P ≤ 0.02). None of the chosen housekeeping genes showed significant circadian periodicity and either did not associate or were weakly associated with the 24 confounders examined, with one exception, 60% of genes were altered by mouthwash. ATP6, ACTB and B2M represented genes with the highest mean baseline expression  and were detected in all samples. Combining these housekeeping genes for normalization purposes did not decrease inter-individual variance, but increased the robustness. In summary, our work addresses critical confounders and provides important information for the successful examination of gene expression in human whole saliva.Isolation of RNA from whole saliva, a non-invasive and easily accessible biofluid that is an attractive alternative to blood for high-throughput biodosimetry of radiological/nuclear victims might be of clinical significance for prediction and diagnosis of disease. In a previous analysis of 12 human samples we identified two challenges to measuring gene expression from total RNA: (1) the fraction of human RNA in whole saliva was low and (2) the bacterial contamination was overwhelming. To overcome these challenges, we performed selective cDNA synthesis for human RNA species only by employing poly(A)+-tail primers followed by qRT-PCR. In the current study, this approach was independently validated on 91 samples from 61 healthy donors. Additionally, we used the ratio of human to bacterial RNA to adjust the input RNA to include equal amounts of human RNA across all samples before cDNA synthesis, which then ensured comparable analysis using the same base human input material. Furthermore, we examined relative levels of ten known housekeeping genes, and assessed inter- and intra-individual differences in 61 salivary RNA isolates, while considering effects of demographical factors , epidemiological factors comprising social habits , oral hygiene , previous radiological diagnostic procedures (e.g. number of CT-scans) and saliva collection time (circadian periodic). Total human RNA amounts appeared significantly associated with age only ( Because saliva is derived from several tissue sources and also contains large amounts of total RNA it is one of the most attractive diagnostic, prognostic, and monitoring tools for both systemic and oral diseases7. As such, saliva has been shown to contain RNA biomarkers for prediction and diagnosis of several diseases especially of the oral cavity such as oral cancer10 and disorders of the salivary glands12. Saliva represents information from several bodily sources, including blood, because saliva is a plasma ultra-filtrate. This means that most compounds found in blood are also in saliva, leading to the aphorism that saliva is a “mirror of the body”14.Over the last two decades, saliva has become of increased interest as an easily accessible and non-invasive source of human biomarkers. Besides DNA, proteins and various metabolites, RNA (mRNA and miRNA species) has also been shown to be a promising marker in all tissues and body fluids15. The previous study was based on 12 samples and served as an initial “proof-of-concept” study. Following this, in the current work, we have modified the methodology to (1) select only human RNA during cDNA synthesis by targeting the poly(A)+-tailed mRNA and (2) introduced pre-amplification of human RNA before qRT-PCR15. The combination of these two important modifications to our protocols resulted in sufficient amounts of high quality and quantity human RNA. In this context, we also discussed numerous advantages of collecting whole saliva as a source for RNA biomarkers instead of focusing on salivary supernatant15.Also, collection of saliva samples represents an easy, cheap, non-invasive alternative to blood collection. In a previous study, we identified two problematic issues not coherently described before when working with human whole saliva: (1) the fraction of human RNA in whole saliva was low and (2) the bacterial contamination was overwhelming15 using 91 instead of 12 samples  and introduced the adaption of human RNA input for cDNA-synthesis to ensure equal human RNA amounts and improved comparability among different samples. (II) We examined inter- and intra-individual differences in salivary isolates considering potential demographic and epidemiological confounding factors (n = 24) as well as different saliva collection time points. (III) Human 18S rRNA, as a commonly known housekeeping gene, cannot be used for normalization purposes in gene expression analysis in the current application due to its lack of a poly-A-tail15. We examined baseline gene expression values of ten commonly used housekeeping genes in human whole saliva to accomplish the above mentioned tasks and present our analysis here.Not being a sterile medium like blood, saliva can be affected by many confounding variables, which then influence the quality and quantity of RNA that can be isolated from human whole saliva. The aim of this manuscript is to examine if there are any other prerequisites that have to be considered for satisfying results in this approach e.g. the optimal saliva sampling time or the impact of potential confounders such as demographics  or epidemiological factors comprising social habits , oral hygiene  or previous radiological diagnostic procedures (e.g. number of computed tomography/CT-scans). In the current exploratory study, we addressed the following aspects/tasks Fig. : (I) ForWhole saliva samples were collected using ORAgene®RNA  vial collection kits from DNA Genotek according to the manufacturer’s instructions . The kit is an all-in-one system for the collection, stabilization and transportation of RNA from saliva. Unstimulated whole saliva was collected from 61 healthy donors  as described in detail elsewhere15. In brief, the samples were heated at 50 °C (1 h), three aliquots (of 1000 µl) were generated, incubated at 90 °C (15 min), cooled to room temperature, 40 µl ORAgene® neutralizer solution  was added, incubated on ice, centrifuged at 13,000g (3 min) and the cell-free clear supernatant was collected for further processing. At this step, we switched to the mirVana™ kit protocol17 by adding the Lysis/Binding Solution. With the mirVana™ kit, total RNA, including human and bacterial RNA species, was isolated by combining a Phenol–Chloroform RNA precipitation with further processing using silica membranes. After several washing procedures to purify RNA from other residual debris, DNA residuals were digested on the membrane . RNA was eluted with 100 µl RNAse free water in a collection tube and the aliquots were pooled for each sample. In order to increase the input RNA amount for downstream gene expression analysis, samples were steamed at 45 °C for 90 min followed by elution with 30 µl of RNase free water before freezing at − 20 °C.Total RNA comprising a mixture of human and bacterial RNA, was isolated from whole saliva samples following a combination of the ORAgene® RNA purification protocolQuality and quantity of isolated total RNA were measured spectrophotometrically using NanoDrop™ One Microvolume UV–Vis spectrophotometer . RNA integrity was assessed by the 2100 Agilent Bioanalyzer  and DNA contamination was controlled by conventional PCR using actin primers.18 . The amount of total RNA input was always determined to 500 ng measured by NanoDrop™ One. After reverse transcription, cDNA was diluted to a concentration of 0.01 ng/10 µl, which was used for qRT-PCR detection of 16S and 18S rRNA.For analyzing gene expression of human rRNA (18S) and pan-bacterial rRNA , total salivary RNA was converted into complementary DNA (cDNA) via reverse transcription using the High-capacity cDNA reverse transcription kit15 was the adaption of human RNA input for cDNA synthesis with the SuperScript® III First-Strand Synthesis System  was reverse transcribed to cDNA by using the SuperScript® III First-Strand Synthesis System with Oligo (dT)20. In the present work, 10 different TaqMan® Gene Expression Assays  were utilized and pooled to enable the multiplex amplification of specific cDNA targets. Those are commonly used house-keeping genes already employed in other experimental set ups (www.genomics-online.com).To detect low abundance mRNA species, pre-amplification was required. We used TaqMan® PreAmp Master Mix  to increase the amount of specific cDNA targets, synthesized with the SuperScript® III First-Strand Synthesis System. According to the manufacturer, pre-amplification with this kit is linear when a minimum amount of cDNA molecules is present  and multiplex amplification can be performed by pooling up to 100 TaqMan® Gene Expression Assays. Pre-amplification was performed according to the TaqMan® PreAmp master mix kit protocolACTB, B2M, GUSB, MT-ATP6, PGK1, PP1A, RPL13A, RPLP0, TBP, YWHAZ), SuperScript™ III First-Strand Synthesis SuperMix synthesized, i.e. human cDNA with and without 14× pre-amplification was used for detection of each of these genes in each sample. The qRT-PCR reaction contained TaqMan® Universal PCR Master Mix and one of the inventoried TaqMan® Gene Expression Assays for separate detection of transcripts. All measurements were run in duplicate, using a 96-well-format TaqMan® qRT-PCR platform and the QuantStudio™ 12 K Flex Real-Time PCR System.Using different sets of primers, two kinds of cDNAs were utilized for qRT-PCR: for human  and pan-bacterial  primer probe designs, cDNA from High-capacity cDNA reverse transcription kit was used, whereas for the primer probe designs representing the potential house-keeping genes  were calculated for continuous variables such as gene expression data and age. Frequency tables of categorical data were examined for statistical differences using the chi-square test for equal proportion. Comparisons of categorical variables with gene expression values were performed using the non-parametric Kruskal–Wallis (KW) test. We assessed the assumptions of normality (Kolmogorov–Smirnov) and boxcox transformed the continuous variable “age”. All calculations were performed using SAS . Graphical presentations were performed using Sigma Plot 14 .The merged set of raw data is provided within supplemental Table 260/280 ratios at a mean of 2.1 was isolated from saliva. A mean RNA integrity number (RIN) of 5.9 (SD ± 1.4) was detected for saliva samples and all saliva samples showed gel-like image bands of human 28S and 18S rRNA that did not provide any indications for severe degradation. No DNA contamination could be detected by beta actin PCR in all samples (data not shown).From 91 samples an average of 93.3 µg (SD ± 141.7) total RNA per 2 ml saliva could be isolated. Moreover, high purity RNA with OD28.5–17.1) times more bacterial RNA copy numbers relative to human RNA in whole saliva . A mean human 18S rRNA raw Ct-value of 28.5  and a mean bacterial 16S rRNA raw Ct-value of 17.1  implied on average about 2,702 (2(Ct18S rRNA—Ct16S rRNA)), a defined amount of human RNA (4 ng) was reverse transcribed in a second cDNA synthesis, again using High-capacity cDNA reverse transcription kit, followed by qRT-PCR analysis for detection of human 18S rRNA and bacterial 16S rRNA Ct-values  from 7 Ct values of unadjusted input RNA  to 1.5 Ct values after considering the degree of bacterial contamination and adjusting