Peritoneal metastasis (PM) in gastric cancer (GC) remains an untreatable disease, and is difficult to diagnose preoperatively. Here, we aim to establish a novel prediction model.The clinicopathologic characteristics of a cohort that included 86 non-metastatic GC patients and 43 PMGC patients from Zhongshan Hospital were retrospectively analysed to identify PM associated variables. Additionally, mass spectrometry and glycomic analysis were applied in the same cohort to find glycomic biomarkers in serum for the diagnosis of PM. A nomogram was established based on the associations between potential risk variables and PM.Overexpression of 4 N-glycans ; weight loss ≥ 5 kg; tumour size ≥ 3 cm; signet ring cell or mucinous adenocarcinoma histology type; poor differentiation; diffuse or mixed Lauren classification; increased CA19-9, CA125, and CA724 levels; decreased lymphocyte count, haemoglobin, albumin, and pre-albumin levels were identified to be associated with PM. A nomogram that integrated with five independent risk factors  achieved a good performance for diagnosis . When 160 was set as the cut-off threshold value, the proposed nomogram represented a perfectly discriminating power for both sensitivity (0.97) and specificity (0.88).The nomogram achieved an individualized assessment of the risk of PM in GC patients; thus, the nomogram could be used to assist clinical decision-making before surgery. Although the incidence and mortality rate of gastric cancer have both decreased worldwide, gastric cancer remains the fifth most common malignancy and the third leading cause of cancer deaths in the world . PeritonAlthough computed tomography (CT), positron emission tomography-computed tomography (PET-CT), and magnetic resonance imaging (MRI) technologies have greatly improved, predicting peritoneal metastasis preoperatively remains difficult in clinical practice. A meta-analysis consisting of 15 CT studies and 4 PET-CT studies indicated that CT and PET-CT had a fairly low sensitivity for detecting peritoneal metastasis  [Recent research has preliminarily demonstrated the predictive effects of several clinical pathological characteristics, including tumour size, differentiation, histology type, and tumour biomarkers , 6, as wSerological glycomic profiling is an emerging non-invasive screening tool for finding potential biomarkers in a variety of cancers –11. SeruIn this study, we aimed to discover peritoneal metastasis associated glycan biomarkers by using glycomics analysis based on the MALDI-TOF MS method and clinicopathologic characteristics to establish a nomogram for predicting the risk of peritoneal metastasis in gastric cancer patients.The clinical and pathological data of a consecutive cohort of 728 gastric cancer patients who were initially treated in the general department of Zhongshan Hospital from April to November 2015 were retrospectively collected. Patients with distant metastasis at other sites  and with three or more missing clinical data were excluded. We then applied a 2:1 ratio of non-distant metastasis patients (n = 86) and peritoneal metastasis patients (n = 43) for further examination and analysis. Patients with peritoneal metastasis were consecutively selected. The exclusion criteria and study cohort flow diagram are summarized in Fig. Every patient received a CT or PET-CT scan before surgery. When suspected peritoneal metastasis was reported in the examination, our multidisciplinary team (MDT) then discussed the case. If the evidence of PM was regarded as sufficient by the MDT, then the diagnosis was made; in contrast, if the evidence was not sufficient, diagnostic laparoscopy was used. When peritoneal metastasis was not reported in the examination, exploratory laparotomy or diagnostic laparoscopy accompanied by peritoneal lavage and exfoliative cytologic examination was applied before gastrectomy was performed.N-acetylhexosamine, F = fucose, L = α 2,3-sialic acid, E = α 2,6-sialic acid).N-glycans were released from serum glycoproteins and derived by ethyl esterification reagent according to the method described in previous study . Briefly2 test for categorical variables and Mann–Whitney test for continuous variables. A forest plot was created according to the result of multivariable logistic analysis. A nomogram was created with R software  using the ‘rms’ package to be a new prediction model. A calibration plot was generated to examine the performance characteristics of the nomogram. Receiver operating characteristic (ROC) curve was used to compare the sensitivity and specificity for the prediction of PM by the parameters. Area under the curve (AUC) was used to judge the predictive accuracy of different parameters. A two-sided significance level of 0.05 was used in all statistical tests. Statistical analysis were performed using SPSS package  and R package.Clinicopathologic characteristics between non-distant metastasis and peritoneal metastasis groups were compared by using Pearson’s χA total of 728 gastric cancer patients, which included 663 91%) non-metastatic patients and 65 (9%) distant metastatic patients, were consecutively treated in the General Department of Zhongshan Hospital from April 2015 to December 2015. The TNM stage and distant metastasis details of the patients are depicted in Additional file % non-metAmong the 47 patients with peritoneal metastasis, 25 patients received a CT scan, 12 patients received a PET-CT scan, and 10 patients underwent both examinations preoperatively. Nine cases of PM were detected by CT scans, with a detection rate of 25.7% (9/35), and ten cases were detected by PET-CT scans, with a detection rate of 45.5% (10/22). Importantly, all metastatic cases detected by imageological examination were finally confirmed by surgery with 100% accuracy. The results indicated that CT or PET-CT had a low sensitivity but extraordinarily high accuracy for peritoneal metastasis diagnosis. Due to the low detection rate of the CT or PET-CT scan, more than 60% (29/47) of patients had to undergo invasion examinations. Among them, the majority of patients (28/29) were observed to have macroscopic lesions with peritoneal metastasis.After 2:1 cohort matching, 86 non-metastatic patients consecutively treated from May to June and 43 patients with peritoneal metastasis were finally recruited for further analysis.N-glycomic profiles were identified by MALDI-MS according to our previous study [The us study . Based oP < 0.05)  were considered valuable for predicting peritoneal metastasis and were included for further analysis , CA19-9 ≥ 37 U/mL , CA125 ≥ 35 U/mL , lymphocyte count < 2.0 * 10–9/L , and H5N5F1E2 (m/z 2650.98) expression ≥ 0.0017  were identified as independent risk factors for peritoneal metastasis after the adjustment of covariates  and 95% confidence interval (CI) of the risk factors for peritoneal metastasis in gastric cancer patients, univariable and multivariable analysis were conducted using a logistic regression model. Weight loss ≥ 5 kg , tumour size ≥ 3 cm, signet ring cell or mucinous adenocarcinoma histology type, poor differentiation, diffuse or mixed Lauren classification, increased carbohydrate antigens , decreased lymphocyte count, and haemoglobin, albumin, pre-albumin, and increased N-glycan levels were identified as risk factors for peritoneal metastasis   Fig. c.The number of nomogram points was significantly higher in patients with peritoneal metastasis than in patients without distant metastasis Fig. a. OptimaPeritoneal metastasis is regarded as the most prevalent incurable cause of gastric cancer. In our 728 consecutive gastric cancer patients, peritoneal metastasis was also identified as the most common mode of distant metastasis 47/65: 72%). In patients with peritoneal metastasis, although controversy regarding surgical application still remains, palliative chemotherapy is preferred . From th5: 72%. I18F-fluoro-2-deoxyglucose) uptake, which is predominantly related to glucose transporter 1 (GLUT1) expression [However, in clinical practice, it is often difficult to make diagnosis of peritoneal metastasis by conventional imaging modalities, such as CT, PET-CT or MRI, due to their limitations in terms of detection sensitivity . Consistpression . GLUT1 ipression . ConsequClinicopathologic characteristics and serum biomarkers were first explored to distinguish peritoneal metastasis. A large tumour size, diffuse types, poor differentiation, or mucinous adenocarcinoma histopathological type have been reported to be positively associated with peritoneal metastasis , which iRecently, some markers reflecting the systemic inflammatory response or immune responses of the host in patients with cancer have been indicated to be associated with cancer metastasis , 26. A cN-glycans is implicated in the modulation of cell-ECM (extracellular matrix) associations as well as in cell–cell adhesion and migration, which are closely related to the metastasis potential of tumours [N-glycans for providing comprehensive glycomic analysis due to the insufficient sensitivity of the methods [N-glycans showed significant difference in PM samples. Among these N-glycans, the abundances of 9 N-glycans  containing sialylation were remarkably higher in PM samples. Sialic acids were reported to play important roles in cell–cell interaction, recognition and immunological response [N-glycans were high mannose glycans , which were reported to be abundantly expressed in metastatic cholangiocarcinoma, and promote metastasis of cholangiocarcinoma by enhancing the ability to translocate, invade surrounding basement membrane matrix, and migrate [N-glycans were bisecting GlcNAc , which showed significantly decreased levels in PM. N-glycans with bisecting GlcNAc have been identified to be valuable for detecting the early peritoneal metastasis in ovarian cancer [In addition to these clinicopathologic characteristics and laboratory parameters reported previously, we first identified PM specifically related serum glycans by MALDI-TOF MS and glycomic profile analysis. Glycosylation is a kind of posttranslational modification in the majority of proteins to modulate and control their biological roles. Abnormal glycosylation is associated with tumour progression and metastasis in a variety of cancers –31. It i tumours . The rol tumours . As a re methods , 35. In  methods , 36. Comresponse , as wellresponse . Besides migrate . We alson cancer .Though these PM-associated variables were identified, the diagnostic accuracy of each single parameter was underpowered. Some variables, such as lymphocyte count and H5N5F1E2 expression level, achieved good sensitivity but had low specificity. However, others showed better specificity than sensitivity, such as weight loss, CA19-9 and CA-125 levels. Thus, a nomogram was introduced here to build a statistical model for evaluating the risk of peritoneal metastasis. Many investigations have shown that the nomogram is a visualized graphical statistical model for the individualized assessment of the quantified risk of clinical events by a variety of factors , 42. In Patients with gastric cancer showed an increased risk of developing peritoneal metastasis with more than 5 kg of body weight loss, elevated CA19-9 and CA-125 levels, decreased lymphocyte counts, and increased H5N5F1E2 expression. By incorporating these five variables, a nomogram predicting peritoneal metastasis was developed. Then, the incidence of peritoneal metastasis in an individual gastric cancer patient could be estimated. However, further internal and external validation of the application of the nomogram model is required.Additional file 1: Figure S1. Representative MALDI-TOF spectra of serum N-glycomics profile in gastric cancer. Table S1. Consecutive GC patients treated in Zhongshan Hospital from April 2015 to November 2015. Table S2. Compositions detected by positive reflectron mode MALDI-TOF-MS after ethyl esterification. Table S3. List of the 22 serum N-glycans that were evaluated to be significantly different between non-metastatic GC and PMGC. Table S4. Nomogram point of each variable.


