We would like to acknowledge funding from the United States Department of Agriculture-Agricultural Research Service and from the National Science Foundation (IOS 1025881 and IOS 1361554). We thank R. Caspers for greenhouse maintenance and plant care. We also thank the University of Minnesota Genomics Center, where the Illumina library preparations and sequencing were performed. We are also grateful for the comments from the journal editors and four anonymous reviewers that further strengthened our manuscript. We also thank OECD for a fellowship to SK to integrate these protocols for collaborative projects with colleagues in Japan.

<ol>
	<li>Liberatore, K. L., Dukowic-Schulze, S., Miller, M. E., Chen, C., Kianian, S. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+role+of+mitochondria+in+plant+development+and+stress+tolerance.">The role of mitochondria in plant development and stress tolerance.</a> Free Radic Biol Med. 100, 238-256 (2016).</li><li>Samaniego Castruita, J. A., Zepeda Mendoza, M. L., Barnett, R., Wales, N., Gilbert, M. 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The authors declare that they have no competing interests.
Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the US Department of Agriculture. USDA is an equal opportunity provider and employer.

To date, most organellar sequencing studies center on traditional DC methods to enrich for specific DNA. Methods to isolate organelles from diverse plants have been described, including moss40; monocots such as wheat15 and oats11; and dicots such as arabidopsis11, sunflower17, and rapeseed14. Most protocols focus on leaf tissue13,14,15,16,17, with some having been adapted for a variety of tissue types, including seeds11. The isolation of organelles from protoplasts has also been demonstrated41. However, this not amenable to all systems, nor is it feasible when the tissue of interest is limited. Many of these organellar isolation methods were designed to recover intact organelles for specific experiments, such as physiological studies. These protocols are cumbersome and typically require the use of density gradients, such as sucrose or Percoll gradients, which are very efficient at isolating specific organellar fractions but require a large tissue input (i.e., in excess of 5 g and upwards of kilograms, depending on the tissue type). However, the DC method may be optimized to enrich for specific cellular fractions, such as the mitochondria or chloroplast, by changing spin speeds and density gradients. In contrast, the MF approach requires far less starting material (20 mg), but mitochondrial and plastid DNAs will be present per their relative abundances in the tissue used for DNA extraction. Nonetheless, the MF protocol offers an alternative approach for isolating mixed organellar DNA and is particularly beneficial for starting with small amounts of tissue.
To assess sample purity following organelle isolation, most studies to date only use end-point PCR and gel electrophoresis11,12. This gives a fair qualitative measure of sample purity. However, low levels of amplification may not be visualized on an agarose gel. Few reports include more quantitative measures of quality control, such as qPCR14. For a quantitative assessment of DNA sample purity isolated from both methods, we utilized qPCR and sequencing to determine how much nuclear DNA remains in the sample, as well as the relative proportions of mitochondrial versus chloroplast DNA. Both methods evaluated here are efficient at removing nuclear DNA. Both methods yield a mix of mitochondrial and chloroplast DNA, albeit at different proportions.
Growing plants in the dark (etiolation) is reported to help facilitate organellar isolation due to a reduction of phenolics13. However, in this comparison, we did not find an appreciable advantage to working with etiolated tissue over light-grown samples. Although the proportion of specialized chloroplasts will likely be higher when light-grown, the total plastid number, as reflected in the proportion of reads mapping to the chloroplast genome, is unchanged under differing light conditions. Therefore, for downstream functional analyses, such as the assessment of heteroplasmy in different tissues or under different stressors or for expression analyses, we recommend performing genomic sequencing on plants grown under physiologically relevant conditions.
For application with short-read sequencing technologies, both techniques compared here yield adequate DNA quantity and quality. However, to achieve long reads of &#62;20 kb for single-molecule sequencing applications, a greater amount of higher-quality DNA is necessary. For instance, ideally, &#62; 1 &#181;g of pure organellar wheat DNA with a molecular weight &#62;20 kb is necessary for in-house, low-input protocols for 20-kb insert library preparations42. New user-developed, low-input protocols may reduce DNA requirements (i.e., to 50 ng or even less20), but the challenge remains to have high-quality, high-molecular weight DNA going into the library preparations. It is essential that a majority of the DNA is &#62;20 kb, as smaller fragments will be preferentially inserted into the SMRTbell and throw off the size distribution of the library43. We tried a number of homemade DNA extraction protocols and a number of commercial protocols for DNA extraction (not shown). For wheat leaf tissue, the best balance between DNA quantity and quality, particularly length, was obtained using a commercial kit27,29. Depending on the plant species and tissue of interest, alternative extraction protocols may be equally suited or more fruitful. Nonetheless, we conclude that the total extraction of high-molecular weight genomic DNA &#62;50 kb in size, followed by fractionation with the MBD2-Fc pulldown approach28, is amenable to long-read sequencing from limited starting material. Future work should test the limits of the starting material required following fractionation for long-insert library preparation and subsequent long-read sequencing. Critically, this approach could provide a robust method to isolate DNA from a subsample of a single leaf that is suitable for long-read sequencing, without whole-genome amplification. We anticipate that this approach will be easily adaptable to additional tissue types and broadly applicable to other plant species. It will be particularly useful in situations where the tissue amounts are limiting, such as sequencing at individual generations in a crossing scheme or in rarer tissue types.