for it.Using the generated ratio  and males  or linear scale , which was not shown for bacterial RNA (16S rRNA). We built two groups based on relatively high  and relatively low  amounts of human RNA. Within the younger age group (< 30 years) the mean human RNA amount (18S rRNA as representative) was 676-fold higher between both human 18S rRNA groups, but neither sociodemographic nor epidemiological parameters appeared significantly associated (data not shown). In the older (> 30 years old) compared to younger donor group (≤ 30 years old), examinations regarding the high yield human 18S rRNA group showed significantly more alcohol consumption per week (P = 0.012), higher frequency of former smoker (P = 0.0058), radiological examinations (P = 0.012) and dentures (P = 0.032).Total RNA concentration increased almost three-fold with higher alcohol consumption (data not shown). Only human RNA (18S rRNA) amount as well as the ratio of 18S/16S rRNA appeared significantly associated with age on a categorical (P = 0.022), but distribution of RIN values at 9 am of the following day were similar to the 9 am values of the previous day  with a small variance  whereas the amount of human RNA was lower  with a large variance  revealed baseline levels > 30 Ct-values without pre-amplification , ACTB  and B2M  indicating high expression levels and detection in all samples  but significant association of altered gene expression and oral hygiene like mouth wash and flossing (Table PGK1 (P = 0.03), age with RPLPO (P = 0.034) and radiological examination during the last 6 months with ATP6 gene expression (P = 0.046). Only TBP showed no significant association with sociodemographic and epidemiological characteristics  did not reduce inter-individual variability significantly  can be measured. Measurement of total RNA quantity is relatively uninformative considering the high and inhomogeneous bacterial contamination of saliva samples. To resolve this issue, we relatively quantified human RNA using 18S rRNA as surrogate and bacterial contamination using 16S rRNA as surrogate, and introduced a correction factor for the same starting material (human RNA amount) for downstream cDNA-synthesis. By performing a second cDNA-synthesis with the High-capacity Kit  we confirmed that we were able to adjust the amount of human RNA input for downstream cDNA synthesis with the SuperScript® III First-Strand Synthesis System  to equal amounts.In the current study, we confirmed previous results increasing the sample size from 12 to 91 whole saliva samples. Furthermore, we modified the previously described workflow to ensure equal human RNA input for cDNA-synthesis as a prerequisite for comparability among samples when performing quantitative RT-PCR. In addition to normalization using a housekeeping gene, this normalization step via RNA quantification proved to be a robust method when measuring RNA expression between samples28. Our study indicated no significant differences in RNA amounts between the collection time points, but intra-individual differences were high (> 1000-fold). Also, RIN values slightly improved at later (3 pm and 9 pm) relative to early collection time points (9 am), indicating that saliva samples can be collected during the whole day, thus, widening the applicability of this approach, e.g. for clinical use.In the current study we also examined factors with potential impact on RNA quantity and quality. Those included different collection time points for saliva sampling or extraneous factors, demographic and epidemiological. Concerning circadian periodic rhythmicity, published protocols from other research groups recommend highest yields of RNA as well as best quality when sampling between 9 and 11 am We did not observe any significant differences concerning sociodemographic and epidemiological characteristics that would explain the observed magnitude of inter-individual variance of human RNA yields. Furthermore, the differences in amounts of human RNA  between the samples could not be explained by sociodemographic or epidemiological characteristics. In this study, the addressed sociodemographic and epidemiological conditions seemed to be of minor relevance for interpretation of saliva gene expression results.15. We examined the baseline gene expression values of ten commonly used housekeeping genes. These genes appeared not or only weakly altered by sociodemographic or epidemiological factors which adds to their robustness. ATP6, ACTB and B2M appeared most suitable, because sufficiently high copy numbers ensured detection in all samples indicating methodological robustness. However, inter-individual differences in gene expression and certain time points were found in several donors. These patterns occurred among all genes, suggesting they may be caused by methodological reasons. Combining all three housekeeping genes in our study did not reduce the variance significantly, but normalization based on more than one reference gene has been increasingly suggested by others31. Certainly, combining ATP6, ACTB and B2M as housekeeping genes for expression studies using human saliva will increase the robustness and, therefore, would be suggested. Nevertheless, planned future studies on saliva samples from irradiated donors will finally show whether radiation impacts these three identified genes, which would render them unsuitable as housekeeping genes. The applicability of suggested housekeeping genes has to be proven in future independent studies.Human 18S rRNA, as a commonly known housekeeping gene, cannot be used as a normalizer in gene expression analysis in the current application due to the lack of a poly(A)+-tail Finally, some limitations of this manuscript need to be considered. The major limitation lies in the fact that epidemiological data was gathered from the healthy donors by anamnesis. For example, the oral health status was self-reported and not confirmed by medical examination.Nevertheless, a main strength of this study is that it represents a comprehensive examination of different facets when working with human whole saliva. The enhancement of the methodology and the proper examination of potential confounders like the influence of sociodemographic and epidemiologic characteristics that could potentially influence salivary isolates are completely novel findings, not described before in the literature. Considering saliva as an emerging source of body fluid for gene expression examinations underlines the importance of those findings. A key strength of the present study was the sample size: 91 samples from 61 donors in total are remarkable numbers considering molecular biological studies. These numbers and the numerous endpoints in this study are sufficient for creating reliable hypotheses.ATP6, ACTB and B2M) and suggested their combination to increase robustness of saliva-based gene expression studies.In summary, we (I) improved the comparability of gene expression measurements among different saliva samples, (II) demonstrated that quality and quantity of RNA isolates is highly robust considering potential confounding factors such as demographics/epidemiologic and the saliva sampling time, making the approach of saliva collection even more attractive for further biomarker studies and (III) identified a set of potential housekeeping genes (Supplementary Figure 1.Supplementary Table 1.Supplementary Table 2.


The discovery of various noncoding RNAs (ncRNAs) and their biological implications is a growing area in cell biology. Increasing evidence has revealed canonical and noncanonical functions of long and small ncRNAs, including microRNAs, long ncRNAs (lncRNAs), circular RNAs, PIWI-interacting RNAs, and tRNA-derived fragments. These ncRNAs have the ability to regulate gene expression and modify metabolic pathways. Thus, they may have important roles as diagnostic biomarkers or therapeutic targets in various diseases, including neurodegenerative disorders, especially Parkinson’s disease. Recently, through diverse sequencing technologies and a wide variety of bioinformatic analytical tools, such as reverse transcriptase quantitative PCR, microarrays, next-generation sequencing and long-read sequencing, numerous ncRNAs have been shown to be associated with neurodegenerative disorders, including Parkinson’s disease. In this review article, we will first introduce the biogenesis of different ncRNAs, including microRNAs, PIWI-interacting RNAs, circular RNAs, long noncoding RNAs, and tRNA-derived fragments. The pros and cons of the detection platforms of ncRNAs and the reproducibility of bioinformatic analytical tools will be discussed in the second part. Finally, the recent discovery of numerous PD-associated ncRNAs and their association with the diagnosis and pathophysiology of PD are reviewed, and microRNAs and long ncRNAs that are transported by exosomes in biofluids are particularly emphasized.The online version contains supplementary material available at 10.1186/s12929-021-00775-x. After Alzheimer’s disease (AD), Parkinson’s disease (PD) is the second most common neurodegenerative disease worldwide. Gross pathology shows excessive loss of dopaminergic neurons in the substantia nigra (SN) at the midbrain and subsequent dopamine deficiency in the nerve terminals of the basal ganglia. Conventionally, if more than 50% of dopaminergic neurons are damaged , individIn addition, α-syn pathology (synucleinopathy) is found in other central nervous system diseases. The deposition of α-syn in oligodendrocytes is the hallmark of multiple system atrophy (MSA), which is characterized by cerebellar ataxia, pyramidal signs, and autonomic dysfunction , 62. PatRecently, dysregulation of noncoding RNA (ncRNA) levels has been reported in several neurodegenerative disorders such as AD, PD and Huntington disease (HD) . AlthougCell in 2014 [J Biomed Sci in 2020 [A substantial variety of ncRNAs have been uncovered in recent decades. In ncRNA history, transfer RNAs (tRNAs) and ribosomal RNAs (rRNAs) were first discovered in the 1950s and were also recognized as ncRNAs. The generic term ncRNA was not officially proposed until the discovery of small nuclear RNAs (snRNAs) and their neighbours, small nucleolar RNAs (snoRNAs), in the 1980s , 42. How in 2014  and J Bi in 2020 . Here, wCaenorhabditis elegans and shown to participate in aberrant temporal regulation in the early developmental stage of C. elegans larvae [MicroRNAs are small noncoding RNAs of approximately 21–25 nucleotides (nt) in length. The first miRNA, “lin-4 RNA”, was discovered in s larvae , 204. Sis larvae  and trans larvae .The biogenesis of miRNAs comprises a series of processes, generally including canonical and noncanonical pathways . Both paIn addition, the mechanisms by which miRNAs promote gene silencing through the inhibition of translation and the degradation of target mRNAs are continuously being discovered. Recent studies have focused on the mechanisms of miRNA-mediated post-transcriptional regulation , mostly Similar to mRNAs in many ways, lncRNAs have a 5' terminal methyl guanosine (5'mG) cap and a 3' terminal polyadenylated tail, contain exons and introns, include several alternative splicing sites, and have no open reading frame (ORF) . GenomesGiven that the mammalian brain contains up to 40% heavily