Adequate comparisons of DNA and cDNA libraries from complex environments require methods for co-extraction of DNA and RNA due to the inherent heterogeneity of such samples, or risk bias caused by variations in lysis and extraction efficiencies. Still, there are few methods and kits allowing simultaneous extraction of DNA and RNA from the same sample, and the existing ones generally require optimization. The proprietary nature of kit components, however, makes modifications of individual steps in the manufacturer’s recommended procedure difficult. Surprisingly, enzymatic treatments are often performed before purification procedures are complete, which we have identified here as a major problem when seeking efficient genomic DNA removal from RNA extracts. Here, we tested several DNA/RNA co-extraction commercial kits on inhibitor-rich soils, and compared them to a commonly used phenol-chloroform co-extraction method. Since none of the kits/methods co-extracted high-quality nucleic acid material, we optimized the extraction workflow by introducing small but important improvements. In particular, we illustrate the need for extensive purification prior to all enzymatic procedures, with special focus on the DNase digestion step in RNA extraction. These adjustments led to the removal of enzymatic inhibition in RNA extracts and made it possible to reduce genomic DNA to below detectable levels as determined by quantitative PCR. Notably, we confirmed that DNase digestion may not be uniform in replicate extraction reactions, thus the analysis of “representative samples” is insufficient. The modular nature of our workflow protocol allows optimization of individual steps. It also increases focus on additional purification procedures prior to enzymatic processes, in particular DNases, yielding genomic DNA-free RNA extracts suitable for metatranscriptomic analysis. With the advent of the meta-omics era, it has become increasingly commonplace to aim for metagenomic/metatranscriptomic analyses of environmental samples. Despite advances in the sequencing front, upstream methods required to obtain the high quality DNA and RNA needed for these analyses have fallen behind and there is often a need to optimize existing methods when applying them to a new sample type. The choice of extraction method affects the ensuing purity and yield of nucleic acid material, which in turn affects subsequent downstream processes. This calls for rapid and simple extraction and/or purification methods that yield high quality and quantities of nucleic acids. However, this is but a pipe dream in many cases, due to the presence of “inhibitory compounds.” These well-known, yet poorly understood compounds are ubiquitous to most environments. They are abundant in most soils and are often classified under the blanket term of “humic and fulvic compounds, and/or polyphenolic compounds” , yet thePresently available methods can be divided into two: those that co-extract both DNA and RNA from single reactions, and those that extract DNA and RNA from separate reactions. While extracting nucleic acids separately is markedly simpler, with a wider variety of highly optimized kits and methods available, single reaction DNA/RNA co-extractions offer the benefit of more comparable data, especially from highly heterogeneous samples such as soils. This has spawned a multitude of novel methods and kits from independent researchers  and largMetatranscriptomic analyses require high quality RNA that is free of inhibitors and genomic DNA (gDNA). The presence of inhibitors greatly affects RNA high throughput sequencing due to the relatively large quantities of RNA required. Unlike DNA-based analyses, where “diluting out the inhibitor effect” is always an option, metatranscriptomic analyses often require concentrating samples in order to achieve sufficient material for the sequencing process, thus further exacerbating the inhibitory effect. Even if we ignored any effect the inhibitory compounds may have on the RNA extraction and DNA removal process, this need to concentrate samples makes inhibitor removal an extremely important step in RNA analysis. Thus, there is a consistent necessity to optimize existing methods and/or kits to suit one’s needs. Although commercial kits have the potential to yield high quality nucleic acids, the proprietary nature of kit components make it difficult for optimization or up-scaling. Such changes to the extraction procedure or increased sample volumes may be necessary for samples with low biomass and/or activity, containing little mRNA, when metatranscriptomic analysis is sought after.The present study aimed to identify and overcome key problematic steps during the co-extraction of high quality DNA and RNA from inhibitor-rich soil samples for the purposes of meta-omic analyses. The efficacy of commercially available nucleic acid extraction kits were tested, and the nucleic acid extracts’ yield and purity were compared to the extracts obtained using the method by 3 of shell sand per hectare of soil (2 (1:5 (ww to volume) soil to CaCl2 solution) immediately prior to using the soil. Soils FH and FL were used in the testing of all kits and methods, and soil Å was only used as a comparison for kits/methods that showed at least some success with the other two soils.Three agricultural soils, chosen because of their extraction difficulty with commercial kits and non-kit methods , were us of soil . Soil Å In the present study we targeted denitrification gene transcripts to evaluate methods for DNA/mRNA isolation. Several successive experiments were performed where different extraction kits/methods were tested (see below). Using field-fresh soil for each of these would introduce undesired variation, due to seasonal differences in the soil. Instead, to achieve the best possible comparison of extraction methods, all soils used in this study were sampled at the same time and kept at 4°C until use .-1 soil wet weight (ww), amended with 8–11 mM nitrate (in soil moisture), then incubated at 15°C for 72 h. Soil Å was used in a separate experiment  and was exposed to different lengths of oxic and anoxic periods over 4 weeks in glass vials incubated at 15°C. During this incubation, clover ) and nitrate  was added every 2–5 days  to maintain microbial activity.A small amount of a natural C source was added, to standardize the conditions and to secure that the organisms would have enough energy to induce transcription of the targeted denitrification genes . Soils F2, O2, NO, N2O, and N2) produced in the headspace were measured every 3 h with a GC and NO analyzer (2O gas to N2 gas was taken as an indicator for nitrous oxide reductase gene (nosZ) transcription. For each sample, one vial was opened and the soil within was snap frozen in liquid nitrogen then stored at -80°C until nucleic acid extraction.At the end of the 72 h (FH and FL) or 4 weeks (Å) incubation, the soils were transferred to air-tight glass vials and sealed with butyl-rubber septa and aluminum crimps, then made anoxic by six cycles of gas evacuation and helium filling . These vanalyzer , and useFigure 1 presents a schematic diagram of the different key steps examined to obtain an optimized protocol for co-extraction of DNA and RNA from soil. Our criteria for the successful application of a kit or method was the ability to obtain high quality DNA and RNA (both rRNA and mRNA) from our samples. Quality was assessed as follows: (1) DNA extracts should be amplifiable with little or no inhibition, as judged by successful PCR amplification and comparable qPCR efficiency to plasmid standards; and (2) RNA extracts must be free of gDNA , and should yield positive results when reverse transcribed and assessed with qPCR. Three DNA- and three TNA extraction kits were tested for their ability to extract nucleic acids that are suitable for downstream processes, according to manufacturer’s instructions (Table 1). In the present paper, the RNA PowerSoil kits are considered one kit because the DNA Accessory Kit (AK) cannot be used separately. Where applicable, lysis was achieved by bead-beating as described below.The PowerLyzer DNA (PL), FastDNA SPIN (FDS), and ZR Soil (SM) kits were used as benchmark DNA extractions because of their previous success in our laboratory with soil FH and in the literature in extracting DNA from soil and other environmental samples . The resThe lysis step of Nicolaisen’s method was optimized by testing different lysis options (FastPrep-24 Instrument vs. vortex), lysis beads type (garnet vs. glass), one size  vs. multiple bead sizes , and the number of cycles of lysis . Different buffers for the lysis of bacteria were also tested: CTAB (hexadecyltrimethylammonium bromide) buffer  with 1% (w/v) polyvinylpolypyrrolidone (PVPP); GES (guanidinium thiocyanate-EDTA-sarcosyl) buffer (pH 4); and phenol (pH 4 or 8)  were tested in various combinations on the primary extracts: MinElute Reaction Cleanup Kit (MRC), RNeasy Mini Kit (RM) (both from QIAGEN), E.Z.N.A. Cycle Pure Kit (CP) (Omega Bio-Tek), Genomic DNA Clean & Concentrator (gDCC), RNA Clean & Concentrator-5 (RCC) and OneStep PCR Inhibitor Removal Kit (OPIR) .In the following, the term “primary” when used to describe nucleic acids refers to the resuspended or eluted nucleic acids obtained from the extraction procedure or kit, and is equivalent to “Extract I” in free DNase Kit . All DNases were used according to manufacturers’ instructions, with the exception of incubation time, which we varied from 15 min to 2 h. The efficiency of each DNase treatment was determined by comparing the purified DNA fractions  with the non-reverse transcribed RNA , via quantitative PCR (qPCR) amplification of the 16S rRNA or the nosZ genes (details below).Based on our previous experience , residuanosZ cDNA in the absence of gDNA. Reverse transcriptase efficiency was not assessed in this study. The following reverse transcriptases were tested according to manufacturers’ instructions: High Capacity RNA-to-cDNA Master Mix (Applied Biosystems), SuperScript VILO MasterMix (Invitrogen), PrimeScript RT Reagent Kit (Takara Bio), and Maxima Reverse Transcriptase (Thermo Scientific). Random hexamer primers and dNTPs  were used with all reverse transcriptases. To improve the rate of successful nosZ transcript reverse transcription (present in low quantities in the samples compared to 16S rRNA), the maximum volume of RNA template  was used in each reaction. Due to the comparatively low quantities of RNA in the extracts (compared to pure culture RNA extractions), the quantity of RNA in these volumes never exceeded the manufacturers’ recommended maximum quantity of RNA template . Additionally, the differing template quantities/volumes used in this study did not affect the failure or success of