Utilising identical genetic aberrations but targeting different cells, Zhang and colleagues seek to uncover how the cell of origin influences high-grade serous ovarian cancer biology, metastasis and response to treatment. This has impeded the development of physiologically relevant genetically engineered mouse models (GEMMs). Historically, the two main contenders for the HGSOC cell of origin are the ovarian surface epithelium (OSE) and more recently the fallopian tube epithelium (FTE). Just how the cell of origin impacts the development and progression of HGSOC has not been investigated thoroughly until now. The recent study by Zhang et al.2 demonstrates, using mouse models and tumour organoids, that the cell of origin significantly impacts HGSOC growth, metastasis and the HGSOC transcriptome. Compellingly, they also show the cell of origin may play an important role in the response to first-line chemotherapy.Identification of the cell of origin of high-grade serous ovarian cancer (HGSOC) has been a major challenge. The disease is generally diagnosed after the tumour has metastasised throughout the peritoneal cavity3. However, direct evidence of this is limited and no bona fide precursor lesions have been observed in the ovary4. The FTE was originally identified as a strong contender for the cell of origin of HGSOC in BRCA1/2 mutation carriers who underwent risk-reducing salpingo-oophorectomy5. Examination of the fallopian tubes from these women demonstrated the presence of proliferative lesions now designated as serous tubal intra-epithelial carcinomas (STICs)6. STICs have subsequently been shown to also occur in non-BRCA1/2-associated HGSOCs, often contain identical TP53 mutations as their associated tumours and are now considered a potential precursor lesion of HGSOC7. However, experimental evidence that identifies the cell-of-origin as either arising from the OSE or FTE is often contentious, with current evidence suggesting that both the FTE and OSE could give rise to HGSOC9. Determining the cell of origin of HGSOC is necessary for the development of better in vitro and in vivo models of early carcinogenesis, studying prevention strategies, improving early detection and may also be important for determining optimal treatment strategies.Originally, ovarian cancers were thought to arise from OSE that had undergone metaplasia to Müllerian duct-like epithelia before progressing to HGSOC9. Importantly, these studies highlight the likelihood that HGSOC may arise from more than one cell type. However, demonstrating this using GEMMs has proven difficult due to the lack of OSE-specific promoters. Early attempts to model HGSOC in mice occurred when the prevalent opinion was that tumours arise from the OSE. Conditional genetic alterations were introduced into the OSE either using Amhr2-cre transgenic mice or by directly injecting adenoviral Cre recombinase under the ovarian bursa10. While these methods were able to target the desired genetic changes to the OSE, they are not OSE-specific; Amhr2 is expressed throughout the entire Müllerian duct and after injection under the ovarian bursa there is also the high possibility of exposure of adenovirus in the mouse oviduct. Furthermore, not all models developed using these techniques resulted in tumours resembling HGSOC, with some mice developing tumours resembling other ovarian cancer subtypes. These limitations make it difficult to deduce that the OSE represents the cell-of-origin of HGSOC. Following the identification of the FTE as a potential cell of origin for HGSOC, mouse models were developed targeting mutations to the mouse FTE using the Pax811 or Ovgp112 promoters to drive Cre recombinase expression. Depending on the targeted genes, several of these models demonstrated the development of STICs as well as ovarian tumours with HGSOC features. However, due to the limitations of specifically targeting the OSE, there have been no direct comparisons to demonstrate the difference the cell of origin makes to the evolution of HGSOC.While direct experimental evidence demonstrating the definitive cell of origin of HGSOC is lacking, gene expression studies using human FTE, OSE and HGSOC samples reveal that the majority of HGSOC more closely resemble FTE, although a significant proportion of HGSOC share similar expression patterns to the OSETP53 mutation and aberrations in the RB family occur in up to 67% of patients13. To target RB inactivation (using T121) and/or Trp53 mutations to the FTE, Zhang et al. used the established Pax8rtTA;TetOCre mice, which have previously been used to model HGSOC in mice with some success11. To target the same genetic aberrations to the OSE, they used Lgr5CreERT2 mice, and verified that when activated in adult mice Lgr5 is restricted to the OSE and absent in the FTE. Lgr5 was originally discovered in the stem cells of the intestine and colon, but its expression has also been demonstrated in the OSE and FTE14. However, Lgr5 has not previously been used to establish OSE-driven mouse models of HGSOC. A summary of the phenotypes seen in the different mouse models used is shown in Table 10. This study demonstrates Lgr5 can be used to target the OSE to induce HGSOC in mice and this model represents a mouse model of HGSOC derived exclusively from the OSE.The study by Zhang et al. comprehensively unravels the key differences the cell of origin can have on the course of HGSOC. By using strategies that target identical genetic aberrations in the FTE and the OSE, the authors were able to compare differences in tumour development, metastasis and response to therapy. HGSOC is characterised by virtually pathognomonic Pax8rtTA;TetOcre;Tp53R172H/fl;T121 mice dying early from enlarged thymi, found to be due to leakiness of the TetOcre in the thymic epithelium. To circumvent this, the authors established organoids from the FTE or the OSE of the GEMMs and then induced the genetic aberrations in vitro, before transplanting the organoids into recipient mice to allow them to form tumours. Their approach is depicted in Fig. Even though Zhang et al. were successful in targeting genetic aberrations to the FTE or OSE, they were unable to directly compare differences in HGSOC progression in FTE-derived and OSE-derived mouse models. This was due to Pax8 is also expressed in the endometrial and renal tubular epithelium and the Pax8 promoter has previously been used to generate mouse models of renal cancer15. Lgr5 is expressed in the stem cells of the intestine and although the authors mention no intestinal carcinomas were found in Lgr5-Cre;Tp53R172H/fl;T121 mice, future use of the Lgr5 promoter to develop OSE-derived mouse models of HGSOC should be approached with caution as any genetic mutations being studied in the OSE will also be altered in the intestines. The limitations of the chosen promoters, combined with the leakiness seen from the TetOcre mice seen in this study, make the use of organoids an attractive alternative as it bypasses the possible oncogenic changes that may be seen in other organs. The use of organoids has steadily been increasing in recent years as a more physiologically relevant way to study cancer in vitro. Zhang et al. also showed that organoids can be transplanted into recipient mice to generate tumours that are able to recapitulate many of the features of HGSOC including the development of metastases and ascites.The development of reliable, highly penetrant HGSOC GEMMs that faithfully recapitulate the human disease from the pre-invasive stages all the way through to metastatic disease has been largely impeded by the lack of highly specific and reliable methods to target the candidate cell of origins (i.e. the OSE and the FTE), rather than a lack of knowledge as to the best genetic drivers to use. Even the promoters used in this study are not highly specific to the OSE or the FTE. Zhang et al. provide strong evidence that the cell of origin plays an important part in the progression of HGSOC and may influence treatment response. They also highlight the potential of using organoids for the development of more reliable, efficient and cost-effective ways to model HGSOC in the mouse. With the improvement of gene editing technologies, it is anticipated that inducing tumorigenic organoids from human FTE or OSE is the next step in understanding the impact of the cell of origin on the initiation and progression of HGSOC.


Sesamum indicum L.). To understand the molecular mechanisms of this oilseed crop in response to salt stress, we examined the transcriptome and proteome profiles of two sesame varieties, with contrasting tolerances to salinity. Here, RNA sequencing and quantitative proteomic analyses of 30 samples from salt-tolerant and -sensitive sesame seedlings under salt stress were carried out. These data can be used for differential gene expression and protein accumulation analyses, based on a genetic aberration or phenotypic differences in sesame responses to salt stress. Our dataset provides an extensive resource for understanding the molecular mechanisms underlying the adaptation of sesame to salt stress, and may constitute useful a resource for increasing the tolerance of major crop plants to raised salinity levels in soils.Soil salinity is a major abiotic factor affecting the growth and development of important crops such as sesame ( Specifications TableValue of the data•These RNA-seq and iTRAQ data obtained from the selected 2 sesame varieties which represent the first complete set of transcriptome and proteomic data generated from sesame varieties with contrasting tolerances to salt.•These datasets permit comparative transcriptomics and proteomics between salt-tolerant and salt-sensitive sesame varieties. Differential gene and protein expression profiles between varieties could help in understanding the salinity response and tolerance mechanisms of sesame, which helps plant breeders develop traditional breeding and biotechnological approaches to improve stress resistance in sesame.•These datasets will be of value for future characterization of functional genes and proteins involved in salt stress responses in sesame.•These datasets are also expected to provide valuable information for the study of molecular mechanisms underlying salt tolerance in other plants.This dataset aims to provide the transcriptomic and proteomic profiling of 30 samples, from salt-tolerant and salt-sensitive sesame varieties. 22.1The seeds of two sesame varieties were sown and germinated in a box containing half-strength Hoagland solution. The whole cultivation process was accomplished in a growth chamber with a 16/8 h light/dark cycle at 28 °C 2.2Ⓡ RNA Assay Kit and QubitⓇ 2.0 Fluorometer  and the RNA integrity number (RIN) was assessed using the RNA Nano 6000 Assay Kit for the Bioanalyzer 2100 system . RNA libraries were prepared using 3 µg RNA per sample, using a NEBNextⓇ Ultra™ RNA Library Prep Kit for IlluminaⓇ , following manufacturer's instructions. Library preparations were sequenced on an Illumina HiSeq X ten platform at the Novogene Corporation  and 150 bp paired-end reads were generated, using methods described previously For each sample, an EASYspin Plus kit  was used to extract RNA following manufacturer's recommendations. The RNA concentration was measured using a Qubit2.3P value <0.05 were assigned as statistically significant differentially expressed.The raw data (Data Citation 1: NCBI Sequence Read Archive SRP186970) were filtered using Fastq clean v2.0, and clean reads were obtained by removing low quality reads and those containing adapter or ploy-N reads, according to parameters previously reported 2.4Ⓡ Reagent-8PLEX Multiplex Kit, Sigma) following manufacturer's instructions. Differently labeled peptides were mixed equally and then desalted in 100 mg SCX columns. The iTRAQ-labeled peptide mix was fractionated using a C18 column  on a Rigol L3000 HPLC operating at 1 ml/min and subsequently analyzed on an AB SCIEX nanoLC-MS/MS system (Triple TOF 6600) at Novogene Genetics, Beijing, China.Protein was extracted from each sample using methods described previously 2.5PXD013013) were analyzed using Proteome Discoverer 2.2 software . Search parameters included a mass tolerance of 10 ppm for the precursor ion scans and a mass tolerance of 0.02 Da for the product ion scans. Carbamidomethyl was specified in PD 2.2 as a fixed modification. The oxidation of methionine, acetylation of the N-terminus and iTRAQ 8-plex of tyrosine and lysine were specified in PD 2.2 as variable modifications. A maximum of two mis-cleavage sites were allowed. Protein identification and relative abundance quantitation was carried out based on the sesame genome annotation database (https://www.ncbi.nlm.nih.gov/genome/?term=sesamum) as previously reported P value < 0.05 and fold-changes > 1.5 or < 0.67, were used to screen differentially expressed proteins (DEP) https://software.broadinstitute.org/morpheus/). Finally, R software version 3.4.3 was used to analyze the relationship between mRNA and protein expression levels of selected genes or proteins.The raw LC–MS/MS data (Data Citation 2: ProteomeXchange The RNA-Seq and iTRAQ raw data were deposited in the Sequence Read Archive (SRA) of NCBI, under accession number SRP186970 (Data Citation 1) and the ProteomeXchange with identifier PXD013013 (Data Citation 2). Detailed descriptions of the raw data in the SRA and ProteomeXchange are provided in RIN is positively correlated on uniquely mapped reads in RNA-Seq, and all RNA samples with Agilent Bioanalyzer RIN scores above 6.3 were used to construct RNA libraries. Protein quality was analyzed by SDS-PAGE and all protein samples, used for this study, showed high quality . Quality values for RNA and protein samples are listed in 2 > 0.91, Table S4).The quality of the RNA-seq data was assessed and all samples were deemed of high quality in this study . For eac2 > 0.88, Fig. S2).In this study, 30 protein samples, labeled with iTRAQ tags, were divided into five run groups . To eval12019. NCBI Sequence Read Archive. SRP18697022019. ProteomeXchange. PXD013013Authors declare no conflict of interest.