transcribed lncRNAs , many asVarious researchers found a novel-at-the-time class of ncRNAs that is abundant in the fly  and mousIn fact, the abundance of PIWI proteins and their homologues in animals, at least in pigs, is highest in the gonad, the kidney, and finally the brain . EmerginCircular RNAs were first reported as a plant viroid RNA structure in 1976 . TypicalThe first study describing the biological function of circRNAs in human brain development was published in 2013, proposing that a circRNA that is antisense to cerebellar degeneration-related protein 1 (CDR1as) can sponge many miRNAs, particularly miR-7, and therefore achieve post-translational gene regulation . Since tTransfer RNA is transcribed by RNA polymerase III and highly abundant, accounting for up to 15% of all RNA in tissues and cells . PrecursAccumulating evidence has revealed that fragments of mature and precursor tRNAs, including tRFs and tiRNAs, once considered redundant in cell biology, have additional noncanonical functions, such as miRNA gene silencing, post-translational modification of mRNA, protein interactions, epigenetic modification, cellular stress responses, and sperm maturation . HoweverTotal RNA, when originally isolated, is composed of multiple RNA species, including rRNA, precursor messenger RNA (pre-mRNA), messenger RNA (mRNA), and several types of ncRNAs. Noncoding RNAs are highly heterogeneous in terms of their length and conformation. These molecules can be separated into 3 categories: (1) small ncRNAs (< 50 nt), including microRNAs , small interfering RNAs (19–29 nt), PIWI-interacting RNAs , and other functional small RNAs, such as transcription initiation RNAs , tRNA-derived fragments , snoRNA-derived RNAs (17–24 nt or > 27 nt), and sectional ribosomal RNA-derived fragments ; (2) intermediate-sized ncRNAs (50–500 nt), including 5S rRNAs (~ 120 nt), 5.8S rRNA (~ 150 nt), tRNAs (76–90 nt), snoRNAs (60–300 nt), and small nuclear RNAs ; and (3) long noncoding transcripts greater than 500 nt, including linear lncRNAs and circular circRNAs . Over 90Because many of these ncRNAs are tissue- and disease-specific, the expression profile (the RNA sequences and expression levels) of these ncRNAs can be used as biomarkers for diseases. Different platforms can provide different information about the sequence and expression level of ncRNAs. Sometimes a combination of these platforms could be used to obtain a more comprehensive view of the expression profiles of multiple RNAs. In this part of the review, the basic principle of five common platforms, microarray, RT-qPCR, Illumina NGS, PacBio and Nanopore, will be discussed. We will focus on how each platform can be used to generate the expression profiles of ncRNAs, including known and unknown transcripts. Non-coding RNAs include small RNAs  and long RNAs (lncRNAs and circRNA).When total RNA is extracted from biological materials , subsets of RNA molecules need to be isolated or enriched using specific protocol, such as a ribodepletion protocol to remote ribosomal RNAs or size selection by electrophoresis, to filter out unwanted transcripts . RNA isoWith a mixture of small RNAs, since miRNAs (19–25 nt), piRNAs (25–31 nt), and tRFs (14–36 nt) are all similar in size, they cannot be easily distinguished by examining their sizes. Luckily, most of the time these small RNAs could be identified in the database that contains previously reported transcripts. In some cases when the database or reference sequences are not available, specific characteristics of miRNAs, piRNAs and tRFs need to be considered to aid the identification of the novel transcripts.Typical miRNAs have the following characteristics: (1) their length distribution is narrow and often between 21 and 23 nt; (2) their precursor forms a hairpin structure; therefore, the genomic sequencing flanking a miRNA sequence contains a highly complementary 20- to 30-nt segment; (3) in most cases, pre-miRNA processing results in asymmetric strand accumulation; (4) a miRNA 5' end is most often uridine; (5) a 3' end is usually variable and, at low frequency, can be post-transcriptionally modified by the addition of adenosine or uridine; and (6) mature miRNAs, and often pre-miRNA sequences, are often conserved in closely related species; (7) in most cases, miRNAs originate from nonrepetitive genomic sequences . During To determine the expression profile of these small RNAs, researchers can use different platforms. Microarray is often chosen if the sequences of the sample are known and we want to determine the differential expression.Microarrays are a major high-throughput (reads per run) tool that can simultaneously provide the relative concentration or expression levels of hundreds of target RNA templates  based onRT-qPCR is another platform that has often been used to analyse RNA or DNA expression profiles. Because RT-qPCR can provide high sensitivity and specificity for transcripts with various sizes, it is suitable for quantitative study. This method is also widely used in most research labs and by researchers. In fact, RT-qPCR is currently the gold standard method to verify data obtained by microarrays or next generation sequencing (NGS) approaches . HoweverNGS is a good tool if we do not know the sequence of the target RNAs or want to study minor mutations or polymorphisms. We will focus on Illumina NGS since it is the major NGS platform currently in the market. NGS has been widely tested and used by many lapidaries, and the bias and error profiles are well understood. A large catalogue of compatible methods and computational workflows is also available. RNA-seq has considerable advantages for examining transcriptome fine structure, such as the detection of novel transcripts, allele-specific expression and splice junctions .NGS is a very high throughput (reads per run) platform and currently provides 100–1000 times more reads per run than long-read platforms. NGS can not only profile the expression of known RNA sequences but is also suitable for identifying various unknown variants  and can RNA-seq analysis is vulnerable to the general biases and errors inherent in the NGS technology. Because library preparation includes sequence fragmentation, adaptor ligation, cDNA synthesis and PCR amplification, errors can occur . Uneven The expression level of each RNA unit is measured by the number of sequenced fragments that map to the reference transcript, which is expected to correlate directly with its abundance level. RNA-seq data (counts of mapped reads) are fundamentally different from microarray data (computed scores of image intensity) in terms of the expression level analysis. In RNA-seq, the expression signal of a transcript is dependent on the sequencing depth and the expression levels of other transcripts, whereas in array-based methods, probe intensities are independent of each other . NormaliThe nature of the features encompassed by read count data depends on what the mapping reference (database) is and what type of aligner tool was used. Some types of analytical software are designed to handle isoform differences , 191, anThere are currently three main platforms for long RNA sequencing and expression profiling: Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT). For long RNA sequencing, including lncRNA and circRNA sequencing, similar to short-read RNA sequencing, the target ncRNAs will firstly be specifically enriched as much as possible to reduce interference signals from other transcripts , 189. LiLike small RNAs, databases are available for most of the lncRNAs and circRNAs. If certain lncRNAs or circRNAs cannot be found in the database, specific characteristics of lncRNAs and circRNAs should be considered to aid the identification of novel lncRNAs and circRNAs.CircRNAs are a type of long RNA formed by covalent binding of the 3′ and 5′ ends after reverse splicing , 208. ThLncRNAs are non-coding RNAs with lengths greater than 200 nt , 208. LnAlthough microarrays and RT-qPCR can be used in long RNA expression analysis , since tThe two main long-read platforms are PacBio and Oxford Nanopore Technology (ONT). Long-read sequencing is more accurate and sensitive in structural variation and isoform detection . BecausePacBio single-molecule real-time (SMRT) isoform sequencing (Iso-Seq) can capture the full length of transcripts, thereby presenting an easier and more accurate method for gene annotation, isoform identification, and lncRNA discovery . Due to The limitation of lower throughput and higher error rate is most obvious when performing the large-scale differential expression analysis. In these studies, the expression of ncRNA needs to be precisely profiled to attain sufficient statistical power to have confidence in the transcriptome fold changes. For the pros and cons of PacBio, please refer to Fig. ONT sequencers measure changes in ionic current when the DNA fragments translocate through protein nanopores in a semisynthetic insulated membrane; this process does not require enzyme-based nucleotide incorporation or detection of fluorescence signals .Since PCR amplification is optional and direct cDNA sequencing is possible, some errors during DNA polymerase could be avoided , leading to higher-quality results. However, the sequencing yields (numbers of reads) were higher for PCR-amplified cDNA libraries. If PCR is performed, users could start with much smaller amounts of input RNA . SimilarFor PacBio and ONT Pc data, we found that short read lengths (< 500 bp) had low alignment rates. This finding is likely due to a larger portion of adapter and linker sequences in this short-length data bin . In pracPacBio technology is now widely used for the characterization of cancer transcriptomes, where detection of novel isoforms and fusion transcript is superior to that of short-read technologies. This approach surpasses mapping-based or assembly-based approaches. The effectiveness of MinION in the accurate quantification of transcripts, in the detection of transcript variants and fused genes, in transcript-based haplotype phasing and allele-specific expression and in single-cell expression profiling has been shown in multiple studies . In addiEach platform has its strengths and weaknesses and can be used to address different research questions in RNA expression profiling. There is currently no one-size-fits-all approach for all aspects of RNA profiling. Microarrays and RT-qPCR provide direct quantification of target RNAs, but they are unable to identify novel RNA sequences and can only work with known sequences. Long-read sequencing platforms (such as PacBio and ONT) can be used to detect and analyse complete full-length RNA sequences and identify new RNA transcripts, structural variations and RNA isoforms , but becRecent advances in sequencing technologies have enabled high-resolution RNA profiling and thus has provided insights into PD-associated ncRNAs. In the final section, the role of each RNA  in serving as potential biomarkers for PD is summarized. Their functional aspects in the pathogenesis of PD are also discussed separately.SNCA, the gene encoding alpha-synuclein. Herein, we highlight some novel and innovative mechanisms of miRNAs that will shed light on future works in this section.Many studies and review articles emphasize the