Standard RNA-seq Research Articles

DRUG-seq for miniaturized high-throughput transcriptome profiling in drug discovery

Here we report Digital RNA with pertUrbation of Genes (DRUG-seq), a high-throughput platform for drug discovery. Pharmaceutical discovery relies on high-throughput screening, yet current platforms have limited readouts. RNA-seq is a powerful tool to investigate drug effects using transcriptome changes as a proxy, yet standard library construction is costly. DRUG-seq captures transcriptional changes detected in standard RNA-seq at 1/100th the cost. In proof-of-concept experiments profiling 433 compounds across 8 doses, transcription profiles generated from DRUG-seq successfully grouped compounds into functional clusters by mechanism of actions (MoAs) based on their intended targets. Perturbation differences reflected in transcriptome changes were detected for compounds engaging the same target, demonstrating the value of using DRUG-seq for understanding on and off-target activities. We demonstrate DRUG-seq captures common mechanisms, as well as differences between compound treatment and CRISPR on the same target. DRUG-seq provides a powerful tool for comprehensive transcriptome readout in a high-throughput screening environment.

The 'TranSeq' 3'-end sequencing method for high-throughput transcriptomics and gene space refinement in plant genomes.

High-throughput RNA sequencing has proven invaluable not only to explore gene expression but also for both gene prediction and genome annotation. However, RNA sequencing, carried out on tens or even hundreds of samples, requires easy and cost-effective sample preparation methods using minute RNA amounts. Here, we present TranSeq, a high-throughput 3'-end sequencing procedure that requires 10- to 20-fold fewer sequence reads than the current transcriptomics procedures. TranSeq significantly reduces costs and allows a greater increase in size of sample sets analyzed in a single experiment. Moreover, in comparison with other 3'-end sequencing methods reported to date, we demonstrate here the reliability and immediate applicability of TranSeq and show that it not only provides accurate transcriptome profiles but also produces precise expression measurements of specific gene family members possessing high sequence similarity. This is difficult to achieve in standard RNA-seq methods, in which sequence reads cover the entire transcript. Furthermore, mapping TranSeq reads to the reference tomato genome facilitated the annotation of new transcripts improving >45% of the existing gene models. Hence, we anticipate that using TranSeq will boost large-scale transcriptome assays and increase the spatial and temporal resolution of gene expression data, in both model and non-model plant species. Moreover, as already performed for tomato (ITAG3.0; www.solgenomics.net), we strongly advocate its integration into current and future genome annotations.

A Composite Mode Differential Gene Regulatory Architecture based on Temporal Expression Profiles.

Exploring the complex interactive mechanism in a Gene Regulatory Network (GRN) developed using transcriptome data obtained from standard microarray and/or RNA-seq experiments helps us to understand the triggering factors in cancer research. The Transcription Factor (TF) genes generate protein complexes which affect the transcription of various target genes. However, considering the mode of regulation in a time frame such transcriptional activities are dependent on some specific activation time points only. It is also crucial to check whether the regulating capabilities are uniform across varied stages, especially when periodicity is a big issue. In this context, we propose an algorithm called RIFT which helps to monitor the temporal differential regulatory pattern of a Differentially Expressed (DE) target gene either by a TF gene or a group of TF genes from a large time series (TS) data. We have tested our algorithm on HeLa cell cycle data and compared the result with its most advanced state of the art counterpart proposed so far. As our algorithm yields up stringent mode and target specific significant valid TF genes for a DE gene, we can expect to have new forms of genetic interactions.