Genome sequencing is a powerful tool to dissect the underlying genetic basis of important plant traits. Most genome-sequencing studies focus on the nuclear genome content, as the majority of genes are located in the nucleus. However, organellar genomes, including the mitochondria (across eukaryotes) and plastids (in plants; the specialized form, the chloroplast, works in photosynthesis) contribute significant genetic information essential to organismal development, stress response, and overall fitness1. Organellar genomes are typically included in total DNA extractions intended for nuclear genome sequencing, although methods to reduce organelle numbers prior to DNA extraction are also employed2. Many studies have used sequencing results from total gDNA extractions to assemble organellar genomes3,4,5,6,7. However, when the target of the study is to focus on organellar genomes, using the total gDNA increases the sequencing costs because many reads are &#34;lost&#34; to the nuclear DNA sequences, particularly in plants with large nuclear genomes. Moreover, due to the duplication and transfer of organellar sequences into the nuclear genome and between organelles, resolving the correct mapping position of sequencing reads to the proper genome is bioinformatically challenging2,8. The purification of organellar genomes from the nuclear genome is one strategy to reduce these problems. Further bioinformatics strategies may be used to separate reads that map to regions of homology between the mitochondria and chloroplasts.
While the organellar genomes from many plant species have been sequenced, little is known about the breadth of organellar genome diversity available in wild populations or in cultivated breeding pools. Organellar genomes are also known to be dynamic molecules that undergo significant structural rearrangement due to recombination between repeat sequences9. Moreover, multiple copies of the organellar genome are contained within each organelle, and multiple organelles are contained within each cell. Not all copies of these genomes are identical, which is known as heteroplasmy. In contrast to the canonical picture of &#34;master circles,&#34; there is now growing evidence for a more complex picture of organellar genome structures, including sub-genomic circles, linear chromosomes, linear concatamers, and branched structures10. The assembly of plant organellar genomes is further complicated by their relatively large sizes and substantial inverted and direct repeats.
Traditional protocols for organellar isolation, DNA purification, and subsequent genome sequencing are often cumbersome and require large volumes of tissue input, with several grams to upwards of hundreds of grams of young leaf tissue necessary as a starting point11,12,13,14,15,16,17. This makes organellar genome sequencing inaccessible when tissue is limited. In some situations, seed amounts are limited, such as when it is necessary to sequence on a generational basis or in male sterile lines that have to be maintained via crossing. In these situations, organellar DNA can be purified and then subjected to whole-genome amplification. However, whole-genome amplification can introduce significant sequencing bias, which is a particular problem when assessing structural variation, sub-genomic structures, and heteroplasmy levels18. Recent advances in library preparation for short-read sequencing technologies have overcome low-input barriers to avoid whole-genome amplification. For example, the Illumina Nextera XT library preparation kit allows for as little as 1 ng of DNA to be used as input19. However, standard library preparations for long-read sequencing applications, such as PacBio or Oxford Nanopore sequencing technologies, still require a relatively high amount of input DNA, which can pose a challenge for organellar genome sequencing. Recently, new user-made, long-read sequencing protocols have been developed to reduce the input amounts and to help facilitate genome sequencing in samples where obtaining microgram-quantities of DNA is difficult20,21. However, obtaining high-molecular weight, pure organellar fractions to feed into these library preparations remains a challenge.
We sought to compare and optimize organellar DNA enrichment and isolation methods suitable for NGS without the need of whole-genome amplification. Specifically, our goal was to determine best practices to enrich for high-molecular weight organellar DNA from limited starting materials, such as a subsample of a leaf. This work presents a comparative analysis of methods to enrich for organellar DNA: (1) a modified, traditional differential centrifugation protocol versus (2) a DNA fractionation protocol based on the use of a commercially available DNA CpG-methyl-binding domain protein pulldown approach22 applied to plant tissue23. We recommend best practices for the isolation of organellar DNA from wheat leaf tissue, which may be readily extended to other plants and tissue types.