orchestrated role of miRNAs in the pathogenesis, differential diagnosis, and prognosis of PD. Previous studies of microRNAs associated with PD are summarized in Table MiRNAs can be extracted from various biofluids, including blood, serum, plasma and other sources. Since a recent study reported that the levels of miR-153 and miR-223 were decreased in the saliva of 83 PD patients versus 77 HCs, as detected by quantitative RT-qPCR , salivarMany studies extend their interest from circulating free-form miRNAs to tissue-specific miRNAs, such as brain-derived miRNAs. Circulating brain-enriched miRNAs would enable us to detect CNS signals from the peripheral bloodstream. For example, a combined group of 12 brain-enriched miRNAs, including miR-7, miR-124, miR-129, miR-139, and miR-431, could help us to differentiate individuals with PD from healthy individuals, although the discrimination using the aforementioned 4 miRNAs was moderate, with an area under the curve of 0.705 . IntriguResearchers have attempted to identify techniques that not only determine miRNA identities but also show the specific tissues, cells, organelles, or extracellular vesicles (EVs) from which they are derived. The characteristics of EVs, such as surface markers, size, and cargo, are extremely complicated and interwoven. Hence, the effort to categorize various EVs is an emerging field of science worldwide. By size exclusion, exosomes are one of the categories of EVs characterized by lipid bilayers and sizes of 40–160 nm . ExosomeSeveral studies have described the diagnostic role of exo-miR in PD . Gui et In addition to exo-miR’s high permeability across biological membranes and blood–brain barriers, its low immunogenicity with low rejection response compared to intracranial or intravenous stem cell therapy and tissue- or cell-specific targeting ability also make it a good candidate for therapeutic druggable targets of PD. However, further research is required to confirm the therapeutic potential of exosome-based therapy in clinical applications. One of the hot topics that seems promising is using stem cell-derived exosomes. Crude exosome pellets extracted from mesenchymal stem cells (MSCs) were found to be beneficial to the outcome of traumatic brain injury  and cognThe pathogenesis of PD comprises multiple mechanisms and can be generally classified into several categories: increased a-syn production, decreased a-syn clearance (owing to lysosome-autophagosome impairment), mitochondrial dysfunction, enhanced neuroinflammation, reduced neuronal survival, and dysregulated PD-related genes such as SNCA, PTEN-induced putative kinase 1 (PINK1), leucine-rich repeat kinase 2 (LRRK2), glucosylceramidase beta (GBA), or Parkin RBR E3 ubiquitin protein ligase (PRKN). The targeted genes of miRNAs could directly deactivate the transcription of protein-coding genes and subsequently inhibit their protein products, which is the earliest concept we know about miRNAs. For example, miR-16–1 decreases alpha-synuclein clearance by inhibiting heat shock protein 70 (hsp70) and inducing lysosomal dysfunction  in an exThe anti-neuroinflammatory effect of miR-29c has been extensively demonstrated in a series of studies published by Wang R. et al. in an MPTP-treated mouse model of PD and neuronal cell lines. In brief, miR-29c reduces the levels of proinflammatory interleukins  , miR-29cC. elegans PD model [The autophagosome-lysosome network is a major intracellular degradation system that digests unwanted proteins, including α-syn, in cells . GeneticPD model . The invPD model  in primaPD model  warrantsPD model , and difPD model ), serum PD model ), CSF (uPD model  and let-PD model ), and CSPD model ) . Through sophisticated experiments, Fan’s team ultimately demonstrated that these lncRNAs are enriched in dopaminergic neurons and detected in both peripheral and central tissues; thus, they are suitable as diagnostic markers of PD.Several human studies that used lncRNAs as diagnostic biomarkers are summarized Table . ElkouriC131056.3–001, HOTAIRM1, lnc-MOK-6:1, and RF01976.1–201) had upregulated expression in leukocytes of PD patients [AC131056.3–001 and HOTAIRM1) promoted apoptosis in dopaminergic neurons. Other researchers have focused on PD and other neurodegenerative diseases. Garofalo and his colleagues explored the issue by comparing the whole picture of RNA metabolism in three neurodegenerative diseases, PD, AD, and amyotrophic lateral sclerosis (ALS) [Using a microarray, Fan and his colleagues also found that four differentially expressed lncRNAs (Apatients . The dysis (ALS) . High-thExosomal lncRNAs are an emerging field, as we found with miRNAs. In the plasma of 93 PD patients and 85 controls, Zou et al. isolated likely CNS-derived exosomes containing L1CAM by a microbead-based method, screened the lncRNA profile by microarray, and emphasized only one lncRNA, lnc-POU3F3, which showed highly upregulated expression in PD . AlthougSome studies screened for lncRNA targets by higher-throughput RNA-seq. A recent study published by Wang et al. revealed that in 7 PD patients and 7 HCs, RNA-seq identified 15 PD-relevant exosomal lncRNAs with upregulated expression and 24 with downregulated expression by ultracentrifugation of isolated exosomes . Among those differentially expressed lncRNAs, MSTRG.336210.1 and lnc‐MKRN2‐42:1 were highly expressed among controls, while MSTRG.242001.1 and MSTRG.169261.1 were highly expressed in patients. The researchers focused on motor severity-correlated lnc-MKRN2-42:1 because some of its targeted genes also had downregulated expression in the plasma of 24 PD patients and 11 HCs. Indeed, future works are still needed to validate the results in a larger cohort and clarify the biological functions of lnc-MKRN2-42:1 in animal or cell models. However, it is worth noting that Wang et al. also sequenced circRNAs and uncovered 62 circRNAs with upregulated expression and 37 with downregulated expression in their cohort, but a detailed description of these molecules is not available. We look forward to further progress in the field of lncRNAs and other ncRNAs, including circRNAs, piRNAs, and tRFs, as we will discuss in the following sections.Given that circRNAs are brain-enriched  and predHBB, SIN3A, FBXW7, ITGAL, SIN3A) of the highlighted circRNAs were predominantly linked to functions of homeostasis and oxidative stress response, indicating the central role of reactive oxidative stress and dyshomeostasis in PD [A recent study investigated the RNA-seq profiles of circRNAs along with mRNAs and miRNAs in peripheral blood from a small number of participants, with only 4 patients with PD and 4 HCs . A totalis in PD .In 2020, Hanan et al. studied circRNAs in the human brains of PD patients and examined brain tissues from three distinct regions, the amygdala, substantia nigra (SN), and mesial temporal gyrus, from 42 PD patients and 27 healthy individuals using pooled existing databases . In addiAnother recent study utilized the most fundamental method, rt-qPCR, to screen known brain-drived circRNAs in human PBMCs , where tTo date, only two published papers have reported their findings of specific piRNA profiles in PD based on cell and worm models, as well as postmortem tissues. The first study of piRNAs in PD was published by Schulze et al. in 2018, comparing transcriptomic and epigenomic analysis using RNA-seq between 15 lines of fibroblasts , 24 lines of iPSCs , and 10 lines of differentiated neurons  . There w. [C. elegans nematodes overexpressing human α-syn wild type (WT) and the A53T mutant (HASNWT OX andHASNA53T OX) were crossbred with C. elegans with knockout (KO) of the human TDP-43-like protein tdp-1 (tdp-1 KO). Interestingly, among 6 various genotypes, various ncRNAs were differentially expressed between HASNA53T OX and WT, including 32 miRNAs and 112 piRNAs. However, the differentially expressed ncRNAs between HASNWT OX and WT only included 8 miRNAs and no piRNAs. More strikingly, a major difference in 31 miRNAs and 440 piRNAs was also uncovered when comparing HASNWT OX and HASNA53T OX.Another study published by Shen et al.  used a wApparently, there is still no convincing experimental evidence demonstrating that WT and A53T α-syn have diverse impacts in influencing TEs through distinct piRNA alterations. However, the dramatic change of piRNAs in A53T mutant nematodes suggests that maybe it is mutant strain rather wild type α-syn that should be investigated in relation to the piRNA—TE loop deregulation. Another misfolded protein commonly associated with neurodegenerative disease, tau, is a hot topic in studying tau-depleted piRNA and dysregulated TE patterns in AD  and tauoA recent review summarizing the discovery of tRFs in neurodegenerative diseases, including PD, is available . UnfortuPark2, or Parkin. In 43 PD-related PARK families, autosomal-recessive early onset PD induced by Parkin mutation is the most common genetic cause of familial PD worldwide [Another study focused on brain-derived tRFs in mice called senescence-accelerated mouse prone 8 (SAMP8) . Intriguorldwide . Howeverorldwide , charactThe discovery of diagnosis, treatment, prognosis-related ncRNAs in PD is challenging, largely owing to the complexity of disease itself, the variability of sample origins , the non-standardized method using distinct RNA-seq platforms and software pipelines, and inconsistent results of ncRNA candidates in human association studies. Validations of robust ncRNAs relevant to the differential diagnosis between PD, HC or disease controls, and disease progression in motor, psychiatric, cognitive domains should be further clarified with the highest interest. To date, only a short list of ncRNA candidates can be replicated in mechanisms-based experiments using cell or animal models. Most of pathogenic miRNAs in PD which also have significant alteration in human studies were highlighted as aforementioned. Discoveries of safer virus-  and noveThe discovery of novel ncRNAs with diverse biological functions within different species and cell types makes ncRNAs one of the most exciting scientific topics. In this review, we have introduced many ncRNAs, including miRNAs, lncRNAs, piRNAs, circRNAs, and tRFs. MicroRNAs, lncRNAs and circRNAs can serve as diagnostic biomarkers, while miRNAs are the most promising therapeutic targets of PD, especially coupled with exosomal transportation. However, the evidence for piRNAs and tRFs is less convincing at this stage.There are still many new classes of ncRNA sequences to be found by higher-throughput, longer-read, and extensive sequencing platforms. A complementary approach using multiple platforms is often recommended when generating RNA profiles or analysing RNA expression. However, their biological functions still warrant further clarification by functional analysis in the pathophysiology of PD.The complex network of ncRNAs with DNA, proteins, and other ncRNAs makes this puzzle hard to unravel. With the help of collaborative sequencing and analytical methods and proper selection of model organisms, we can expect to gain clearer insight to identify disease-relevant ncRNAs as diagnostic biomarkers or therapeutic targets of PD in all biological aspects in the near future.Additional file 1. Appendix. Pros and cons of RNA sequencing platforms.