cDNA synthesis, as determined by the absence or presence of amplifiable nosZ cDNA (see Test of DNases and Reverse Transcriptases).Several reverse transcriptases were compared using RNA extracts obtained from soils FL and FH during the iterative method optimization. The purpose was to ensure successful cDNA synthesis in extraction replicates from inhibitor-rich soils. Because trials with RNA extracts from Nicolaisen’s method and the extraction kits were not able to yield cDNA , and phenol-chloroform-isoamyl alcohol (25:24:1), and the nucleic acids were washed with ethanol then precipitated. The following are the differences to Nicolaisen’s method: (i) Three sizes of glass beads were used for lysis ; (ii) the samples were lysed in a FastPrep-24 Instrument by two cycles at 6.0 m s-1 for 45 s, with intermittent cooling between each cycle to prevent overheating of the samples and instrument; (iii) after removing residual phenol with chloroform, up to 500 μL of the aqueous phase was transferred; (iv) the nucleic acids (both DNA and RNA) were precipitated with 0.2 volumes of 3 M sodium acetate  and an equal volume of isopropanol, then continuously inverted for 2 min at room temperature; and (v) the ethanol-washed TNA pellet was dried in a SpeedVac Concentrator then resuspended in DEPC-treated nuclease-free water.Based on the results from the above tests  was purified with the OPIR kit, according to manufacturer’s instructions. Extract II  was then divided in two fractions, one for DNA and one for RNA. To ensure maximum removal of inhibitory compounds, the DNA fraction was further purified with the gDCC kit. For the RNA fraction, gDNA was removed with the TURBO DNase kit, before purification with the RCC kit. If residual gDNA was detected in the eluate (via qPCR using primers targeting the 16S rRNA or nosZ genes), a second round of DNase digestion and purification with the RCC kit was performed (but without the OPIR kit prior to digestion). Additional use of OPIR prior to the second digestion did not improve RNA purity, but instead resulted in the loss of material (data not shown). The qPCR-certified gDNA-free RNA was then reverse transcribed to cDNA with random hexamers using the Maxima Reverse Transcriptase, both according to manufacturer’s instructions. All resulting nucleic acids  were quantified after extraction and/or purification (see below), then stored at -80°C until use. This procedure of ‘purification before enzymatic processes’ was also used on primary extracts from the most effective extraction kit, RNA Powersoil kit (PS), to ensure high quality RNA for sequencing (see Results section).After this primary extraction, and before any further enzymatic downstream treatment, the resuspended TNA  were quantified by spectrofluorometry using the Qubit dsDNA BR Assay Kit and Qubit RNA BR Assay Kit . Spectrophotometric analysis  was used for preliminary evaluation of nucleic acid quality, via the assessment of the absorbance ratios A260/230 and A260/280. As is common practice, A260/230 absorbance ratios nearing 2.0 were regarded as contaminated with humic substances, whereas ratios below 1.5 were regarded as failure to extract nucleic acids  were used without dilution. For PCR, each 25 μL amplification reaction contained 1 μL of template, 0.4 μM of each primer, 0.125 U of TaKaRa Taq (Takara Bio), 400 μM of each dNTP and 2.5 μL of 10X PCR Buffer. The primers used were: 27F and 518R for the 16S rRNA gene , or 60°C (narG and nosZ gene). For qPCR the StepOnePlus Real-Time PCR System (Applied Biosystems) was used. All samples were amplified in simultaneous reactions to compare the DNase digestion and reverse transcription efficiency. Each 20 μL reaction contained SYBR Premix Ex Taq II  used according to manufacturer’s instructions, and included 0.4 μM of each primer and 2 μL of template. The qPCR cycling conditions for all primer sets were the same as above, with the following exceptions: an additional 20 s at 82°C at the end of each cycle to measure the fluorescent signal, thereby reducing background signals from primer dimers and unspecific PCR products; the extension time for the primers targeting the nosZ gene was prolonged to 60 s; a final melting curve analysis from 60 to 95°C was performed to determine the specificity of amplicons, in lieu of the final extension step; and the amplification reactions were performed for 40 cycles. The detection limit of each qPCR run was five copies per microliter of reaction, which ranged from 4 × 102 to 4 × 105 copies g-1 soil (ww).To confirm amplifiability of extracted DNA and synthesized cDNA, and the complete digestion of gDNA in RNA samples, the presence of the 16S rRNA, RNA gene , 1960f aarG gene , and Z-FosZ gene . The optThe raw qPCR fluorescence data was imported into the LinRegPCR program . Unlike Multiple samples of DNA and RNA extracted from all three soils using our revised extraction method, and PS kit-extracted (and further purified as described in the simplified extraction method) soil Å RNA extracts, were sent for metagenomic and metatranscriptomic sequencing at The Roy J. Carver Biotechnology Center (CBC)/W. M. Keck Center for Comparative and Functional Genomics at the University of Illinois at Urbana-Champaign, using HiSeq 2500 technology. Prior to shipping on liquid nitrogen vapor (Cryoport), we confirmed that all nucleic acids were of high quality (DNA or gDNA-free RNA as verified by qPCR). Independent verification of the RNA quality, including confirmation of the absence of gDNA, was also performed at the CBC. A sample of the sequenced reads from soil FH and FL were trimmed for adaptors and quality using Trimmomatic  . The triTable 2. For a comparison of all kits tested, see Supplementary Table -1 soil (ww) and amplifiable cDNA (16S rRNA) in the absence of amplifiable gDNA. The long extraction time required at room temperature may potentially compromise the quality and quantity of extracted mRNA, which puts any absence or low mRNA copy numbers in doubt. The only available option provided by the manufacturer was the application of positive pressure to the top of the column. Unfortunately, the outcome varied between soil types Å and FL: High quality rRNA and mRNA was obtained from soil Å, although a supplementary two rounds of ‘purification-digestion-purification’ was required . In contrast, for soil FL, positive pressure application co-extracted such large quantities of inhibitory compounds that both the extracted DNA (eluted with the AK kit) and RNA remained brown (suggesting a high content of organic compounds) and was unusable in downstream processes in spite of attempted clean-up with additional purification kits. Moreover, the extracts were not reliably quantifiable prior to further purification . NanoDrop quality assessments revealed highly variable A260/280 ratio ranges that failed to improve with additional purification: 1.41–1.58 for the DNA eluate and 1.34–1.79 for the RNA eluate  are compared to the unmodified Nicolaisen’s method in Figure 1), various purification kits (listed in Table 1) were tested on FH and FL extracts from the best extraction kits (listed in Table 2) and our optimized version of Nicolaisen’s method . Regardless of method or kit used for the extraction, the DNA yielded from both FH and FL in Extract I  was amplifiable, but the results were variable in consistency and strength  when the same quantity of pre-purification DNA was used, independent of primers used  were removed by purification with DNA clean-up kits .These further purification steps, regardless of the purification kit used, greatly improved the purity of DNA extracts. For example, purification of FL extracts with gDCC improved the For RNA, on the other hand, the quality of the extracts varied, as seen from differences in residual gDNA for soils FH and FL below. We were able to obtain gDNA-free RNA from soil FH , although DNase digestion was always required to remove the residual gDNA, regardless of kit or method used . These RNA extracts from soil FH were successfully reverse transcribed, as judged from the amplification of the resulting cDNA using qPCR (detected after ≤35 cycles). In contrast, RNA extracts from soil FL often contained qPCR-amplifiable gDNA (detected after ≤35 cycles) that was not removable even after repeated rounds of extended DNase digestion (1–2 h) and RNA clean-up kit purification (regardless of purification kit used). There was often residual gDNA in these primary extracts even after a second digestion or, in cases where gDNA was completely digested (in the qPCR), the RNA in the sample was no longer detectable .Figure 1), we observed that enzymatic issues in the RNA fraction  coincided with Taq polymerase inhibition in the DNA fraction , we used the OPIR kit, a TNA purification kit that specializes in inhibitor removal, on the primary TNA Extract I  prior to any enzymatic process (including DNase digestion). In addition to improved DNA quality, we observed little loss of nucleic acid material. For example, purification of 3–4 μg of DNA g-1 soil (ww) resulted in 2.5–3.5 μg using OPIR (compared to 2–2.3 μg using gDCC), and the Extract II  DNA was as equally amplifiable as Extract III  DNA purified with dedicated DNA purification kits, confirming the removal of Taq polymerase inhibitors. The improved TNA quality was also observed by enhanced DNase digestion. A single, non-extended digestion using the TURBO DNase kit (see below), performed according to manufacturer’s instructions, reduced the quantity of residual gDNA in the digested RNA Extracts V  from FH and FL soils to below the limit of PCR and qPCR detection .During the first two stages of optimization , OPIR kit purified, inhibitor-free extracts from all three soil types were used to test different DNases (Extract II) and reverse transcriptases (Extract V). Of the DNases tested, TURBO DNase was the most active at 2 Units μL-1 (as described in the respective product information sheets), and was also the most efficient at removing gDNA from samples even in the presence of low quantities of inhibitors . Coupling this DNase with the OPIR kit made a potent combination for alleviating the inhibitory effect, thus digesting more gDNA in the TNA extracts.In the second part of Stage 3 optimization . The soil had been exposed to different oxygen regimes, and incubated anoxically for different time periods , but these treatments did not affect the copy numbers of nosZ in the gDNA content of the samples . Although residual gDNA persisted in some samples from soil Å after the first DNase digestion (Extract IV), purification with an RNA purification kit  followed by a second DNase digestion often completely removed the remaining gDNA in Extract V . The first digestion ensured that any RNA clean-up kit used  did not become