<table border="0" cellpadding="0" cellspacing="0" width="685">
	<tbody>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">2-mercaptoethanol (beta-mercaptoethanol; BME)</td>
			<td style="width:183px;">Sigma Aldrich</td>
			<td style="width:119px;">M3148-100ml</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">2-propanol (Isopropyl alcohol/isopropanol), bioreagent</td>
			<td>Sigma Aldrich</td>
			<td>I9516</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">agarose, Bio-Rad Cetified Megabase agarose</td>
			<td>Bio-Rad</td>
			<td>1613108</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">analytical balance</td>
			<td>Mettler Toledo</td>
			<td>AB54-S</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">balance</td>
			<td>Mettler Toledo</td>
			<td>PB1502-S</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">bovine serum albumin (BSA)</td>
			<td>Sigma Aldrich</td>
			<td>B4287-25G</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">Ceramic grinding cylinders, 3/8in x 7/8in</td>
			<td>SPEX SamplePrep</td>
			<td>2183</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="63">
			<td height="63" style="height:63px;width:216px;">Cryogenic Blocks compatible with tissue homogenizer for holding 50 ml tubes</td>
			<td>SPEX SamplePrep</td>
			<td>2664</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">DNaseI</td>
			<td>Sigma</td>
			<td>DN25</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">ethanol, absolute</td>
			<td>Decon Laboratories</td>
			<td>2716</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">Ethylenediamine Tetraacetic Acid (EDTA), 0.5M Solution, pH8.0</td>
			<td>Fisher</td>
			<td>BP2482-500</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">gel imaging system</td>
			<td></td>
			<td></td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;width:216px;">gel stain</td>
			<td></td>
			<td></td>
			<td style="width:167px;">Such as GelRed or Ethidium Bromide</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">grinding pestle, wide tip for 2 ml conical tubes</td>
			<td></td>
			<td></td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Guanidine-HCl, 8M solution</td>
			<td>ThermoFisher</td>
			<td>24115</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">LightCycler 480 SYBR Green I Master</td>
			<td>Roche</td>
			<td>4707516001</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">liquid nitrogen</td>
			<td></td>
			<td></td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">Lysing enzymes from Trichoderma harzianum</td>
			<td>Sigma</td>
			<td>L1412</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Magnesium Chloride</td>
			<td>G Bioscience</td>
			<td>24115</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">magnetic rack</td>
			<td>ThermoFisher</td>
			<td>A13346</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">microcentrifuge tubes, LoBind 1.5 ml&#160;</td>
			<td>Eppendorf</td>
			<td>22431021</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">microcentrifuge tubes, standard nuclease-free 1.5 ml</td>
			<td>Eppendorf</td>
			<td></td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="120">
			<td height="120" style="height:120px;width:216px;">microcentrifuge, refrigerated</td>
			<td>Sorvall&#160;</td>
			<td>Legend X1R</td>
			<td style="width:167px;">Or equivalent product, must be capable of reaching at least 18,000 x g with rotors for 50 ml tubes, Oak Ridge tubes, and 1.5 ml tubes</td>
		</tr>
		<tr height="120">
			<td height="120" style="height:120px;width:216px;">microcentrifuge, room temperature</td>
			<td>Eppendorf</td>
			<td>5424</td>
			<td style="width:167px;">Or equivalent product, must be capable of reaching at least 18,000 x g with rotor for 1.5 ml and 2 ml microcentrifuge tubes</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">Microcon DNA Fast Flow Centrifugal Filter Units</td>
			<td>EMD Millipore</td>
			<td>MRCFOR100</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">Miracloth, 1 square per sample cut to fit funnel</td>
			<td>EMD Millipore</td>
			<td>475855</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">NEBNext Microbiome DNA Enrichment Kit</td>
			<td>New England Biolabs</td>
			<td>E2612L</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">parafilm</td>
			<td>Parafilm M</td>
			<td>PM992</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">plastic pots and trays</td>
			<td></td>