Transcriptome profiling can provide information of great value in clinical decision-making, yet RNA from readily available formalin-fixed paraffin-embedded (FFPE) tissue is often too degraded for quality sequencing. To assess the clinical utility of FFPE-derived RNA, we performed ribo-deplete RNA extractions on > 3200 FFPE slide samples; 25 of these had direct FFPE vs. fresh frozen (FF) replicates, 57 were sequenced in 2 different labs, 87 underwent multiple library analyses, and 16 had direct microdissected vs. macrodissected replicates. Poly-A versus ribo-depletion RNA extraction methods were compared using transcriptomes of TCGA cohort and 3116 FFPE samples. Compared to FF, FFPE transcripts coding for nuclear/cytoplasmic proteins involved in DNA packaging, replication, and protein synthesis were detected at lower rates and zinc finger family transcripts were of poorer quality. The greatest difference in extraction methods was in histone transcripts which typically lack poly-A tails. Encouragingly, the overall sequencing success rate was 81%. Exome coverage was highly concordant in direct FFPE and FF replicates, with 98% agreement in coding exon coverage and a median correlation of whole transcriptome profiles of 0.95. We provide strong rationale for clinical use of FFPE-derived RNA based on the robustness, reproducibility, and consistency of whole transcriptome profiling. RNA-Seq data is used for gene expression5 and allele-specific expression profiling6, fusion detection7, variant calling8 and selection of immunotherapy9. However, often only formalin-fixed paraffin-embedded (FFPE) tissues are available for analysis in clinical settings and RNA extracted from such samples can be highly degraded10. Despite this, FFPE RNA is routinely used for analysis in several FDA-approved tests13.Tumor genomic and transcriptomic data typically from targeted panels or microarrays are widely used in clinical cancer diagnosis, prognosis, and therapy recommendations. Numerous studies have shown the utility of RNA sequencing (RNA-Seq) for advancing precision medicine and informing clinical decisions. have developed improved methods for extracting high-quality FFPE-RNA by integrating a new micro-homogenizing (mH) tool14. In library prep, Zhao et al.15 demonstrated that compared to mRNA-Seq and microarray, Ribo-Zero-Seq provides equivalent mRNA coverage uniformity, genome-based aligned reads, and high-quality transcript quantification; while having the ability to recover non-polyadenylated and short-transcript RNAs. The ribosomal RNA depletion approach has been widely accepted as the preferred method of RNA isolation for degraded or low-input samples18. Another improvement comprising use of an “RNA Direct” workflow including targeted capture using the Agilent Strand-Specific RNA Library Prep kit has also been described19.To improve data reliability of FFPE-derived RNA, others such as Hoover et alAs a contribution to the ongoing improvement in utility of FFPE-RNA, herein we describe our assessment of the reproducibility, quality, and robustness of clinical FFPE-derived RNA at every step of the extraction and sequencing process Fig.  and prov21 of FFPE-derived RNA-Seq with RNA isolation by rRNA depletion. Exon regions and ncRNAs had the highest coverage .The consistency of coverage of individual transcripts shows moderate systematic differences between FFPE and FF/OCT replicates. In comparison to their FF/OCT counterparts, some FFPE replicates have many more genes with low TIN Fig. . Per-gen28 (n = 31) demonstrated similar expression levels in FFPE and FF/OCT samples. In fact, all of the genes were within absolute magnitude of 2 log expression levels .We found again that FFPE versus FF/OCT replicate samples are most similar to their corresponding replicate pair Fig. D. Transcels Fig. A, indicaels Fig. B. Transcp = 2.92e−18), consistent with our findings in direct FFPE versus FF replicates. We found that TCGA samples exhibit strong 3′ transcript bias , comprising a wide variety of cancer types  and TCGA FF n = 10,266) samples to the clinical FFPE cohort separately. We found that TCGA FFPE expression is more consistent with clinical FFPE samples, with few highly differential outliers  as a tumor classifier/target for therapeutic intervention35. But the use of RNA-Seq in clinical settings remains infrequent because of uncertainty concerning the quality of FFPE-derived RNA.RNA-Seq data from FFPE-derived RNA has shown sufficient precision and sensitivity for biomarker discovery in breast cancer37 and have investigated which protocols are best for preparation of RNA libraries20 or tools for read alignment on FFPE-derived samples39, our study concentrated on examining any intrinsic biases in the FFPE-derived gene expression data and assessing its utility in clinical applications. Our study also contains the largest, in number of samples, FFPE cohort published to date. We performed comprehensive analysis of various types of replicates at every stage of RNA sample fixation/storage, extraction, and sequencing and show that—when quality is controlled—gene expression quantification of FFPE-derived RNA is robust, consistent and reproducible. We introduce methodology for evaluating sample RNA quality post-sequencing, and show that when this method is applied to samples, it produces high quality, high confidence results in replicate datasets.While others have addressed the issue of FFPE and FF comparison in smaller studies40. Further, while FF-derived RNA transcripts with a poly-A tails may be deemed more clinically relevant because they are destined for translation, some non-polyadenylated transcripts have been shown to be translated into peptides41 and are important in tumorigenesis or biomarkers in cancer44.We identified systematic trends and technical artifacts that affect gene expression profiling in FFPE samples. For example, we demonstrate that the sample fixation method accounts for the apparent increased expression of nuclear and cytoplasmic proteins in FF/OCT samples and the apparent increased expression of transmembrane and secreted proteins in FFPE samples. Similarly, some of the transcripts detected at higher rates in FF/OCT samples are genes that encode for histone proteins, which have been shown to undergo critical chemical modifications in FFPE samples.45 demonstrated that subtyping accuracy of FFPE and FF triple negative breast cancer (TNBC) samples increased with sequencing depth, and that samples with shorter archiving time (< 4 years) were subtyped more accurately, presumably due to reduced RNA degradation which is consistent with others investigators46. However, Jovanovic focused their analysis on genes that did not vary between platform type, tissue processing, and RNA isolation techniques, tacitly suggesting that certain transcripts were unreliable and should be excluded. Like Jovanovic, we noticed an inverse relationship between the archiving time and RNA quality of the samples. Here, we present evidence that focusing on driver genes does not increase quality substantially, but rather that quality metrics such as TIN and TB can be used to exclude poor quality samples and transcripts post-sequencing. Our study suggests a quality threshold cutoff for TIN metric that can be used to identify samples with RNA quality too poor to perform reliable genomic analysis. Additionally, we provide a computational methodology to normalize out systemic differences in detected ribosomal RNA content in sequenced samples resulting from the differences in degree of sample degradation as well as differences in rRNA depletion among different FFPE samples.The results presented here comport with and represent a significant expansion on previous findings. Jovanovic et alFortifying confidence in transcriptomic analysis of FFPE-derived RNA in clinical settings opens up a broad spectrum of possibilities to advance patient care and may lead to RNA sequencing becoming indispensable as a diagnostic and prognostic technology that may also reveal new targets for therapeutic intervention.Tumor samples were obtained from patients across multiple clinical centers. These samples were analyzed as part of routine clinical care when the oncologists ordered a molecular diagnostic assay from NantOmics/NantHealth, Inc. These samples comprised a wide variety of solid tumor types  staining and reviewed by a board-certified pathologist who identified and marked regions of malignancy. Using the marked-up image as a guide, areas of tissue were macrodissected to collect the desired regions.Laser microdissection of FFPE sections was performed on a subset of FFPE samples for comparison of direct microdissection vs. macrodissected replicates (n pairs = 16). The pathologist marked up an H&E image, then mirrored that mark-up onto the FFPE section to guide the laser microdissection.The RNeasy FFPE Kit  was used for extraction and extracted material quantified using a Qubit fluorometer . Samples with total RNA quantities of greater than 600 ng were prepped in duplicate using a KAPA Stranded RNA-Seq Kit with RiboErase  and sequenced to a target depth of 200 M reads on the Illumina HiSeq platform . Duplicate preps were both sequenced if there was enough material to do separate technical library preparations for each, otherwise 2× material from a single library prep was loaded to reach 200 M reads.47, RSEM, and custom software were used for alignment and transcript quantification. STAR48 alignments were performed on a subset of clinical FFPE samples (n = 114). RefSeq49 (build 73) on hg19 was used to define transcripts for each pipeline. See methods for read and base composition analysis in Bowtiehttps://portal.gdc.cancer.gov/legacy-archive/) and applied the same bioinformatics pipeline for transcript quantification to these data as for the FFPE clinical cases. Data from our processing of TCGA data are available upon request (email corresponding author).We downloaded TCGA RNA-Seq FASTQ files from the GDC Legacy Data Archive  score—to specifically address 5′–3′ coverage bias. This measure is based on the difference between the observed distribution of reads and a uniform distribution with the same average coverage. Details of TB score calculation can be found in 50 R package to perform linear model fit using weighted least squares for each gene.To compute differential gene expression we utilized the LIMMATo quantify expression levels differences for each replicate pair and gene, we compared the average expression vs. expression difference plotted as Bland–Altman plots  queried against KEGG and GO cellular component MSigDBWe sought to functionally annotate genes that are differentially detected between the library preps used in FFPE vs. TCGA. First, we limited analysis to tissues with at least n = 30 samples in both datasets  to ensure that tissue-specific expression did not bias analysis. Next, we extracted the top 150 most-differentially expressed genes (up- or down-regulated) by t-test. Next, we applied a hypergeometric test using the MSigDB pathway online tool with GO cellular component gene sets to find pathways that were enriched for said genes.Transcript Integrity Number (TIN) score and TB score; enrichment analysis of genes with low transcript integrity in FFPE vs. FF/OCT replicates and FFPE vs. TCGA cohorts; normalization to overcome variations in rRNA depletion quality across the FFPE cohort; exomic feature annotations and other genomic features considered; features coverage and read and base composition analysis; analysis of enhancers; per-transcript and per-HUGO-gene expression quantification from RNA-seq data; mapping of TCGA data into the FFPR RNA-seq space are described in Supplementary Information 1.Supplementary Legends.Supplementary Table 1.Supplementary Information 2.Supplementary Figure 1.Supplementary Figure 2.Supplementary Figure 3.Supplementary Figure 4.Supplementary Figure 5.Supplementary Figure 6.Supplementary Figure 7.Supplementary Figure 8.Supplementary Figure 9.Supplementary Figure 10.Supplementary Figure 11.Supplementary Figure 12.Supplementary Figure 13.Supplementary Figure 14.Supplementary Figure 15.Supplementary Figure 16.Supplementary Figure 17.Supplementary Figure 18.Supplementary Figure 19.Supplementary Figure 20.Supplementary Figure 21.Supplementary Figure 22.Supplementary Figure 23.Supplementary Figure 24.Supplementary Figure 25.Supplementary Figure 26.Supplementary Figure 27.Supplementary Figure 28.Supplementary Figure 29.Supplementary Figure 30.Supplementary Figure 31.Supplementary Figure 32.Supplementary Figure 33.