VIPER: Visualization Pipeline for RNA-seq, a Snakemake workflow for efficient and complete RNA-seq analysis

BackgroundRNA sequencing has become a ubiquitous technology used throughout life sciences as an effective method of measuring RNA abundance quantitatively in tissues and cells. The increase in use of RNA-seq technology has led to the continuous development of new tools for every step of analysis from alignment to downstream pathway analysis. However, effectively using these analysis tools in a scalable and reproducible way can be challenging, especially for non-experts.ResultsUsing the workflow management system Snakemake we have developed a user friendly, fast, efficient, and comprehensive pipeline for RNA-seq analysis. VIPER (Visualization Pipeline for RNA-seq analysis) is an analysis workflow that combines some of the most popular tools to take RNA-seq analysis from raw sequencing data, through alignment and quality control, into downstream differential expression and pathway analysis. VIPER has been created in a modular fashion to allow for the rapid incorporation of new tools to expand the capabilities. This capacity has already been exploited to include very recently developed tools that explore immune infiltrate and T-cell CDR (Complementarity-Determining Regions) reconstruction abilities. The pipeline has been conveniently packaged such that minimal computational skills are required to download and install the dozens of software packages that VIPER uses.ConclusionsVIPER is a comprehensive solution that performs most standard RNA-seq analyses quickly and effectively with a built-in capacity for customization and expansion.

ROP: dumpster diving in RNA-sequencing to find the source of 1 trillion reads across diverse adult human tissues

High-throughput RNA-sequencing (RNA-seq) technologies provide an unprecedented opportunity to explore the individual transcriptome. Unmapped reads are a large and often overlooked output of standard RNA-seq analyses. Here, we present Read Origin Protocol (ROP), a tool for discovering the source of all reads originating from complex RNA molecules. We apply ROP to samples across 2630 individuals from 54 diverse human tissues. Our approach can account for 99.9% of 1 trillion reads of various read length. Additionally, we use ROP to investigate the functional mechanisms underlying connections between the immune system, microbiome, and disease. ROP is freely available at https://github.com/smangul1/rop/wiki.

Accurate RNA Sequencing From Formalin-Fixed Cancer Tissue To Represent High-Quality Transcriptome From Frozen Tissue.

PurposeAccurate transcriptional sequencing (RNA-seq) from formalin-fixed paraffin-embedded (FFPE) tumor samples presents an important challenge for translational research and diagnostic development. In addition, there are now several different protocols to prepare a sequencing library from total RNA. We evaluated the accuracy of RNA-seq data generated from FFPE samples in terms of expression profiling.MethodsWe designed a biospecimen study to directly compare gene expression results from different protocols to prepare libraries for RNA-seq from human breast cancer tissues, with randomization to fresh frozen (FF) or FFPE conditions. The protocols were compared using multiple computational methods to assess alignment of reads to a reference genome, the uniformity and continuity of coverage, the variance and correlation of overall gene expression, patterns of measuring coding sequence, phenotypic patterns of gene expression, and measurements from representative multigene signatures.ResultsThe principal determinant of variance in gene expression was use of exon capture probes, followed by the conditions of preservation (FF v FFPE) and phenotypic differences between breast cancers. One protocol, with RNase H-based ribosomal RNA depletion, exhibited the least variability of gene expression measurements and strongest correlation between FF and FFPE samples and was generally representative of the transcriptome from standard FF RNA-seq protocols.ConclusionMethod of RNA-seq library preparation from FFPE samples had a marked effect on the accuracy of gene expression measurement compared with matched FF samples. Nevertheless, some protocols produced highly concordant expression data from FFPE RNA-seq data, compared with RNA-seq results from matched frozen samples.

Phenotype Classification Using Moment Features of Single-Cell Data.

Features for standard expression microarray and RNA-Seq classification are expression averages over collections of cells. Single cell provides expression measurements for individual cells in a collection of cells from a particular tissue sample. Hence, it can yield feature vectors consisting of higher order and mixed moments. This article demonstrates the advantage of using these expression moments in cancer-related classification. We use synthetic data generated from 2 real networks, the mammalian cell cycle network and a melanoma-related pathway network, and real single-cell data generated via fluorescent protein reporters from 2 cell lines, HT-29 and HCT-116. The networks consist of hidden binary regulatory networks with Gaussian observations. The steady-state distributions of both the original and mutated networks are found, and data are drawn from these for moment-based classification using the mean, variance, skewness, and mixed moments. For the real data, we only observe 1 gene at a time, so that only the mean, variance, and skewness are considered, the analysis being done for 2 genes, EGFR and ERRB2. For the synthetic data, classification improves as we move from just the mean to mean, variance, and skewness and then to these plus the mixed moments. Comparisons are done with 3, 4, or 5 features, using feature selection. Sample size effects are considered. For the real data, we only consider mean, variance, and skewness, with results improving when the higher order moments are used as features.

