We report here the in‐cell NMR‐spectroscopic observation of the binding of the cognate ligand 2′‐deoxyguanosine to the aptamer domain of the bacterial 2′‐deoxyguanosine‐sensing riboswitch in eukaryotic cells, namely Xenopus laevis oocytes and in human HeLa cells. The riboswitch is sufficiently stable in both cell types to allow for detection of binding of the ligand to the riboswitch. Most importantly, we show that the binding mode established by in vitro characterization of this prokaryotic riboswitch is maintained in eukaryotic cellular environment. Our data also bring important methodological insights: Thus far, in‐cell NMR studies on RNA in mammalian cells have been limited to investigations of short (<15 nt) RNA fragments that were extensively modified by protecting groups to limit their degradation in the intracellular space. Here, we show that the in‐cell NMR setup can be adjusted for characterization of much larger (≈70 nt) functional and chemically non‐modified RNA. RNA aptamers find increasing application in synthetic biology as exogenous gene control elements, but high‐resolution structural characterization in vivo is challenging. We report here the NMR‐spectroscopic observation of ligand binding by the 2′‐deoxyguanosine riboswitch aptamer in eukaryotic cells and show that the binding mode established by in vitro characterization of this aptamer is maintained in eukaryotic cellular environment. RNA aptamers occur in nature as ligand recognition domains of riboswitches.For rational design, high‐resolution structural data of the RNA aptamer domain and their binding capability to ligands is of great importance. X‐ray crystallography has provided numerous high resolution structures of the aptamer domain,A major challenge for structural studies is the investigation of RNA aptamers under (near)‐physiological conditions. We previously demonstrated by 2D NMR that the 2′‐deoxyguanosine riboswitch aptamer is capable of binding its cognate ligand 2′‐deoxyguanosine and its non‐cognate ligand guanosine under “in‐cell‐like” in vitro conditions using bacterial cell extract as well as metabolic extract.Xenopus laevis oocytes and in HeLa cells, using state‐of‐the‐art approach of in‐cell NMR spectroscopy.[Here, we seize the unique opportunity to directly test ligand binding in living eukaryotic cells, namely in troscopy.13, 141H, 15N‐correlated 2D NMR spectra.The sv‐2′‐dG aptamer Figure , which i15N‐labeled 2′‐dG was injected into living oocytes and 1D 15N‐edited imino proton spectra were acquired. Ligand binding in living oocytes was observed by the characteristic H‐bond imino proton signal of 2′‐dG to C74 of the RNA . G25, which is a direct reporter signal for ligand binding,D of sv‐aptamer is higher than that of aptamer 70mer, which enables near‐cognate metabolites in the cell extract to compete for the binding site, thereby preventing stable complex formation and complex detection. For the same reasons, we assume that it will not be possible to detect the complex in cells.Interestingly, ligand binding for the sv‐aptamer could only be detected in vitro in intraoocyte buffer Figure  and in vThe lifetime of sv‐2′‐dG aptamer is sufficient for recording a 2D in‐cell spectrum. Imino proton linewidths increase considerably compared to in vitro spectra and therefore, spectral resolution is limited. We attribute the line broadening to predominantly arise from the high solvent viscosity, accompanied by molecular crowding. Still, all imino proton signals expected in the canonical region can be observed as well as the two spectrally well resolved signals of G57 and U69, arising from the G‐U wobble base pair in helix P3. In the free form of the aptamer domain, the number of detectable imino signals is limited, because a large portion of the aptamer is dynamic in the ligand‐free state. Non‐observable reporter signals include signals from U45 and U75; they become detectable only in the ligand‐bound form of the RNA aptamer, but are missing in the spectra of the free aptamer.m is well above the abundance of any endogenous RNA in the cell and thus, the stability of the injected RNA may be attributed to this high concentration. In addition, the injected hybrid RNA aptamer is a sequence‐modified construct, which we originally designed for high structural stability in NMR buffer. However, even given these limitations, the stability of the intact structure of this RNA in living cells is surprising and shows that functional RNAs can in fact be characterized by 2D in‐cell NMR spectroscopy.Both the oocyte extract and the entire oocytes contain endogenous RNases and numerous other RNA‐modifying enzymes that ultimately degrade any RNA in the cell. In addition, the intracellular environment is strikingly different from in vitro conditions in terms of molecular crowding, potential unspecific interaction partners and general changes of the entire system over time. The intracellular concentration of the freely tumbling RNA aptamer of ≈100–130 μX. laevis oocytes, we next aimed at obtaining spectra inside human cells. Since HeLa cells cannot be injected with RNA samples such as the large oocyte cells, the capability of electroporation for the delivery of a preformed RNA–ligand complex was investigated.Encouraged by the spectral quality of 70mer RNA–ligand complex in Recently, Yamaoki et al. reported NMR signals of RNA in living human cells.Here, we report our efforts to improve in‐cell NMR of RNA in human HeLa cells aiming at: a) elimination of artifacts in in‐cell NMR readout stemming from both leakage of RNA from cells in the course of in‐cell NMR spectra acquisition and high fraction of death cells in the in‐cell NMR sample ,In terms of the method improvement, we hypothesized that both, leakage of RNA from cells during NMR spectra acquisition and high cell mortality observed in original work by Yamaoki et al. (2018),To allow monitoring the transfection efficiency and localization of transfected RNA in the cells with flow cytometry (FCM) and confocal microscopy, the unlabeled RNA was supplemented with 5′‐FAM‐labeled RNA (in a ratio of 40:1). The confocal microscope images of transfected cells indicated that the delivered RNA was localized in both, cell nucleus and cytosol, and was homogeneously dispersed all over the cell Figure . Yet, FC1H in‐cell NMR spectra of electroporated HeLa cells were recorded in a time window of 4 hours to monitor RNA stability in the intracellular space . The signal can reproducibly be detected as strong signal  were measured  of both, cells and Leibovitz L15 medium. The signals in the range from 7.7–8.0 ppm of the in‐cell spectrum of the complex (and leakage)  Figure  were theHowever, the differences in the region highlighted in green Figure  of the sm ligand in‐cell NMR sample was measured. The spectrum showed leakage of the ligand. However, FCM analysis showed that more than 90 % of the cells were viable after transfection with 400 μm/2 mm ligand  which was also supported by confocal microscope images taken after transfection with ligand, which showed intact cells , indicating that the ligand was not toxic for the cells.After completion of spectra acquisition the supernatant of the 2 mIn this report, we extend in‐cell NMR spectroscopy for RNA both in terms of methodology as well as complexity of the detected RNA system. By detecting a functional riboswitch aptamer domain bound to its native ligand in living oocytes and in viable human HeLa cells, we demonstrated the applicability of in‐cell NMR on non‐modified RNA, which proved to maintain structural integrity long enough to allow for recording of NMR spectra. It should be particularly emphasized that the binding mode of this prokaryotic riboswitch aptamer determined by means of in vitro studies was identical in eukaryotic cells, although prokaryotic and eukaryotic cells as well as in vitro conditions differ with regard to their composition. In contrast to in‐cell studies in bacteria, which showed that the cellular milieu can have influence on the protein conformation and stability,19F nuclei might provide solution to this issue. With 19F labeling of the RNA one can obtain background free in‐cell spectra because biological molecules do not contain fluorine atoms. A number of 19F RNA labeling strategies were developed for monitoring of RNA structural transitions and interactions in vitro in past two decades.19F in‐cell NMR readout on the RNA G‐quadruplex structure. Whether this strategy is applicable to in‐cell NMR studies of other (non‐G‐quadruplex) RNAs is not currently known.The interference from cellular background represents one of the major problems in in‐cell NMR studies of biomolecules including RNA. In principle, the site‐specific labeling of the RNA with 100 %‐abundant m can be reached via micro‐injection, which offers sensitivity almost comparable to in vitro conditions. Furthermore, oocytes remain viable at 18 °C for several hours, thus enabling long (2D) NMR experiments. However, inside the HeLa cells more relevant to the human physiology, the intracellular concentration that can be reached by available transfection methods is around 10 μm. Due to combined effects of RNA degradation, low intracellular RNA concentration, and fast relaxation of NMR signals inherently encountered for large RNAs (>50 nt) the acquisition of multi‐dimensional in‐cell NMR spectra is impractical, but the parameters of in‐cell NMR sample are sufficient for observing RNA fingerprint resonances on isotope‐labeled sample.For oocyte cells, in‐cell RNA concentrations of ≈120 μDetecting ligand binding to an exogenously prepared prokaryotic riboswitch in two different eukaryotic cells provides substantial and direct support for Systems Biology application where bacterial riboswitch typically not existing in eukaryotes can fundamentally be utilized as exogenous regulation element. Further, riboswitch RNA with submicromolar affinities to their cognate ligand can also be utilized as structural readout for ligands and changes in their concentration in eukaryotic cells. Careful tailoring of aptamer and cellular system, in‐cell studies of RNA are thus feasible utilizing methodology outline here.The authors declare no conflict of interest.As a service to our authors and readers, this journal provides supporting information supplied by the authors. Such materials are peer reviewed and may be re‐organized for online delivery, but are not copy‐edited or typeset. Technical support issues arising from supporting information (other than missing files) should be addressed to the authors.SupplementaryClick here for additional data file.


