scholarly journals Identification of sheep lncRNAs related to the immune response to vaccines and aluminium adjuvants

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Martin Bilbao-Arribas ◽  
Endika Varela-Martínez ◽  
Naiara Abendaño ◽  
Damián de Andrés ◽  
Lluís Luján ◽  
...  
Keyword(s):  
Immune Response ◽  
Expression Analysis ◽  
Expression Patterns ◽  
Differential Expression Analysis ◽  
Rna Seq ◽  
Protein Coding ◽  
Protein Coding Genes ◽  
Livestock Species ◽  
Non Coding Rnas ◽  
Aluminium Adjuvant

Abstract Background Long non-coding RNAs (lncRNAs) are involved in several immune processes, including the immune response to vaccination, but most of them remain uncharacterised in livestock species. The mechanism of action of aluminium adjuvants as vaccine components is neither not fully understood. Results We built a transcriptome from sheep PBMCs RNA-seq data in order to identify unannotated lncRNAs and analysed their expression patterns along protein coding genes. We found 2284 novel lncRNAs and assessed their conservation in terms of sequence and synteny. Differential expression analysis performed between animals inoculated with commercial vaccines or aluminium adjuvant alone and the co-expression analysis revealed lncRNAs related to the immune response to vaccines and adjuvants. A group of co-expressed genes enriched in cytokine signalling and production highlighted the differences between different treatments. A number of differentially expressed lncRNAs were correlated with a divergently located protein-coding gene, such as the OSM cytokine. Other lncRNAs were predicted to act as sponges of miRNAs involved in immune response regulation. Conclusions This work enlarges the lncRNA catalogue in sheep and puts an accent on their involvement in the immune response to repetitive vaccination, providing a basis for further characterisation of the non-coding sheep transcriptome within different immune cells.

scholarly journals Identifying inaccuracies in gene expression estimates from unstranded RNA-seq data

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Mikhail Pomaznoy ◽  
Ashu Sethi ◽  
Jason Greenbaum ◽  
Bjoern Peters
Keyword(s):  
Gene Expression ◽  
Differential Expression Analysis ◽  
Cell Types ◽  
Library Preparation ◽  
Rna Seq ◽  
Protein Coding ◽  
Protein Coding Genes ◽  
Machine Learning Model ◽  
Specific Manner ◽  
Library Preparation Protocol

Abstract RNA-seq methods are widely utilized for transcriptomic profiling of biological samples. However, there are known caveats of this technology which can skew the gene expression estimates. Specifically, if the library preparation protocol does not retain RNA strand information then some genes can be erroneously quantitated. Although strand-specific protocols have been established, a significant portion of RNA-seq data is generated in non-strand-specific manner. We used a comprehensive stranded RNA-seq dataset of 15 blood cell types to identify genes for which expression would be erroneously estimated if strand information was not available. We found that about 10% of all genes and 2.5% of protein coding genes have a two-fold or higher difference in estimated expression when strand information of the reads was ignored. We used parameters of read alignments of these genes to construct a machine learning model that can identify which genes in an unstranded dataset might have incorrect expression estimates and which ones do not. We also show that differential expression analysis of genes with biased expression estimates in unstranded read data can be recovered by limiting the reads considered to those which span exonic boundaries. The resulting approach is implemented as a package available at https://github.com/mikpom/uslcount.

scholarly journals Emerging Roles of Estrogen-Regulated Enhancer and Long Non-Coding RNAs

2020 ◽  
Vol 21 (10) ◽  
pp. 3711
Author(s):  
Melina J. Sedano ◽  
Alana L. Harrison ◽  
Mina Zilaie ◽  
Chandrima Das ◽  
Ramesh Choudhari ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Expression Patterns ◽  
Biological Significance ◽  
Rna Seq ◽  
Biological Functions ◽  
Protein Coding ◽  
Rna Molecules ◽  
Non Coding Rna ◽  
Genome Wide ◽  
Non Coding Rnas

Genome-wide RNA sequencing has shown that only a small fraction of the human genome is transcribed into protein-coding mRNAs. While once thought to be “junk” DNA, recent findings indicate that the rest of the genome encodes many types of non-coding RNA molecules with a myriad of functions still being determined. Among the non-coding RNAs, long non-coding RNAs (lncRNA) and enhancer RNAs (eRNA) are found to be most copious. While their exact biological functions and mechanisms of action are currently unknown, technologies such as next-generation RNA sequencing (RNA-seq) and global nuclear run-on sequencing (GRO-seq) have begun deciphering their expression patterns and biological significance. In addition to their identification, it has been shown that the expression of long non-coding RNAs and enhancer RNAs can vary due to spatial, temporal, developmental, or hormonal variations. In this review, we explore newly reported information on estrogen-regulated eRNAs and lncRNAs and their associated biological functions to help outline their markedly prominent roles in estrogen-dependent signaling.