			<td></td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">polyvinylpyrrolidone (PVP)</td>
			<td>Fisher</td>
			<td>BP431-100</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Proteinase K</td>
			<td>Qiagen</td>
			<td>19131</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Pulsed-Field Gel Electrophoresis rig (e.g. CHEF DR III)</td>
			<td>Bio-Rad</td>
			<td>1703697</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">purification beads, Agencourt AMpureXP beads</td>
			<td>Beckman Coulter</td>
			<td>A63881</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">QIAamp DNA Mini Kit</td>
			<td>Qiagen</td>
			<td>51304</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">Qiagen 20/g Genomic Tip DNA Extraction Kit</td>
			<td>Qiagen</td>
			<td>10223</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Qiagen Buffer EB (elution buffer)</td>
			<td>Qiagen</td>
			<td>19086</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">Qiagen DNA Extraction Buffer Set</td>
			<td>Qiagen</td>
			<td>19060</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">QiaRack</td>
			<td>Qiagen</td>
			<td>19015</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">qPCR machine (e.g. Roche Light Cycler 480)</td>
			<td>Roche</td>
			<td></td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">qPCR plate sealing film</td>
			<td>Roche</td>
			<td>4729757001</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">qPCR plate, 96 well plate</td>
			<td>Roche</td>
			<td>4729692001</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Qubit&#160;assay tubes</td>
			<td>Life Technologies</td>
			<td>Q32856</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Qubit Broad Spectrum assay kit</td>
			<td>Life Technologies</td>
			<td>Q32850</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Qubit High Sensitivity assay kit</td>
			<td>Life Technologies</td>
			<td>Q32851</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">RNaseA</td>
			<td>Qiagen</td>
			<td>19101</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">Serological pipettes (20 ml) and pipet-aid</td>
			<td>Fisher</td>
			<td>13-678-11</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Small funnels, 1 per sample</td>
			<td></td>
			<td></td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Sodium Chloride</td>
			<td>Ambion</td>
			<td>AM9759</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Soft paintbrush, 2 per sample</td>
			<td></td>
			<td></td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="260">
			<td height="260" style="height:260px;width:216px;">SPEX SamplePrep 2010 Geno/Grinder or another type of tissue homogenizer</td>
			<td>SPEX SamplePrep</td>
			<td></td>
			<td style="width:167px;">Or another comparable tissue homogenizer. If you do not have access to a tissue homogenizer, then grinding in a pre-chilled mortar and pestle will suffice (see protocol for details). However, a homogenizer will give more consistent results and total homogenization time is reduced.</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Sucrose</td>
			<td>Omnipure</td>
			<td>8550</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">TBE</td>
			<td></td>
			<td></td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">thermomixer</td>
			<td></td>
			<td></td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Tris</td>
			<td>Sigma</td>
			<td>T2819-100ml</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Triton X-100</td>
			<td>Promega</td>
			<td>H5142</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">tube rotater</td>
			<td></td>
			<td></td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">tubes, 50 mL conical polypropylene</td>
			<td>Corning</td>
			<td>352070</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">tubes, 50 ml high-speed polypropylene&#160;</td>
			<td>ThermoScientific/Nalgene</td>
			<td>3119-0050</td>
			<td style="width:167px;">e.g. Nalgene Oakridge tubes or equivalent</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">vermiculite</td>
			<td></td>
			<td></td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">water bath</td>
			<td></td>
			<td></td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">water, sterile and certified Nuclease-free&#160;</td>
			<td>Fisher</td>
			<td>1481</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">water, sterile milliQ</td>
			<td></td>
			<td></td>
			<td style="width:167px;"></td>
		</tr>
	</tbody>
</table>