This raises important questions about the roles of D-RNAs, both in nature and in tissue culture, during viral infection and whether their influence is constrained by packaging requirements. Further, during the course of these studies, we found a novel type of alphavirus D-RNA that is enriched intracellularly; dubbed subgenomic D-RNAs (sgD-RNAs), they are defined by deletion boundaries between the capsid-E3 region and the E1-3′ untranslated region (UTR) and are common to chikungunya, Mayaro, Sindbis, and Aura viruses. These sgD-RNAs are enriched intracellularly and do not appear to be selectively packaged, and additionally, they may exist as subgenome-derived transcripts.Our understanding of viral defective RNAs (D-RNAs), or truncated viral genomes, comes largely from passaging studies in tissue culture under artificial conditions and/or packaged viral RNAs. Here, we show that specific populations of alphavirus D-RNAs arise  de novo intracellularly that may not be packaged and thus have remained undetected. To this end, we generated next-generation sequencing libraries using RNA derived from passage 1 (P1) stock chikungunya virus (CHIKV) 181/clone 25, intracellular virus, and P2 virions and analyzed samples for D-RNA expression, followed by diversity and differential expression analyses. We found that the diversity of D-RNA species is significantly higher for intracellular D-RNA populations than P2 virions and that specific populations of D-RNAs are differentially expressed between intracellular and extracellular compartments. Importantly, these trends were likewise observed in a murine model of CHIKV AF15561 infection, as well as in vitro studies using related Mayaro, Sindbis, and Aura viruses. Additionally, we identified a novel subtype of subgenomic D-RNA that is conserved across arthritogenic alphaviruses. D-RNAs specific to intracellular populations were defined by recombination events specifically in the subgenomic region, which were confirmed by direct RNA nanopore sequencing of intracellular CHIKV RNAs. Together, these studies show that only a portion of D-RNAs generated intracellularly are packaged and D-RNAs readily arise de novo in the absence of transmitted template.Alphaviruses are positive-sense RNA arboviruses that can cause either a chronic arthritis or a potentially lethal encephalitis. Like other RNA viruses, alphaviruses produce truncated, defective viral RNAs featuring large deletions during replication. These defective RNAs (D-RNAs) have primarily been isolated from virions after high-multiplicity-of-infection passaging. Here, we aimed to characterize both intracellular and packaged viral D-RNA populations during early-passage infections under the hypothesis that D-RNAs arise  Alphavirus encompasses many important human pathogens, including the equine encephalitis viruses, chikungunya virus (CHIKV), Sindbis virus (SINV), and Mayaro virus (MAYV), among others. Pathogenic alphaviruses are transmitted by a mosquito vector and generally follow the same replication strategy despite infecting distinct organisms and tissues. The alphavirus genome consists of single-stranded positive-sense RNA and carries 4 nonstructural genes, nsP1 to nsP4, and 5 structural genes, encoding capsid, E3, E2, TF/6K, and E1. The viral genome is capped and polyadenylated, allowing translation of the nonstructural genes upon entry of viral genomic RNA in the cytosol. From this, the viral replication machinery synthesizes antisense genomes, which are subsequently used to synthesize new genomic sense RNA  or undiluted virus over the course of multiple viral passages. In this way, D-RNAs have been discovered for nearly every pathogenic RNA virus family in tissue culture  D-RNAs isolated from human nasopharyngeal samples were correlated with heightened immune response to infection and subsequently with improved patient outcomes  , 26 and 25–34–d salmon , 40. Addd salmon . Finallyosophila .in vitro passaging studies. Moreover, all D-RNA studies to date have focused on packaged D-RNA populations, assaying intracellular compartments only after D-RNAs have accumulated over multiple passages. Thus, important questions about the biogenesis and intracellular functions of D-RNAs produced de novo remain largely unexplored. In this study, we characterized both intracellular and packaged D-RNA populations during early viral passages, under the hypothesis that numerous D-RNAs arise de novo intracellularly that are not packaged. By thoroughly investigating intracellular RNA diversity and its relation to packaged RNA populations, we can define D-RNA subtypes and thereby elucidate specific roles D-RNAs play during infection. To this end, we used Illumina sequencing to sequence RNA from P1 alphavirus stock, intracellular RNA (P1.5), and resultant P2 virion RNA, and we used various bioinformatics approaches to analyze differences in D-RNA diversity and expression. Intracellular D-RNA expression was also evaluated using Oxford Nanopore Technologies’ direct RNA sequencing. Finally, we utilized a murine model of CHIKV infection to demonstrate that these in vitro trends hold true in a complex biological system.Despite increasing interest in the biological consequences of D-RNA production, the majority of D-RNA research still generally relies on identifying D-RNAs through serial in vitro  were infected with CHIKV 181/clone 25 at a multiplicity of infection (MOI) of 2. After 12 h of incubation, cells were washed three times with phosphate-buffered saline (PBS), and then total cellular RNA was extracted and rRNA depleted. Concomitant infections were incubated for 48 h, and then supernatant was collected, clarified by centrifugation, and concentrated using polyethylene glycol (PEG)-NaCl precipitation; concentrated virus was incubated with 2 μg RNase A for 1 h at room temperature to remove nonpackaged RNAs prior to RNA extraction. By following this study design, we were able to maximize available viral RNA from both compartments and thus identify rare events. RNA sequencing (RNA-seq) libraries were then constructed using the previously described ClickSeq library preparation method optimized for the discovery of rare recombination events , 43 and in vitro . All reain vitro . Median in vitro .10.1128/mBio.00731-20.1FIG S1FIG S1, TIF file, 0.8 MB.Distribution of nucleotides at CHIKV recombination junctions. Three replicates of intracellular RNA from chikungunya virus (CHIKV)-infected Vero cells were collected 12 h post-infection and then sequenced and evaluated for defective RNA expression using ViReMa v1.5. Nucleotides observed upstream and downstream of recombination junctions were then enumerated and converted to percentage of recombination events. Native nucleotide distribution (expected) is indicated by solid black lines, intracellular samples are indicated by blue lines, and P2 virion samples are indicated by pink lines. (Top) donor sites with nucleotides A, U, G, and C (left to right); (bottom) acceptor sites with nucleotides A, U, G, and C (left to right). Download Copyright © 2020 Langsjoen et al.2020Langsjoen et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the t test, P < 0.01 for all) (H) for D-RNAs is significantly higher for intracellular RNAs than virion-derived RNAs    and B, ifor all) , which f≪ 0.005) ; thus, i≪ 0.005) .de novo that are not efficiently packaged.A principal-component analysis PCA) of recombination event count frequencies was performed for intracellular and P2 virion D-RNAs , which rA of reco6 mapped reads were illustrated using heat maps, along with associated coverage data  were combined; normalized count data for D-RNAs present in at least two of three samples were averaged, and then the boundaries for D-RNAs with at least 1 count per 2 × 10age data . While sage data . This il10.1128/mBio.00731-20.2FIG S26 CHIKV-mapped reads, and then average nucleotide count for donor sites (top) and acceptor sites (bottom) were plotted. Download FIG S2, TIF file, 0.5 MB.Recombination junction counts for CHIKV D-RNAs. Three replicates of intracellular RNA from chikungunya virus (CHIKV)-infected Vero cells were collected 12 h post-infection and then sequenced and evaluated for defective RNA expression using ViReMa v1.5. Recombinant count data were normalized to count per 10Copyright © 2020 Langsjoen et al.2020Langsjoen et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mBio.00731-20.3FIG S36 CHIKV-mapped reads, and then subgenomic recombination events  were averaged and then ranked from highest to lowest count; the top 200 in total are shown. Top line, donor site (Jct 1); bottom line, acceptor (Jct 2). (A) 3′ UTR-specific recombination events; (B) subgenomic deletions; (C) subgenomic insertion/duplication events. Download FIG S3, TIF file, 1.0 MB.CHIKV subgenomic recombination junctions. Three replicates of intracellular RNA from chikungunya virus (CHIKV)-infected Vero cells were collected 12 h post-infection and then sequenced and evaluated for defective RNA expression using ViReMa v1.5. Recombinant count data were normalized to count per 10Copyright © 2020 Langsjoen et al.2020Langsjoen et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the For the recombination events observed in the subgenomic region in intracellular samples, it is possible that they occur on either genomic or sg transcripts. Thus, to put these subgenomic deletions (sgDels) into context, two separate Oxford Nanopore Technologies (ONT) direct RNA sequencing (DRS) libraries were generated using two intracellular samples, extracted as described above at 12 h post-infection (hpi) (derived from independent infections that utilized completely distinct CHIKV 181/clone 25 stocks). ONT DRS directly sequences the input RNA on a single-molecule-by-molecule basis (there is no PCR or amplification process), maintaining native features of the RNA . Therefo10.1128/mBio.00731-20.4FIG S410 nucleotide coverage for both reactions (DRS1 [A] and DRS2 [B]) was plotted. Download FIG S4, TIF file, 0.4 MB.Coverage data for CHIKV MinION direct RNA sequencing reactions. Two replicates of intracellular RNA from chikungunya virus (CHIKV)-infected Vero cells were collected 12 h post-infection, and then two separate sequencing libraries were constructed using Oxford Nanopore Technologies’ direct RNA sequencing kit and sequenced on a MinION sequencer. After aligning reads to the CHIKV genome, logCopyright © 2020 Langsjoen et al.2020Langsjoen et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the First, reads were placed into one of two categories: (i) genomic reads containing deletions, defined as reads beginning between nucleotides (nt) 1 and 7450, and (ii) sg reads containing deletions, defined as reads beginning between nt 7500 and 7550. Beyond these nucleotides, it is not possible to discern between true sgRNA reads and genomic reads that may have simply been truncated during the sequencing process (either due to RNA fragmentation or incomplete 3′-to-5′ nanopore sequencing of the RNA). The largest deletions for genomic and sgRNA reads are shown for one of the libraries , Sindbis virus (SINV), and Aura virus (AURV)  . Of thess (AURV) . Additios (AURV) –60. Simi (AURV) –. Similar (AURV) –10.1128/mBio.00731-20.5FIG S5FIG S5, TIF file, 1.1 MB.Distribution of nucleotides at MAYV, SINV, and AURV donor sites. Three replicates of intracellular RNA from Mayaro virus (MAYV)-, Sindbis virus (SINV)-, and Aura virus (AURV)-infected Vero cells were collected 12 h post-infection and then sequenced and evaluated for defective RNA expression using ViReMa v1.5. Nucleotides observed upstream and downstream of donor junctions were then enumerated and converted to percentage of recombination events. Native nucleotide distribution (expected) is indicated by solid black lines, intracellular samples are indicated by blue lines, and P2 virion samples are indicated by pink lines. MAYV donor sites (top), SINV donor sites (middle), and AURV donor sites (bottom) with nucleotides A, U, G, and C (left to right) are shown. Download Copyright © 2020 Langsjoen et al.2020Langsjoen et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mBio.00731-20.6FIG S6FIG S6, TIF file, 1.1 MB.Distribution of nucleotides at MAYV, SINV, and AURV donor sites. Three replicates of intracellular RNA from Mayaro virus (MAYV)-, Sindbis virus (SINV)-, and Aura virus (AURV)-infected Vero cells were collected 12 h post-infection and then sequenced and evaluated for defective RNA expression using ViReMa v1.5. Nucleotides observed upstream and downstream of donor junctions were then enumerated using and converted to percentage of recombination events. Native nucleotide distribution (expected) is indicated by solid black