scholarly journals Dynamic Expression of Long Non-Coding RNAs Throughout Parasite Sexual and Neural Maturation in Schistosoma Japonicum

2020 ◽  
Vol 6 (2) ◽  
pp. 15 ◽  
Author(s):  
Lucas Maciel ◽  
David Morales-Vicente ◽  
Sergio Verjovski-Almeida
Keyword(s):  
Schistosoma Japonicum ◽  
Sexual Maturation ◽  
System Development ◽  
Sequence Similarity ◽  
Nervous System Development ◽  
Rna Seq ◽  
Protein Coding ◽  
Protein Coding Genes ◽  
Synteny Conservation ◽  
Non Coding Rnas

Schistosoma japonicum is a flatworm that causes schistosomiasis, a neglected tropical disease. S. japonicum RNA-Seq analyses has been previously reported in the literature on females and males obtained during sexual maturation from 14 to 28 days post-infection in mouse, resulting in the identification of protein-coding genes and pathways, whose expression levels were related to sexual development. However, this work did not include an analysis of long non-coding RNAs (lncRNAs). Here, we applied a pipeline to identify and annotate lncRNAs in 66 S. japonicum RNA-Seq publicly available libraries, from different life-cycle stages. We also performed co-expression analyses to find stage-specific lncRNAs possibly related to sexual maturation. We identified 12,291 S. japonicum expressed lncRNAs. Sequence similarity search and synteny conservation indicated that some 14% of S. japonicum intergenic lncRNAs have synteny conservation with S. mansoni intergenic lncRNAs. Co-expression analyses showed that lncRNAs and protein-coding genes in S. japonicum males and females have a dynamic co-expression throughout sexual maturation, showing differential expression between the sexes; the protein-coding genes were related to the nervous system development, lipid and drug metabolism, and overall parasite survival. Co-expression pattern suggests that lncRNAs possibly regulate these processes or are regulated by the same activation program as that of protein-coding genes.

scholarly journals RNA-Seq Implies Divergent Regulation Patterns of LincRNA on Spermatogenesis and Testis Growth in Goats

Animals ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 625
Author(s):  
Dongdong Bo ◽  
Xunping Jiang ◽  
Guiqiong Liu ◽  
Ruixue Hu ◽  
Yuqing Chong
Keyword(s):  
Target Genes ◽  
Expression Patterns ◽  
Testicular Development ◽  
Related Protein ◽  
Rna Seq ◽  
Down Regulation ◽  
Protein Coding ◽  
Protein Coding Genes ◽  
Regulation Of Growth ◽  
Fresh Insight

Long intergenic non-coding RNAs (lincRNAs) regulate testicular development by acting on protein-coding genes. However, little is known about whether lincRNAs and protein-coding genes exhibit the same expression pattern in the same phase of postnatal testicular development in goats. Therefore, this study aimed to demonstrate the expression patterns and roles of lincRNAs during the postnatal development of the goat testis. Herein, the testes of Yiling goats with average ages of 0, 30, 60, 90, 120, 150, and 180 days postnatal (DP) were used for RNA-seq. In total, 20,269 lincRNAs were identified, including 16,931 novel lincRNAs. We identified seven time-specifically diverse lincRNA modules and six mRNA modules by weighted gene co-expression network analysis (WGCNA). Interestingly, the down-regulation of growth-related lincRNAs was nearly one month earlier than the up-regulation of spermatogenesis-related lincRNAs, while the down-regulation of growth-related protein-coding genes and the correspondent up-regulation of spermatogenesis-related protein-coding genes occurred at the same age. Then, potential lincRNA target genes were predicted. Moreover, the co-expression network of lincRNAs demonstrated that ENSCHIT00000000777, ENSCHIT00000002069, and ENSCHIT00000005076 were the key lincRNAs in the process of testis development. Our study discovered the divergent regulation patterns of lincRNA on spermatogenesis and testis growth, providing a fresh insight into age-biased changes in lincRNA expression in the goat testis.

scholarly journals An integrative atlas of chicken long non-coding genes and their annotations across 25 tissues

