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

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.

Download Full-text

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

Scientific Reports ◽

10.1038/s41598-020-77586-x ◽

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.

Download Full-text

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

BMC Genomics ◽

10.1186/s12864-021-08086-z ◽

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.

Download Full-text

Predicting the Functions of Long Noncoding RNAs Using RNA-Seq Based on Bayesian Network

BioMed Research International ◽

10.1155/2015/839590 ◽

2015 ◽

Vol 2015 ◽

pp. 1-14 ◽

Cited By ~ 11

Author(s):

Yun Xiao ◽

Yanling Lv ◽

Hongying Zhao ◽

Yonghui Gong ◽

Jing Hu ◽

...

Keyword(s):

Bayesian Network ◽

Regulatory Network ◽

Noncoding Rnas ◽

Nervous System Development ◽

Long Noncoding Rnas ◽

Functional Enrichment ◽

Biological Processes ◽

Rna Seq ◽

Protein Coding ◽

Protein Coding Genes

Long noncoding RNAs (lncRNAs) have been shown to play key roles in various biological processes. However, functions of most lncRNAs are poorly characterized. Here, we represent a framework to predict functions of lncRNAs through construction of a regulatory network between lncRNAs and protein-coding genes. Using RNA-seq data, the transcript profiles of lncRNAs and protein-coding genes are constructed. Using the Bayesian network method, a regulatory network, which implies dependency relations between lncRNAs and protein-coding genes, was built. In combining protein interaction network, highly connected coding genes linked by a given lncRNA were subsequently used to predict functions of the lncRNA through functional enrichment. Application of our method to prostate RNA-seq data showed that 762 lncRNAs in the constructed regulatory network were assigned functions. We found that lncRNAs are involved in diverse biological processes, such as tissue development or embryo development (e.g., nervous system development and mesoderm development). By comparison with functions inferred using the neighboring gene-based method and functions determined using lncRNA knockdown experiments, our method can provide comparable predicted functions of lncRNAs. Overall, our method can be applied to emerging RNA-seq data, which will help researchers identify complex relations between lncRNAs and coding genes and reveal important functions of lncRNAs.

Download Full-text

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

10.1101/246967 ◽

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.

Download Full-text