In Silico Typing of Classical and Non-classical HLA Alleles from Standard RNA-Seq Reads.

Next-Generation Sequencing (NGS) enables the rapid generation of billions of short nucleic acid sequence fragments (i.e., "sequencing reads"). Especially, the adoption of gene expression profiling using whole transcriptome sequencing (i.e., "RNA-Seq") has been rapid. Here, we describe an in silico method, seq2HLA, that takes standard RNA-Seq reads as input and determines a sample's (classical and non-classical) HLA class I and class II types as well as HLA expression. We demonstrate the application of seq2HLA using publicly available RNA-Seq data from the Burkitt's lymphoma cell line DAUDI and the choriocarcinoma cell line JEG-3.

Primer Extension, Capture, and On-Bead cDNA Ligation: An Efficient RNAseq Library Prep Method for Determining Reverse Transcription Termination Sites.

In this chapter, we describe a method for making Illumina-compatible sequencing libraries from RNA. This protocol can be used for standard RNAseq analysis for detecting differentially expressed genes. In addition, this protocol is ideally suited for adapting to RIPseq, 5'-RACE, RNA structural probing, nascent RNA sequencing, and other protocols where polymerase termination sites need to be profiled. The utilization of solid-phase bead chemistries facilitates simple workflow and efficient library yields.

Simultaneously Monitoring Immune Response and Microbial Infections during Pregnancy through Plasma cfRNA Sequencing.

Plasma cell-free RNA (cfRNA) encompasses a broad spectrum of RNA species that can be derived from both human cells and microbes. Because cfRNA is fragmented and of low concentration, it has been challenging to profile its transcriptome using standard RNA-seq methods. We assessed several recently developed RNA-seq methods on cfRNA samples. We then analyzed the dynamic changes of both the human transcriptome and the microbiome of plasma during pregnancy from 60 women. cfRNA reflects a well-orchestrated immune modulation during pregnancy: an up-regulation of antiinflammatory genes and an increased abundance of antimicrobial genes. We observed that the plasma microbiome remained relatively stable during pregnancy. The bacteria Ureaplasma shows an increased prevalence and increased abundance at postpartum, which is likely to be associated with postpartum infection. We demonstrated that cfRNA-seq can be used to monitor viral infections. We detected a number of human pathogens in our patients, including an undiagnosed patient with a high load of human parvovirus B19 virus (B19V), which is known to be a potential cause of complications in pregnancy. Plasma cfRNA-seq demonstrates the potential to simultaneously monitor immune response and microbial infections during pregnancy.

Dysregulation of Alternative Poly-adenylation as a Potential Player in Autism Spectrum Disorder.

We present here the hypothesis that alternative poly-adenylation (APA) is dysregulated in the brains of individuals affected by Autism Spectrum Disorder (ASD), due to disruptions in the calcium signaling networks. APA, the process of selecting different poly-adenylation sites on the same gene, yielding transcripts with different-length 3′ untranslated regions (UTRs), has been documented in different tissues, stages of development and pathologic conditions. Differential use of poly-adenylation sites has been shown to regulate the function, stability, localization and translation efficiency of target RNAs. However, the role of APA remains rather unexplored in neurodevelopmental conditions. In the human brain, where transcripts have the longest 3′ UTRs and are thus likely to be under more complex post-transcriptional regulation, erratic APA could be particularly detrimental. In the context of ASD, a condition that affects individuals in markedly different ways and whose symptoms exhibit a spectrum of severity, APA dysregulation could be amplified or dampened depending on the individual and the extent of the effect on specific genes would likely vary with genetic and environmental factors. If this hypothesis is correct, dysregulated APA events might be responsible for certain aspects of the phenotypes associated with ASD. Evidence supporting our hypothesis is derived from standard RNA-seq transcriptomic data but we suggest that future experiments should focus on techniques that probe the actual poly-adenylation site (3′ sequencing). To address issues arising from the use of post-mortem tissue and low numbers of heterogeneous samples affected by confounding factors (such as the age, gender and health of the individuals), carefully controlled in vitro systems will be required to model the effect of calcium signaling dysregulation in the ASD brain.