Flaviviridae comprise one of the largest families of positive-sense single-stranded RNA (+ssRNA) and are divided into the Flavivirus, Pestivirus, Pegivirus, and Hepacivirus genera. The genus Flavivirus contains many medically relevant viruses such as Zika virus, dengue virus, and Powassan virus. In these, a part of the RNA of the virus twists up into a distinct three-dimensional shape called an exoribonuclease-resistant RNA (xrRNA) that blocks the ability of the cell to “chew up” the viral RNA. Hence, part of the RNA of the virus remains intact, and this protected part is important for viral infection. These xrRNAs were known to occur in flaviviruses, but whether they existed in the other members of the family was not known. In this study, we identified a new subclass of xrRNA found not only in flaviviruses but also in the remaining three genera. The fact that these structured viral RNAs exist throughout the Flaviviridae family suggests they are important parts of the infection strategy of diverse pathogens, which could lead to new avenues of research.The members of the  Flavivirus genus (in the family Flaviviridae), where during flaviviral infection, pathogenic subgenomic flaviviral RNAs (sfRNAs) accumulate in the cell. These sfRNAs are formed when a host cell 5′ to 3′ exoribonuclease degrades the viral genomic RNA but is blocked by an exoribonuclease-resistant RNA structure (xrRNA) located in the viral genome’s 3′ untranslated region (UTR). Although known to exist in several Flaviviridae genera, the full distribution and diversity of xrRNAs in this family were unknown. Using the recently solved high-resolution structure of an xrRNA from the divergent flavivirus Tamana bat virus (TABV) as a reference, we used bioinformatic searches to identify xrRNAs in the remaining three genera of Flaviviridae: Pegivirus, Pestivirus, and Hepacivirus. We biochemically and structurally characterized several examples, determining that they are genuine xrRNAs with a conserved fold. These new xrRNAs look superficially similar to the previously described xrRNAs but possess structural differences making them distinct from previous classes of xrRNAs. Overall, we have identified the presence of xrRNA in all four genera of Flaviviridae, but not in all species. Our findings thus require adjustments of previous xrRNA classification schemes and expand the previously known distribution of xrRNA in Flaviviridae.Viruses have developed innovative strategies to exploit the cellular machinery and overcome the antiviral defenses of the host, often using specifically structured RNA elements. Examples are found in the  Viruses face continuous evolutionary pressure to evolve innovative strategies that exploit the host cell’s biological machinery and overcome its antiviral defenses. Often these are based on specifically structured RNA elements, which is not surprising given RNA’s functional diversity and ability to influence virtually every cellular process. Important examples are found in positive-sense single-stranded RNA (+ssRNA) viruses such as arthropod-borne flaviviruses, which use several structured elements within their RNA genome to direct or regulate important processes during infection –13.–Among the important structured RNA elements found in flaviviruses are exoribonuclease-resistant RNAs (xrRNAs), which enable an elegant mechanism of noncoding RNA biogenesis. Originally identified in mosquito-borne flaviviruses (MBFVs) –19 and l–23–29–Tombusviridae and Luteoviridae viral families, although the structural strategies used to form and stabilize the ring are different , Tamana bat virus (TABV), and tick-borne encephalitis virus (TBEV) xrRNA. Representative xrRNA secondary structures from (A) ZIKV , (B) TABCopyright © 2020 Szucs et al.2020Szucs et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the Flaviviridae family consists of four major genera: Flavivirus, Pegivirus, Pestivirus, and Hepacivirus   , 45, 46,vivirus)  37). If. IfFlaviFlaviviridae.New questions about variation in flavivirus xrRNA structure arise from the recently solved structure of an xrRNA found in the 3′ UTR of TABV . While tPegivirus, Pestivirus, and Hepacivirus genera that we verified using in vitro functional studies and characterized by chemical probing. Analysis of sequences and secondary structures revealed fundamental similarities to the TABV xrRNA. Together, these xrRNAs comprise an xrRNA subclass similar to class 1 but bioinformatically and structurally distinct; we now assign them to subclass 1b and accordingly place those species previously classified in class 1 (MBFV) into subclass 1a. Thus, our results show that all genera  within Flaviviridae contain xrRNAs that exist in at least three distinct structural classes/subclasses.Using computational tools that take into account both structural and sequence constraints and findings that are informed by crystal structures, we exploited the burgeoning availability of genomic data from new viral species. We identified putative xrRNAs in the RALEE v. 0.8 and a text editor , Pegivirus (n = 28), Pestivirus (n = 7), and Hepacivirus (n = 10)  using the program Infernal . MultiplFrom this expanded comparative sequence alignment, we generated a covariance model of the newly proposed xrRNAs , right. −7 to 1.29 × 10−5 (P1), 2.17 × 10−14 to 1.75 × 10−3 (P2), and 1.45 × 10−4 to 1.77 × 10−9 (P3). Stem length varies less for P1 (3 to 5 bp) and P3 (4 to 5 bp) than for P2 (up to 21 predicted base pairs) (−8 to 1.20 × 10−13)  , right. e pairs)  37). Th. Th−7 to× 10−13) .Rodent hepacivirus isolate rn-1 . Among them were xr4 from Pestivirus, Pegivirus, and Hepacivirus to in vitro Xrn1 resistance assays (in vitro with a 22-nucleotide (nt) leader sequence, which allowed Xrn1 to load onto the 5′ end of the transcribed RNA. Some of the previous work from our laboratory showed that a wild-type leader sequence of an arbitrary length could generate false-negative results in Xrn1 resistance assays, likely due to RNA misfolding. Hence, for our assays of potential subclass 1b xrRNAs, the leader sequence contained the same normalization hairpin (xxxx-GAGUA-xxxx) and spacer sequences used for the chemical probing experiments described in the next section  (RNAs were treated with RNA 5′-pyrophosphohydrolase (RppH), leaving a monophosphorylated 5′ end . When anNA (xr1) . An RNA NA (xr1) . When chNA (xr1) . This re10.1128/mBio.02352-20.2FIG S2in vitro degradation assay gels. Raw gel images were used and were cropped for FIG S2, PDF file, 2.5 MB.Uncropped subclass 1b Copyright © 2020 Szucs et al.2020Szucs et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the Because the predictions of the secondary structures were based on sequence alignments and observed nucleotide covariation, we tested these predictions through chemical probing with dimethyl sulfate  and N-methylisatoic anhydride   53, 54), 5453, 510.1128/mBio.02352-20.3FIG S3Pestivirus, Pegivirus, and Hepacivirus chemical probing data. Each of the following pages contains one of several xrRNA constructs with similar labels and notation, showing the raw data for chemical probing experiments. The eight constructs are ordered by genera as follows: Pestivirus, Pegivirus, and Hepacivirus. (A) Normalized DMS and NMIA reactivities mapped onto the secondary structure representation of the xrRNA, including the putative P4 region. (B and C) CE lanes and reactivity plot for DMS (B) and NMIA (C) used in the experiment. Red circles around the peaks in the reactivity plot represent a higher degree of standard deviation observed for that point. Download FIG S3, PDF file, 1.4 MB.Copyright © 2020 Szucs et al.2020Szucs et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the Pegivirus, Pestivirus, and Hepacivirus xrRNAs tested for Xrn1 resistance to compare each probing fingerprint to that of TABV. The resultant patterns were consistent with the pattern from the TABV xrRNA, indicating that they adopt similar secondary structures , SPgV (A54), and GBVB (A47) , and I. On the basis of the sequence covariation observed and our structural analysis, xrRNAs from subclass 1b are more compact than those from subclass 1a, except for the P2 stem . SubclasThe L3 loop comprising the Pk2 pseudoknot is the second defining feature of the subclass 1b xrRNAs. The L3 region exists in a 3-3-3 (86%), 4-3-3/3-3-4 (13%), or 4-3-4 (1%) nucleotide configuration (the number of nucleotides refers to the length of the 5′ side of L3 and of Pk2 and to that of the 3′ side of L3 or L3′) . SequencNext, a defining structural feature of subclass 1a lacking in subclass 1b is the presence of a conserved cytosine between P2 and P3. In subclass 1a xrRNAs, this nucleotide is important for tertiary interactions that support folding and the formation of the ring-like structure around the 5′ end of the xrRNA. Studies have shown that mutating this C disrupts the ability of xrRNA to resist Xrn1 . BecauseFinally, the covariation pattern of Pk1 is another key feature of the subclass 1b xrRNAs, although the data are less distinctive between the two subclasses. According to sequence alignment and probing data, this region accommodates either two base pairs (11% of the sequences identified) or three base pairs (89% of the sequences) . Of the Flaviviridae, one of the largest families of RNA viruses, expanding on the known distribution of these RNA structures within Flaviviridae . Subclasses 1a and 1b deviate in (i) the sequence patterns of their P1 and P3 stems, (ii) the nucleotide patterns of the L3 loop, (iii) the Pk2 region, and (iv) whether a particular nucleotide is present between P2 and P3. In addition, the pattern of almost absolutely conserved nucleotides that is indicative of subclass 1a is not present in subclass 1b   .Flaviviridae sequences, subclasses 1a and 1b do not appear in the same viral species. In other words, no viral sequence within the phylogenetic groups characterized as having subclass 1a xrRNAs possesses a subclass 1b xrRNA and vice versa. When several xrRNAs are present in the same 3′ UTR . The recovered plasmid stocks were verified through sequencing (Eton Bioscience).Each xrRNA construct  was desiIn vitro transcription was carried out in a volume of 2.5 ml comprising 1.0 ml of PCR as the template. The transcription reaction mixture contained ∼0.2 M template DNA, a 6 mM concentration of each rNTP (ribonucleoside triphosphate), 60 mM MgCl2, 30 mM Tris (pH 8.0), 10 mM dithiothreitol (DTT), 0.1% spermidine, 0.1% Triton X-100, T7 RNA polymerase, and 2 μl