RNA Editing as a General Trait of Ebolaviruses.

RNA editing has been discovered as an essential mechanism for the transcription of the glycoprotein (GP) gene of Ebola virus but not Marburg virus. We developed a rapid transcript quantification assay (RTQA) to analyze RNA transcripts generated through RNA editing and used immunoblotting with a pan-ebolavirus monoclonal antibody to confirm different GP gene-derived products. RTQA successfully quantified GP gene transcripts during infection with representative members of 5 ebolavirus species. Immunoblotting verified expression of the soluble GP and the transmembrane GP. Our results defined RNA editing as a general trait of ebolaviruses. The degree of editing, however, varies among ebolaviruses with Reston virus showing the lowest and Bundibugyo virus the highest degree of editing.

The effect of alginate as an elicitor on transcription of steviol glycosides biosynthesis pathway related key genes and sweeteners content in in vitro cultured Stevia rebaudiana.

BACKGROUND: Stevia rebaudiana is a medicinal herb that accumulates non-caloric sweeteners called steviol glycosides (SGs) which are approximately 300 times sweeter than sucrose. This study used alginate (ALG) as an elicitor to increase steviol glycosides accumulation and elucidate gene transcription in the steviol glycosides biosynthesis pathway. METHODS AND RESULTS: To minimize the grassy taste associated with stevia sweeteners, plantlets were grown in complete darkness. ALG was applied to stevia plants grown in suspension culture with a Murashige and Skoog (MS) medium to determine its effect on SGs' content and the transcription profile of SG-related genes using the HPLC and RT-qPCR methods, respectively. Treatment with alginate did not significantly affect plantlet growth parameters such as shoot number, dry and fresh weight. Rebaudioside A (Reb A) content increased approximately sixfold in the presence of 1g L-1 alginate and KS, KAH, and UGT74G1 genes showed significant up-regulation. When the concentration was increased to 2g L-1, the transcription of KO and UGT76G1, responsible for the conversion of stevioside to Reb A, was increased about twofold. CONCLUSIONS: The current study proposes that adding alginate to the MS suspension medium can increase Reb A levels by altering the SG biosynthesize pathway's transcription profile. The present experiment provides new insights into the biochemical and transcriptional response mechanisms of suspension-cultured stevia plants to alginate.

Next-generation transcriptome assembly and analysis: Impact of ploidy.

Whole genome duplications (WGD) occur widely in plants, but the effects of these events impact all branches of life. WGD events have major evolutionary impacts, often leading to major structural changes within the chromosomes and massive changes in gene expression that facilitate rapid speciation and gene diversification. Even for species that currently have diploid genomes, the impact of ancestral duplication events is still present in the genomes, especially in the context of highly similar gene families that are retained from WGD. However, the impact of these ploidies on various bioinformatics workflows has not been studied well. In this review, we overview biological significance of polyploidy in different organisms. We describe the impact of having polyploid transcriptomes on bioinformatics analyses, especially focusing on transcriptome assembly and transcript quantification. We discuss the benefits of using simulated benchmarking data when we examine the performance of various methods. We also present an example strategy to generate simulated allopolyploid transcriptomes and RNAseq datasets and how these benchmark datasets can be used to assess the performance of transcript assembly and quantification methods. Our benchmarking study shows that all transcriptome assembly methods are affected by having polyploid genomes. Quantification accuracy is also impacted by polyploidy depending on the method. These simulated datasets can be adapted for testing, such as, read mapping, variant calling, and differential expression using biologically realistic conditions.

Loss of HCN2 in Dorsal Hippocampus of Young Adult Mice Induces Specific Apoptosis of the CA1 Pyramidal Neuron Layer.

Neurons inevitably rely on a proper repertoire and distribution of membrane-bound ion-conducting channels. Among these proteins, the family of hyperpolarization-activated and cyclic nucleotide-gated (HCN) channels possesses unique properties giving rise to the corresponding Ih-current that contributes to various aspects of neural signaling. In mammals, four genes (hcn1-4) encode subunits of HCN channels. These subunits can assemble as hetero- or homotetrameric ion-conducting channels. In order to elaborate on the specific role of the HCN2 subunit in shaping electrical properties of neurons, we applied an Adeno-associated virus (AAV)-mediated, RNAi-based knock-down strategy of hcn2 gene expression both in vitro and in vivo. Electrophysiological measurements showed that HCN2 subunit knock-down resulted in specific yet anticipated changes in Ih-current properties in primary hippocampal neurons and, in addition, corroborated that the HCN2 subunit participates in postsynaptic signal integration. To further address the role of the HCN2 subunit in vivo, we injected recombinant (r)AAVs into the dorsal hippocampus of young adult male mice. Behavioral and biochemical analyses were conducted to assess the contribution of HCN2-containing channels in shaping hippocampal network properties. Surprisingly, knock-down of hcn2 expression resulted in a severe degeneration of the CA1 pyramidal cell layer, which did not occur in mice injected with control rAAV constructs. This finding might pinpoint to a vital and yet unknown contribution of HCN2 channels in establishing or maintaining the proper function of CA1 pyramidal neurons of the dorsal hippocampus.