1505P - Analysis of expression of immunomodulation factors in alveolar soft-part sarcoma: a retrospective study from the Spanish Group for Research on Sarcoma (GEIS)

Background: Alveolar soft-part sarcoma (ASPS) is an exceedingly rare entity. We previously reported on a series of 12 ASPS the expression of PD-1/PD-L1 and lymphocytic infiltrates. PD-L1 staining was positive in 50% of the cases, suggesting a role for immunomodulatory interventions. We now enlarge the series and explore the expression of immune-related genes with the HTG EdgeSeq System and analyze their possible prognostic role. Methods: Patients (pts) diagnosed with ASPS from Dec 1994 to Jul 2016 were reviewed. Clinical characteristics and outcome were collected. Immunohistochemical expression was tested on archived Formalin-Fixed Paraffin-Embedded (FFPE) blocks using PD-L1 (ab205921; Abcam), CD8 (ab4055; Abcam) antibodies. An ImmunOncology (IO) panel of 549 mRNAs was evaluated with the HTG EdgeSeq System from one 5µm FFPE section. This novel technology consists in a tissue digestion followed by a pre-hybridization with specific probes using quantitative nuclease protection assay (qNPA) and quantified by a standard RNA-seq protocol in a NGS sequencer. Results: We identified 16 ASPS pts: median age 23y (6-62), M/F=5/11. Stage localized/metastatic in 11/5. With a median FU of 91 mos (4-151), 6/11 pts (54%) relapsed, with a median RFS of 19 mos (95% CI 1-75) and 2 pts died. PD-L1 was pos in tumor in 10/16 (62%) pts. HTG showed differential expression of immune-related genes according to stage (localized vs metastatic) in MNDA (log2 fold change: -3.01, p < 0.00001) and PRAME (log2 fold change: 1.9, p < 0.0002). Pts relapsing differentially expressed ABCC2 (log2 fold change -1,64, p < 0.04) when compared with those not relapsing. PD-L1 pos vs neg tumors (pos>5% membrane staining for PD-L1) differentially expressed TNFSF11 (fold change: -1,77, p = 0.05). Tumors infiltrated/not for CD8 lymphs (pos>10% of CD8) had a differential expression of CCL18 (log2 fold change: 4,89, p < 0.0001) and SLAMF7 (log2 fold change 2,37, p < 0.0089). Conclusions: PD-L1 is expressed in more than a half of ASPS of our series. Differential expression in immune-associated genes suggest an immune related gene profile on this entity with clinical and pathological implications. Further exploration of immune-modulation in ASPS is ongoing. Legal entity responsible for the study: Spanish Group for Research on Sarcoma Funding: Spanish Group for Research on Sarcoma (GEIS). Disclosure: All authors have declared no conflicts of interest.

O77: Are baseline joint patterns in the sagittal plane indicative for the success of botulinum toxin injections in children with cerebral palsy?

The genome annotation for the model plant Arabidopsis thaliana does not include the primary transcripts from which MIRNAs are processed. Here we present and analyze the raw mRNA sequencing data from wild type and serrate-1 globular stage embryos of A. thaliana, ecotype Columbia. Because SERRATE is required for pri-miRNA processing, these precursors accumulate in serrate-1 mutants, facilitating their detection using standard RNA-Seq protocols. We first use the mapping of the RNA-Seq reads to the reference genome to annotate the potential primary transcripts of MIRNAs expressed in the embryo. We then quantify these pri-miRNAs in wild type and serrate-1 mutants. Finally, we use differential expression analysis to determine which are up-regulated in serrate-1 compared to wild type, to select the best candidates for bona fide pri-miRNAs expressed in the globular stage embryos. In addition, we analyze a previously published RNA-Seq dataset of wild type and dicer-like 1 mutant embryos at the globular stage [1]. Our data are interpreted and discussed in a separate article [2].

