Arabidopsis thaliana. The complete loss of mTERF2 function results in embryo lethality, whereas directed, microRNA (amiR)-mediated knockdown of MTERF2 is associated with perturbed plant development and reduced chlorophyll content. Moreover, photosynthesis is impaired in amiR-mterf2 plants, as indicated by reduced levels of photosystem subunits, although the levels of the corresponding messenger RNAs are not affected. RNA immunoprecipitation followed by RNA sequencing (RIP-Seq) experiments, combined with whole-genome RNA-Seq, RNA gel-blot, and quantitative RT-PCR analyses, revealed that mTERF2 is required for the splicing of the group IIB introns of ycf3 (intron 1) and rps12.Plastid gene expression (PGE) is essential for chloroplast biogenesis and function and, hence, for plant development. However, many aspects of PGE remain obscure due to the complexity of the process. A hallmark of nuclear-organellar coordination of gene expression is the emergence of nucleus-encoded protein families, including nucleic-acid binding proteins, during the evolution of the green plant lineage. One of these is the mitochondrial transcription termination factor (mTERF) family, the members of which regulate various steps in gene expression in chloroplasts and/or mitochondria. Here, we describe the molecular function of the chloroplast-localized mTERF2 in  Plastids of the group Plantae arose more than one-billion years ago from a single endosymbiosis event, in which a heterotrophic protist was engulfed and retained a cyanobacterium in its cytoplasm . During Arabidopsis thaliana  mTERF proteins are targeted to mitochondria and/or chloroplasts  ethanol and the surface-sterilized seeds were stratified for at least two days at 4 °C. Where indicated, the seedlings were grown on autoclaved half-strength MS medium containing 1.0% (w/v) sucrose, 0.05% MES (pH 5.8), and 0.8 % plant agar  at 22 °C under illumination that was provided by white fluorescent lamps (100 μmol photons m−2 s−1).The mutants w/v) sodium dodecyl sulfate (SDS). After centrifugation, DNA was precipitated from the supernatant by adding isopropyl alcohol. After washing with 70% (v/v) ethanol, the DNA was dissolved in distilled water. For RNA isolation, frozen tissue was ground in liquid nitrogen. The total RNA was extracted with the Direct-zol™ RNA Miniprep Plus Kit , according to the manufacturer’s instructions. The RNA quality  and concentration were assessed by agarose gel electrophoresis and spectrophotometry, respectively. The isolated RNA was kept at -80 °C prior to use.For DNA isolation, the leaf tissue was homogenized in extraction buffer containing 200 mM Tris-HCl (pH 7.5), 25 mM NaCl, 25 mM EDTA, and 0.5% .For complementary DNA (cDNA) synthesis, 1-μg aliquots of total RNA were employed in order to synthesize cDNA while using the iScript cDNA Synthesis Kit . Real-time quantitative PCR experiments were performed on a Bio-Rad iQ5 real-time instrument with the iQ SYBR Green Supermix  with a standard thermal profile . Each sample was quantified in triplicate and, as an internal control, normalized to the values of mterf2–amiR1 and amiR2 mutant plants were generated while using an artificial microRNA (amiRNA)-mediated knockdown technique  glycerol, 0.05 M sodium metabisulfite) containing 1 mM phenylmethylsulfonyl fluoride (PMSF) and protease inhibitor cocktail , as described in  and washing buffer . RNA was extracted from the precipitated complexes with TRIzol reagent (Thermo Fisher Scientific) and rRNA was depleted with the RiboMinus Plant Kit for RNA-seq (Thermo Fisher Scientific) and RNA-seq libraries were generated with the NEBNext Ultra™ RNA library Prep Kit and multiplex oligos for Illumina .The sample preparation for RIP was conducted essentially as described in . In brief, leaves of two-week-old transgenic Arabidopsis plants expressing the mTERF2-c-myc fusion protein were vacuum-infiltrated in 1% FA  for 15 min.; the fixation was stopped by additional vacuum infiltration with 125 mM glycine, and the tissue was then ground to a powder in liquid nitrogen. RNA–protein complexes were immunoprecipitated using an anti-c-myc antibody , as described previously ,43, excew/v) agarose gel and transferred to a nylon membrane . The blotted membrane was cross-linked by exposure to UV light, stained in 0.04% (w/v) methylene blue in 0.5 M sodium acetate (pH 5.2) buffer, and then used as loading control. The probes were amplified from cDNA and labeled with [α-32P]dCTP, probes for tRNA detection were generated by end-labeling corresponding primers with [γ-32P]ATP using polynucleotide T4 kinase  or at 44 °C (detection of trn), as previously described  were resolved on a formaldehyde-containing denaturing gel, transferred onto a nylon membrane, and then probed with [γ-32P]ATP end-labeled oligonucleotide probes specific for the second exon of trnI.2 and first exon of trnA.1, and [α-32P]dCTP-labeled cDNA fragments that are specific for rpoC1, ycf3, and nhdA exons and introns, as indicated. Ribosomal RNA was visualized by staining the membrane with Methylene Blue (M. B.) as a loading control. The arrows or the asterisks (*) in the blots indicate mature transcripts or the excised intron, respectively, and the question-mark indicates uncertainty regarding the size of the mature transcript or intron, respectively.The levels and splicing patterns of ndhA intron is detected in the weaker amiR1–2-mterf2 mutant. However, defective ndhA splicing has also been detected in A. thaliana and rice mutants lacking PPR4, a protein that is specifically required for rps12-1 splicing, and this effect has been discussed as a possible secondary consequence of reduced translation . Moreover, one mTERF can serve different functions . In this context, the question arises as to whether mammalian mTERFs are also involved in splicing. However, until recently it was thought that, in contrast to the plant mitochondrial and plastid genomes, the mammalian mitochondrial genome does not contain any introns. This view was challenged when introns were detected in mammalian mitochondrial transcripts by next-generation sequencing technology [The functions that are summarized above indicate that some mTERFs might be specific for a single target  in land plants are encoded in the nucleus. Progress in uncovering the functions of these proteins provides insights into the molecular innovations that emerged during the coevolution of nuclear and organellar genomes. Thus, studies have revealed that gene expression in plant organelles is even more complicated than first expected, and it involves a still growing number of nucleus-encoded proteins from diverse families. The mTERF proteins that have been characterized so far also participate either in aspects of OGE that are not typical of the nuclear/cytosolic compartment , or in processes characteristic for bacteria that have not previously been shown to take place in plant organelles . It has become evident that some mTERFs are essential , the lack of others  provokes milder phenotypes, some of which  only become pronounced under adverse environmental conditions . Changes


Automated lying-posture tracking is important in preventing bed-related disorders, such as pressure injuries, sleep apnea, and lower-back pain. Prior research studied in-bed lying posture tracking using sensors of different modalities . However, there remain significant gaps in research regarding how to design efficient in-bed lying posture tracking systems. These gaps can be articulated through several research questions, as follows. First, can we design a single-sensor, pervasive, and inexpensive system that can accurately detect lying postures? Second, what computational models are most effective in the accurate detection of lying postures? Finally, what physical configuration of the sensor system is most effective for lying posture tracking? To answer these important research questions, in this article we propose a comprehensive approach for designing a sensor system that uses a single accelerometer along with machine learning algorithms for in-bed lying posture classification. We design two categories of machine learning algorithms based on deep learning and traditional classification with handcrafted features to detect lying postures. We also investigate what wearing sites are the most effective in the accurate detection of lying postures. We extensively evaluate the performance of the proposed algorithms on nine different body locations and four human lying postures using two datasets. Our results show that a system with a single accelerometer can be used with either deep learning or traditional classifiers to accurately detect lying postures. The best models in our approach achieve an  Keeping track of in-bed lying postures and the transitions between them provide useful clinical information regarding the patients’ mobility ,2, risk Deep learning (DL) has emerged as the leading approach in the field of computer vision, voice recognition, and natural language processing in recent years. Deep neural networks are known as learners of high-level features for a specific problem domain. This makes DL models suitable models for human posture estimation. Moreover, DL models tend to reduce the overhead of feature engineering compared to traditional machine learning models . To dateMore specific contributions of this paper are as follows. We (1) investigate the efficacy of a single accelerometer for lying-posture tracking while using feature engineered machine learning models and deep LSTM networks; (2) identify the set of optimal time-domain features for accurate lying posture detection using traditional ML; (3) compare traditional machine learning with deep learning in recognition of lying postures; (4) evaluate nine different body sites to determine the most appropriate site to attach the accelerometer for accurate lying posture tracking.Human posture detection is an active research area. There have been many studies to explored models to distinguish human lying and sitting postures ,24,25. IA study in  conducteof 83.6% . Moreoveof 83.6%  developeThe above-mentioned algorithms that rely on data from multiple sensors attached to the different locations on the user’s body cause discomfort and limit usability, especially for long-term monitoring during sleep. Moreover, the possibility of sensor rotation during sleep might alter the angular axes of the sensors relative to each other, therefore decrease the accuracy of the orientation-based lying posture tracking. In this paper, we proposed lying posture detection algorithms that only use data from a single accelerometer sensor, which can be placed on one of the nine different body locations, including chest, thighs, ankles, arms, and wrists. Therefore, the proposed models using a single accelerometer are more comfortable to the end-users are favored over those using multi-sensor.Monitoring lying postures while using a single sensor improves the usability and wearability of the system comparing to the multi-sensor framework. These advantages in single-sensor monitoring become more evident in older adults and users with cognitive impairment, who might face difficulties to recall and follow the protocols . MoreoveIn a study conducted by Razjouyan et al. in , the auter study  by Zhangapproach . Howeverapproach  proposedWe define an episode of