Gly/GCC and tRNAVal-CAC were the most abundant sequences across all sperm samples. A total of 324 small RNAs were differentially expressed between samples with high (n = 18) and low (n = 14) blastocyst rates (p-adj < 0.05). Ninety three miRNAs were differentially expressed between these groups (p-adj < 0.05). Differentially expressed transfer RNA fragments included: 5'-tRF-Asp-GTC; 5'-tRF-Phe-GAA; and 3'-tRF-Ser-GCA. Differentially expressed miRNAs included: let-7f-2-5p; miR-4755-3p; and miR-92a-3p. This study provides the foundation on which to validate a clinical panel of fertility-related sperm small RNAs, as well as to pursue potential mechanisms through which they alter blastocyst development.Clinical testing strategies for diagnosing male factor infertility are limited. A deeper analysis of spermatozoa-derived factors could potentially diagnose some cases of ‘unexplained infertility’. Spermatozoa carry a rich and dynamic profile of small RNAs, which have demonstrated potential developmental importance and association with fertility status. We used next-generation sequencing to correlate sperm small RNA profiles of normozoospermic males (n = 54) with differing blastocyst development rates, when using young donor oocytes. While ribosomal RNAs accounted for the highest number of sequencing reads, transfer RNA fragments of tRNA Female partners experience the largest burden during clinical management of infertility, often undergoing laboratory testing, ultrasonography, hysterosalpingography, and surgical procedures3. However, male etiological factors are estimated to contribute to 50% of infertility, and are the sole cause in 20–30% of cases4. Therefore, effective diagnosis and management of male factor infertility is critical.Infertility has a considerable global impact, affecting roughly 15% of individuals and couples of reproductive age, and can have profound financial and psychosocial consequences for individuals undergoing fertility treatment5. These types of etiologies are routinely discerned through a reproductive history, a physical examination, blood testing and semen analyses7. However, the underlying cause of male subfertility is unidentified in 30–50% of cases, suggesting there may be uncharacterized spermatozoal defects, which are not adequately assessed by the current diagnostic work-up8. Thus, a more comprehensive analysis of sperm-borne genomic and extragenomic contents could be valuable.Many cases of male infertility are attributed to low sperm counts (oligozoospermia), low motility (asthenozoospermia), morphological deformities (teratozoospermia), male anatomic abnormalities or hormonal abnormalities9. Despite translational inactivity, sperm carry an epigenetically-marked genome and a collection of ribonucleic acids (RNAs) and proteins10. Of particular interest, sperm carry a rich profile of small, non-coding RNAs, including microRNAs (miRNAs), PIWI-interacting RNAs (piRNAs), ribosomal RNAs (rRNAs) and transfer RNA (tRNA) fragments13. Numerous groups have suggested that the sperm small RNA profile is drastically altered as maturing sperm transition through the male reproductive axis18. Vesicles trafficked to maturing sperm in the epididymis, known as epididymosomes, have demonstrated the ability to deliver small RNAs to sperm, which may suggest selective packaging and delivery19. Furthermore, numerous studies have demonstrated plasticity of the sperm RNA profile in response to paternal toxin exposures, including exposure to dibutyl phthalate and bisphenol A; as well as changes in lifestyle, including short-term diet- and exercise-based interventions25. Such paternal exposures can modify the sperm RNA payload that is delivered to the oocyte at fertilization.It is increasingly clear that the developmental role of spermatozoa extends beyond simply transmitting the paternal haploid genome27. While the precise function of sperm small RNA elements in the developing embryo are unclear, they may influence oocyte activation, zygotic genome activation, regulate early embryonic gene expression, and affect offspring phenotypes28. In fact, animal studies suggest they are indispensable for normal developmental competence30. For instance, an sncRNA-deficiency, achieved via gene knockout, significantly reduced preimplantation development in mice, and this was rescued when sncRNA-deficient sperm was co-injected with either wild-type sperm total or small RNAs29.It is fairly well-established that sperm-borne RNAs are present in the embryo and persist throughout early development13. In a clinical study, human sperm piRNAs (piR-31704 and piR-39888) were lower in males with oligoasthenozoospermia, compared to fertile controls, and higher in spermatozoa from couples with higher 2PN rates31. Moreover, increases in a number of human sperm miRNAs (miR-122 and miR-383) have also been noted in cases of moderate and severe oligoasthenoteratozoospermia32. In addition, specific tsRNAs (Gln-TTG) demonstrated association with sperm quality and 3PN outcomes33.Consequently, sperm-borne RNAs have garnered interest as clinical infertility markers. Numerous studies have correlated various sperm small RNAs with semen parameters, embryo quality, and fertility status in animals and humans34. Recently, a number of tRNA fragments  were found to be increased in the seminal plasma from male partners of couples with multiple failed ART cycles35. Further validation is needed to develop a clinically useful and reproducible panel of differentially expressed small RNAs. If proven to be a reliable and efficient strategy, surveying sperm small RNAs, used in conjunction with the traditional male factor infertility diagnostic work-up, could reduce time to pregnancy and be leveraged to assess therapeutic strategies aimed at improving male reproductive health.However, the diagnostic value of the sperm small RNA profile depends on its ability to distinguish idiopathic infertile males, who display poorer preimplantation embryonic developmental outcomes despite a normozoospermic presentation. Hua et al. observed that a number of tsRNAs, rsRNAs, and miRNAs are differentially expressed in human sperm in accordance with embryo quality, despite a normal semen-parameter assessment using traditional methodologyIn the present study, sperm small RNA profiles of normozoospermic males with differing levels of preimplantation embryo development were analyzed via next-generation RNA-sequencing to determine whether males with idiopathic infertility have aberrantly expressed sperm small RNAs.This study was approved by Veritas IRB . Informed consent was obtained from all participants and methods were performed in accordance with the Human Assisted Reproduction Act . Human semen samples were collected between April 2017 and August 2020 from a total of 54 male patients presenting to the CReATe Fertility Centre, Toronto, Canada, for fertility evaluation. Studied male subjects were either: (1) undergoing 50% of donor oocytes fertilized from each member of a same-sex male couple (50/50); or (2) a single sperm provider with donor oocytes. All patients included in the study were normozoospermic, according to standard semen analysis; males with a diagnosis of oligozoospermia, asthenozoospermia or teratozoospermia were excluded. Blastocyst rates were calculated by dividing the number of 2PN zygotes by the number of blastocysts obtained. For 50/50 couples, only couples with a minimum of 4 ova fertilized for each partner, and a minimum blast rate discrepancy of 35% were included. Samples with a blastocyst rate higher or lower than one standard deviation from the mean were designated as the high (Hi) and low (Lo) groups, respectively. The mean blastocyst rate of all included 50/50 couples and single sperm provider patients grouped together were used to group the single sperm provider patients. Single sperm provider patients within one standard deviation from the mean formed the average (Av) group. Patient characteristics and IVF data are summarized in Table 36.Fresh ejaculate samples, collected for IVF, were allowed to liquefy at room temperature. A standard computer-aided semen analysis (CASA), using the HTM-CEROS Sperm Analyzer (Hamilton Thorne), was undertaken within 60 min of collection. Sperm concentration and motility were evaluated by CASA, while morphology was assessed manually adhering to WHO recommendationsRemaining sample volumes and corresponding de-identified data were collected by CReATe Biobank personnel. The CReATe Biobank is certified by the CTRNet Biobank Program. The collection and biobanking of biological materials for research was REB approved. Purified sperm was collected 24 h post-washing; samples were centrifuged at 420 g for 10 min for effective partitioning of seminal plasma from spermatozoa. The resulting spermatozoal pellet was resuspended with 0.5 mL of sperm wash medium and immediately frozen at − 80 °C for downstream assessment.37. An input of 2 million sperm, estimated by a Countess™ 3 Automated Cell Counter (Invitrogen), was used to account for variance in sperm concentration. The RNeasy Kit (Qiagen) used in conjunction with the MiRNeasy Kit (Qiagen) allowed for the small RNA fraction to be obtained. Briefly, samples were lysed and homogenized and dilute ethanol was introduced to optimize binding conditions; QIAzol phenol/guanidine-based lysis and isolation was used. A silica-based membrane allowed for RNA binding and isolation, and impurities were washed away. Purified RNA was eluted in water. Starting RNA input was evaluated by Qubit™ microRNA Assay Kit (Invitrogen) prior to entering the library preparation workflow.RNA isolation procedures were adapted from an established sperm RNA extraction protocolSmall RNA libraries were prepared using the NEXTFLEX Small RNA-Seq Kit v3 (Bioo Scientific). An input of 2 ng of isolated small RNA was used for all samples. Small RNAs were ligated with adapters and sequential reverse-transcription and cDNA barcoding and amplification was performed. No rRNA depletion steps were included within this protocol. Pooled libraries were further size-selected using Pippin HT (Sage Science) to a range of 140–190 base pairs; this was performed to eliminate dimers which may have formed throughout library preparation and isolate small RNA species of interest. Final library traces were assessed using the Bioanalyzer 2100 (Agilent technologies). Resulting small RNA libraries had an average length of approximately 150 base pairs.The NextSeq 550 Sequencing System (Illumina) was used for small RNA sequencing. Samples were divided into separate pools to prevent barcode overlap, as the number of samples exceeded the number of unique barcodes included in the NEXTFLEX Small RNA-Seq Kit. Pooled small RNA libraries were denatured, diluted according to the manufacturer’s protocols, and sequenced at a single-end read length of 75 bp.38. Briefly, samples were demultiplexed with Bcl2Fastq (Illumina) and adapters were removed with the FASTX-Toolkit. Multiqc (SciLifeLab) provided additional sequencing quality metrics and assessment of cleaned sequences. Low quality and short (< 18 nt) reads were removed from the analysis. Samples with below 200,000 clean reads were excluded from further analysis. Reads were then mapped to the latest available small RNA databases using Unitas, in the following order: tRNA database (22.03.2022), piRNA cluster database (22.03.2022), Ensembl (Release 88), EnsemblGenomes (Release 35), tRNA database (09.04.2019), SILVA rRNA databases (Release 132), miRBase (Release 22)43. Sequences