Abstract 487: A comprehensive target enrichment panel for fusion detection

Abstract Growing interest in cancer classification and progression has accelerated the rate of novel gene fusions discovery with increasing recognition of their roles as biomarkers. RNA-Seq is an attractive method for expressed fusion discovery and detection because of its ability to provide unbiased fusion sequencing information. The ability to detect low expressing fusion transcripts, however, require high sequencing depth and represents a significant financial barrier and identification of clinically relevant fusion sequences from a large data set can be a bioinformatics challenge. To address these challenges we have tested the Ovation® Fusion Panel Target Enrichment System V2, a targeted RNA sequencing method using the Single Primer Enrichment Technology (SPET), with a number of control and clinical samples. Initial studies were performed using a comprehensive target enrichment panel targeting 502 genes with three samples from Horizon DX containing known fusions. Target enriched libraries were constructed with 10 ng and 100 ng inputs and the data was analyzed using the NuFuseD pipeline (available as a point and click BaseSpace application or downloadable linux package) which has been optimized for fusion analysis from this data. Expected fusions were identified at both input levels, even when down sampled to 500K reads, with fewer fusion calls compared to other publically available fusion detection software (Chimerascan and SOAPFuse), suggesting a lower false positive rate. NuFuseD fusion calls are provided with a P-value to help prioritize the identified fusions for subsequent validation. Additionally, NuFuseD detected novel fusions in the control samples demonstrating the advantage of a comprehensive panel compared to more restricted panels. We further validated the target panel using control RNA (UHR and Human Brain) and fresh or FFPE cell lines (NCI-H2228, HCC1937) to further demonstrate our ability to identify known fusions. Finally, the system was evaluated at an external site using patient FFPE samples. These samples (N=8) were from a set of breast, liver and ovarian cancers, containing a unique fusion in 4 of the samples based on DNA based sequencing. Only 1 of the 4 expected fusions were identified using whole transcriptome data (100 million reads) while 3 of the 4 fusions were detected with this assay (10 million reads) demonstrating its ability to generate targeted RNA sequencing libraries with increased sensitivity of gene fusion detection and reduced sequencing costs compared to standard RNA-Seq methods. Citation Format: Ashesh Saraiya, Brandon Young, Tobias Meißner, Brian L. Jones, Stephanie C. Huelga, Doug A. Amorese. A comprehensive target enrichment panel for fusion detection [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 487. doi:10.1158/1538-7445.AM2017-487

MR-seq: measuring a single cell’s transcriptome repeatedly by RNA-seq

We described a novel single-cell RNA-seq technique called MR-seq (measure a single-cell transcriptome repeatedly), which permits statistically assessing the technical variation and identifying the differentially expressed genes between just two single cells by measuring each single cell twice. We demonstrated that MR-seq gave sensitivity and reproducibility similar to the standard single-cell RNA-seq and increased the positive predicate value. Application of MR-seq to early mouse embryos identified hundreds of candidate intra-embryonic heterogeneous genes among mouse 2-, 4- and 8-cell stage embryos. MR-seq should be useful for detecting differentially expressed genes among a small number of cells.

