Search

Long non-coding RNAs (lncRNAs) are a heterogeneous group of transcripts that lack protein coding potential and display regulatory functions in various cellular processes. As a result of their cell- and cancer-specific expression patterns, lncRNAs have emerged as potential diagnostic and therapeutic targets. The accurate characterization of lncRNAs in bulk transcriptome data remains challenging due to their low abundance compared to protein coding genes. To tackle this issue, we describe a unique short-read custom lncRNA capture sequencing approach that relies on a comprehensive set of 565,878 capture probes for 49,372 human lncRNA genes. This custom lncRNA capture approach was evaluated on various sample types ranging from artificial high-quality RNA mixtures to more challenging formalin-fixed paraffin-embedded tissue and biofluid material. The custom enrichment approach allows the detection of a more diverse repertoire of lncRNAs, with better reproducibility and higher coverage compared to classic total RNA-sequencing.

Understanding the molecular composition of pathogenic tissues is a critical step in understanding the pathophysiology of disease and designing therapeutics. First described in 2009, single cell RNA sequencing (scRNAseq) is a methodology whereby thousands of cells are simultaneously isolated into individual micro-environments that can be altered experimentally and the genome-wide RNA expression of each cell is captured. It has undergone significant technological improvement over the last decade and gained tremendous popularity. scRNAseq is an improvement over prior pooled RNA analyses which cannot identify the cellular composition and heterogeneity of a tissue of interest. This new approach offers new opportunity for new discovery, as tissue samples can now be sub-categorized into groups of cell types based on genome-wide gene expression in an unbiased fashion. As ophthalmologists, we are uniquely positioned to obtain pathologic samples from the eye for further study. ScRNAseq has already been applied in ophthalmology to characterize retinal tissue, and it may offer the key to understanding various pathological processes in the future.

In the era of precision medicine, accurate and reproducible assignment of cell-of-origin (COO) in diffuse large B-cell lymphoma patients has become important. The Lymph2Cx assay is accurately determining COO by analyzing RNA expression of 20 selected genes while the Hans algorithm based on immunohistochemistry is the most popular method for routine daily diagnosis. However, there are discrepancies between the 2 methods, which need to be evaluated for better correlation. We prospectively analyzed 156 cases of diffuse large B-cell lymphoma, not otherwise specified to analyze the characteristics of discrepancy groups of COO determined by Lymph2Cx and Hans algorithm. We investigated the pattern and cause of discrepancy of COO assigned by the 2 methods. Hans algorithm classified 50 cases (32%) as germinal-center B-cell-like (GCB) type and 106 cases (68%) as non-GCB type. Lymph2Cx assay assigned 43 cases (28%) as GCB type, 94 cases (60%) as activated B-cell-like type, and 19 cases (12%) as intermediate/unclassified type. The agreement rate was 86% after exclusion of unclassified type. With regard to the clinicopathologic factors related with discrepancy between Hans algorithm and Lymph2Cx assay, endoscopic biopsy of the gastrointestinal tract (4/11, 36%) showed higher discrepancy rate (P=0.052). Immunophenotypically, CD10/BCL6/MUM1 GCB type and CD10/BCL6//MUM1 (=30%, low level expression) non-GCB type exhibited a significantly higher discrepancy rate (6/13, 46%; 4/13, 31%) (P=0.0001). Activated B-cell-like subgroup via Lymph2Cx assay predicted poor progression-free survival (mean survival duration 28.6 mo, P=0.049) compared with the GCB and unclassified type. Hans algorithm revealed no significant difference in progression-free survival and overall survival (P=0.122 and 0.121). These results suggest that when assigning COO via Hans algorithm, CD10/BCL6/MUM1 GCB type and CD10/BCL6/MUM1 (=30%, low level) non-GCB type require careful interpretation, especially if the MUM1 staining is weak and heterogeneous in the biopsied specimen.