SHAPE-JuMP is a concise strategy for identifying close-in-space interactions in RNA molecules. Nucleotides in close three-dimensional proximity are crosslinked with a bi-reactive reagent that covalently links the 2’-hydroxyl groups of the ribose moieties. The identities of crosslinked nucleotides are determined using an engineered reverse transcriptase that jumps across crosslinked sites, resulting in a deletion in the cDNA that is detected using massively parallel sequencing. Here we introduce ShapeJumper, a bioinformatics pipeline to process SHAPE-JuMP sequencing data and to accurately identify through-space interactions, as observed in complex JuMP datasets. ShapeJumper identifies proximal interactions with near-nucleotide resolution using an alignment strategy that is optimized to tolerate the unique non-templated reverse-transcription profile of the engineered crosslink-traversing reverse-transcriptase. JuMP-inspired strategies are now poised to replace adapter-ligation for detecting RNA-RNA interactions in most crosslinking experiments. In principle, crosslinking represents a simple and elegant way to measure important features of RNA structure. Crosslinking-derived, close-in-space structural information can be highly useful for modeling complex higher-order RNA structure and for generating hypotheses regarding how an RNA functions. In practice, extracting the information from an RNA crosslinking experiment, rigorously and at nucleotide resolution, has been difficult and imprecise. This work outlines the development and optimization of an analysis pipeline, called ShapeJumper, that substantially facilitates analysis of RNA crosslinking experiments, based on easily implemented JuMP technology. Both the crosslinking experiment and the analysis software described here are readily implemented by non-expert users. PLOS Computational Biology Software paper.This is a RNA molecules form multiple levels of intra- and inter-molecular higher order structure, and these structures often have important functions. Secondary structures form via base pairing, and secondary structures may further fold into compact tertiary structures mediated by interactions involving canonically and non-canonically interacting nucleotides ,2. DevelIn principle, RNA crosslinking should be able to identify short through-space interactions. Chemical probes such as psoralen analogs –16, formselective 2’-hydroxyl acylation analyzed by primer extension and juxtaposed merged pairs)  at 37°C for 30 minutes, divided into three 18 μL aliquots, and transferred to amber tubes. One aliquot was treated with 1/9 volume 2 mg/mL AMT (Sigma-Aldrich A4330), dissolved in water, to yield a final concentration of 200 ng/mL AMT. The other two aliquots were treated with the same volume of water, one to serve as a control and the other to be crosslinked with short wavelength UV. The samples were incubated at 37°C for an additional 15 minutes then placed on ice for crosslinking. The control and AMT samples were exposed to 365 nm  for 30 minutes. The short-wavelength UV sample was exposed to 295 nm  for 15 minutes. The RNA was purified by size-exclusion chromatography  and kept on ice until reverse transcription. Reverse transcription was then performed using target-specific primers under SHAPE-JuMP conditions  and incubated at 37°C for 30 minutes. RNA was treated with 1/10 volume 500 mM N,N’-disuccinimidyl carbonate (Sigma-Aldrich) in DMSO . The RNA was incubated at 4°C for 1 hour, extracted once with phenol:chloroform:isoamyl alcohol, and recovered by precipitation with isopropanol. RNA was partitioned using a denaturing polyacrylamide gel , and 2 mM EDTA]) at 180 V for 3.5 hours. The low mobility band was extracted and eluted into water overnight at 4°C. The extracted, crosslinked RNA was concentrated  and stored at -20°C.The 2mhi RNA  was prodExtracted, crosslinked RNA and non-crosslinked RNA were subject to JuMP reverse transcription . The cDNOne application of SHAPE-JuMP and the ShapeJumper pipeline is to identify restraints useful for three-dimensional structure modeling of complex RNAs. A detailed algorithm for SHAPE-JuMP based structure modeling is provided in a companion manuscript .S1 FigA) Definition and example of an ambiguous deletion. An ambiguous deletion cannot be mapped to a unique site (left); an unambiguous deletion can (right). (B) Representative contact map of deletion sites from synthetic deletion read alignments containing (left) and without (right) ambiguous deletions. Ambiguous deletions enclosed in gray outline. Sites with no mapped deletions are white. Note extensive purple regions (0% ppv) are eliminated by removing ambiguous deletions. (C) Effect of enforcing exact edge matching (of 3 nucleotides) at a deletion site that also contain an insertion. (D) Representative contact map of deletion sites from synthetic deletion-insertion read alignments with ambiguous deletions removed without (left) and with (right) edge matching. Ambiguous deletions sites are removed in both cases. All contact maps  are colored on the same scale by the percent of deletions correctly mapped to a specific nucleotide pair.((EPS)Click here for additional data file.S2 FigA) Comparison of distributions of normalized deletion rates for crosslinked (TBIA) and mono-adduct (IA) RNase P RNA experiments. RNase P data used here show trends found in all RNAs examined to date. (B) Distribution of crosslink-induced deletion rates after mono-adduct subtraction. (C) Deletion sites corresponding to the 3% most frequent deletion rates, pre and post mono-adduct subtraction. Deletion sites are mapped onto the reference tertiary structure [D) Ability of ShapeJumper to identify short distance interactions. ROC curve comparison based on TBIA-mediated crosslinking of the RNase P RNA [(tructure  and colose P RNA . Tertiar(EPS)Click here for additional data file.S3 FigMisalignment distance is the sequence distance between assigned and known deletion end points. Mean misalignment distance (red line) as a function of insertion length in red. Standard deviation of misalignment is shown by red shading. Observed frequency of each insertion length in experimental SHAPE-JuMP RNase P data  is shown(EPS)Click here for additional data file.S4 FigIdentification of short distance interactions, as examined by receiver operating characteristic (ROC) curve analysis. Classifier: Inter-nucleotide distance less than 15 Å with a contact distance > 10 see , based o(EPS)Click here for additional data file.S5 FigAUC values summarize the results of replicate experiments in terms of ability to measure close-in-space interactions, defined as through-space distances less than 15 Å and contact distances greater than 10. Each column represents a step in the ShapeJumper pipeline. The mono-adduct control shows the AUC for (non-crosslinked) IA samples after processing by the optimized pipeline.(EPS)Click here for additional data file.S1 Data(ZIP)Click here for additional data file.