overloaded by the large quantities of extracted gDNA, which would result in the loss of RNA. Using qPCR on these RNA extracts, we showed that two rounds of DNase digestion reduced the number of nosZ gene copies to below the qPCR detection limit ) for all samples . This is compared to a single DNase digestion, where only 6 of 45 samples had undetectable quantities of nosZ DNA, and the residual gDNA in the remaining samples was 0.002 ± 0.002% of the original. Although these percentage numbers are small, they translate to a residual gDNA of between 900 and 60 000 copies of nosZ genes g-1 soil (ww). Notably, the soil samples retained different quantities of residual gDNA in RNA fractions despite identical extraction procedures, as indicated by qPCR . This differed from the DNA fractions that contained equally amplifiable and relatively similar quantities of gDNA in replicate extractions .To investigate the reproducibility of gDNA removal, we quantified the nosZ cDNA was always undetectable in partially purified RNA, and consistently detectable in high-quality RNA, regardless of the reverse transcriptase used. In this study, Maxima Reverse Transcriptase was chosen for use with the optimized method because it had the highest capacity and was thus the least likely to be overloaded by the total RNA in each sample . Thus, for the optimized method, we used a combination of the OPIR and RCC purification kits and TURBO DNase to obtain high-quality RNA extracts prior to cDNA synthesis with the Maxima Reverse Transcriptase.Using these high quality gDNA-free RNA extracts for reverse transcription, there was no observable difference in the cDNA synthesis success rate between the reverse transcriptases tested – A260/280 and A260/230 ratios before purification (Extract I) were 1.84 and 1.66, respectively, and the crude extracted quantities were 50–150 μg DNA g-1 soil (ww) and 15–18 μg RNA g-1 soil (ww). Analysis by agarose gel electrophoresis revealed reproducible TNA extraction, with large quantities of extracted rRNA that was clearly visible on the gel  contained no detectable copies of gDNA. Average 16S rRNA copies were reduced from 1.08 × 1011 ± 3.32 × 1010 (soil FH) and 3.15 × 1010 ± 1.19 × 1010 (soil FL) copies g-1 soil (ww) to below the detection limit of qPCR  in RNA extracts. The RNA extracts were also successfully reverse transcribed to cDNA, and qPCR-amplifiable with primers targeting the nosZ gene .Using qPCR analysis and primers targeting the 16S rRNA and Table 3), confirming the absence of amplification or dsDNA-binding dye inhibitors in all our amplification reactions. Although these individual amplification efficiencies appear to be low, similar efficiencies seen in the standards indicate that the lower-than-expected efficiencies are likely an effect of poor primer-template matches or the formation of primer dimers affecting the amplification reaction, rather than the presence of inhibitory compounds. For comparison to other studies, the calibration plot-based method of efficiency calculation yields amplification efficiencies of 95.1 and 99.1% for the 16S rRNA and nosZ genes, respectively.Analysis of the raw qPCR fluorescence data using LinRegPCR revealed similar efficiencies for both the samples and the purified plasmid standards (Table 4). There was minor variation for some genes in the RNA duplicates , but the reproducibility for the other genes points toward variability in fusA gene expression due to incubation conditions, rather than an extraction bias. Together, the sequenced metagenomes and metatranscriptomes give evidence to the reproducibility of DNA and RNA co-extraction using the optimized method.DNA and RNA (Extracts III and V) yielded by our simplified TNA extraction method  and RNA (Extract V) from the PS kit (soil Å) , were sent for Illumina HiSeq sequencing at the CBC. All samples were independently verified to be of high quality: RNA extracts were confirmed to be free of gDNA, and both DNA and RNA were successfully sequenced with HiSeq 2500 technology. The resulting sequences were annotated using MG-RAST, and a summary of the annotated data has been included in the Supplementary Table Table 2). Although the PS kit was able to yield usable nucleic acids, varying quantities were extracted from equal starting amounts of a single soil type . Considering the inherent variations in the soil, methods yielding poor replication will only further complicate matters and lead to erroneous conclusions and hypotheses. Previous studies comparing multiple methods have also concluded that extraction methods may substantially affect any downstream data . While the DNA extraction kits fared well, none of the RNA extraction kits tested worked for all our soil samples. Even the best kit tested, the PS kit, only worked for soil Å and FH, but not for soil FL  aims to remove the problems upstream, thereby circumventing downstream problems and avoiding the struggle with persistent residual gDNA or otherwise poor quality nucleic acids. In the current study, we have chosen relative ease and speed over cost, and have opted to use commercial purification kits for each purification step. But, as suggested in our data and indicated in Figure 2, it is not the purification kit that determines the usability of the material downstream, but the point during extraction at which the purification step takes place – as early as possible and before enzymatic processes, but without compromising RNA stability. As such, the use of similar purification kits or methods  would achieve the same effect, and at a reduced cost. Similarly, the core of our suggested workflow is designed for gene expression analyses, and the restriction of total sample processing time  played a big role in the creation of our proposed workflow . Thus, our workflow reflects time-limited sample processing that is incompatible with early purification procedures that require pre-optimization, such as the addition of Al2(SO4)3 to remove inhibitors prior to soil disruption (We took the study by sruption .-1 soil (ww) and 2.3–7.2 μg RNA g-1 soil (ww). Additionally, nosZ transcripts that were previously only quantifiable in soil FH  but completely undetectable in soil FL  sample lysis, (2) phenol-chloroform purification, and (3) nucleic acid precipitation . These p soil FL , were nonosZ transcripts in soil FL, could be that the higher extraction efficiency of the optimized method provided a “deeper” transcript profile. The nucleic acid yield of the optimized method presented here was ≈ 10 times that of the unmodified Nicolaisen’s method  are adjusted to correspond with a 10 times lower efficiency , it is still well above than the detection limit of 8.4 × 103 copies g-1 soil (ww) of nosZ transcript detection in soil FL. Furthermore, while the quality and quantity of RNA from soil FL yielded by Nicolaisen’s method was previously too poor for sequencing . We hypothesized that many commercial extraction kits failed to yield gDNA-free RNA from the inhibitor-rich soil FL, because DNase is often applied to the primary TNA extract (Extract I) before purification. The aforementioned use of the OPIR kit to purify primary TNA extracts prior to all enzymatic processes was the major breakthrough in the optimization and simplification of the extraction process. By using a specialized method to remove inhibitory compounds prior to DNase digestion, digestion efficiencies were greatly improved and the procedure was shortened significantly. In contrast, the relatively common practice of attempting to remove gDNA without purification via prolonged incubations at non-ideal RNA preservation temperatures potentially compromised the extracted RNA. Thus, it is our recommendation to purify samples prior to the digestion of gDNA to ensure maximal efficiency and speed.If commercial kits are used for purification prior to DNase digestion, two important factors must be considered: (1) Whether or not the purification kit is RNase-free, and (2) The maximum nucleic acid holding capacity of the kit, especially for column-based purification kits. Unfortunately, DNA purification kits have higher load capacities but are not always RNase-free , and the load capacities of the RNA purification kit columns tested were too low to capture all extracted nucleic acids . Using these potentially RNase-contaminated DNA purification kits could result in RNA digestion, whereas the RNA kits would be severely overloaded by DNA from the TNA sample. On the other hand, our kit trials revealed that the dedicated RNA purification kits are more capable of removing inhibitors than the TNA purification kit, and their use to remove residual inhibitors prior to reverse transcription was irreplaceable. Hence, while it is critical for TNA extracts (Extract II) to be purified prior to digestion, it is also essential to purify the digested extracts (Extract IV) with dedicated RNA kits to obtain high quality RNA extracts.Figure 3). There was no correlation between the quantity of residual gDNA and the starting gDNA quantities (R2 = 0.0189). The reason behind this is unclear, but uneven spread of inhibitors creates non-uniform DNase digestion of otherwise identical samples. The presence of samples with residual gDNA alongside those with no amplifiable gDNA highlights the importance of checking all samples for the presence of DNA and not only “representative samples.” Such use of “representative samples” to extrapolate the lack of contaminating residual gDNA in all RNA samples may potentially introduce severe biases with respect to the quantification and sequencing of mRNA.Using our optimized extraction and purification method, both DNA and RNA fractions were used as templates in qPCR reactions with primers targeting the 16S rRNA gene to determine the quantity and amplifiability of gDNA  or Nanodrop/Qubit quantification, was not considered sufficient evidence of samples free of amplifiable gDNA because neither is sufficiently sensitive to detect trace quantities of gDNA. Among papers published in Applied and Environmental Microbiology in 2012, 2013, and 2014, only 36, 31, and 13% clearly indicated the lack of gDNA in their RNA extracts according to our definition. This problem is not isolated to one journal, as papers published in 2014 in ISME Journal showed a similar trend, with only 37% of papers clearly addressing the residual gDNA question in RNA extracts. While more papers published in 2015 in Applied and Environmental Microbiology (47%) clearly indicated DNA-free RNA samples, the rest still either provided insufficient evidence, or failed to report that the samples had been quality-controlled prior to further downstream analysis.Figure 3), even though gel visualization (not shown) failed to reveal the presence of gDNA in the purified RNA