Characterization of in vitro transcription amplification linearity and variability in the low copy number regime using External RNA Control Consortium (ERCC) spike-ins

Loss of Paip1 causes translation reduction and induces apoptotic cell death through ISR activation and Xrp1

Regulation of protein translation initiation is tightly associated with cell growth and survival. Here, we identify Paip1, the Drosophila homolog of the translation initiation factor PAIP1, and analyze its role during development. Through genetic analysis, we find that loss of Paip1 causes reduced protein translation and pupal lethality. Furthermore, tissue specific knockdown of Paip1 results in apoptotic cell death in the wing imaginal disc. Paip1 depletion leads to increased proteotoxic stress and activation of the integrated stress response (ISR) pathway. Mechanistically, we show that loss of Paip1 promotes phosphorylation of eIF2α via the kinase PERK, leading to apoptotic cell death. Moreover, Paip1 depletion upregulates the transcription factor gene Xrp1, which contributes to apoptotic cell death and eIF2α phosphorylation. We further show that loss of Paip1 leads to an increase in Xrp1 translation mediated by its 5'UTR. These findings uncover a novel mechanism that links translation impairment to tissue homeostasis and establish a role of ISR activation and Xrp1 in promoting cell death.

Simultaneous sequencing of coding and noncoding RNA reveals a human transcriptome dominated by a small number of highly expressed noncoding genes

Comparing the abundance of one RNA molecule to another is crucial for understanding cellular functions but most sequencing techniques can target only specific subsets of RNA. In this study, we used a new fragmented ribodepleted TGIRT sequencing method that uses a thermostable group II intron reverse transcriptase (TGIRT) to generate a portrait of the human transcriptome depicting the quantitative relationship of all classes of nonribosomal RNA longer than 60 nt. Comparison between different sequencing methods indicated that FRT is more accurate in ranking both mRNA and noncoding RNA than viral reverse transcriptase-based sequencing methods, even those that specifically target these species. Measurements of RNA abundance in different cell lines using this method correlate with biochemical estimates, confirming tRNA as the most abundant nonribosomal RNA biotype. However, the single most abundant transcript is 7SL RNA, a component of the signal recognition particle. Structured noncoding RNAs (sncRNAs) associated with the same biological process are expressed at similar levels, with the exception of RNAs with multiple functions like U1 snRNA. In general, sncRNAs forming RNPs are hundreds to thousands of times more abundant than their mRNA counterparts. Surprisingly, only 50 sncRNA genes produce half of the non-rRNA transcripts detected in two different cell lines. Together the results indicate that the human transcriptome is dominated by a small number of highly expressed sncRNAs specializing in functions related to translation and splicing.

High-throughput annotation of full-length long noncoding RNAs with capture long-read sequencing

Accurate annotation of genes and their transcripts is a foundation of genomics, but currently no annotation technique combines throughput and accuracy. As a result, reference gene collections remain incomplete-many gene models are fragmentary, and thousands more remain uncataloged, particularly for long noncoding RNAs (lncRNAs). To accelerate lncRNA annotation, the GENCODE consortium has developed RNA Capture Long Seq (CLS), which combines targeted RNA capture with third-generation long-read sequencing. Here we present an experimental reannotation of the GENCODE intergenic lncRNA populations in matched human and mouse tissues that resulted in novel transcript models for 3,574 and 561 gene loci, respectively. CLS approximately doubled the annotated complexity of targeted loci, outperforming existing short-read techniques. Full-length transcript models produced by CLS enabled us to definitively characterize the genomic features of lncRNAs, including promoter and gene structure, and protein-coding potential. Thus, CLS removes a long-standing bottleneck in transcriptome annotation and generates manual-quality full-length transcript models at high-throughput scales.

Short bowel syndrome results in increased gene expression associated with proliferation, inflammation, bile acid synthesis and immune system activation: RNA sequencing a zebrafish SBS model

Background

Much of the morbidity associated with short bowel syndrome (SBS) is attributed to effects of decreased enteral nutrition and administration of total parenteral nutrition (TPN). We hypothesized that acute SBS alone has significant effects on gene expression beyond epithelial proliferation, and tested this in a zebrafish SBS model.

Methods

In a model of SBS in zebrafish (laparotomy, proximal stoma, distal ligation, n = 29) or sham (laparotomy alone, n = 28) surgery, RNA-Seq was performed after 2 weeks. The proximal intestine was harvested and RNA isolated. The three samples from each group with the highest amount of RNA were spiked with external RNA controls consortium (ERCC) controls, sequenced and aligned to reference genome with gene ontology (GO) enrichment analysis performed. Gene expression of ctnnb1, ccnb1, ccnd1, cyp7a1a, dkk3, ifng1-2, igf2a, il1b, lef1, nos2b, saa1, stat3, tnfa and wnt5a were confirmed to be elevated in SBS by RT-qPCR.

Results

RNA-seq analysis identified 1346 significantly upregulated genes and 678 significantly downregulated genes in SBS zebrafish intestine compared to sham with Ingenuity analysis. The upregulated genes were involved in cell proliferation, acute phase response signaling, innate and adaptive immunity, bile acid regulation, production of nitric oxide and reactive oxygen species, cellular barrier and coagulation. The downregulated genes were involved in folate synthesis, gluconeogenesis, glycogenolysis, fatty-acid oxidation and activation and drug and steroid metabolism. RT-qPCR confirmed gene expression differences from RNA-Sequencing.

Conclusion

Changes of gene expression after 2 weeks of SBS indicate complex and extensive alterations of multiple pathways, some previously implicated as effects of TPN. The systemic sequelae of SBS alone are significant and indicate multiple targets for investigating future therapies.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-3433-4) contains supplementary material, which is available to authorized users.