Novel Technologies to Detect Minimal Residual Disease

The widespread use of ribonucleic acid (RNA) analysis as a measure of minimal residual disease (MRD) in leukemia has primarily been performed via real-time quantitative polymerase chain reaction (PCR) (RQ-PCR) for common translocations, insertions/deletions and duplications (e.g. FLT3-ITD, MLL-PTD). While RQ-PCR for known rearrangements can offer a limit of detection as low as 1:106, these make up a minority of the total leukemia cases. MLL-rearrangements are generally not amenable to RQ-PCR due to the many fusion partners and breakpoints in these translocations. The ability to use quantitative RNA sequencing (RNA-seq) for MRD assessment would enable detection of allele-specific gene expression, alternative splicing and cryptic splice forms in addition to common rearrangements. While a few institutions are performing reflexive genome, transcription and methylome sequencing for cancer patients, standard RNA-seq library preparation is relatively low efficiency, which causes loss of rare transcripts1 required for meaningful MRD assessment. Three methods currently employed to overcome this inherent bias in RNA-seq are targeted RNA sequencing of cancer-related genes to reduce off target, non-specific sequencing which improves read depth and mutation detection2; single cell transcriptomes to obtain a digital readout of gene expression in single cells rather than a population-based average; and the addition of unique molecular indexes (UMI) to normalize amplification bias and obtain digital quantitation of transcripts in populations of cells. None of these methods have been carefully studied for their applicability to diagnostic testing, but single cell transcriptome profiling has been applied to solid tumor biology3,4. In this presentation, we explore whether digital RNA-seq can serve as a viable modality for MRD either as a stand-alone method or in conjunction with other methods of genomic profiling or immunophenotyping. While single-cell RNA profiling allows for co-localization of mutations within the same cell and is now capable of surveying hundreds to thousands of cells in a single experiment for reasonable costs, this scale remains far below the throughput necessary for MRD detection of cells present at 1:105. One would need to process tens of thousands of cells and analyze the resulting data to confidently identify any residual leukemia cells, which is not currently feasible in timescales amenable to clinical decision making. We present data from the combination of targeted RNA sequencing of a pan-cancer gene expression array with the addition of UMIs to normalize amplification bias and computationally eliminate errors introduced by the sequencing platform while specifically sequencing cancer-associated genes to high read depths. This enables high-throughput detection of allele and isoform-specific transcripts at frequencies below 1:5,000. This method can be coupled with an equivalent DNA sequencing analysis from the same sample to ascertain the relative expression of leukemia-associated mutant alleles. However, one significant drawback to the addition of UMIs compared to single-cell RNA-seq is that the ability to co-localize mutations is lost. As epigenetic modifiers and targeted agents continue to permeate cancer therapy, these evolving technologies should offer more than direct quantification of residual leukemia cells, but a rich series of data on expression differences in healthy and cancer cells before, during and after therapy.

Traditional versus 3′ RNA-seq in a non-model species

One limitation of the widely used RNA-seq method is that long transcripts are represented by more reads than shorter transcripts, resulting in a biased estimation of expression levels. The 3′ RNA-seq method, which yields only one sequence per transcript, bypasses this limitation. Here, RNA was extracted from two samples, in which we expected to find differentially expressed genes. Each was processed by both traditional and 3′ RNA-seq protocols. Both methods yielded similar differentially expressed genes and estimated expression levels in a comparable way, confirming they both represent valid tools for RNA-seq analysis. Notably, however, we identified more differentially expressed transcripts with the 3′ RNA-seq method, suggesting a greater power to detect expression variation using this method. Hence, when little genomic information is available for the species studied, the standard RNA-seq presents a better cost-benefit compromise, whereas for model species, the 3′ RNA-seq method might more accurately detect differential expression.

Assembly-based inference of B-cell receptor repertoires from short read RNA sequencing data with V'DJer.

Motivation: B-cell receptor (BCR) repertoire profiling is an important tool for understanding the biology of diverse immunologic processes. Current methods for analyzing adaptive immune receptor repertoires depend upon PCR amplification of VDJ rearrangements followed by long read amplicon sequencing spanning the VDJ junctions. While this approach has proven to be effective, it is frequently not feasible due to cost or limited sample material. Additionally, there are many existing datasets where short-read RNA sequencing data are available but PCR amplified BCR data are not.Results: We present here V’DJer, an assembly-based method that reconstructs adaptive immune receptor repertoires from short-read RNA sequencing data. This method captures expressed BCR loci from a standard RNA-seq assay. We applied this method to 473 Melanoma samples from The Cancer Genome Atlas and demonstrate V’DJer’s ability to accurately reconstruct BCR repertoires from short read mRNA-seq data.Availability and Implementation: V’DJer is implemented in C/C ++, freely available for academic use and can be downloaded from Github: https://github.com/mozack/vdjerContact: benjamin_vincent@med.unc.edu or parkerjs@email.unc.eduSupplementary information: Supplementary data are available at Bioinformatics online.

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations.