RNasin RNase inhibitor (Promega). The transcription reaction mixture was incubated overnight at 37°C. The RNA was precipitated with 4 volumes of ice-cold 100% ethanol, incubating overnight at –20°C. Precipitated RNA was gel purified via the use of 7 M urea–8% denaturing polyacrylamide gel electrophoresis (dPAGE)–1× Tris-borate-EDTA (TBE). The RNA was visualized by UV light, excised from the gel, and eluted from the gel by the crush and soak method overnight at 4°C, using ∼50 ml of diethylpyrocarbonate (DEPC)-treated Milli-Q (Millipore) filtered water. Amicon Ultra spin concentrators (Millipore-Sigma)  were used to concentrate the eluted RNA to 2.5 mg/ml, and the reaction mixture was then stored at –20°C in DEPC-treated H2O until use.Template DNA was amplified by PCR using custom DNA primers  and reco2, and 1 mM DTT). Next, 3 μl of recombinant RppH (0.5 μg/μl stock) was added to the mixture, which was then aliquoted into two volumes of 20 μl. A 1.5-μl volume of recombinant Xrn1 (0.8 μg/μl stock) was added to one of the aliquots, and the reaction mixture was incubated in a thermocycler at 37°C for 2 h. A 10-μl volume of RNA from each reaction (with or without Xrn1) was resolved by the use of a 7 M urea 8% dPAGE gel and visualized with ethidium bromide.A total of 5 μg RNA was incubated at 90°C for 3 min and then at 20°C for 5 min in 40 μl of refolding buffer , 4.1 nM FAM-labeled RT primers, and 13.3 mM MgCl2. This reaction mixture was incubated at 37°C for 15 min. The RNA mixture was centrifuged at maximum speed  in a tabletop centrifuge, and the full 15-μl volume was added to 1 well of the 96-well plate where the remainder of the chemical probing experiments occurred. When setting up this experiment, enough folded RNA was made to fill the contents of a 96-well plate.The method used for chemical probing was adapted from a previously published method . RNA 1. at 95°C 2O. This 1% DMS solution was then diluted to 0.5% with DEPC-treated Milli-Q H2O. NMIA solution was prepared by dissolving 12 mg of NMIA in dimethyl sulfoxide (DMSO) . A stock of DMSO alone was used as the no-modification control. For each condition , 5 μl of the appropriate stock was added to a well containing the 15 μl of folded RNA, followed by incubation of the reaction mixture at room temperature for 30 min. Specific chemical modifier quenching solutions were prepared which consisted of 1.5 μl of cleaned oligo(dT)25 magnetic beads (Invitrogen), 3 μl of 5 M NaCl, 0.25 μl of DEPC-treated Milli-Q H2O, and 5 μl of the corresponding quenching solution. Fresh 2-mercaptoethanol  was used to quench the DMS reaction and 0.5 M 2-(N-morpholino)ethanesulfonic acid sodium salt (Na-MES)  to quench the NMIA reaction. The appropriate quenching solution was added to each well and incubated at room temperature for 10 min. The chemically modified RNAs bound to the beads were washed three times with 100 μl of 70% ethyl alcohol (EtOH) on a magnetic rack. The washed beads were air dried for ∼15 min and then resuspended in 2.5 μl of 2 M betaine .Fresh stocks of 0.5% dimethyl sulfate (DMS)  and 12 mg/ml N-methylisatoic anhydride (NMIA)  were made immediately before probing was performed. Chemical modifier stocks were prepared as follows. A 10-μl volume of DMS was first added to 90 μl of 100% ethanol, which was then added to 900 μl of H2O, and 0.1 μl of SuperScript III reverse transcriptase (Thermo Fisher). The mixture was incubated in a 52°C water bath for 50 min. Upon completion of the incubation, 5 μl of 0.4 M NaOH was added to each well and incubated at 65°C for 20 min to hydrolyze the RNA. The samples were then cooled in an ice bath for 2 min, and each reaction was neutralized with 5 μl of an acid quench mixture . The supernatant was aspirated from each well, and the cDNA bound to the beads was washed three times with 100 μl of 70% EtOH and allowed to air dry for ∼15 min. The cDNA was then eluted from the beads in 11 μl of a ROX-formamide mixture  with 1.2 ml of HiDi-formamide (Applied Biosystems), incubating at room temperature for 15 min. The 11 μl cDNA mixture was transferred to an optical plate and analyzed with an ABI 3500 Genetic Analyzer capillary electrophoresis (CE) machine.The RNA was reverse transcribed on beads with 2.5 μl of a reverse transcription mixture comprised of 1.0 μl of 5× first-strand buffer (Thermo Fisher), 0.25 μl of 0.1 M DTT, 0.4 μl of a 10 mM equimolar mixture of deoxynucleoside triphosphates (dNTPs), 0.75 μl of DEPC-treated HAlongside the reverse transcription for the chemically probed RNAs, Sanger sequencing ladders were constructed through reverse transcription of unmodified xrRNA in the presence of dideoxynucleoside triphosphates (ddNTPs). A 2.5-μl volume of an RNA mixture consisting of 0.2 μl of 0.25 μM FAM primer, 1.5 μl of oligo(dT) beads, 0.5 μl of 2.4 μM RNA, and 0.25 μl of 5 M betaine was added to 2.5 μl of a ladder reverse transcription mixture consisting of 1.0 μl of 5× first-strand buffer, 0.25 μl of 0.1 M DTT, 0.4 μl of 1.0 mM dNTP, 0.4 μl of a 1.0 mM concentration of the appropriate ddNTP, 0.1 μl of SuperScript III reverse transcriptase, and 0.25 μl of 5 M betaine. The outlined reverse transcription protocol was followed as described earlier.https://ribokit.github.io/HiTRACE/) with MATLAB (v8.3.0.532) was used to analyze the chemical probing data as described previously (The HiTRACE MATLAB suite (eviously . CE traceviously .


We found that the half-life of recently produced RNA increased from 4.0 days at 20°C to 15.8 days at 4°C, and 215 days at −4°C, while no degradation was detected at −18°C during a 1-year period. We discuss the implications of the strong temperature dependency of rRNA turnover for interpretation of microbiome data based on rRNA isolated from environmental samples.Ribosomal RNA (rRNA) is used widely to investigate potentially active microorganisms in environmental samples, including soil microorganisms and other microbial communities that are subjected to pronounced seasonal variation in temperature. This raises a question about the turnover of intracellular microbial rRNA at environmentally relevant temperatures. We analyzed the turnover at four temperatures of RNA isolated from soil bacteria amended with  Microbial DNA isolated from environmental samples cannot differentiate between the live and dead fraction of the microbiome and is generally believed to persist for longer time spans compared to RNA, e.g., in soils where extracellular DNA from dead organisms may persist for years . The perBacterial rRNA is more stable than messenger RNA (mRNA) likely as a consequence of its incorporation into ribosomes and protection by ribosomal proteins , but rRN14C-radiolabeled uridine (an RNA nucleoside), and finally returning the bacteria to the soil for incubation at −18, −4, 4, and 20°C, respectively. At different time points, RNA was isolated from the incubated soil samples and its content of 14C was measured.Studies of RNA turnover in bacterial cells often involve inhibition of RNA synthesis by actinomycin D  or rifamSoil was sampled in April 2016 at 0–10 cm depth from a permanent grassland at the University of Copenhagen experimental farm Højbakkegård, Tåstrup, Denmark. The mean annual air temperature is 8.2°C. The soil is a sandy loam with pH 6.4 and had a moisture content of 22.7% of dry weight at the time of sampling. The soil was gently homogenized by hand and sieved at 4 mm.g for 5 min at 4°C. Thirty mL of the supernatant and 60 mL 1-% TSB were transferred to eight incubation flasks for a final concentration of 1/150 TSB. The flasks were incubated at 4°C with shaking at 150 rpm. After 24 h, TSB was added to a final concentration of 1/75, and following an additional 24 h, TSB was added to a final concentration of 1/10 and the flasks were incubated for 24 h. A pilot experiment revealed that this gradual increase in TSB concentration was needed to achieve adequate incorporation of 14C in RNA. The pilot experiment involved incubation of extracted bacteria at three separate concentrations of TSB , and scintillation counting was conducted after 3 days of incubation to quantify the incorporation of 14C into cells following amendment with 14C-radiolabeled uridine . Twenty hours after addition of labeled uridine, the remaining radioactivity in the media was 11.2, 14.2, and 91.5% of the total added following incubation at 1/150, 1/75, and 1/10 TSB, respectively, and after centrifugation, 5.4% of the radioactivity was found in cells incubated with 1/150 TSB, 6.4% for 1/75 TSB, and 69.5% for 1/10 TSB. The low incorporation of radioactivity by cells incubated with 1/150 and 1/75 TSB was most likely caused by bacteria using the labeled uridine mainly as an energy source resulting in a loss of 14C as 14CO2.Two batches of 100 g soil in 320 mL phosphate-buffered saline (PBS) were blended in a sterilized blender for 3 min. An additional 80 mL PBS was used to wash away remaining soil particles after transferring the blended soil slurry to eight 50-mL tubes, which were centrifuged at 100014C-uridine to a final concentration of 6.2 × 105 disintegrations per min (DPM) mL–1. Uridine is specifically incorporated into RNA or used as an energy, carbon and/or nitrogen source by the soil microorganisms, but RNA can be selectively isolated from other cell substances that may contain some of the 14C.The contents of the incubation flasks were combined and 60 mL were transferred to eight 250-mL flasks and amended with 60 μL of g for 5 min at 10°C. The supernatant was decanted and cells were re-suspended in 30 mL PBS by vortexing. The cells were washed twice in 4 mL PBS by centrifuging as described above. 