Evaluation of the External RNA Controls Consortium (ERCC) reference material using a modified Latin square design

BACKGROUND

Highly multiplexed assays for quantitation of RNA transcripts are being used in many areas of biology and medicine. Using data generated by these transcriptomic assays requires measurement assurance with appropriate controls. Methods to prototype and evaluate multiple RNA controls were developed as part of the External RNA Controls Consortium (ERCC) assessment process. These approaches included a modified Latin square design to provide a broad dynamic range of relative abundance with known differences between four complex pools of ERCC RNA transcripts spiked into a human liver total RNA background.

RESULTS

ERCC pools were analyzed on four different microarray platforms: Agilent 1- and 2-color, Illumina bead, and NIAID lab-made spotted microarrays; and two different second-generation sequencing platforms: the Life Technologies 5500xl and the Illumina HiSeq 2500. Individual ERCC controls were assessed for reproducible performance in signal response to concentration among the platforms. Most demonstrated linear behavior if they were not located near one of the extremes of the dynamic range. Performance issues with any individual ERCC transcript could be attributed to detection limitations, platform-specific target probe issues, or potential mixing errors. Collectively, these pools of spike-in RNA controls were evaluated for suitability as surrogates for endogenous transcripts to interrogate the performance of the RNA measurement process of each platform. The controls were useful for establishing the dynamic range of the assay, as well as delineating the useable region of that range where differential expression measurements, expressed as ratios, would be expected to be accurate.

CONCLUSIONS

The modified Latin square design presented here uses a composite testing scheme for the evaluation of multiple performance characteristics: linear performance of individual controls, signal response within dynamic range pools of controls, and ratio detection between pairs of dynamic range pools. This compact design provides an economical sample format for the evaluation of multiple external RNA controls within a single experiment per platform. These results indicate that well-designed pools of RNA controls, spiked into samples, provide measurement assurance for endogenous gene expression studies.

Quantitative large scale gene expression profiling from human stem cell culture micro samples using multiplex pre-amplification

Transcriptional profiling is a powerful tool to study biological mechanisms during stem cell differentiation and reprogramming. Genome-wide methods like microarrays or next generation sequencing are expensive, time consuming, and require special equipment and bioinformatics expertise. Quantitative RT-PCR remains one of today's most widely accepted and used methods for analyzing gene expression in biological samples. However, limitations in the amount of starting materials often hinder the quantity and quality of information that could be obtained from a given sample. Here, we present a fast 4-step workflow allowing direct, column-free RNA isolation from limited human pluripotent stem cell (hPSC) cultures that is directly compatible with subsequent reverse transcription, target specific multiplex pre-amplification, and standard SYBR-Green quantitative PCR (qPCR) analysis. The workflow delivers excellent correlations in normalized gene-expression data obtained from different samples of hPSCs over a wide range of cell numbers (500-50,000 cells). We demonstrate accurate and unbiased target gene quantification in limiting stem cell cultures which allows for monitoring embryoid body differentiation and induced pluripotent stem cell (iPSC) reprogramming. This method highlights a rapid and cost effective screening process, allowing reduction of culture formats and increase of processing throughputs for various stem cell applications.

Validation of Tuba1a as appropriate internal control for normalization of gene expression analysis during mouse lung development

The expression ratio between the analysed gene and an internal control gene is the most widely used normalization method for quantitative RT-PCR (qRT-PCR) expression analysis. The ideal reference gene for a specific experiment is the one whose expression is not affected by the different experimental conditions tested. In this study, we validate the applicability of five commonly used reference genes during different stages of mouse lung development. The stability of expression of five different reference genes (Tuba1a, Actb Gapdh, Rn18S and Hist4h4) was calculated within five experimental groups using the statistical algorithm of geNorm software. Overall, Tuba1a showed the least variability in expression among the different stages of lung development, while Hist4h4 and Rn18S showed the maximum variability in their expression. Expression analysis of two lung specific markers, surfactant protein C (SftpC) and Clara cell-specific 10 kDA protein (Scgb1a1), normalized to each of the five reference genes tested here, confirmed our results and showed that incorrect reference gene choice can lead to artefacts. Moreover, a combination of two internal controls for normalization of expression analysis during lung development will increase the accuracy and reliability of results.

mRNA enrichment protocols determine the quantification characteristics of external RNA spike-in controls in RNA-Seq studies

RNA-Seq promises to be used in clinical settings as a gene-expression profiling tool; however, questions about its variability and biases remain and need to be addressed. Thus, RNA controls with known concentrations and sequence identities originally developed by the External RNA Control Consortium (ERCC) for microarray and qPCR platforms have recently been proposed for RNA-Seq platforms, but only with a limited number of samples. In this study, we report our analysis of RNA-Seq data from 92 ERCC controls spiked in a diverse collection of 447 RNA samples from eight ongoing studies involving five species (human, rat, mouse, chicken, and Schistosoma japonicum) and two mRNA enrichment protocols, i.e., poly(A) and RiboZero. The entire collection of datasets consisted of 15650143175 short sequence reads, 131603796 (i.e., 0.84%) of which were mapped to the 92 ERCC references. The overall ERCC mapping ratio of 0.84% is close to the expected value of 1.0% when assuming a 2.0% mRNA fraction in total RNA, but showed a difference of 2.8-fold across studies and 4.3-fold among samples from the same study with one tissue type. This level of fluctuation may prevent the ERCC controls from being used for cross-sample normalization in RNA-Seq. Furthermore, we observed striking biases of quantification between poly(A) and RiboZero which are transcript-specific. For example, ERCC-00116 showed a 7.3-fold under-enrichment in poly(A) compared to RiboZero. Extra care is needed in integrative analysis of multiple datasets and technical artifacts of protocol differences should not be taken as true biological findings.