lines, intracellular samples are indicated by blue lines, and P2 virion samples are indicated by pink lines. MAYV acceptor sites (top), SINV acceptor sites (middle), and AURV acceptor sites (bottom) with nucleotides A, U, G, and C (left to right) are shown. Download Copyright © 2020 Langsjoen et al.2020Langsjoen et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the de novo without the aid of a transmitted template. Interestingly, the two New World representatives, MAYV and AURV, shared similar intracellular and virion D-RNA diversity indices, whereas the two Old World representatives, CHIKV and SINV, shared similar intracellular, but not virion, indices. Further, these differences in population diversity, as with CHIKV, were largely driven by recombination events in the subgenomic region for MAYV  and acceptor sites (bottom) were plotted for each. Download FIG S7, TIF file, 1.0 MB.Recombination junction counts for MAYV D-RNAs. Three replicates of intracellular RNA from Mayaro virus (MAYV) (A)-, Sindbis virus (SINV) (B)-, and Aura virus (AURV) (C)-infected Vero cells were collected 12 h post-infection and then sequenced and evaluated for defective RNA expression using ViReMa v1.5. Recombinant count data were normalized to count per 10Copyright © 2020 Langsjoen et al.2020Langsjoen et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the in vitro D-RNA expression patterns are recapitulated in vivo, alpha interferon receptor-null (IFN-αR−/−) (A129) mice were infected with 103 PFU of CHIKV AF15561, the parental strain to CHIKV 181/clone 25  was higher in tissues  than in serum (virion RNAs) , these animals were not perfused prior to tissue collection, and thus, excess serum virus may have diluted the diversity of some tissue samples and subsequently affected standard deviation. Viremia ranged between 1.5 × 106 and 1.2 × 108 PFU/ml, and no correlation between diversity measures and viremia was observed  . Furtherobserved . Murine observed , with th10.1128/mBio.00731-20.8FIG S8H) and viremia among mice infected with CHIKV. Four A129 mice were infected with CHIKV AF15561 in the left rear footpad. Animals were humanely euthanized, and serum and organs were collected 4 days post-infection. RNA was then extracted from serum and tissues and sequenced on an Illumina NextSeq550. Defective RNAs were identified and quantified using ViReMa v1.5. Viremia is plotted on the x axis, while Shannon’s entropy (H) is plotted on the y axis, with dotted trend lines in pink and various shades of blue and R2 values indicated next to trend lines. Download FIG S8, TIF file, 0.2 MB.Relationship between organ-specific Shannon’s entropy , contralateral leg muscle (B), injection site leg muscle (C), cardiac muscle (D), and mouse 1 kidney (E)  are shown. (F) Hierarchical analysis of D-RNA expression and organ clustering for mouse 1. Download Copyright © 2020 Langsjoen et al.2020Langsjoen et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mBio.00731-20.10FIG S10FIG S10, TIF file, 0.5 MB.Top insertion-duplication events observed among CHIKV-infected mouse organs. Four A129 mice were infected with CHIKV AF15561 in the left rear footpad, and then animals were humanely euthanized. Serum and organs were collected 4 days post-infection. RNA was then extracted from serum and tissues and sequenced on an Illumina NextSeq550 system. Defective RNAs were identified and quantified using ViReMa v1.5. The top 21 insertion/duplication events for mouse 2 serum (A), contralateral leg muscle (B), injection site leg muscle (C), cardiac muscle (D), and kidney (E) are shown. Download Copyright © 2020 Langsjoen et al.2020Langsjoen et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the de novo without requiring packaging and subsequent accumulation through high-MOI passaging. By sequencing early-passage, low-MOI-derived stock, intracellular P1.5 virus, and subsequent P2 virion (passage 2 virus), we have shown that D-RNA diversity and composition are fundamentally different between intracellular and resulting P2 virion RNAs, both in vitro and in vivo, for 5 alphavirus strains from 4 distinct species and 2 distinct phylogenetic groups. As a result of these studies, we additionally observed a novel conserved subtype of alphavirus D-RNA originating from recombination events in the subgenomic region, specifically between the capsid-E3 region and the end of the E1-3′ UTR subgenomic region, which is heavily enriched intracellularly but does not appear to be commonly packaged. We call this new heterogeneous subpopulation subgenomic D-RNAs (sgD-RNAs).The goal of these studies was to determine how and whether intracellular D-RNA populations differ from virion populations, addressing whether D-RNAs can arise H) is significantly higher in intracellular samples, indicating greater alpha diversity intracellularly. These trends held for in vivo studies, where D-RNA populations from specific tissues were more diverse than those from serum. Broadly, this indicates a packaging bottleneck between intracellular and P2 virion populations, which likewise means that production of D-RNAs occurs de novo without a strict requirement for transmitted template. These results also distinguish packaged D-RNAs and D-RNAs produced de novo as two separate populations. Packaged D-RNAs have been shown to play multiple roles during viral infection, which range from interfering with replication of wild-type parental virus RNA and promoting viral persistence in tissue culture to stimulating the host immune response in vivo. However, unpackaged D-RNAs have not been appreciated as a distinct population before now, and subsequently, the role of these D-RNAs remains unclear. These studies therefore provide a critical foundation for further investigation of the role of de novo D-RNA production during viral infection by establishing this important distinction between packaged and unpackaged D-RNAs.Viruses, especially RNA viruses, are notorious for their ability to generate highly diverse populations, particularly in terms of point mutations that are generated during replication and may contribute to overall viral fitness within a host , 63. AltThere is some evidence that recombination events leading to D-RNA formation are largely, though not exclusively, polymerase driven –66 and a–de novo implies that transmitted template is not a strict requirement for D-RNA production. In addition, differential D-RNA diversity between intracellular and P2 virion alphavirus RNA was due almost entirely to recombination events in the subgenomic region encoding the structural proteins. The packaging signals of alphaviruses are thought to be in the nsP1  and SINV that gave rise to the Western equine encephalitis virus (WEEV) species , 74. Litin vivo were similar to ones observed in vitro during early passages, suggesting that expression of sgD-RNAs is a programmed phenomenon with potential biological functions, recapitulating observations made with IAV  that sgD-RNAs are an artifact of another replicative process, such as the binding of host and/or viral proteins to viral RNA, and (ii) that sgD-RNAs are a deliberate by-product of viral replication with specific cellular functions. Additionally, the function of alphavirus sgD-RNAs and other de novo-produced D-RNAs remains unknown, necessitating further investigation.In all, the present study provides critical evidence of discrete types of D-RNAs, virion/transmissible and intracellular/Aedes albopictus mosquito (C7/10) cells, kindly provided by Scott Weaver, were maintained in DMEM containing 1× PSA, 10% FBS, 1× nonessential amino acids , 1× sodium pyruvate (Gibco), and 5 ml 1× tryptose phosphate buffer (Gibco).Vero cells  were maintained in Dulbecco’s modified Eagle medium  containing 1× penicillin-streptomycin-amphotericin  and 5% fetal bovine serum . 6 and 107 PFU/ml from Vero cells and 107 to 109 PFU/ml from C7/10 cells.CHIKV 181/clone 25 , CHIKV AViral infections were performed as follows: virus was first diluted in serum- and antibiotic-free DMEM to a target MOI of 2 infectious units/cell. Culture medium was removed from cells, and virus medium was added for 1 h, rocking plates every 15 min. Cells were then washed 3 times with PBS, and normal culture medium was added.g for 30 min at 4°C, and supernatant was removed; the pellet was resuspended in 100 μl 10% FBS-DMEM and then incubated with 10 μg RNase A for 2 h at room temperature (to remove contaminating nonpackaged RNAs). TRIzol (500 μl) was then added to RNase-treated sample and incubated for 5 min. For both intracellular and packaged RNAs, RNA was then extracted using Phasemaker tubes (Invitrogen) according to the manufacturer’s instructions. Additionally, rRNA was removed from intracellular RNA samples using a Lexogen RiboCop kit according to manufacturer’s instructions .Intracellular RNA was collected 11 to 12 h post-infection as follows: supernatant was removed from target wells, followed by 3 washes with 2 ml PBS, and then 500 μl TRIzol  was added directly to plate and incubated for 5 min prior to collection. Packaged RNA was collected 48 h post-infection as follows: supernatant was collected, clarified by centrifugation for 5 min at a relative centrifugal force (rcf) of 1,000, and incubated in 7% PEG 8000–NaCl overnight at 4°C. Precipitate was then pelleted by centrifugation at 3,200 × −/− (A129) mice of different ages and genders were kindly provided by Slobodan Paessler. Mice were infected with 103 PFU CHIKV AF15561 in 10 μl PBS in the left rear footpad and monitored for weight and signs of disease. Animals were euthanized 4 days post-infection by CO2 overdose followed by cardiac puncture, whereupon blood, skeletal muscle from the infected and contralateral legs, heart, kidney, liver, and spleen were collected; organs were placed in 300 μl RNAlater solution, while whole blood was centrifuged at an rcf of 3,380 for 5 min, and serum was collected and stored at −80°C until further use. Upon processing, tissue was removed from RNAlater solution, rinsed in sterile PBS, and placed in a 2-ml microcentrifuge tube with 300 μl Buffer RLT and a stainless steel ball bearing. Samples were heat inactivated at 60°C for 15 min, and then organs were homogenized using a TissueLyser II  with shaking at 26 frequency 1/s for 5 min. RNA was then extracted from organs in Buffer RLT using RNeasy fibrous tissue minikits following the manufacturer’s instructions (Qiagen), while RNA was collected from serum using TRIzol according to the manufacturer’s instructions. Mouse studies were performed in an animal biosafety level 3 facility according to approved University of Texas Medical Branch (UTMB) Institutional Animal Care and Use Committee (IACUC) protocol number 1708051. The University of Texas Medical Branch complies with NIH policy on animal welfare, the Animal Welfare Act, and all other applicable federal, state, and local laws.Type 1 interferon-deficient IFN-αRClickSeq libraries were generated as previously described , 43. Oxf[script location]/ViReMa.py [reference genome].txt [input file].fastq [output file designation].sam –Output_Dir [output folder] –p 4 –MaxIters 10 –MicroInDel_Length 5.For AURV BeAr93375 and SINV CGLT599, corrected reference genomes were generated using Pilon based on publicly available sequences . For CHIH), weighted by median nucleotide coverage, utilizing a previously described custom python script . Data formatted by DESeq2 were used for principal-component analyses performed in SigmaPlot.All statistics were performed in SigmaPlot. For Shannon’s entropy and unique/total D-RNA comparisons, data were tested for normality using the Shapiro-Wilk test, followed by Student’s PRJNA613616.All raw data files for Illumina and default quality-filtered, base-called data for direct RNA nanopore sequencing data sets associated with this paper are available in the SRA NCBI archive under study number


