Long Noncoding RNAs in Erythropoiesis and Megakaryopoiesis.

Blood ◽  
2012 ◽  
Vol 120 (21) ◽  
pp. 2331-2331
Author(s):  
Vikram R Paralkar ◽  
Tejaswini Mishra ◽  
Jing Luan ◽  
Yu Yao ◽  
Neeraja Konuthula ◽  
...  

Abstract Abstract 2331 Lnc (long noncoding) RNAs are RNA transcripts greater than 200nt that regulate gene expression independent of protein coding potential. It is estimated that thousands of lncRNAs play vital roles in diverse cellular processes and are involved in numerous diseases, including cancer. We hypothesize that multiple lncRNAs regulate erythrocyte and megakaryocyte formation by modulating gene expression. To identify lncRNAs in erythro-megakaryopoiesis, we purified two biological replicates each of murine Ter119+ erythroblasts, CD41+ megakaryocytes and bipotential megakaryocyte-erythroid progenitors (MEPs) [Lin− Kit+, Sca1−, CD16/32−, CD34−]. We performed strand-specific, paired-end, 200nt-read-length deep sequencing (RNA-Seq) to a depth of ∼200 million reads per sample using the Illumina GAII platform. We used the Tophat and Cufflinks suite of bioinformatic tools to assemble and compare de-novo transcriptomes from these three cell types, producing a high-confidence set of 69,488 transcripts. We confirmed that the RNA-seq assemblies accurately reflect gene expression predicted from prior studies. For example, Ter119+ cells were highly enriched for key erythroid transcripts encoding globins, heme synthetic enzymes and specialized membrane proteins. Megakaryocytes expressed high levels of gene encoding lineage-specific integrins and platelet markers. MEPs expressed numerous progenitor genes including Gata2, Kit and Myc. Thus, the RNA-seq data are of high-quality and sufficient complexity to accurately represent erythroid, megakaryocytic and MEP transcriptomes. We used a series of Unix-based bioinformatic filtering tools to identify lncRNAs that are expressed in these transcriptomes. We identified 605 “stringent” lncRNAs, and 813 “potential noncoding” transcripts. 47% of the lncRNAs are novel unannotated transcripts, validating the use of de-novo RNA-Seq in unique cell populations for lncRNA discovery. Among the 605 “stringent” lncRNAs, 103 are erythroid-restricted, 133 are meg-restricted and 280 are MEP-restricted, consistent with reports that lncRNAs exhibit exquisitely cell-type specific expression. Current efforts are aimed at generating a more comprehensive map of lncRNA expression at specific stages of erythroid and megakaryocyte/platelet development, and performing high throughput functional screens to analyze currently identified lncRNAs. Our studies are beginning to define new layers of gene regulation in normal erythro-megakaryopoiesis and are relevant to the pathophysiology of related disorders including various anemias, myeloproliferative and myelodysplastic syndromes and leukemias. Disclosures: No relevant conflicts of interest to declare.

Blood ◽  
2013 ◽  
Vol 122 (21) ◽  
pp. SCI-28-SCI-28
Author(s):  
Mitchell J. Weiss

