15 Differentially Expressed Long Non-coding RNA Associated with Gastrointestinal Nematode Infection in Divergent Immune Response Sheep

Gastrointestinal nematode infection is one of the major production problems for sheep producers worldwide due its high incidence, morbidity, and mortality in affected flocks. The study of long non-coding RNA (lncRNA) in liver tissue of high (HIR) and low immune responder (LIR) sheep to GINs using RNA-Sequencing technology may provide a better understanding of the gene regulation mechanism associated with the host response to the infection. The aim of this study was to identify differentially expressed (DE) lncRNA between HIR and LIR natural infested sheep and control group. Liver tissue samples from the 13 divergent animals (out of a population of 211) based on their immunoglobulin G levels after vaccination using Hen Egg White (HEW) Lysozyme, and immature abomasum worm counts [HIR (> 4000) (n = 5), LIR (< 1500) (n=5) and control (no parasite challenge) (n=4) groups] were used to perform transcriptomic analysis using RNA-Sequencing. The “Large Gap read mapping “and “Transcript Discovery” tools from CLC Genomics Workbench 20.0.4 (CLC Bio, Aarhus, Denmark), were used to map reads to a reference genome (Oar_rambouillet_v1.0) and transcript discovery, respectively. The FEELnc software was used to identify, from predicted transcript model, potential lncRNAs and classify those transcripts into intro putative lncRNAs and protein coding RNAs. As preliminary results, 8 and 48 DE lncRNAs for HIR and LIR compared to control group were identified, respectively using an adjusted p-value False Discovery Rate (FDR) < 0.05 and Fold change (FC) abs > 2. Functional analyses using the list of DE lncRNAs identified metabolic pathways related to immune function. In depth analysis will help to better understand the physiological mechanisms of resilience of high immune sheep.

Novel Transcript Discovery Expands the Repertoire of Pathologically-Associated, Long Non-Coding RNAs in Vascular Smooth Muscle Cells

Vascular smooth muscle cells (VSMCs) provide vital contractile force within blood vessel walls, yet can also propagate cardiovascular pathologies through proliferative and pro-inflammatory activities. Such phenotypes are driven, in part, by the diverse effects of long non-coding RNAs (lncRNAs) on gene expression. However, lncRNA characterisation in VSMCs in pathological states is hampered by incomplete lncRNA representation in reference annotation. We aimed to improve lncRNA representation in such contexts by assembling non-reference transcripts in RNA sequencing datasets describing VSMCs stimulated in vitro with cytokines, growth factors, or mechanical stress, as well as those isolated from atherosclerotic plaques. All transcripts were then subjected to a rigorous lncRNA prediction pipeline. We substantially improved coverage of lncRNAs responding to pro-mitogenic stimuli, with non-reference lncRNAs contributing 21–32% for each dataset. We also demonstrate non-reference lncRNAs were biased towards enriched expression within VSMCs, and transcription from enhancer sites, suggesting particular relevance to VSMC processes, and the regulation of neighbouring protein-coding genes. Both VSMC-enriched and enhancer-transcribed lncRNAs were large components of lncRNAs responding to pathological stimuli, yet without novel transcript discovery 33–46% of these lncRNAs would remain hidden. Our comprehensive VSMC lncRNA repertoire allows proper prioritisation of candidates for characterisation and exemplifies a strategy to broaden our knowledge of lncRNA across a range of disease states.

PRAM: a novel pooling approach for discovering intergenic transcripts from large-scale RNA sequencing experiments

Publicly available RNA-seq data is routinely used for retrospective analysis to elucidate new biology. Novel transcript discovery enabled by joint examination of large collections of RNA-seq datasets has emerged as one such analysis. Current methods for transcript discovery rely on a ‘2-Step’ approach where the first step encompasses building transcripts from individual datasets, followed by the second step that merges predicted transcripts across datasets. To increase the power of transcript discovery from large collections of RNA-seq datasets, we developed a novel ‘1-Step’ approach named Pooling RNA-seq and Assembling Models (PRAM) that builds transcript models from pooled RNA-seq datasets. We demonstrate in a computational benchmark that ‘1-Step’ outperforms ‘2-Step’ approaches in predicting overall transcript structures and individual splice junctions, while performing competitively in detecting exonic nucleotides. Applying PRAM to 30 human ENCODE RNA-seq datasets identified unannotated transcripts with epigenetic and RAMPAGE signatures similar to those of recently annotated transcripts. In a case study, we discovered and experimentally validated new transcripts through the application of PRAM to mouse hematopoietic RNA-seq datasets. Notably, we uncovered new transcripts that share a differential expression pattern with a neighboring genePik3cgimplicated in human hematopoietic phenotypes, and we provided evidence for the conservation of this relationship in human. PRAM is implemented as an R/Bioconductor package and is available athttps://bioconductor.org/packages/pram.

Discovery and Annotation of Plant Endogenous Target Mimicry Sequences from Public Transcriptome Libraries: A Case Study of Prunus persica

Novel transcript discovery through RNA sequencing has substantially improved our understanding of the transcriptome dynamics of biological systems. Endogenous target mimicry (eTM) transcripts, a novel class of regulatory molecules, bind to their target microRNAs (miRNAs) by base pairing and block their biological activity. The objective of this study was to provide a computational analysis framework for the prediction of putative eTM sequences in plants, and as an example, to discover previously un-annotated eTMs in Prunus persica (peach) transcriptome. Therefore, two public peach transcriptome libraries downloaded from Sequence Read Archive (SRA) and a previously published set of long non-coding RNAs (lncRNAs) were investigated with multi-step analysis pipeline, and 44 putative eTMs were found. Additionally, an eTM-miRNA-mRNA regulatory network module associated with peach fruit organ development was built via integration of the miRNA target information and predicted eTM-miRNA interactions. My findings suggest that one of the most widely expressed miRNA families among diverse plant species, miR156, might be potentially sponged by seven putative eTMs. Besides, the study indicates eTMs potentially play roles in the regulation of development processes in peach fruit via targeting specific miRNAs. In conclusion, by following the step-by step instructions provided in this study, novel eTMs can be identified and annotated effectively in public plant transcriptome libraries.

A dual transcript-discovery approach to improve the delimitation of gene features from RNA-seq data in the chicken model

The sequence of the chicken genome, like several other draft genome sequences, is presently not fully covered. Gaps, contigs assigned with low confidence and uncharacterized chromosomes result in gene fragmentation and imprecise gene annotation. Transcript abundance estimation from RNA sequencing (RNA-seq) data relies on read quality, library complexity and expression normalization. In addition, the quality of the genome sequence used to map sequencing reads and the gene annotation that defines gene features must also be taken into account. Partially covered genome sequence causes the loss of sequencing reads from the mapping step, while an inaccurate definition of gene features induces imprecise read counts from the assignment step. Both steps can significantly bias interpretation of RNA-seq data. Here, we describe a dual transcript-discovery approach combining a genome-guided gene prediction and ade novotranscriptome assembly. This dual approach enabled us to increase the assignment rate of RNA-seq data by nearly 20% as compared to when using only the chicken reference annotation, contributing therefore to a more accurate estimation of transcript abundance. More generally, this strategy could be applied to any organism with partial genome sequence and/or lacking a manually-curated reference annotation in order to improve the accuracy of gene expression studies.