optimization and comparative analysis of plant organellar dna enrichment methods suitable for next-generation sequencing

Plant organellar genomes contain large, repetitive elements that may undergo pairing or recombination to form complex structures and/or sub-genomic fragments. Organellar genomes also exist in admixtures within a given cell or tissue type (heteroplasmy), and an abundance of subtypes may change throughout development or when under stress (sub-stoichiometric shifting). Next-generation sequencing (NGS) technologies are required to obtain deeper understanding of organellar genome structure and function. Traditional sequencing studies use several methods to obtain organellar DNA: (1) If a large amount of starting tissue is used, it is homogenized and subjected to differential centrifugation and/or gradient purification. (2) If a smaller amount of tissue is used (i.e., if seeds, material, or space is limited), the same process is performed as in (1), followed by whole-genome amplification to obtain sufficient DNA. (3) Bioinformatics analysis can be used to sequence the total genomic DNA and to parse out organellar reads. All these methods have inherent challenges and tradeoffs. In (1), it may be difficult to obtain such a large amount of starting tissue; in (2), whole-genome amplification could introduce a sequencing bias; and in (3), homology between nuclear and organellar genomes could interfere with assembly and analysis. In plants with large nuclear genomes, it is advantageous to enrich for organellar DNA to reduce sequencing costs and sequence complexity for bioinformatics analyses. Here, we compare a traditional differential centrifugation method with a fourth method, an adapted CpG-methyl pulldown approach, to separate the total genomic DNA into nuclear and organellar fractions. Both methods yield sufficient DNA for NGS, DNA that is highly enriched for organellar sequences, albeit at different ratios in mitochondria and chloroplasts. We present the optimization of these methods for wheat leaf tissue and discuss major advantages and disadvantages of each approach in the context of sample input, protocol ease, and downstream application.

Watch this Scientific Journal Video about Optimization and Comparative Analysis of Plant Organellar DNA Enrichment Methods Suitable for Next-generation Sequencing at JoVE.com

Optimization and Comparative Analysis of Plant Organellar DNA Enrichment Methods Suitable for Next-generation Sequencing

The comparison and optimization of two plant organellar DNA enrichment methods are presented: traditional differential centrifugation and fractionation of the total gDNA based on methylation status. We assess the resulting DNA quantity and quality, demonstrate performance in short-read next-generation sequencing, and discuss the potential for use in long-read single-molecule sequencing.

Plant organellar genomes contain large, repetitive elements that may undergo pairing or recombination to form complex structures and/or sub-genomic ...

optimization-comparative-analysis-plant-organellar-dna-enrichment

Nanyang Technological University

Research

JoVE Journal

Genetics

12.8K Views.  United States Department of Agriculture-Agricultural Research Service. The comparison and optimization of two plant organellar DNA enrichment methods are presented: traditional differential centrifugation and fractionation of the total gDNA based on methylation status. We assess the resulting DNA quantity and quality, demonstrate performance in short-read next-generation sequencing, and discuss the potential for use in long-read single-molecule sequencing.

Video: Optimization and Comparative Analysis of Plant Organellar DNA Enrichment Methods Suitable for Next-generation Sequencing

Methyl-binding DNA capture Sequencing for Patient Tissues

Methylation is one of the essential epigenetic modifications to the DNA, which is responsible for the precise regulation of genes required for stable development and differentiation of different tissue types. Dysregulation of this process is often the hallmark of various diseases like cancer. Here, we outline one of the recent sequencing techniques, Methyl-Binding DNA Capture sequencing (MBDCap-seq), used to quantify methylation in various normal and disease tissues for large patient cohorts. We describe a detailed protocol of this affinity enrichment approach along with a bioinformatics pipeline to achieve optimal quantification. This technique has been used to sequence hundreds of patients across various cancer types as a part of the 1,000 methylome project (Cancer Methylome System).

Here we present a protocol to investigate genome wide DNA methylation in large scale clinical patient screening studies using the Methyl-Binding DNA Capture sequencing (MBDCap-seq or MBD-seq) technology and the subsequent bioinformatics analysis pipeline.

Methylation is one of the essential epigenetic modifications to the DNA, which is responsible for the precise regulation of genes required for stable ...