generating a single read were removed and count matrices were assembled with awk scripts.Data was analyzed as previously described by our groupThe top 100 most abundant sequences across all samples which failed to annotate to known small RNAs were further investigated. Some of the sequences were identified using BLAST® (NCBI) and through a survey of existing literature containing RNA sequencing small RNA annotation data. These re-annotated sequences are listed in supplementary Table 44. Two sets of DE analyses were performed: (1) all annotated small RNAs, and (2) human miRNAs only. Counts were collapsed by unique annotations, excluding sequence variants (eg. isomiRs). The design formula included the categorical variables age, percentage of library annotated, sperm concentration and blastocyst development rate. Data was explored using principal component analyses  and unsupervised hierarchical clustering analyses using Pheatmap (version 1.0.12) and the complete linkage method.Data was imported into R (R Development Core Team 2013) for differential expression (DE) analysis with DESeq246. The top 10 miRNA target genes with the highest cumulative weighted context +  + score (combining 14 features which determine a ranking of significant gene targets) were compiled into single gene lists for each category of interest47. Absolute values for cumulative weighted context +  + scores ranged from 0.033 to 2.67. The resulting lists of miRNA target genes were used to conduct pathway analysis and identify relevant functional genomics data. Pathways of these gene targets were further investigated using the Kyoto Encyclopedia of Genes and Genomes (KEGG)48. NCBI-GeneIDs were converted to KEGG identifiers and pathway searches were conducted using the KEGG Mapper Search tool (https://www.kegg.jp). The compiled list of gene targets of up-regulated, down-regulated and abundant miRNAs were further cross-referenced with human imprinted genes cataloged in Geneimprint (https://www.geneimprint.com/).Functional gene ontology (GO)-enrichment analyses of miRNAs were completed through: (1) generating categorical lists of the most highly annotated (n = 10) miRNAs and the DE miRNAs (n = 10); 5 up-regulated and 5 down-regulated; (2) identifying a list of predicted gene targets via TargetscanHuman 8.0; and (3) conducting a pathway analysis search using the ShinyGO v0.75: Gene Ontology Enrichment Analysis tool49. Our protocol, by contrast, captures all rRNA-derived sequences50.Approximately 461 million raw reads were generated, with a mean of roughly 8.2 million reads per sample. In the samples analyzed, 55,338 unique annotated sequences were identified. The number of unique annotations did not differ among Hi, Av, and Lo groups (data not shown). Overall, the highest number of reads were mapped to rRNAs (55%), tsRNAs (18%)–including both genomic tsRNAs (14%) and mitochondrial tsRNAs (4%)–miRNAs (7%), protein-coding fragments (7%), and piRNAs (5%), and the remaining 7% of reads were attributed to other miscellaneous coding and non-coding small RNAs . While protein-coding RNAs  in sperm are also believed to have no functional roles beyond spermatogenesis, their notable presence in mature sperm is intriguing54. The most abundant piRNA clusters identified in our dataset include: hsa-0088, hsa-0175, hsa-0084, and hsa-0142  embryos; potentially contributing to protein synthesis during embryonic development via RNA elements delivered by sperm58. While the underlying mechanisms through which tRNA fragments reportedly influence zygotic activation and early embryonic development remain unclear, it has been suggested that they may direct transposon silencing59. Specifically, they may regulate embryonic expression of retroelements, including long interspersed nuclear elements and long terminal repeat retrotransposons, which may contribute to programming early embryonic development61. However, other mechanistic actions, including translation regulation and mRNA degradation, have also been proposed59. Functional studies of mouse embryonic stem cells have demonstrated that 5′-tRNA halves  may regulate translation of the pluripotency‐promoting factor c‐Myc through RNA-binding protein (IGF2BP1) action; thereby regulating cell proliferation and metabolism63.Importantly, transfer RNA fragments from tRNA-Gly-GCC and tRNA-Val-CAC were the most abundant individual sequences present in our data, despite transfer RNA fragments, as a whole, only accounting for 18% of total reads. In fact, tRNA-Gly-GCC was present in all samples. These sequences have been reported elsewhere, in animal and in vitro studies, to impart developmental ability, and are suggested to be indispensable for successful preimplantation embryonic development64. Furthermore, miR-21-5p expression levels in sperm samples and spent culture media have been associated with embryo quality in couples undergoing IVF, with significantly lower miR-21-5p expression in samples yielding good quality embryos, compared to those with poorer quality embryos65. Over-expression of miR-21 in cumulus oocyte complexes has also been associated with increases in cleavage rate and blastocyst formation66. The let-7 miRNA family was also highly annotated. Mouse embryo studies have suggested that repression of the miR-let-7 family, via lin28a inhibition, and associated Wnt/β-catenin signaling, may promote embryo implantation, as well as the embryonic epithelial-mesenchymal transition67.In total, 11,845 miRNA sequences were identified. With isoforms collapsed, these sequences were considerably reduced to 2011 unique human miRNAs. Top annotated miRNAs include: miR-12136-5p; miR-21-5p; miR-122-5p; miR-26a-5p; and miR-375-3p. A number of sources have identified miR-122 in sperm and seminal plasma, and have noted differences in its expression among infertile males70.Long, non-coding RNA (lncRNA) and long intergenic, non-coding RNA (lincRNA) fragments accounted for 2% and less than 1% (0.73%) of total reads, respectively. However, a number of sequences mapping to lncAB370.3, lnc_000681, and lnc_000754 represented highly abundant sequences. While these specific lncRNAs have been reported elsewhere in RNA-seq supplementary datasets and have been identified in both spongiosa tissue and macrophages in humans , they lack functional characterizationThe range of sequence lengths detected for each small RNA type, and overall , are shown in supplementary Figure 71. Specifically, the changes in the sperm RNA transcriptome that accompany paternal aging, and the related mechanisms, are not well characterized but may be related to neurodevelopmental disorders72. Though some studies have noted an overall decrease in genomic integrity with increasing paternal age, the apparent clustering in the intermediate age groups  noted in the present study differs from this trend73. While this could simply represent stochastic clustering, other studies have noted sperm small RNA variation amongst animals of particular ages74. For the miRNA-only analysis, considerable clustering of samples with low blastocyst rates was also noted, as well as the 37–41 and 47–64 age groups. Samples with high blastocyst rates also appear to cluster, similar to the low group.Principal component analysis (PCA) was used to visualize the dimensionality-reduced small RNA differences between normalized samples to identify potential contributors to variation in the data Fig. a. This pp value of < 0.05). Moreover, 221 and 84 DE small RNAs were identified when comparing the Average  group with the Hi and Lo groups, respectively (p-adj < 0.05). Together, this data suggests that the Hi group has the most unique expression profile, particularly when compared with the Lo group. In fact, it was observed that most DE RNAs demonstrated markedly higher expression in the Hi group. This is a perhaps suggestive of a functional difference, in which the relative abundance of particular small RNA relative to the other groups confers developmental competence. Figure p-adj < 0.05) among the Hi and Lo groups is included in the supplementary Table Leu/CAG and tRNAArg/TCG were both down-regulated in the Hi group. DE piRNA clusters were all up-regulated in the Hi group and include: hsa-0841; hsa-0861; and hsa-0877. Fragments of small subunit (SSU) rRNA and lincRNAs, including lincRNA-1694, lincRNA-2211, and lincRNA-957 were also DE. Over 100 protein coding RNA fragments were found to be DE, though many are from uncharacterized genes.Importantly, 324 small RNAs were DE between the High  and Low  blastocyst rate groups (FDR adjusted p-adj < 0.05), 37 of which were present in at least 5 samples  could potentially explain variances in embryo development among males with seemingly normal sperm.The results are limited by a moderate sequencing depth. Furthermore, fertility groups were determined by blastocyst rate, taking into account the number of blastocysts resulting from mature (MII) donor oocytes. Confounding variables in this group may include: technical skill of the embryologist; embryo culturing conditions, which have been shown to influence blastocyst development; and maternal factors, despite the use of viable oocytes retrieved from young, healthy donors. Furthermore, the data was parsed by a number of covariates beyond blastocyst rate, including, sperm concentration, percent mapped to the genome, age, and post-extraction RNA concentration. While no substantial PCA clustering was observed in most cases, moderate clustering based on paternal age is an interesting finding, suggesting that the sperm RNA profile is distinct among certain age groups. Our study was limited by a higher-than-average patient age (42.7 years) in the demographic of interest  when compared to infertile patients as a whole. Further work to investigate the relationship between paternal age and the sperm sncRNA transcriptome is in progress. The observed clustering based on blastocyst rate supports our contention that the identified differences in the small RNA profile are indeed related to fertility outcomes of interest.Given some of the limitations of our findings, future studies are required to validate the differentially expressed small RNAs we identified, and evaluate the clinical utility of our small RNA panel for diagnosing male infertility. Moreover, in future investigations we will assess how changes in sperm small RNAs delivered to oocytes impact early embryo development, and the underlying mechanisms for small RNA-derived effects. Other sperm related factors, including large coding and non-coding RNAs, proteins and epigenetic signatures also warrant further investigation. This could allow us to gain a holistic perspective of how sperm-derived factors intersect to influence fertilization and development.With additional validation, a clinically-useful panel of differentially expressed sperm small RNAs could potentially be used to better assess male factor infertility and predict IVF success. Such diagnostic improvements could greatly reduce time to pregnancy for individuals and couples attempting to conceive, while mitigating the considerable psychosocial impacts of infertility and fertility treatment. In addition to their diagnostic utility, sperm-borne small RNAs which influence development to blastocyst may be helpful proxies for improving sperm quality through lifestyle or pharmacologic interventions.Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.Supplementary Information 4.Supplementary Information 5.Supplementary Information 6.Supplementary Information 7.Supplementary Information 8.


