We thank James Shan for his efforts in field video recording. We thank Jon Byron and Matthew Semerau for their work in video editing. We thank Ansen Luo, Harry Du, and Frank Deng for their support in locating the test field. We thank Maria Rubinsky for her valuable comments on the whole manuscript. All the acknowledged persons are employees of Herbalife International of America, Inc.

1. Sample collection
<ol>
	<li>Set up a testing area in the field with a flat and horizontal surface.</li>
	<li>Identify a representative plant that reflects the characteristics of majority of the plants in the chamomile flower field (Figure 4).</li>
	<li>Pick a flower head from the representative plant using sterile gloves.</li>
	<li>Place the sample into a 2.0 mL collection tube.</li>
	<li>Repeat steps 1.3 to 1.4 and collect a leaflet (approximately 0.5&#8211;0.7 cm long) from the same plant. 
	NOTE: M. chamomilla flower and leaf are small enough to sit at the bottom of a 2.0 mL collection tube. For other botanicals with larger surface area, a paper punch or scissors (rinsed in 70% ethanol prior to use) can be used to isolate tissue samples for testing. When multiple sampling is required, rinse the paper punch or scissors between handling of different samples.</li>
</ol>
2. DNA extraction
<ol>
	<li>Preheat the dry bath incubator to 95 &#176;C.</li>
	<li>To each collection tube, add 100 &#181;L of the extraction solution from the plant DNA extraction kit (listed in Table of Materials). For better DNA extraction efficiency, grind the tissue sample in the extraction solution using a tissue pestle.</li>
	<li>Close the tube. Ensure that the botanical tissue is covered with the extraction solution throughout the extraction process.</li>
	<li>Place the collection tubes in a preheated dry bath incubator and incubate the collection tubes at 95 &#176;C for 10 min.</li>
	<li>After 10 min, take the tubes out of the dry bath incubator.</li>
	<li>Add 100 &#181;L of the dilution solution from the same DNA extraction kit and mix the solution by pipetting up and down several times.</li>
	<li>Repeat the same step for leaflet extraction.</li>
	<li>Shake to mix the solution further. The plant tissue usually does not appear to be degraded after this treatment. The liquid color may change and become cloudy. 
	NOTE: The diluted solution can be stored at room temperature overnight if not proceeding immediately. It is not necessary to remove the cellular debris from plant tissue before storage. The liquid in the tubes holds the DNA templates for downstream PCR amplification.</li>
</ol>
3. PCR reaction setup
<ol>
	<li>Configure the qPCR instrument thermocycling conditions according to the manufacturer&#39;s specifications. Apply the PCR thermocycling profile listed in (Table 1), which starts with a constant temperature step for initial denaturation, followed by 25 cycles of amplification, and ends with temperature ramping to obtain a high-resolution melting curve.</li>
	<li>Thaw the qPCR Master Mix and primers (Table 2) at room temperature prior to use.</li>
	<li>Plan the reaction that will be loaded in each well: wells containing positive control with target species, positive control with non-target species, samples, and negative control (Figure 5).
	<ol>
		<li>In this example, ten wells are used &#8211; five for the German chamomile identification test and the remaining five for the Roman chamomile identification test. For each type of species identification test, one well contains positive control with DNA extracted from targeted species&#8217; reference material, one well contains positive control with DNA extracted from non-targeted species&#8217; reference material, two wells are filled with flower and leaf DNA samples extracted from the field, and one well is allocated for a negative control. Table 3 describes each well type.</li>
	</ol>
	</li>
	<li>Prepare a reaction master-mix according to the manual for each botanical species identification test. A typical reaction master-mix contains universal qPCR Master Mix (2x), forward and reverse species-specific primers, and nuclease-free water. Table 4 lists the reaction system composition. 
	NOTE: If not using immediately, store the qPCR reaction master-mix at +2 &#176;C to +8 &#176;C in a cooler or mini-fridge.</li>
	<li>Thoroughly mix the reaction master-mix by pipetting before use.</li>
	<li>Place the qPCR cartridge face-up on a flat and stable surface.</li>
	<li>Load 18 &#181;L of the reaction master-mix configured in step 3.4 into the cartridge wells according to the wells defined in step 3.3. For this demonstration, add the German chamomile identification test reaction master mix into wells labeled for GC test (GCT in wells 1, 3, 5, 7, 9) and the Roman chamomile identification test reaction master-mix into wells labeled for RC test (RCT in wells 2, 4, 6, 8, 10) (see Figure 5).</li>
	<li>Transfer 2 &#181;L of sample DNA from the supernatant of DNA extraction tubes and pre-extracted DNA positive controls into cartridge wells preloaded with qPCR master mix. After adding each DNA template to the qPCR master mix, gently mix the solution by pipetting. 
	NOTE: Avoid floating cellular debris when transferring DNA from the DNA extraction tube. Use minicentrifuge to separate the supernatant and cellular debris, if necessary.</li>
	<li>Carefully seal the cartridge with adhesive film. Load the cartridge onto the thermocycling chamber and close it.</li>
	<li>Set the qPCR instrument to run.</li>
</ol>

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We certify that Zhengxiu Yang, Zheng Quan, Tiffany Chua, Leo Li, Yanjun Zhang, Silva Babajanian, Tricia Chua, Peter Chang, Gary Swanson, Zhengfei Lu are employees of Herbalife International of America, Inc. We certify that Francesco Buongiorno, Isabella Della Noce, and Lorenzo Colombo are employees of Hyris Ltd. that produces the portable qPCR instrument used in this article.