Transcriptomic profile analysis of mouse neural tube development by RNA-Seq.

The neural tube is the primordium of the central nervous system (CNS) in which its development is not entirely clear. Understanding the cellular and molecular basis of neural tube development could, therefore, provide vital clues to the mechanism of neural tube defects (NTDs). Here, we investigated the gene expression profiles of three different time points (embryonic day (E) 8.5, 9.5 and 10.5) of mouse neural tube by using RNA-seq approach. About 391 differentially expressed genes (DEGs) were screened during mouse neural tube development, including 45 DEGs involved in CNS development, among which Bmp2, Ascl1, Olig2, Lhx1, Wnt7b and Eomes might play the important roles. Of 45 DEGs, Foxp2, Eomes, Hoxb3, Gpr56, Hap1, Nkx2-1, Sez6l2, Wnt7b, Tbx20, Nfib, Cntn1 and Dcx had different isoforms, and the opposite expression pattern of different isoforms was observed for Gpr56, Nkx2-1 and Sez6l2. In addition, alternative splicing, such as mutually exclusive exon, retained intron, skipped exon and alternative 3' splice site was identified in 10 neural related differentially splicing genes, including Ngrn, Ddr1, Dctn1, Dnmt3b, Ect2, Map2, Mbnl1, Meis2, Vcan and App. Moreover, seven neural splicing factors, such as Nova1/2, nSR100/Srrm4, Elavl3/4, Celf3 and Rbfox1 were differentially expressed during mouse neural tube development. Interestingly, nine DEGs identified above were dysregulated in retinoic acid-induced NTDs model, indicating the possible important role of these genes in NTDs. Taken together, our study provides more comprehensive information on mouse neural tube development, which might provide new insights on NTDs occurrence. © 2017 IUBMB Life, 69(9):706-719, 2017.

Web-based bioinformatics workflows for end-to-end RNA-seq data computation and analysis in agricultural animal species.

BACKGROUND: Remarkable advances in Next Generation Sequencing (NGS) technologies, bioinformatics algorithms and computational technologies have significantly accelerated genomic research. However, complicated NGS data analysis still remains as a major bottleneck. RNA-seq, as one of the major area in the NGS field, also confronts great challenges in data analysis. RESULTS: To address the challenges in RNA-seq data analysis, we developed a web portal that offers three integrated workflows that can perform end-to-end compute and analysis, including sequence quality control, read-mapping, transcriptome assembly, reconstruction and quantification, and differential analysis. The first workflow utilizes Tuxedo (Tophat, Cufflink, Cuffmerge and Cuffdiff suite of tools). The second workflow deploys Trinity for de novo assembly and uses RSEM for transcript quantification and EdgeR for differential analysis. The third combines STAR, RSEM, and EdgeR for data analysis. All these workflows support multiple samples and multiple groups of samples and perform differential analysis between groups in a single workflow job submission. The calculated results are available for download and post-analysis. The supported animal species include chicken, cow, duck, goat, pig, horse, rabbit, sheep, turkey, as well as several other model organisms including yeast, C. elegans, Drosophila, and human, with genomic sequences and annotations obtained from ENSEMBL. The RNA-seq portal is freely available from http://weizhongli-lab.org/RNA-seq . CONCLUSIONS: The web portal offers not only bioinformatics software, workflows, computation and reference data, but also an integrated environment for complex RNA-seq data analysis for agricultural animal species. In this project, our aim is not to develop new RNA-seq tools, but to build web workflows for using popular existing RNA-seq methods and make these tools more accessible to the communities.

Oarfish: Enhanced probabilistic modeling leads to improved accuracy in long read transcriptome quantification.