ILF3) gene that has been found to influence RNA metabolism at several levels, including pre-RNA splicing, mRNA turnover, and translation. To systematically identify the RNAs that interact with NF90, we carried out iCLIP  analysis in the human embryonic fibroblast cell line HEK-293. Interestingly, many of the identified RNAs encoded proteins involved in the response to viral infection and RNA metabolism. We validated a subset of targets and investigated the impact of NF90 on their expression levels. Two of the top targets, IRF3 and IRF9 mRNAs, encode the proteins IRF3 and IRF9, crucial regulators of the interferon pathway involved in the SARS-CoV-2 immune response. Our results support a role for NF90 in modulating key genes implicated in the immune response and offer insight into the immunological response to the SARS-CoV-2 infection.RNA-binding proteins (RBPs) interact with and determine the fate of many cellular RNAs directing numerous essential roles in cellular physiology. Nuclear Factor 90 (NF90) is an RBP encoded by the interleukin enhancer-binding factor 3 ( Consequently, RBPs are major factors in the response of cells to both intracellular and extracellular stimuli. Nuclear Factor 90 (NF90) is a multifunctional DNA- and RNA-binding protein found mainly in the nucleus and encoded by the interleukin enhancer-binding factor 3 (ILF3) gene. The role of NF90 as a DNA-binding transcription factor was first identified in 1994 by Crothesy and colleagues while studying the human transcription factor complex NFAT (nuclear factor of activated T cells), which is involved in the transcriptional regulation of the IL2 and IL13 genes3. Deeper characterization of NF90 as transcription factor revealed that it induces the proliferation of K562 erythroleukemia cells and regulates the expression of immediate early genes5, genes which are rapidly activated in response to several stimuli7.RNA-binding proteins (RBPs) robustly influence gene expression programs by modulating a range of post-transcriptional steps, including pre-mRNA splicing and processing, as well as mRNA export to the cytoplasm, mRNA turnover, and translation8, through which it binds single- and double-stranded RNA from viruses and mammalian cells4. In turn, NF90 modulates RNA metabolism by influencing mRNA splicing, export to the cytoplasm, turnover, and translation11. NF90 regulates mRNA stability and translation efficiency by binding to the 3'-untranslated region (UTR) of target mRNAs, many of them encoding proteins involved in cell proliferation and in the immune response. NF90 was shown to bind a subset of RNAs containing AU-rich sequences in the 3'UTR, including IL2 mRNA during T cell activation and MKP-1 mRNA in response to oxidative damage12. NF90 stabilizes the mRNAs encoding the cyclin-dependent kinase inhibitor CDKN1A (p21) and the myogenic transcription factor MYOD in developing skeletal muscle13, while it promotes cell proliferation by binding the 3'UTR of CCNE1 mRNA, encoding cyclin E1, and stabilizing it, an effect that was further enhanced by the long noncoding (lnc) RNA LINC0047015. NF90 was found to suppress the translation of several mRNAs encoding proinflammatory factors; NF90 levels declined in senescent fibroblasts, leading to derepression and increased translation of several inflammatory cytokines16. Our studies found that NF90 regulates the stability of several cytokines following exposure to Plasmodium falciparum antigens17, repressed the translation of the cytokine BAFF in THP-1 cells, and cooperated with microRNA miR-15a in binding BAFF mRNA to inhibit translation19.As an RBP, NF90 possesses two double-stranded RNA-binding motifs (dsRBM), a zinc finger domain (ZNF), and a nucleic acid-binding arginine/glycine-rich (RGG) motif20 and YWHAH, a protein implicated in susceptibility for schizophrenia and Parkinson’s disease22, as well as the lncRNA XIST, a known NF90 target23. IRFs are implicated in processes including the regulation of innate immunity and cell cycle progression20, and were recently shown to have a relevant role in the immune response during infection by the coronavirus SARS-CoV-226. Our results support a role of NF90 as a major regulator of RNA metabolism in a range of cellular responses, particularly viral infection.In order to systematically identify the collection of NF90 target mRNAs and further determine the sites of interaction with target mRNAs, we carried out iCLIP  analysis in HEK-293 cells. We found several RNAs that encoded proteins implicated in RNA metabolism, the response to viral infection, and signaling through RAP1. We validated these interactions by ribonucleoprotein (RNP) immunoprecipitation (RIP) analysis and investigated the impact of NF90 on the stability and translation of these target mRNAs. Among the most interesting NF90 targets, we identified several mRNAs encoding transcription factors in the interferon regulatory factor (IRF) family32P-ATP, size-separated by using SDS-containing polyacrylamide gels, transferred to a nitrocellulose membrane, and recovered from the membrane by digesting the protein with proteinase K. After reverse transcription of the RNA into cDNA, circularization, and linearization, the protected RNA was identified by high-throughput RNA-sequencing (RNA-seq) analysis . Coverage profile of query regions at feature boundaries indicated that the signals distributed preferentially towards the end of the 5'UTR and the beginning of the 3'UTR , Kyoto Encyclopedia of Genes and Genome (KEGG) pathway analyses and Gene Ontology (GO) to identify functional information from NF90 targets. Reactome PA revealed that the proteins encoded by NF90 target mRNAs were mainly implicated in biological processes related to RNA degradation, SARS-CoV-2 infection, and WNT signaling pathways. Similarly, enriched KEGG pathways included eukaryotic translation, nonsense-mediated RNA decay independent of exon junction complexes, and regulation of viral translation Fig. D. These 28. NF90 RNP complexes were isolated from HEK-293 lysates using a specific antibody directed at NF90  analysis using native (non-denaturing) conditions that preserved the binding of endogenous RNAs to NF90, and measuring their enrichment in NF90 RIP samples by reverse transcription (RT) followed by quantitative (q)PCR analysisZFR) and proteasome 20S subunit alpha 3 (PSMA3) mRNAs, which showed strong interaction with NF90 by RIP analysis, the Tyrosine 3-Monooxygenase/Tryptophan 5-Monooxygenase Activation Protein Eta (YWHAH) mRNA, which showed weaker interaction, and two transcripts encoding Interferon Regulatory Factors (IRFs), which are associated with SARS-CoV-2 immune response, IRF3 and IRF9 mRNAs . ZFR, PMSA3, YWHAH mRNAs showed stronger binding at the 3’UTR RNAs, while for IRF3 and IRF9 mRNAs, preferential binding was seen with the 5’UTR RNAs. Biotinylated RNA fragments from coding regions of PSMA3 and IRF9 mRNAs were also found to bind NF90. A biotinylated fragment synthesized to contain a region of BAFF 3'UTR mRNA, which we previously demonstrated not to bind with NF9030, was included as a negative control (Neg)  and 48 h later, total RNA was collected, and the levels of target mRNAs were measured by RT-qPCR analysis. NF90 downregulation did not dramatically influence the steady-state levels of most mRNAs analyzed RNA specifically targeting The impact of NF90 on total RNA levels was tested globally by RNA sequencing (RNA-seq) analysis of HEK-293 cells in which NF90 was left unchanged or was silenced, as shown in Fig. To study if the impact of NF90 on target mRNAs was associated with changes in the levels of encoded proteins, we performed Western blot analysis of several target mRNAs and quantified them using ImageJ Fig. B than atMYC mRNA was included in the analysis as a positive control for the effect of actinomycin D31, while a stable transcript, GAPDH mRNA, was used as a negative control.The results showed in Fig. We then turned our attention to the possibility that NF90 might influence the translation of some target mRNAs. We specifically assessed whether the association of the mRNA with polysomes might be influenced by NF90 levels Fig. . Cells eUBC mRNA, encoding the housekeeping protein Ubiquitin C, as its association with polysomes does not change in an NF90-dependent manner. The relative distribution of a specific target mRNA in each fraction was plotted as the relative distribution (%) of that mRNA in each fraction calculated over the total levels of that mRNA in the entire gradient. For the majority of the mRNAs tested  were reduced after NF90 silencing. Similar shifts towards lighter fractions (less translation) were observed for other mRNAs after silencing NF90, including RBM6, PSMA3 and ZFR mRNAs. These data suggest that NF90 promotes the translation of several NF90 target mRNAs in unstimulated HEK-293 cells.We then performed RT-qPCR analysis of the same subset of mRNAs in Fig. ted Fig. B, cells IRF3 and IRF9 mRNAs in immune cells and then studied the impact of NF90 on their expression levels in peripheral blood mononuclear cells (PBMCs) purified from a small heterogenous group of patients infected with the SARS-CoV-2 virus.Considering that some of the identified NF90 targets encode proteins with direct roles in fighting the SARS-CoV-2 infection, we further explored the binding of NF90 to IRF3 and IRF9 mRNAs by RIP analysis using Jurkat cells , which are known to express high levels of both IRF3 and IRF932. NF90 RIP analysis in Jurkat cells was performed as described in Fig. 28. As shown, IRF3 and IRF9 mRNAs were highly enriched in NF90 IP as compared to IgG IP samples  Fig. C, and al19.In this study, we report a comprehensive analysis of RNA targets of NF90 in HEK-293 cells using iCLIP. We found that many NF90 target mRNAs encode proteins with roles in eukaryotic and viral RNA metabolism Fig. , includiYWHAH mRNA, which showed increased stability after silencing NF90  associating with a given mRNA varies depending on the mRNA sequence, the cell type, and the growth conditions.For other target mRNAs examined, particularly 35. Interestingly, it was recently reported that SARS-CoV-2 infected patients produce aberrant levels of type I IFNs and alterations in the function of IRF proteins25. Indeed, several SARS-CoV-2 proteins, including proteins of the viral non-structural proteins (nsp) and the nucleocapsid (N) protein, have been reported to inhibit type I IFN activation and the corresponding responses through regulation of IRF proteins36. For example, Wang and colleagues found that the SARS-CoV-2 nsp12 protein attenuates type I IFN induction by inhibiting IRF3 nuclear translocation37. Given the potential relevance to the COVID-19 pandemic, we sought to confirm the association of NF90 with IRF3 and IRF9 mRNAs in PBMCs and the steady-state levels of IRF3, IRF9 and NF90 in PBMCs purified from SARS-CoV-2-positive patients and healthy controls. Although in PBMCs and immortalized lymphocytes NF90 associated with IRF3 and IRF9 mRNAs, there was a striking loss of NF90 in PBMCs from SARS-CoV-2-positive individuals associated with a decline in IRF3 and IRF9 levels  response against viral infectionIL2 mRNA during T cell activation9. We