The design of primers and template selection are the crucial steps in obtaining an efficient and specific qPCR amplification. After identifying a suitable template, primer design software is typically used to aid selection of primers based on design variables such as primer length, melting temperature, and GC content33,34. Optimization and validation can be performed under the expected experimental conditions of the assay to ensure specificity, sensitivity, and robustness of a PCR reaction35. Sub-optimal primer design may result in primer-dimer formation, wherein primer interactions produce non-specific products36.
The no template controls (NTC) used in this study check for both DNA contamination and the presence of primer-dimers that could affect the assay. Results showed no amplification, a good indication that both DNA contamination and primer-dimers are not a concern. DNA contamination and primer-dimers are manifested in melt curves through no template controls, and as extra peaks in melt curves of positive controls. Typically, the melt curve of a positive control is expected to contain a single peak, unless AT-rich subdomains in the template cause uneven melting. Double peaks could be predicted by simulating melting assays using the uMELT software37. In this study, the gold standard of running the PCR product on agarose gel was used to confirm the presence of target PCR product and absence of contamination and primer-dimers.
A considerable challenge in botanical material molecular analysis is obtaining good-quality DNA following the botanical DNA extraction process. Botanical materials are traded and consumed for the active chemical compounds that are associated with health benefits. In the process of DNA extraction, these chemical compounds will also be released into the DNA extraction solution, potentially causing PCR inhibition, thereby resulting in failures in PCR amplification. Various plant DNA purification kits using organic solvents and columns have been developed to remove chemical compounds derived from botanicals38. However, fume hood and high-speed centrifuge required to assist these kits are not available in the field.
In the current protocol, the simplified DNA extraction method uses a commercial plant DNA extraction kit (see Table of Materials for details). It had the ability to neutralize common inhibitory substances for reproducible results and produced consistent results for M. chamomilla and C. nobile. Both M. chamomilla and C. nobile flower heads and leaves yielded PCR amplicons with specific melt peaks, indicating that the presence of PCR inhibitors was not a concern. For other plants with higher levels of PCR inhibitors, amplifying DNA in their original extraction may be less efficient. To reduce inhibition and improve amplification efficiency, with access to the whole plant, other plant parts with lower polysaccharide and polyphenol content can also be used for identification purposes. If there is limited access to different plant parts, younger leaves or petals dissected from flower heads, which typically have lower phenolic content39, may offer a better chance of success. Since DNA sequences are consistent across the whole plant, any plant part may be used to confirm species identity. If PCR amplification is still suboptimal, the original DNA extract can be further diluted before PCR, or more sophisticated laboratory purification protocols can be used.
Another challenge for PCR analysis is false positive results caused by DNA contamination, which can negatively impact data interpretation. It is usually controlled by active housekeeping, using dedicated equipment, and restricting work to designated areas. Using qPCR, all PCR analysis can be accomplished in a closed system, which greatly reduces the chance of PCR amplicon contamination in an environment that is not well controlled. Besides, environmental DNA should also not show a false positive due to the specificity of the assay, according to a previous validation study40.
There is room for improvement. In the protocol presented here, intercalating dye was used to show target fragment amplification in real-time. The specificity of the method is further confirmed by the characteristic melting temperature, which is distinct between M. chamomilla and C. nobile amplicons. Therefore, intercalating dye-based PCR can efficiently answer the question &#8220;What is this plant species?&#8221; in the field. However, in addition to the need of performing botanical identification on a single plant isolated from the field, in many circumstances, botanical powders or extracts in the warehouse will also benefit from an on-site rapid identity assessment. For these types of materials, additional questions may need to be addressed, such as &#8220;What is in this material?&#8221;, &#8220;Does it contain the botanical species I am looking for?&#8221;, &#8220;Does it contain adulterants I want to avoid?&#8221;, and &#8220;Is it substituted wholly or partially by other botanical species that are harmful?&#8221;. Instead of using intercalating dye, different qPCR probes can be designed to target amplicons from different botanicals in one reaction system, while maintain high specificity and efficiency of the assay. Development of probe-based qPCR and utilization of a portable qPCR system that offers multiple channel detection can further extend the application of field testing as a fit-for-purpose assay to a broader environment setting, such as botanical material warehouses and distribution centers to answer more complicated questions. In addition, using multiple probes also allows the user to include internal amplification in each reaction system, so that more information will be available when PCR inhibition is suspected.
The protocol presented here has the following advantages compared to existing technologies used for the same purpose. First, for traditional morphological and chemical identification methods, the procedure and its results need to be conducted and interpreted by experts. qPCR-based identification tests can be conducted by people with basic molecular biology training and interpreted in a more standardized manner. Second, compared to qPCR-based species identification and differentiation normally performed in the laboratory, the field identification protocol using a portable instrument does not require instruments with a large footprint, such as a high-speed centrifuge, DNA quality evaluation equipment, thermal cycler with fluorescence detector, and a computer running a special software. Thus, DNA-based species identification can be performed in any setting without delay. Third, searching for botanical materials is a task that requires a global operation. With advancements in cloud services and artificial intelligence, a portable device can potentially receive methods developed and validated by experts in the laboratory, be operated by non-experts in remote areas, and produce objective certifications from third parties. Therefore, this option is more compelling than ever with remote work becoming the trend.
In summary, the protocol here demonstrated field identification of M. chamomilla using a portable qPCR system. The successful application of this technique will generate highly accurate results on botanical identification and help botanical manufacturers and suppliers qualify botanical materials in a timely and cost-efficient manner.

The practice of using botanicals to maintain and improve health dates back to thousands of years. Due to stresses on the supply chain brought by increased consumer demand1, unsustainable harvesting practices and climate change2, botanical adulteration is becoming a growing concern in the food and dietary supplement industry3. The presence of undeclared or misidentified botanical species may lead to reduced efficacy, or even safety issues. For example, black cohosh (Actaea racemosa), used for treating premenstrual discomfort, may be substituted with a low-priced Asian species with limited clinical data support for its efficacy4. In a more serious case, substitution of Aristolochia fangchi for Stephania terandra in a clinical study for weight loss using Chinese herbs led to severe nephrotoxicity and renal failure in some participants5,6. The two different species shared a Chinese common name &#8220;Fang Ji&#8221;. These cases highlight the need for more stringent quality control, starting with the identification of raw materials7, preferably as close to the point of origin of the supply chain as possible, so resources can be efficiently allocated to the material of correct identity.
A number of orthogonal approaches can be used for botanical identification. Traditionally, botanical identification is performed through morphological assessment8,9 and chemical analytical methods10,11,12,13. Morphological identification is based on differences in macroscopic and microscopic features of plant materials if differences exist (Figure 1). However, the lack of training programs on classical botany in the recent years has resulted in a shortage of experts14, making this approach impractical for routine quality control. Its application in powdered botanical materials is also limited. Chemical analytical methods are widely used in pharmacopoeias and laboratories, but are not ideal for field testing due to the size of instruments such as High Performance Thin Layer Chromatography (HPTLC), High Performance Liquid Chromatography (HPLC), and Nuclear Magnetic Resonance Spectroscopy (NMR) (Figure 2), and environment requirements. Recently, genomic methods have emerged as an alternative technique for botanical species&#39; authentication and substitution detection and has proven to be efficient and precise. Genomic methods exploit the high fidelity and specificity of genetic information in plant materials15,16,17,18,19. Molecular diagnostic tools are available in the form of portable devices, and often include automated data interpretation tools that lower the barrier to technology use, making this approach ideal for field identification20,21,22,23,24. Once the molecular analysis method has been designed and validated25,26,27, it can be performed by any personnel with basic molecular biology training. Among the different portable tools available, real-time PCR on DNA sequences is one of the cost-effective choices28. The combination of a portable device, together with customized and validated molecular analysis, allows verification of botanical species and ingredients outside the laboratory, such as in farms and botanical material warehouses, reducing the time and costs associated with traditional methods.
The goal of this protocol is to introduce a method for botanical identification in situations where access to laboratory equipment and expertise is limited or unavailable, using a portable qPCR system. The method is demonstrated on a field of Matricaria chamomilla (Figure 3A), commonly known as German chamomile, widely used for its anti-inflammatory and antioxidant properties29. It can be confused with related species of similar appearance or odor, especially from the genera Chamaemelum, Tanacetum, and Chrysanthemum30,31,32. Among the related species, Chamaemelum nobile, also known as Roman chamomile, is a noticeable one with comparable production levels in commerce (Figure 3B). The method demonstrated was designed to not only identify the target botanical species M. chamomilla, but also detect its close relative, C. nobile, based on specific amplification of DNA sequences.
This article explains, in detail, how to perform field botanical identification of M. chamomilla using intercalating dye-based qPCR and melt curve analysis on a portable device. The protocol includes the collection of botanical samples from the field, on-site DNA extraction, and set up of real-time PCR reaction. To ensure a valid conclusion, target botanical M. chamomilla and non-target botanical C. nobile genomic DNA, pre-extracted from certified botanical reference materials, are used as positive control. The specificity of this method is demonstrated by performing both M. chamomilla and C. nobile identification tests individually on samples and controls. Non-template negative control is used to exclude false positive results caused by PCR contamination.

