne pathogens, is crucial to prevent further inoculum production and proliferation of plant diseases in the field. The method developed here using a portable real-time PCR detection system enables on-site diagnosis under field conditions.

On-site diagnosis of plant diseases can be a useful tool for growers for timely decisions enabling the earlier implementation of disease management ...

The Effect of the Application of Thyme Essential Oil on Microbial Load During Meat Drying

Meat is a high protein meal that is used in the preparation of jerky, a popular food snack, where preservation and safety are important. To assure food safety and to extend the shelf life of meat and meat products, the use of either synthetic or natural preservatives have been applied to control and eliminate foodborne bacteria. A growing interest in the application of natural food additives for meat has increased. Microorganisms, such as Escherichia coli, contaminate meat and meat products, causing foodborne illnesses. Therefore, it is necessary to improve the meat conservation process. However, the use of essential oils when the meat is being dried has not been deeply studied. In this regard, there is an opportunity to increase the value of dried meat and reduce the risk of foodborne illnesses by applying essential oils during the drying process. In this protocol, we present a novel method of applying thyme essential oil (TEO) during meat drying, specifically in vapor form directly in a drying chamber. For evaluation, we use Minimal Inhibitory Concentration (MIC) to detect the number of harmful bacteria in the treated samples compared to raw samples. The preliminary results show that this method is a viable and alternative option to synthetic preservatives and that it significantly reduces microbial load in dried meat.

Microorganisms such as Escherichia coli that contaminate meat products cause foodborne illnesses. The use of essential oils in the meat drying process has not been deeply studied. Here, we present a novel method of applying thyme essential oil to meat during drying to reduce the microbial load in dried meat.

Meat is a high protein meal that is used in the preparation of jerky, a popular food snack, where preservation and safety are important. To assure food ...

Wastewater Irrigation Impacts on Soil Hydraulic Conductivity: Coupled Field Sampling and Laboratory Determination of Saturated Hydraulic Conductivity

Since the early 1960s, an alternative wastewater discharge practice at The Pennsylvania State University has been researched and its impacts monitored. Rather than discharging treated wastewater to a stream, and thereby directly impacting the stream quality, the effluent is applied to forested and cropped land managed by the University. Concerns related to reductions in soil hydraulic conductivity occur when considering wastewater reuse. The methodology described in this manuscript, matching soil sample size with the size of the laboratory-based hydraulic conductivity measurement apparatus, provides the benefits of a relatively rapid collection of samples with the benefits of controlled laboratory boundary conditions. The results suggest that there may have been some impact of wastewater reuse on the soil&#39;s ability to transmit water at deeper depths in the depressional areas of the site. Most of the reductions in the soil hydraulic conductivity in the depressions appear to be related to the depth from which the sample was collected, and by inference, associated with the soil structural and textural differences.

Here we present a methodology which matches a soil sample size and a hydraulic conductivity measurement device to prevent the so-called wall flow along the inside of the soil container from being erroneously included in water flow measurements. Its use is demonstrated with samples collected from a wastewater irrigation site.

Since the early 1960s, an alternative wastewater discharge practice at The Pennsylvania State University has been researched and its impacts monitored. ...

Evaluating Dryocosmus Kuriphilus-induced Damage on Castanea Sativa

Dryocosmus kuriphilus Yasumatsu has become a major pest for Castaneasativa since its arrival in Europe. Its galling activity results in the formation of different gall types and prevents the development of normal shoots. Repeated and uncontrolled attacks cause, besides the production of galls and the attendant gall-related reduction in leaf area, progressive corruption of the branch architecture, including the death of branch parts, and an increase in dormant bud activation. Thus far, there have been few attempts to quantify branch architecture damage. Further, the different methods for assessing infestation degree (MAID) that have been developed focus only on the galls' presence and abundance.
Using the leaf area to sapwood area relationship as a green biomass indicator, we developed in a previous study a damage composite index (DCI) that takes into account the most important branch architectural features, allowing for realistic damage assessment during the entire epidemic process. 
The aim of this study is to present this novel method and highlight differences in the damage description with respect to other broadly used indices. Results show how the DCI depicts branch damage better, especially during the epidemic peak, compared to MAID, which tend to underestimate it. We conclude by suggesting how to properly evaluate the overall impact of the pest by means of our composite damage index, the infestation degree using classic methods, and crown transparency evaluations.

Evaluating Dryocosmus Kuriphilus-induced Damage on Castanea Sativa

It is common practice to assess the damage caused by Dryocosmus kuriphilus by considering the abundance of galls alone rather than by also taking related branch corruption into consideration. We propose a composite damage index that takes into account the most important branch features, thus enabling more realistic damage assessment.

Dryocosmus kuriphilus Yasumatsu has become a major pest for Castaneasativa since its arrival in Europe. Its galling activity results in the formation of ...

Quantification of the Potential Impact of Glyphosate-Based Products on Microbiomes

Glyphosate-based products (GBP) are the most common broad-spectrum herbicides worldwide. The target of glyphosate is the enzyme 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS) in the shikimate pathway, which is virtually universal in plants. The inhibition of the enzyme stops the production of three essential amino acids: phenylalanine, tyrosine, and tryptophan. EPSPS is also present in fungi and prokaryotes, such as archaea and bacteria; thus, the use of GBP may have an impact on the microbiome composition of soils, plants, herbivores, and secondary consumers. This article aims to present general guidelines to assess the effect of GBP on microbiomes from field experiments to bioinformatics analyses and provide a few testable hypotheses. Two field experiments are presented to test the GBP on non-target organisms. First, plant-associated microbes from 10 replicated control and GBP treatment plots simulating no-till cropping are sampled and analyzed. In the second experiment, samples from experimental plots fertilized by either poultry manure containing glyphosate residues or non-treated control manure were obtained. Bioinformatics analysis of EPSPS protein sequences is utilized to determine the potential sensitivity of microbes to glyphosate. The first step in estimating the effect of GBP on microbiomes is to determine their potential sensitivity to the target enzyme (EPSPS). Microbial sequences can be obtained either from public repositories or by means of PCR amplification. However, in the majority of field studies, microbiome composition has been determined based on universal DNA markers such as the 16S rRNA and the internal transcribed spacer (ITS). In these cases, sensitivity to glyphosate can only be estimated through a probabilistic analysis of EPSPS sequences using closely related species. The quantification of the potential sensitivity of organisms to glyphosate, based on the EPSPS enzyme, provides a robust approach for further experiments to study target and non-target resistant mechanisms.

Glyphosate-based products (GBP) are the most common broad-spectrum herbicides worldwide. In this article, we introduce general guidelines to quantify the effect of GBP on microbiomes, from field experiments to bioinformatics analyses.