also reported the role of NF90 in modulating several cytokines in response to Plasmodium falciparum antigens17 and the role of NF90 as a repressor of the translation of the cytokine BAFF in THP-1 cells18. Additionally, we confirmed that NF90 binds A- and U-rich sequences present in target mRNAs as previously reported27  on PKR phosphorylation during influenza A infection43.Besides binding and regulating mammalian mRNAs, NF90 and its isoforms also bind viral genomes (RNA and DNA). Depending on the virus, NF90 can act as either a negative or a positive regulator of the expression of viral genesIRF3 and IRF9 mRNAs, two key factors in the IFN response. Our findings set the stage for a deeper investigation of the impact of NF90 on the immune response during SARS-CoV-2 infection, and a search for therapeutic modulators of NF90 function.In closing, using comprehensive, high-throughput approaches, we have identified numerous NF90 target mRNAs, and found that the encoded proteins are predicted to influence mammalian and viral RNA metabolism. We confirmed earlier observations that NF90 can influence mRNA turnover and translation, and characterized in molecular detail how NF90 affects the stability and translation of Human Embryonic Kidney 293 (HEK-293) cells were cultured in Dulbecco's modified Eagle's medium  supplemented with 10% fetal bovine serum and antibiotics. HEK-293 cells expressing low levels of NF90 (Ctrl or NF90 siRNA) and high levels of NF90 were generated (pcDNA3-MYC-NF90 expression vector) using the Lipofectamine 2000 transfection reagent (Invitrogen Life Technologies) following the manufacturer’s instructions. Jurkat cells (human T lymphocyte cell line) were cultured in RPMI 1640 medium supplemented with 10% fetal bovine serum and antibiotics.−ΔΔCt method and the levels of β-actin (ACTB) mRNA were used for normalization. Gene-specific primer pairs are listed in Table Total RNA was isolated using the TRIzol isolation reagent (Thermo Fisher) following the manufacturer’s protocol and RNA integrity was verified by agarose gel electrophoresis. First-strand cDNA synthesis was performed using Maxima reverse transcriptase (Thermo Fisher) and random hexamers. The generated cDNA was diluted tenfold and used as a template for quantitative (q)PCR analysis using SYBR Green mix (Kapa Biosystems). Relative levels of RNA were calculated using the 2RNA sequencing (RNA-seq) and RNA-seq analysis were performed by Quick Biology. Briefly, RNA integrity was first assessed by using the Agilent Bioanalyzer 2100. Only samples with clean rRNA profiles were used for subsequent steps. Libraries for RNA-seq analysis were prepared according to KAPA Stranded RNA-Seq Kit with RiboErase (KAPA Biosystems) system. Paired-end sequencing was performed using Illumina HighSeq 4000 instrument (Illumina Inc.).28. Briefly, PEB buffer  was used to prepare lysates of HEK-293 cells and M-PER Mammalian Protein Extraction Reagent (Thermo Scientific) was used to prepare lysates of PBMCs. After preclearing (10 min on ice followed by 15 min centrifugation), 500 μg of lysates were incubated  with a suspension of protein-A Sepharose beads  precoated overnight with 5 μg of a NF90 specific antibody (BD Transduction Laboratories) or normal mouse IgG (Santa Cruz Biotechnology) used as a control. Subsequently, the beads were washed with NT2 buffer  and then treated with RNase-free DNase I . RNAs that associated with this NF90 were then isolated using TRIzol and their levels of enrichment were assessed by RT-qPCR analysis using the primers listed in Table ACTB mRNA levels, and then enrichments were calculated as the relative abundance of a target mRNA in NF90 IP as compared to IgG control IP.RIP analysis was performed as previously describedHEK-293 and PBMC cell lysates were prepared with RIPA buffer ; 20 μg protein aliquots from each sample were denatured and size-fractionated by electrophoresis through SDS-containing, 4–12% gradient polyacrylamide gels (Thermo Fisher Scientific), SDS-PAGE. The proteins were then transferred onto nitrocellulose membranes using Trans-Blot Turbo Transfer System (Biorad). Full-length membranes or membranes cropped based on molecular weight were incubated for 16 h with the appropriate primary antibodies recognizing target proteins NF90 (BD Transduction Laboratories) EPS15L1 (OriGene Technologies), RGS14, EBNA1BP2 , PSMD11, DDX39B, RBM6, NFKBID, ZFR, YWHAH, PSMA3, IRF3, IRF9 (Abcam), ACTB (Santa Cruz Biotechnology). After incubation with appropriate secondary antibodies conjugated with horseradish peroxidase (HRP) (Kindle Biosciences), protein signals were detected by enhanced chemiluminescence (Euroclone). Images of uncropped membrane are present in Supplemental Fig. 44. Fractions were collected using a density gradient fractionation system and monitored by optical density measurement (A254) (Brandel). The RNA in each fraction was isolated using TRIzol reagent and reverse-transcribed; the relative levels of a given mRNA in a specific gradient fraction were calculated using qPCR analysis with the primers listed . Cytoplasmic extracts were then loaded on top of a linear sucrose gradient [10–50% (w/v)] and size-separated by ultracentrifugation as previously reportedBAFF 3'UTR RNA that was previously demonstrated not to bind with NF90 was used as negative control30. Bi-RNA pulldown assay was performed as previously described45 using 200 µg of whole-cell lysates from HEK-293 cells. Briefly, cells were lysed using PEB buffer  and incubated 30 min with 1 μg of purified biotinylated transcripts for 1 h at 25ºC. Complexes were isolated with Streptavidin-coupled Dynabeads (Invitrogen) and the proteins present in the pulldown material were detected by Western Blot analysis.To synthesize biotinylated RNA, gene-specific forward PCR primers containing the T7 RNA polymerase promoter sequence were used. After purification of the PCR products, the following biotin-RNAs (bi-RNA) were synthetized using MaxiScript rT7 kit (Ambion): bi-ZFR, bi-YHAW, bi-PSMA3, bi-IRF3, and bi-IRF9. A fragment of ACTB mRNA levels as normalization control. mRNA decay from Act D assays were processed using Prism8.2.1 software.Actinomycin D  was used to inhibit de novo transcription in cultured HEK-293 cells. Cells were then harvested between 1 and 6 h and total RNA extracted using TRIzol reagent (Invitrogen Life Technologies) according to the manufacturer's instructions. RT-qPCR analysis was then performed using 46. Briefly, HEK-293 cells were cultured in 10-cm plates and stably transfected with pcDNA3-MYC-NF90. After irradiation with 150 mJ/cm2 at 254 nm in a UV Stratalinker 2400 (Stratagene) on ice, cells were collected and snap-frozen at -80 °C. Cells were lysed using lysis buffer  supplemented with Protease and RNase inhibitors, sonicated and treated with 2 μl of RNase I (Ambion) diluted 1:150. Lysate samples were then immunoprecipitated using anti-MYC antibody overnight at 4 °C with rotation. For library preparation and visualization, the RNA was dephosphorylated, an adapter was ligated at the 3’ end and it was radioactively labeled at the 5’ end with γ-32P-ATP. The protein-RNA complexes were separated by SDS–PAGE and then isolated from a nitrocellulose membrane according to the expected size; protein digestion was performed using proteinase K and RNA was isolated. RNA fragments were reverse-transcribed using primers that introduce two cleavable adapter regions and barcode sequences. Circularization of the cDNA was carried out followed by linearization using the restriction enzyme BamHI. Linearization generated appropriate templates for PCR amplification optimized to avoid the generation of secondary products. Libraries were finally sequenced using high-throughput RNA-seq analysis.iCLIP was performed as previously descriedcutadapt47 and sickle48. More than 75 million reads were retained. The overlapping forward and reverse reads were merged using Pandaseq to generate longer and complete sequences49. The randomized 5-bp molecular barcodes were extracted using UMI-tools50 and the reads were demultiplexed using reaper51. A total of 2,518,715 reads were obtained and demultiplexed reads were then mapped to hg19 using STAR aligner52. In more details, the demultiplexed reads were processed following the eCLIP ENCODE pipeline described in https://www.encodeproject.org/pipelines/ENCPL357ADL/: (i) reads were mapped to human specific version of RepBase (accessed 18 set 2018) with the STAR alignment tool to remove repetitive elements; (ii) unmapped reads from the first STAR run were than sorted and aligned to the human genome (hg19 with Gencode V19 annotations); (iii) mapped reads were then deduplicated (to remove PCR duplicates) with the command umi_tools dedup. Finally, (iv) output BAM files were then merged into an unique indexed and sorted BAM file. Identification of Crosslinked sites was performed with iCount, a Python module (Python software 4.10.2), and associated command-line interface (CLI), which provides all the commands needed to process protein-RNA iCLIP interaction data and to identify and quantify sites of protein-RNA interactions on RNA53. Binding signals were also confirmed with the Clipper tool54. Mean coverage of iCLIP data was calculated from different genomic regions using only signal with significant p-value (< 0.05). Motif analysis was performed using HOMER (Hypergeometric Optimization of Motif EnRichment) a suite of tools for Motif Discovery (HOMER software v4.11.)55.Raw reads were obtained using bcl2fastq from the NextSeq500 run. Adapter and low-quality bases (< Q30) were trimmed using https://bioconductor.org/packages/release/bioc/html/clusterProfiler.html]. Given a vector of genes, the function enrichGO returns the enrichment GO categories after FDR control.Pathway enrichment analysis was conducted with the R library (R software 4.1.2) cluster Profiler [Peripheral blood samples were collected from adult donors positive for COVID-19 infection and adult healthy donors. All of the human samples used in this study were previously anonymized as required by the Italian Data Protection Code (Legislative Decree 196/2003) and the general authorizations issued by the Data Protection Authority. Ethics Committee approval was deemed unnecessary because, under Italian law, it is only required in the case of prospective clinical trials (Art. 6 and Art. 3 of Legislative Decree 211/2003). However, all of the patients gave their written informed consent to the medical procedures/interventions carried out for routine treatment purposes. Peripheral blood mononuclear cells (PBMCs) were purified from a small heterogenous group of patients infected with the SARS-CoV-2 virus and healthy human donors using Histopaque 1077. About 15 ml of blood was collected from each donor using BD Vacutainer CPT tubes containing 0.1 M sodium citrate and PBMCs were isolated following the manufacturer’s instructions. The isolated PBMCs were then used for Western Blot and RIP analysis.t-tests, as indicated in the Figure legends. Values were considered significant when p < 0.05.Statistical significance was determined using two-tailed Supplementary Figures.Supplementary Table S1.Supplementary Table S2.