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved (Non-model) Organisms

This workflow allows novice researchers to leverage advanced computational resources such as cloud computing to carry out pairwise comparative transcriptomics. It also serves as a primer for biologists to develop data scientist computational skills, e.g. executing bash commands, visualization and management of large data sets. All command line code and further explanations of each command or step can be found on the wiki (<a href="https://wiki.cyverse.org/wiki/x/dgGtAQ">https://wiki.cyverse.org/wiki/x/dgGtAQ</a>). The Discovery Environment and Atmosphere platforms are connected together through the CyVerse Data Store. As such, once the initial raw sequencing data has been uploaded there is no more need to transfer large data files over an Internet connection, minimizing the amount of time needed to conduct analyses. This protocol is designed to analyze only two experimental treatments or conditions. Differential gene expression analysis is conducted through pairwise comparisons, and will not be suitable to test multiple factors. This workflow is also designed to be manual rather than automated. Each step must be executed and investigated by the user, yielding a better understanding of data and analytical outputs, and therefore better results for the user. Once complete, this protocol will yield de novo assembled transcriptome(s) for underserved (non-model) organisms without the need to map to previously assembled reference genomes (which are usually not available in underserved organism). These de novo transcriptomes are further used in pairwise differential gene expression analysis to investigate genes differing between two experimental conditions. Differentially expressed genes are then functionally annotated to understand the genetic response organisms have to experimental conditions. In total, the data derived from this protocol is used to test hypotheses about biological responses of underserved organisms.

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms

This protocol outlines a comparative de novo transcriptome assembly and annotation workflow for novice bioinformaticians. The workflow is available for free entirely through CyVerse and connected by the Data Store. Command line and graphical user interfaces are used, but all code needed is available to copy and paste.

This workflow allows novice researchers to leverage advanced computational resources such as cloud computing to carry out pairwise comparative ...

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 ...

Purification of High Molecular Weight Genomic DNA from Powdery Mildew for Long-Read Sequencing

The powdery mildew fungi are a group of economically important fungal plant pathogens. Relatively little is known about the molecular biology and genetics of these pathogens, in part due to a lack of well-developed genetic and genomic resources. These organisms have large, repetitive genomes, which have made genome sequencing and assembly prohibitively difficult. Here, we describe methods for the collection, extraction, purification and quality control assessment of high molecular weight genomic DNA from one powdery mildew species, Golovinomyces cichoracearum. The protocol described includes mechanical disruption of spores followed by an optimized phenol/chloroform genomic DNA extraction. A typical yield was 7 &#181;g DNA per 150 mg conidia. The genomic DNA that is isolated using this procedure is suitable for long-read sequencing (i.e., &#62; 48.5 kbp). Quality control measures to ensure the size, yield, and purity of the genomic DNA are also described in this method. Sequencing of the genomic DNA of the quality described here will allow for the assembly and comparison of multiple powdery mildew genomes, which in turn will lead to a better understanding and improved control of this agricultural pathogen.

Described here is a method for the extraction, purification, and quality control of genomic DNA from the obligate biotrophic fungal pathogen, powdery mildew, for use in long-read genome sequencing.

The powdery mildew fungi are a group of economically important fungal plant pathogens. Relatively little is known about the molecular biology and genetics ...

Robust DNA Isolation and High-throughput Sequencing Library Construction for Herbarium Specimens

Herbaria are an invaluable source of plant material that can be used in a variety of biological studies. The use of herbarium specimens is associated with a number of challenges including sample preservation quality, degraded DNA, and destructive sampling of rare specimens. In order to more effectively use herbarium material in large sequencing projects, a dependable and scalable method of DNA isolation and library preparation is needed. This paper demonstrates a robust, beginning-to-end protocol for DNA isolation and high-throughput library construction from herbarium specimens that does not require modification for individual samples. This protocol is tailored for low quality dried plant material and takes advantage of existing methods by optimizing tissue grinding, modifying library size selection, and introducing an optional reamplification step for low yield libraries. Reamplification of low yield DNA libraries can rescue samples derived from irreplaceable and potentially valuable herbarium specimens, negating the need for additional destructive sampling and without introducing discernible sequencing bias for common phylogenetic applications. The protocol has been tested on hundreds of grass species, but is expected to be adaptable for use in other plant lineages after verification. This protocol can be limited by extremely degraded DNA, where fragments do not exist in the desired size range, and by secondary metabolites present in some plant material that inhibit clean DNA isolation. Overall, this protocol introduces a fast and comprehensive method that allows for DNA isolation and library preparation of 24 samples in less than 13 h, with only 8 h of active hands-on time with minimal modifications.