fraction. Additionally, using either spectrofluoro- or spectrophotometric methods to quantify residual gDNA relies heavily on exceeding minimum detection limits, as well as the assumption that the fluorophores have not been otherwise inhibited , and the residual gDNA may vary between samples and replicates. Thus, we strongly recommend the examination of all samples for residual gDNA and not only “representative samples.” Furthermore, we propose the use of the more sensitive qPCR method as an indicator of residual gDNA, rather than less sensitive methods such as electrophoretic analysis of unamplified nucleic acid extracts.As is known from other studies and indicated in All authors contributed to the planning of the work and the revision of the manuscript. In addition, NL and CR performed the experimental work detailed in this paper. NL performed data analysis and the drafting of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Ribonucleic acid (RNA) is widely used in molecular biology assays, and some of the most common assays include: northern blotting and RT-PCR gene expression analysis. RNA is generally extracted by two methods: phenol-chloroform or commercially available silica spin column kits. Phenol-chloroform extraction is generally more economical; however, it produces hazardous byproducts, and leftover chemicals in the sample that can inhibit downstream applications. Commercial kits usually have simple set ups and short preparation time; however, they can introduce a significant expense to laboratory budgets. Here we have created a method to extract RNA using generic silica columns and readily available reagents while maintaining a high yield and purity. Ribonucleic acid (RNA) is a ribose based nucleic acid that uses guanine, uracil, adenine, and cytosine as nitrogenous bases. RNA is found throughout the cell exhibiting functions that range from a template for protein synthesis  to catal2. After 48 h of growth, the cells were trypsinized, counted and prepared for RNA extraction.GMMe cells obtained from the American Type Culture Collection  were grown in Dulbecco’s Modified Eagle Medium/Nutrient Mixture F-12  with 10% FBS  at 37°C and 5% COTable 1. Buffer A was composed of 4 M guanidine thiocyanate , 10 mM 2-(N-morpholino)ethanesulfonic acid  pH 5.5, and 1% β-mercaptoethanol . β-M was added immediately before starting the extraction. Buffer B was composed of 1 M GITC, 10 mM tris(hydroxymethyl)aminomethane  pH 7. Buffer C was composed of 80% Ethanol , and 10 mM Tris buffer pH 7.Buffer composition is summarized in 2. After incubation, the cell mixture was transferred into a new 50 ml centrifuge tube  and centrifuged at 1600 G for 6 min at 25°C. The supernatant was carefully removed and the subsequent cell pellet was re-suspended with 3 ml of DMEM/F12 with 10% FBS. A 10 µl aliquot sample of cell mixture was then retrieved and mixed with 10 µl of trypan blue . The cell-trypan blue mixture was then loaded into a Countess II FL Automated Cell Counter  and a cell count was calculated. The cell mixture was then aliquoted into new 1.5 ml centrifuge tubes containing approximately 4.5 × 105 cells per tube. Tubes containing cells were then divided into three extraction procedures that consisted of the proposed silica column procedure, an RNeasy mini kit  and a GenCatch total RNA miniprep kit .Cell culture media was carefully removed from flasks (T-160) and the cells were washed in 15 ml phosphate-buffered saline (PBS). Cells were then incubated for 10 min with 4 ml of trypsin  and 12 ml of PBS at 37°C, and 5% CO−80°C.Both kit procedures followed the manufacturer’s instructions with no modification and the subsequent RNA obtained was stored at −80°C. For the silica column procedure, cell culture media was carefully removed and the remaining cells were washed in 1 ml PBS. Following the washing and removal of PBS, 300 µl of Buffer A was added to each well with cells. Cells were then incubated with Buffer A for 15 min. After incubation, 300 µl of 70% ethanol was added to each well and the mixture was added to a silica column  and centrifuged  at 8000 G for 30 s. The resultant flow through was then discarded. The column was then washed again by the addition of 600 µl of Buffer B followed by centrifugation at 8000 G for 30 s. The resultant flow through was again discarded. The column was then washed twice with 500 µl of Buffer C followed each time by centrifugation at 8000 G for 30 s and the flow through was discarded. Following the washing steps, the column was centrifuged again for 2 min and then placed in a new 1.5 ml centrifuge tube . The RNA was captured by adding 30 µl of RNase free Mili-Q water to the column followed by centrifugation at 8000 G for 30 s. The solution containing the RNA was treated with DNase  by adding 1 µl of 10 × reaction buffer and 1 µl of DNase. The reaction volume was then adjusted to 10 µl with RNase-free water and then incubated at 37°C for 30 min after which 1 µl of 50 mM ethylenediaminetetraacetic acid (EDTA) was added. Following the addition of EDTA the reaction was incubated at 65°C for 10 min and then stored at −80°C. For the silica column procedure, approximately 30 mg of tissue was placed in 2 ml centrifuge tubes  followed by the addition of 600 µl of Buffer A. Tissue was disrupted using a homogenizer . The resultant mixture was centrifuged at 8000 G for 3 min and the supernatant was transferred to a new 2 ml centrifuge tube and one volume of 70% ethanol was added. The mixture was then added to a silica column and centrifuged at 8000 G for 30 s. The resultant flow through was discarded. The column was then washed with 600 µl of Buffer B followed by centrifugation at 8000 G for 30 s and the flow through was discarded. The column was then washed twice with 500 µl of Buffer C followed each time by centrifugation at 8000 G for 30 s and flow through was discarded. To clear any remaining buffer, the column was centrifuged for 2 min and then placed in a new 1.5 ml centrifuge tube. The RNA was captured by adding 30 µl of RNase free Mili-Q water to the column followed by centrifugation at 8000 G for 30 s. The solution containing the RNA was treated with DNase and then stored at −80°C. Additionally, to test the efficacy of the silica column against varying amounts of tissue, we performed incremental extractions from starting tissue samples that ranged from 10 mg to 60 mg .Rat (Rattus norvegicus) liver tissue was donated by Clair Eckersell (Brigham Young University - Idaho). Identical to the cell RNA extraction protocols, both kit procedures followed the manufacturer’s instructions using 30 mg of tissue with no modification and the subsequent RNA obtained was stored at ® 2.0), and using spectrometer . RNA fragment was analyzed using a fragment analyzer  and the results were analyzed by fragment analysis software .RNA was sent to Idaho State University’s Molecular Research Core Facility for the quantification and the fragment analysis. RNA was quantified using an RNA fluorescence assay  following the manufacturer’s instructions, with a fluorometer . The RNA fragment analyzer scores RNA integrity with a unit called the RNA quality number (RQN). Although spectroscopy can be used to determine the concentration and purity of RNA it lacks the power to determine the integrity of the RNA. The RQN can estimate the integrity of the RNA using a proprietary algorithm that uses the area before the 18 s peak, the total area of the 18 s and 28 s peaks, and the ratio of the 28 s and 18 s peaks as obtained from the fragment analyzer graph. The RQN has been shown to correlate to the RNA integrity number (RIN) [Following RNA fragment analysis , we found that our high efficiency and low cost extraction technique for RNA from both cells and tissue showed little to no signs of degradation (er (RIN) . The RINer (RIN) .Table 2) we hypothesize that this difference in concentrations was the result of the variation in the number of cells obtained from cell culture vs the amount of cells used for the procedure or variability in the tissue samples. Our average cell count was around 4 × 105 cells, consistent with the average 12 well plate which has around 4 × 105 cells [6 cells [Although the cell samples showed a significantly smaller concentration of RNA in comparison to the tissue , anti-foam agents  or transcription inhibitors (like sodium lauroyl sarcosinate). However, in order to keep the method simple and cost effective, these reagents were omitted for a later study.Fig. 3 and 4). These results support the claim that the proposed protocol can extract RNA at the same quality and level as the more expensive commercial kits. Although the silica column procedure was effective in extracting pure and consistent RNA, our data suggests that there is a binding limit. After approximately 30 mg of tissue, additional material did not significantly affect the amount of RNA extracted .The RNA results comparing the commercial kits to the silica column procedure showed no significant difference in the amount or quality of RNA . With the same omission, we also found small amounts of genomic DNA in commercial kits (unpublished data). Overall, this article aims to aid new laboratories or scientists under tight budget constraints to obtain the same quality of results for RNA isolation as commercially available kits, while using less expensive yet widely available reagents.