RNA molecules have the capacity to form a multitude of distinct secondary and tertiary structures, but only the most energetically favorable conformations are adopted at any given time. Formation of such structures strongly depends on the environment and consequently, these structures are highly dynamic and may refold as their surroundings change. Temperature is one of the most direct physical parameters that influence RNA structure dynamics, and in turn, thermosensitive RNA structures can be harnessed by a cell to perceive and respond to its temperature environment. Indeed, many thermosensitive RNA structures with biological function have been identified in prokaryotic organisms, but for a long time such structures remained elusive in eukaryotes. Recent discoveries, however, reveal that thermosensitive RNA structures are also found in plants, where they affect RNA stability, pre-mRNA splicing and translation efficiency in a temperature-dependent manner. In this minireview, we provide a short overview of thermosensitive RNA structures in prokaryotes and eukaryotes, highlight recent advances made in identifying such structures in plants and discuss their similarities and differences to established prokaryotic RNA thermosensors. RNA molecules play vital roles in all extant forms of life: they serve as blueprints for protein synthesis, exert regulatory effects on DNA, other RNAs and proteins and even act as biocatalysts. Underpinning all these functions is their secondary and tertiary structure: inter- and intramolecular base pairing results in the formation of hairpins, apical and internal loops, three-way junctions and pseudo-knots . The criThermodynamic free energy is a strong determinant of RNA secondary structure and thus, only the most energetically favorable will be adopted at any given time from a multitude of possible structures. While RNA folding is an intrinsic property of each RNA molecule, it is highly sensitive to the molecular environment and is influenced by RNA-binding proteins and ligands in addition to physical and chemical parameters. The free energy of RNA folding is directly dependent on temperature and therefore temperature changes can have profound and immediate consequences on RNA conformation. Transcriptome-wide RNA structure probing indeed revealed that shifts to higher temperature globally reduce RNA structure formation in several bacterial species  as well Temperature affects every physical and biochemical process within the cell; it is thus vital for every organism to tightly monitor its temperature environment. Many organisms are known to undergo substantial physiological and developmental changes when their ambient temperature deviates from the desired optimum. As its structure is intrinsically thermosensitive, RNA represents an attractive candidate molecule to perceive temperature signals. While many conformational changes triggered by a rise or drop in temperature are likely to be subtle with limited functional consequences, some RNA structures undergo substantial rearrangements across a physiologically relevant temperature gradient, and it is these structures that can be harnessed by cells to monitor their temperature surroundings. Such RNA-based thermosensors are best described in bacteria, including plant symbionts and pathogens, where the so-called RNA thermometers control translation initiation in direct response to the temperature environment .Drosophila and Trypanosomes predict the possibility of mRNA 5′-UTR secondary structure mediated regulation in heat shock translation response  seedlings reveals global structural changes in the transcriptome in response to high temperature due to unfolding of mRNA 5′ and 3′-untranslated regions (UTRs)  . This res (UTRs) . Severalrevisiae  and morepersicum . Yet anoesponses . Likewisesponses .In this minireview, we compare recently identified thermosensitive RNA structures in plants with structures found in prokaryotic and other eukaryotic systems and contrast the different modes of action employed by these structures to control gene expression.Temperature-dependent changes in RNA secondary and tertiary structures are effectively utilized by many bacteria to modulate gene expression. These RNA thermometers form a powerful tool for rapid control of bacterial protein synthesis in response to cellular events such as entry into host, heat or cold stress . BacteriEscherichia coli ibpA RNA thermometer, which regulates expression of an inclusion-body associated protein, encompasses three RNA hairpins at the 5′-UTR. The two proximal hairpins were found to be stable at temperatures ranging from 20 to 50°C and may be involved in proper folding of the distal temperature sensitive hairpin encompassing the SD sequence and start codon  elements which control the expression of small heat shock genes. Commonly found in Proteobacteria, these 5′-UTR hairpins undergo gradual melting in response to increasing temperature, which then leads to complete melting at high temperature (∼42°C) and full liberation of the SD and AUG start codon, allowing enhanced translation . These mrt codon .cis-acting ROSE elements. Structural and mutational studies on the small heat shock protein (HspA) transcript from Bradyrhizobium japonicum, a nitrogen-fixing root nodule bacterium, revealed a functional ROSE domain in the 5′-UTR that encompasses the transcript’s SD sequence. This domain forms an elaborate secondary structure consisting of four hairpins, which help in the ROSE at temperatures <30°C , Mesorhizobium loti, and are functionally interchangeable among the species , a trans-acting small RNA (sRNA). This sRNA in turn responds to elevated levels of S-adenosyl methionine (SAM) such as when inside the mammalian host, and functions in trans by binding to the prfA RNA thermosensor causing repression of PrfA synthesis. Thus, an additional feedback signal is integrated to regulate translation of the virulence regulator PrfA, thereby fine-tuning expression of several virulence genes and host immune modulators   . Despitetrans-acting RNA forming an essential regulatory component of the mammalian heat-shock-factor-1 (HSF-1) by assisting autoregulation of HSPs. This RNA element, termed heat-shock RNA1 (HSR1), undergoes temperature-dependent changes in conformation driving activation of HSF-1 leading to transcription of heat-shock protein genes. In addition to humans and rodents, these highly conserved HSR1 sequences were also identified in Drosophila, Caenorhabditis elegans, Arabidopsis, and bacteria , where they modulate RNA stability and consequently affect translation rates of the associated coding region.Eukaryotic heat shock responses can be affected by temperature-sensitive RNA structures, often found in the 3′-UTR of the corresponding transcripts. Examples of these type of control have been reported in O. sativa L.) seedlings revealed global structural changes in the transcriptome at elevated temperature of 42°C due to RNA unfolding. However, a parallel Ribo-seq analysis showed no correlation between RNA unfolding and heat-induced changes in translation, while RNA-seq based transcript quantification indicates that the observed structural changes promote transcript degradation. Hence the authors conclude that the downregulation of global translation in rice, which reduces heat-stress induced damage, is facilitated by increased access of the RNA degradation machinery to unfolded mRNA 5′ and 3′-UTRs. Yet another study analyzed the nucleotide compositions of transcriptomes of 906 land plants , lead to enhanced mRNA stability and adaptation to cold , allowing interactions with the translation initiation complex. This may in turn lead to circularization of the mRNA by bridging of the 5′ and 3′-ends, thereby enhancing protein synthesis at elevated temperatures  located between positions 312 and 341. Computer-based structure predictions showed that this region is positioned on a highly probable secondary structure. Thermal melting profiles at 260 nm and RNAase H assays of WT and mutated 3′-UTRs suggest that this regulatory region undergoes partial melting during a temperature shift from 26 to 37°C, indicating the presence of a thermosensory RNA sequence. The study also shows that eratures . While aeratures .Arabidopsis demonstrates, for the first time, the presence of plant RNA ThermoSwitches which directly control the expression of several transcripts in response to warm temperatures (Arabidopsis has been investigated in detail for transcript encoding transcription factor PIF7 (PHYTOCHROME INTERACTING FACTOR 7): it is mediated by the formation an mRNA hairpin in the 5′-UTR, approximately 30 nucleotides upstream of the start codon. Further biophysical studies combined with in vitro translation experiments suggest that the hairpin remains stable at temperatures <22°C but switches to a partially melted open conformation at temperatures of 27–32°C. This structural change results in a direct increase in PIF7 protein synthesis, and high PIF7 protein levels in turn promote the transcription of several downstream genes, including those of the auxin biosynthetic pathway, thereby enhancing elongation growth during warm daytime . Therefore, rather than providing accessibility for the ribosomal subunits to join the mRNA, the plant RNA ThermoSwitch is likely to regulate protein synthesis through the scanning process, possibly assuming a dual role: while its stable structure at temperatures less than 22°C may well impede movement of the scanning complex, its more relaxed structure at 27–32°C acts as a translational enhancer, increasing translation initiation rates selectively at these temperatures  plastids (Likewise, plant RNA thermosensors can be utilized for advanced synthetic biology applications such as in inducible expression of plant pigments, phytochemicals and other biologicals of interest to biotechnological, pharmaceutical and food industry. Some recent studies involving the development of novel inducible riboswitches for metabolic engineering in green alga nhardtii  and in tplastids  are encoBC proposed the manuscript. BC and ST researched the content. All authors contributed to writing of the manuscript and approved the final version.