Motivation: Long read sequencing technology is becoming an increasingly indispensable tool in genomic and transcriptomic analysis. In transcriptomics in particular, long reads offer the possibility of sequencing full-length isoforms, which can vastly simplify the identification of novel transcripts and transcript quantification. However, despite this promise, the focus of much long read method development to date has been on transcript identification, with comparatively little attention paid to quantification. Yet, due to differences in the underlying protocols and technologies, lower throughput (i.e. fewer reads sequenced per sample compared to short read technologies), as well as technical artifacts, long read quantification remains a challenge, motivating the continued development and assessment of quantification methods tailored to this increasingly prevalent type of data. Results: We introduce a new method and software tool for long read transcript quantification called oarfish. Our model incorporates a novel and innovative coverage score, which affects the conditional probability of fragment assignment in the underlying probabilistic model. We demonstrate that by accounting for this coverage information, oarfish is able to produce more accurate quantification estimates than existing long read quantification methods, particularly when one considers the primary isoforms present in a particular cell line or tissue type. Availability and Implementation: Oarfish is implemented in the Rust programming language, and is made available as free and open-source software under the BSD 3-clause license. The source code is available at https://www.github.com/COMBINE-lab/oarfish.

SCAPTURE: a deep learning-embedded pipeline that captures polyadenylation information from 3' tag-based RNA-seq of single cells.

Single-cell RNA-seq (scRNA-seq) profiles gene expression with high resolution. Here, we develop a stepwise computational method-called SCAPTURE to identify, evaluate, and quantify cleavage and polyadenylation sites (PASs) from 3' tag-based scRNA-seq. SCAPTURE detects PASs de novo in single cells with high sensitivity and accuracy, enabling detection of previously unannotated PASs. Quantified alternative PAS transcripts refine cell identity analysis beyond gene expression, enriching information extracted from scRNA-seq data. Using SCAPTURE, we show changes of PAS usage in PBMCs from infected versus healthy individuals at single-cell resolution.

Exact transcript quantification over splice graphs.

BACKGROUND: The probability of sequencing a set of RNA-seq reads can be directly modeled using the abundances of splice junctions in splice graphs instead of the abundances of a list of transcripts. We call this model graph quantification, which was first proposed by Bernard et al. (Bioinformatics 30:2447-55, 2014). The model can be viewed as a generalization of transcript expression quantification where every full path in the splice graph is a possible transcript. However, the previous graph quantification model assumes the length of single-end reads or paired-end fragments is fixed. RESULTS: We provide an improvement of this model to handle variable-length reads or fragments and incorporate bias correction. We prove that our model is equivalent to running a transcript quantifier with exactly the set of all compatible transcripts. The key to our method is constructing an extension of the splice graph based on Aho-Corasick automata. The proof of equivalence is based on a novel reparameterization of the read generation model of a state-of-art transcript quantification method. CONCLUSION: We propose a new approach for graph quantification, which is useful for modeling scenarios where reference transcriptome is incomplete or not available and can be further used in transcriptome assembly or alternative splicing analysis.

AAV-Mediated CRISPRi and RNAi Based Gene Silencing in Mouse Hippocampal Neurons.

Uncovering the physiological role of individual proteins that are part of the intricate process of cellular signaling is often a complex and challenging task. A straightforward strategy of studying a protein's function is by manipulating the expression rate of its gene. In recent years, the Clustered Regularly Interspaced Short Palindromic Repeat (CRISPR)/Cas9-based technology was established as a powerful gene-editing tool for generating sequence specific changes in proliferating cells. However, obtaining homogeneous populations of transgenic post-mitotic neurons by CRISPR/Cas9 turned out to be challenging. These constraints can be partially overcome by CRISPR interference (CRISPRi), which mediates the inhibition of gene expression by competing with the transcription machinery for promoter binding and, thus, transcription initiation. Notably, CRISPR/Cas is only one of several described approaches for the manipulation of gene expression. Here, we targeted neurons with recombinant Adeno-associated viruses to induce either CRISPRi or RNA interference (RNAi), a well-established method for impairing de novo protein biosynthesis by using cellular regulatory mechanisms that induce the degradation of pre-existing mRNA. We specifically targeted hyperpolarization-activated and cyclic nucleotide-gated (HCN) channels, which are widely expressed in neuronal tissues and play essential physiological roles in maintaining biophysical characteristics in neurons. Both of the strategies reduced the expression levels of three HCN isoforms (HCN1, 2, and 4) with high specificity. Furthermore, detailed analysis revealed that the knock-down of just a single HCN isoform (HCN4) in hippocampal neurons did not affect basic electrical parameters of transduced neurons, whereas substantial changes emerged in HCN-current specific properties.

AP-TSS: A New Method for the Analysis of RNA Expression from Particular and Challenging Transcription Start Sites.