<table><tbody><tr><td>Battery</td><td>TNE</td><td>78000mAh</td><td>Provide field power supply</td></tr><tr><td>bCUBE</td><td>HYRIS</td><td>bCUBE 2.0</td><td>Portable qPCR instrument</td></tr><tr><td>Cartridges(16 Well)</td><td>HYRIS</td><td>NA</td><td>Consumables for bCUBE</td></tr><tr><td>Electric pipette</td><td>Eppendorf</td><td>NA</td><td>Handling liquid</td></tr><tr><td>Extract-N-Amp&lt;sup&gt;TM&lt;/sup&gt; plant PCR kit</td><td>SIGMA</td><td>XNAP2-1KT</td><td>Plant DNA extraction kit</td></tr><tr><td>German chamomile (&lt;em&gt;Matricaria chamomilla L&lt;/em&gt;) flower botanical reference materials</td><td>AHP</td><td>2264</td><td>Used as positive control</td></tr><tr><td>Mini dry bath</td><td>Yooning</td><td>MiniH-100L</td><td>For DNA extraction</td></tr><tr><td>Nuclease-free water</td><td>AMBION</td><td>AM9937</td><td>qPCR reaction</td></tr><tr><td>Primer</td><td>Thermo Fisher Scientific</td><td>NA</td><td>qPCR reaction</td></tr><tr><td>Roman chamomile (&lt;em&gt;Chamaemelum nobile&lt;/em&gt;) flower botancial reference materials</td><td>ChromaDex</td><td>ASB-00030806-005</td><td>Used as positive control</td></tr><tr><td>Luna universal qPCR master mix</td><td>NEB</td><td>M3003L</td><td>qPCR reaction</td></tr></tbody></table>

field identification of matricaria chamomilla using a portable qpcr system

Quality control in botanical products begins with the raw material supply. Traditionally, botanical identification is performed through morphological assessment and chemical analytical methods. However, the lack of availability of botanists, especially in recent years, coupled with the need to enhance quality control to combat the stresses on the supply chain brought by increasing consumer demand and climate change, necessitates alternative approaches. The goal of this protocol is to facilitate botanical species identification using a portable qPCR system on the field or in any setting, where access to laboratory equipment and expertise is limited. Target DNA is amplified using dye-based qPCR, with DNA extracted from botanical reference materials serving as a positive control. The target DNA is identified by its specific amplification and matching its melting peak against the positive control. A detailed description of the steps and parameters, from hands-on field sample collection, to DNA extraction, PCR amplification, followed by data interpretation, has been included to ensure that readers can replicate this protocol. The results produced align with traditional laboratory botanical identification methods. The protocol is easy to perform and cost-effective, enabling quality testing on raw materials as close to the point of origin of the supply chain as possible.