Interfering RNA technology has been established as an effective strategy to protect plants against viral infection. Despite this success, interfering RNA (RNAi) has rarely been applied due to the regulatory barriers that confront genetically engineered plants and concerns over possible environmental and health risks posed by non-endogenous small RNAs. ‘HoneySweet’ was developed as a virus-resistant plum variety that is protected by an RNAi-mediated process against Sharka disease caused by the plum pox virus. ‘HoneySweet’ has been approved for cultivation in the United States but not in countries where the plum pox virus is endemic. In this study, we evaluated the long-term efficacy of virus resistance in ‘HoneySweet,’ the nature and stability of its sRNA profile, and the potential health risks of consuming ‘HoneySweet’ plums. Graft-challenged ‘HoneySweet’ trees carrying large non-transgenic infected limbs remained virus-free after more than 10 years in the field, and the viral sequences from the non-transgenic infected limbs showed no evidence of adaptation to the RNAi-based resistance. Small RNA profiling revealed that transgene-derived sRNA levels were stable across different environments and, on average, were more than 10 times lower than those present in symptom-less fruits from virus-infected trees. Comprehensive 90-day mouse feeding studies showed no adverse health impacts in mice, and there was no evidence for potential siRNA off-target pathologies predicted by comparisons of the most abundant transgene-derived sRNAs to the mouse genome. Collectively, the data confirmed that RNAi provides a highly effective, stable, and safe strategy to combat virus diseases in crop plants.

We detected precise coordination of RNA levels between two latent genes of the Kaposi sarcoma-associated herpesvirus (KSHV) using single-cell RNA sequencing. LANA and vIL6 are expressed during latency by different promoters on remote regions of the episome.….

Classifying diffuse large B-cell lymphoma (DLBCL) according to the cell-of-origin (COO) was first proposed using gene expression profiling; accordingly, DLBCL is classified into germinal-center B-cell type and activated B-cell type. Immunohistochemistry (IHC)-based classification using different algorithms is used widely due to the ability to use formalin-fixed paraffin-embedded tissue. Recently, newer techniques using RNA expression from formalin-fixed paraffin-embedded were introduced including the nCounter NanoString platform assay. In this brief report, we study the degree of concordance between the NanoString assay and 6 commonly utilized IHC-based algorithms to classify DLBCL cases by COO. Stains for CD10, BCL2, BCL6, FOXP-1, MUM-1, and LOM2 were used to classify a cohort of DLBCL by COO according to the respective IHC-algorithms. Then, RNA was extracted from the same cases for NanoString assay classification. The degree of concordance was calculated between the NanoString classification and each IHC-algorithm as well as among the different IHC-algorithm themselves. The concordance in COO classification of DLBCL between NanonoString assay and IHC-based algorithms is variable depending on the used IHC-algorithm; the highest concordance is seen with the Visco algorithm (κ=0.69; P=0.001). Therefore, discrepancies between the recently introduced NanoString assay and the commonly utilized IHC-algorithms are expected to some extent and should be taken into consideration when interpreting conflicting results.

Motivation Next-generation sequencing techniques revolutionized the study of RNA expression by permitting whole transcriptome analysis. However, sequencing reads generated from nested and multi-copy genes are often either misassigned or discarded, which greatly reduces both quantification accuracy and gene coverage. Results Here we present count corrector (CoCo), a read assignment pipeline that takes into account the multitude of overlapping and repetitive genes in the transcriptome of higher eukaryotes. CoCo uses a modified annotation file that highlights nested genes and proportionally distributes multimapped reads between repeated sequences. CoCo salvages over 15% of discarded aligned RNA-seq reads and significantly changes the abundance estimates for both coding and non-coding RNA as validated by PCR and bedgraph comparisons. Availability and implementation The CoCo software is an open source package written in Python and available from http://gitlabscottgroup.med.usherbrooke.ca/scott-group/coco. Supplementary information Supplementary data are available at Bioinformatics online.

BACKGROUND The aims of this study were to use RNA expression profile bioinformatics data from cases of thyroid cancer from the Cancer Genome Atlas (TCGA), the Kyoto Encyclopedia of Genes and Genomes (KEGG), and the Gene Ontology (GO) databases to construct a competing endogenous RNA (ceRNA) network of mRNAs, long noncoding RNAs (lncRNAs), and microRNAs (miRNAs). MATERIAL AND METHODS TCGA provided RNA profiles from 515 thyroid cancer tissues and 56 normal thyroid tissues. The DESeq R package analyzed high-throughput sequencing data on differentially expressed RNAs. GO and KEGG pathway analysis used the DAVID 6.8 and the ClusterProfile R package. Kaplan-Meier survival statistics and Cox regression analysis were performed. The thyroid cancer ceRNA network was constructed based on the miRDB, miRTarBase, and TargetScan databases. RESULTS There were 1,098 mRNAs associated with thyroid cancer; 101 mRNAs were associated with overall survival (OS). Multivariate analysis developed a risk scoring system that identified seven signature mRNAs, with a discriminative value of 0.88, determined by receiver operating characteristic (ROC) curve analysis. A ceRNA network included 13 mRNAs, 31 lncRNAs, and seven miRNAs. Four out of the 31 lncRNAs and all miRNAs were down-regulated, and the remaining RNAs were upregulated. Two lncRNAs (MIR1281A2HG and OPCML-IT1) and one miRNA (miR-184) were significantly associated with OS in patients with thyroid cancer. CONCLUSIONS Differential RNA expression profiling in thyroid cancer was used to construct a ceRNA network of mRNAs, lncRNAs, and miRNAs that showed potential in evaluating prognosis.