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Frédéric Jehl ◽  
Kévin Muret ◽  
Maria Bernard ◽  
Morgane Boutin ◽  
Laetitia Lagoutte ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Biological Processes ◽  
Rna Seq ◽  
Protein Coding ◽  
Tissue Specific ◽  
Protein Coding Genes ◽  
Intergenic Regions ◽  
Immune Tissues ◽  
Non Coding Rnas

AbstractLong non-coding RNAs (LNC) regulate numerous biological processes. In contrast to human, the identification of LNC in farm species, like chicken, is still lacunar. We propose a catalogue of 52,075 chicken genes enriched in LNC (http://www.fragencode.org/), built from the Ensembl reference extended using novel LNC modelled here from 364 RNA-seq and LNC from four public databases. The Ensembl reference grew from 4,643 to 30,084 LNC, of which 59% and 41% with expression ≥ 0.5 and ≥ 1 TPM respectively. Characterization of these LNC relatively to the closest protein coding genes (PCG) revealed that 79% of LNC are in intergenic regions, as in other species. Expression analysis across 25 tissues revealed an enrichment of co-expressed LNC:PCG pairs, suggesting co-regulation and/or co-function. As expected LNC were more tissue-specific than PCG (25% vs. 10%). Similarly to human, 16% of chicken LNC hosted one or more miRNA. We highlighted a new chicken LNC, hosting miR155, conserved in human, highly expressed in immune tissues like miR155, and correlated with immunity-related PCG in both species. Among LNC:PCG pairs tissue-specific in the same tissue, we revealed an enrichment of divergent pairs with the PCG coding transcription factors, as for example LHX5, HXD3 and TBX4, in both human and chicken.

scholarly journals Long non-coding RNA transcriptome of uncharacterized samples can be accurately imputed using protein-coding genes

2019 ◽  
Vol 21 (2) ◽  
pp. 637-648 ◽  
Author(s):  
Aritro Nath ◽  
Paul Geeleher ◽  
R Stephanie Huang
Keyword(s):  
Expression Patterns ◽  
Imputation Accuracy ◽  
High Quality ◽  
Protein Coding ◽  
Protein Coding Genes ◽  
Non Coding Rna ◽  
Non Coding Rnas ◽  
Cancer Tissues ◽  
Long Non Coding Rna

Abstract Long non-coding RNAs (lncRNAs) play an important role in gene regulation and are increasingly being recognized as crucial mediators of disease pathogenesis. However, the vast majority of published transcriptome datasets lack high-quality lncRNA profiles compared to protein-coding genes (PCGs). Here we propose a framework to harnesses the correlative expression patterns between lncRNA and PCGs to impute unknown lncRNA profiles. The lncRNA expression imputation (LEXI) framework enables characterization of lncRNA transcriptome of samples lacking any lncRNA data using only their PCG profiles. We compare various machine learning and missing value imputation algorithms to implement LEXI and demonstrate the feasibility of this approach to impute lncRNA transcriptome of normal and cancer tissues. Additionally, we determine the factors that influence imputation accuracy and provide guidelines for implementing this approach.

scholarly journals Spatio-Temporal Transcriptional Dynamics of Maize Long Non-Coding RNAs Responsive to Drought Stress

Genes ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 138 ◽  
Author(s):  
Junling Pang ◽  
Xia Zhang ◽  
Xuhui Ma ◽  
Jun Zhao
Keyword(s):  
Drought Stress ◽  
Developmental Stages ◽  
Expression Patterns ◽  
Functional Enrichment ◽  
Transcriptional Analysis ◽  
Protein Coding ◽  
Altered Expression ◽  
Protein Coding Genes ◽  
A Genome ◽  
Non Coding Rnas

Long non-coding RNAs (lncRNAs) have emerged as important regulators in plant stress response. Here, we report a genome-wide lncRNA transcriptional analysis in response to drought stress using an expanded series of maize samples collected from three distinct tissues spanning four developmental stages. In total, 3488 high-confidence lncRNAs were identified, among which 1535 were characterized as drought responsive. By characterizing the genomic structure and expression pattern, we found that lncRNA structures were less complex than protein-coding genes, showing shorter transcripts and fewer exons. Moreover, drought-responsive lncRNAs exhibited higher tissue- and development-specificity than protein-coding genes. By exploring the temporal expression patterns of drought-responsive lncRNAs at different developmental stages, we discovered that the reproductive stage R1 was the most sensitive growth stage with more lncRNAs showing altered expression upon drought stress. Furthermore, lncRNA target prediction revealed 653 potential lncRNA-messenger RNA (mRNA) pairs, among which 124 pairs function in cis-acting mode and 529 in trans. Functional enrichment analysis showed that the targets were significantly enriched in molecular functions related to oxidoreductase activity, water binding, and electron carrier activity. Multiple promising targets of drought-responsive lncRNAs were discovered, including the V-ATPase encoding gene, vpp4. These findings extend our knowledge of lncRNAs as important regulators in maize drought response.