Following the protocol described in section 1, botanical DNA from flower head and leaf were extracted into the supernatant after heat incubation of the collection tube at 95 &#176;C for 10 min. In the current study, the supernatant showed a yellow and greenish color for both flower and leaf, indicating that a variety of natural compounds were released into the supernatant with botanical DNA (Figure 6). Although reliable PCR amplification was achieved later in triplicate for all field extracted DNA template, DNA quality assessment was performed in the laboratory as reference. The concentration of flower head DNA extract, determined by fluorometry, ranged from 3.69&#8211;5.36 ng/&#181;L, while the concentration of leaf DNA extract ranged from 6.42&#8211;9.29 ng/&#181;L. The A260/A280 and A260/A230 absorbance ratios of flower and leaf DNA extracts were measured by spectrophotometry. However, due to the overlap between DNA and phytochemical UV absorption spectrum, these ratios could not be reliability measured (data not shown).
Intercalating fluorescent dye was used to monitor the amplification of target fragments in real-time. Since both the specific primers M. chamomilla and C. nobile target the internal transcribed spacer 2 (ITS2) region, which has tens to hundreds of copies in the plant genome, 25 PCR amplification cycles are sufficient to generate enough amplicons for the identification of chamomile species. In Figure 7, the Ct value for M. chamomilla positive control in M. chamomilla identification test was less than 25 (GCP_GCT), while after 25 amplification cycles, the fluorescence of the same control in C. nobile identification test was below the detection threshold (GCP_RCT). On the other hand, after 25 cycles, the fluorescence for C. nobile positive control in M. chamomilla identification test was below the detection threshold (RCP_GCT), while the Ct value for the same control in C. nobile identification test was less than 25 (RCP_RCT). The amplification of target and non-target positive controls in their respective assays demonstrate the specificity of the M. chamomilla identification assay. For sample DNA, field flower head and leaf DNA extract yielded Ct values of 15.18 and 19.41 in M. chamomilla identification test, respectively (Sample1(FLOWER)_GCT and Sample2(LEAF)_GCT). Both of these samples were not amplified in C. nobile identification test (Sample1(FLOWER)_RCT and Sample2(LEAF)_RCT). The amplification patterns of both the samples matched the amplification pattern of M. chamomilla positive control. Negative controls were not amplified in both M. chamomilla and C. nobile identification tests (NC_GCT and NC_RCT), excluding the possibility of false positives caused by PCR contamination. To further confirm specific amplification in positive controls and samples, fractions of PCR end product from each well were run on 2% agarose gel in the laboratory (Figure 8). For M. chamomilla identification test, both field samples yielded amplicons running at the same position as the M. chamomilla positive control with an estimated size slightly above 100 bp (theoretical size 102 bp). For C. nobile identification test, non-target species C. nobile positive control yielded a band between 50 and 100 bp, fitting the theoretical size of 65 bp. The rest of the lanes showed no specific amplification product, which was in agreement with the absence of fluorescent signal for these wells, as observed in field testing.
Following PCR amplification, a melting curve analysis was performed to assess the dissociation characteristics of double-stranded DNA (dsDNA) during heating. As the temperature ramped up during the final cycle for melt curve analysis, increases in temperature caused the double-strand amplicons to dissociate. The intercalating fluorescent dye was gradually released into the solution, decreasing fluorescence intensity (Figure 9A). The inflection point of the first derivative curve was used to determine the melting temperature (Tm) (Figure 9B), which depends mainly on DNA fragment length and GC content. Combining Ct value with melting temperature can increase the specificity of qPCR analysis. In the current study, the melting temperature peak of M. chamomilla positive control PCR amplicon occurred at 85.6 &#176;C (GCP_GCT) and it was distinct from the melting temperature peak of C. nobile positive control PCR amplicon at 79.1 &#176;C (RCP_RCT). The PCR amplicon from field flower head and leaf produced melting temperature peaks at 85.2 &#176;C and 84.8 &#176;C, respectively (Sample1(FLOWER)_GCT and Sample2(LEAF)_GCT). To assess melting temperature variations measured by the portable qPCR system, additional datapoints were collected to confirm that sample melting temperatures were always close to the melting temperature obtained from M. chamomilla positive control (within 2 &#176;C) and were far away from the melting temperature of C. nobile positive control amplicon (Figure 10). Melting temperature peaks were sometimes reported in other wells. However, their Ct values were not less than 25 and melting temperature peaks were not close to M. chamomilla or C. nobile positive control (more than 2 &#176;C apart).
In summary, field M. chamomilla identification test can be interpreted based on decision rules summarized in Table 5. With all the positive controls testing positive for the putative botanical species, negative for the other species, and negative controls testing negative, both field samples were determined to contain M. chamomilla but not C. nobile. In addition, to align field testing results with other analytical techniques, field conclusions were further confirmed by a previously validated DNA barcoding method25 (data not shown).
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/60940/60940fig01.jpg" /> 
Figure 1: Morphological identification of botanical materials.&#160;(A) Hibiscus rosa-sinensis flowers, Curcuma longa roots, Malva Sylvestris leaves, Rosmarinus officinalis leaves, Coriandrum sativum seeds, Zingiber officinale roots. (B) Petroselinum crispum and Apium graveolens flakes are difficult to differentiate. <a href="https://www.jove.com/files/ftp_upload/60940/60940fig01large.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/60940/60940fig02.jpg" /> 
Figure 2: Chemical identification of botanical materials.&#160;(A) HPTLC instrument and a representative HPTLC chromatogram. (B) HPLC instrument and a representative HPLC chromatogram. <a href="https://www.jove.com/files/ftp_upload/60940/60940fig02large.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/60940/60940fig03.jpg" /> 
Figure 3: Matricaria chamomilla and Chamaemelum nobile in the field.&#160;(A) Matricaria chamomilla, adapted from Wikipedia under CC BY-SA 3.0, https://en.wikipedia.org/wiki/Matricaria_chamomilla#/media/File:Matricaria_February_2008-1.jpg. (B) Chamaemelum nobile, adapted from Wikipedia under CC BY-SA 3.0, https://en.wikipedia.org/wiki/Chamomile#/media/File:Chamaemelum_nobile_001.JPG. <a href="https://www.jove.com/files/ftp_upload/60940/60940fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/60940/60940fig04.jpg" /> 
Figure 4: Collecting M. chamomilla plant parts from the field. <a href="https://www.jove.com/files/ftp_upload/60940/60940fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/60940/60940fig05.jpg" /> 
Figure 5: Layout of testing wells in the demonstration. <a href="https://www.jove.com/files/ftp_upload/60940/60940fig05large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/60940/60940fig06.jpg" /> 