APP, SCA3, SNCA and SOD1 genes, carrying C-to-G, G-to-C, G-to-A, A-to-G and C-to-U substitutions. The results show that certain arrangements of mismatches enhance discrimination between wild type and mutant SNP alleles of RNA in vitro as well as in HeLa cells. Among the over 120 gapmers tested, we found two gapmers that caused preferential degradation of the mutant allele APP 692 G and one that led to preferential cleavage of the mutant SNCA 53 A allele, both in vitro and in cells. However, several gapmers promoted selective cleavage of mRNA mutant alleles in in vitro experiments only.This study focused on determining design rules for gapmer-type antisense oligonucleotides (ASOs), that can differentiate cleavability of two SNP variants of RNA in the presence of ribonuclease H based on the mismatch type and position in the heteroduplex. We describe the influence of structural motifs formed by several arrangements of multiple mismatches (various types of mismatches and their position within the ASO/target RNA duplex) on RNase H cleavage selectivity of five different SNP types. The targets were mRNA fragments of  This situation is present in many neurodegenerative diseases, among which Huntington’s disease and different types of spinocerebellar ataxia that result from expanded trinucleotide repeats are the most studied targets9. The bases for distinguishing between wild type and mutant alleles are primarily SNPs (single nucleotide polymorphisms) or the length of trinucleotide repeats. In the majority of cases, SNPs are not the primary cause of disease, but variants correlate with the occurrence of wild type and mutant alleles. In turn, when targeting expanded trinucleotide repeats, the chance of antisense oligonucleotide binding is increased due to multiplication of the target sequence. However, depending on the targeted number of repeats, ASOs may be too short to directly distinguish alleles, and quantitative differentiation of alleles results from an increased frequency of binding of the oligonucleotide tools to the expanded target. Moreover, some RNAs containing expanded trinucleotide repeats are susceptible to forming hairpin structures10, which may be less accessible to binding by ASOs than single-stranded regions. Alleles that differ by small deletions or insertions may also be used for this purpose11, but in general, their occurrence in correlation with the target genes seems to be less frequent.For more than a decade, allele-selective approaches using antisense technologies have been explored as a promising way to eliminate pathogenic alleles to treat various genetic disorders. This type of treatment may be achieved at the RNA level by enzymatic degradation of mutated mRNA by specific ribonucleases. The best targets for such approaches are genes that act in a dominant manner and present heterozygosity, meaning that in addition to the mutant allele there is also wild type one that is masked until the expression of the dominant mutant allele is repressed13.Currently, after cardiovascular diseases and cancer, neurodegenerative disorders are one of the major diseases afflicting humans. An increase in the frequency of their occurrence is associated with aging in human populations. Neurodegeneration is a complex, progressive and irreversible process of nerve cell deterioration, eventually leading to cell death. In the majority, mature neurons do not undergo cell division, which results in a strong limitation of their ability to regenerate. The accumulation of mutations, both sporadic and inherited, leads to impaired biochemical functions of many proteins in the nervous system, resulting in aggregation and formation of insoluble, toxic deposits. These pathomorphological changes in brain tissue are common in many neurodegenerative diseases, each of them involving different proteins15. RNA interference is an evolutionarily conserved process to repress target genes in a sequence-specific manner in a response to the presence of dsRNA molecules16. Small interfering RNAs (siRNAs) are agents that may be designed to induce RNAi pathways. Their presence in the cell cytoplasm induces assembly of the RISC-complex, in which they mediate cleavage of complementary mRNA targets by the Argonaute-2 (Ago2) protein17. The activity and specificity of RNA degradation by ASO and siRNA is increased if the constructs contain chemically modified nucleotides22.Antisense strategies provide several nucleic acid tools for RNA degradation in the context of gene silencing. Among these, the most commonly used are antisense oligonucleotides and RNAi reagents. ASOs recruit cellular RNase H1 to cleave RNA duplexed with DNA. At least five successive unmodified nucleotides at the 2′ position are required for nucleolytic activity of the enzyme25. Targeting SNPs by ASOs to distinguish between wild type and mutant alleles is based on the occurrence of a single mismatch in one of the two RNA/ASO duplexes. Differentiation between the cleavage rates of these duplexes by RNase H might vary depending on the mismatch type and position with the ASO/target RNA duplex26. Single mismatch discrimination cleavage of a target RNA with RNase H was reported by Giles et al., who used chimeric methylphosphonodiester/phosphodiester oligonucleotides27. Later, it was shown that the sequence and structure of DNA/RNA heteroduplexes affect RNase H cleavage sites and rates29. It was found that a single mismatch results in a several-fold decrease in RNase H cleavage, and three and more mismatches completely abolished RNase H cleavage32.Although antisense oligonucleotides have been known for some time, RNAi discovery has led to the rapid development of allele-selective approaches. Nevertheless, despite having a less specific mechanism of RNA degradation, antisense oligonucleotides remain an attractive tool for gene silencing. Through a wide range of novel chemical modifications of nucleotides, the specificity and selectivity ASOs can be considerably improvedin vitro and in HeLa cells. The five most common SNP types in the human genome35, occurring in APP, SNCA, SOD1 and SCA3 genes were chosen as the targets of this research. These SNPs, except for SCA3, are direct causes of rare versions of Alzheimer’s disease40, Parkinson’s disease42 and Amyotrophic Lateral Sclerosis (ALS)44. The G-to-C SNP of the SCA3 gene (rs12895357) is commonly targeted as being associated with the expanded trinucleotide repeat allele46. By investigating this approach on different SNPs placed in model RNAs, we aimed to define the rules regarding the influence of mismatches on RNase H allele-selective cleavage of RNA within DNA/RNA duplexes with modified antisense gapmers.In this study, we present the influence of several structural motifs of the RNA/ASO duplex, including bulges and different arrangements of multiplied mismatches  that enhances processivity of the enzyme, and flexible spacer region of ~60 amino acid residues that connects it with catalytic, C-terminal domain52. Despite only 34% sequence identity, human and E. coli ribonucleases H share common catalytic mechanism and their 3D structures exhibit remarkably similarity53. Unlike B.halodurans enzyme and HIV-1 reverse transcriptase subunit, E. coli and human RNases H both contain a basic protrusion motif responsible for magnesium-dependent catalytic activity, deleting of which markedly reduces binding affinity for RNA/DNA hybrids51. The presence of additional hybrid binding domain in human RNase H1 influences precise positioning of the substrate within the active site resulting from a significantly tighter binding affinity. Thus, compared to E. coli, human enzyme shows reduced ability to move on the substrate, making the human RNase H1 cleavage more precise and focusing it between 7–10 nucleotides from the 3′-DNA/5′-RNA terminus15. Keeping in mind the length and composition of gapmers applied, results of E. coli RNase HI assay in this study showed that cleavage sites overlap within those positions of heteroduplex, in which cleavage by human RNase H1 is predicted.This study aimed to determine the parameters for designing oligonucleotides that can preferentially differentiate cleavage of two SNP variants of RNA (WT and Mut RNA) by ribonuclease H. Several arrangements of mismatches within each RNA/ASO duplex were designed to improve discrimination of single nucleotides between RNA alleles by RNase H Fig. . The preAPP, SNCA, SOD1 and SCA3 genes. Initially, the selection of ASO was based on in vitro RNase HI assays. For gapmers that induced selective degradation of mutant RNA, thermodynamic parameters for both RNA/ASO duplexes (wild type and mutant) were determined, and ASO gapmer transfection of HeLa cells was performed. The results showed that the structure of the heteroduplex, which was perturbed by mismatches, affects RNase HI cleavage yield, pattern or both, and the mismatch position may determine discrimination between RNA single nucleotide substitutions. We supposed that the SNP type may have a strong influence on the level of differentiation between alleles, because each mismatch resulted in different conformational changes to the helix, which are often dependent on the adjacent base pairs. The results supported our conjecture in this regard.In total, over 120 antisense oligonucleotides were tested to cause allele-preferential cleavage of Mut RNA of the APP gene occurring in exon 17, which encodes amyloid β fragment of APP protein, were chosen as targets. They are all missense mutations that increase production of amyloid β, a product of transmembrane cleavage of APP by β- and γ-secretases, the deposition of which outside neurons is a main cause of Alzheimer’s disease.Three dominant point mutations in APP Flemish variant results from a C-to-G nucleotide transversion and leads to upregulation of the secretion of both Aβ40 (less amyloidogenic) and Aβ42 (more amyloidogenic) forms of β-amyloid. This point mutation increases the solubility of Aβ peptides and the stability of peptide oligomers54. Conformational changes in the peptide induced by this transversion facilitate peptide adherence to the vascular endothelium, creating nidi for amyloid growth in the blood vessels of the central nervous system, and as such it is also linked to cerebral amyloid angiopathy (CAA)37.The 56, but it was not known whether it will alter the activity of RNase H toward the wild type allele. In vitro RNase HI assays showed that the mismatch position is important for RNA cleavage yields. A single C-dC mismatch that was formed in wild type RNA/ASO duplexes caused strong thermodynamic destabilization of the helix and affected RNase HI cleavage yield of RNA 692, depending on the position within the duplex . All these ASO gapmers introduce single or tandem mismatches at the site of RNase H cleavage of the duplex (at the 5′-end of the DNA gap) or in the center of the duplex. However, the presence of a C-dC mismatch determines selective inhibition of cleavage of WT RNA. When tandem purine mismatches occur at the site of cleavage by RNase H , mutant as well as wild type 692 RNA are cleaved with equal efficiency. The presence of a C-dC mismatch as a third mismatch , as the second mismatch of a tandem mismatch at the cleavage site (b13 and b14 gapmers) or even as a single mismatch at the cleavage site , abolishes RNase HI activity. Additionally, a G-dG mismatch as the third mismatch in heteroduplex reduces RNase HI cleavage efficiency (Mut RNA-b9 gapmer). A triple mismatch containing C-dC forms a small internal loop that, when placed in the center of the duplex, inhibits RNase HI cleavage of WT RNA (b12 gapmer). However, WT RNA cleavage is half as efficient when the C-dC mismatch is separated from the tandem mismatch by a few nucleotides (b8 gapmer). When the C-dC mismatch was placed in a tandem mismatch at the 3′-end of the DNA gap , no difference in cleavage yield of RNase HI between two RNA SNP variants was observed.The change of cytosine to guanine in target RNA 692 results in a C-dC mismatch in the duplex of wild type RNA and a mutant-complementary ASO. The C-dC mismatch strongly destabilizes duplexeslex Fig. . They inlex Fig. . In vitrThe effectiveness of the triple-mismatch