Genomic prediction typically relies on associations between single-site polymorphisms and traits of interest. This representation of genomic variability has been successful for predicting many complex traits. However, it usually cannot capture the combination of alleles in haplotypes and it has generated little insight about the biological function of polymorphisms. Here we present a novel and cost-effective method for imputing cis haplotype associated RNA expression (HARE), studied their transferability across tissues, and evaluated genomic prediction models within and across populations. HARE focuses on tightly linked cis acting causal variants in the immediate vicinity of the gene, while excluding trans effects from diffusion and metabolism. Therefore, HARE estimates were more transferrable across different tissues and populations compared to measured transcript expression. We also showed that HARE estimates captured one-third of the variation in gene expression. HARE estimates were used in genomic prediction models evaluated within and across two diverse maize panels–a diverse association panel (Goodman Association panel) and a large half-sib panel (Nested Association Mapping panel)–for predicting 26 complex traits. HARE resulted in up to 15% higher prediction accuracy than control approaches that preserved haplotype structure, suggesting that HARE carried functional information in addition to information about haplotype structure. The largest increase was observed when the model was trained in the Nested Association Mapping panel and tested in the Goodman Association panel. Additionally, HARE yielded higher within-population prediction accuracy as compared to measured expression values. The accuracy achieved by measured expression was variable across tissues, whereas accuracy by HARE was more stable across tissues. Therefore, imputing RNA expression of genes by haplotype is stable, cost-effective, and transferable across populations., Author summary Genomic marker data is widely used in the prediction of many traits. However, prediction has been primarily carried out within populations and without explicit modeling of RNA or protein expression. In this study, we explored the prediction of field traits within and across populations using estimated RNA expression attributable to only the DNA sequence around a gene. We showed that the estimated RNA expression was more transferable across populations and tissues than measured RNA expression. We improved prediction of field traits up to 15% using estimated gene expression as compared to observed expression or gene sequence alone. Overall, these findings indicate that structural and functional information in the gene sequence is highly transferable.

Tracking the expression of RNA in a cell-specific manner is a major challenge in basic and disease research. Herein we outline the current state of employing chemical approaches for cell-specific RNA expression studies. We define the utility of metabolic labels for tracking RNA synthesis, the approaches for characterizing metabolic incorporation and enrichment of labeled RNAs, and finally outline how these approaches have been used to study biological systems by providing mechanistic insights into transcriptional dynamics. Further efforts on this front will be the continued development of novel chemical handles for RNA enrichment and profiling as well as innovative approaches to control cell-specific incorporation of chemically modified metabolic probes. These advancements in RNA metabolic labeling techniques permit sensitive detection of RNA expression dynamics within relatively small subsets of cells in living tissues and organisms that are critical to performing complex developmental and pathological processes. This article is categorized under: RNA Methods > RNA Analyses in Cells RNA Evolution and Genomics > Ribonomics RNA Structure and Dynamics > RNA Structure, Dynamics and Chemistry.

MotivationCancers are composed by several heterogeneous subpopulations, each one harbouring different genetic and epigenetic somatic alterations that contribute to disease onset and therapy response. In recent years, copy number alterations leading to tumour aneuploidy have been identified as potential key drivers of such populations, but the definition of the precise makeup of cancer subclones from sequencing assays remains challenging. In the end, little is known about the mapping between complex copy number alterations and their effect on cancer phenotypes.ResultsWe introduce CONGAS, a Bayesian probabilistic method to phase bulk DNA and single-cell RNA measurements from independent assays. CONGAS jointly identifies clusters of single cells with subclonal copy number alterations, and differences in RNA expression. The model builds statistical priors leveraging bulk DNA sequencing data, does not require a normal reference and scales fast thanks to a GPU backend and variational inference. We test CONGAS on both simulated and real data, and find that it can determine the tumour subclonal composition at the single-cell level together with clone-specific RNA phenotypes in tumour data generated from both 10x and Smart-Seq assays.AvailabilityCONGAS is available as 2 packages: CONGAS (https://github.com/caravagnalab/congas), which implements the model in Python, and RCONGAS (https://caravagnalab.github.io/rcongas/), which provides R functions to process inputs, outputs, and run CONGAS fits. The analysis of real data and scripts to generate figures of this paper are available via RCONGAS; code associated to simulations is available at https://github.com/caravagnalab/rcongas_test.Contactgcaravagna@units.itSupplementary informationSupplementary data are available at Bioinformatics online.