Spermatogenesis is a complex differentiating developmental process in which undifferentiated spermatogonial germ cells differentiate into spermatocytes, spermatids, and finally, to mature spermatozoa. This multistage developmental process of spermatogenesis involves the expression of many male germ cell-specific long noncoding RNAs (lncRNAs) and highly regulated and specific gene expression. LncRNAs are a recently discovered large class of noncoding cellular transcripts that are still relatively unexplored. Only a few of them have post-meiotic; however, lncRNAs are involved in many cellular biological processes. The expression of lncRNAs is biologically relevant in the highly dynamic and complex program of spermatogenesis and has become a research focus in recent genome studies. This review considers the important roles and novel regulatory functions whereby lncRNAs modulate mammalian spermatogenesis. H19  [Mammalian spermatogenesis is the process by which male germline cells divide and differentiate into mature spermatozoa . It is anscript) . This afnscript) . After tnscript) . In thisnscript) ; howevernscript) .Mammals transcribe a large amount of RNAs; however, only a small fraction (around 2%) of transcripts encode proteins. The remaining large number of transcripts represent small noncoding RNAs and lncRNAs , 9. LncRThe molecular functions of lncRNAs have been studied in different cell lines , 17 by lMany lncRNAs have been noted as being involved in spermatogenesis; however, very few of them have been validated and functionally characterized. Thus, the majority of lncRNAs expressed during mammalian spermatogenesis need to be validated and their molecular functions should be specified experimentally. This paper discusses the expression patterns and known functions of certain lncRNAs and their suggested roles in mammalian spermatogenesis. The main purpose of this review was to provide a better understanding of mammalian spermatogenesis in terms of lncRNAs and to explain the classification, expression, and potential roles of lncRNAs in regulating spermatogenesis.The developmental process of mammalian spermatogenesis involves the continuous production of functional sperm, starting from spermatogonial stem cells and involving different cell types. Spermatogenesis involves a series of differentiation of morphologically undifferentiated tissues and cells into highly functional cells, including self-renewing stem cells . The expThere are three principal phases or stages of spermatogenesis: Mitosis, meiosis, and post meiosis stages. Spermatogonial cells undergo mitosis for self-renewal and proliferation, which promotes spermatogonial differentiation. During the second stage, meiosis and genetic recombination occur in the spermatocytes. No further replication occurs and the haploid male germ cells differentiate in the post-replicative stage into spermatids. In mice, the mitotic, meiotic, and post meiotic phases last around 10, 11, and 14 days, respectively. The spermatogenic stem cells differentiate six times during the mitotic phase to form type A spermatogonia, intermediate spermatogonia, and type B spermatogonia. The final division of this phase produces preleptotene spermatocytes that commence meiotic division and undergo the S phase of the spermatogenesis in their last cell cycle. The second meiotic stage is succeeded by two meiotic divisions producing spermatids in the post-meiotic stage. At this stage, DNA replication does not occur and the spermatids develop into fully mature spermatozoa via spermiogenesis by remodeling through the process of acrosome formation, condensation of the nucleus, development of flagellum, and loss of a large portion of the cytoplasm –24.Thus, spermatogonial stem cells undergo mitotic divisions giving rise to meiotic spermatocytes. Then, the reductive divisions of meiosis lead to the formation of round spermatids (haploid). The round spermatids undergo a series of differentiation to develop into elongated spermatids and then fully develop into mature spermatozoa Fig. .Fig. 1SPost-genomic studies have identified a diversity of transcriptional products, such as many small regulatory RNAs and a large number of polyadenylated and non-polyadenylated lncRNAs. These lncRNAs are classified according to their protein-coding loci as intergenic, antisense, intronic, or overlapping lncRNAs. Novel lncRNAs may be similarly classified and subtyped as intergenic, ambiguous, antisense, and intronic lncRNAs according to their genomic locations  as shownLncRNAs can be categorized into three groups according to their relationship with the adjacent protein-coding genes –26 as in1) Antisense or sense: These lncRNAs are located and transcribed on the opposite or the same strand of the adjacent protein-coding genes . 2) ConvMost lncRNAs are non-coding, but play diverse roles in many biological processes and are associated with disease occurrence . LncRNAsI)Signaling: The transcriptional activity of lncRNAs can be described by their response to diverse stimuli, the production of signals, and cell type-specific expression. The molecular mechanism of the majority of the lncRNAs may indicate considerable transcriptional control on a molecular basis by RNA polymerase II. The occurrence of lncRNAs during the developmental processes of specific tissues and cells at a specified time demonstrates the effectiveness of transcriptional control by interpreting the cellular context as well as gene regulation and acts as a molecular signal . The lncII)cis and in trans, for distantly located genes [Guides: The interaction of lncRNAs with their target molecules may enhance the proper positioning as the transcription machinery on adjacent genes by guiding, in ed genes . A lncRNIII)TERRA), which is large noncoding RNA, interacts with the telomerase protein to inhibit its function [Decoys: Enhancers and promoters play an important role in transcriptional regulation of lncRNAs, both negatively and positively . Effectofunction  and the function , are exafunction .IV)Scaffold: The lncRNAs form a distinct complex from two or more proteins to act as a molecular scaffold as shown in Fig. The lncRNAs use different mechanisms to carry out complex functions and have played some of the following roles  as shownComprehensive datasets play an important role in facilitating the classification, validation, functional prediction, and assessment in low and high throughput lncRNA experiments. Evidence for the biological properties of well-studied and identified lncRNAs has been deposited in specialized databases that have been developed to provide and improve information resources for lncRNA research in the public domain. These developed lncRNA databases have been designed to contain important and comprehensive information about interactions among macromolecules, genomic structure, conservation potential, expression profile, epigenetic modifications, and functional annotations .The lncRNA databases can be categorized as annotation, interaction, and specific databases as shown in Table . Phylogenetic, conservational, and comparative genomic analyses will also help to determine the evolution and origin of testis enriched lncRNAs. Cell-based in vitro studies of genomic loss of function [LncRNAs can be identified and functionally characterized systematically in different ways . Genome-function , includifunction , 65, 66.Mammalian spermatogenesis plays an important role in fertility and the continual replacement of species from generation to generation. Previously, many studies have analyzed the molecular mechanisms of mammalian spermatogenesis, focusing on microRNAs, Piwi-interacting RNAs (piRNAs), and protein-coding genes; however, limited studies have been performed on lncRNAs in mammalian spermatogenesis. Some studies reported testis and germ cells expression profiles of lncRNAs in different ages and stages of development . Bao et lnc32058, lnc09522, and lnc98487 are upregulated in asthenozoospermic sperm, exhibiting specific expression patterns in sperm and testis and are related to the progressive motility of sperm, providing insights into the causes of male infertility [HOTAIR (Hox transcript antisense intergenic RNA) in the spermatozoa of patients with asthenozoospermia and oligoasthenozoospermia, which might be related with the motility and vitality of sperm [Recent studies have assessed the lncRNA expression during spermatogenesis. Many lncRNAs show different expression levels and tissue specificities in different stages of mammalian spermatogenesis , suggestertility . Zhang eof sperm . Evoluti1700108J01Rik and long intergenic noncoding RNA 1700101O22Rik [Tcam1) in round spermatids than in somatic cells or germ cells in mice, with lncRNA-Tcam1 being localized in 45% of the cells found in testis [Liang et al. analyzed the overall sequential expression of lncRNAs in four specific stages of spermatogenesis, mainly in spermatogonial stem cells (SSC), type A spermatogonia (AS), pachytene spermatocytes (PS), and round spermatids (RS) in mice using microarray analysis. They reported that intergenic lncRNAs were highly expressed in the germ cell types, acting as tissue-specific lncRNA genes and as housekeeping genes, sense overlap lncRNA, antisense lncRNA, and bidirectional lncRNA . Further01O22Rik  in meiotn testis . See theTherefore, increasing amounts of research have demonstrated the dynamic changes in gene expression in the complex process of spermatogenesis. The findings of these studies provide new insights into lncRNAs expression levels and their tissue specificity in testicular spermatogenesis, suggesting their specific roles in mammalian spermatogenesis.LncRNAs are mRNA-like transcripts of greater than 200 nucleotide sequences exhibiting little or no protein-coding ability  and reprin trans during late spermatogenesis [in cis-mediated mechanism to regulate neighboring gene expression [Wen et al. reported the partial and full rescue of testis-specific lncRNAs in a trans-configuration using transgenes, indicating their DNA regulatory functions ogenesis . This stpression , 86. Sonpression .1700108J01Rik and 1700101O22Rik using in situ hybridization, suggesting their function in post-transcriptional gene regulation mainly in the nucleus; however, they are not involved in epigenetic and transcriptional regulation [Dmrt1 on chromosome 5 in spermatogenesis and testis development in mice [Mammals exhibit condensation and remodeling of their chromatin material during late spermatogenesis by omitting excess cytoplasm and replacing histones with protamine for spermatid individualization, leading to a highly compact sperm nucleus . Enhancegulation . Similar in mice . A study in mice . The autMrhl (Meiotic recombination hot spot1 locus) interacts with the p68 (DEAD-box helicase 5) protein and binds to the SOX8 (SRY-box 8) promoter to inhibit the Wnt signaling pathway in mouse spermatogonial cells to ensure normal sperm production [H19 through the IGF-1 signaling pathway [Gm2044 is mediated by the miR-202-Rbfox2 molecular signaling pathway in the proliferation of human testicular embryonic carcinoma cells. This provides new clues to understand male reproduction and the importance of lncRNAs in the miR-202-Rbfox2 molecular signaling pathway in the pathogenesis and treatment of male infertility [LncRNAs play an important role in diverse biological processes by interacting with regulatory pathways of epigenetics . Zhang eoduction . The apoertility . Wichmanertility .Advances in transcriptomic studies, especially in RNA sequencing, have increased the identification of lncRNAs. Recently developed computational methods for lncRNA identification and functional prediction have increased our knowledge of the various functions of lncRNAs. This has helped us to understand the complex lncRNA associated processes in various systems, lncRNAs’ molecular functions, and their mechanisms. LncRNAs have an important function in mammalian spermatogenesis; however, the specific roles of only a few lncRNAs have been determined. Therefore, it remains challenging to develop accurate and efficient methods to characterize lncRNAs and lacks detailed information about the specific roles of lncRNAs and the interaction between lncRNAs, genes, proteins, and other molecules in specific tissues and cells. Therefore, developing more powerful computational methods and comprehensive databases, and performing further experimental studies are required to determine the molecular functions, mechanisms, specific expression, and tissue specificity of lncRNAs in different cellular developmental processes. Future studies combining different approaches in lncRNA research are likely to provide a comprehensive understanding and exciting insights into this rapidly evolving field of study in complex biological processes.The localization of lncRNAs in the vicinity of protein-coding genes and the existence of conserved lncRNAs indicate closer interactions and interrelated functions of lncRNAs in the mammalian genome. The conserved lncRNAs might also represent new prognostic biomarkers and novel diagnostic options to design therapeutic drugs. Recent advances in nucleic acid drug development have also started to provide successful solutions to male sterility problems, male contraceptives, and will allow the exploration of lncRNAs viable targets to combat male infertility, pathogenesis , and othCurrently, rapid advances are being made in lncRNA pathway studies and understanding their molecular functions , 92, 93.