Dear editor,In the past few decades, a flurry of studies has revealed the importance of RNA methylation. These modifications are present in various types of RNAs and collaborate with “writers”, “erasers” and “readers” to influence RNA metabolism and regulate cell differentiation and transformation. In particular, protein synthesis can be directly influenced by RNA codon‐recognition and structure‐based factors or indirectly by RNA methylation reader proteins. Here, we briefly describe the important role of RNA methylation in tumorigenesis and stem cell differentiation, and focus on how major RNA methylations alter translation rates via ribosomal activity or codon usage. These regulatory mechanisms potentially regulate protein diversity through amino acid polymorphisms. With the improvement of single‐base modification and amino acid sequencing technologies, the complex roles of RNA modifications in ribosomal translation and cell fate determination are being revealed.In recent decades, with the development of next generation sequencing (NGS), various detection methods for RNA modifications have been gradually developed and refined. On this basis, a basic understanding of the types, abundance and distribution patterns of RNA modifications has been obtained.6A), 5‐methylcytosine (m5C), N1‐methyladenosine (m1A), and N7‐methyladenosine (m7G). How do cells generate these modifications in response to internal and external metabolism during cell fate determination, e.g., differentiation and transformation? What are the implications of these modifications in the process of genetic information transmission?Among the diverse types of RNA modifications, the main ones that have been studied are N6‐methyladenosine  directly leads to ribosome pausing. Conversely, CDS m6A binding to YTHDC2 facilitates mRNA secondary structure opening and increases translation efficiency.6A is present at the tRNA's anticodon stem and loop (ASL) domain, the N6‐adenosine electron clouds and dynamic structure of the ASL can be altered to produce codon wobble, which affects translation fidelity and causes protein noise.6A1832 and 28S m6A4220 are known to stabilize ribosome structure and subunit assembly.6A regulates codon diversity and protein translation, which alters the transmission rate of genetic information. This could be another reason why this modification plays an important role in regulating embryonic development,Although m5C is a class of cytosine methylation in many RNAs. It is mainly catalyzed by NOL1/NOP2/sun domain family proteins (NSUNs) or DNA methyltransferase 2 (DNMT2).5C has an important role in regulating cell fate and development. For example, during the maternal‐to‐zygotic transition (MZT) in zebrafish embryogenesis, m5C‐modified maternal mRNAs are more stabilized by recruiting Y‐box binding protein 1 (YBX 1) and poly (A) binding protein cytoplasmic 1a (Pabpc1a).5C protects tRNA from cleavage into non‐coding 5′tRNA fragments, thereby affecting global protein synthesis patterns.5C is present in the wobble position of tRNA anticodons, thereby regulating the translation efficiency of leucine and proline, with resultant effects on the oxidative stress response in yeast and the heat stress response in Caenorhabditis elegans.5C at any of the codon positions resulted in a 40% decrease in protein production.m5C in many cancers' progression has been widely studied. The m5C methyltransferases (including NSUN2 and DNMT2) are highly expressed in a variety of tumour tissues, causing multiple oncogene mRNAs to be hypermethylated and stabilized.5C content of heparin binding growth factor's (HDGF) mRNA in bladder cancer, recruiting YBX1 and ELAV‐like RNA binding protein 1 (ELAVL1) to maintain the high expression of HDGF mRNA, thereby promoting the proliferation and invasion of bladder cancer cells.The emerging role of m1A modification is about one‐tenth of that of m6A, and it is mainly distributed in tRNA, rRNA, 5′UTR of mRNA and mitochondrial DNA‐encoded transcripts.6A, m1A has effects on the tertiary conformation of tRNA and rRNA, and regulates overall translation efficiency. For instance, AlkB homologue 1 (ALKBH1) was identified as a tRNA demethylase, which mediates the demethylation of m1A, resulting in attenuated translation initiation and reduced total protein synthesis.1A674 was found to be catalyzed by Rram‐1 in Caenorhabditis elegans,1A645 was found to be catalyzed by Rrp8 in yeast,1A drives cancer cell proliferation and promotes the development of gastrointestinal cancer,Studies have shown that the level of m7G was initially identified as a signature modification in the mRNA 5′ cap structure. Subsequently, m7G was also found in rRNA, tRNA and internal mRNA, with an especially high enrichment in the 5′ UTR region and AG‐rich regions.7G in pri‐miRNA, and this methylation promotes miRNA processing by antagonizing G‐quadruplex structures, thereby increasing let‐7e‐5p miRNA.7G methylome, thereby regulating global mRNA translation, stem cell self‐renewal and differentiation.The m6A, m5C, m1A and m7G in many biological processes and diseases. In collaboration with writers, readers and erasers, RNA methylation codes affect the tertiary conformation, processing and stability of RNA, with multiple effects on translation initiation, elongation and termination. In fact, due to the low abundance of these RNA modifications (except for m6A perhaps), more sensitive detection techniques are still needed, and the roles of RNA methylation in cell differentiation and transformation remain to be further explored. On the other hand, because the influence of RNA methylation on the specificity of codon‐anticodon pairing has direct relevance to Crick's wobble hypothesis, the resultant amino acid polymorphisms in protein distributions may be a very interesting biological phenomenon that deserves further studies.In recent decades, with the establishment of various new techniques to detect rare RNA modifications, researchers have gradually revealed the roles of mAll authors declare that they have no conflict of interest.Dan Song and Ng Shyh‐Chang designed and wrote the manuscript.


Lys can protect and/or stabilize the Spinach RNA aptamer interaction with the fluorophore DFHBI enabling detectable levels of green fluorescence to be emitted. The tRNALys-Spinach-tRNALys, once delivered into “chloroplast-free” onion epidermal cells can emit strong green fluorescence in the presence of DFHBI. Our results demonstrate for the first time that Spinach-based RNA visualization has the potential for in vivo monitoring of RNAs in plant cells.Spinach RNA-mimicking GFP (S-RMG) has been successfully used to monitor cellular RNAs including microRNAs in bacterium, yeast, and human cells. However, S-RMG has not been established in plants. In this study, we found that like bacterial, yeast, and human cellular tRNAs, plant tRNAs such as tRNAThe online version contains supplementary material available at 10.1007/s10142-022-00835-x. BEL5 mRNA moves from the leaf to stolon tip to promote potato tuber formation and development  mRNAs regulate flowering in Arabidopsis , RNA-binding labeled proteins (RBLPs), and RNA aptamer-based approaches , (ii) 80-nt Spinach (S), (iii) 152-nt AttRNALys-AttRNALys (KK), (iv) 250-nt AttRNALys-Spinach-AttRNALys (KSK), (v) 227-nt T7 promoter-KK, and (vi) 374-nt T7 promoter-K-Spinach-K (KSK) are listed in Data Set MluI/BspEI sites of the Potato virus X (PVX)-based vector  73-nucleotides (nt) Data Set . The KK Data Set  and subcely Fig. . We clonKSK Fig. . The KSKKSK Fig.  and subcKSK Fig. . In pMD1KSK Fig. . The inthttp://www.lucernatechnologies.com/fluorophores-c17/). DFHBI was dissolved in DMSO to prepare a 40 mM stock solution. It was then diluted with 100 mM HEPES buffer (pH 7.5) to produce a 2 mM DFHBI/5% DMSO working solution  was bought from Lucerna™ company (Escherichia coli 2 T1R cells (Thermo Fisher Scientific) using QIAprep Spin Miniprep Kit, and their concentration was adjusted to 1 µg/µl. Gold particles were coated with DNA, and onion epidermal cells were particle-bombarded as described , and bombardment was carried out using a PDS-1000/He Biolistic Particle Delivery System (Bio-Rad). After 12-h culture in a hypertonic medium  basal medium, 0.256 M (46.67 g/L) sorbitol and 0.256 M (46.67 g/L) mannitol), onion epidermis was immersed into 100 µM DFHBI  for 30 min, examined and photographed using a Zeiss LSM 710 confocal laser scanning microscope . Purified RNA transcripts were routinely dissolved in 40 µl in RNase-free water. Ten microliters of in vitro RNA transcripts were mixed with 10 µl 200 µM DFHBI, incubated at 75℃ for 5 min, then immediately cooled on ice, and examined and photographed using a Nikon fluorescent stereomicroscope.Production of infectious RNA transcripts was produced by in vitro transcription as described  and Spinach (24-2 min)  or AttRNALys-Spinach(24-2 min)-AttRNALys (KSK) into the pMD-19/T vector to generate pMD19-T/KK and pMD19-T/KSK constructs, respectively Supplementary file2 (XLSX 12 KB)Supplementary file3 (PPTX 4828 KB)Below is the link to the electronic supplementary material.