data as a sequence of signals collected from one subject while performing a run of a specific posture . The raw accelerometer signal and lying posture labels are fed into the data processing unit. The extracted episodes consist of four major lying postures, such as lying on the back (supine), lying on the front (prone), and lying on sides  that are more common across hospitalized patients and healthy individuals. The processing unit normalizes the signal to remove possible subject-based variations and segments it into different lying posture episodes based on the labels.The proposed traditional lying posture tracking consists of two main steps: (1) feature preparation in which an exhaustive set of features are extracted from each input episode; and, (2) ensemble model learning, which trains an ensemble of 100 decision trees on the features and lying posture labels.ith episode by averaging all of the extracted features from multiple windows over that episode.We extract 48 time-domain features  from a sliding window over each episode of data  with a 50% overlap. We set the window size to the minimum episode length in the training dataset . The selected features, shown in A total of 48 features are obtained from the previous step. We train an ensemble of 100 decision trees on the features and lying posture labels. We choose the bagging technique as the ensemble method for reducing the variance of the decision tree and overfitting to the existing data. A decision tree is selected as the weak learner because of the high dimension of the input features. As shown in Recurrent Neural Networks (RNNs) are a type of deep learners that are well-suited to model sequential data. However, RNNs fail to learn long-term dependencies in the data due to the problem of vanishing/exploding gradients. The Long Short-term Memory (LSTM) network has been introduced to address this issue and capture long-term dependencies from the sequential time series data . LSTM net, a bi-directional LSTM network maintains two hidden layers, one for the forward propagation and another for the backward propagation. At time-step t, each intermediate LSTM unit from layer At each time-step, tamp hti−, and staquations . Both fotamp hti−, and staquations . The finEquation . The finquations  and 4)..t, a bi-U is the weight matrix that corresponds to f and g are the activation function that is applied to the output of each LSTM unit  and the final hidden state At each layer In the next section, we define lying posture prediction from the sequences of raw sensor data as an optimization problem, and then design a deep learning architecture using Bi-LSTM networks as the solution.N sequences of variable lengths where each sequence jth class for ith sequence. Given these, the problem is to estimate a set of labels Y for summed over the sequences in Equation , an LSTM Network with an adaptive learning rate method with a decaying learning rate schedule. AdaLSTM receives the sequences of raw accelerometer sensor data as the input and estimates one label for each sequence. As shown in ptimizer  is used We perform training and validation of the models on two publicly available datasets: (1) Class-Act dataset , which cClass-Act is a human posture and activity classification dataset from 22 healthy participants  . The parThe DAS dataset contains data from eight subjects  that performed 19 activities of daily living for five minutes each . The parWe combined the lying posture episodes for the shared sensor locations  validation in order to minimize overfitting to a specific subject or a specific pattern of performing a lying posture. We report accuracy, analysis  to compaAccuracy is defined as the average effectiveness of the classifier over all the class labels.l is the number of class labels. Precision and Recall.Precision refers to the average agreement of the actual class labels and classifier-predicted labels and Recall is the average effectiveness of the classifier to identify each class label. Precision and Recall are computed by the following equations.BalancedAccuracy is defined as the average of the true positives and true negatives for each class label.i , i, while i. ve class ,46.The coefficient of variation  the accelerometer data captured from one of the subjects was not converted to gravity (g) and was stored in analog format. (2) The data collectors labeled multiple episodes of different lying posture as the same posture for one of the subjects. In this section, we validate the feature-based ensemble tree classifier in detecting three major lying postures  using the Class-Act dataset, including 12 subjects and nine sensor locations.The performance decline in the upper body parts originated from inter-subject variations in the placement of the arms and the wrists and nocturnal movements of them, such as bending and rotating while maintaining the same lying posture. The trend in the standard deviation of accuracy, balanced accuracy, and In this section, we evaluate the performance of the AdaLSTM classifier in detecting three major lying postures. Specifically, we compare the performance of the trained model on nine different on-body locations from the Class-Act dataset.subjects . AdaLSTMIn this section, we investigate the possibility of replacing feature-based machine learning models with deep recurrent neural networks (RNNs). For this purpose, we validate the proposed and state-of-the-art deep learning and feature-based classifiers in order to detect four major lying postures from the integrated dataset.In addition, we performed Kruskal’s statistical test between the We compare the performance of the proposed models against the state-of-the-art in lying posture detection while using a single accelerometer sensor. The proposed and competing models are described, as follows.ET is the proposed feature-based classifier, which is an ensemble of decision trees trained on 48 time-domain features.AdaLSTM is the proposed deep learning model, which is an adaptive long short-term memory network with Adam optimizer and decaying learning rate.LDA, as proposed by Zhang et al., is a linear discriminate analysis (LDA) classifier trained on the mean value of the signal .SVM, as proposed by Jeng et al., is a multi-class linear kernel support vector machine classifier trained on the mean value of the tri-axial accelerometer signal .LSTM is a long short-term memory network with the same structure as the AdaLSTM, but with a fixed learning rate of 0.01.As shown in h as LDA  and SVM h as LDA  achieve Moreover, the proposed models show lower We compared the performance of lying posture tracking while using a single accelerometer on nine different body locations in this study. When the ensemble tree classifier is trained on the data that were collected from the sensors on the chest and thighs the lying posture tracking achieves the highest performance and the least cross-subject variations, while classifiers for the wrists and the arms show the least performance and highest within-subject variations. These results demonstrate that individuals might devise arbitrary and dissimilar hand movements during the same lying posture. We further investigated the possibility of replacing traditional machine learning with deep learning. Our study showed that deep RNNs such as LSTM can replace the traditional machine learning classifiers as long as adequately designed. The fact that end-to-end deep learning neural networks could not improve the performance significantly when comparing to the feature-based classifiers demonstrates the lack of sufficient data as a limitation to this study . We beliWe implemented a traditional machine learning classifier, ensemble tree, with time-domain features and a deep recurrent neural network, AdaLSTM, with the decaying learning rate to detect four major lying postures, including supine, prone, left side, and right side, while using a single tri-axial accelerometer sensor. We identified amplitude, mean, minimum, and maximum values of the lateral and vertical axes as the optimal set of time-domain features in traditional machine learning for accurate lying posture tracking while using a single accelerometer sensor. We determined the optimal wearing sites of a single accelerometer sensor (thighs and chest) to accurately detect lying postures. Finally, we evaluated the performance of the proposed models against deep learning and state-of-the-art feature-based lying posture tracking methods while using two publicly available human posture and activity datasets. The proposed AdaLSTM using data from the left thigh and AdaLSTM on the chest locations achieved the highest


MAPT gene transcript. Our results demonstrated that RNV563, an arm-loop-arm-type DNAzyme targeting exon 13, and an AO candidate AO4, targeting exon 4, efficiently downregulated MAPT RNA expression by 58% and 96%, respectively. In addition, AO4 also reduced the MAPT protein level by 74%. In line with our results, we believe that AO4 could be used as a potential therapeutic molecule for Alzheimer’s disease and other tauopathies.The hyperphosphorylation of the microtubule-associated protein tau (MAPT) has been implicated in various neurological diseases, including Alzheimer’s disease. It has been hypothesized that the reduction of MAPT would result in depolymerizing neurofibrillary tangles and could be a potential strategy for the treatment of Alzheimer’s disease and other tauopathies. In this study, we report the development of novel DNAzymes and splice-modulating antisense oligonucleotides (AOs) for the efficient inhibition of MAPT. We designed and synthesized a range of DNAzymes and 2ʹ-O-methyl (2’-OMe)-modified AOs on a phosphorothioate (PS) backbone targeting various exons across the  MAPT), which is a protein that belongs to the microtubule-associated protein family [MAPT pre-mRNA [Synthetic nucleic acid-based technologies offer great promise for the development of precision therapeutics for the treatment of various diseases. So far, nine oligonucleotide drug molecules have been approved for clinical use, including Vitravene for viral retinitis, Macugen for age-related macular degeneration, Kynamro for familial hypercholesterolemia, Exondys51 and Vyondys53 for Duchenne muscular dystrophy, Spinraza for spinal muscular atrophy, Onpattro and Tegsedi for hereditary transthyretin amyloidosis, and Waylivra for familial chylomicronemia syndrome . Inspirepre-mRNA ,5  against l trials . Anotherl trials . Althougl trials . Furtherl trials ,9. FurthDNAzymes are a unique class of oligonucleotides that have catalytic activity and can be used for various applications, including gene silencing, and in diagnostic tools as biosensor molecules ,11,12,13Steric-blocking AOs bind to the target mRNA and can block the access of cellular machinery to pre-mRNA and mRNA, and can therefore modulate splicing, repair defective RNA, restore protein production, or downregulate gene expression without degrading the RNA ,19,20. SMAPT variant 2, which is the longest isoform found in the adult brain. The 2’-OMePS AOs were synthesized in-house on the AKTA Oligopilot 10 synthesizer  using standard phosphoramidite chemistry at a 1 µmole scale , as per the manufacturer’s protocol. Transfection was carried out for 24 h before harvesting RNA for transcript analysis. For protein analysis, transfection was carried