Kaposi’s sarcoma-associated herpesvirus (KSHV) induces life-long infections and has evolved many ways to exert extensive control over its host’s transcriptional and post-transcriptional machinery to gain better access to resources and dampened immune sensing. The hallmark of this takeover is how KSHV reshapes RNA fate both to control expression of its own gene but also that of its host. From the nucleus to the cytoplasm, control of RNA expression, localization, and decay is a process that is carefully tuned by a multitude of factors and that can adapt or react to rapid changes in the environment. Intriguingly, it appears that KSHV has found ways to co-opt each of these pathways for its own benefit. Here we provide a comprehensive review of recent work in this area and in particular recent advances on the post-transcriptional modifications front. Overall, this review highlights the myriad of ways KSHV uses to control RNA fate and gathers novel insights gained from the past decade of research at the interface of RNA biology and the field of KSHV research.

Motivation Long-read sequencing technologies are invaluable for determining complex RNA transcript architectures but are error-prone. Numerous ‘hybrid correction’ algorithms have been developed for genomic data that correct long reads by exploiting the accuracy and depth of short reads sequenced from the same sample. These algorithms are not suited for correcting more complex transcriptome sequencing data. Results We have created a novel reference-free algorithm called Transcript-level Aware Long-Read Correction (TALC) which models changes in RNA expression and isoform representation in a weighted De Bruijn graph to correct long reads from transcriptome studies. We show that transcript-level aware correction by TALC improves the accuracy of the whole spectrum of downstream RNA-seq applications and is thus necessary for transcriptome analyses that use long read technology. Availability and implementation TALC is implemented in C++ and available at https://github.com/lbroseus/TALC. Supplementary information Supplementary data are available at Bioinformatics online.

Long non-coding RNA (lncRNA)-mediated competitive endogenous RNA (ceRNA) networks act as essential mechanisms in tumor initiation and progression, but their diagnostic and prognostic significance in prostate cancer (PCa) remains poorly understood. Presently, using the RNA expression data derived from multiple independent PCa-related studies, we constructed a high confidence and PCa-specific core ceRNA network by employing three lncRNA-gene inference approaches and key node filter strategies and then established a logistic model and risk score formula to evaluate its diagnostic and prognostic values, respectively. The core ceRNA network consists of 10 nodes, all of which are significantly associated with clinical outcomes. Combination of expression of the 10 ceRNAs with a logistic model achieved AUC of ROC and PR curve up to ∼96 and 99% in excluding normal prostate samples, respectively. Additionally, a risk score formula constructed with the ceRNAs exhibited significant association with disease-free survival. More importantly, utilizing the expression of RNAs in the core ceRNA network as a molecular signature, the TCGA-PRAD cohort was divided into four novel clinically relevant subgroups with distinct expression patterns, highlighting a feasible way for improving patient stratification in the future. Overall, we constructed a PCa-specific core ceRNA network, which provides diagnostic and prognostic value.

There is no consensus as to how a precursor lesion, germ cell neoplasia in situ (GCNIS), develops into the histologic types of testicular germ cell tumor type II (TGCT). The present meta-analysis examined RNA expressions of 24 candidate genes in three datasets. They included 203 samples of normal testis (NT) and histologic types of TGCT. The Fisher&rsquo, s test for combined p values was used for meta-analysis of the RNA expressions in the three datasets. The histologic types differed in RNA expression of PRAME, KIT, SOX17, NANOG, KLF4, POU5F1, RB1, DNMT3B, and LIN28A (p &lt, 0.01). The histologic types had concordant differences in RNA expression of the genes in the three datasets. Eight genes had overlap with a high RNA expression in at least two histologic types. In contrast, only seminoma (SE) had a high RNA expression of KLF4 and only embryonal carcinoma (EC) had a high RNA expression of DNMT3B. In conclusion, the meta-analysis showed that the development of the histologic types of TGCT was driven by changes in RNA expression of candidate genes. According to the RNA expressions of the ten genes, TGCT develops from NT over GCNIS, SE, EC, to the differentiated types of TGCT.