Figure 6: Field DNA extract in collection tubes.&#160;Botanical tissue remains in the original tube and is covered by yellowish DNA extract. <a href="https://www.jove.com/files/ftp_upload/60940/60940fig06large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/60940/60940fig07.jpg" /> 
Figure 7: Fluorescence plot showing the accumulation of PCR products over 25 cycles of thermocycling.&#160;M. chamomilla positive control and C. nobile positive control show Ct values less than 25 in M. chamomilla and C. nobile identification tests, respectively. The field flower and leaf samples were amplified by M. chamomilla identification test with Ct values of 15.18 and 19.41. The rest of the wells were not amplified. <a href="https://www.jove.com/files/ftp_upload/60940/60940fig07large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 8" class="xfigimg" src="/files/ftp_upload/60940/60940fig08.jpg" /> 
Figure 8: Gel electrophoresis of field PCR amplification products. <a href="https://www.jove.com/files/ftp_upload/60940/60940fig08large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 9" class="xfigimg" src="/files/ftp_upload/60940/60940fig09.jpg" /> 
Figure 9: Melting temperature analysis.&#160;(A) The fluorescence signal in each well decreases with the increasing temperature. (B) The identity of the PCR products was confirmed by the melting temperature peak in melting curve analysis. The field flower and leaf samples show peaks at 85.2 &#176;C and 84.8 &#176;C. These are close to the peak produced by M. chamomilla positive control. The C. nobile positive control produced a peak at 79.1 &#176;C, which is different from the other three samples. <a href="https://www.jove.com/files/ftp_upload/60940/60940fig09large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 10" class="xfigimg" src="/files/ftp_upload/60940/60940fig10.jpg" /> 
Figure 10: Melting temperature peak variation between positive control and field samples. <a href="https://www.jove.com/files/ftp_upload/60940/60940fig10large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Stage</td>
			<td>Cycle</td>
			<td>Temperature</td>
			<td>Time</td>
		</tr>
		<tr>
			<td>Constant Temperature</td>
			<td>1</td>
			<td>95 &#176;C</td>
			<td>60s</td>
		</tr>
		<tr>
			<td rowspan="2">Amplification</td>
			<td rowspan="2">25</td>
			<td>95 &#176;C</td>
			<td>30s</td>
		</tr>
		<tr>
			<td>60 &#176;C</td>
			<td>30s</td>
		</tr>
		<tr>
			<td rowspan="2">Melting Curve</td>
			<td rowspan="2">1</td>
			<td>60 &#176;C</td>
			<td rowspan="2">Ramp 0.05 &#176;C/s</td>
		</tr>
		<tr>
			<td>95 &#176;C</td>
		</tr>
	</tbody>
</table>
Table 1: qPCR thermocycling conditions for M. chamomilla and C. nobile identification tests.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Assay</td>
			<td>Primer name</td>
			<td colspan="2">Sequence 5&#39;-3&#39;</td>
			<td>Position</td>
			<td>Region</td>
			<td>Amplicon Size</td>
		</tr>
		<tr>
			<td rowspan="2">Matricaria recutita</td>
			<td>ZL3</td>
			<td colspan="2">TCGTCGGTCGCAAGGATAAG</td>
			<td>Forward</td>
			<td rowspan="2">ITS2</td>
			<td rowspan="2">102 bp</td>
		</tr>
		<tr>
			<td>ZL4</td>
			<td colspan="2">TAAACTCAGCGGGTAGTCCC</td>
			<td>Reverse</td>
		</tr>
		<tr>
			<td rowspan="2">Chamaemelum nobile</td>
			<td>ZL11</td>
			<td colspan="2">TGTCGCACGTTGCTAGGAAGCA</td>
			<td>Forward</td>
			<td rowspan="2">ITS2</td>
			<td rowspan="2">65 bp</td>
		</tr>
		<tr>
			<td>ZL12</td>
			<td colspan="2">TCGAAGCGTCATCCTAAGACAAC</td>
			<td>Reverse</td>
		</tr>
	</tbody>
</table>
Table 2: Primer pairs for M. chamomilla and C. nobile identification tests.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Well position</td>
			<td>Well name</td>
			<td colspan="2">Description</td>
		</tr>
		<tr>
			<td>1</td>
			<td>GC_PosCtrl_GC_Test</td>
			<td colspan="2">German chamomile positive control under GC Test</td>
		</tr>
		<tr>
			<td>2</td>
			<td>GC_PosCtrl_RC_Test</td>
			<td colspan="2">German chamomile positive control under RC Test</td>
		</tr>
		<tr>
			<td>3</td>
			<td>RC_PosCtrl_GC_Test</td>
			<td colspan="2">Roman chamomile positive control under GC Test</td>
		</tr>
		<tr>
			<td>4</td>
			<td>RC_PosCtrl_RC_Test</td>
			<td colspan="2">Roman chamomile positive control under RC Test</td>
		</tr>
		<tr>
			<td>5</td>
			<td>Field_Sample_GC_Test</td>
			<td colspan="2">Sample of leaf tissue under GC Test</td>
		</tr>
		<tr>
			<td>6</td>
			<td>Field_Sample_RC_Test</td>
			<td colspan="2">Sample of leaf tissue under RC Test</td>
		</tr>
		<tr>
			<td>7</td>
			<td>Field_Sample_GC_Test</td>
			<td colspan="2">Sample of flower tissue under GC Test</td>
		</tr>
		<tr>
			<td>8</td>
			<td>Field_Sample_RC_Test</td>
			<td colspan="2">Sample of flower tissue under RC Test</td>
		</tr>
		<tr>
			<td>9</td>
			<td>NegCtrl_GC_Test</td>
			<td colspan="2">Negative control sample under GC Test</td>
		</tr>
		<tr>
			<td>10</td>
			<td>NegCtrl_RC_Test</td>
			<td colspan="2">Negative control sample under RC Test</td>
		</tr>
	</tbody>
</table>
Table 3: Well types and descriptions for M. chamomilla and C. nobile identification tests.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Reagent</td>
			<td>GC_Test</td>
			<td>RC_Test</td>
		</tr>
		<tr>
			<td>Universal qPCR Mix*</td>
			<td>10 &#181;L</td>
			<td>10 &#181;L</td>
		</tr>
		<tr>
			<td>ZL3 primer (10 &#181;M)</td>
			<td>0.4 &#181;L</td>
			<td>NA</td>
		</tr>
		<tr>
			<td>ZL4 primer (10 &#181;M)</td>
			<td>0.4 &#181;L</td>
			<td>NA</td>
		</tr>
		<tr>
			<td>ZL11 primer (10 &#181;M)</td>
			<td>NA</td>
			<td>0.4 &#181;L</td>
		</tr>
		<tr>
			<td>ZL12 primer (10 &#181;M)</td>
			<td>NA</td>
			<td>0.4 &#181;L</td>
		</tr>
		<tr>
			<td>H2O (Nuclease-free)</td>
			<td>7.2 &#181;L</td>
			<td>7.2 &#181;L</td>
		</tr>
		<tr>
			<td colspan="3">* contains Hot Start Taq DNA Polymerase</td>
		</tr>
	</tbody>
</table>
Table 4: Master-mix composition for M. chamomilla and C. nobile identification tests.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td rowspan="2">Well Name</td>
			<td rowspan="2">Expected Result</td>
			<td>Positive Result Criteria</td>
			<td>Negative Result Criteria</td>
		</tr>
		<tr>
			<td>Detected / Present</td>
			<td>Not Detected / Absent</td>
		</tr>
		<tr>
			<td>GC_PosCtrl_GC_Test</td>
			<td>Detected</td>
			<td>Ct &#60; 25 and 84 &#60;= Tm &#60;= 86</td>
			<td>-</td>
		</tr>
		<tr>
			<td>GC_PosCtrl_RC_Test</td>
			<td>Not Detected</td>
			<td>-</td>
			<td>No Ct value within 25 cycles</td>
		</tr>
		<tr>
			<td>RC_PosCtrl_GC_Test</td>
			<td>Not Detected</td>
			<td>-</td>
			<td>No Ct value within 25 cycles</td>
		</tr>
		<tr>
			<td>RC_PosCtrl_RC_Test</td>
			<td>Detected</td>
			<td>Ct &#60; 25 and 79 &#60;= Tm &#60;= 81</td>
			<td>-</td>
		</tr>
		<tr>
			<td>Field_Sample_Leaf_GC_Test</td>
			<td>Present</td>
			<td>Ct &#60; 25 and 84 &#60;= Tm &#60;= 86</td>
			<td>No Ct value within 25 cycles</td>
		</tr>
		<tr>
			<td>Field_Sample_Leaf_RC_Test</td>
			<td>Absent</td>
			<td>-</td>
			<td>No Ct value within 25 cycles</td>
		</tr>
		<tr>
			<td>Field_Sample_Flower_GC_Test</td>
			<td>Present</td>
			<td>Ct &#60; 25 and 84 &#60;= Tm &#60;= 86</td>
			<td>No Ct value within 25 cycles</td>
		</tr>
		<tr>
			<td>Field_Sample_Flower_RC_Test</td>
			<td>Absent</td>
			<td>-</td>
			<td>No Ct value within 25 cycles</td>
		</tr>
		<tr>
			<td>NegCtrl_GC_Test</td>
			<td>Not Detected</td>
			<td>-</td>
			<td>No Ct value within 25 cycles</td>
		</tr>
		<tr>
			<td>NegCtrl_RC_Test</td>
			<td>Not Detected</td>
			<td>-</td>
			<td>No Ct value within 25 cycles</td>
		</tr>
	</tbody>
</table>
Table 5: Rules for qPCR result interpretation.