out 24 h, 48 h (re-transfected at 24 h), and six days (re-transfected at 24 and 48 h) before collecting the cell pellet. Cell culture media and supplements were purchased from Life technologies, Australia, unless specified. SH-SY5Y cells were obtained from Cell Bank Australia (kindly provided by Prof. Sue Fletcher and Prof. Steve Wilton). SH-SY5Y cells were propagated in Dulbecco’s modified Eagle’s medium: F12 medium and 10% fetal bovine serum  in a humidified atmosphere in a 37 °C incubator with 5% COMAPT isoform—variant 2. GAPDH was used as a loading control and the primer set (ordered from IDT), and RT-PCR conditions for GAPDH are given in Total RNA was extracted using the Bioline Isolate II RNA MiniPrep Kit , following the manufacturer’s protocol. A total of 50 ng of total RNA was analysed using primer sets (ordered from IDT) listed in MAPT bands  in different DNAzyme- or AO-treated samples was measured and normalized to the intensity of the corresponding GAPDH bands, before being compared to the band intensity of the scrambled samples or untreated samples. The percentage of the longest MAPT isoform, calculated as variant 2 transcript knockdown in SH-SY5Y cells, was expressed as the activity of DNAzyme or AO. All percentages given in this study refer to the longest MAPT isoform—variant 2.Densitometry (measuring the band intensity) of the bands from the gels was performed using Image J Software . The intMAPT transcript was used. The experiments were performed by incubating DNAzymes RNV563 and their second-generation variants with an FAM-labeled RNA template in the presence of divalent metal ions, and the products were separated and analysed on a denaturing polyacrylamide gel. A total of 1.76 µM DNAzymes was incubated with an equal molar concentration of FAM-conjugated MAPT RNA (5′-FAM-UGUGGCUCAUUAGGCAACAUCCAUCAUAAACCAG-3′) in 5 µL of buffer containing Mg2+ divalent cations (10 mM MgCl2) at 37 °C. The reaction was stopped by adding 10 µL of formamide to 10 µL of the reaction mixture at 0 min, 30 min, 60 min and 2 h. Scrambled DNAzyme RNV563-SCR (AACATCCTCGTTGTAGCTAGCCTCATAAAC), RNV563-Mut1 (GTTTATGAAACTAGCTACAACGAGGATGTT), and RNV563-Mut2 (GTTTATGAGGCTAAATACAACGAGGATGTT) were used as negative controls, and the untreated samples did not have any DNAzyme. The underlined bases are the mutated bases and the two double mutants RNV563-Mut1 and RNV563-Mut2 were designed based on previous papers [The cleavage efficiency of the second-generation DNAzymes in vitro was tested using the in vitro cleavage assay. A synthetic fluorescein dye (FAM)-labeled RNA target specific to the exon 11 region of the s papers ,34. The MAPT protein in comparison to the scrambled AO-treated and untreated samples. Frozen transfected cell pellets were thawed and homogenized in SDS lysis buffer , and 10% glycerol (v/v)) containing protease inhibitor . The proteins were separated on a 10% polyacrylamide gel containing 400 mM Tris-HCL (pH 8.8) and 0.1% SDS in a Tris-glycine-SDS running buffer, before being transferred to a 0.2 µm nitrocellulose membrane  in a Tris-glycine-methanol transfer buffer. The membranes were blocked in 5% skim milk Tris-buffered saline with 0.1% Tween. The membrane was washed in Tris-buffered saline with 0.1% Tween, and incubated with primary antibodies overnight at 4 °C, 1:2000 anti-total tau , and 1:1000 anti-GAPDH . After washing the membrane in Tris-buffered saline with 0.1% Tween, the membrane was incubated with the secondary antibody  for 1 h at room temperature before washing. The antibodies were detected using a Clarity Western ECL detection kit  according to the manufacturer’s protocol and visualized using a chemiluminescence-based protocol on the Fusion Fx gel documentation system .Western blot analysis was performed on the best performing AO to evaluate the effect of the AO on the inhibition of the MAPT transcript  for 24 h. Total RNA was extracted from the cell lysate, and the integrity of the MAPT transcript was assessed by performing RT-PCR. Dose-dependent reduction of the full-length MAPT transcript was observed at various levels for RNV559, RNV561, RNV563, RNV569, RNV570, and RNV571 DNAzyme candidate-treated samples   . Based on the initial screening outputs, the best performing DNAzyme RNV563 was selected for further modifications in order to improve the efficacy by systematically increasing and decreasing the binding arm lengths. Many studies have shown that modifying the length of the hybridization arms on either side of the catalytic motif can increase the binding affinity and efficacy ,38,39. IMAPT transcript, we tested the cleavage efficacy in vitro using a synthetic fluorescein dye (FAM)-labeled RNA target specific to the exon 11 region of the MAPT transcript. The experiments were performed by incubating DNAzymes RNV563 and their second-generation variants with an FAM-labeled RNA template in the presence of divalent metal ions, and the products were separated and analysed on a denaturing polyacrylamide gel. Briefly, 1.76 µM DNAzymes was incubated with 1.76 µM FAM-conjugated MAPT RNA in the presence of Mg2+ for 30, 60, and 120 min at 37 °C. The reactions were stopped by adding 10 µL of formamide solution. The products were then separated on a 15% denaturing polyacrylamide gel and visualized using the Fusion Fx gel documentation system (Vilber Lourmat). The cleavage of the 34mer full-length FAM-conjugated RNA target is expected to yield 22 nucleotide-long products. A scrambled (SCR) sequence and RNV563 mutants with different mutations (RNV563Mut1 and RNV563Mut2) within the catalytic region of RNV563 were used as negative controls in parallel and an untreated (UT) sample with no DNAzyme was also included. DNAzymes RNV563, RNV610, RNV611, and RNV612 showed efficient cleavage of the MAPT RNA template in vitro, whereas the RNV608 and RNV609 with decreased arm lengths failed to yield any RNA cleavage  at a 50 nM concentration  and AO5 (76%) also resulted in dose-dependent downregulation of the MAPT transcript and were most efficient at 50 nM. Although AO19 downregulated the MAPT transcript, it failed to demonstrate dose-dependent inhibition. AO4 was further validated in inhibiting the expression of the MAPT protein as it was found to be the most efficient candidate at inhibiting the MAPT transcript.Firstly, we evaluated the exon-skipping efficiency of all AOs targeting different exons  in vitroin vitro . AO4, AOt levels A,C, evenntration A,C. The MAPT expression, the total MAPT protein level was evaluated in SH-SY5Y cells after 24 h, 48 h, and six days of transfection with AO4 and RNV563. Briefly, AO4- and RNV563-treated SH-SY5Y cells were incubated for 24 h, 48 h, and six days before collection. The cell pellet was collected and lysed, and the proteins were separated on a 10% separating gel and transferred onto a nitrocellulose membrane. The membrane was incubated with 1:2000 dilution of the MAPT  antibody overnight at 4 °C. The results clearly demonstrated that there was downregulation of the MAPT gene transcript and MAPT protein in cells 24 h, 48 h, and six days after AO4 treatment; however, the highest MAPT protein downregulation (74%) was seen six days after transfection  was seen 48 hours after RNV563 transfection . Although some early work focused on developing nucleic acid-based shown in . Therefo5Y cells  and the ariant 2 , confirmGA4 mRNA , and othITGA4-targeting DNAzyme RNV143 [MAPT with asymmetrical arms. The most efficient first-generation DNAzyme RNV563 was selected for further analysis by increasing and decreasing the arm lengths based on the cleavage efficiency. Decreasing the arm length of DNAzyme RNV563 resulted in a decreased cleavage efficiency in both SY-SY5Y cells and in the in vitro cleavage assay. The decreased cleavage efficiency observed could be due to the decreased binding affinity of the shorter arm lengths of the DNAzymes to their target RNA. In contrast, increasing the arm length of DNAyzme RNV563 resulted in a reduced cleavage efficiency in SH-SY5Y cells, but improved cleavage efficiency of the synthetic RNA in the in vitro cleavage assay. Increasing the arm lengths of the DNAzyme RNV563 may increase the binding affinity to the target RNA, leading to an increased cleavage efficiency of the synthetic RNA in vitro; however as shown by a previous study, cleavage of the shorter unstructured RNA is more efficient than longer structured RNA and therefore, the in vitro cleavage efficiency may not be a good representative of the in-cell environment and the synthetic RNA used in the in vitro assay may not be a good representative of the longer and more complex structure of the RNA naturally found in cells [The optimal arm length, the symmetry or asymmetry of the arms, and the GC content may all affect the catalytic efficiency of the resulting DNAzyme, although how they affect the DNAzyme activity is unclear. The target binding arm of the DNAzyme is an important factor in determining the cleavage efficiency. Similar to our previously reported e RNV143 , we desiin cells ,41 FurthMAPT transcript in this study. AOs were designed to target regions in exons 1, 4, 5, 7, 9, and 12. Exon 1 contains the initiation codon, and the skipping of 5, 7, 9, and 12 exons would induce a frameshift and generate a premature stop codon . Inducing exon 4 skipping does not change the reading frame; however, it is structurally important [MAPT transcript simultaneously. The full-length product and exon-skipped RT-PCR products from AO4-treated samples were sequenced to confirm that the exon-skipped product is missing exon 4 , whereas AO4 was very effective, even at a concentration as low as 6.25 nM (>60% knockdown). The lead AO and DNAzyme candidates, AO4 and RNV563, were both further validated by testing the tau protein levels in SH-SY5Y cells, which demonstrated a reduction in the tau level at 24 and 48 h, and this effect lasted at least 6 days after transfection with AO4, but not with RNV563. Tau has a relatively long half-life of about 12–14 h [The most effective AO candidate, AO4 targeting exon 4, was found to be highly efficient in downregulating the  12–14 h , explain 12–14 h , the appMAPT transcript both in vitro and in SH-SY5Y cells, by screening different DNAzyme constructs targeting various exons of the MAPT gene transcript. The DNAzyme, RNV563, can downregulate the tau protein by more than 50%. We have also identified a 2’-OMePS antisense oligonucleotide, AO4, that induces efficient exon 4 skipping and downregulates the MAPT transcript in SH-SY5Y cells at concentrations as low as 6.25 nM, and downregulates the tau protein by more than 70%. We believe that our findings will contribute towards tau targeting drug development and help to improve the knowledge in the field. In summary, we have successfully identified an arm-loop-arm-type DNAzyme, RNV563, with a 10–23 catalytic motif that cleaves the