Differences in RNA expression can provide insights into the molecular identity of a cell, pathways involved in human diseases, and variation in RNA levels across patients associated with clinical phenotypes. RNA modifications such as m6A have been found to contribute to molecular functions of RNAs. However, quantification of differences in RNA modifications has been challenging. Here we develop a computational method (xPore) to identify differential RNA modifications from direct RNA sequencing data. We evaluate our method on transcriptome-wide m6A profiling data, demonstrating that xPore identifies positions of m6A sites at single base resolution, estimates the fraction of modified RNAs in the cell, and quantifies the differential modification rate across conditions. We apply the method to direct RNA-Sequencing data from 6 cell lines and find that many m6A sites are preserved, while a subset of m6A sites show significant differences in their modification rates across cell types. Together, we show that RNA modifications can be identified from direct RNA-sequencing with high accuracy, enabling the analysis of differential modifications and expression from a single high throughput experiment.AvailabilityxPore is available as open source software (https://github.com/GoekeLab/xpore)

The NanoString RNA counting assay for formalin-fixed paraffin embedded samples is unique in its sensitivity, technical reproducibility and robustness for analysis of clinical and archival samples. While commercial normalization methods are provided by NanoString, they are not optimal for all settings, particularly when samples exhibit strong technical or biological variation or where housekeeping genes have variable performance across the cohort. Here, we develop and evaluate a more comprehensive normalization procedure for NanoString data with steps for quality control, selection of housekeeping targets, normalization and iterative data visualization and biological validation. The approach was evaluated using a large cohort ($N=\kern0.5em 1649$) from the Carolina Breast Cancer Study, two cohorts of moderate sample size ($N=359$ and$130$) and a small published dataset ($N=12$). The iterative process developed here eliminates technical variation (e.g. from different study phases or sites) more reliably than the three other methods, including NanoString’s commercial package, without diminishing biological variation, especially in long-term longitudinal multiphase or multisite cohorts. We also find that probe sets validated for nCounter, such as the PAM50 gene signature, are impervious to batch issues. This work emphasizes that systematic quality control, normalization and visualization of NanoString nCounter data are an imperative component of study design that influences results in downstream analyses.

MotivationLong-read sequencing technologies are invaluable for determining complex RNA transcript architectures but are error-prone. Numerous “hybrid correction” algorithms have been developed for genomic data that correct long reads by exploiting the accuracy and depth of short reads sequenced from the same sample. These algorithms are not suited for correcting more complex transcriptome sequencing data.ResultsWe have created a novel reference-free algorithm called TALC (Transcription Aware Long Read Correction) which models changes in RNA expression and isoform representation in a weighted De-Bruijn graph to correct long reads from transcriptome studies. We show that transcription aware correction by TALC improves the accuracy of the whole spectrum of downstream RNA-seq applications and is thus necessary for transcriptome analyses that use long read technology.Availability and ImplementationTALC is implemented in C++ and available athttps://gitlab.igh.cnrs.fr/lbroseus/TALC.Contactwilliam.ritchie@igh.cnrs.fr

Profiling RNA expression in a cell-specific manner continues to be a grand challenge in biochemical research. Bioorthogonal nucleosides can be utilized to track RNA expression; however, these methods currently have limitations due to background and incorporation of analogs into undesired cells. Herein, we design and demonstrate that uracil phosphoribosyltransferase can be engineered to match 5-vinyluracil for cell-specific metabolic labeling of RNA with exceptional specificity and stringency. University of California System United States Department of Health & Human Services National Institutes of Health (NIH) - USA (V1DP2GM119164) NIH Director's New Innovator Award (5T32CA009054-40) 5.100 JCR (2020) Q2, 87/295 Biochemistry & Molecular Biology 1.899 SJR (2020) Q2, 62/438 Biochemistry No data IDR 2020 UEM