Pleuroparenchymal fibroelastosis (PPFE) is a rare fibrosing lung disease with a predilection for the upper lobe and its progression causes hypoventilation, resulting in hypercapnia. Even though the association between sleep-related hypoventilation (SRH) and chronic obstructive pulmonary disease was well documented, its impact in patients with PPFE was not evaluated. The aim of this study is to clarify the impact of SRH on prognosis in PPFE.A retrospective review of the medical records of 52 patients with PPFE who underwent transcutaneous carbon dioxide monitoring during sleep was done.Patients were stratified into SRH (n = 28) and non-SRH (n = 24) groups based on American Academy of Sleep Medicine criteria. The impact of SRH on the prognosis of PPFE, as well as the clinical factors and comorbidities of PPFE associated with SRH, were evaluated.1), forced vital capacity (FVC), total lung capacity (TLC), and carbon monoxide diffusing capacity (DLco) in the SRH group were significantly lower than the non-SRH group (P < .01). Chronic pulmonary aspergillosis (CPA) was found at a higher rate in the SRH group (P = .02). The median survival time for SRH patients was 330 days, whereas roughly 80% of non-SRH patients were alive during the 3-year observation period (P < .01). Body mass index was a significant prognostic factor in PPFE patients with SRH . Home oxygen therapy (HOT) during the day and noninvasive positive pressure ventilation (NPPV) at night while sleeping tended to improve prognosis in the SRH group, as indicated by HR of .25 (P = .07).Forced expiratory volume in the first second (FEVSRH may be a poor prognostic factor for PPFE. Additionally, SRH may modify susceptibility to Aspergillosis in patients with PPFE. HOT plus NPPV may improve the disease outcomes in patients with SRH. PPFEher IIPs . The mecrd edition as the impairment of ventilation during sleep, resulting in the elevation of the PaCO2 [2 during sleep increases > 55 mmHg for ≥ 10 min or PaCO2 during sleep increases > 10 mmHg compared with awake supine values exceeding 50 mmHg for ≥ 10 min [Sleep-related hypoventilation (SRH) was one of the sleep-related respiratory disorders described in the International Classification of Sleep Disorder (ICSD) 3he PaCO2 , 5. The ≥ 10 min . SRH is ≥ 10 min , which l≥ 10 min –8. Howev2) during sleep over a span of three years. The study protocol was approved by the Institutional Ethics Committee (No. 2019-027). All procedures in this study were performed according to the ethical standards of the Institutional Research Committee and adhered to the tenets of the Helsinki Declaration (1964). The opt-out method was used for patient consent instead of obtaining informed consent.We retrospectively reviewed the medical records of 52 consecutive patients diagnosed with PPFE at a tertiary care center who were assessed with transcutaneous carbon dioxide monitoring (PtCOCO) using the single-breath method (CHESTAC-8900) were also measured. The PFT was conducted according to the American Thoracic Society (ATS) recommendations for acceptability and reproducibility [1), forced vital capacity (FVC), FEV1/FVC, VC, total lung capacity (TLC), residual volume (RV), DLCO, and DLCO/ alveolar volume (VA) were evaluated. The values of FEV1, FVC, VC, TLC, RV, RV/TLC, DLCO, and DLCO/VA were expressed as percentages of the predicted values. Additionally, the gender-age-physiology (GAP) score was evaluated, as the prognostic value for idiopathic pulmonary fibrosis (IPF) [Parameters such as age, sex, body mass index (BMI), smoking history, serum levels of Krebs von den lungen-6 (KL-6), arterial blood gas analysis (ABG), and respiratory comorbidities were evaluated. The pulmonary function test (PFT)  and diffusing capacity for carbon monoxide (DLis (IPF) .2 and hydrogen carbonate (HCO3−) were evaluated within the normal pH range of 7.35 to 7.45. To analyze nocturnal PtCO2 during sleep, a transcutaneous blood gas monitoring system (TOSCA®) was used. The sensor worn on the earlobe had a built-in heating system that raised the skin temperature to 42 °C to increase both blood flow and CO2 diffusion and detects CO2 value [2 is higher than blood PaCO2; therefore, PtCO2 was carbureted, thereby calculating approximate PaCO2 [2 monitoring systems are extremely precise and have an accuracy level within that of PtCO2 and PaCO2 [2 and the mean and maximum values of PtCO2 during sleep were evaluated.ABG samples were collected when a patient was awake, in a resting state, and in the supine position. The levels of PaCOO2 value . Generalte PaCO2 . Modern nd PaCO2 , 11, 12.To analyze the association between SRH and platythorax, a flat chest index was estimated . For eacThe GAP score was classified 0–3 for Stage I, 4 and 5 for Stage II, and 6–8 for Stage III, and as the stage progressed, the prognosis became worse in IPF . Based oIt has been reported that the thoracic cage becomes flat with the progression of PPFE, resulting from fibrotic collapse in the upper lobes , 17. ThePPFE was mainly diagnosed by HRCT based on Reddy’s criteria  and clinComorbidities assessed in the study included COPD, bronchial asthma (BA), radiological lower-lobe interstitial lung disease (ILD) in usual interstitial pneumonia (UIP), nonspecific interstitial pneumonia (NSIP), and chronic pulmonary aspergillosis (CPA). The diagnosis of COPD, BA, and CPA was based on established criteria –23. UIP 2 and PaCO2 [2 during sleep were substituted by PtCO2, while awakening PaCO2 was measured by arterial blood gas (ABG) in a resting state and in the supine position. In this regard, the SRH criteria in the AASM were modified to PtCO2 increasing > 55 mmHg for ≥ 10 min or PtCO2 increasing > 10 mmHg compared with awake supine values in PaCO2 of ABG exceeding 50 mmHg for ≥ 10 min. PtCO2 was performed for PPFE patients who had been suspected of having ventilation disturbance based on symptoms, results of ABG, pulmonary function tests (PFT), chest X-ray (CXR), and HRCT.SRH was diagnosed using AASM criteria . Some rend PaCO2 , 12; the2 to ABG, (3) acute respiratory diseases that were complicated during or just before PtCO2, (4) tranquilizers were used before PtCO2, and (5) patients who were managed with mechanical ventilation by intubation, non-invasive positive pressure ventilation (NPPV), continuous positive airway pressure, or nasal high flow oxygen therapy.To assess the condition of each patient over the course of the disease, we excluded patients with acute respiratory failure as follows: (1) patients were in the intensive care unit (ICU), (2) acute changes in the condition of the patient in the time period from examining PtCO2, HCO3−, PtCO2 mean, PtCO2 max, %FEV1, FEV1/FVC, %VC, %FVC, %TLC, %RV, %RV/TLC, %DLCO, %DLCO/VA, KL-6, GAP score stage, flat chest index, and HOT + NPPV.All data were presented as medians (interquartile ranges) for continuous variables and as numbers or percentages for categorical variables. The comparison between two categorical variables was evaluated using the chi-squared or Fisher’s exact test. The Mann–Whitney U-test was used to compare continuous variables. For analyzing overall survival for 3 years after TOSCA® evaluation, the Kaplan–Meier method was used to present the estimated survival probabilities and the comparison of survival rates was made by log-rank test. Additionally, univariate and multivariate Cox proportional hazards models were applied to analyze independent predictors of survival. Stepwise regression was used to select explanatory variables in the multivariate analysis. The predictive factors were age, smoking history, BMI, male gender, PaCOIn this study, all variables with P < .05 were defined as statistically significant. Data analysis was performed using R software .2 in the SRH group were higher than in the non-SRH group with a significant statistical difference. Differences were also observed in the results of pulmonary function tests and DLCO using the single-breath method. The SRH group progressed with pulmonary restrictive changes, namely decreased VC, and FVC, compared with the non-SRH group. The FRC, TLC, and RV in the SRH group were lower than those in the non-SRH group, indicating that the lung volume in the SRH group tended to shrink significantly compared to the non-SRH group. Moreover, the pulmonary diffusion capacity, DLCO, and DLCO/VA, of the SRH group were also worse than that of the non-SRH group, which likely highlighted the destruction of the alveolar wall and pulmonary capillary bed due to disease progression. Additionally, the results of ABG also reflected the difference between the SRH and the non-SRH group; levels of PaCO2 and HCO3− within the normal range of pH in the SRH group were obviously higher than those of the non-SRH group, which reflected chronic alveolar hypoventilation.Among 52 consecutive patients with PPFE, 28 (53.8%) were diagnosed as having SRH. The clinical characteristics of the study participants are listed in Table A comparison of baseline comorbidities between the SRH and non-SRH groups in PPFE patients is shown in Table In patients with SRH in PPFE (n = 28), there were some events including death during the follow-up period Table . PneumotDuring the three years follow-up period, there were 16 deaths in 23 patients with SRH and 4 deaths in 22 traceable patients with non-SRH. Seven patients were untraceable because of transfer to another hospital or self-interruption of medical check-ups. The median survival time in the SRH group was 330 days while about 80% patients are alive in the follow-up period in the non-SRH group (log-rank P < .01). The Cox proportional hazard ratio demonstrated a hazard ratio (HR) of 5.36; 95% confidence interval (CI); 1.94–14.82; P < .01, which identified SRH as a prognostic factor in PPFE.Univariate Coxproportional hazards models were used to identify the prognostic factors in patients with SRH in PPFE, as listed in Table 2 , HCO3− , PtCO2 mean , PtCO2 max , %VC , %FVC ,Furthermore, univariate and multivariate Cox proportional hazards models were used to construct prognostic models for all patients with PPFE, as shown in Table CO , GAP score stage , and SRH . For these factors, stepwise regression was performed to select the explanatory variables for multivariate Cox proportional hazards models. As a result, GAP score stage, BMI, and SRH were selected. The results of the multivariate analysis , BMI , and SRH .The results indicated that SRH with BMI and GAP score stage may be useful factors as prognostic models in PPFE.This study aimed to determine whether the complication of SRH in patients with PPFE has an impact on their survival. To the best of our knowledge, there is no research on the relationship between PPFE and SRH. In this study, the SRH group had poor prognosis than the non-SRH group. Worsening of hypercapnic chronic respiratory failure was common reason of death in PPFE , 25. In CO also decreases while preserving DLCO/VA [CO in the SRH group were significantly lower than those in the non-SRH group. These changes strongly affected the GAP score stage, with the SRH group demonstrating an advanced stage with significant differences. In contrast, the ratio of RV/TLC was high and DLCO/VA was low in both groups, without significant differences. There was no difference between the SRH and the non-SRH groups in the flat chest index presenting with platythorax, which may have no association with hypercapnia. These results indicate that the presence of SRH reflects more restrictive and shrinking lung states in PPFE. During sleep, these changes may occur even in the early stages of PPFE [In general, the progression of the fibrotic process in PPFE tends to worsen hypercapnic respiratory failure, which is a characteristic clinical course compared to other ILDs , 25. The DLCO/VA . In this of PPFE , 5, 25.  of PPFE . In PPFE of PPFE , 25, 27.2 and probably survival, especially in COPD [2 compared with oxygen supplementation alone [It has been reported that NPPV may improve diurnal and nocturnal PaCO in COPD –7. Whileon alone , this apon alone , 30; the2 and PtCO2 measured using the TOSCA® during sleep. In a single-center, 46-comparison study, Kelly et al. found that the average difference between PaCO2 and PtCO2 was 6.1 mmHg [2 has been shown to have a slight positive bias compared to PaCO2 [2 during sleep need to be further improved in terms of accuracy. Third, it has not been revealed whether CPA was one of the causes of secondary PPFE, however, there were some reports indicating that CPA associated with construction of PPFE [The present study had several limitations. First, this study was performed at a single center with a relatively small sample size and had a retrospective study design. Future multicenter prospective studies with adequate sample sizes will be required to validate the data obtained. Second, there may be a small gap between PaCO6.1 mmHg . PtCO2 hto PaCO2 . In the  of PPFE . In thisThis study indicates that SRH is a poor prognostic factor for PPFE. Progression of pulmonary fibrosis and reduction in lung volume may be associated with SRH in patients with PPFE. HOT plus NPPV may improve the prognosis of PPFE patients with SRH, although the risk of pneumothorax and oxygen-induced nocturnal hypoventilation must be accurately assessed. In addition, SRH may modify Aspergillus infection in PPFE.