scholarly journals Genetic Dissection of Hypertrophic Cardiomyopathy with Myocardial RNA-Seq

2020 ◽  
Vol 21 (9) ◽  
pp. 3040 ◽  
Author(s):  
Jun Gao ◽  
John Collyer ◽  
Maochun Wang ◽  
Fengping Sun ◽  
Fuyi Xu
Keyword(s):  
Hypertrophic Cardiomyopathy ◽  
Heart Development ◽  
Gene Networks ◽  
Complex Disease ◽  
Differential Expression Analysis ◽  
Genetic Modifiers ◽  
Rna Seq ◽  
Protein Coding ◽  
Network Analyses ◽  
Protein Coding Genes

Hypertrophic cardiomyopathy (HCM) is an inherited disorder of the myocardium, and pathogenic mutations in the sarcomere genes myosin heavy chain 7 (MYH7) and myosin-binding protein C (MYBPC3) explain 60%–70% of observed clinical cases. The heterogeneity of phenotypes observed in HCM patients, however, suggests that novel causative genes or genetic modifiers likely exist. Here, we systemically evaluated RNA-seq data from 28 HCM patients and 9 healthy controls with pathogenic variant identification, differential expression analysis, and gene co-expression and protein–protein interaction network analyses. We identified 43 potential pathogenic variants in 19 genes in 24 HCM patients. Genes with more than one variant included the following: MYBPC3, TTN, MYH7, PSEN2, and LDB3. A total of 2538 protein-coding genes, six microRNAs (miRNAs), and 1617 long noncoding RNAs (lncRNAs) were identified differentially expressed between the groups, including several well-characterized cardiomyopathy-related genes (ANKRD1, FHL2, TGFB3, miR-30d, and miR-154). Gene enrichment analysis revealed that those genes are significantly involved in heart development and physiology. Furthermore, we highlighted four subnetworks: mtDNA-subnetwork, DSP-subnetwork, MYH7-subnetwork, and MYBPC3-subnetwork, which could play significant roles in the progression of HCM. Our findings further illustrate that HCM is a complex disease, which results from mutations in multiple protein-coding genes, modulation by non-coding RNAs and perturbations in gene networks.

scholarly journals Limitation of alignment-free tools in total RNA-seq quantification

2018 ◽  
Author(s):  
Douglas C. Wu ◽  
Jun Yao ◽  
Kevin S. Ho ◽  
Alan M. Lambowitz ◽  
Claus O. Wilke
Keyword(s):  
Small Rnas ◽  
State Of The Art ◽  
Rna Seq ◽  
Protein Coding ◽  
Total Rna ◽  
Protein Coding Genes ◽  
Gene Quantification ◽  
Rna Quantification ◽  
Alignment Free ◽  
Non Coding Rnas

AbstractBackgroundAlignment-free RNA quantification tools have significantly increased the speed of RNA-seq analysis. However, it is unclear whether these state-of-the-art RNA-seq analysis pipelines can quantify small RNAs as accurately as they do with long RNAs in the context of total RNA quantification.ResultWe comprehensively tested and compared four RNA-seq pipelines on the accuracies of gene quantification and fold-change estimation on a novel total RNA benchmarking dataset, in which small non-coding RNAs are highly represented along with other long RNAs. The four RNA-seq pipelines were of two commonly-used alignment-free pipelines and two variants of alignment-based pipelines. We found that all pipelines showed high accuracies for quantifying the expressions of long and highly-abundant genes. However, alignment-free pipelines showed systematically poorer performances in quantifying lowly-abundant and small RNAs.ConclusionWe have shown that alignment-free and traditional alignment-based quantification methods performed similarly for common gene targets, such as protein-coding genes. However, we identified a potential pitfall in analyzing and quantifying lowly-expressed genes and small RNAs with alignment-free pipelines, especially when these small RNAs contain mutations.

Sign in / Sign up

Export Citation Format

Share Document