RNA sequencing (RNAseq) is a versatile method that can be utilized to detect and characterize gene expression, mutations, gene fusions, and noncoding RNAs. Standard RNAseq requires 30 - 100 million sequencing reads and can include multiple RNA products such as mRNA and noncoding RNAs. We demonstrate how targeted RNAseq (capture) permits a focused study on selected RNA products using a desktop sequencer. RNAseq capture can characterize unannotated, low, or transiently expressed transcripts that may otherwise be missed using traditional RNAseq methods. Here we describe the extraction of RNA from cell lines, ribosomal RNA depletion, cDNA synthesis, preparation of barcoded libraries, hybridization and capture of targeted transcripts and multiplex sequencing on a desktop sequencer. We also outline the computational analysis pipeline, which includes quality control assessment, alignment, fusion detection, gene expression quantification and identification of single nucleotide variants. This assay allows for targeted transcript sequencing to characterize gene expression, gene fusions, and mutations.

Abstract 2665: SHOT-RNAs: A novel class of tRNA-derived functional RNAs expressed in hormone-dependent cancers

Abstract Sex hormones and their receptors play critical roles in the genesis and progression of breast and prostate cancers. We here present that sex hormone signaling pathways promote the expression of specific tRNA halves termed Sex HOrmone-dependent TRNA-derived RNAs (SHOT-RNAs). Screenings using 94 cancer cell lines indicated that SHOT-RNAs are specifically and abundantly expressed in estrogen receptor-positive (ER+) breast cancer and androgen receptor-positive (AR+) prostate cancer. None of the ER− breast cancer, AR− prostate cancer, or other examined cancer cell lines from other tissues exhibited abundant expression of SHOT-RNAs. ER-dependent accumulation of SHOT-RNAs is not limited to a cell culture system, but it also occurs in luminal-type breast cancer patient tissues. The expression specificity allowed us to hypothesize that sex hormones and their receptors are the crucial molecular factors for SHOT-RNA generation. Our hypothesis has been confirmed by the following three observations: (1) siRNA-mediated reduction of ER or AR decreased SHOT-RNA levels, (2) hormone-depletion from the culture medium decreased SHOT-RNA levels, and (3) addition of the sex hormone, estrogen or androgen, to the medium increased SHOT-RNA levels. These results indicate the clear dependency of SHOT-RNA expressions on sex hormones and their receptors. SHOT-RNAs are produced from aminoacylated mature tRNAs by angiogenin (ANG)-mediated anticodon cleavage. Resultant 5′- and 3′-SHOT-RNAs, corresponding to 5′- and 3′-tRNA halves, bear 3′-terminal modifications, cyclic phosphate (cP) and amino acid, respectively. Thus, SHOT-RNAs would not be captured by standard RNA-seq methods because the modification inhibits adapter ligation steps. This could be the reason why SHOT-RNAs had not been discovered in spite of their abundance and expression specificity. To identify a comprehensive repertoire of SHOT-RNAs, we developed a method named “cP-RNA-seq” in which 3′-termini of all RNAs, except for those containing a 3′-terminal cP, were cleaved by a periodate treatment, thereby selectively amplifying and sequencing only cP-containing RNAs. The application of cP-RNA-seq to breast cancer cells identified eight cytoplasmic tRNA species as major sources of 5′-SHOT-RNAs. We further investigated the functional significance of SHOT-RNA expression. Intriguingly, siRNA-directed reduction of each one of the three different species of 5′-SHOT-RNA severely impaired cell proliferation, whereas 3′-SHOT-RNA reduction showed no impact. These results strongly suggest that at least 5′-SHOT-RNAs are expressed as functional RNA molecules promoting cell growth. Our results have unveiled a novel tRNA-engaged pathway in tumorigenesis of hormone-dependent cancers and implicate SHOT-RNAs as potential candidates for biomarkers and therapeutic targets. We will discuss our working model and future challenges. Citation Format: Shozo Honda, Megumi Shigematsu, Phillipe Loher, Juan P. Palazzo, Issei Imoto, Isidore Rigoutsos, Yohei Kirino. SHOT-RNAs: A novel class of tRNA-derived functional RNAs expressed in hormone-dependent cancers. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2665.