This article demonstrates a detailed protocol for DNA isolation and high-throughput sequencing library construction from herbarium material including rescue of exceptionally poor-quality DNA.

Herbaria are an invaluable source of plant material that can be used in a variety of biological studies. The use of herbarium specimens is associated with ...

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Next-generation sequencing (NGS) is quickly revolutionizing how research into the genetic determinants of constitutional disease is performed. The technique is highly efficient with millions of sequencing reads being produced in a short time span and at relatively low cost. Specifically, targeted NGS is able to focus investigations to genomic regions of particular interest based on the disease of study. Not only does this further reduce costs and increase the speed of the process, but it lessens the computational burden that often accompanies NGS. Although targeted NGS is restricted to certain regions of the genome, preventing identification of potential novel loci of interest, it can be an excellent technique when faced with a phenotypically and genetically heterogeneous disease, for which there are previously known genetic associations. Because of the complex nature of the sequencing technique, it is important to closely adhere to protocols and methodologies in order to achieve sequencing reads of high coverage and quality. Further, once sequencing reads are obtained, a sophisticated bioinformatics workflow is utilized to accurately map reads to a reference genome, to call variants, and to ensure the variants pass quality metrics. Variants must also be annotated and curated based on their clinical significance, which can be standardized by applying the American College of Medical Genetics and Genomics Pathogenicity Guidelines. The methods presented herein will display the steps involved in generating and analyzing NGS data from a targeted sequencing panel, using the ONDRISeq neurodegenerative disease panel as a model, to identify variants that may be of clinical significance.

Targeted next-generation sequencing is a time- and cost-efficient approach that is becoming increasingly popular in both disease research and clinical diagnostics. The protocol described here presents the complex workflow required for sequencing and the bioinformatics process used to identify genetic variants that contribute to disease.

Next-generation sequencing (NGS) is quickly revolutionizing how research into the genetic determinants of constitutional disease is performed. The ...

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Conventional next-generation sequencing techniques (NGS) have allowed for immense genomic characterization for over a decade. Specifically, NGS has been used to analyze the spectrum of clonal mutations in malignancy. Though far more efficient than traditional Sanger methods, NGS struggles with identifying rare clonal and subclonal mutations due to its high error rate of ~0.5&#8211;2.0%. Thus, standard NGS has a limit of detection for mutations that are &#62;0.02 variant allele fraction (VAF). While the clinical significance for mutations this rare in patients without known disease remains unclear, patients treated for leukemia have significantly improved outcomes when residual disease is &#60;0.0001 by flow cytometry. In order to mitigate this artefactual background of NGS, numerous methods have been developed. Here we describe a method for Error-corrected DNA and RNA Sequencing (ECS), which involves tagging individual molecules with both a 16 bp random index for error-correction and an 8 bp patient-specific index for multiplexing. Our method can detect and track clonal mutations at variant allele fractions (VAFs) two orders of magnitude lower than the detection limit of NGS and as rare as 0.0001 VAF.

Next-generation sequencing (NGS) is a powerful tool for genomic characterization that is limited by the high error rate of the platform (~0.5&#8211;2.0%). We describe our methods of error-corrected sequencing that allow us to obviate the NGS error rate and detect mutations at variant allele fractions as rare as 0.0001.

Conventional next-generation sequencing techniques (NGS) have allowed for immense genomic characterization for over a decade. Specifically, NGS has been ...

Amplification of Near Full-length HIV-1 Proviruses for Next-Generation Sequencing

The Full-Length Individual Proviral Sequencing (FLIPS) assay is an efficient and high-throughput method designed to amplify and sequence single, near full-length (intact and defective), HIV-1 proviruses. FLIPS allows determination of the genetic composition of integrated HIV-1 within a cell population. Through identifying defects within HIV-1 proviral sequences that arise during reverse transcription, such as large internal deletions, deleterious stop codons/hypermutation, frameshift mutations, and mutations/deletions in cis acting elements required for virion maturation, FLIPS can identify integrated proviruses incapable of replication. The FLIPS assay can be utilized to identify HIV-1 proviruses that lack these defects and are&#160;therefore potentially replication-competent. The FLIPS protocol involves: lysis of HIV-1 infected cells, nested PCR of near full-length HIV-1 proviruses (using primers targeted to the HIV-1 5&#39; and 3&#39; LTR), DNA purification and quantification, library preparation for Next-generation Sequencing (NGS), NGS, de novo assembly of proviral contigs, and a simple process of elimination for identifying replication-competent proviruses. FLIPS provides advantages over traditional methods designed to sequence integrated HIV-1 proviruses, such as single-proviral sequencing. FLIPS amplifies and sequences near full-length proviruses enabling replication competency to be determined, and also uses fewer amplification primers, preventing the consequences of primer mismatches. FLIPS is a useful tool for understanding the genetic landscape of integrated HIV-1 proviruses, especially within the latent reservoir, however, its utilization can extend to any application in which the genetic composition of integrated HIV-1 is required.