Background Alzheimer’s disease (AD) is a complex disorder caused by a combination of genetic and non-genetic risk factors. In addition, an increasing evidence suggests that epigenetic mechanisms also accompany AD. Genetic and epigenetic factors are not independent, but multiple loci show genetic-epigenetic interactions, the so-called quantitative trait loci (QTLs). Recently, we identified the first QTL association with AD, namely Peptidase M20 Domain Containing 1 (PM20D1). We observed that PM20D1 DNA methylation, RNA expression, and genetic background are correlated and, in turn, associated with AD. We provided mechanistic insights for these correlations and had shown that by genetically increasing and decreasing PM20D1 levels, AD-related pathologies were decreased and accelerated, respectively. However, since the PM20D1 QTL region encompasses also other genes, namely Nuclear Casein Kinase and Cyclin Dependent Kinase Substrate 1 (NUCKS1); RAB7, member RAS oncogene family-like 1 (RAB7L1); and Solute Carrier Family 41 Member 1 (SLC41A1), we investigated whether these genes might also contribute to the described AD association. Results Here, we report a comprehensive analysis of these QTL genes using a repertoire of in silico methods as well as in vivo and in vitro experimental approaches. First, we analyzed publicly available databases to pinpoint the major QTL correlations. Then, we validated these correlations using a well-characterized set of samples and locus-specific approaches—i.e., Sanger sequencing for the genotype, cloning/sequencing and pyrosequencing for the DNA methylation, and allele-specific and real-time PCR for the RNA expression. Finally, we defined the functional relevance of the observed alterations in the context of AD in vitro. Using this approach, we show that only PM20D1 DNA methylation and expression are significantly correlated with the AD-risk associated background. We find that the expression of SLC41A1 and PM20D1—but not NUCKS1 and RAB7L1—is increased in mouse models and human samples of AD, respectively. However, SLC41A1 and PM20D1 are differentially regulated by AD-related stressors, with only PM20D1 being upregulated by amyloid-β and reactive oxygen species, and with only PM20D1 being neuroprotective when overexpressed in cell and primary cultures. Conclusions Our findings reinforce PM20D1 as the most likely gene responsible of the previously reported PM20D1 QTL association with AD.

PD-L1 expression levels derived from >16,000 samples guided the selection of tumor types likely to benefit from pembrolizuamb monotherapy in clinical trials. Although not fail-proof, FDA approvals for most of the prioritized indications speak to the power of RNA expression profiling and the value of large genomic datasets. See related article by Ayers et al., p. 1564

ObjectiveGastroenteropancreatic neuroendocrine tumours (GEP-NETs) encompass a diverse group of neoplasms that vary in their secretory products and in their location within the gastrointestinal tract. Their prevalence in the USA is increasing among all adult age groups.AimTo identify the possible derivation of GEP-NETs using genome-wide analyses to distinguish small intestinal neuroendocrine tumours, specifically duodenal gastrinomas (DGASTs), from pancreatic neuroendocrine tumours.DesignWhole exome sequencing and RNA-sequencing were performed on surgically resected GEP-NETs (discovery cohort). RNA transcript profiles available in the Gene Expression Omnibus were analysed using R integrated software (validation cohort). Digital spatial profiling (DSP) was used to analyse paraffin-embedded GEP-NETs. Human duodenal organoids were treated with 5 or 10 ng/mL of tumor necrosis factor alpha (TNFα) prior to qPCR and western blot analysis of neuroendocrine cell specification genes.ResultsBoth the discovery and validation cohorts of small intestinal neuroendocrine tumours induced expression of mesenchymal and calcium signalling pathways coincident with a decrease in intestine-specific genes. In particular, calcium-related, smooth muscle and cytoskeletal genes increased in DGASTs, but did not correlate with MEN1 mutation status. Interleukin 17 (IL-17) and tumor necrosis factor alpha (TNFα) signalling pathways were elevated in the DGAST RNA-sequencing. However, DSP analysis confirmed a paucity of immune cells in DGASTs compared with the adjacent tumour-associated Brunner’s glands. Immunofluorescent analysis showed production of these proinflammatory cytokines and phosphorylated signal transducer and activator of transcription 3 (pSTAT3) by the tumours and stroma. Human duodenal organoids treated with TNFα induced neuroendocrine tumour genes, SYP, CHGA and NKX6.3.ConclusionsStromal–epithelial interactions induce proinflammatory cytokines that promote Brunner’s gland reprogramming.