motifs described above introduced to 13 nucleotide WT or Mut RNA/ASO duplexes was also confirmed for 15 and 17 nucleotide-long gapmers, which were marked with the suffix “–a” and “–b,” respectively. In the case of 15-mer gapmers, the gap region was elongated from 7 to 9 nucleotides, and the selective Mut RNA cleavage effect was maintained. In the case of 17-mer gapmers, the flanking region was elongated from three to five nucleotides on both sides, which made the resulting RNA/ASO duplex more rigid and minimized the destabilizing effect of the three mismatches. As a consequence, selective degradation of Mut RNA by RNase HI was abolished, which confirmed the presumption that disruption of the helical structure at the cleavage site of the duplex may lower RNase H activity.in vitro, because they reduced the level of mutant RNA at least two-fold without affecting WT RNA , were cotransfected into HeLa cells at five different concentrations in the range of 0–150 nM ASO together with plasmids carrying an over one-hundred nucleotide fragment of the APP gene with the C-to-G substitution. To compare the allele-selective effect of the gapmers, EC50 values were determined for each RNA variant-gapmer pair  were selected in RNase HI assays RNA Fig. . These eair Fig. . Under cmer Fig. .Figure 4HeLa cells concerning selectivity of gapmer action depending on the pattern of gapmer elongation. A comparison of the WT and Mut RNA ratios obtained after HeLa treatment with b8 and b12 gapmers and their elongated versions  demonstrated more preferential cleavage of Mut RNA with b8-a and b12-b. This means that in the case of separated mismatches (b8 pattern), gapmer selectivity was improved by elongation of the gap region by two nucleotides, but for coupled mismatches that occur centrally in the duplex (b12 pattern), better selectivity was observed when the flanking wings of the gapmer was elongated  is about two times weaker than to the complementary mutant RNA 26. However, the central position of this mismatch type did not influence RNase HI selectivity. Among the few mismatch arrangements tested , which corresponds to amino acid 46 in the N-terminal fragment of the alpha-synuclein protein. This change results in a significant increase in α-synuclein binding to negatively charged liposomes. Moreover, it leads to increased rate and amount of filaments assembly, a step in α-synuclein aggregation that results in the formation of the brain lesions that are the hallmarks of Parkinson’s disease60. The second G-to-A transition in the SNCA transcript results in a change in codon 53 from alanine to threonine (A53T) and it is only approximately 20 nucleotides away from E46K. It also increases rate and amount of filaments assembly, that differ from those formed by the 46 K mutant and by wild type as well. Targeting these two G-to-A transitions was a way of checking if this type of substitution truly has no effect on RNase H cleavage, independently of the position they occupy within the RNA/gapmer duplex.Two more G-to-A point mutations in a different gene context were also analyzed as potential targets for the SNP-preferential antisense approach. They both occur in the α-synuclein transcript and lead to different type of neurodegeneration related to development of early-onset Parkinson’s diseasein vitro RNase HI assay indicated small differences in cleavage yields between the two RNA alleles. In both cases of these SNCA transitions, the most selective cleavage of the mutant A-allele was observed with gapmers k2 and h2, which introduced tandem mismatches in the RNase H cleavage region (two nucleotides at the 5′-end of the DNA gap), and the SNP site was placed centrally within duplex. Additionally, in both cases tandem mismatches were purine mismatches (GG-dGdG for RNA 46 with gapmer k2 and AA-dAdA for RNA 53 with gapmer h2). UV-melting experiments showed only small differences in the thermodynamic stability between duplexes of WT and Mut RNA and the gapmer . Despite the large differences in stability between the tandem mismatch-containing (k2 and h2) and tandem mismatch-free duplexes , the efficiency of their cleavage by RNase HI is almost unaffected until the third centrally placed mismatch occurs. Its presence significantly reduces the cleavage efficiency of WT RNA, by 75% in case of RNA 46 and by 50% in case of RNA 53. Interestingly, in both cases of this transition, when the third, centrally placed mismatch occurred in Mut RNA duplexes and WT RNA duplexes contained only tandem mismatches at the 5′-end of the DNA gap, preferential cleavage of wild type RNA occurred . This observation confirms the structural significance of this arrangement of mismatches for selective RNase HI degradation of RNA alleles.The HeLa cell assays, we observed different results for each G-to-A target. The preference of the h2 gapmer toward Mut RNA 53 degradation was confirmed, demonstrating that its effective inhibitory concentration in the case of Mut RNA was over 6-fold lower than for WT RNA  and it is specific for the U.S. population. Approximately 50% of SOD1-ALS patients in U.S. carry the A4V substitution61. It acts in a dominant manner (as most of the known ALS-causing SOD1 mutations), and a single mutant copy of the gene is sufficient to cause the disease. The mechanism by which the A4V mutation causes ALS probably relies on disturbing the formation of the SOD1 homodimer, which is the active form of the protein. As a result of loss of stability of the dimer structure, SOD1 aggregation is accelerated, which causes faster progression of neuronal degeneration62.One of the most frequent substitutions in the human genome is the C-to-T transition, because of the major mechanism for newly occurring mutations, which is deamination of cytosine to uracil. In the context of neurodegeneration, we decided to target our SNP-selective antisense approach to the A4V mutation in the SOD1 transcript was a single C-dA mismatch, occurring in the wild type SOD1 RNA 4 duplex. When placed in the center of the duplex, the mismatch caused moderate thermodynamic destabilization of the RNA/gapmer duplex, but did not decrease the efficiency of RNase HI cleavage of WT RNA , of which variants G and C are associated with wild type and mutant alleles, respectively65. We chose this SNP as a target for searching for allele-selective gapmers, because it has already been used for similar purposes by other research groups66.The SCA3 target many different options of double mismatches, including the G-dG mismatch and tandem mismatches, were tested in vitro with RNase HI assays . The reason for this different localization was to minimize the number of repeats in the target sequence. We realized that such placement is not in the cleavage site and expected it would have a rather negligible influence on the yield of cleavage by RNase HI. However, this single G-dG mismatch turned out to limit the cleavage efficiency of WT RNA by approximately 50% and Mut RNA only by approximately 15%, without a change in cleavage pattern  shifts of the RNA cleavage site out of the DNA gap region of the duplex were observed to occur opposite from the LNA and 2′-O-methylRNA nucleotides of the ASO. The sugar residues of these nucleotides adopt a different conformation (C2′-endo) that limits RNase H cleavage activity. However, in the cases described above, despite the fact that RNA cleavage occurred in this unexpected site within duplexes, it did not significantly influence cleavage efficiency.Often, the presence of mismatches caused a change of the major cleavage sites into minor ones or conversely, shifted them by one or two nucleotides Figure . The shiin vitro, there is no such possibility in cells. Many factors including accessibility of the hybridization site, the number of potentially existing sequence-dependent and independent off-targets, the actual concentration of antisense oligonucleotide due to differences in transfection efficiency, the condition of the cells and the phase of cell cycle during transfection determine the final effect that is observed. Therefore, the consistency between cleavage results in in vitro assays and RNA profiles from HeLa cells seemed interesting. Several sets of control experiments were designed to validate gapmers activity in cells. Firstly, GFP levels in HeLa cells were measured, to which empty plasmid (without an insert) was co-transfected with the gapmer. Often we observed a decrease in GFP expression, despite gapmers’ target was absent in the cell . Certainly, these nonspecific effects also occur in the presence of the target, diminishing the pool of ASO that might successfully bind. In case of allele-preferential RNA cleavage based on SNPs, the concentration of ASO and proper mismatch localization are equally important. When concentration of ASO is too low, the gapmer might not reach its target site, and when it is too high, it acts mostly nonspecifically. For SNP-selectivity of ASO, its concentration is the most important parameter to determine, because the behavior is similar to competitive inhibition, where stoichiometry plays crucial role. Dose-response curves of ASO activity in 68. Their presence results in changes to the local duplex structure that may be beneficial for differentiating cleavage of the target RNA alleles by RNase H. We observed this effect in vitro in RNase HI assays and successfully used it to selectively degrade Mut RNAs in some SNPs. The approach worked best with C-to-G transversion in the context of the APP RNA target, for which two gapmers were found that caused preferential degradation of mutant RNA by RNase H1 in HeLa cells. Their manner of action is based on proper localization of a defined set of mismatches within the RNA/ASO heteroduplex.Mismatches form non-canonical base pairs that vary in strength and may significantly influence duplex stability and structure50 values for WT and Mut RNAs. Three ASOs which were characterized by a significantly lower EC50 value in case of Mut RNA than WT RNA  will be further evaluated, also in different cell line. In most cases, mismatches that appear as a consequence of SNP change do not drastically alter the efficiency of RNA degradation, and the observed mutual deviation of the curves for both alleles is small.The general conclusion for all analyzed SNP types is that the effect of preferential degradation of specific RNA alleles is strongly dependent on the ASO concentration. Comparison of dose–response curves of ASO gapmers illustrates this dependency. For ASO, which can cause SNP-preferential RNA cleavage relative to concentration, dose-response curves for the levels of two RNA alleles did not overlap. The more allele-preferential properties gapmer demonstrated, the clearer this relationship was. To determine the overall