E. coli O157:H7, Salmonella enteritidis, and Listeria monocytogenes, which were selected based on their impact on economic loss or prevalence in recent outbreaks. Further evaluation and validation confirmed the accuracy of direct metatranscriptome RNA-seq and multiplex RT-PCR amplicon sequencing using Sanger sequencing and selective media. The study also included a comparison of different bioinformatic pipelines for metatranscriptomic and amplicon genomic analysis. MEGAN without rRNA mapping showed the highest accuracy of multiplex identification using the metatranscriptomic data. EPI2ME also demonstrated high accuracy using multiplex RT-PCR amplicon sequencing. In addition, a systemic comparison was drawn between Nanopore sequencing of the direct metatranscriptome RNA-seq and RT-PCR amplicons. Both methods are comparable in accuracy and time. Nanopore sequencing of RT-PCR amplicons has higher sensitivity, but Nanopore metatranscriptome sequencing excels in read length and dealing with complex microbiome and non-bacterial transcriptome backgrounds.Viable pathogenic bacteria are major biohazards that pose a significant threat to food safety. Despite the recent developments in detection platforms, multiplex identification of viable pathogens in food remains a major challenge. A novel strategy is developed through direct metatranscriptome RNA-seq and multiplex RT-PCR amplicon sequencing on Nanopore MinION to achieve real-time multiplex identification of viable pathogens in food. Specifically, this study reports an optimized universal Nanopore sample extraction and library preparation protocol applicable to both Gram-positive and Gram-negative pathogenic bacteria, demonstrated using a cocktail culture of  Biological threats, including bacteria, viruses, and parasites, remain as the top food safety challenge in the United States. According to the CDC surveillance for foodborne disease outbreaks most recent annual report, in 2016 outbreaks attributed to bacterial infection comprised 44% of the total 645 outbreaks and caused 76% of the 847 hospitalization cases . More imCommercial food safety testing methods include traditional plate counting methods, immunological techniques such as enzyme-linked immunosorbent assay (ELISA), lateral flow immunoassay chip, electrochemical biosensors, and chromatography, as well as nucleic acid-based approaches . As summin situ hybridization (FISH), nucleic acid sequence-based amplification (NASBA), and loop-mediated isothermal amplification (LAMP). PCR based methods are sensitive and specific but easily generate cross contamination between pre-PCR and post-PCR products. Insufficient permeability of cell walls and the inherent autofluorescence of the substrate will decrease the efficiency of FISH , Salmonella enteritidis (S. enteritidis), and Listeria monocytogenes (L. monocytogenes) in both standard general-purpose media and a food model. The three bacteria were selected based on their impact on economic loss or prevalence in recent outbreaks.Therefore, for the first time, RNA-enabled Nanopore sequencing is evaluated for its potential in achieving multiplex identification of viable pathogens in this study . SpecifiE. coli O157:H7 samples collected from 5 time points. E. coli O157:H7 growth curve  (Bacillus (still misidentified as 63.6%), which again was likely caused by rRNA or other housekeeping genes. Therefore, MEGAN with non-rRNA mapping was performed and successfully identified all three primary bacteria of Listeria, E. coli O157:H7 and Salmonella without any miss-identification  . MEGAN wfication .The results of multiplex RT-PCR sequencing showed that three targeted bacteria were anchored accurately by MinKNOW , and no RT-PCR was used to verify the presence of all three bacteria in the cocktail culture, and PCR was used to verify the complete removal of DNA contamination using the protocol described above.stx, invA and inlA in the 24-h cocktail culture, which confirms the presence of all three target pathogens. No bands appeared on negative controls using only PCR without the RT step, which indicates the absence of DNA contamination.stx), 244 (invA) and 153 (inlA) bp  bp  line 2. E. coli O157:H7, S. enteritidis, and L. monocytogenes in both standard general-purpose media and a food model.In this study, RNA-enabled Nanopore sequencing is evaluated, for the first time, for its potential in achieving multiplex identification of viable pathogens. The optimized universal RNA extraction and DNA digestion method was developed to simplify and standardize the RNA preparation for both Gram-positive and Gram-negative bacteria. Direct metatranscriptome RNA-seq and multiplex RT-PCR amplicon sequencing were evaluated and compared using a cocktail culture of False positives are a major issue for DNA-based approaches. This is due to the inability to differentiate DNA molecules in viable bacterial cells from the genomic background, which is comprised of stable DNA molecules from the microbiota, the food matrices, and dead pathogens inactivated during food processing and storage. Both approaches developed in this study only utilize RNA, especially mRNA, as the ultimate sequencing target, which eliminated false positive identification typically caused by DNA contamination.Random and unknown threats from multiple infectious bacteria poses a significant threat to the safety and security of food supplies worldwide. A feasible strategy should enable multiplex identification without the need to customize for an individual threat. Therefore, the developed universal protocol is applicable to both Gram-positive and Gram-negative bacteria. RNA from multiple pathogens in one food sample can be collected from one extraction and library preparation step, followed by the universal sequencing protocol.The developed method successfully identified all three bacteria from cocktail culture in BHI and LJE by direct metatranscriptome RNA-seq and multiplex RT-PCR amplicon sequencing. Nonetheless, the two sequencing approaches entail different capacities and challenges. Direct metatranscriptome RNA-seq does not require assay customization for an individual biohazard if the bioinformatic database includes the target microbiota. Multiplex RT-PCR amplicons comprise the target gene copies, and they can be easily captured by the motor membrane protein when passing through the nanopores. As a result, it shows higher accuracy, greater quality score, better quality control, and less turnaround time.E. coli O157:H7, S. enteritidis and L. monocytogenes cocktail culture was 16S RNA with 1250–2100 nucleotides, and 23S RNA with 2250–3950 nucleotides, respectively. Direct metatranscriptome RNA-seq sequencing successfully identified all three bacteria. The RNA read length ranged from 0 to 3000 nucleotides, with the most abundant read length between 400–1600 nucleotides. The read length from direct metatranscriptome RNA-seq is approximately the full length of RNA (stx), 244 bp (invA) and 153 bp (inlA), respectively. Some reports suggest that Nanopore excels in long RNA reads up to thousands of nucleotides, and sequencing of short reads tends to be more challenging due to their higher and non-uniform error profiles, which might result in a large fraction of reads remaining unmapped or unused , library preparation (1.5 h), Nanopore sequencing (1 h), and bioinformatic analysis (0.5 h). Multiplex amplicon sequencing takes approximately 6 h, which includes RNA purification (3.5 h), RT-PCR (2 h), library preparation (0.5 h), Nanopore sequencing (15 min), and bioinformatics analysis (0.5 h).1–104 CFU/g. 500 ng RNA input on Nanopore MinION is recommended by the supplier for metatranscriptomic direct RNA-seq. However, significant RNA loss was observed during the library preparation due to the three purification steps. The initial purified RNA concentration before library preparation was 3490 ng and 1338 ng in BHI and LJE, respectively, and only 744 and 130 ng were yielded for Nanopore sequencing. RNA loss can be as high as 80–90%, which significantly restricted sensitivity of the assay. Redesign of the library preparation protocol to minimize RNA loss can have profound significance for assay sensitivity and feasibility for clinical applications.Multiplex RT-PCR amplicon sequencing requires substantially less RNA input, which could translate into less microbial input. The method only requires 36.5 ng RNA input for multiplex RT-PCR, and 33.8 ng amplicon for library preparation and sequencing . The ampThe two strategies pose different levels of complexities. Direct metatranscriptome RNA-seq may be applicable in foods with a complex microbiome (e.g. cultured food). Direct metatranscriptome RNA-seq does not require assay customization for an individual biohazard, if the bioinformatic database includes the target microbiota. The multiplex RT-PCR amplicon sequencing requires complex primer design and validation. Not all RT-PCR primers work in multiplex RT-PCR, due to potential primer interaction, non-specific amplification, and amplification bias. The amplicon sequencing may be more suitable for high-throughput and continuous monitoring of foodborne pathogens with high risk factors.1. MinION chemistry provides a simplified and rapid report of nanopore running, including read number, read length, cumulative read, taxonomy tree and quality control. MEGAN and MG-RAST are popular software or service for metagenome or metatranscriptome analysis. The similarity between them is that they perform computational analysis of multiple datasets for taxonomic content based on family and genus level. In contrast, MEGAN is able to perform taxonomical, functional and interactive analyses, which is the comparison of taxonomic and functional contents based on the SEED hierarchy and KEGG pathways  from this study confirmed the identification of the three pathogens performed by Nanopore direct metatranscriptome RNA-seq, even though NGS had time-consuming and cost prohibitive limitations. The study also included a comparison of different bioinformatic pipelines for metatranscriptomic and amplicon genomic analysis. In addition, direct metatranscriptome RNA-seq and RT-PCR amplicon sequencing were compared for their respective advantages in sample inputs, accuracy, sensitivity, and time effectiveness for potential applications.Both direct metatranscriptome RNA-seq and multiplex RT-PCR amplicon sequencing require further development to address some pressing challenges. (A) Optimization of direct metatranscriptome RNA-seq sequencing may include minimizing RNA loss in the library preparation step; comparison of bioinformatic pipelines to eliminate miss-identified and unclassified targets; cross-domain identification of prokaryotes, eukaryotes, and viruses. This is especially important in complex mixtures. For example, less RNA loss during library preparation often leads to increased RNA yield and input on Nanopore device, which improves data quality and accuracy of the taxonomical identification. (B) Multiplex RT-PCR amplicon sequencing can benefit from: multiplex primer development; inclusivity/exclusivity evaluations; reduced amplification bias.To the best of our knowledge, this is the first report of metatranscriptome sequencing of cocktail microbial RNAs on the emerging Nanopore platform. Direct RNA-seq and RT-PCR amplicon sequencing of the metatranscriptome enable the direct identification of nucleotide analogs in RNAs, which is highly informative for determining microbial identities while detecting ecologically relevant processes. The information pertained in this study could be important for future revelatory research including predicting antibiotic resistance, elucidating host-pathogen interactions, prognosing disease progression, and investigating microbial ecology, etc.Escherichia coli O157:H7 (ATCC 43895), S. enteritidis (ATCC 13076), and L. monocytogenes (ATCC 19115) were acquired from ATCC . The three bacteria were cultured using Brain Heart Infusion (BHI) broth and agar  at 37°C for 24 h either in separate individual cultures or in cocktail cultures. Romaine lettuce (Lactuca sativa L. var. longifolia) juice extract (LJE) was used as a food model in this study. Romaine LJE was prepared according to our previous publications (g for 10 min (low speed centrifugation), the supernatant from low speed centrifugation was then centrifuged at 3200 × g for 30 min (high speed centrifugation). High speed centrifugation supernatant was then filtered through 0.2 micro filter membrane  and diluted to 4% using sterilized DI water (COD = 800 ppm) to grow bacteria.ications . BrieflyE. coli O157:H7, S. enteritidis, and L. monocytogenes in BHI or LJE were obtained by inoculating appropriate volume of 24-h stock culture of the individual bacteria to achieve the initial concentration shown in Listeria selective agar base (Oxford formulation) with Oxford modified Listeria selective supplement was used for the selective quantification of L. monocytogenes. MacConkey agar  was used to differentiate and quantify E. coli O157:H7 and S. enteritidis. Both cultures were incubated at 37°C for 24 h before quantification using an automated plate counter .Cocktail culture of Escherichia coli O157:H7 was selected as a model organism to demonstrate that mRNA is a valid indicator for bacteria viability. Aliquot of overnight E. coli O157:H7 culture was inoculated at 3-log CFU/mL in BHI broth and incubated at 37°C. Culture was sampled at 0, 4, 8, 24, and 72 h, fractions from the sample culture were taken and plated on BHI agar to determine viable bacteria counts. Remaining fractions were used for DNA and RNA purification using DNeasy blood and tissue kit  and Monarch Total RNA Miniprep Kit  following the supplier protocols, respectively. In RNA preparation, to lyse the cells, the cell pellet obtained from initial centrifugation was incubated at 37°C for 1 h with 300 rpm mixing in 250 μL 3 mg/mL lysozyme  in Tris-EDTA buffer . Purified DNA and RNA from these four time points were quantified using Qubit dsDNA HS Assay Kit and Qubit RNA HS Assay Kit , and also quantified using NEB Luna Universal qPCR Master Mix and Luna Universal One-Step RT-qPCR Kit  following supplier protocols., A Biorad CFX-96 Touch real time PCR detection system was used for qPCR and RT-qPCR testing. The primer pairs used in this test were designated as Stx1A and sequence was listed in E. coli O157:H7 inactivated with 13.4 mmol/L of sodium hypochlorite was used as the negative control to test whether mRNA and/or DNA can be used as viability biomarkers  which was confirmed by multiplex RT-PCR and gel electrophoresis. DNA was completed digested using DNase I , which was verified by multiplex PCR and gel electrophoresis. The primers stx, invA and LisA2 were selected for E. coli O157:H7, S. enteritidis, and L. monocytogenes, respectively. RT-PCR products were analyzed using gel electrophoresis with 1.2% agarose gel. The One Taq One-Step RT-PCR Kit (NEB # E5310S) was used for nucleic acid amplification. The thermal cycler condition: reverse transcription at 48°C for 15 min; initial denaturation at 94°C for 1 min; denaturation at 94°C for 15 s, annealing at 53°C for 30 s, extension at 68°C for 40 s with 40 cycles; final extension at 68°C for 5 min.One dimensional direct metatranscriptome RNA-seq was performed using 24-h cocktail culture of The prepared RNA samples were further modified with poly(A) tailing and library preparation by following suppliers’ protocols. Direct metatranscriptome RNA-seq was developed based on supplier’s direct RNA-seq protocol . The MinION flow cell was primed using a priming mix, and then 75 μl of sample was loaded to the SpotON sample port dropwise to avoid bubbles. After adding the sample, MinKNOW software was initiated to start a sequencing run. Cocktail culture inactivated with 13.4 mmol/L of sodium hypochlorite was used as the negative control to test whether metatranscriptome sequencing can eliminate false positive identification.E. coli O157:H7, S. enteritidis, and L. monocytogenes in BHI or LJE was obtained by NEB Monarch Total RNA Miniprep Kit including DNase I as we did for direct metatranscriptome RNA-seq on a MinION sequencer (Oxford Nanopore). The quantification and quality control of samples were tested by Qubit RNA HS Assay (Thermo Fisher Scientific) and 2100 Bioanalyzer system . NEBNext Ultra II Directional RNA Library Prep Kit (NEB # E7760), NEBNext rRNA Depletion Kit (NEB # E6310) and NEBNext Multiplex Oligos for Illumina  were used for NGS library prep.RNA extracts of 24-h cocktail culture of RNA samples from BHI (quadruplicate) were developed by following the NEB Protocol for use with FFPE RNA, NEBNext rRNA Depletion Kit (Human/Mouse/Rat) (E6310) and NEBNext Ultra II Directional RNA Library Prep Kit for Illumina  as their RNA Integrity Number (RIN) was equal to 1 to 2, while samples from LJE (quadruplicate) were treated by following NEB Protocol for use with NEBNext rRNA Depletion Kit (Human/Mouse/Rat) (NEB # 6310) as their RIN was above 7. Briefly, 100 ng of FFPE RNA was diluted in 12 μl of Nuclease-free Water for RNA probe hybridization. Twelve microliters of total RNA was mixed with 1 μl of NEBNext rRNA Depletion Solution and 2 μl of Probe Hybridization Buffer by following the thermal condition: heated lid at 105°C; 2 min at 95°C; ramp down to 22°C at 0.1°C/s; 5 min hold at 22°C. After that, RNase H Digestion and DNase I Digestion were performed. First, 15 μl of sample were mixed with 5 μl of RNase H master mix  and incubated for 30 min at 37°C (heated lid at 40°C), and then 20 μl of sample was added to 30 μl of DNase I digestion master mix  and incubated for another 30 min with the same condition. After RNA purification by beads , 5 μl of sample with Nuclease-free Water was reacted with 1 μl of random primers for 5 min at 65°C  for priming. The 6 μl of primed RNA was mixed with 4 μl of NEBNext First Strand Synthesis Reaction Buffer, 8 μl of NEBNext Strand Specificity Reagent and 2 μl of NEBNext First Strand Synthesis Enzyme Mix to form the first strand cDNA , followed by second strand cDNA synthesis by reacting with 8 μl of NEBNext Second Strand Synthesis Reaction Buffer with dUTP, 4 μl of NEBNext Second Strand Synthesis Enzyme Mix and 48 μl of Nuclease-free Water for 1 h at 16°C. After cDNA synthesis, a bead purification (1.8X) was performed as before and 50 μl of purified cDNA was eluted. End Prep of cDNA was carried out in a reaction of 50 μl of Second Strand Synthesis Product, 7 μl of NEBNext Ultra II End Prep Reaction Buffer, 3 μl of NEBNext Ultra II End Prep Enzyme Mix , and Adaptor Ligation in a reaction of 60 μl of End Prepped DNA, 2.5 μl of 25-fold Diluted Adapter, 1 μl of NEBNext Ligation Enhancer and 30 μl of NEBNext Ultra II Ligation Master Mix with incubation for 15 min at 20°C, followed by another 15 min incubation at 37°C by adding 3 μl of USER Enzyme. DNA libraries were purified with 0.9X DNA purification beads. Finally, 15 μl of adaptor ligated DNA, 25 μl of NEBNext Ultra II Q5 Master Mix, 5 μl of Universal PCR Primer/i5 Primer, 5 μl of Index (X) Primer/i7 Primer  were mixed together for PCR Enrichment . After PCR enrichment, 0.9X beads were used to purify DNA and 0.1X TE was used to elute DNA for Assess Library Quality on Agilent 2100 Bioanalyzer. RNA samples from LJE and BHI samples were treated the same, however, the LJE samples had an additional step of RNA Fragmentation via mixing 5 μl of sample with 4 μl of NEBNext First Strand Synthesis Reaction Buffer and 1 μl of random primers, following the incubation at 94°C for 15 min.Library quantification was done on CFX-96 Touch system  using NEBNext Library Quant Kit (NEB # E7630) by following NEBNext Library Quant Kit Protocol. Libraries were diluted at 1:1,000, 1:10,000 and 1:100,000 and 4 μl of template was used in each 20 μl total reaction volume. The thermal condition: initial denaturation at 95°C for 1 min; denaturation at 95°C for 15 s with 35 cycles; extension at 63°C for 45 s. The calculation of quantitative library DNA was achieved by using NEBioCalculator v1.10.0.E. coli O157:H7, L. monocytogenes, and S. enteritidis as a control dataset. All eight libraries were normalized to a 1 nM concentration and 5 μl of library pool was diluted to a 50 pM loading concentration. PhiX was spiked-in to the library pool at 10%. This value was used in order to increase base diversity. Paired-end reads were generated at 151 cycles each with a 6-nucleotide indexing read.An iSeq 100 system  was used to sequence 24-h cocktail cultures of BHI-derived and LJE-derived E. coli O157:H7, S. enteritidis, and L. monocytogenes in BHI or LJE. RNA extraction and DNA digestion were performed and verified using the same protocols listed above. Major virulent genes selected in this study include: stx and stx1A localized to the lambdoid prophages H19B and H19J in E. coli O157:H7  in S. enteritidis  in L. monocytogenes  was carried out for the RT-PCR products, followed by a ligation step using NEB Ultra II ligation master mix. Oxford Nanopore Ligation Sequencing Kit SQK-LSK108 and Library Loading Bead Kit EXP-LLB001 were used for the library preparation of RT-PCR amplicon. To validate Nanopore DNA sequencing results, the RT-PCR products were also sequenced using an Applied Biosystems 3130xl genetic analyzer by following a protocol at NEB. The DNA sequencing data collected by the sequencer was analyzed using EPI2ME and MG-RAST to confirm the identities of the three bacteria. Cocktail culture inactivated with 13.4 mmol/L of sodium hypochlorite was used as the negative control to test whether multiplex RT-PCR amplicon sequencing can eliminate false positive identification. Detailed protocols for RT-PCR are provided in the Sequencing reads were base-called via the local base-calling algorithm with MinKNOW software (v. 1.4.3). All FASTQ files of passed base-called reads were collected and combined to one file for analysis. EPI2ME, MG-RAST and MEGAN were used for metagenomics and taxonomic analysis.2. Sequencing reads were uploaded to Galaxy  and adapter sequences were trimmed using Cutadapt  with a quality cutoff of 20. The program trimmed the adapter sequence in both the 5′ and 3′ orientations. Forward and Reverse reads were joined using FASTQjoiner . FASTQ files were converted to FASTA files using FASTQ to FASTA program3 . FASTA files were then aligned and mapped on NCBI BLAST by its refseq_rna and blastn programs. The Common Taxonomic tree and Alignment Summary Viewer tools were used to analyze alignments per bacterial species and to generate phylogenetic trees.Sequencing reads were demultiplexed by unique six nucleotide barcodes using the bcl2fastq programThis manuscript has been released as a Pre-Print at bioRxiv .https://www.ncbi.nlm.nih.gov/bioproject/609733, Accession number: PRJNA609733.The datasets generated for this study can be found here: MY, AC, XL, DS, SL, TG, and LS provided assistance and conducted the experiments throughout the project. MY, HD, and JL aided in NGS validation. YL, MX, and BZ provided guidance and wrote the manuscript.AC, DS, SL, LS, and MX were employed by the New England Biolabs, Inc. The remaining 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.