Canonical RNAi, one of the so-called RNA-silencing mechanisms, is defined as sequence-specific RNA degradation induced by long double-stranded RNA (dsRNA). RNAi occurs in four basic steps: (i) processing of long dsRNA by RNase III Dicer into small interfering RNA (siRNA) duplexes, (ii) loading of one of the siRNA strands on an Argonaute protein possessing endonucleolytic activity, (iii) target recognition through siRNA basepairing, and (iv) cleavage of the target by the Argonaute’s endonucleolytic activity. This basic pathway diversified and blended with other RNA silencing pathways employing small RNAs. In some organisms, RNAi is extended by an amplification loop employing an RNA-dependent RNA polymerase, which generates secondary siRNAs from targets of primary siRNAs. Given the high specificity of RNAi and its presence in invertebrates, it offers an opportunity for highly selective pest control. The aim of this text is to provide an introductory overview of key mechanistic aspects of RNA interference for understanding its potential and constraints for its use in pest control. C. elegans and defined as sequence-specific mRNA degradation induced by long double-stranded RNA (dsRNA) (RNA interference (RNAi) is one of the pathways, collectively named RNA silencing pathways, that employ small RNAs as guides for sequence-specific silencing [reviewed in ]. RNAi w (dsRNA) . Althoug (dsRNA) ], this rThe primary aim of this contribution is to provide an overview of RNA interference mechanism with focus on selected aspects concerning RNAi targeting and off-targeting in animals as these would be most relevant features for discussing the use of RNAi for pest control. Therefore, I will purposefully not go into the details. Interested readers should check out referenced reviews or original articles. For a thorough overview of RNAi, readers are welcome to refer to a comprehensive compilation of information on RNAi and related pathways in different animal taxons and plants, which we assembled with colleagues for the European Food and Safety Authority .Some kind of RNA silencing pathway . RNA silRNA substrates giving rise to small RNA guides in RNA silencing pathways vary in structure. They include double-stranded RNA (dsRNA) with blunt ends, small and long RNA hairpins with perfect and less-than-perfect complementarity, sense and antisense RNA (basepaired or not), or single-stranded “aberrant” RNA that would be converted to dsRNA by an RNA-dependent RNA polymerases (RdRP) or converted directly to small RNAs. Substrates can be converted to a small RNA either by Dicer, an RNase III cleaving dsRNA and/or canonical microRNA (miRNA) precursors, or by some Dicer-independent mechanism [reviewed in ].de novo DNA methylation or transcriptionally repressive histone modifications.Target repression can be post-transcriptional or transcriptional. Post-transcriptional RNA silencing could have a form of endonucleolytic cleavage of cognate RNA , or translational repression coupled with mRNA destabilization . Transcriptional RNA silencing is common in plants but rare among animals [reviewed in ]. It mayCommon biological roles of RNA silencing pathways include regulation of endogenous gene expression, antiviral immunity, and genome protection against transposable elements [summarized in ]. DuringC. elegans), RNAi pathways employ the above-mentioned RdRPs, which can provide an amplification loop synthesizing small RNAs or dsRNA on targeted RNA templates  employsDrosophila, where both pathways genetically diverged such that each pathway has a dedicated Dicer and AGO protein, while the crosstalk between the two pathways is minimal. Dicer in the RNAi pathway is phylogenetically more derived, which would be consistent with its engagement in dsRNA-based antiviral defense and host-pathogen evolutionary arms race . PhasiRNAs themselves are a complex small RNA category as they can be generated by different Dicers and mediate target cleavage as well as transcriptional silencing.  uncovered how canonical Dicer generates small RNAs of defined length from long dsRNA substrates . While Argonaute proteins interact with many other proteins [reviewed in ], the miC. elegans employs a single Dicer protein, but evolved an extreme diversity of Argonaute proteins among common model systems . Togethgermline . The exoBP RDE-4 . This isBP RDE-4 . C. elegproteins . Drosoph insects ] is coup insects . Mammals insects . This isRecognition of targets is coupled with the loaded Argonaute structure . AGO2 biA small RNA loaded onto an animal AGO protein has five distinct sequence modules: the anchor, seed, central, 3’ supplementary, and tail . The 5’ For the Argonaute function in RNAi, a “two-state” model was proposed , where tD for seed-matching targets, which are in a picomolar range (3.7 pM for mouse AGO2 and 20 pM for Drosophila AGO2)  . Importala AGO2) .Drosophila showed that perfect complementarity at positions 2–17 is sufficient for RNAi , Cnidaria (hydra), Chelicerata (tick), Hemichordata (acorn worm), Urochordata (sea squirt), but appear absent in the genomes of others, including Platyhelminthes (planaria), Hexapoda (Drosophila), or Craniata (vertebrates). Consequently, transitive RNAi generating secondary sequences upstream of the region targeted by siRNAs was not observed in Drosophila or mouse  or through alternative endocytosis  has varreported . Importabona fide RNAi suppressor proteins, which are known defense strategy against RNAi used by viruses , mutations within RNAi pathway factors of the pest (including uptake mechanisms), or evolution of  viruses .rde-1 mutant in C. elegans (C. elegans vary in the RNAi response and may exhibit different degrees of resistance to RNAi (Animals lacking RNAi may be viable and fertile, as shown in an  elegans . In fact to RNAi  despite  to RNAi . A simil to RNAi . It was  to RNAi . TherefoRNAi offers selective gene targeting in a species-specific manner. RNAi induced by long dsRNA or unmodified siRNA offers a species-specific biodegradable pesticide. RNAi can be a particularly potent tool against pests that display environmental and systemic RNAi. The risk of potential off-targeting effects can be minimized when selecting the target and its sequence. Off-targeting effects can be monitored in closely related species and targets and, if identified, they would disappear after termination of the RNAi treatment.The author confirms being the sole contributor of this work and has approved it for publication.This work was supported by the Ministry of Education, Youth, and Sports project NPU1 LO1419. The content is based on a presentation given at the OECD Conference on RNAi-based Pesticides, which was sponsored by the OECD Co-operative Research Programme: Biological Resource Management for Sustainable Agricultural Systems whose financial support made it possible for the author to participate in the workshop.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Using existing protocols, RNA extracted from seeds rich in starch often results in poor quality RNA, making it inappropriate for downstream applications. Though some methods are proposed for extracting RNA from plant tissue rich in starch and other polysaccharides, they invariably yield less and poor quality RNA. In order to obtain high yield and quality RNA from seeds and other plant tissues including roots a modified SDS-LiCl method was compared with existing methods, including TRIZOL kit (Invitrogen), Plant RNeasy mini kit (Qiagen), Furtado (2014) method, and CTAB-LiCl method. Modifications in the extraction buffer and solutions used for RNA precipitation resulted in a robust method for extracting RNA in seeds and roots, where extracting quality RNA is challenging. The modified SDS-LiCl method revealed intense RNA bands through gel electrophoresis and a nanodrop spectrophotometer detected ratios of ≥ 2 and 1.8 for A260/A230 and A260/A280, respectively. The absence of starch co-precipitation during RNA extraction resulted in enhanced yield and quality of RNA with RIN values of 7–9, quantified using a bioanalyzer. The high-quality RNA obtained was demonstrated to be suitable for downstream applications, such as cDNA synthesis, gene amplification, and RT-qPCR. The method was also effective in extracting RNA from seeds of other cereals including field-grown sorghum and corn. The modified SDS-LiCl method is a robust and highly reproducible RNA extraction method for plant tissues rich in starch and other secondary metabolites. The modified SDS-LiCl method successfully extracted high yield and quality RNA from mature, developing, and germinated seeds, leaves, and roots exposed to different abiotic stresses. Modern biotechnological advances in functional genomics and the access to whole-genome sequences are invaluable tools for crop improvement. Identifying the molecular factors affecting crop growth and productivity under harsh environmental conditions and evaluating their functional responses will help decode pathways enhancing stress-tolerance in crops. Deciphering these molecular factors and stress tolerance mechanisms demands high-quality RNA, which is a pre-requisite for various techniques in molecular biology, including gene expression quantification by RT-PCR, RT-qPCR, RNA-seq, northern blot analysis, construction of cDNA libraries and PCR amplification-based gene cloning.5. Several RNA isolation methods have been reported to yield quality RNA from wheat and other seeds rich in starch, polysaccharides, and other secondary metabolites6. A few limitations of existing RNA extraction methods mentioned above include—(i) need more than one extraction buffers2, (ii) methods developed are restricted to extracting RNA only from seeds, (iii) require expensive TRIZOL and/or commercial kits for extraction6 and comparatively cumbersome2. Hence, there is a need to develop a rapid and universal RNA extraction method using basic laboratory chemicals that can be applicable to a wide range of tissues containing interfering substances that alter the quality of extracted RNA.Seeds are complex storage organs, composed of embryo and endosperm covered by a hard seed coat. The endosperm, which occupies the major proportion of the seed, contains significantly high quantity of starch followed by other polysaccharides , proteins, lipids, and secondary metabolites. Most of these components in seeds have structural similarities with nucleic acids and hence co-precipitate along with RNA, thus challenging the extraction of high yield and quality RNA1. Solidification of starch enhances the co-precipitation of starch/polysaccharides along with RNA due to their structural similarities, for example presence of ribose sugar. The large polysaccharide fractions in the seed can physically trap RNA and be entrained during centrifugation and gets discarded during the phase separation, thus leading to low yield. Additionally, small polysaccharide particles can be partitioned into the aqueous phase during phase separation and co-precipitate along with RNA, which further reduces RNA yield and renders it less suitable for downstream applications9. Therefore, to overcome the starch solidification encountered by existing methods, additional purification steps or alternative methods are required11. In addition, extraction of high-quality RNA from plant tissues subjected to different environmental stresses such as heat, drought and cold stresses can be difficult due to increased accumulation of high molecular weight polysaccharides and secondary metabolites that interfere with RNA isolation. Secondary metabolites in the oxidized form irreversibly bind to the nucleic acids and act as inhibitors for downstream applications or degrade the RNA15. Isolating high-quality RNA free from proteins, polyphenols, and polysaccharide contaminants from samples exposed to abiotic stresses is essential for downstream applications. Moreover, the quality and quantity of RNA extracted using existing RNA extraction methods and commercial kits differ depending on the plant species, genotype and tissue type17. Hence, it is evident that a universal RNA extraction method that can be used across different plant parts, including seeds, which contain high levels of starch and secondary metabolites and roots would be of great value to the crop science community.RNA isolation methods with guanidine isothiocyanate based extraction buffers fail to extract quality RNA from seeds rich in starch, as guanidine isothiocyanate induces solidification of