Watch this Scientific Journal Video about Field Identification of Matricaria chamomilla using a Portable qPCR System at JoVE.com

Field Identification of Matricaria chamomilla using a Portable qPCR System

Presented here is a protocol for field identification of Matricaria chamomilla using a portable qPCR system. This easy-to-perform protocol is ideal as a method to confirm the identity of a botanical species at locations where access to laboratory equipment and expertise is limited, such as farms and warehouses.

Field Identification of Matricaria chamomilla using a Portable qPCR System

Quality control in botanical products begins with the raw material supply. Traditionally, botanical identification is performed through morphological ...

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Research

JoVE Journal

Biology

6.7K Views.  Herbalife NatSource (Hunan) Natural Products Co., Ltd.. Presented here is a protocol for field identification of Matricaria chamomilla using a portable qPCR system. This easy-to-perform protocol is ideal as a method to confirm the identity of a botanical species at locations where access to laboratory equipment and expertise is limited, such as farms and warehouses.

Video: Field Identification of Matricaria chamomilla using a Portable qPCR System

Assessment of DNA Contamination in RNA Samples Based on Ribosomal DNA

One method extensively used for the quantification of gene expression changes and transcript abundances is reverse-transcription quantitative real-time PCR (RT-qPCR). It provides accurate, sensitive, reliable, and reproducible results. Several factors can affect the sensitivity and specificity of RT-qPCR. Residual genomic DNA (gDNA) contaminating RNA samples is one of them. In gene expression analysis, non-specific amplification due to gDNA contamination will overestimate the abundance of transcript levels and can affect the RT-qPCR results. Generally, gDNA is detected by qRT-PCR using primer pairs annealing to intergenic regions or an intron of the gene of interest. Unfortunately, intron/exon annotations are not yet known for all genes from vertebrate, bacteria, protist, fungi, plant, and invertebrate metazoan species.
Here we present a protocol for detection of gDNA contamination in RNA samples by using ribosomal DNA (rDNA)-based primers. The method is based on the unique features of rDNA: their multigene nature, highly conserved sequences, and high frequency in the genome. Also as a case study, a unique set of primers were designed based on the conserved region of ribosomal DNA (rDNA) in the Poaceae family. The universality of these primer pairs was tested by melt curve analysis and agarose gel electrophoresis. Although our method explains how rDNA-based primers can be applied for the gDNA contamination assay in the Poaceae family,&#160;it could be easily used to other prokaryote and eukaryote species

Here, we present a protocol for tracing genomic DNA (gDNA) contamination in RNA samples. The presented method utilizes primers specific for the internal transcribed spacer region (ITS) of ribosomal DNA (rDNA) genes. The method is suited for reliable and sensitive detection of DNA contamination in most eukaryotes and prokaryotes.

One method extensively used for the quantification of gene expression changes and transcript abundances is reverse-transcription quantitative real-time ...

Genetics

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 ...

Environment

A Loop-mediated Isothermal Amplification (LAMP) Assay for Rapid Identification of Bemisia tabaci

The whitefly Bemisia tabaci (Gennadius) is an invasive pest of considerable importance, affecting the production of vegetable and ornamental crops in many countries around the world. Severe yield losses are caused by direct feeding, and even more importantly, also by the transmission of more than 100 harmful plant pathogenic viruses. As for other invasive pests, increased international trade facilitates the dispersal of B. tabaci to areas beyond its native range. Inspections of plant import products at points of entry such as seaports and airports are, therefore, seen as an important prevention measure. However, this last line of defense against pest invasions is only effective if rapid identification methods for suspicious insect specimens are readily available. Because the morphological differentiation between the regulated B. tabaci and close relatives without quarantine status is difficult for non-taxonomists, a rapid molecular identification assay based on the loop-mediated isothermal amplification (LAMP) technology has been developed. This publication reports the detailed protocol of the novel assay describing rapid DNA extraction, set-up of the LAMP reaction, as well as interpretation of its read-out, which allows identifying B. tabaci specimens within one hour. Compared to existing protocols for the detection of specific B. tabaci biotypes, the developed method targets the whole B. tabaci species complex in one assay. Moreover the assay is designed to be applied on-site by plant health inspectors with minimal laboratory training directly at points of entry. Thorough validation performed under laboratory and on-site conditions demonstrates that the reported LAMP assay is a rapid and reliable identification tool, improving the management of B. tabaci.

A Loop-mediated Isothermal Amplification LAMP Assay for Rapid Identification of Bemisia tabaci

This paper reports the protocol for a rapid identification assay for Bemisia tabaci based on loop-mediated isothermal amplification (LAMP) technology. The protocol requires minimal laboratory training and can, therefore, be implemented on-site at points of entry for plant imports such as seaports and airports.

The whitefly Bemisia tabaci (Gennadius) is an invasive pest of considerable importance, affecting the production of vegetable and ornamental crops in many ...

Discrimintion and Mapping of the Primary and Processed Transcripts in Maize Mitochondrion Using a Circular RT-PCR-based Strategy

In plant mitochondria, some steady-state transcripts have 5&#39; triphosphate derived from transcription initiation (primary transcripts), while the others contain 5&#39; monophosphate generated post-transcriptionally (processed transcripts). To discriminate between the two types of transcripts, several strategies have been developed, and most of them depend on presence/absence of 5&#39; triphosphate. However, the triphosphate at primary 5&#39; termini is unstable, and it hinders a clear discrimination of the two types of transcripts. To systematically differentiate and map the primary and processed transcripts stably accumulated in maize mitochondrion, we have developed a circular RT-PCR (cRT-PCR)-based strategy by combining cRT-PCR, RNA 5&#39; polyphoshpatase treatment, quantitative RT-PCR (RT-qPCR), and Northern blot. As an improvement, this strategy includes an RNA normalization step to minimize the influence of unstable 5&#39; triphosphate.
In this protocol, the enriched mitochondrial RNA is pre-treated by RNA 5&#39; polyphosphatase, which converts 5&#39; triphsophate to monophosphate. After circularization and reverse transcription, the two cDNAs derived from 5&#39; polyphosphatase-treated and non-treated RNAs are normalized by maize 26S mature rRNA, which has a processed 5&#39; end and is insensitive to 5&#39; polyphosphatase. After normalization, the primary and processed transcripts are discriminated by comparing cRT-PCR and RT-qPCR products obtained from the treated and non-treated RNAs. The transcript termini are determined by cloning and sequencing of the cRT-PCR products, and then verified by Northern blot.
By using this strategy, most steady-state transcripts in maize mitochondrion have been determined. Due to the complicated transcript pattern of some mitochondrial genes, a few steady-state transcripts were not differentiated and/or mapped, though they were detected in a Northern blot. We are not sure whether this strategy is suitable to discriminate and map the steady-state transcripts in other plant mitochondria or in plastids.