SNP-preference of specific ASOs, we compared EC70. For RNase H degradation efficiency, the most important factor is the cleavage region of the RNA/ASO duplex, which depends mostly on duplex length and structure. Therefore, selective mutant RNA cleavage using gapmers depends on ASO length, and the type, number and position of mismatches, which must be determined on the basis of target sequence. Our studies revealed that in the case of maintaining allele-preference of RNase H cleavage, elongation of the DNA gap is more favorable than elongation of the modified wings of the gapmer. Although it is extremely difficult to design a universal gapmer template for selective degradation of SNP-mutants at the RNA level, the knowledge of properties of different types of mismatches will be very useful to design antisense oligonucleotides that support allele-selective RNA cleavage with ribonuclease H.Thermodynamic data concerning RNA/RNA as well as RNA/DNA short duplexes indicate that the central position of the mismatch causes the highest impairment of duplex stability and structure71, and commercially available phosphoramidites . The details of oligoribonucleotide deprotection and purification are described previously73.Short RNA oligonucleotides , ASO gapmers  and primers for PCR reactions were synthesized on a BioAutomation MerMade12 DNA/RNA synthesizer using β-cyanoethyl phosphoramidite chemistry2EDTA, at pH 7. Only in the case of the SCA3 G-to-C SNP, buffer containing 10 mM NaCl was used, because of the inability to calculate thermodynamic parameters for 100 mM NaCl. Single-strand oligonucleotide concentrations were calculated from the absorbance above 80 °C, and single strand extinction coefficients were approximated using a nearest-neighbor model75. Absorbance vs. temperature melting curves were measured at 260 nm with a heating rate of 1 °C/min in the range of 2 to 90 °C on a Beckman DU 640 or JASCO V-650 spectrophotometer with a thermoprogrammer. Melting curves were analyzed, and thermodynamic parameters were calculated from a two-state model with the program MeltWin 3.576. Statistical analysis of thermodynamic data was carried out using MeltWin 3.5 software. The methods for determining sampling errors in ∆G°37, ∆H° and ∆S° from the linear regression of TM−1 vs. ln(CT/4) plots using standard statistical analysis has been previously described77. Based on results obtained for nine concentrations of each duplex, TM−1 vs. ln(CT/4) dependence was determined, from which the calculated ∆G°37 values for averaged 10–4 M concentrations were compared with that derived from averaging the fits to individual melting curves. Differences below 15% indicated that a two-state model is reasonable for melting of the duplex analyzed.Oligonucleotides were melted in buffer containing 100 mM NaCl, 20 mM sodium cacodylate and 0.5 mM Na32P–ATP using T4 polynucleotide kinase (long RNAs) or with fluorescein (FAM) during oligonucleotide chemical synthesis (short RNAs). Labeled RNA was mixed with ASO in H-buffer  at proportions optimized in a separate experiment (10:1 for RNA/gapmers). The mixture was denatured for 3 min at 80 °C and renatured at room temperature for 5 min to form duplexes. Next, 0.4 U of RNase H (E. coli) was added, and the mixture was incubated for 20 min in 37 °C. The time of RNase H digestion, as well as the enzyme concentration, was optimized according to the fully complementary RNA/gapmer duplexes. Experiments were done with recombinant E. coli RNase H because of its wide commercial availability. Cleavage products were separated on a 16% polyacrylamide gel with 7 M urea and visualized by autoradiography. Digestion efficiency was evaluated from gel images using the MultiGauge 3.0 program (Fuji). The same assay was used to determine the kinetics of RNase H cleavage. A mixture of the appropriate duplex in H-buffer with ribonuclease H was incubated at 37 °C, and 10 μL aliquot samples were collected after 30 sec, 1, 5, 10, and 20 min of reaction. In most cases, 20 min was sufficient for complete RNA hydrolysis. The assay was performed at least three times for each tested oligonucleotide. Mean ± SD of digestion efficiency was evaluated with Microsoft Excel software.Screening of antisense gapmers was carried out using an RNase H assay. RNAs were labeled at 5′-end with γHeLa cell line experiments were performed using the 99-nucleotide fragment of the gene of interest (in Wild-type or Mut variant) inserted into a pEGFP expression plasmid upstream of the GFP protein. One day before transfection, HeLa cells were passaged to 24-well plates and cultured in a standard RPMI 1640 medium containing 10% FBS (Invitrogen), 1x Antibiotic-Antimycotic solution (Sigma Aldrich) and 1x MEM vitamins solution (Sigma Aldrich). When the confluence reached ca. 90% per well, co-transfection of the pEGFP plasmid constructs (Wild-type or Mut) at a concentration of 1 μM and gapmers at concentrations of 0, 10, 25, 50, 75 and 100 nM was carried out with Lipofectamine 2000 (Invitrogen) in medium without antibiotics. For certain gapmers, 200 and 300 nM concentrations were also tested. After 24 h incubation, cells were washed with phosphate-buffered saline (PBS) and viewed under a fluorescence microscope, and then total RNA was extracted. Each transfection experiment (using different concentrations of various ASOs) was repeated at least three times. For quantitative analysis of silencing, total RNA from cells was isolated using the TRIzol method78 as described previously79. Next, 0.5 μg of RNA was subjected to DNase I (Life Technologies) treatment. RNA quality was controlled by separation on 1.5% agarose gels. cDNA, which was the template for qPCR, was obtained from a reverse transcription reaction using iScript cDNA Synthesis Kit (Bio-rad). qPCR was performed on a CFX96 real-time PCR system (Bio-Rad) using iTaq SYBR Green Supermix (Bio-rad) and 96-well clear plates. The level of GFP mRNA was quantified with the use of target gene primers: GFPf 5′-GCTGACCCTGAAGTTCATC, GFPr 5′-GCTCCTGGACGTAGCCTTC, and normalized to human β-actin levels . The qPCR cycles were as follows: 95 °C for 5 min; (95 °C for 10 sec and 60 °C for 1 min) for 40 cycles. Each qPCR experiment was repeated at least twice.HeLa cells. Semi-log plots of dose-response dependency were designated for the oligonucleotides in OriginLab 8.0 software, and they served as estimations of the EC50 values of each antisense oligonucleotide – RNA variant pair. Comparison of EC50 values for wild type and mutant RNA variants allowed to evaluate allele-preference of antisense oligonucleotides.Five different concentrations ranging from 0–100 nM  were examined for each ASO transfected to Statistical analysis of the qPCR results was performed with Bio-rad CFX Manager 3.0 and OriginLab 8.0 software. Antisense oligonucleotides were tested in various concentration sets, and transfection of each set was repeated two to five times . Quantitative PCR for each set was repeated at least twice . Standard curves for target and reference genes were determined to control for PCR efficiency of each reaction, which ranged from 91 to 97%. The results from technical repeats were gathered for each concentration set transfected with WT or Mut constructs to determine mean relative expression and standard deviation (Bio-rad CFX Manager 3.0). Samples for which the obtained Cq value differed by more than 0.5 from the others were excluded from the analysis. Next, using one-way ANOVA analysis (OriginLab 8.0) the normalized relative expression of GFP  from biological replicates for WT and Mutant was compared at a significance level of 0.05 for each of the conditions analyzed. Statistically significant differences in mean expression between WT and Mut alleles (P < 0.05) were observed only at certain concentrations. .Dataset 1Dataset 2Dataset 3


WTG1 is involved in early chloroplast development and may function in RNA editing by enhancing the function of RNA editosomes in Arabidopsis WHITE TO GREEN1 (WTG1), was identified to have a function in chloroplast development and plastid gene expression by screening Arabidopsis leaf coloration mutants. WTG1 encodes a chloroplast-localized tetratricopeptide repeat protein that is expressed widely in Arabidopsis cells. Disruption of WTG1 suppresses plant growth, retards leaf greening and chloroplast development, and represses photosynthetic gene expression, but complemented expression of WTG1 restored a normal phenotype. Moreover, WTG1 protein is associated with the organelle RNA editing factors MORF8 and MORF9, and RNA editing of the plastid petL-5 and ndhG-50 transcripts was affected in wtg1 mutants. These results indicate that WTG1 affects both transcriptional and posttranscriptional regulation of plastid gene expression, and provide evidence for the involvement of a tetratricopeptide repeat protein in chloroplast RNA editing in Arabidopsis.The chloroplast is essential for plant photosynthesis and production, but the regulatory mechanism of chloroplast development is still elusive. Here, a novel gene,  The funtrans-factors that recognize editing sites  to uridines (U) in mitochondria and plastids  . TPR prowtg1) have an albinic and dwarf phenotype, retarded chloroplast development, disturbed expression of chloroplast-related genes, and significantly lowered editing rates at two (petL-5 and ndhG-50) of 43 plastid RNA editing sites. WTG1 did not bind the cis-elements of petL-5 and ndhG-50 sites; instead, WTG1 interacted with plastid-localized MORFs. These findings suggest that WTG1 is required for chloroplast development and may function in RNA editing in Arabidopsis by enhancing the function of RNA editosomes.Here, a novel TPR protein was identified to affect chloroplast development in Arabidopsis and designated WHITE TO GREEN1 (WTG1). T-DNA insertion mutant plants  was used as the wild type in this work. The T-DNA insertion alleles in WTG1  were obtained from the Arabidopsis Biological Resource Center (ABRC) stock center . All seeds were cultivated on agar plates containing 50% Murashige and Skoog medium (−2 s−1)/dark cycle.g medium  and tranTotal genomic DNA was isolated as described previously  according to the manufacturer’s instructions. To synthesize cDNA, total RNA was reverse-transcribed using the primeScript 1st Strand cDNA Synthesis Kit (TaKaRa). Primer sets L2/R2 and L2/LBa1 were used to determine the abundance of the WTG1, total RNA isolated from roots, stems, leaves, inflorescences, and siliques was subjected to RT-PCR using the L1/R1 primers. UBQ5 was amplified as a control using the UBQ5F/UBQ5R primers.To investigate the expression pattern of To detect the expression levels of chloroplast-related genes, quantitative real-time PCR (qRT-PCR) analysis was performed. qRT-PCR amplification was carried out