starch6 method, CTAB-LiCl method3, and our modified SDS-LiCl method, were used to compare the quantity and quality of RNA extracted from mature wheat seeds collected from field-grown plants exposed to ambient, and high night temperatures . The extracted RNA was then used to assess the quality through RT-qPCR analysis. In addition, RNA from different tissues of wheat plants including mature, developing and germinated seeds, leaf, and roots, exposed to cold, freezing, and high night temperature stresses were isolated using the modified SDS-LiCl method. The robustness of our modified SDS-LiCl method was further tested on developing sorghum seeds and developing and mature maize seeds, obtained from field grown plants. Given the variability of RNA isolation methods, the ability to achieve comparative analysis of genomes and transcriptomes across crops and tissue types continues to be a challenge for researchers. Here we describe a universal method that can be applied in seed tissues containing high starch and other plant tissues including roots, which contain high fiber and secondary metabolites. Our intent in presenting this modified method is not to oppose other methods that work well for specific crops and tissues, but rather to present a universal method that is more robust, rapid and works equally well across different field crops and plant tissues.The major objective of the present study was to develop a robust and reliable RNA extraction method to obtain high-quality RNA from different plant tissues, including roots and seeds exposed to different abiotic stresses. To achieve this objective, five RNA extraction methods, i.e. TRIZOL kit (Invitrogen), Plant RNeasy mini kit (Qiagen), FurtadoTriticum aestivum L.) genotypes Tascosa and Tx86A5606 with contrasting responses to high night temperature (HNT) stress were used to isolate RNA from different plant tissue and growth conditions. RNA was isolated from physiologically mature wheat seeds collected from field grown plants18, flag leaves from controlled environment chamber grown plants exposed to HNT and control conditions19, germinated seeds under cold stress, roots of wheat seedlings grown on Murashige and Skoog (MS) media exposed to freezing stress. Our modified SDS-LiCl method was extensively compared to other currently available methods developed by public institutes and private companies. RNA was also isolated from plant samples exposed to cold, freezing and HNT stress to demonstrate that the efficiency of the new method can be extended to plants exposed to different abiotic stress conditions. In addition, the method was adopted for extracting RNA from developing and mature seeds of field-grown maize and sorghum. Details of the sample collection and stress imposition and associated references are detailed below.Two winter wheat (18. The heat tents maintained temperatures < 1 °C higher compared to ambient conditions during the day. Higher night-time (19:00 h to 6:00 h) temperature was achieved using a custom built semi-automated heating system operating independently in each tent during grain filling . Physiologically mature seeds were collected from two winter wheat genotypes (Tascosa and Tx86A5606) grown under ambient night temperature  and an elevated night temperature of + 3.2 °C (HNT stress) during the grain filling, frozen in liquid nitrogen, and stored at − 80 °C for RNA extraction.Physiologically mature wheat seeds were collected from field grown plants, 21 days after 50% flowering. The field experiment was carried out at Kansas State University, Agronomy research farm at Manhattan, Kansas. High night temperature (HNT) stress was imposed by placing custom built field-based heat tents on already established wheat plants from 10 days after 50% flowering till maturity20). HNT stress treatments were imposed immediately after the main spike (first emerged) had completed flowering and maintained until physiological maturity. Three biological replicates of main spikes from all the five night temperature treatments were collected at 14 days after the stress was initiated, and immediately frozen in liquid nitrogen, and stored at − 80 °C for RNA extraction.Developing wheat seeds were collected from controlled environment chamber grown plants at 14 days after heading/HNT stress exposure, from control and HNT treatments. Two winter wheat genotypes, SY Monument (Tolerant) and KS07077M-1 (Sensitive) contrasting for HNT stress were exposed to control (28/15 °C) and a range of HNT treatments  using the controlled environment chamber facility, at the Department of Agronomy, Kansas State University . Three replicate samples were collected for each genotype and treatment. Samples were immediately frozen in liquid nitrogen and stored at − 80 °C for RNA extraction.Flag leaf samples were collected from the controlled environment chamber grown wheat genotypes, Tascosa and Tx86A5606 at 14 days after heading/HNT stress exposure, from control (26/15 °C) and HNT (26/23 °C) treatments. HNT stress treatments were imposed immediately after the main spike (first emerged) had completed flowering and maintained until physiological maturity  were germinated in plastic Petri dishes (100 mm by 20 mm). Seeds were sterilized with 70% ethanol for 2 min, followed by 3% sodium hypochlorite for 5 min. Subsequently, seeds were rinsed 2 to 3 times with sterile distilled water before placing them on the germination paper. Ten seeds were placed in each petri dish containing the germination paper moistened with sterile distilled water. The Petri dishes were incubated in an incufridge  maintained at 15 °C to impose cold stress, and at 30 °C for control treatmentSeeds of Tascosa and Tx86A5606 were surface sterilized with 70% ethanol for 2 min and rinsed twice with sterile distilled water. Subsequently, the seeds were washed with 3% sodium hypochlorite for 5 min and rinsed three times with sterile distilled water. Later, the seeds were dried under laminar airflow and were inoculated into sterile test tubes containing half-strength Murashige and Skoog (MS) media  with 0.6% w/v solidified agar. The test tubes were incubated in a growth chamber maintained at 25 °C with 16/8 h day/night photoperiod. After ten days of incubation, the seedlings were exposed to a freezing temperature of − 4 °C in the freezer for 16 h. At the end of the 16 h stress exposure, root samples from control and freezing temperatures were collected into liquid nitrogen and immediately stored at − 80 °C for RNA extraction.The developing seeds of Sorghum hybrid (Pioneer 87P06) and developing and mature seeds of Corn (Pioneer P0805AM) hybrid were collected from the plants grown under field conditions at Kansas State University, Agronomy research farm. The samples harvested in the field were immediately frozen in liquid nitrogen and stored at − 80 °C for RNA extraction.All the solutions used in the RNA extraction procedure were prepared with autoclaved 0.1% DEPC (diethyl pyrocarbonate) treated MilliQ water. All the plastic- and glassware and mortar and pestle were pretreated with DEPC and autoclaved.Ambion TRIZOL reagent Kit : Total RNA was extracted according to the manufacturer's instructions. Extraction buffer used in this method contains guanidium thiocyanate and RNA was precipitated in isopropanol.RNeasy Plant Mini Kit : RNA was extracted according to the manufacturer's instructions. The method uses guanidium thiocyanate based lysis buffer and RNA purification was done using silica-membrane column.6 method: The total RNA was isolated based on the combination of both TRIZOL and RNeasy Plant Mini Kit (Qiagen) method. RNA extraction was carried out in the TRIZOL reagent, and the collected aqueous phase was extracted in RLC buffer, followed by steps in RNeasy Plant Mini Kit manufactures method.FurtadoCTAB-LiCl method: RNA was extracted based on the method described by White et al.3. In this method, ionic detergent cetyltrimethylammonium bromide (CTAB) was used in the extraction buffer, and LiCl was used to precipitate RNA.New Modified method (SDS-LiCl): This method follows the SDS-Phenol based extraction and RNA precipitation with sodium acetate and LiCl  at 260 nm and 280 nm by a Nanodrop-2000 Ultraviolet Spectrophotometer . The RNA quality of the sample was measured by monitoring the absorbance at A260/280 and A260/230 ratios Table . The purThe RNA extracted from developing and mature seed samples were subjected to DNase 1 treatment  to eliminate remaining traces of genomic DNA (gDNA). The first strand of cDNA was synthesized using RevertAid First Strand cDNA Synthesis Kit (Thermo Scientific) according to the manufacturer’s instructions. For polymerase chain reaction (PCR) amplification of 5-enolpyruvyl-shikimate-3-phosphate synthase (EPSPS) gene product (1229 bp), ~ 100 ng of cDNA samples of total RNA extracted from the mature wheat seed tissue using five different RNA extraction protocols were added to a 25 µl PCR reaction mix containing GoTaq G2 Green Master Mix  and 0.4 µM of each gene-specific primer (EPSPSF- AAGTCGCTMTCCAAYCGRATCCT and EPSPSR- GGGAAGGTCTTNCGGGTGCA). The amplification was carried out in a thermal cycler  with PCR conditions as follows: 5 min denaturing at 95 °C; 32 cycles of 30 s denaturation at 95 °C, 30 s annealing at 55 °C, 45 s elongation at 72 °C and a final extension for 10 min at 72 °C.Actin  and Beta-amylase (BMY) . Quantitative PCR/Real-Time PCR (RT-qPCR) was performed to determine the quality of the RNA extracted from the seed samples using the BMY (Beta Amylase) gene. For all the RT-qPCR analysis three technical replicates were used for each of the three biological replicates and RT-qPCR was performed with a reaction mixture containing 8 μl of SYBR Green master mixture , 2 μl each of forward and reverse primers (5 μM), and 20 ng cDNA (2 μl) to make the final reaction volume of 14 μl. RT-qPCR  was performed at 95 °C for 5 min, and 40 cycles of 95 °C for 30 s and 58 °C for 30 s. Relative gene expression was quantified using the 2−ΔΔCT method as described in Livak and Schmittgen22, and BMY gene expression was normalized using Actin as a reference gene. A melt curve profile was incorporated in the thermal cycling method to check the specificity  of the RT-qPCR reaction.For RT-qPCR analysis, the following reference and gene-specific primers were used to measure the corresponding genes (Fwd/Rev): 24. Modified SDS-LiCl method described here provides a robust method to obtain high-yield and high-quality RNA from wheat seeds containing high starch and other polysaccharides, and other tissues such as roots that are rich in fiber. In addition, this method has the potential to extract high yield and quality RNA from developing and mature seeds exposed to different abiotic stresses. Analysis of total RNA from mature wheat seed on agarose gel electrophoresis revealed that ribosomal RNA bands were intact and intense, demonstrating that the total RNA extracted by modified SDS-LiCl was not degraded , including proteins and other biopolymers (polysaccharides and secondary metabolites). Therefore, due to the higher affinity of CTAB with both nucleic acids and other biopolymers, leads to reduced yield of RNA. On the other hand, anionic surfactant SDS does not interact with nucleic acids (anionic) but highly reactive with proteins and other polysaccharides and forms aggregates. Thus, SDS is more effective in separating RNA from other biopolymers during RNA extraction29. Moreover, CTAB-LiCl method has a lengthy procedure and is time consuming compared to the modified SDS-LiCl method. Higher RNA yield with the modified SDS-LiCl extraction method suggests the absence of starch interference during RNA precipitation.Evaluation of total RNA quality using Agilent bioanalyzer 2100 revealed superior quality of RNA extracted using modified SDS-LiCl method with the electropherograms showing distinct, prominent peaks for ribosomal RNAs (rRNAs), whereas other methods exhibited either many small peaks and/or no protuberant peaks for ribosomal RNAs Fig. –S5. RIN 32. In addition, post-grinding addition of SDS directly to the homogenate suppressed excessive foaming and facilitated proper lysis of seed samples and prevented starch solidification33. Additionally, in the modified SDS-LiCl method, phase purification using acidic phenol (pH 4.3) saturated with citrate buffer helped to improve the RNA quality by separating RNA from bulk starch residues compared to CTAB-LiCl method. Efficient separation of bulk starch residues and degraded