Alsuviricetes and consists of viruses with simple genomes that typically encode only RNA-dependent RNA polymerase (RdRP), capping enzyme and capsid protein. Several clades within the Yangshan assemblage independently evolved domain permutation in the RdRP. Another previously unknown clade shares ancestry with Potyviridae, the largest known plant virus family. The ‘Aquatic picorna-like viruses/Marnaviridae’ clade was greatly expanded, with more than 800 added viruses. Several RdRP-linked protein domains not previously detected in any RNA viruses were identified, such as the small ubiquitin-like modifier (SUMO) domain, phospholipase A2 and PrsW-family protease domain. Multiple viruses utilize alternative genetic codes implying protist  hosts. The results reveal a vast RNA virome that includes many previously unknown groups. However, phylogenetic analysis of the RdRPs supports the previously established five-branch structure of the RNA virus evolutionary tree, with no additional phyla.RNA viruses in aquatic environments remain poorly studied. Here, we analysed the RNA virome from approximately 10 l water from Yangshan Deep-Water Harbour near the Yangtze River estuary in China and identified more than 4,500 distinct RNA viruses, doubling the previously known set of viruses. Phylogenomic analysis identified several major lineages, roughly, at the taxonomic ranks of class, order and family. The 719-member-strong Yangshan virus assemblage is the sister clade to the expansive class  Metagenomic analysis of a single RNA virome from the Yangshan Deep-Water Harbour in China enabled the recovery of more than 4,500 distinct RNA viruses, doubling the known set of RNA viruses to date, and provided insights into their biology. The International Committee on Taxonomy of Viruses has approved formal classification of viruses characterized solely by metagenomics10. The rapid advances in metaviromics have substantially expanded the known diversity of RNA viruses, yielding vast amounts of sequences for comprehensive studies on RNA virus evolution17.Metagenomics and metaviromics  have led to rapid progress in virus discovery2. Rich RNA viromes have been described in aquatic environments as diverse as Antarctic seas and wastewater20. Although metaviromic analyses do not typically identify the virus hosts, some of the marine RNA virome components have been phylogenetically anchored through similarity to viruses with known hosts. Perhaps the best characterized group of such viruses is the family Marnaviridae, which combines picorna-like viruses of diatoms and other stramenopiles26 with a growing number of species defined by metagenomics as probably infecting related aquatic unicellular eukaryotes28 (hereafter referred to as ‘protists’).Metagenomic investigation of various aquatic environments provides access to viromes of diverse prokaryotes and unicellular eukaryotes that could harbour ancient lineages of the RNA viruses33. The high diversity of the invertebrate RNA virome suggests that RNA viruses of land plants and vertebrates evolved from viruses infecting invertebrates2. The known RNA viromes of plants, fungi, protists and bacteria have also expanded through meta-transcriptome sequencing, albeit not as massively as the invertebrate virome41.Another key development has been meta-transcriptome sequencing of invertebrate holobionts, doubling the size of the known RNA virome42. The deepest branch 1 includes the only known group of positive-sense (+)RNA viruses of prokaryotes, the leviviruses and their eukaryote-infecting descendants (narna- and ourmia-like viruses). The remaining four branches consist mostly of RNA viruses that infect eukaryotes. Branch 2 includes the assemblage of +RNA viruses denoted ‘picornavirus-like supergroup’, along with some of the smallest +RNA viruses in the Solemoviridae family and the largest +RNA viruses of the order Nidovirales. Branch 2 also contains two families of double-stranded RNA (dsRNA) viruses, Partitiviridae and Picobirnaviridae. Branch 3 consists solely of +RNA viruses, including the ‘Alphavirus supergroup’, a variety of viruses with small genomes resembling tombusviruses and nodaviruses, and the ‘Flavivirus supergroup’. Branch 4 consists of diverse dsRNA viruses, including the large families Reoviridae and Totiviridae, and the only known family of prokaryotic dsRNA viruses, Cystoviridae. Finally, branch 5 includes all known negative-sense (−)RNA viruses. A comprehensive virus ‘megataxonomy’ has been recently proposed and subsequently formally approved by the International Committee on Taxonomy of Viruses, in which the five major branches of the RdRPs correspond to five phyla in the kingdom Orthornavirae44. Despite these advances, a pressing question remains: would the current view of the RNA virome change substantially with deeper sampling, or are we getting close to an effectively complete coarse-grain picture of the global RNA virome? Is it likely that additional phyla of RNA viruses remain to be discovered?A comprehensive phylogenetic analysis of RdRP, the only universally conserved protein of RNA viruses, produced a phylogenetic tree comprised of five major branches44.Here we report an extensive analysis of an RNA virome in water samples from Yangshan Deep-Water Harbour near Shanghai, China, where the Yangtze River meets the East China Sea Fig. . This anRNA virome analysis performed using complementary DNA derived from approximately 10 l of samples from Yangshan Deep-Water Harbour yielded 4,593 nearly full-length RNA virus RdRPs that formed 2,192 clusters at 75% amino acid identity which represents virus diversity at a level between species and genus. Among the RdRP sequences from GenBank (October 2018), 2,021 comparable clusters were detected. Thus, the 10 l water sample analysed here more than doubles the known diversity of RNA viruses.Lenarviricota, Pisuviricota and Kitrinoviricota, whereas no members of Negarnaviricota were found. Only six dsRNA viruses (Duplornaviricota) were identified, but were not further analysed. No enveloped +RNA viruses of the families Flaviviridae and Togaviridae were detected. Common +RNA viruses of terrestrial vertebrates and plants  were also absent from the Yangshan virome.Phylogenetic analysis assigned 85% of the RdRPs from the Yangshan RNA virome to 9 clades and one complex assemblage, each comprising more than 100 RdRps from several clusters Fig. . All thePicornavirales)42 in the phylum Pisuviricota , with 653 members  joined the lineage that includes plant Solemoviridae, fungal Barnaviridae and protist Alvernaviridae.Pisuviricota , likely inherited from the common ancestor of all eukaryotic RNA viruses42. In contrast, filamentous potyviruses encode a distinct capsid protein51, which is homologous to nucleocapsid proteins of (−)RNA viruses53. These findings are consistent with the ancestral status of protopotyviruses with respect to potyviruses.The fourth clade (101 members) of the Yangshan virome RdRPs within Kitrinoviricota; this was unexpected, given that so far, Kitrinoviricota consisted largely of viruses of terrestrial plants and animals54. Two virus groups from the Yangshan virome fell within the Tombus-like (589 members) and Noda-like (414 members) clades of Kitrinoviricota  comprising viruses that infect mostly plants, as well as animals and fungi.More than 1,700 Yangshan virome RdRPs belong to ota Fig. . NodavirAlsuviricetes within Kitrinoviricota  consists of several clades positioned between the noda-like viruses and ta Figs.  and 3. TPermutotertraviridae and Birnaviridae63, but were excluded from our previous analysis due to interference with multiple RdRP alignments caused by the permutation42. Here we developed a procedure for swapping domains in the permuted RdRPs to restore the original domain order and included these reconstructed RdRP sequences in the phylogenetic analysis  and by encoding a capping enzyme similar to the distinct capping enzyme of nodaviruses  consists of 113 members Fig.  and inclses Fig. .Fig. 4Di30. The phylogenomic diversity within the Yangshan assemblage seems to justify the establishment of a virus class, subdivided into multiple orders and families.The third major clade in the Yangshan assemblage, denoted ‘Shanghai’, harbours 74 Yangshan RdRPs and the unclassified ‘eunivirus’ (KF412900), which was identified in a wastewater virome Fig. ; OV.15. Kitrinoviricota (‘Brandma-like’ viruses) combines 282 RdRPs , or with ourmia-like viruses (382 members), the eukaryote-infecting descendants of +RNA bacteriophages37  spacers yielded a single match between one of the Yangshan RNA virome contigs bearing a levi-like RdRP and the reverse transcriptase-associated type III-B CRISPR locus of the bacterium Candidatus Accumulibacter sp. SK-02  and identified 13 singleton RdRP sequences. The further expansion of the global RNA virome is expected to allow more confident assignment of these divergent RdRPs to additional clades, as was the case with the Yan-like, Zhao-like, Brandma-like and Protopoty clades.2+-binding and methyltransferase domains, as well as conserved domains of unknown function. Collectively, these observations reveal dynamic acquisition of multiple functional domains that might be involved in distinct virus–host interactions.Analysis of the domain content of the longer Yangshan contigs indicates that the genome organizations are typically similar within clusters enriched in Yangshan viruses and closely resemble the genome organizations of the previously known viruses from the same clades. Nevertheless, we identified several domains that have not been previously observed in any RNA viruses Fig. , includiLenarviricota, Aquatic picorna-like and Dicistro-like viruses in Pisuviricota and Tombus-like and Noda-like viruses in Kitrinoviricota , order  and family RNA viruses in protists. A similar logic applies to the absence of the enveloped viruses of the Alsuviricetes and Flasuviricetes in the Yangshan virome: none of these viruses are known to infect protists. However, we cannot rule out that some unidentified technical bias in the procedures employed in this work also contributed to the dominance of +RNA viruses in the Yangshan virome.The Yangshan virome could also shed light on RNA virus ecology. Quantitative analysis of contig occurrence revealed several extremely abundant viruses that are likely to reflect virus blooms on the most abundant hosts  was used for extraction of either DNA using Purification Resin and Mini Column (Promega)69, or RNA by using TRIzol LS Reagent (Invitrogen) and the Fast Total RNA Kit (Generay Biotech)  were used. Two 150 bp paired-end libraries  were generated using random-hexamer priming with the TruSeq RNA