Alternative promoter usage involved in the regulation of transcription, splicing, and translation contributes to proteome diversity and is involved in a large number of diseases, in particular, cancer. Epigenetic mechanisms and cis regulatory elements are involved in alternative promoter activity. Multiple transcript isoforms can be produced from a gene, due to the initiation of transcription at different transcription start sites (TSS). These transcripts may not have regions that allow discrimination during RT-qPCR, making quantification technically challenging. This study presents a general method for the relative quantification of a transcript synthesized from a particular TSS that we called AP-TSS (analysis of particular TSS). AP-TSS is based on the specific elongation of the cDNA of interest, followed by its quantification by qPCR. As proof of principle, AP-TSS was applied to two non-coding RNA: telomeric repeat-containing RNAs (TERRA) from a particular subtelomeric TSS, and Alu transcripts. The treatment of cells with a DNA methylation inhibitor was associated with a global increase of the total TERRA level, but the TERRA expression from the TSS of interest did not change in HT1080 cells, and only modestly increased in HeLa cells. This result suggests that TERRA upregulation induced by global demethylation of the genome is mainly due to activation from sites other than this particular TSS. For Alu RNA, the signal obtained by AP-TSS is specific for the RNA Polymerase III-dependent Alu transcript. In summary, our method provides a tool to study regulation of gene expression from a given transcription start site, in different conditions that could be applied to many genes. In particular, AP-TSS can be used to investigate the epigenetic regulation of alternative TSS usage that is of importance for the development of epigenetic-targeted therapies.

QuaPra: Efficient transcript assembly and quantification using quadratic programming with Apriori algorithm.

RNA sequencing (RNA-seq) has greatly facilitated the exploring of transcriptome landscape for diverse organisms. However, transcriptome reconstruction is still challenging due to various limitations of current tools and sequencing technologies. Here, we introduce an efficient tool, QuaPra (Quadratic Programming combined with Apriori), for accurate transcriptome assembly and quantification. QuaPra could detect at least 26.5% more low abundance (0.1-1 FPKM) transcripts with over 2.1% increase of sensitivity and precision on simulated data compared to other currently popular tools. Moreover, around one-quarter more known transcripts were correctly assembled by QuaPra than other assemblers on real sequencing data. QuaPra is freely available at https://doi.org/www.megabionet.org/QuaPra/ .

Quantitative Detection of Low-Abundance Transcripts at Single-Cell Level in Human Epidermal Keratinocytes by Digital Droplet Reverse Transcription-Polymerase Chain Reaction.

Genetic and epigenetic characterization of the large cellular diversity observed within tissues is essential to understanding the molecular networks that ensure the regulation of homeostasis, repair, and regeneration, but also pathophysiological processes. Skin is composed of multiple cell lineages and is therefore fully concerned by this complexity. Even within one particular lineage, such as epidermal keratinocytes, different immaturity statuses or differentiation stages are represented, which are still incompletely characterized. Accordingly, there is presently great demand for methods and technologies enabling molecular investigation at single-cell level. Also, most current methods used to analyze gene expression at RNA level, such as RT-qPCR, do not directly provide quantitative data, but rather comparative ratios between two conditions. A second important need in skin biology is thus to determine the number of RNA molecules in a given cell sample. Here, we describe a workflow that we have set up to meet these specific needs, by means of transcript quantification in cellular micro-samples using flow cytometry sorting and reverse transcription-digital droplet polymerase chain reaction. As a proof-of-principle, the workflow was tested for the detection of transcription factor transcripts expressed at low levels in keratinocyte precursor cells. A linear correlation was found between quantification values and keratinocyte input numbers in a low quantity range from 40 cells to 1 cell. Interpretable signals were repeatedly obtained from single-cell samples corresponding to estimated expression levels as low as 10-20 transcript copies per keratinocyte or less. The present workflow may have broad applications for the detection and quantification of low-abundance nucleic acid species in single cells, opening up perspectives for the study of cell-to-cell genetic and molecular heterogeneity. Interestingly, the process described here does not require internal references such as house-keeping gene expression, as it is initiated with defined cell numbers, precisely sorted by flow cytometry.

Computational Analysis of RNA-Seq Data from Airway Epithelial Cells for Studying Lung Disease.