// Jing Wang 1, * , Xue Yang 1, * , Haofeng Chen 2 , Xuewei Wang 1 , Xiangyu Wang 1 , Yi Fang 1 , Zhenyu Jia 3 and Jidong Gao 1 1 Department of Breast Surgical Oncology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China 2 Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China 3 Department of Botany and Plant Sciences, University of California, Riverside, California, United States * These authors have contributed equally to this work Correspondence to: Jidong Gao, email: gaojidong@cicams.ac.cn Yi Fang, email: fangyi0501@vip.sina.com Zhenyu Jia, email: zhenyuj@ucr.edu Keywords: breast cancer, next generation sequencing, RNA expression, MLPA, 21-gene Received: January 17, 2017 Accepted: March 28, 2017 Published: May 02, 2017 ABSTRACT RNA in formalin-fixed and paraffin-embedded (FFPE) tissues provides large amount of information indicating disease stages, histological tumor types and grades, as well as clinical outcomes. However, Detection of RNA expression levels in formalin-fixed and paraffin-embedded samples is extremely difficult due to poor RNA quality. Here we developed a high-throughput method, Reverse Transcription-Multiple Ligation-dependent Probe Sequencing (RT-MLPSeq), to determine expression levels of multiple transcripts in FFPE samples. By combining Reverse Transcription-Multiple Ligation-dependent Amplification method and next generation sequencing technology, RT-MLPSeq overcomes the limit of probe length in multiplex ligation-dependent probe amplification assay and thus could detect expression levels of transcripts without quantitative limitations. We proved that different RT-MLPSeq probes targeting on the same transcripts have highly consistent results and the starting RNA/cDNA input could be as little as 1 ng. RT-MLPSeq also presented consistent relative RNA levels of selected 13 genes with reverse transcription quantitative PCR. Finally, we demonstrated the application of the new RT-MLPSeq method by measuring the mRNA expression levels of 21 genes which can be used for accurate calculation of the breast cancer recurrence score – an index that has been widely used for managing breast cancer patients.

Motivation Circular RNAs (circRNAs) are a class of non-coding RNAs that are widely expressed in various cell lines and tissues of many organisms. Although the exact function of many circRNAs is largely unknown, the cell type—and tissue-specific circRNA expression has implicated their crucial functions in many biological processes. Hence, the quantification of circRNA expression from high-throughput RNA-seq data is becoming important to ascertain. Although many model-based methods have been developed to quantify linear RNA expression from RNA-seq data, these methods are not applicable to circRNA quantification. Results Here, we proposed a novel strategy that transforms circular transcripts to pseudo-linear transcripts and estimates the expression values of both circular and linear transcripts using an existing model-based algorithm, Sailfish. The new strategy can accurately estimate transcript expression of both linear and circular transcripts from RNA-seq data. Several factors, such as gene length, amount of expression and the ratio of circular to linear transcripts, had impacts on quantification performance of circular transcripts. In comparison to count-based tools, the new computational framework had superior performance in estimating the amount of circRNA expression from both simulated and real ribosomal RNA-depleted (rRNA-depleted) RNA-seq datasets. On the other hand, the consideration of circular transcripts in expression quantification from rRNA-depleted RNA-seq data showed substantial increased accuracy of linear transcript expression. Our proposed strategy was implemented in a program named Sailfish-cir. Availability and Implementation Sailfish-cir is freely available at https://github.com/zerodel/Sailfish-cir. Supplementary information Supplementary data are available at Bioinformatics online.

Stringent chemical methods to profile RNA expression within discrete cellular populations remains a key challenge in biology. To address this issue, we developed a chemical-genetic strategy for metabolic labeling of RNA. Cell-specific labeling of RNA can be profiled and imaged using bioorthogonal chemistry. We anticipate that this platform will provide the community with a much-needed chemical toolset for cell-type specific profiling of cell-specific transcriptomes derived from complex biological systems.

We have carried an exploratory study by blood transcriptome to find RNA expression signatures in familial ADHD. Samples were collected from three cases with familial ADHD and their paired controls and evaluated by RNA-Seq. Transcriptome profiling identified 7 differentially expressed transcripts with a FDR0.05 that were involved in pathways in Huntington's disease or axonal guidance signaling previously implicated in ADHD, and enriched for signal peptide, growth factor binding, and notably the lipid metabolism pathways. These findings show that blood transcriptome can have an associated signature and highlight a potential to use blood transcriptome to identify patterns of ADHD.

The bacterial-defence system CRISPR–Cas can store DNA snippets that correspond to encountered viral RNA sequences. One such system has now been harnessed to record gene expression over time in bacteria. RNA expression can be recorded in bacterial cells.