We present a circular RT-PCR-based strategy by combining circular RT-PCR, quantitative RT-PCR, RNA 5&#39; polyphosphatase-treatment, and Northern blot. This protocol includes a normalization step to minimize the influence of unstable 5&#39; triphosphate, and it is suitable for discriminating and mapping the primary and processed transcripts stably accumulated in maize mitochondrion.

In plant mitochondria, some steady-state transcripts have 5&#39; triphosphate derived from transcription initiation (primary transcripts), while the ...

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.

Glyphosate-based products (GBP) are the most common broad-spectrum herbicides worldwide. The target of glyphosate is the enzyme ...

DNA Barcoding for Medicinal Plants: Identification and Phylogenetic Analysis

Application of DNA Barcoding to Identify Medicinal Plants

Medicinal plants are valuable resources globally and are used worldwide to maintain health and treat disease; however, the presence of adulteration obstructs their development. DNA barcoding, a technique for species identification by standard DNA regions, facilitates prompt and accurate identification of traditional medicinal plants. The process of DNA barcoding entails six basic steps: 1) processing the medicinal plants, 2) extracting high-quality total DNA from the medicinal plants using centrifugal column method, 3) amplifying target DNA region internal transcribed spacer 2 (ITS2) with universal primers of plants and performing Sanger sequencing, 4) splicing and aligning sequence to obtain the target sequence, 5) matching the barcode sequence against the barcode library for identification, 6) aligning sequence, comparing intraspecific and interspecific variation, constructing phylogenetic neighbor-joining tree. As shown in the results, the universal primer can amplify the target region. Basic Local Alignment Search Tool (BLAST) demonstrates the percentage identified was 100%, and the neighbor-joining tree demonstrates that the splicing sequences were clustered with the A. sinensis OR879715.1 clade, and the clade support value is 100. This protocol provides a reference for applying DNA barcoding technology as an effective method to identify medicinal plants and adulterants.

Author Spotlight: Harnessing DNA Barcode Technology to Enhance the Efficiency of Medicinal Plant Identification

The present protocol describes the use of DNA barcoding technology to authenticate plant-based medicinal materials, and the medicinal plant Angelica sinensis (Oliv.) Diels was identified as an example.

Medicinal plants are valuable resources globally and are used worldwide to maintain health and treat disease; however, the presence of adulteration ...

Visual Detection of Multiple Nucleic Acids in a Capillary Array

Multi-target, short time, and resource-affordable methodologies for the detection of multiple nucleic acids in a single, easy to operate test are urgently needed in disease diagnosis, microbial monitoring, genetically modified organism (GMO) detection, and forensic analysis. We have previously described the platform called CALM (Capillary Array-based Loop-mediated isothermal amplification for Multiplex visual detection of nucleic acids). Herein, we describe improved fabrication and performance processes for this platform. Here, we apply a small, ready-to-use cassette assembled by capillary array for multiplex visual detection of nucleic acids. The capillary array is pre-treated into a hydrophobic and hydrophilic pattern before fixing loop-mediated isothermal amplification (LAMP) primer sets in capillaries. After assembly of the loading adaptor, LAMP reaction mixture is loaded and isolated into each capillary, due to capillary force by a single pipetting step. The LAMP reactions are performed in parallel in the capillaries. The results are visually read out by illumination with a hand-held UV flashlight. Using this platform, we demonstrate monitoring of 8 frequently appearing elements and genes in GMO samples with high specificity and sensitivity. In summary, the platform described herein is intended to facilitate the detection of multiple nucleic acids. We believe it will be widely applicable in fields where high-throughput nucleic acid analysis is required.

This protocol describes the fabrication of a small, ready-to-use cassette that can be applied for visual detection of multiple nucleic acids in a single, test that is easy to operate. In this approach, a capillary array was used for multiplex and highly efficient detection of GMO targets.

Multi-target, short time, and resource-affordable methodologies for the detection of multiple nucleic acids in a single, easy to operate test are urgently ...

Bioengineering

A PCR-based Genotyping Method to Distinguish Between Wild-type and Ornamental Varieties of Imperata cylindrica

Wild-type I. cylindrica (cogongrass) is one of the top ten worst invasive plants in the world, negatively impacting agricultural and natural resources in 73 different countries throughout Africa, Asia, Europe, New Zealand, Oceania and the Americas1-2. Cogongrass forms rapidly-spreading, monodominant stands that displace a large variety of native plant species and in turn threaten the native animals that depend on the displaced native plant species for forage and shelter. To add to the problem, an ornamental variety [I. cylindrica var. koenigii (Retzius)] is widely marketed under the names of Imperata cylindrica 'Rubra', Red Baron, and Japanese blood grass (JBG). This variety is putatively sterile and noninvasive and is considered a desirable ornamental for its red-colored leaves. However, under the correct conditions, JBG can produce viable seed (Carol Holko, 2009 personal communication) and can revert to a green invasive form that is often indistinguishable from cogongrass as it takes on the distinguishing characteristics of the wild-type invasive variety4 (Figure 1). This makes identification using morphology a difficult task even for well-trained plant taxonomists. Reversion of JBG to an aggressive green phenotype is also not a rare occurrence. Using sequence comparisons of coding and variable regions in both nuclear and chloroplast DNA, we have confirmed that JBG has reverted to the green invasive within the states of Maryland, South Carolina, and Missouri. JBG has been sold and planted in just about every state in the continental U.S. where there is not an active cogongrass infestation. The extent of the revert problem in not well understood because reverted plants are undocumented and often destroyed.
Application of this molecular protocol provides a method to identify JBG reverts and can help keep these varieties from co-occurring and possibly hybridizing. Cogongrass is an obligate outcrosser and, when crossed with a different genotype, can produce viable wind-dispersed seeds that spread cogongrass over wide distances5-7. JBG has a slightly different genotype than cogongrass and may be able to form viable hybrids with cogongrass. To add to the problem, JBG is more cold and shade tolerant than cogongrass8-10, and gene flow between these two varieties is likely to generate hybrids that are more aggressive, shade tolerant, and cold hardy than wild-type cogongrass. While wild-type cogongrass currently infests over 490 million hectares worldwide, in the Southeast U.S. it infests over 500,000 hectares and is capable of occupying most of the U.S. as it rapidly spreads northward due to its broad niche and geographic potential3,7,11. The potential of a genetic crossing is a serious concern for the USDA-APHIS Federal Noxious Week Program. Currently, the USDA-APHIS prohibits JBG in states where there are major cogongrass infestations (e.g., Florida, Alabama, Mississippi). However, preventing the two varieties from combining can prove more difficult as cogongrass and JBG expand their distributions. Furthermore, the distribution of the JBG revert is currently unknown and without the ability to identify these varieties through morphology, some cogongrass infestations may be the result of JBG reverts. Unfortunately, current molecular methods of identification typically rely on AFLP (Amplified Fragment Length Polymorphisms) and DNA sequencing, both of which are time consuming and costly. Here, we present the first cost-effective and reliable PCR-based molecular genotyping method to accurately distinguish between cogongrass and JBG revert.