DNAzymes are short pieces of DNA with catalytic activity, capable of cleaving RNA. DNAzymes have multiple applications as biosensors and in therapeutics. The high specificity and low toxicity of these molecules make them particularly suitable as therapeutics, and clinical trials have shown that they are effective in patients. However, the development of DNAzymes has been limited due to the lack of specific tools to identify efficient molecules, and users often resort to time-consuming/costly large-scale screens. Here, we propose a computational methodology to identify 10–23 DNAzymes that can be used to triage thousands of potential molecules, specific to a target RNA, to identify those that are predicted to be efficient. The method is based on a logistic regression and can be trained to incorporate additional DNAzyme efficiency data, improving its performance with time. We first trained the method with published data, and then we validated, and further refined it, by testing additional newly synthesized DNAzymes in the laboratory. We found that although binding free energy between the DNAzyme and its RNA target is the primary determinant of efficiency, other factors such as internal structure of the DNAzyme also have an important effect. A program implementing the proposed method is publicly available. DNAzymes or deoxyribozymes are catalytically active single-stranded DNA molecules . Multipl2+, via a de-esterification reaction  and short hairpin RNA (shRNA). DNAzymes have several advantages over RNA-based reagents, being more cost-effective and stable . HoweverThe use of computational tools for the identification of 10–23 DNAzymes is currently limited. As a result, researchers often have to perform large costly and time-consuming screens to identify lead molecules. The lack of tools has hampered the development of this technology, and hence there is a great need for computational methods to aid in DNAzyme design. Currently available bioinformatics sites and software are mainly limited to ‘oligo analysers’, such as those provided by oligo suppliers that are primarily used for primer design. These tools can be used to aid in the prediction of inter/intramolecular structures in DNAzymes but are limited in their suitability for the assessment of other parameters. A recent advancement in the field has been the development of DNAzyme repositories  and DNAzin vitro cleavage reactions and possibly the consideration of a small number of parameters that have been described to be important in DNAzyme efficiency.Here, we employed the use of other online packages to facilitate the identification of DNAzymes (such as those provided by ViennaRNA Web Services or the Frieburg RNA Tools), despite them mainly catering for duplex formation between RNA molecules rather than the heteroduplex (RNA:DNA) seen with these functional oligonucleotides ,29. The et al. have provided further insight into the cleavage efficiency of different purine:pyrimidine junctions, with the efficiency of the different combinations being AU = GU GC >>AC, where AU/GU junctions prove most efficient followed by GC and lastly AC  resulting from the pairing of nucleotides and is often referred to as Δur model . This mour model . The ΔG roduplex . The forroduplex . The freOligonucleotides were synthesized by Sigma Aldrich . All DNAzymes were constructed using the ‘10–23’ catalytic core motif (5′-GGCTAGCTACAACGA-3′) and nine nucleotide substrate binding arms.in vitro transcribed from a template plasmid containing an upstream T7 promoter sequence . Prior to in vitro transcription, the plasmid was linearized using a 3′ NotI restriction site. In vitro transcription was performed on 1 μg of purified linearized plasmid DNA using the TranscriptAid High Yield T7 RNA polymerase kit  and fluorescein-12-dUTPs for labelling , following the manufacturer’s instructions. RNA was purified using a RNeasy MinElute Cleanup Kit , and RNA length, quality and yield were checked using gel electrophoresis and a nanodrop spectrophotometer.The HPV16 E6/E7 RNA transcript was 2, 0.01% sodium dodecylsulphate) , were incubated separately at 37°C for 10 min in equal volumes of reaction buffer  . The DNASamples were boiled at 70°C for 10 min and cooled on ice for 2 min prior to electrophoresis on 5% denaturing urea polyacrylamide gels (run at 120–150 V for 1 h). Gels were imaged using the Fusion FX Imager  and band intensity measured using ImageJ v1.52k . The relPotential DNAzyme cleavage sites were identified in the HPV16 E6/E7 transcript sequence (accession number: MH937393) by looking for purine:pyrimidine (RY) junctions. The initial scan of HPV16 E6/E7 identified 205 potential target sites. DNAzymes containing either GC or AC purine:pyrimidine junctions were excluded from further screening, as it is already known that these are the least efficient DNAzymes . DNAzymeet al. . We observed that DNAzyme efficiency has a sigmoid-like distribution, where high binding energy interactions (i.e. less stable) have low efficiencies and vice versa, where the transition between low and high efficiencies is approximately −20 kcal/mol . As expected, very high energy interactions were associated with cleavage in the model. However, some of these DNAzymes had poor efficiency. Hence, we fitted a second logistic curve considering a good efficiency when the DNAzyme cleaves over 40% of the target DNA within an hour . In this case, although the fit was good, relatively low binding energies associated with some of the DNAzymes corresponded with low efficiencies . In agreement with others, binding energy alone is not sufficient to predict DNAzyme efficiency.We first chose an efficiency threshold of 20% to define whether a DNAzyme is cleaving a target and fitted a logistic curve . To see if homodimers affect DNAzyme efficiency, we also fitted a model using the hybridization energy of potential homodimers as the only variable. In contrast to hairpins, regression analysis of the free energy of predicted DNAzyme homodimers was not statistically significant . It therefore appears that hairpin formation, but not homodimers, has an effect upon DNAzyme efficiency.Binding energy alone was not sufficient to predict DNAzyme efficiency. This led us to explore the role of other variables that may affect efficiency. First, we fitted a logistic regression for efficiencies >40% using only the free energy of internal forming structures (i.e. hairpins) with RNAcofold  that the DNAzyme may have. In agreement with the previous results, binding energy and internal structure energy were associated with DNAzyme efficiency, and we did not find a significant association with dimer formation energy Table .To assess our model, we synthesized 15 additional, randomly chosen novel DNAzymes targeting HPV16 E6/E7 and performed cleavage reactions on full-length transcripts Table . CleavagTo investigate whether more data will improve the logistic regressions used for predicting DNAzyme efficiencies, we included our novel DNAzymes into a new logistic regression, resulting in a total of 30 DNAzymes with measured efficiencies. This model had strong statistical support for a contribution of binding energy and internal structure energy in the prediction outcome Table . To indeBy incorporating all 38 DNAzymes assessed in this work, the logistic regression model provides strong evidence that binding and internal energies influence the DNAzyme efficiency Table . ImportaHere, we propose a method to build efficient 10–23 DNAzymes based on our findings about the relationship between binding energy and efficiency. The algorithm  Training . Efficiency values for various DNAzymes can be provided (as we do above) to build a logistic model specific to the target sequence. This step is optional, but it can be useful to improve the model as more data on DNAzyme efficiencies become available. Users can therefore progressively refine their models.Step (1) Scan. The algorithm scans the input RNA sequence looking for Purine:Pyrimidine (RY) junctions, which are potential cleavage sites. A potential 10–23 DNAzyme targeting each site is reconstructed with a custom arm length (9 nucleotides by default) and the catalytic core.Step (2) Parametrization. For each potential DNAzyme, a number of parameters are computed. In the current version proposed, the algorithm computes a predicted binding energy based on a nearest-neighbour algorithm, as well as homodimer and internal structure folding energies , but other parameters can be easily added.Step (3) Sieving. The parameter(s) from the previous step are fed into the pre-built logistic model (provided or generated in step 0). The algorithm will provide a list of DNAzymes that potentially cleave the RNA input sequence at the pre-set efficiency level.By following these simple steps and the software we provide, researchers can iteratively refine their predictions or benefit from already published data to generate efficient DNAzymes in a rational way, without extensive selection screens of random candidate molecules.et al.  using the long transcript, cleaving almost 80% of the target within 60 min. The efficiencies generated were subsequently used to explore the importance of three different ΔG energies in predicting DNAzyme efficiency: hybridization free energy (ΔG), dimer formation energy (intermolecular structures) and hairpin formation (intramolecular structures) energy.The development of 10–23 DNAzymes has been hampered by the lack of tools that can predict DNAzyme efficiency and/or assist with DNAzyme identification. Here, we propose a method for the rational construction of RNA transcript-cleaving 10–23 DNAzymes that complements the work in the laboratory to identify the most efficient DNAzymes specific to an RNA target, generally transcripts. To develop this model, we used HPV16 E6/E7 RNA as a target, and first evaluated the DNAzymes previously proposed and tested by Cairns et al. . Cairns anscript . For ourG or hybridization free energy was previously highlighted by Cairns et al.  with high efficiency that our current model did not predict to be efficient. In this case, the dimer energy for this DNAzyme was low, and it is therefore possible that the model interprets this as an issue against high efficiency.Using the DNAzyme efficiency data, a multiple logistic regression model was developed, taking into account ΔTo reconcile these issues, future implementation of the method can include other parameters accounting for nucleotide content biases or specific motif signatures. This is possible since the logistic regression can be modeled to include not only continuous and ranked variables but also categorical variables. For instance, in the computational scan of siRNA molecules there are additional factors that can be taken into account, such as internal structure of the target RNA  and specSeveral other parameters/factors are also likely to affect DNAzyme efficiency, and these should also be taken into consideration when selecting DNAzymes for testing. For example, the folding of the target RNA is likely to play an important role in DNAzyme efficiency. However, the effect of RNA folding has not been systematically explored in relation to DNAzymes and further work is therefore needed to assess this. Another factor that will affect cleavage efficiency is substrate binding arm length. The longer the arm length, the more specific and stable that the interaction will be. However, longer arm lengths are more likely to form secondary structures, and/or increase heteroduplex formation, potentially reducing cleavage efficiency. Santoro and Joyce first described an increased efficiency for DNAzymes with substrate binding arms over 4 nt . ImportaA balance between specificity, the strength of the heteroduplex formation and secondary structures therefore needs to be found. As the number of parameters and permutations increase exponentially, heuristic algorithms will need to be implemented to consider these variables. As additional data regarding DNAzyme efficiency are generated experimentally, this information can be fed into the analysis of these parameters, increasing the accuracy of our predictions.This work is a first step in the development of tools to identify efficient RNA-cleaving DNAzymes. Also, it is an important step towards understanding parameters involved in heteroduplex formation and stability, and whether these affect 10–23 DNAzyme catalytic activity. This can only be determined by evaluating myriads of DNAzymes under different assay conditions, and in terms of using DNAzymes as potential therapeutics, in different cellular contexts. However, we do recognize that the number of DNAzymes used in the study is relatively low and the future inclusion of additional cleavage efficiencies will strengthen the predictions. Further, it should be noted that the fluorescent labelling of the target RNA may have had an effect upon cleavage efficiency. Future analysis of non-labelled target RNA will resolve this potential issue. Here, we focused on 10–23 DNAzymes due to availability of published cleavage efficiency data and also due to the therapeutic potential of these molecules. Importantly, this method could be modified to evaluate other types of molecules, such as 8–17 DNAzymes ,42 and/ohttps://github.com/antoniomarco/DNAzymes_10_23 and Zenodo at https://doi.org/10.5281/zenodo.7415194.Scripts to reproduce the statistical analyses in the paper, as well as a script to scan RNA sequences for potential DNAzymes, are publicly available from GitHub at lqac098_Supplemental_FileClick here for additional data file.