Airway epithelial cells (AECs) play a central role in the pathogenesis of many lung diseases. Consequently, advancements in our understanding of the underlying causes of lung diseases, and the development of novel treatments, depend on continued detailed study of these cells. Generation and analysis of high-throughput gene expression data provide an indispensable tool for carrying out the type of broad-scale investigations needed to identify the key genes and molecular pathways that regulate, distinguish, and predict distinct pulmonary pathologies. Of the available technologies for generating genome-wide expression data, RNA sequencing (RNA-seq) has emerged as the most powerful. Hence many researchers are turning to this approach in their studies of lung disease. For the relatively uninitiated, computational analysis of RNA-seq data can be daunting, given the large number of methods and software packages currently available. The aim of this chapter is to provide a broad overview of the major steps involved in processing and analyzing RNA-seq data, with a special focus on methods optimized for data generated from AECs. We take the reader from the point of obtaining sequence reads from the lab to the point of making biological inferences with expression data. Along the way, we discuss the statistical and computational considerations one typically confronts during different phases of analysis and point to key methods, software packages, papers, online guides, and other resources that can facilitate successful RNA-seq analysis.

Improved RNA-seq Workflows Using CyVerse Cyberinfrastructure.

RNA-seq is a vital method for understanding gene structure and expression patterns. Typical RNA-seq analysis protocols use sequencing reads of length 50 to 150 nucleotides for alignment to the reference genome and assembly of transcripts. The resultant transcripts are quantified and used for differential expression and visualization. Existing tools and protocols for RNA-seq are vast and diverse; given their differences in performance, it is critical to select an analysis protocol that is scalable, accurate, and easy to use. Tuxedo, a popular alignment-based protocol for RNA-seq analysis, has been updated with HISAT2, StringTie, StringTie-merge, and Ballgown, and the updated protocol outperforms its predecessor. Similarly, new pseudo-alignment-based protocols like Kallisto and Sleuth reduce runtime and improve performance. However, these tools are challenging for researchers lacking command-line experience. Here, we describe two new RNA-seq analysis protocols, in which all tools are deployed on CyVerse Cyberinfrastructure with user-friendly graphical user interfaces, and validate their performance using plant RNA-seq data. © 2018 by John Wiley & Sons, Inc.

Transcript Quantification of Genes Involved in Steviol Glycoside Biosynthesis in Stevia rebaudiana Bertoni by Real-Time Polymerase Chain Reaction (RT-PCR).

Stevia (Stevia rebaudiana Bertoni) is a medicinal plant having sweet, diterpenoid glycosides known as steviol glycosides which are 200-300 times sweeter than sucrose (0.4 % solution). They are synthesized mainly in the leaves via plastid localized 2-C-methyl-D-erythrose-4-phosphate pathway (MEP pathway). Fifteen genes are involved in the formation of these glycosides. In the present protocol, a method for the quantification of transcripts of these genes is shown. The work involves RNA extraction and cDNA preparation, and therefore, procedures for the confirmation of DNA-free cDNA preparation have also been illustrated. Moreover, details of plant treatments are not mentioned as this protocol may apply to relative gene expression profile in any medicinal plant with any treatment. The treatments are numbered as T0 (Control), T1, T2, T3, and T4.

Evaluation of UL99 transcript as a target for antiviral treatment efficacy.

Human cytomegalovirus (HCMV) is a virus belonging to the Beta Herpes virus family. Its genome contains many different genes clustered in immediate early, early and late genes. This last cluster includes UL99, a late gene that encodes for a tegument protein called pp28. In immunocompetent patients, HCMV infection occurs asymptomatically, while its reactivation in immunocompromised patients can be a cause of pneumonia, retinitis and gastrointestinal diseases. To prevent or to contrast HCMV infection, several drugs (such as Ganciclovir, Acyclovir, Foscarnet) are available, and their efficiency is evaluated by HCMV DNA load monitoring, as also for antiviral resistance onset that may occur after the therapy. In this study is described the development of a Real Time PCR for the detection and quantification of UL99 transcript and the clearance of this target compared to HCMV DNA, both in vitro and in vivo on bronchoalveolar lavage samples.

Digital Droplet PCR: CNV Analysis and Other Applications.

Digital droplet PCR (ddPCR) is an assay that combines state-of-the-art microfluidics technology with TaqMan-based PCR to achieve precise target DNA quantification at high levels of sensitivity and specificity. Because quantification is achieved without the need for standard assays in an easy to interpret, unambiguous digital readout, ddPCR is far simpler, faster, and less error prone than real-time qPCR. The basic protocol can be modified with minor adjustments to suit a wide range of applications, such as CNV analysis, rare variant detection, SNP genotyping, and transcript quantification. This unit describes the ddPCR workflow in detail for the Bio-Rad QX100 system, but the theory and data interpretation are generalizable to any ddPCR system.