The disruptions have resulted in failure to access family planning (FP) clinics, emergency contraception, abortion facilities in cases of unintended pregnancies, treatment for sexually transmitted infections (STIs) and attendance for sexual and gender-based violence (SGBV) victims, and disruptions in human papilloma virus (HPV) vaccination programmes.1 This correspondence is, therefore, a call for prioritisation of restoration and maintenance of robust SRH services for adolescent girls in Zimbabwe to militate against further negative impact on SRH outcomes in this population.Almost 18 months into the coronavirus disease 2019 (COVID-19) pandemic, sexual and reproductive health (SRH) concerns for adolescent girls in Zimbabwe remain unresolved.3 In Zimbabwe, this has been worsened by increasing healthcare worker (HCW) attrition, HCW fear of contracting COVID-19, lack of personal protective equipment (PPE) and failing to timeously access healthcare services. The public transport crisis in the country has continued to worsen because of restrictions in public transport provision, poor roads and digital networks. Based on the three-delays conceptual framework, first-stage delays are occurring because of late decisions in seeking care at primary facilities, second-stage delays as a result of logistical challenges in getting patients to higher facilities, and third-stage delays at higher level facilities owing to lack of human resources, PPE, medicines and other sundries.At the beginning of the pandemic, it was projected that there would be an increase in maternal deaths in sub-Saharan Africa because of disruptions in SRH services.5 Thus, an increased incidence of teenage pregnancies has been widely reported. Many teenagers who fall pregnant never go back to school, and they end up in abusive relationships or informal jobs, including commercial sex work and cross-border trading. This may expose them to multiple sexual partners and frequent unprotected intercourse, which are high-risk behaviours for SRH.6Prolonged closure of schools resulted in young girls spending more time in the community and indulging in early premarital sexual activities.We, therefore, urge the government and relevant public health stakeholders to prioritise restoring and maintaining robust SRH services for adolescent girls. Amongst other things, the actions should include restoring FP services, HPV vaccination programmes, rape and SGBV clinics, to ensure timely access to treatment and prevention services. In addition, re-opening of schools and appropriate SRH education for this population remain indispensable components of adolescent SRH.


The drastic loss of biodiversity has alarmed the public and raised sociopolitical demand for chemical pesticide-free plant production, which is now treated by governments worldwide as a top priority. Given this global challenge, RNAi-based technologies are rapidly evolving as a promising substitute to conventional chemical pesticides. Primarily, genetically modified (GM) crops expressing double-stranded (ds)RNA-mediating gene silencing of foreign transcripts have been developed. However, since the cultivation of GM RNAi crops is viewed negatively in numerous countries, GM-free exogenous RNA spray applications attract tremendous scientific and political interest. The sudden rise in demand for pesticide alternatives has boosted research on sprayable RNA biopesticides, generating significant technological developments and advancing the potential for field applications in the near future. Here we review the latest advances that could pave the way for a quick lab-to-field transition for RNA sprays, which, as safe, selective, broadly applicable, and cost-effective biopesticides, represent an innovation in sustainable crop production. Given these latest advances, we further discuss technological limitations, knowledge gaps in the research, safety concerns and regulatory requirements that need to be considered and addressed before RNA sprays can become a reliable and realistic agricultural approach. Modern agriculture faces diverse challenges related to the fact that the world population is expected to rise by 26% by 2050. Given this projection, agriculturists must boost productivity to meet emerging food needs . Major cIntergovernmental initiatives such as the EU’s farm-to-fork strategy  aim to rethink and redesign food systems, for example by developing frameworks for sustainable food production. A central goal is to reduce the overall use and risk of chemical pesticides by 50% by 2030 . Given tA promising alternative to chemical pesticides relies on harnessing the mechanistic of RNA interference (RNAi). Briefly, RNAi is a conserved cellular mechanism that regulates and protects eukaryotic cells against harmful nucleic acids. In this natural phenomenon, double-stranded RNAs (dsRNAs) are processed by Dicer enzymes into 21–24 nucleotide (nt) small interfering RNAs (siRNAs). These siRNAs either mediate transcriptional gene silencing (TGS) by ensuring inhibitory DNA and histone modifications as well as chromatin remodeling, or they facilitate post-transcriptional gene silencing (PTGS) by preventing translation of targeted mRNA transcripts. Both TGS and PTGS served as blueprints for the development and continuous improvement of RNAi toolkits. In agriculture, RNAi has proven to be an effective strategy in controlling pathogens such as viruses, bacteria, and fungi, as well as insect pests, mites, and nematodes .A decade ago, studies focused on the transgene-based endogenous expression and formation of dsRNA in host plants, termed host-induced gene silencing (HIGS) . NowadayThe immense potential of sprayable RNA biopesticides for meeting current agro-economic challenges has prompted the development of RNAi technology. Initial euphoria was a major driver leading to a rapid increase of reports, resulting in 54 studies over the last 3 years  demonstrating academic proof-of-concept for several pathosystems . However1” which focus on broad, cost-efficient topical RNAi uses in agriculture. Their final dsRNA products are advertised as safe, ready to use and stabler than naked dsRNAs. This advancement represents a step toward lowering the amount of applied RNA biopesticides since it is estimated that 2–10 g of dsRNA are required to protect 1 ha . Thus, subsequent attempts have focused on large-scale production and on the purification of sprayable RNAs to make them commercially competitive and economically achievable, with great success. For example, recent research efforts have rapidly produced cost-effective, large-scale microbial-based dsRNA production using bacteria, such as E. coli , processing and translocation has begun to emerge.Fusarium graminearum  and V. dahliae (12 hpt). In addition, they demonstrated the antifungal activity of topically applied dsRNAs targeting fungal vesicle pathway genes or DCL genes in B. cinerea, S. sclerotinia, R. solani, and A. niger  surface and those that enter plant cells. We predict that surface interaction of dsRNA or siRNA with microbial targets will require intense stabilization formulas, with potential risks for environmental accumulation and pollution. Cellular uptake, on the other hand, may have a protective effect, but promoting fast processing or even bearing the risk of enzymatic and lytic degradation together with dilution over distance. Although we seek RNA-stabilizing formulas, at the same time we question RNA degradability as a selling point. Recently, dsRNA spray effects against he plant . AlthougE. coli-derived anucleated minicells can be used as a cost-effective platform for dsRNA production and encapsulation, shielding dsRNA from RNase degradation    and strad dsRNA)  already d dsRNA) . Notablyhosystem ,b. BasedHenosepilachna vigintioctopunctata (28-spotted ladybird) in the greenhouse (E. coli-expressed dsRNAs targeting the ecdysone receptor (EcR) gene onto the foliage of greenhouse-growing potato plants provoked the death of third and fourth instar larvae and reduced leaf damage  caused larval lethality  as well as social-political demands (acceptance and regulations). To meet these challenges, significant advances in developing RNA sprays that guarantee stability in the field and minimize off-target risks for non-target organisms have been made, for example, by using nanomaterial-based formulations and the fusion of different technologies. However, we lack a sufficient data basis for understanding how nano-formulated RNA biopesticides will behave under field conditions. Moreover, we urgently require knowledge on durability, persistence and systemic effects of RNA sprays to develop recommendations for handling in the field. For example, when and how often do RNA biopesticides need to be applied or renewed to confer maximum protection? Based on the answer, we could calculate real costs for each pathosystem. To achieve broad applicability, which is often used as a selling point over GMO-based RNAi strategies (HIGS), we need to clarify the question of why RNA sprays work well for some pathosystems and not for others. If we know what determines SIGS efficiencies, we may have a starting point for developing solutions that demonstrate broad transferability. Elucidating the molecular mechanisms of uptake, processing and translocation of spray-delivered RNAs is crucial for making RNA sprays realistic and achievable in future field applications. In addition, emerging data that suggest extending RNA sprays to exploit the mechanisms of TGS that will allow epigenetic modifications of plant genes  should bTM technology) will access the market. Finally, we need to develop educational work and information campaigns for public outreach and transparency as early as possible.In contrast to lab experiments, where dsRNA and siRNA effects are tested in isolation and on small scales, field environments are unpredictable and highly dynamic. For example, the occurrence of pathogens and pests, as well as their coincidences, represents a major challenge for determining application time and frequencies. Moreover, plants grown under environmental conditions develop differently from plants grown in greenhouses, especially in terms of cuticle properties, which may impact adherence, stability, and cellular uptake. Thus, we need smart application and formulation strategies, such as multi-targeting and site-directed targeting, to ensure high efficacies on the species level. For example, combining RNA biopesticides with distinct molecular targets may not only confer stronger effects but also allow high efficiencies on a species-specific scale. In other words, the majority of highly effective dsRNAs and siRNAs target highly conserved genes, which bear high off-target risks. They are suitable candidates when conducting proof-of-concept studies but are inappropriate for field trials. Thus, identifying molecular targets on species level and their simultaneous combinatory (multi-targeting) applications may exhibit strong effects and at the same time minimize off-target risks. However, lab-to-field transitions require further precision and accuracy in off-target predictions and their controllability for adequate risk assessment. Currently, data requirements specific for RNA spray-based plant protection products are not yet in place under the regulatory frameworks for pesticides and plant protection products in the United States and EU . The OECBoth authors contributed to wrote the manuscript and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.