in a LightCycler® 480 Real-Time PCR System. Primer sets are listed in wtg1, and complemented lines at the 18- and 50-day-old stages. mRNA enriched from total RNA was fragmented and reverse-transcribed using random hexamer primers. The library was then constructed and sequenced using an Illumina HiSeq 4000 (BIOPIC-Beijing). Clean reads were aligned to the A. thaliana genome (TAIR 10.0). Levels of gene expression were calculated using the RPKM (reads per kilobase transcript per million reads) method.Total RNA was isolated from leaves of wild-type, http://iseq.cbi.pku.edu.cn) by fold change less than 0.33 or greater than 3. Gene ontology analysis was performed by DAVID 6.7  was determined through iSeq (WTG1 coding sequence (CDS) without the stop codon was PCR-amplified from wild-type cDNA with 53080cds_L1/53080link_R1 primers as a WTG1-link fragment. green fluorescent protein (GFP) was amplified from the pGreen0029-DUO1-DIPS-GFP-NOS plasmid with linkGFP_L1/linkGFP_R1 primers to yield a link-GFP fragment. Using the WTG1-link and link-GFP fragments as a template, 53080cds_L1/linkGFP_R1 primers were used to generate a WTG1-GFP fragment, which was digested with BamHI and SalI and ligated into the BamHI-SalI site of pWM101 to yield a pWM101-35S-WTG1-GFP vector. A 35S-GFP construct was used as a negative control.The WTG1 coding region was amplified from genomic DNA (ecotype Col-0) using BamHI-53080-F/53080-EcoRI-R primers, after which the product was cut by BamHI and EcoRI and cloned into the pGEX-4T-1 vector  within the BamHI and EcoRI sites to yield a pGEX-WTG1 vector.The full-length For the yeast two-hybrid assay, the CDSs of WTG1, OTP82, MORF2, MORF8, MORF9, ORRM1, OZ1, and PPO1 were ampliﬁed from wild-type cDNA using gene-speciﬁc primer sets. Full-length WTG1 was ligated into pGBKT7 (BD) to generate a pBD-WTG1 plasmid, while full-length OTP82, MORF2, MORF8, MORF9, ORRM1, OZ1, and PPO1 were cloned into pGADT7 (AD) to yield pAD-OTP82, pAD-ORF2, pAD-MORF8, pAD-MORF9, pAD-ORRM1, pAD-OZ1, and pAD-PPO1 plasmids, respectively (see For the bimolecular fluorescence complementation (BiFC) assay, MORF8, MORF9, and WTG1 were cloned into binary BiFC vectors pSPYNE173 and pSPYCE (M) to produce MORF8/9-eYNE and WTG1-eYCE plasmids , respectAgrobacterium tumefaciens strain GV3101 by electroporation and transformed into homozygous mutants (wtg1-1/wtg1-1) by ﬂoral dipping , as well as 18-day-old albino leaves and 35-day-old pale green leaves from wtg1 mutants. Samples (approximately 1 mm2) were cut with a new blade, fixed with 4% glutaraldehyde for 4 h at room temperature, and post-fixed in 2% osmium tetroxide for 1 h at room temperature. The samples were rinsed twice in 20 mM phosphate buffer (pH 7.0) and dehydrated in 30 min steps in a graded series of ethanol concentrations . We transferred the dehydrated samples to a gradient mixture of Spurr’s embedding medium and 100% ethanol  at room temperature. Next, the samples were transferred into pure Spurr’s medium and incubated in a mixer for 24 h at room temperature. Finally, the samples were allowed to sit at 65 °C for 20 h to complete the embedding. Ultrathin sections of the samples were cut with a diamond knife on an ultramicrotome (Leica UC7) and collected on single-mesh copper grids. The sections were stained with 2% aqueous uranyl acetate and lead citrate before being viewed using an electron microscope (FEI Tecnai G2 20).We harvested 50-day-old leaves from wild-type plants, A. tumefaciens strain GV3101. Wild-type plants were stably transformed with the pWM101-35S-WTG1-GFP and pWM101-35S-GFP transformants via the floral dipping method  and ndhGLpyro-F/ndhGpyro-R (biotinylated) primers, respectively. PCR products were converted into single-stranded DNA templates using a PyroMark Q24 Vacuum Workstation (Qiagen) according to the manufacturer’s instructions. Pyrosequencing reactions were performed in a PyroMark Q24 Advanced System (Qiagen). C:U ratios were analyzed using PyroMark Q24 Advanced Software (Qiagen).To verify the C (unedited) to U (edited) ratios at the in vitro RNA transcription by T7 RNA polymerase (NEB) following the manufacturer’s instructions. The templates were digested with RNase-free DNase I (TaKaRa), after which RNA probes were purified with the RNeasy Mini Kit (Qiagen) and then incubated with WTG1 protein at 30 °C for 30 min in a 20 μl system containing 20 mM Tris-acetate (pH 7.9), 50 mM potassium acetate, 10 mM magnesium, 2.5 mM dithiothreitol, 500 μg/ml BSA, and 40 units of RNase inhibitor. To prevent non-specific binding, 500 μg/ml BSA was added to the reaction system. RNA–protein complexes were resolved on a 1.5% agarose gel and detected by GelRed staining using the methods described by RNA electrophoretic mobility-shift assay (REMSA) was performed as described previously . Briefly35S-WTG1-GFP transgenic seedlings (18 days old) were fixed with 1% formaldehyde. Chloroplast isolation and subsequent immunoprecipitation of specific RNA–protein complexes were performed as described previously . Plasmids without Agrobacterium tumefaciens strain GV3103 was co-transformed with MORF8-eYNE and WTG1-eYCE, or MORF9-eYNE and WTG1-eYCE. The MORF2-eYNE and WTG1-eYCE combination was used as the negative control, whereas the MORF2-eYNE and MORF9-eYCE combination was used as the positive control. Each combination was introduced into A. tumefaciens strain GV3103 by electroporation. The transformants were then used to inﬁltrate Nicotiana benthamiana leaves as described previously (eviously . After 4Sequence data from this report can be found in the Arabidopsis Genome Initiative or GenBank/EMBL databases under accession numbers At5g53080 (WTG1) and AP000423 (Arabidopsis plastid genome).White-To-Green1-1 (wtg1-1). The homozygous mutants (wtg1-1/wtg1-1) germinated as dwarf and albino seedlings with serrated leaves, which, interestingly, turned green as they matured; these plants were fertile and had short siliques with a reduced seed set (2 (3:1)=0.241<χ20.85]. These results indicate that the defective leaf coloration phenotype of wtg1-1 mutants was regulated by a recessive allele and inherited by Mendelian genetic principles.To study the mechanism of chloroplast development, we screened abnormal leaf coloration mutants from T-DNA insertion Arabidopsis lines  and idenseed set . Self-poWTG1 transcripts were absent in wtg1-1 homologous mutants  and SALK_027718 (wtg1-3)  of At5g53080 was transformed into homozygous wtg1 plants. In total, 42 T1 transgenic plants were identified; all transformants displayed normal morphology and were indistinguishable from wild-type plants  , were obe plants . These rwtg1 mutants transitioned from albino to pale green to green, representing three stages of development  possessed etioplasts, whereas pale green leaves (stage 2) possessed pre-chloroplasts, and green leaves (stage 3) possessed mature chloroplasts  showed that WTG1 is widely expressed in Arabidopsis  and cell communications, e.g. ‘glycoprotein’ (P=6.6e-29) and ‘signal’ (P=3.8e-28), while the up-regulated DEGs were enriched for GO terms related to DNA processing and metabolism, including ‘DNA replication’ (P=3.7e-17), ‘DNA damage’ (P=4.1e-8), ‘DNA recombination’ (P=1.5e-7), and ‘DNA repair’ (P=4.5e-7).To determine whether these affected genes belonged to particular gene classes, we analyzed their gene ontology (GO) classifications in the biological process category , C. The wtg1 leaves turned green, we compared the transcriptional levels in 50-day-old and 18-day-old leaves in wtg1, wild-type, and complemented seedlings by RNA-seq  and two ribosomal protein-encoding genes (rpl24 and rpl16) in the wtg1 mutants were almost the same as, or even higher than, those in the wild type. However, the relative expression levels of the ribosomal RNA genes rrn16 and rrn23 were significantly reduced in the mutant compared with the wild type. Expression of the ribosomal protein small subunit gene rps17 was also downregulated in the wtg1 mutants, with a transcript level of 41.61% of that in the wild type. . Similarly, the editing rate of the ndhG-50 site in the wtg1 mutants was approximately 60%, which was notably lower than that of the wild-type plants (nearly 100%). It is notable that the editing rates of both sites within the complemented transgenic line were identical to those of the wild-type plants (both nearly 100% edited). The transcript abundance of petL was unchanged in the wtg1 mutants, whereas that of ndhG-50 was increased for unknown reasons . At the ndhG-50 site in wtg1 mutants, the editing rate was 54% in pale green leaves and 75% in green leaves, while the editing rate of wild-type plants was determined to be approximately 83% using pyrosequencing. Moreover, we detected a decrease in ndhG and petL RNA abundance in the 50-day-old leaves compared with the 18-day-old leaves of wtg1  and green (50-day-old) leaves of greening . The ediquencing . The edi of wtg1 , implyincis-element 5′-adjacent to the target editable Cs, as is typical for PPR proteins  in a Petunia hybrid (Nicotiana tabacum and Zea mays (The extent of RNA editing can vary with RNA abundance. This can be observed in the correlation between transcript and editing levels. Reported examples include the mitochondrial gene a hybrid  and plasZea mays . Howeverwtg1 were largely altered compared with the wild type (wtg1 plants on the changes in RNA abundance. However, after careful analysis of our results, we believe that the influence of petL and ndhG RNA editing on RNA abundance in wtg1 is minor; that is, WTG1 is likely to participate in RNA editing. This is because, among the genes with greatly altered transcript abundances, such as accD, psbF, and ndhB (petL and ndhG showed a deficiency in RNA editing (petL transcripts remained unchanged in wtg1 plants (Our results showed that the expression profiles of plastid-encoded genes in ild type . As descand ndhB , only tw editing . In fact1 plants . More im1 plants , F. Thes1 plants , consist1 plants , we propJXB online.Supplementary data are available at WTG1 reduces the yield trait of Arabidopsis.Fig. S1. Loss of Fig. S2. WTG1 is expressed ubiquitously in Arabidopsis and targeted to chloroplasts.wtg1 leaves.Fig. S3. RNA-seq analysis of wild-type and wtg1 by RNA-seq.Fig. S4. Transcript analysis of chloroplast-associated genes in 18-day-old Fig. S5. Controls for the bimolecular fluorescence complementation assay.Table S1. List of primers used in this study.wtg1 mutant plants.Table S2. Plastid RNA editing sites affected in the Trans-factors involved in petL-5 and/or ndhG-50 editing.Table S3. wtg1 plants.Data S1. List of genes that are differentially expressed in wild-type and supplementary_figures_S1_S5_tables_S1_S3Click here for additional data file.supplementary_dataset_S1Click here for additional data file.