proteins during the centrifugation of RNA containing aqueous phase using acid phenol saturated with 0.1 M citrate buffer (pH 4.3) supports earlier findings2.Even though the extraction buffer used in the SDS-LiCl method contains NaCl, PVP, Tris/HCl, EDTA, β-mercaptoethanol, and SDS (see supplementary document), similar to existing methods, we optimized the concentrations of these chemicals for seeds and other tissues including roots. The chemical composition confirmed that 2.5 M NaCl, 2.5% PVP, 100 mM Tris, 25 mM EDTA, 2.5% β-mercaptoethanol, and 2% SDS, helps obtain high quality and yield of RNA without the interference of starch and other impurities. The enhanced yield and quality of RNA achieved in the modified SDS-LiCl method compared to CTAB-LiCl is due to the addition of SDS, and higher concentration of other chemicals (PVP and NaCl) in the extraction buffer. In agreement with previous studies, an increased concentration of NaCl, PVP, β- mercaptoethanol and SDS in the extraction buffer of the modified SDS-LiCl method, not only promotes cell lysis but also prevents RNAse and polyphenols in the isolated RNA34. Adding both lithium chloride and sodium acetate in the precipitation step enhanced the yield, whereas in CTAB-LiCl method, there was no phenol separation and LiCl alone was used for precipitation. Overall, the RNA obtained from modified SDS-LiCl method from field-grown wheat seeds under both control and HNT stress had high yield and quality, demonstrating a marked improvement compared to all other existing methods.A combination of 2 M Lithium chloride and 1/10 volumes of 3 M sodium acetate (pH 4.8) possibly facilitated better RNA precipitation. This is in agreement with previous studies wherein lithium chloride and sodium acetate were used to promote precipitation and resulted in high RNA yieldPCR amplification of EPSPS gene full length CDS with 1.2 kb product size and a prominent band provides support that cDNA obtained from RNA using the modified SDS-LiCl method is high quality compared to other methods. In addition, it indicates that RNA integrity is sufficient to amplify large size gene fragments . The expression analysis of 21 genes encoding enzymes involved in the starch metabolism in developing wheat seeds exposed to a range of increasing night temperatures was carried out using the RNA extracted by the modified SDS-LiCl method . A single peak in the melt curve analysis and lower standard errors between the Ct values for a gene indicated the specificity of the primers to bind to the cDNA synthesized from the RNA, with no interference of PCR inhibitors  was performed with the cDNA synthesized from RNA isolated from mature seeds using the modified SDS-LiCl method. Amplification of all the samples with strait amplification curves and Ct values within the range permitted to perform meaningful analysis of expression data . Previous studies have reported decreased concentration of RNA from the samples subjected to abiotic stress35. Similarly, in our study we observed less RNA concentration in the developing grain samples of KS07077M-1 subjected to HNT stress compared to control using the SDS-LiCl method (Supplementary Table 6) did not differ between control and HNT stress samples due to interference of starch. Similarly, an independent study recorded no difference in the RNA yield of wheat leaf tissue subjected to freezing stress13. Previous studies have reported that reduced concentration of RNA and interference of secondary metabolites in RNA extraction depends on the severity of stress and tissue age12 also the type of tissue13. Hence, the lack of a significant impact on RNA concentration in this study could be due to the short duration of exposure under different stresses or due to moderate HNT stress18 levels.Further, the modified SDS-LiCl method was used successfully to extract high-quality RNA from germinated wheat seeds, flag leaves, and root tissues that were exposed to cold, freezing and HNT stress. Agarose gel electrophoretic separation and Nanodrop UV spectrophotometer measurements of total RNA from different tissues of wheat genotypes exposed to different abiotic stresses were confirmed for RNA quality with A260/280 and A260/A230 ratios of ≥ 2 and a similar RNA yield to that of control samples Fig. –S9. Anal36, whereas the modified SDS-LiCl method was suitable for extracting high quality RNA from various crop tissues differing in developmental stages and exposed to different abiotic stresses under controlled environment and field conditions.Modified SDS-LiCl method also yielded high quality RNA from other cereals seeds (mature and developing maize seeds and developing sorghum seeds) rich in starch, protein, and polyphenols (Supplementary Figs. 24 or only leaf tissues3. The protocol standardized here has been modified to be a rapid yet reliable, using basic laboratory chemicals that are easily accessible by researchers, whereas previously methods needed more chemicals in the extraction buffer, or expensive reagents kits5. Besides, the versatility of the universal protocol is demonstrated by extracting high-quality RNA from other cereal crop seeds. A comparative assessment including the advantages of the SDS-LiCl method compared to previous protocols is presented in Table In summary, the modified protocol was exclusively developed for extraction of RNA from cereal seed tissue containing high starch, as it is one of the major hurdles challenging functional studies involving developing or mature seeds. This protocol is cost-effective compared to commercially available kits and has been demonstrated to be successful in obtaining quality RNA from mature wheat grains, while TRIZOL, CTAB, and other kits generally failed. The robustness of the modified SDS-LiCl method helped to extract significantly higher yield and quality of RNA from different wheat plant tissues, including, mature, developing and germinated seeds, leaves and roots, exposed to various abiotic stresses, whereas previously published protocols have limited the protocol to either seed tissues of wheat or other cerealsIn conclusion, we have optimized a robust method for extracting high yield and quality RNA from wheat seeds, free from starch and other contaminates. The obtained RNA quality was appropriate for downstream applications demonstrating the effectiveness of the protocol with seeds, that contain high starch and polysaccharides. The modified SDS-LiCl method extracted significantly higher yield and quality of RNA from different wheat tissues, including mature, developing and germinated seeds, leaves and roots exposed to different abiotic stresses. Besides, the versatility of the protocol was further strengthened with high quality RNA extracted from field grown maize and sorghum seeds.Supplementary file1Supplementary file2


Even though several computational methods for rhythmicity detection and analysis of biological data have been proposed in recent years, classical trigonometric regression based on cosinor still has several advantages over these methods and is still widely used. Different software packages for cosinor-based rhythmometry exist, but lack certain functionalities and require data in different, non-unified input formats.We present CosinorPy, a Python implementation of cosinor-based methods for rhythmicity detection and analysis. CosinorPy merges and extends the functionalities of existing cosinor packages. It supports the analysis of rhythmic data using single- or multi-component cosinor models, automatic selection of the best model, population-mean cosinor regression, and differential rhythmicity assessment. Moreover, it implements functions that can be used in a design of experiments, a synthetic data generator, and import and export of data in different formats.https://github.com/mmoskon/CosinorPy. CosinorPy can be installed manually or by using pip, the package manager for Python packages. The implementation reported in this paper corresponds to the software release v1.1.CosinorPy is an easy-to-use Python package for straightforward detection and analysis of rhythmicity requiring minimal statistical knowledge, and produces publication-ready figures. Its code, examples, and documentation are available to download from  Many biological processes display oscillations that are under the control of different biological clocks. For example, circadian clocks display daily oscillations, i.e., with a periodicity of approximately 24 h , and mayDetection and analysis of rhythmicity requires designated computational approaches. These approaches are focused on the identification of rhythmic datasets that correspond to specific biological entities  and evaluation of their rhythmicity parameters. Several non-parametric methods for circadian data analysis have been proposed recently, such as JTK CYCLE and its extensions –9 and RAt corresponds to the observed time points in the time series, N is the number of components in the model, namely number of cosine curves, and C and P are the parameters of the model, with M being the MESOR (Midline Statistic Of Rhythm), P the period of the observed rhythm, and e(t) the error term [Cosinor presents a fundamental method for rhythmicity detection and analysis using cosine curve fitting , 15. It timation .Cosinor has been widely applied to the analysis of rhythmicity detection and evaluation of rhythmicity parameters in time series data. Different software packages for cosinor-based rhythmometry, such as cosinor , cosinorxlsx and csv files and generating synthetic data. The cosinor module implements the functionalities based on a single- or multi-component cosinor model that include model fitting, identification of the most suitable model, and analysis of differential rhythmicity. The cosinor1 module implements similar functionalities, which are adapted to a single-component cosinor model, and thus provide more exhaustive results and additional statistics such as the significance of acrophase shifts within the differential rhythmicity analysis. The implementation of specific functionalities within the package are described below. Thorough documentation of the package is available at https://github.com/mmoskon/CosinorPy/blob/master/docs/docs.md.CosinorPy is implemented in Python and relies on the state-of-the-art Python packages for data management, scientific computing, visualisation, and statistical modelling, namely pandas, NumPy, SciPy, Matplotlib, and statsmodels. CosinorPy is comprised of three Python modules, namely file_parser, cosinor, and cosinor1. The file_parser module implements reading and writing of A) and acrophase  or to the same variable in different conditions . We are usually interested in amplitude changes and acrophase shifts between the groups. Several different methods, which we use in our implementation, have been proposed to assess these differences.g equals 0 if the data belong to the group a, and 1 if the data belong to the group b. Based on the assessed parameter values, we can estimate the acrophases and amplitudes of each of the groups, as well as the differences between these values and their significance [If the data describing both groups can be modelled with single-component cosinor models, a single-component cosinor can as well be used to assess the differential rhythmicity of these two groups. This model is implemented asificance . Moreoveificance , 19.b, namely LimoRhyde  presentsa, respectively, and b, respectively. CosinorPy as well implements differential rhythmicity assessments based on non-linear regression. However, this approach does not provide any additional information to the approach described in Equation Non-linear regression might as well be used to evaluate the differential rhythmicity parameters and their confidence intervals as described in CircaCompare . This ishttps://github.com/mmoskon/CosinorPy.We demonstrate the application of selected CosinorPy functionalities on two typical case studies using four groups of synthetically generated time series data with the attributes presented in Table https://github.com/mmoskon/CosinorPy/blob/master/demo_independent.ipynb). Results obtained with the multi-component cosinor regression are presented in Fig. In our first case study, we presume that the measurements in each group are independent. This scenario complies with a transcriptomics data analysis or an analysis of qPCR data. CosinorPy successfully identifies the most suitable model, namely a 1-component model in the first two