A PCR-based Genotyping Method to Distinguish Between Wild-type and Ornamental Varieties of Imperata cylindrica

We provide a cost-effective and rapid molecular genotyping protocol that employs variety-specific PCR primers that target DNA sequence differences within the chloroplast trnL-F spacer region to differentiate between varieties of Imperata cylindrica (cogongrass) that cannot be distinguished by morphology alone. These varieties include the federally listed noxious weed, cogongrass and closely-related, wide-spread ornamental variety, I. cylindrica var. koenigii (Japanese blood grass).

Wild-type I. cylindrica (cogongrass) is one of the top ten worst invasive plants in the world, negatively impacting agricultural and natural resources in ...

Genotyping of Plant and Animal Samples without Prior DNA Purification

The Direct PCR approach facilitates PCR amplification directly from small amounts of unpurified samples, and is demonstrated here for several plant and animal tissues (Figure 1). Direct PCR is based on specially engineered Thermo Scientific Phusion and Phire DNA Polymerases, which include a double-stranded DNA binding domain that gives them unique properties such as high tolerance of inhibitors.
PCR-based target DNA detection has numerous applications in plant research, including plant genotype analysis and verification of transgenes. PCR from plant tissues traditionally involves an initial DNA isolation step, which may require expensive or toxic reagents. The process is time consuming and increases the risk of cross contamination1, 2. Conversely, by using Thermo Scientific Phire Plant Direct PCR Kit the target DNA can be easily detected, without prior DNA extraction. In the model demonstrated here, an example of derived cleaved amplified polymorphic sequence analysis (dCAPS)3,4 is performed directly from Arabidopsis plant leaves. dCAPS genotyping assays can be used to identify single nucleotide polymorphisms (SNPs) by SNP allele-specific restriction endonuclease digestion3. 
Some plant samples tend to be more challenging when using Direct PCR methods as they contain components that interfere with PCR, such as phenolic compounds. In these cases, an additional step to remove the compounds is traditionally required2,5. Here, this problem is overcome by using a quick and easy dilution protocol followed by Direct PCR amplification (Figure 1). Fifteen year-old oak leaves are used as a model for challenging plants as the specimen contains high amounts of phenolic compounds including tannins. 
Gene transfer into mice is broadly used to study the roles of genes in development, physiology and human disease. The use of these animals requires screening for the presence of the transgene, usually with PCR. Traditionally, this involves a time consuming DNA isolation step, during which DNA for PCR analysis is purified from ear, tail or toe tissues6,7. However, with the Thermo Scientific Phire Animal Tissue Direct PCR Kit transgenic mice can be genotyped without prior DNA purification. In this protocol transgenic mouse genotyping is achieved directly from mouse ear tissues, as demonstrated here for a challenging example where only one primer set is used for amplification of two fragments differing greatly in size.

The Direct PCR approach presented here facilitates PCR amplification directly from small amounts of unpurified plant and animal tissue.

The Direct PCR approach facilitates PCR amplification directly from small amounts of unpurified samples, and is demonstrated here for several plant and ...

Field-Deployable Candidatus Liberibacter asiaticus Detection Using Recombinase Polymerase Amplification Combined with CRISPR-Cas12a

The early detection of Candidatus Liberibacter asiaticus (CLas) by citrus growers facilitates early intervention and prevents the spread of disease. A simple method for rapid and portable Huanglongbing (HLB) diagnosis is presented here that combines recombinase polymerase amplification and a fluorescent reporter utilizing the nuclease activity of the clustered regularly interspaced short palindromic repeats/CRISPR-associated 12a (CRISPR-Cas12a) system. The sensitivity of this technique is much higher than PCR. Furthermore, this method showed similar results to qPCR when leaf samples were used. Compared with conventional CLas detection methods, the detection method presented here can be completed in 90 min and works in an isothermal condition that does not require the use of PCR machines. In addition, the results can be visualized through a handheld fluorescent detection device in the field.

Field-Deployable Candidatus Liberibacter asiaticus Detection Using Recombinase Polymerase Amplification Combined with CRISPR-Cas12a

In this work, a rapid, sensitive, and portable detection method for Candidatus Liberibacter asiaticus based on recombinase polymerase amplification combined with CRISPR-Cas12a was developed.

The early detection of Candidatus Liberibacter asiaticus (CLas) by citrus growers facilitates early intervention and prevents the spread of disease. A ...

Field Identification of Matricaria chamomilla using a Portable qPCR System

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Rozdziały w tym wideo

Assessment of DNA Contamination in RNA Samples Based on Ribosomal DNA

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

A Loop-mediated Isothermal Amplification (LAMP) Assay for Rapid Identification of Bemisia tabaci

Discrimintion and Mapping of the Primary and Processed Transcripts in Maize Mitochondrion Using a Circular RT-PCR-based Strategy

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

Application of DNA Barcoding to Identify Medicinal Plants

Visual Detection of Multiple Nucleic Acids in a Capillary Array

A PCR-based Genotyping Method to Distinguish Between Wild-type and Ornamental Varieties of Imperata cylindrica

Genotyping of Plant and Animal Samples without Prior DNA Purification

Field-Deployable Candidatus Liberibacter asiaticus Detection Using Recombinase Polymerase Amplification Combined with CRISPR-Cas12a