Library Prep Kit (Illumina) for the virus RNA extracts with or without DNase I digestion. Two single-end libraries were generated for the DNase I treated viral RNA extract using template-switching reverse transcription with the SMARTER stranded total RNA-seq kit (Clontech): one without fragmentation, and one with 4 min fragmentation at 94 °C, according to manufacturer’s instructions. The TruSeq Nano DNA HT Library Prep Kit (Illumina) was used to generate a 150-bp paired-end DNA library from the virus DNA extracts . To obtain a ‘clean’ RNA dataset, DNA-matching reads were computationally subtracted from the pool of RNA reads before virus genome assembly using a k-mer based approach. All unique 30-mers present in the DNA library were collected and RNA reads with an exact match to any 30-mer in the DNA library (on either read in the mate-pair for the paired-end datasets) were then excluded prior to contig assembly. Then, 20- and 25-mers were also tested to ensure that the subtraction was not sensitive to the k-mer length. As anticipated by a priori calculations, while subtraction using 20-mers resulted in gross overfiltering, 25- and 30-mers resulted in very similar numbers of removed reads. We also repeated the subtraction separately for the RNA libraries with or without DNase I treatment using 30-mers from the DNA dataset, and found no substantial difference in the numbers of removed reads (about 50% in each case), thereby underscoring the importance of in silico DNA subtraction.Sequencing adapters were first removed, and nucleotides with quality scores lower than 20 were trimmed from the ends of reads using the cutadapt tool . After assembly, the two sets of contigs were unified into a single set of non-redundant contigs by excluding any contig from the template-switching dataset that shared more than 90% of its 15-mer sub-sequences with any contig in the random-priming dataset.42 were used to generate query position-specific scoring matrices. Sequences that covered at least 75% of the query profile length were considered to contain full-length RdRP cores. This analysis identified almost 75,000 contigs  encoding predicted proteins with significant amino acid sequence similarity to previously identified RdRP. Of these, 4,593 proteins were operationally considered ‘full-length’ RdRP. Initial clustering of the identified full-length RdRPs was performed using MMSEQ270 with sequence similarity threshold of 0.5. When the same position-specific scoring matrices were employed to search the protein sequences from GenBank, 5,481 full-length, non-redundant (<90% identity) RdRP sequences were identified that formed 2,021 clusters. After the addition of 4,593 full-length sequences from the Yangshan dataset, the combined set of 10,074 sequences produced 4,213 clusters under the same clustering procedure, increasing the number of clusters by a factor of 2.08.RdRp sequences were identified using PSI-BLAST, which was run against the six-frame end-to-end translations of all contig sequences. Multiple alignments of virus RdRPs and reverse transcriptases from group II intron and non-long-terminal-repeat retrotransposons71. Cluster-derived profiles were compared to existing profiles using the HHsearch program72 to broadly assign the Yangshan sequences to the five major branches of RdRPs42. Iterations of clustering using HHsearch and profile–profile alignments using HHalign were performed to refine the positions of the Yangshan sequences within the RdRP tree. The clusters were delineated such as to include sufficiently diverse sequences and to be significantly enriched with sequences from the metaviromic sample. This procedure yielded 323 clusters (OV.1 to OV.323 in Supplementary Dataset 73 with the WAG evolutionary model and gamma-distributed site rates. Nearly monophyletic groups of Yangshan RdRPs (containing at least 90% of Yangshan metagenome sequences) or mixed, but shallow groups of Yangshan RdRPs (corresponding to the tree depth of less than 1.0 substitution per site) were considered to be distinct Yangshan clusters.Multiple alignments of sequences within clusters were generated using MUSCLE47 using a procedure that involved several iterations of aligning Yangshan RdRPs with those from GenBank, constructing preliminary trees, and extracting Yangshan RdRPs that grouped together. The overwhelming majority of the Yangshan sequences  and all large clusters (31 clusters encompassing 22 or more sequences each) were affiliated with previously identified RdRP lineages . Then the de-permuted sequences were returned to the pool, replacing the permuted originals. This procedure was used to generate Extended Data Fig. In addition to the RdRPs that could be assigned to previously identified clades at different depths of the phylogenetic tree, we attempted to detect putative highly divergent RdRPs. First, all long RNA contigs  from the virome were translated stop-to-stop in 6 frames, and any which encoded open reading frames for more than 400 amino acids were selected and clustered by sequence similarity. The 37 profiles constructed from the resulting cluster alignments of 10 or more sequences were used as queries to search sequence databases with HHPred search. No RdRPs were found among these clusters. Second, open reading frames derived from 33 of the longest contigs in our dataset were analysed one at a time using HHPred; this procedure resulted in the identification of 13 singleton RdRP sequences .74 and 102 were developed in-house77. PSI-BLAST searches initiated by these profiles were competed against other, unrelated PFAM profiles in the Conserved Domains Database. Significant  hits were recorded; contigs containing these hits were tentatively assigned to the respective virus group. Sampled sequences were manually curated using HHPred to verify or correct assignments.The nucleotide sequences of DNA viruses were identified by comparing position-specific scoring matrices for the respective capsid proteins to the 6-frame translated sequences of the DNA metagenomic contigs using PSI-BLAST. The set of scoring matrices consists of 200 profiles derived from multiple alignments of capsid and coat proteins of eukaryotic, bacterial and archaeal DNA viruses. Of these, 98 alignments were taken from National Center for Biotechnology Information Conserved Domains DatabasePhycodnaviridae (for example YP_004062106.1/NC_014767.1 matching NODE_1923356 contig). Many of these also have close matches in ‘bacterial’ assemblies from marine metagenomes (MAB60321.1/NYUE01000104.1). All four parvovirus contigs showed only distant similarity (about 30% protein identity) to vertebrate parvoviruses (for example APQ44761.1/KY053092.1 matching NODE_10537 contig), suggesting that these are viruses of unidentified hosts rather than vertebrate virus contaminants.For many of the Polinton-like virus contigs, the best hits in the NR database are (erroneously) annotated as bacteria assembled from marine metagenomes . These ‘bacterial’ assemblies probably contain numerous fragments derived from the marine virome. All nucleo-cytoplasmic large DNA virus (NCDLV) contigs were found to be highly similar to 72. The identification procedure was run iteratively. First, profiles for each in silico-translated protein sequence were generated by performing one iteration against uniclust30_2018_08 database78 with HHblits79. The generated profiles were then compared against the previous generated RNA virus profile database42. Protein regions longer than 100 residues that did not display significant hits were extracted and clustered with CLANS80. Groups containing at least five members were identified using convex clustering algorithm implicated in CLANS, aligned with MUSCLE71, annotated when possible and added to the RNA virus profile database. In addition, extracted protein regions were searched against the European Bioinformatics Institute metagenomics database81, supplemented with the RNA virus protein sequences from the current study by performing one iteration of Jackhmmer82. Profiles with statistically significant hits (probability >95%) were annotated and added to the RNA virus profile database. Finally, domain identification procedure was repeated using the updated RNA virus profile database.To identify protein domains, we performed sensitive profile–profile comparisons using HHsearch83.CRISPR spacers  were matched against the set of oceanic virus contigs; 90% identity, 90% coverage criteria were used for matches, as previously described84. A bowtie2 index was generated from the combined non-redundant contigs assembled from all RNA libraries, and bowtie2 was then used to map reads from each experiment back to these contigs. All RdRP-bearing contigs were more than 95% covered by mapped reads. The abundance of each contig was calculated as mapped reads per kilobase per million (RPKM) total reads in the library.The abundances of viruses present in each virus cluster were calculated by mapping DNA-subtracted RNA sequencing reads back to RdRP-bearing contigs using bowtie2The distribution of contig abundances covers several orders of magnitude, is unimodal with a peak at ~18 RPKM and a median of 21.3 RPKM, and resembles a log-normal distribution , suggesting an overall prevalence of eukaryotic aquatic plankton. The Tombus-like clade was also well represented, largely, due to the most abundant virus mentioned above. The Yan-like and Zhao-like clades within the Yangshan assemblage contained several highly abundant viruses as well. Finally, several ourmia-like (OV.6) and levi-like viruses were prominent, particularly, the most abundant putative +RNA phage (OV.81).The contigs were then grouped by cluster or by clade to identify over-represented lineages  and used for the further phylogenetic analysis.Supplementary Data 2Clade-specific phylogenies for Yangshan RNA viruses. Each tree contains representatives of the indicated clade  as well as phylogenetically close reference viruses. Genome maps for the sequences and some reference viruses are shown on the right. The functional domains are colour-coded and the key is provided at the bottom of each panel. MP, movement protein; RBP, RNA-binding protein; GTase, guanylyltransferase; rXXX.0, uncharacterized conserved domains.Supplementary Data 3The RdRP core domains in the Yangshan RNA virome encoded in alternative genetic codes.Supplementary Data 4The relative abundance of the virus contigs in the Yangshan RNA virome.Supplementary Data 5The list of full-length RdRPs in the Yangshan RNA virome. Correspondence between the amino acid sequence IDs (‘orf.nnn’), contig IDs (‘NODE_xxx’), coordinates of the RdRP core in the nucleotide sequence and GenBank contig IDs are shown.


Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), responsible for the ongoing coronavirus disease (COVID-19) pandemic, is frequently shed in faeces during infection, and viral RNA has recently been detected in sewage in some countries. We have investigated the presence of SARS-CoV-2 RNA in wastewater samples from South-East England between 14th January and 12th May 2020. A novel nested RT-PCR approach targeting five different regions of the viral genome improved the sensitivity of RT-qPCR assays and generated nucleotide sequences at sites with known sequence polymorphisms among SARS-CoV-2 isolates. We were able to detect co-circulating virus variants, some specifically prevalent in England, and to identify changes in viral RNA sequences with time consistent with the recently reported increasing global dominance of Spike protein G614 pandemic variant. Low levels of viral RNA were detected in a sample from 11th February, 3 days before the first case was reported in the sewage plant catchment area. SARS-CoV-2 RNA concentration increased in March and April, and a sharp reduction was observed in May, showing the effects of lockdown measures. We conclude that viral RNA sequences found in sewage closely resemble those from clinical samples and that environmental surveillance can be used to monitor SARS-CoV-2 transmission, tracing virus variants and detecting virus importations. A global pandemic of coronavirus disease COVID-19) caused by a new betacoronavirus named Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is currently ongoing 9 caused . RespiraSARS-CoV-2 has spread very rapidly in the population resulting in a high number of people requiring hospitalization. Consequently, many countries have been forced to implement severe lockdown measures to ensure physical distance between people and interrupt virus transmission ,20,21,22Apart from establishing an alert system for virus detection by ES, phylogenetic analysis of viral RNA found in sewage ,16 can aWith the above scientific information in mind, we aimed at investigating whether we could detect the presence of SARS-CoV-2 in wastewater samples from England (United Kingdom) using standard real-time quantitative reverse transcription-polymerase chain reaction (RTqPCR) assays as reported in different countries. Raw inlet sewage samples collected between 14th January and 12th May 2020 were analysed. In addition, nested reverse transcription-polymerase chain reaction (nPCR) assays specifically targeting SARS-CoV-2 sequences were designed to complement the RTqPCR assays and provide nucleotide sequence information to confirm the association between viral sequences found in clinical samples and those found in sewage. This could eventually help to improve the predicting value of ES for SARS-CoV-2 detection. We have also used next-generation sequencing analysis to investigate whether sequencing data from viral RNA found in sewage can help identify changes in the predominance of different genetic variants that have been reported to occur during the current COVID-19 pandemic. Given the global relevance of the G614 pandemic variant described above, we have targeted genomic regions containing nucleotide variations found in this variant. In addition, nucleotide variants specifically prevalent in England during the period of study were also the target of our sequence analyses.6 people. The samples were transported to the laboratory in chilled packages on the day of sampling and were stored at −80 °C until use. Each sample was processed using a filtration–centrifugation method described before and previously validated for the detection of polio and non-polio enteroviruses during routine ES for poliovirus as part of our role as a WHO Global Specialized polio network laboratory [g, wastewater was filtered through a 500 mL Nalgene Rapid-flow™ 0.45 µM filter  and concentrated using Centriprep centrifugal filter units with a 10 kDa molecular weight cutoff  following manufacturer’s instructions. Starting volumes of raw sewage between 120 and 240 mL yielded 4–6 mL of concentrate. These were further concentrated in a second step, when necessary, with final volumes of concentrates reaching between 150 and 350 µL. A total of five sewage samples was processed, one from each month. At least two aliquots from each raw sewage sample were processed and analysed independently. As shown in the literature, and from our own experience, we know that human viruses in sewage often show a non-homogeneous distribution, particularly when virus concentrations are low [One litre of inlet wastewater composite samples was collected during a 24 h period at a sewage plant in South East England with a catchment area of approximately 4.0 × 10boratory ,44. Brie are low ,45. Aliq are low ,45. For RdRP and E-Sarbeco [https://www.nibsc.org/products/brm_product_catalogue/detail_page.aspx?catid=19/304, accessed on 4 July 2020). The results were expressed in Log10 SARS-CoV-2 gc/L of sewage. Replicate assays were performed for each sample to improve quantification estimates. Good laboratory practices were observed in all assays to reduce the possibility of cross-contamination: i.e., using different laboratory locations for sample processing, preparation of reaction mixtures, template addition, and post-processing analysis. Two different operators tested each sample at least once. RNA extraction and no template controls were included in every assay and were always found to be negative. An additional RT-PCR reaction using enterovirus primers was used as process control to rule out the presence of PCR inhibitors. The presence of live human enteroviruses in wastewater concentrates was also assessed using standard cell culture procedures as part of our routine process for poliovirus surveillance [The SARS-CoV-2 RNA content in wastewater concentrates was estimated by real-time RT-qPCR using a qScript XLT qPCR Toughmix system  in a Rotor-Gene Q instrument (Qiagen) and following a one- RT-PCR protocol. Viral RNA was purified from wastewater concentrates using the High Pure viral RNA kit . Previously reported primer reactions -Sarbeco  were useeillance .Whole-genome SARS-CoV-2 sequences collected up to 31st May 2020 were downloaded from the Global Initiative on Sharing All Influenza Data (GISAID) database  on 4th J® PCR Purification kit  ready for Sanger and next-generation sequencing (NGS) analysis. Primers were tested using serial dilutions of purified RNA from NIBSC’s virus reagent 19/304 referred above. RNA extraction and no template controls were included in every assay and were always found to be negative. Primers used in this study did not closely match viral RNA sequences from seasonal coronavirus that had been circulating worldwide the last several years. Besides this, published nucleotide sequences of seasonal coronavirus serotypes in the PCR regions amplified, are at least 30% different to those from SARS-CoV-2 isolates, which means that full-sequence analysis can unequivocally demonstrate that the sequenced nPCR products from this study were from SARS-CoV-2 and not seasonal coronavirus. All nucleotide sequences of nPCR products in this study were identical or nearly identical to sequences from COVID-19 isolates from England and none resembled those from seasonal coronaviruses.Whole-genome SARS-CoV-2 viral RNA sequences were downloaded from the GISAID database  to identPurified DNA products were sequenced using an Applied Biosystems Prism 3130 genetic analyser. SARS-CoV-2 sequences obtained in this study were compared to those available in the GISAID database . GeneiouNGS libraries were constructed using the DNA Prep kit (formerly known as Nextera DNA Flex) and dual-indexed using Nextera DNA CD Indexes . These libraries were pooled in equimolar concentrations and sequenced with 250 bp paired-end reads on MiSeq v2 (500 cycles) kits (Illumina). Initial demultiplexing was performed on-board by the MiSeq Reporter software. FASTQ sequencing data was adapter and quality trimmed by Cutadapt v2.10 [Further processing and analysis of NGS data was performed with Geneious R10 software using methods described before ,51. FiltE-gene RTqPCR assay was less sensitive and accurate as the limit of quantitation (LOQ) was higher. The LOQ was 32 genome copies of SARS-CoV-2 RNA per reaction for the RdRP-gene assay and 160 genome copies per reaction for the E-gene assay as found using RNA extracted from the NIBSC virus reagent 19/304. These LOQ values correspond to 3.50 and 4.20 Log10 gc/L of sewage, respectively, when maximum concentration is achieved. As shown in Following concentration of raw sewage as described in RdRP-gene reaction and in none of the reactions with E-gene primers, so accurate quantification of viral RNA in this sample was not possible. However, it was clear that there was a large reduction of SARS-CoV-2 RNA concentration in sewage between 14th April and 12th May. Positive and negative results were independently confirmed using a second real-time PCR platform (Stratagene 3000P) in a different NIBSC laboratory. Integrity of process was confirmed through use of previous experience with enteroviruses. This was demonstrated both by detection of enteroviral RNA and recovery of infectious virus in cell cultures from all wastewater concentrates following WHO-recommended protocols as described in The sample from May was only positive in 3 out of 11 replicate assays with the Details of the number of sequences analysed by date and country are given in Differences in nucleotide sequence frequencies at some of these common positions were noticeable indicating a different prevalence of some sequence variants between viral sequences in England and the rest of the world. We used this information to select genomic regions for our sequence analysis. Key nucleotide positions 2480, 2558, 3037, 14,408, and 14,805 were targeted in two nPCR products, nPCR1 and nPCR2. nPCR1 spans nucleotides 2344–3118 and nPCR2 covers nucleotides 14,342–14,913 . The nPCAs can be noted, differences in sequence composition at these positions were notable between clinical samples from different countries/regions, likely reflecting differences in the circulation of different virus variants. Variants A2480G and C2558T were present in very low proportion in Spain, Asia, and USA as compared to the proportion in England. Variant C14480T was particularly prevalent in Spain and increase in the proportion of nucleotide variations characteristic of G614 pandemic variant was delayed in Asia with respect to the other regions analysed. Three additional nPCR assays were designed as described in Five different RNA replicates from each wastewater concentrate were initially used to generate nPCR products with the different primer combinations shown in The amount of SARS-CoV-2 nucleotide sequences obtained by Sanger analysis for each sample is shown in Overall, the nucleotide sequence composition at all five selected nucleotide positions changed between March and April. Viral RNA samples containing A2480G and C2558T nucleotide variations decreased between March and April. The proportion of T at positions 3037 and 14,408, genetic markers of the G614 dominant strain, increased between March and April. Finally, the predominant sequence at position 14,805 also switched from T in March to C in April. The same trend in sequence composition continued in May although a similar in-depth analysis was not possible since fewer replicate nPCRs were sequenced successfully. No mixed bases were identified by Sanger or NGS analysis in any of the nPCR products from the RNA samples from May. Sequence results from four nPCR1 replicates from the May sewage found an A at nucleotide 2480 and a C at residue 2558 in all four replicates and a T at position 3037 in 3/4 of the replicates in agreement with their predominance observed in April. A single nPCR2 product sequenced from May also contained the nucleotide sequences of G614 dominant strain at positions 14,408 and 14,805. Few additional sequence variations were identified in few PCR products, but none were present in more than one replicate.We detected SARS-CoV-2 RNA in wastewater samples collected between February and May 2020. A sample from 14th January was negative and only low levels of viral RNA were detected in the sample from 11th February, 11 days after the first two COVID-19 cases had been confirmed in York, northern England, and 3 days before the first case was reported in the population sampled in our study. The SARS-CoV-2 RNA concentration estimated in wastewater samples was consistent with the number of cases reported at the time of sample collection. Limitations in testing capacity early during the pandemic meant that there was an underestimation of cases in the community, and the extent of community transmission. It is therefore likely that the number of cases in early March was higher, which agrees with the viral RNA levels we found in sewage. Our results showed a large reduction of viral RNA concentration in sewage between April and May, most likely due to the lockdown measures introduced in the country from 23rd March . This isPrevious studies have detected SARS-CoV-2 RNA in wastewater samples worldwide ,16,17,18A novel nested RT-PCR approach targeting five different regions of the viral genome improved the sensitivity of RT-qPCR results. Next generation sequencing analysis of RT-PCR products revealed single nucleotide polymorphisms at five selected nucleotide positions, where sequence variation between viral RNA in clinical samples from England had been observed confirming the co-circulation of virus variants and changes in virus variant predominance with time. The target nucleotide sites for our study were selected following analysis of whole-genome SARS-CoV-2 sequences from the GISAID sequence database . DiffereOverall, our study shows that ES can be used to detect SARS-CoV-2 transmission with viruses identified in sewage resembling those found in clinical samples. We were able to detect virus variants specifically circulating in England and also to identify changes in viral RNA sequences consistent with the increasing global dominance of G614 pandemic variant . Our resWe have shown that environmental surveillance can be used to monitor SARS-CoV-2 transmission detecting virus variants specifically circulating in England and identifying changes in virus variant predominance known to have occurred during the COVID-19 pandemic. Environmental surveillance could be used for the early detection of peaks in virus transmission for public health interventions to be timely implemented.