scenarios and a 3-component model in the last two scenarios. If the rhythmicity period is not known, the user could as well use the automatic identification of the best fitting period together with the best fitting model, or could rely on the period values assessed using periodograms . As in the first case study, CosinorPy is able to identify the most suitable model for each dataset . Among 250 measurements 95 measurement were identified to be circadian using multi-component cosinor models. Among these, 11 measurement were not identified to be circadian using a single-component cosinor model. In all these 11 cases data reflected multiple peaks within a 24-hour period, which cannot be fitted with a single-component model. Multiple peaks were successfully incorporated into multi-component models. However, in some of these cases the statistical significance was marginal, and additional data should be collected to confirm the circadian nature of observed measurements. In the future, large-scale analyses that were performed with single-component cosinor models in the past should be revised using multi-component cosinor models. This could enable us to detect additional rhythmic genes and would thus provide novel insights into circadian dynamics of selected genes.To additionally investigate the benefits of multi-component cosinor models we applied CosinorPy to a larger dataset downloaded from the JTK Cycle repository . We analCosinorPy provides all the functionalities required for a rhythmicity analysis of experimental data. Its features merge and extend the functionalities of existing cosinor-based software packages. These range from data import and pre-processing to identification of the most suitable models, evaluation of rhythmicity parameters and their significance, and assessment of differential rhythmicity between groups of measurements. Moreover, CosinorPy produces publication-ready figures, visualising the results of the fitting process as well as assessed parameter values, e.g., acrophase values in a polar coordinate system. With the vast scope of functionalities, as well as ease of use, the package presents an attractive alternative to other software packages for rhythmicity detection and analysis.Project name: CosinorPyhttps://github.com/mmoskon/CosinorPyProject home page: Operating system(s): Platform independentProgramming language: PythonOther requirements: pandas, Matplotlib, NumPy, SciPy, statsmodels and openpyxl Python librariesLicense: MIT licenseAny restrictions to use by non-academics: noneAdditional file 1: Supplementary Table 1. Results of the fitting process for the first case study using 1-, 2- and 3-component cosinor models with the cosinor module. The results are presented in a CSV format as reported by CosinorPy.Additional file 2: Supplementary Table 2. Results of the fitting process for the first case study using 1-component cosinor models with the cosinor1 module. The results are presented in a CSV format as reported by CosinorPy.Additional file 3: Supplementary Table 3. Results of the comparison analysis for the first case study using 1-component cosinor models with the cosinor1 module. The results are presented in a CSV format as reported by CosinorPy.Additional file 4: Supplementary Table 4. Results of the fitting process for the second case study using 1-, 2- and 3-component cosinor models with the cosinor module. The results are presented in a CSV format as reported by CosinorPy.Additional file 5: Supplementary Table 5. Results of the fitting process for the second case study using 1-component cosinor models with the cosinor1 module. The results are presented in a CSV format as reported by CosinorPy.Additional file 6: Supplementary Table 6. Results of the comparison analysis for the second case study using 1-component cosinor models with the cosinor1 module. The results are presented in a CSV format as reported by CosinorPy.Additional file 7: Supplementary Table 7. Results of the fitting process for the first case study using cosinor and cosinor2 R packages.Additional file 8: Supplementary Table 8 Results of the comparison analysis for the first case study using cosinor and cosinor2 R packages.Additional file 9: Supplementary Table 9. Results of the fitting process for the second case study using cosinor2 R package.Additional file 10: Supplementary Table 10. Results of the comparison analysis for the second case study using cosinor2 R package.


Watasenia scintillans, a sparkling enope squid, has bioluminescence organs to illuminate its body with its own luciferase activity. To clarify the molecular mechanism underlying its scintillation, we analysed high-throughput sequencing data acquired previously and obtained draft genome sequences accomplished with comparative genomic data among the cephalopods. The genome mapped by transcriptome data showed that (1) RNA editing contributed to transcriptome variation of lineage specific genes, such as W. scintillans luciferase, and (2) two types of luciferase enzymes were characterized with reasonable 3D models docked to a luciferin molecule. We report two different types of luciferase in one organism and possibly related to variety of colour types in the W. scintillans fluorescent organs.The online version of this article (10.1007/s10126-020-10001-8) contains supplementary material, which is available to authorized users. Watasenia scintillans, heralds the beginning of spring. The W. scintillans is also known as the “firefly squid,” which is important for both tourism and the fishing industry in Japan. W. scintillans emits light from three different types of photophores  RE variation in W. scintillans is different from that of octopus, and (2) RNA editing was contributed to transcriptome variation of lineage specific genes, such as W. scintillans luciferase. Subsequent detailed analysis of bioluminescence showed that (3) there are at least two types of luciferases in the genome of W. scintillans.Recently, the genome of Hawaiian bobtail squid W. scintillans caught in Toyama Bay were obtained from Hotaruika Aquarium . Genomic DNA was extracted from the brain of a single female individual (L3) by a conventional phenol/chloroform extraction protocol. Briefly, the brain tissue was lysed in 600 μl of the lysis buffer  at 55 °C overnight. The lysate was extracted with an equal volume of phenol/chloroform/isoamyl alcohol (25:24:1) once, and with an equal volume of chloroform once. The genome DNA was precipitated by adding an equal amount of isopropanol to the lysate, washed with 70% ethanol, dried briefly and dissolved in 1× TE. Total RNA samples were also extracted from the brain and the arm tips of the same individual using the Mixer Mill MM301  for homogenization and the E.Z.N.A. Mollusc RNA Kit .A short-insert and two mate-pair libraries were prepared from the brain genomic DNA of an individual using TruSeq DNA sample preparation kit v2  and Nextera mate-pair library preparation kit (Illumina), respectively. Mate-pair libraries were prepared from two size ranges of tagmented DNA fragments, 4 kb and 8 kb in average. RNA-seq libraries were prepared from the total RNA samples of the same individual (brain and arm tips) using TotalScript RNA-Seq Kit . The DNA- and RNA-seq libraries were sequenced on HiSeq1500/2500 systems (Illumina). Paired-end read (150 bp × 2) sequencing was performed using the HiSeq Rapid SBS Kit HS. The conversion of bcl files to fastq files was performed using the CASAVA v1.8.2 (configureBclToFastq.pl) software (Illumina).de novo genome assembly using ALLPATHS-LG v.44837  with species option, human  library and 22.7 million and 23.4 million read pairs from two mate-pair libraries (4 kb and 8 kb) were subjected to 17-mer counting of the Illumina reads using Jellyfish v.2.2.10  and giant squid Architeuthis dux ), all drosophila sequences, all human sequences, all octopus sequences (Octopodiformes (taxid:215451)), all squid sequences (Decapodiformes (taxid:215450)), all Lottia sequences and transcripts from Branchiostoma, Crassostrea, Mytilus and Nematostella invertebrate adenylating enzymes were found by the NCBI web BLASTP search (as of 28 August 2020) using the W. scintillans luciferases as a query . To perform multiple alignment of protein sequences, we utilized MUSCLE v3.8.1551 (Edger 2004) followed by removing suspicious residues using trimAl_v1.4beta   gives more than 24× coverages compared with the estimated haploid genome size, 4.78 Gbp Table . HoweverW. scintillans assembly by default setting (for human) and masked. Then, (2) the repeat libraries of octopus at OIST Octopus Genome site (https://groups.oist.jp/ja/molgenu/octopus-genome) were used as templates, and (3) the repeat libraries of I. paradoxus repeat database which we published previously  were estimated to comprise the repetitive sequence common to Coleoidea. By finally applying method 3, 12,458,350 bp sequences (19.2%) on W. scintillans scaffolds were still found to have high similarity to the I. paradoxus REs. The abundant number of REs after the third screening step indicates that the relative volume of REs common to squids outnumbered REs in octopus  We detected simple repeats and repeat common to metazoan in the  We detecpus Fig. . On the W. scintillans, O. bimaculoides and L. gigantea as an outgroup species. As a result, we found 9535, 11,300 and 9817 orthologous gene groups in W. scintillans, O. bimaculoides and L. gigantea, respectively  are shared in cephalopods. Of these gene groups, we examined the sequence diversity of 1:1:1 core orthologous genes from the divergence point of W. scintillans and O. bimaculoides by setting L. gigantea as an ancestor. The distribution of sequence diversity deduced from the length of branches on each phylogenetic tree seems similar, but sequence diversity of the O. bimaculoides genes is slightly wider than those of the W. scintillans . The sequence was similar to Photinus pyralis (firefly) luciferase. The two sequences we found in the brain RNA transcriptome were also similar to P. pyralis luciferase, and hence our result strengthened the possibility that the protein is indeed the luciferase of W. scintillans  with RR6, which is expected to be coelenterazine on symplectin. Figure W. scintillans contig (#s249267). The C-terminal domain with Cys390 should exist at the region depicted by yellow oval background in Fig. W. scintillans obtained by Gimenez et al. . We found 13 reproducible mismatches between genome and RNA from the same individual. Out of 13, three were A-to-G mismatches and 10 were cytosine to thymine mismatches. However, some RNA reads still supported A as in the DNA in those thirteen mismatch sites; therefore, those mismatches were likely the outcome of RNA editing but not complete edit sites. Those sites were close to each other and located within 30 bp. We found that two out of the three caused synonymous substitutions. An intriguing note should be taken here that wsluc1 was also expressed in the brain, but no RNA editing was found on the brain-expressed wsluc1 RNA molecule.The analyses showed that RNA editing was taken place extensively in 2H2 expansion, which contributed to the evolution of cephalopod neural complexity and morphological innovations. If this scenario is true, common traces of the RE burst should be found throughout the genome across all cephalopods. In the present analysis, we found distinct patterns of RE types in squid and octopus genome sequences. Ceph-SINEs, which are a type of tRNA-derived SINEs originally isolated from squids . He also mentioned in the same paper that there had been a report by other workers that a small scattering of dermal organs on the ventral side had emitted a greenish-yellow light instead of blue light. It has been considered that the same molecular mechanism in different organs is used for the light emission in W. scintillans as suggested by Teranishi and Shimomura , using animal GFPs as queries and found no apparent hit. Different types of substrate or slight difference in amino acid residues in the binding site of substrate is known to change the wavelength, and hence two different types of luciferase may not be needed for different colour in light.W. scintillans can open a way to discuss evolution of luminescence genes. Luciferase enzymes have extremely varied structures, mechanisms and substrate specificities across extant organisms, and hence bioluminescence should have evolved independently at least 30 times across extant organisms ESM 2(PEP 2580 kb)