2.5 mL of re-suspended bacteria were mixed using a spatula into 25 g of soil that had been air-dried for 24 h at room temperature. Care was taken to distribute the suspension evenly in the soil. 1.4⋅105 bacterial cells mL–1 were transferred as estimated by counting the cells in a fluorescence microscope following acridine orange staining. Only bacterial cells were observed in the microscoped samples.Following incubation of the eight flasks for 23 h at 4°C at 150 rpm, the content was transferred to fourteen 50-mL tubes and centrifuged at 600014C-labeled bacteria was distributed in 2.0-g aliquots to 2-mL plastic tubes from the RNA isolation kit , and the soil samples were incubated in our laboratory in quadruplicates at −18°C, −4°C, 4°C, and 20°C, respectively. When sampling for RNA, the tubes were snap frozen in liquid nitrogen and stored at −80°C until RNA isolation using the RNeasy PowerSoil kit. From the final volume of 100 μL eluate, 40 μL was added to 10 mL scintillation liquid in a 20-mL scintillation vial, which was analyzed on a scintillation counter .The soil amended with n = 2) and RNA isolated from the soil samples at initiation of the experiment (n = 15) indicated that the RNA isolation procedure was highly efficient, isolating 97.4% of the labeled RNA pool from the soil samples relative to the cell suspension. As a side note, our experimental setup may, thus, also be used to evaluate the efficiency of protocols for isolation of RNA from soil and other environmental samples.A comparison of the radioactivity in the cell suspension  and 28 μL H2O were added to 20 μL eluate from the RNA isolation procedure, and the mix was incubated at 37°C for 1 h. DNA was isolated from the solution using the Genomic DNA Clean & Concentrator kit  according to the manufacturer’s instructions. All flow-through liquid (presumed to contain nucleotides from degraded RNA) from the washing steps was combined and analyzed for radioactivity. The DNA concentration in the eluate (presumed to contain DNA) was quantified using a Qubit Fluorometer  and the eluate was analyzed for radioactivity. This control experiment revealed that 99.55 ± 0.03% (n = 8) of the radioactivity in the eluate from the RNA isolation was found in the RNA fraction and only 0.45 ± 0.03% of the radioactivity was found in the DNA fraction.We tested if r2 ranged from 0.435 to 0.765 for the four replicate samples incubated at −4°C, from 0.926 to 0.982 for the samples at 4°C, and from 0.924 to 0.944 for the samples at 20°C).As the total RNA pool in the soil may be affected by the experimental treatment we normalized the labeled RNA to the total RNA pool (estimated using Qubit 2.0 [Thermo Fisher Scientific]) at each sampling point see . HereaftIt has previously been shown that approximately 95% of total RNA isolated from soil microbiomes is rRNA and that the majority is bacterial . We assun = 4) at −4°C, 15.8 ± 1.00 days at 4°C, and 4.04 ± 0.44 days at 20°C.We found a strong temperature dependence of soil bacterial rRNA turnover  with und–1 corresponding to a half-life of 3.1 days in a grassland rhizosphere where plants were spiked with 13CO2 (at air temperatures of ca. 15°C). This is faster than the turnover at 20°C in our study even though our experimental setup may overestimate the rRNA turnover. This possible overestimation may be due to the transferred bacteria being stimulated by the  TSB growth medium before transfer to the soil where nutrient availability is expected to be lower, which may lead to starvation of the transferred bacteria and hence enhanced rRNA decay  prevailing at the two temperatures. Ice lowers diffusion rates of nutrients and gases dramatically in the soil environment (10 of 2.3 for this temperature span. However, the Q10 value is based on only two temperatures and more detailed studies are needed to generate robust temperature response curves of the bacterial rRNA half-life in soil and other environments.The large difference in half-life observed at −4 and 4°C is likely caused by not only temperature ironment  and, henin situ of microbial rRNA may be affected by daily or seasonal fluctuations in temperature, drought-induced lowering of soil water content, and other environmental fluctuations.Pronounced seasonal variation in environmental temperature is part of life for microorganisms living in soil, shallow-water sediments, surface water, poikilothermic animals, or on plants. Our data indicate that half of the intracellular rRNA isolated from an environmental sample may have been produced by the microbiome more than 4 days before sampling if the environmental temperature is 20°C, or more than 15 days before if the temperature is 4°C. Thus, the time span during which one or more environmental parameters have affected microbial rRNA composition at the time of sampling varies with seasonal changes in temperature. It should be noted that our experimental set-up involved incubation at stable conditions. However, in most environments conditions are not stable and turnover rates Our study involved a soil at low temperatures, but could be extended to unravel rRNA stability in other environments amenable to RNA preservation, e.g., environments with high osmolarity or low water activity. Precautions should be made when claims are made on the composition of the ‘active’ microbiome in these environments. Also, the stability of fungal and microeukaryotic rRNA awaits further studies.In conclusion, the half-life of rRNA produced by a soil microbiome increased markedly with a decrease in environmentally relevant temperatures and was more than 7 months at −4°C. This means that rRNA may remain long after environmental conditions have changed and, thus, we should interpret with care correlations between rRNA-based microbiome structure and seasonal variation in ephemeral environmental parameters such as precipitation and concentration of soil water, redox potential, oxygen, inorganic nitrogen, and metabolomes.All data generated during this study are included in this article and its All authors designed the project, read, and approved the final manuscript. MS performed the experimental work. AP wrote the first draft of the manuscript with subsequent revisions performed by all authors.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.


Recently, the chloroplast ribonucleoprotein CP33B was identified as a ligand of the psbA mRNA. We here characterized the interaction of CP33B with chloroplast RNAs in greater detail using a combination of RIP-chip, quantitative dot-blot, and RNA-Bind-n-Seq experiments. We demonstrate that CP33B prefers psbA over all other chloroplast RNAs and associates with the vast majority of the psbA transcript pool. The RNA sequence target motif, determined in vitro, does not fully explain CP33B’s preference for psbA, suggesting that there are other determinants of specificity in vivo.Chloroplast RNAs are stabilized and processed by a multitude of nuclear-encoded RNA-binding proteins, often in response to external stimuli like light and temperature. A particularly interesting RNA-based regulation occurs with the  The maturation and translation of chloroplast RNAs depends on numerous RNA-binding proteins (RBPs). With few exceptions, all RBPs involved in chloroplast RNA metabolism are encoded in the nucleus and are post-translationally imported into plastids. The largest family of RBPs in chloroplasts are the pentatricopeptide repeat proteins (PPR proteins), which interact specifically with one or few chloroplast transcripts . In addicpRNPs are characterized by their conserved domain structure. An N-terminal chloroplast import signal (transit peptide), which is cleaved off after transport into the chloroplast, is followed by an acidic domain and two RNA recognition motif (RRM) domains. cpRNPs are able to interact with different nucleic acids  ,4, but tPrediction algorithms for subcellular localization and shotgun proteome analysis identified all ten cpRNPs of Arabidopsis in the chloroplast [summarized in 2]. Fluorescence microscopy of GFP fusion proteins confirmed the chloroplast localization ,12,13,14The expression of cpRNPs is regulated by various external and internal signals. Light especially leads to an accumulation of cpRNPs [summarized in 2]. In general, cpRNPs are involved in a variety of posttranscriptional processes, including 3’-end processing of RNAs , RNA edipsbA, which codes for the D1 protein, the core subunit of photosytem II [psbA mRNA translation. Consistently, a number of proteins have been co-purified with the psbA mRNA in Chlamydomonas, spinach, Arabidopsis and maize, and or have been identified by genetic analyses [psbA mRNA was CP33B (AT2G35410) [psbA mRNA and does show comparatively little binding to other mRNAs [A particularly interesting case of chloroplast gene regulation is the light-induced translation of sytem II ,21,22,23sytem II ,25,26. Manalyses ,33,34,352G35410) . In conter mRNAs . We herepsbA mRNA [Arabidopsis CP33B is predicted to be a chloroplast protein based on algorithms such as Predotar and TargetP . This issbA mRNA . We analThe stroma localization of CP33B was further analyzed by fluorescence microscopy. For this, the coding sequence of CP33B was fused with a GFP tag and transiently expressed in protoplasts. The GFP fluorescence signal is mainly diffusely distributed in chloroplasts, with a partial overlap with chlorophyll autofluorescence a–f. RBPspsbA [psbA mRNA  that CP33B associates with a number of chloroplast mRNAs, but has a preference for psbA . We valipsbA . RIP-chisbA mRNA a. The miitations . In sum,psbA in CP33B RIP-chip experiments is remarkable given that this mRNA is the most abundant mRNA in chloroplasts. The RIP-chip can, however, only give relative insights into RNA quantification, which prompted us to use dot-blot, a semi-quantitative method to determine how much psbA mRNA is pulled down with CP33B. RNA from pellets and supernatants of CP33B precipitates from wt and from cp33b null mutant stroma preparations were blotted onto nylon membranes and probed with radiolabeled RNA probes. Importantly, equal fractions of supernatant and pellet volumes were blotted, which allows for a direct assessment of the efficiency of co-precipitation. Next to probes for several transcripts identified as targets of CP33B in RIP-chip analyses, we also used probes for two transcripts not enriched in an RIP-chip assay, psbF and the 23S rRNA. The hybridization confirmed that these two control RNAs do not co-precipitate with CP33B . Thus, the vast majority of psbA transcripts in the stroma was associated with CP33B. About half of the psaC and ndhK transcripts co-precipitated with CP33B. The rbcL mRNA showed the lowest enrichment of all transcripts analyzed. As a caveat, it needs to be mentioned that RNA degradation occurs during the RIP-procedure. Thus, the enrichment observed reflects only CP33B’s association with the surviving RNA pool. Overall, the dot blot analyses confirmed the results of the RIP chip experiments and demonstrated that a majority of all psbA transcripts is associated with CP33B.The peak signal for th CP33B c. For alpsbA is determined. One explanation would be a target sequence motif that distinguishes the psbA mRNA from other chloroplast mRNAs. To test the RNA sequence preference of CP33B, we used the RNA-Bind-n-Seq (RBNS) assay (We next asked how the specificity