Gemin5 in the Survival Motor Neuron (SMN) complex serves as the RNA-binding protein to deliver small nuclear RNAs (snRNAs) to the small nuclear ribonucleoprotein Sm complex via its N-terminal WD40 domain. Additionally, the C-terminal region plays an important role in regulating RNA translation by directly binding to viral RNAs and cellular mRNAs. Here, we present the three-dimensional structure of the Gemin5 C-terminal region, which adopts a homodecamer architecture comprised of a dimer of pentamers. By structural analysis, mutagenesis, and RNA-binding assays, we find that the intact pentamer/decamer is critical for the Gemin5 C-terminal region to bind cognate RNA ligands and to regulate mRNA translation. The Gemin5 high-order architecture is assembled via pentamerization, allowing binding to RNA ligands in a coordinated manner. We propose a model depicting the regulatory role of Gemin5 in selective RNA binding and translation. Therefore, our work provides insights into the SMN complex-independent function of Gemin5. Structural biology, complemented by biochemistry experiments and RNA-binding assays show that the Gemin5 C-terminal region adopts a decamer architecture. Gemin5 decamerization is essential for its role in regulating mRNA translation. Work done over the years has provided strong evidence for the role of specific RBPs in the regulation of gene expression at the posttranscriptional level via association with the translation machinery5.After completing gene transcription, eukaryotic mature mRNAs are exported from the nucleus to the cytoplasm7. Gemin5, the RBP in the SMN complex, is a 170 kDa protein of 1508 amino acids containing an N-terminal WD40 domain and a C-terminal α-helix rich region10. It has been recently shown that biallelic mutations in the Gemin5 gene, most of which are located in the C-terminal region, lead to human neurodevelopmental disorders13. These patients, however, show phenotypic features apart from those originating from defects in the SMN protein causing spinal muscular atrophy (SMA).The cytoplasmic survival motor neuron (SMN) complex associates with small nuclear RNAs (snRNAs) and facilitates their assembly into small nuclear ribonucleoproteins (snRNPs)7G cap and the Sm site within snRNAs17. In addition to its SMN-dependent role in snRNA delivery, Gemin5 also possesses functions outside of SMN complexes. It has been reported that during foot-and-mouth disease virus (FMDV) infection, Gemin5 undergoes proteolysis to generate a transient C-terminal fragment spanning residues 845–150818, termed G5C herein. Thus, following cleavage, G5C displays biological functions independent of the Gemin5 N-terminal WD40 domain (G5N). Early work showed that the full-length Gemin5 protein interacts with internal ribosome entry site (IRES) elements of two viruses, FMDV and hepatitis C virus (HCV), to inhibit IRES-dependent RNA translation19. However, G5C performs different functions by binding to various types of RNAs21. Intriguingly, transient expression of an internal region of G5C encompassing residues 1287–1400, known as RBS1, associates with its own mRNA to increase translation efficiency22, therefore counteracting the negative effect of Gemin5 on global translation. Thus, Gemin5 serves as a multifunctional RBP to achieve diverse functions through binding to different cognate RNA ligands via G5N and G5C23.We and other groups have reported that the Gemin5 N-terminal WD40 domain spanning residues 1–739 specifically recognizes both the m14, and more recently, the crystal structure of the TPR domain, spanning residues 845–1096 within G5C, was found to form a homodimer24. However, given that the TPR domain alone does not display detectable RNA binding capacity and that the identified RBS1 comprises a nonconventional RNA binding module25, the molecular mechanism underlying the G5C–RNA interaction remains elusive.Our previous work suggested G5C as a polymerTo gain structural insights into the SMN-independent functions of Gemin5, we determined the near-atomic resolution structure of G5C by cryogenicelectron microscopy (cryo-EM). We find that the C-terminus of G5C forms a pentamer, which further dimerizes via the TPR module to adopt a homodecamer (a dimer of pentamers). The protomers within the pentamer establish extensive hydrophobic interactions with each other, which are validated by biochemistry and mutagenesis experiments. Furthermore, by using in vitro RNA binding experiments and in vivo RNA translation, we show that an intact G5C decamer is required for binding to its cognate RNA ligands, and destabilization of the pentamer/decamer impairs both RNA binding and mRNA translation. Therefore, our work sheds light on understanding the role of the G5C decamer as a mediator in mRNA translation outside the SMN complex.14, it is eluted as a high molecular weight (MW) polymer in size-exclusion chromatography (SEC). Static light scattering (SLS) experiments indicated that the molecular weight (MW) of G5C in solution is ~740 kDa with a polydispersity of 1.007  recognizes different RNAs, including domain 5 (D5) of the FMDV IRES element and the stem-loop 1 (SL1) region of Gemin5 mRNA (NM_015465.5) spanning nt 3959–4044RNA Fig. . An elecRNA Fig. . We alsoKDs between G5C and different RNAs. The binding data indicated that G5C bound fluorescein amidite (FAM)-labeled SL1 with a KD of 6.5 µM, whereas a short form of SL1 (SL1short) spanning nt 3959–3990 bound to SL1 10-fold weaker than SL1 (KDs: 67 µM vs. 6.5 µM)  (KD > 500 µM)  binding assay to quantitatively examine the µM) Fig. . As a neµM) Fig. . Collect24 were docked into the map. Consistent with the result from the SLS experiment, G5C forms a homodecamer with an MW of ~750 kDa  curve. Local resolution analysis demonstrated that the resolution of visible G5C regions was within 3.6 Å  form a pentagon-like structure via its C-terminal region, named the pentamer region thereafter Figs. , and 3a.ers Fig. . In summ26 and cyanase (PDB id: 1DW9)27, are characterized by a pentamer of dimers or a dimer of pentamers, which prompted us to compare them with the G5C decamer 2. Specifically, Leu1381 of α28A makes hydrophobic interactions with Leu1465, Leu1468, and Leu1469 of α31B; Ala1382 and Met1384 of α28A contact Leu1469 and Leu1465 of α31B, respectively; Ile1385 of α28A is snugly positioned into a hydrophobic pocket composed of Tyr1286 and Trp1289 of α26B and Leu1465, Val1466, and Leu1469 of α31B; His1388 of α28A makes van der Waals interactions with Leu1461 and Leu1465 of α31B and Leu1372 of α28B; Leu1431 and Thr1435 of α29A make additional hydrophobic interactions with Leu1465 and Leu1468 of α31B and Leu1490 of α32B to stabilize the complex  and α26, α31, and α32 of another protomer , mainly via hydrophobic interactions Fig. , with a lex Fig. . In addixyl Fig. .A, suggesting that the pentamer architecture is conserved among Gemin5 orthologs  using two different reporters. One harbors the SL1 motif of Gemin5 RNA (previously termed luc-H12) on the 3´ end of the mRNA  spatially proximal to the positively charged surface of another molecule (molecule B) Fig. , such th27C Fig. . Hence, 27C Fig. . M4 beha27C Fig.  but disp27C Fig. .Fig. 6Po25, likely serves as the binding site for RNA ligands , by substituting five basic residues at the pentamer interface  in the opposite orientation from the intermolecular concave surface , whereas the M4 mutant weakened the binding affinity by >20-fold (KDs: 138 vs. 6.5 µM)  Fig. , suggestnds Fig. . In summ31. Remarkably, 12 mutations were mapped in G5C, and six of them were found in the TPR domain, including H923P, I988F, S1000P, A1007T, R1016C, and D1019E  was amplified by PCR from a cDNA library and cloned into a modified pET28a vector fused with an N-terminal hexa-histidine tag. The recombinant protein was overexpressed in Escherichia coli BL21 (DE3). Cells were grown in LB medium at 37 °C until the OD600 reached ~0.6. Protein expression was induced with 0.5 mM  β-d-1-thiogalactopyranoside (IPTG) for 20 h at 16 °C. Cells were harvested by centrifugation at 3600 × g for 10 min at 4 °C. Pellets were resuspended in lysis buffer containing 20 mM Tris, pH 7.5, 400 mM NaCl, and 5 mM imidazole. Recombinant proteins were purified by Ni-NTA . After washing with buffer containing 20 mM Tris, pH 7.5, 400 mM NaCl, and 20 mM imidazole, the proteins were eluted with 20 mM Tris, pH 7.5, 400 mM NaCl, and 250–500 mM imidazole. After elution, recombinant Gemin5841–1508 was treated with TEV protease overnight to remove the N-terminal His-tag. Then, the cleaved recombinant proteins were further purified by Superdex 200 gel filtration and mono Q ion exchange . Purified protein was concentrated at 1.5 mg/ml in a buffer containing 20 mM Tris–HCl (pH 7.5), 300 mM NaCl, and 0.5 mM DTT for future use, including cryo-EM sample preparation.The gene encoding the Gemin5 C-terminal fragment  coupled with a DAWN HELEOS 8+ instrument (Wyatt Technology). One hundred microliters of wild-type Gemin5 protein samples (1 mg/ml) were loaded into a Superose 6 Increase 10/300 GL column  pre-equilibrated by a buffer composed of 20 mM Tris–HCl, pH 7.5, and 330 mM NaCl. The data were analyzed with ASTRA software (Wyatt).22 and the stem-loop (nt 416–462) of FMDV IRES RNA21 that were transcribed and purified in vitro as described previously14. The synthesized DNA template (Sangon Biotech.) was amplified by PCR before being used for tRNA transcription. Then, the amplified DNA templates were purified by isopropanol precipitation and dissolved in diethyl pyrocarbonate (DEPC)-treated water. The 20 μl in vitro transcription mixture contained 2 U TranscriptAid Enzyme Mix (Thermo Scientific TranscriptAid T7 High Yield Transcription kit), 4 μl 5 × TranscriptAid buffer, 3.5 μM DNA template, 10 mM NTPs, and DEPC-treated water (Thermo Fisher Scientific Kit). The mixture was incubated at 37 °C for 8 h. After transcription, 2 μl DNase I from the kit was added to the mixture, and the mixture was further incubated at 37 °C for 0.5 h to remove the DNA template.The RNAs used for EMSA were derived from the stem0loop SL1 (nt 18–102) of H12 RNAg for 15 min. The supernatant was collected and further purified by isopropanol precipitation and ethanol precipitation methods. After being dissolved into DEPC-treated water, RNA was further purified by HiTrapTM Q HP . The purified RNA was annealed to generate folded RNA before further use.Each 60 μl of transcription product was treated with 500 μl of RNAiso Plus, shaken for 15 min, and then mixed with 100 μl of chloroform. The mixture was centrifuged at 14,000 × 41. All shifted bands, including the upper bands, were considered for [RNA]Bound, and the fraction bound value was defined as [RNA]Bound/([RNA]Unbound + [RNA]Bound). The curves were analyzed with GraphPad Prism 8. Quantitation data from the EMSA for wild-type G5C binding to SL1 and IRES RNAs are shown in Supplementary Table RNA-binding reactions were carried out in 10 µl of RNA-binding buffer  for 1 h on ice. Increasing amounts of protein were incubated with a constant concentration of SL1 RNA (0.35 μM) or IRES RNA (0.35 μM). Electrophoresis was performed in native 3.0% (19:1) polyacrylamide gels. The gels were run at 110 V for 30 min in 0.5× TBE (Tris/borate) buffer made from a 10× TBE stock solution. Then, the gel was stained by GelRed staining, and the images were processed by ImageJ softwareKDs were obtained by fitting the saturation (%) with protein concentrations. The curve fitting was performed by GraphPad Prism 8.Fluorescence polarization assays were performed with purified G5C WT and its mutants. All RNAs used for the FP assay were labeled with a 5’ 6-FAM group (Beyotime Biotechnology). Experiments were performed in buffer . Each well contained 10 nM RNA (40 nM for poly U) and different protein concentrations (in a range of 0–41 μM) in a total volume of 80 μL. We used black flat bottom 384-well plates  and a CLARIOstar Grating Multi-Microplate Reader for data reading. The excitation and emission wavelengths were 485 and 520 nm, respectively. The dissociation equilibrium constant Three microliters of sample were applied onto glow-discharged 200-mesh R2/1 Quantifoil Au grids. The grids were blotted for 3.5 s in 100% humidity at 8 °C with no blotting offset and rapidly frozen in liquid ethane using a Vitrobot Mark IV (Thermo Fisher).−/pixel/s. Each image was composed of 40 individual frames with an exposure time of 2.5 s. A total of 4888 movie stacks were collected in super-resolution mode with a K3 camera at a nominal magnification of ×81,000 with a defocus range from −2.5 to −1.5 μm.The G5C grids were screened using a Talos Arctica cryo-electron microscope (Thermo Fisher Scientific) operated at 200 kV. Good grids were then imaged in a Titan Krios cryo-electron microscope (Thermo Fisher Scientific) with a GIF energy filter (Gatan). Micrographs were recorded in superresolution mode with a pixel size of 0.535 Å at a dose rate of 8 e42 was used for motion correction and dose weighting. CTFFIND443 was used for the contrast transfer function estimation. A total of 3,126,329 particles were autopicked and extracted in CryoSPARC44, and then extracted particles were subjected to 2D classification with good classes selected for 3D classification. A total of 1,577,779 particles were used for Ab initio 3D reconstruction in CryoSPARC into three classes. Then, the best class containing 870,934 particles was selected for further homogeneous refinement, generating a map of 3.79 Å resolution. Next, nonuniform refinement together with local and global CTF refinement was performed with D5 symmetry imposed, yielding a map with 3.3-Å resolution. To obtain a better structure of the protomer, symmetry (D5) expansion was used, increasing the particle number by 10 times. The final map of the protomer was achieved by local refinement with a resolution of 2.6 Å. Map resolution was estimated by the “gold standard” Fourier shell correlation (FSC) at the 0.143 criterion. Local resolutions were estimated using the Local Map Estimation program in CryoSPARC44, with the local resolution map depicted by UCSF Chimera45.MotionCor2B-factor of −150 Å2 was used for model building in Coot46. By using PHENIX map-to-model47, the solved G5C TPR domain structure (PDB ID: 6RNQ)24 was docked into the cryo-EM map. For the rest of G5C, the predicted model from alphafold  was divided into several fragments and used in model building guided by bulky residues, such as Tyr, Phe, Arg, etc. Manual refinement was performed to remove invisible G5C fragments and to build fragments into the cryo-EM map by Coot46. The final structure contains residues 847–1132, 1155–1293, 1346–1391, and 1430–1496. Structure refinement was carried out by using PHENIX47. PyMOL (https://pymol.org/) and UCSF Chimera45 were used for figure preparation.The final sharpened map with a 845–1508 was previously described48. Constructs pcDNA3-Xpress-G5845–1508–L1469H, pcDNA3-Xpress-G5845–1508-L1468D/L1469D, and pcDNA3-Xpress-G5845–1508-L1381D/M1384D/I1385D were generated by QuickChange mutagenesis (Agilent Technologies) using specific primers (Supplementary Table The plasmid pcDNA3-Xpress-G55) were cotransfected as described20 using a plasmid expressing luciferase in a cap-dependent or IRES-dependent manner 49 and a plasmid expressing Xpress-G5845–1508, Xpress-G5845–1508-L1469H, Xpress-G5845–1508-L1468D/L1469D, Xpress-G5845–1508-L1381D/M1384D/I1385D or the empty vector side by side using Lipofectamine LTX (Thermo Fisher Scientific). Cell lysates were prepared 24 h posttransfection in 150 µl lysis buffer . Luciferase activity (RLU)/µg of total protein was internally normalized to the value obtained with Xpress-G5845–1508 performed side by side. Each experiment was repeated independently three times. Values represent the mean ± SD. We computed P values for a difference in distribution between two samples with the unpaired two-tailed Student’s t test. Differences were considered significant when P < 0.05. The resulting P values are graphically illustrated in figures with asterisks as described in the figure legends.HEK293 cells were cultured in Falcon© six-well plates with Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 5% (v/v) fetal bovine serum (FBS). Cell monolayers . The Clone name for Invitrogen anti-Xpress is reference 46-0528 (lot 2190234). RRID of mouse monoclonal IgG1 is AB_2556552. Immunodetection of tubulin (Merck) was used as a loading control. The anti-tubulin antibody Sigma is monoclonal DM1A (ascites fluid) . Goat anti-mouse (H + L) secondary antibody from Invitrogen  was used according to the manufacturer’s instructions. The signal detected was performed in the linear range of the antibodies. The dilutions for anti-Xpress and anti-tubulin are 1:2000 and 1:4000, respectively. The dilution for the secondary antibody is 1:2000.2O. Reverse transcriptase (RT) reaction was performed to synthesize cDNA from equal amounts of the purified total RNA samples using SuperScriptIII (Thermo Fisher Scientific) and hexanucleotide mix (Merck) as primers. For quantitative PCR (qPCR), the oligonucleotides 5’Luciferase/3’Luciferase20 and Xpress-s/Xpress-as29 were used. qPCR was carried out using the NZYSupreme qPCR Green Master Mix (NZytech) according to the manufacturer’s instructions on a CFX-384 Fast Real-time PCR system (Bio-Rad). Values were normalized against constitutive MYO5A RNA20. The comparative cycle threshold method27 was used to quantify the results.To measure the mRNA steady-state levels, total RNA was isolated from lysates prepared from cells harvested 24 hpt, expressing the corresponding plasmids using TRIzol (Thermo Fisher Scientific), precipitated with isopropanol, and resuspended in RNase-free HFurther information on research design is available in the Supplmentary InformationPeer Review FileReporting Summary