Full-length individual proviral sequencing (FLIPS) provides an efficient and high-throughput method for the amplification and sequencing of single, near full-length (intact and defective) HIV-1 proviruses and allows for determination of their potential replication-competency. FLIPS overcomes limitations of previous assays designed to sequence the latent HIV-1 reservoir.

The Full-Length Individual Proviral Sequencing (FLIPS) assay is an efficient and high-throughput method designed to amplify and sequence single, near ...

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture (4C-seq)

The identification of regulatory elements for a given target gene poses a significant technical challenge owing to the variability in the positioning and effect sizes of regulatory elements to a target gene. Some progress has been made with the bioinformatic prediction of the existence and function of proximal epigenetic modifications associated with activated gene expression using conserved transcription factor binding sites. Chromatin conformation capture studies have revolutionized our ability to discover physical chromatin contacts between sequences and even within an entire genome. Circular chromatin conformation capture coupled with next-generation sequencing (4C-seq), in particular, is designed to discover all possible physical chromatin interactions for a given sequence of interest (viewpoint), such as a target gene or a regulatory enhancer. Current 4C-seq strategies directly sequence from within the viewpoint but require numerous and diverse viewpoints to be simultaneously sequenced to avoid the technical challenges of uniform base calling (imaging) with next generation sequencing platforms. This volume of experiments may not be practical for many laboratories. Here, we report a modified approach to the 4C-seq protocol that incorporates both an additional restriction enzyme digest and qPCR-based amplification steps that are designed to facilitate a greater capture of diverse sequence reads and mitigate the potential for PCR bias, respectively. Our modified 4C method is amenable to the standard molecular biology lab for assessing chromatin architecture.

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture 4C-seq

The identification of physical interactions between genes and regulatory elements is challenging but has been facilitated by chromosome conformation capture methods. This modification to the 4C-seq protocol mitigates PCR bias by minimizing over-amplification of PCR templates and maximizes the mappability of reads by incorporating an addition restriction enzyme digest step.

The identification of regulatory elements for a given target gene poses a significant technical challenge owing to the variability in the positioning and ...

RNA Next-Generation Sequencing and a Bioinformatics Pipeline to Identify Expressed LINE-1s at the Locus-Specific Level

Long INterspersed Elements-1 (LINEs/L1s) are repetitive elements that can copy and randomly insert in the genome resulting in genomic instability and mutagenesis. Understanding the expression patterns of L1 loci at the individual level will lend to the understanding of the biology of this mutagenic element. This autonomous element makes up a significant portion of the human genome with over 500,000 copies, though 99% are truncated and defective. However, their abundance and dominant number of defective copies make it challenging to identify authentically expressed L1s from L1-related sequences expressed as part of other genes. It is also challenging to identify which specific L1 locus is expressed due to the repetitive nature of the elements. Overcoming these challenges, we present an RNA-Seq bioinformatic approach to identify L1 expression at the locus specific level. In summary, we collect cytoplasmic RNA, select for polyadenylated transcripts, and utilize strand-specific RNA-Seq analyses to uniquely map reads to L1 loci in the human reference genome. We visually curate each L1 locus with uniquely mapped reads to confirm transcription from its own promoter and adjust mapped transcript reads to account for mappability of each individual L1 locus. This approach was applied to a prostate tumor cell line, DU145, to demonstrate the ability of this protocol to detect expression from a small number of the full-length L1 elements.

Here, we present a bioinformatic approach and analyses to identify LINE-1 expression at the locus specific level.

Long INterspersed Elements-1 (LINEs/L1s) are repetitive elements that can copy and randomly insert in the genome resulting in genomic instability and ...