of CP33B for S) assay A. RBNS ikmers of selecetd lengths within the sequenced 40mers as the ratio of the frequency of the kmer in the sample pool to the frequency in the input pool. For CP33B, a large number of 6, 7, 8, and 9mer motifs had significant R values  in the library with the highest enrichment (100 nM sample) were used to generate a consensus motif [psbA mRNA. However, when performing sequence searches for derivatives of the top motif, there were dozens of hits throughout the chloroplast genome, but only one hit in the sense direction of the psbA coding region  membranes  . In contArabidopsis thaliana were investigated in greater detail. We confirmed that CP33B associates with a larger number of chloroplast mRNAs. Similar to CP31A and CP29A [atpH, psbC/D, and psaA/B are also targets of the three other cpRNPs, CP31A, CP29, and CP33A, analysed before [cp29a, cp31a, and cp33b mutants, respectively, under normal conditions, where the loss of just one cpRNPs might be compensated by other members of the family.In this study, the RNA targets of CP33B from nd CP29A , CP33B sd before ,8. PossipsbA, with an enrichment of 90% in precipitates as calculated from autoradiograph signals in the pellet versus supernatant fractions. The psbA mRNA is not, or is only a weak, target of CP33A, CP29A, and CP31A [psbF or rbcL, of which more than 50% co-precipitate with CP33A [psbF), or are weak (rbcL), interaction partners of CP33B. How specificity is generated remains unclear. Our RBNS analysis uncovered a sequence motif as prime target that is not enriched in psbA versus other chloroplast transcripts. It does, however, prove that CP33B is capable of binding RNA by itself, without the need for auxiliary proteins, which is in line with previous electro mobility shift assays of cpRNPs [psbA mRNA are the two RBPs CP33C and SRRP1, which were co-purified in precipitations of the psbA mRNA, together with CP33B, although a direct interaction of these proteins was not demonstrated [psbA and to foster translation of this mRNA [psbA might be generated by a larger complex of these three or more RBPs. The three proteins plus CP33B could together form an extended RNA-binding interface that would allow the specific recognition of psbA. CP33B is recruited by one or several of these RBPs and does not contribute to psbA specificity at all.Next to these commonalities in RNA targets, there are also striking differences for a selected low number of targets. Most strikingly, CP33B has a clear preference for a single mRNA, nd CP31A ,8. Vice f cpRNPs . Still, f cpRNPs . For exaf cpRNPs . Candidahis mRNA . SpeculapsbA is recognized by CP33B, it is remarkable that almost the entire psbA mRNA pool that survives the RIP procedure is associated with this protein. Given that psbA is the most abundant chloroplast mRNA, with an estimated 14.000 molecules per chloroplast [psbA mRNA, should be present in excess. psbA, like many of the mRNA targets of CP33B, encodes a photosynthetic membrane protein named D1. Such mRNAs, when translated, become tethered to membranes via nascent peptide chains being inserted co-translationally into the membrane [psbA, according to our oligo-RIP-chip results. This is supported by RBNS data, which point to a sequence motif also found within the coding region of psbA. Binding to the coding region is likely facilitated by the absence of translating ribosomes that would be expected to remove obstacles like CP33B via their intrinsic helicase activity. Thus, translation would decrease the association of CP33B with psbA and this would occur on membranes. This idea is supported by our finding that CP33B prefers stromal psbA over membrane-tethered psbA. We therefore speculate that CP33B’s main role is centered on ribosome-free, stromal psbA. It is however puzzling that mutants of CP33B do not display defects in psbA accumulation or translation [psbA mRNA.While presently unclear how specifically oroplast , CP33B nmembrane . CP33B bnslation . RedundaAs mentioned above, cpRNPs interact with many mRNAs, but where exactly does binding take place within a target transcript and can binding motifs possibly be identified? In order to answer these questions, the binding sites of CP33B should be described in more detail. In the past, the binding motifs of the splicing factor MatK  and varipsbA, ndhB, psaA/psaB/rps14, ndhF and psbD/psbC) was noted. This is in line with the finding that the intact psbA transcript can be recovered in CP33B IPs, as evidenced by RNA gel blot hybridization [However, after performing CP33B RIP-chip analysis and hybridization on an oligonucleotide microarray, no enrichment of isolated short-sequence sections occurred, but rather an enrichment of almost all transcript sections of the target RNAs studied  was obtained from the ABRC  and grown together with wt A. thaliana (ecotype Columbia-0) on soil with a 16-h light/8-h dark cycle at 23 °C.XhoI and NcoI as a translational fusion with GFP under the control of a 35S promoter. The PEND-dsRed construct, under constitutive control of the ubiquitin promoter (pUbi), was kindly provided by Dr R. Lorbiecke and Dr J. Kluth . Mesophyll protoplast preparation and transfection with CP33B:GFP- and PEND-dsRed-fusion constructs was performed as described [OligonucleotideSequence33B.fw.xhoI.TPATACTCGAGATGGCGGTTTTGGAAGC33B.rev.ncoI.cDNAATACCATGGCCACAATGTTTTCTTCGA full-length cDNA sequence of CP33B was amplified by PCR using with oligonucleotides 33B.fw.xhoI.TP and 33B.rev.ncoI.cDNA and cloned via escribed . A Zeiss32P-UTP.Oligo NameSequenceTarget GeneCK_atpH5‘_forGATTTAGATAGGGATTCGATTAGatpHCK_atpH5‘_T7_revGTAATACGACTCACTATAGGGTCCAGGTCCAATAGAAGCAAGatpHCk_psbD_forCTATTTCGCTTTAGGGGGTTGGpsbDCk_psbD_T7_revTAATACGACTCACTATAGGGCAGCAATGGGACCAGAGAATGCpsbDCK_rbcL_forGCAGCATTCCGAGTAACTCCrbcLCK_rbcL_T7GTAATACGACTCACTATAGGGCCACGTAGACATTCATAAACTGCrbcLCK-petB-forCGTCCAACCGTTACTGAAGCpetBCK-petB-T7-revGTAATACGACTCACTATAGGGAATAGCGTCAGGTACACCpetBCK-psbF-forGTCTGGAAGCACAGGAGAACGpsbFCK-psbF-T7-revGTAATACGACTCACTATAGGGCAAAACGGCCTGTTATTAATGGpsbFndhBex1.rpCCGATGGAGAGAAGAACCTATGndhBndhBex1.T7GTAATCGACTCACTATAGGGTAGCCAAGAGAAACCATGAACCndhBndhK.rpCTATGGCCGCTTCTTTATGGndhKndhK.T7GTAATCGACTCACTATAGGGATTTCCAATATGCGGACTGCndhKpsbA.rpCATTCATTGCTGCTCCTCCAGpsbApsbA.t7neuGTAATCGACTCACTATAGGGATTCCTAGAGGCATACCATCAGpsbApsbC.rpAGTGGCCCATTTTGTACCTGpsbCpsbC.T7GTAATCGACTCACTATAGGGCCCCCAAAGGGAGATTTTAGpsbCrrn23.rpCCTAGATGGCGAGAGTCCAGrrn23rrn23.T7GTAATCGACTCACTATAGGGAAGACTCGCTTTCGCTACGrrn23Dot-blot production was described previously . RadiolaThe production and specificities of the CP33B antisera were reported previously . The ant2, 0,5% (v/v) NonidetP-40, 5 μg/mL Aprotinin) and incubated with anti CP33B antisera. All further procedures including data evaluation were carried out analogous to that of the Stroma RIP-chip.CP33B was immunoprecipitated from Arabidopsis chloroplast stroma and co-precipitating RNAs were purified and hybridized to a whole-chloroplast-genome tiling array or a oligonucleotide array, respectively, as previously described . The hybpGEX-6P-1 vector downstream of the GST tag and the PreScission cleavage site of the vector via BamHI and EcoRI restriction sites. The resulting vector was named pGEX-SBP. The coding sequence of CP33B was amplified from cDNA without the predicted signal peptide and cloned into the pGEX-SBP vector via MfeI and XhoI sites using the following oligonucleotides.OligonucleotideSequenceCP33BXhoIrev AATTCTCGAGTCACTCCACAATGTTTTCTTCGG MfeICP33BforAATCAATTGGTCTCCGTACTCTGTTCCGTCA streptavidin-binding protein (SBP) tag was introduced into the The RBNS random RNA input for the RNA Bind-n-Seq experiment was prepared by in vitro transcription of the RBNS T7 template, a DNA oligo containing a 40mer random sequence flanked by primer sites for the Illumina adapters and a T7 promoter sequence . To prodOligonucleotideSequenceRBNS T7 Template CCTTGACACCCGAGAATTCCA -(N)40- GATCGTCGGACTGTAGAACTCCCTATAGTGAGTCGTATTA RNA PCR (RP1) AATGATACGGCGACCACCGAGATCTACACGTTCAGAGTTCTACAGTCCGACGATC RT primer GCCTTGGCACCCGAGAATTCCA T7 Promoter Oligo TAATACGACTCACTATAGGG The RBNS Input Oligo Pool was transcribed with T7 RNA Polymerase. DNA was removed using 2 U TURBODNase (Life Technologies) for 5 min at 37 °C. The reaction was stopped by adding 1.5 μL EDTA 0.5 M and incubation at 75 °C for 10 min. The in vitro transcribed RNA were purified by gel extraction and concentrated by ethanol precipitation.SampleNEXTflex Barcode Primer SequenceInput CGTGATGTGACTGGAGTTCAGACGT- GTGCTCTTCCGATC-s-T-3 ′ 5 ′-CAAGCAGAAGACGGCATACGAGAT- 0 protein sampleTGTAGCCGTGACTGGAGTTCAGACGT- GTGCTCTTCCGATC-s-T-5 ′- CAAGCAGAAGACGGCATACGAGA- 100 nM CP33B TCTGATCGTGACTGGAGTTCAGACGT- GTGCTCTTCCGATC-s-T-3 ′ 5 ′-CAAGCAGAAGACGGCATACGAGA- 1000 nM CP33B AAGCTAGTGACTGGAGTTCAGACGT- 5 ′-CAAGCAGAAGACGGCATACGAGAT- Two different concentrations of SBP-CP33B, 100 and 1000 nM, were equilibrated in 250 μL binding buffer  at 22 °C for 30 min. In addition, a sample with binding buffer only (zero protein sample) was incubated in the same way. The RBNS Input Random RNA was then added to a final concentration of 0.5 μM and incubated for 3 h at 22 °C. Subsequently, streptavidin-coated magnetic beads (Invitrogen) were added and the bead-sample mixture was incubated for 1 h at room temperature. After magnetic separation, the beads were washed with 1 mL Wash Buffer  and then incubated at 70 °C for 10 min in 100 μL Elution Buffer . RNA from the eluates were purified using the RNA Clean & Concentrator Kit by Zymo Research . Half of the purified RNA from each protein concentration and the input RNA pool was reverse-transcribed using the ProtoScript II reverse transcriptase using the RT primer and subsequently amplified by PCR using the Q5 High-Fidelity DNA polymerase and the RP1 primer (see above) plus barcoded primers. The library was sequenced on a NextSeq 500 (Illumina) at LGC (Berlin). For analysis and motif detection, we used the published bioinformatic pipeline ,41,51Sa


Group-B enteroviruses (EV-B) are ubiquitous naked single-stranded positive RNA viral pathogens that are responsible for common acute or persistent human infections. Their genome is composed in the 5′ end by a non-coding region, which is crucial for the initiation of the viral replication and translation processes. RNA domain-I secondary structures can interact with viral or cellular proteins to form viral ribonucleoprotein (RNP) complexes regulating viral genomic replication, whereas RNA domains-II to -VII  are known to interact with cellular ribosomal subunits to initiate the viral translation process. Natural 5′ terminally deleted viral forms lacking some genomic RNA domain-I secondary structures have been described in EV-B induced murine or human infections. Recent in vitro studies have evidenced that the loss of some viral RNP complexes in the RNA domain-I can modulate the viral replication and infectivity levels in EV-B infections. Moreover, the disruption of secondary structures of RNA domain-I could impair viral RNA sensing by RIG-I (Retinoic acid inducible gene I) or MDA5 (melanoma differentiation-associated protein 5) receptors, a way to overcome antiviral innate immune response. Overall, natural 5′ terminally deleted viral genomes resulting in the loss of various structures in the RNA domain-I could be major key players of host–cell interactions driving the development of acute or persistent EV-B infections. Group-B enteroviruses (EV-B) are common human pathogens responsible for a wide range of pathologies such as myocarditis, pancreatitis, hepatitis, or meningitis resulting from acute infections in children and young adults ,2. Some Picornaviridae family [Enterovirus genus is composed of 15 species, classified according to their biological properties and phylogenetic analyses, seven of which are human viruses: EV-A to -D species and three rhinoviruses species (RV-A to -C) (EV-B belong to the e family  and the A to -C)  [9,10,11A to -C) A with a A to -C) B. The sepro) and 3C proteases (3Cpro) into structural viral proteins 1–4 (VP1–4) and non-structural proteins including the RNA polymerase [The viral replication process is well-conserved among EV species . Followilymerase . The sinEnteroviral RNA genomes are known to mutate more easily than DNA viruses, thus leading to mutated and deleted forms of the viral RNA including the 5′-terminally deleted (5′ TD) viral genomes. Natural 5′ TD viral genomes resulting in the loss of different structures ranging from stem “a”, to stem-loop “b” or “c” or part of stem-loop “d” in the 5′ terminal Cloverleaf RNA structure (domain-I) were described in in vitro and in vivo experimental models of EV-B infections. Recently, EV-B populations with 5′ terminal genomic RNA deletions (5′ TD) similar to those described in experimental models were associated with the development of myocarditis or DCM human cases ,17. ThesReported natural deletions within the 5′ NCR of EV-B forms are known to affect functional secondary-structural elements of the RNA domain-I (Cloverleaf-like structure) . The losThe present article reviews the importance of secondary structural elements of 5′ terminal EV-B RNA domain-I for the formation of viral RNP complexes regulating viral genome replication and also for the activation of type I IFN signaling pathway by RLRs  sensors. The present review deciphers functional consequences of the natural loss of some viral 5′ terminal RNA domain-I secondary structures on the viral RNA replication and on the type I IFN pathway activation in EV-B infected human cells. Overall, natural 5′ TD viral genomes resulting in the loss of different structures in the RNA domain-I could be major key players of host–cell interactions driving the development of acute or persistent infections in human target cells.” and three stem-loops “b”, “c”, and “d” and commonly named Cloverleaf-like structure or RNA domain-I  or domain-I, which is crucial for the initiation of the genomic replication for both viral positive- and negative-strands RNA. Enteroviral RNA domain-I presents a conserved structural pattern among EV-B organized in one stem “adomain-I B,C. The domain-I A [21,22,domain-I A. EnteroEnteroviral RNA secondary structure domain-I interacts with viral and cellular proteins to form viral RNP complexes mediating viral genome replication mechanisms in infected cells . The Clopol) into an RNA of negative polarity , which will be used as a template for the synthesis of new viral RNA of positive-polarity that can be translated in turn  interacts with the stem-loop “d”, while PABP binds to the poly(A) located at the end of the 3′ UTR [pro interacts with the cre, which is a RNA stem-loop element in the 2C coding region [pol uridylylation of the viral protein VPg [pol exercises its RNA-dependent RNA polymerase activity using the uridylylated VPg as a primer of negative-strand RNA synthesis  binding proteins 1 and 2 (PCBP1/2) and poly(A) binding proteins (PABP) are major key factors of the viral minus-strands RNA synthesis . Brieflye 3′ UTR ,43  as previously defined with other RNA viruses such as influenza viruses, Sindbis virus, or Sendai virus ,118. DVGpro to the 5′ end of various deleted forms of positive-strand RNA was qualitatively conserved for the 5′ TD viruses independently of the size of the deletion. However, the stability or protein binding affinity of the RNP complexes decreased with the increase in size of the deletion [Partial deletions of the 5′ RNA domain-I sequences significantly disrupt the binding sites of proteins involved in the EV-B RNA synthesis possibly providing a molecular explanation for the low levels of 5′ TD RNA replication in target cells . Investideletion   at 50–60% efficiency compared to that observed with the full-length viral RNA [pro whose levels impact on cell host protein synthesis machinery by cleavage of major host cell proteins as eukaryotic translation initiation factor 4G (eIF4G) [Despite different losses of secondary structures and binding sites of viral or host cell proteins in different known viral RNP complexes, natural 5′ TD forms up to 50 nt show a conservation of the stem-loop “d” structure allowing PCBP2 binding to C-rich sequence spacer 1, which is sufficient to support positive and negative strand synthesis at low levels  Figure C,D. An uiral RNA . Overalliral RNA . These diral RNA . In cult (eIF4G) ,125,126. (eIF4G) . Picornaviridae family and more specifically group-B Enteroviruses, including group-B Coxsackieviruses [The innate immune system plays an essential role in pathogen recognition and initiation of the immune response through the recognition of pathogen-associated molecular profiles  by its Pattern Recognition Receptors (PRRs). The viral nucleic acids, RNAs, or DNAs are recognized as PAMPs. PRRs are essential elements in the detection and induction of the innate response to pathogens including viruses. Recently various PRRs sensors of immunity were identified as Toll-Like Receptors (TLRs), RLRs including RIG-I, MDA5 and LGP2 (Laboratory of Genetics and Physiology 2), and NOD-Like Receptors (NLRs). RLRs have a conserved domain called CTD , which is responsible for attachment specificity to viral RNAs, whether they are double-stranded RNAs, single-stranded RNAs, or RNAs with a tri-phosphate group at the 5′ end. This C-terminal domain is a repressor domain (RD) which inactivates the caspase recruitment domains (CARD domains) and whose negative regulatory function has only been observed for the RIG-I and LGP2 receptors. The recognition specificity of virus families makes it possible to characterize RLRs; RIG-I is activated by numerous viruses such as Paramyxoviruses, Orthomyxoviruses, Rhabdoviruses, Flaviviruses, and Hepatitis C virus, while MDA5 mainly recognizes viruses of the eviruses ,128. In pro activity results in the disruption of MDA5 structures, even though some authors discuss that such MDA5 or mitochondrial antiviral signaling protein (MAVS) cleavage may take place in a proteasome-dependent or activated caspase-3 pathway [pro of EV-B can cleave various intermediate protein factors of the type I IFN pathway [pro activity can mediate the cleavage of key molecules such as transforming growth factor-β activated kinase 1 (TAK1)/TGF-β Activated Kinase 1 (TAB1)/TAB2/TAB3 complex which are known to activate the Nuclear factor kappa B pathway inducing a pro-inflammatory cytokine expression [pro activity was described as capable to cleave Interferon Regulatory Factor 7 (IRF-7) inhibiting its ability to transactivate IFN-β expression by its phosphorylated form during early phase of viral infection [A reported strategy developed by EV-B to overcome antiviral immune response is to cleave or partially disrupt RLRs MDA5 receptor during EV-B infection by viral proteases activities, therefore impairing the innate immune sensing of viral RNA double-strand forms ,131,132  pathway ,133,134. pathway  includin pathway . Indeed,pression  , an absence of detected infectious particle by classical culture assays, a plus- to minus-strand viral RNA ratio <5 and the detection of a viral protein synthesis activity [RNA viruses including picornaviruses have been shown to develop persistent infections in human target tissues ,139,140.activity . EmergenEmergence of 5′ TD viral populations has been proposed as a molecular mechanism for enterovirus persistence in vivo ,140. TheMaintenance of the viral genome is a key step to establish viral RNA persistence in target cells . 5′ TD RThe 5′ NCR of EV-B is involved in key steps of the viral life cycle. The recruitment by RNA domain-I of host cell and viral proteins in RNP complexes is essential for viral translation and genomic replication processes. Natural deletions within the 5′ NCR of EV-B forms affect functional secondary-structural elements of the RNA domain-I. The loss of these secondary structures of RNA Cloverleaf reduces the binding of viral and cellular proteins implicated in EV-B genome replication mechanisms, therefore modulating the viral fitness and infectivity of viral populations. Early emergence of 5′ TD viral populations and their maintenance in infected cells have been recently proposed as a potential molecular mechanism driving the development of EV-B human acute and persistent infections ,17. FurtIn addition, to disturb viral genome RNA replication processes, 5’NCR deletions could impair the viral RNA recognition by RLRs, inducing the modulation of type I IFN expression, specifically by regulating IFN-β and ISG transcription and translation levels in target cells . Other uFinally, published reports and ongoing scientific investigations highlight the importance of secondary structures of RNA domain-I and related functional RNP complexes in the EV-B infectious cycle and related host cell immune response. Overall, natural 5′ TD viral genomes resulting in the loss of different structures in the RNA domain-I could be major key players of host–cell interactions driving the development of acute or persistent EV-B infections in human target cells.