Abstract Long noncoding (Lnc) RNAs are RNA transcripts greater than 200 nucleotides (nt) that regulate gene expression independent of protein coding potential (1-3). It is estimated that thousands of lncRNAs play vital roles in diverse cellular processes. LncRNAs modulate many stages of gene expression by regulating transcription, epigenetics, splicing, translation, and protein localization. We hypothesize that multiple lncRNAs are expressed specifically during erythrocyte and megakaryocyte differentiation, and are likely to have important roles. To identify lncRNAs in erythro-megakaryopoiesis, we performed strand-specific, paired-end deep sequencing (RNA-Seq) to a depth of 200 million reads per sample on two replicates each of murine Ter119+erythroblasts, CD41+ megakaryocytes and bipotential megakaryocyte-erythroid progenitors (MEPs) [lin- Kit+ Sca1- CD16/32- CD34-], and used bioinformatic filtering tools to identify approximately 1,100 candidate lncRNAs. Over 60 percent of these lncRNAs are novel unannotated transcripts with exquisite lineage-specific expression. Using erythroid and megakaryocytic primary cell ChIP-Seq for key transcription factors (TFs) GATA1, TAL1, GATA2,and FLI1, we found that the loci of lncRNAs show similar degree of TF binding as coding genes. We used the erythroid line G1E-ER4 (which expresses estrogen-activated GATA1) to confirm that lncRNAs bound by GATA1 are also directly regulated by it. Furthermore, we used histone methylation ChIP-Seq to show that most lncRNAs arise from classical “promoters” with high H3K4me3 levels and low H3K4me1 levels. Thus, we find that lncRNAs show epigenetic features similar to the promoters of coding genes and are directly regulated by similar TF networks. Comparison of the transcriptomes of mouse fetal liver and human cord blood erythroblasts demonstrated that lncRNAs are expressed in a highly species-specific fashion, i.e., most lncRNAs identifiable in one species are not transcribed in the other, even though the corresponding genomic region is present in both species. Numerous non-conserved but functional lncRNAs are reported in the literature, and the significance of conservation in lncRNA biology is greatly debated. In order to identify functional lncRNAs, we are currently performing RNAi knockdown on numerous candidates to assess how loss of function affects erythroid maturation. We are also performing HITS-CLIP of key chromatin modifying complexes and erythroid transcription factors to identify lncRNAs bound to them. Our studies are beginning to define new layers of gene regulation in normal erythro-megakaryopoiesis, which may be relevant to the pathophysiology of related disorders including various anemias, myeloproliferative and myelodysplastic syndromes and leukemias. 1. Wang K.C., Chang H.Y. Molecular mechanisms of long noncoding RNAs. Molecular Cell. 2011;43(6):904-914. Prepublished on 2011/09/20 as DOI 10.1016/j.molcel.2011.08.018. 2. Hu W., Alvarez-Dominguez J.R., Lodish H.F. Regulation of mammalian cell differentiation by long non-coding RNAs. EMBO reports. 2012;13(11):971-983. Prepublished on 2012/10/17 as DOI 10.1038/embor.2012.145. 3. Paralkar V.R., Weiss M.J. Long noncoding RNAs in biology and hematopoiesis. Blood. 2013;121(24):4842-4846. Prepublished on 2013/05/07 as DOI 10.1182/blood-2013-03-456111. Disclosures: No relevant conflicts of interest to declare.


PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e9585
Author(s):  
Wei Xia ◽  
Yajing Dou ◽  
Rui Liu ◽  
Shufang Gong ◽  
Dongyi Huang ◽  
...  

Long noncoding RNAs (lncRNAs) are an important class of genes and play important roles in a range of biological processes. However, few reports have described the identification of lncRNAs in oil palm. In this study, we applied strand specific RNA-seq with rRNA removal to identify 1,363 lncRNAs from the equally mixed tissues of oil palm spear leaf and six different developmental stages of mesocarp (8–24 weeks). Based on strand specific RNA-seq data and 18 released oil palm transcriptomes, we systematically characterized the expression patterns of lncRNA loci and their target genes. A total of 875 uniq target genes for natural antisense lncRNAs (NAT-lncRNA, 712), long intergenic noncoding RNAs (lincRNAs, 92), intronic-lncRNAs (33), and sense-lncRNAs (52) were predicted. A majority of lncRNA loci (77.8%–89.6%) had low expression in 18 transcriptomes, while only 89 lncRNA loci had medium to high expression in at least one transcriptome. Coexpression analysis between lncRNAs and their target genes indicated that 6% of lncRNAs had expression patterns positively correlated with those of target genes. Based on single nucleotide polymorphism (SNP) markers derived from our previous research, 6,882 SNPs were detected for lncRNAs and 28 SNPs belonging to 21 lncRNAs were associated with the variation of fatty acid contents. Moreover, seven lncRNAs showed expression patterns positively correlated expression pattern with those of genes in de novo fatty acid synthesis pathways. Our study identified a collection of lncRNAs for oil palm and provided clues for further research into lncRNAs that may regulate mesocarp development and lipid metabolism.


2015 ◽  
Vol 36 (5) ◽  
pp. 809-819 ◽  
Author(s):  
Gireesh K. Bogu ◽  
Pedro Vizán ◽  
Lawrence W. Stanton ◽  
Miguel Beato ◽  
Luciano Di Croce ◽  
...  

Discovering and classifying long noncoding RNAs (lncRNAs) across all mammalian tissues and cell lines remains a major challenge. Previously, mouse lncRNAs were identified using transcriptome sequencing (RNA-seq) data from a limited number of tissues or cell lines. Additionally, associating a few hundred lncRNA promoters with chromatin states in a single mouse cell line has identified two classes of chromatin-associated lncRNA. However, the discovery and classification of lncRNAs is still pending in many other tissues in mouse. To address this, we built a comprehensive catalog of lncRNAs by combining known lncRNAs with high-confidence novel lncRNAs identified by mapping andde novoassembling billions of RNA-seq reads from eight tissues and a primary cell line in mouse. Next, we integrated this catalog of lncRNAs with multiple genome-wide chromatin state maps and found two different classes of chromatin state-associated lncRNAs, including promoter-associated (plncRNAs) and enhancer-associated (elncRNAs) lncRNAs, across various tissues. Experimental knockdown of an elncRNA resulted in the downregulation of the neighboring protein-codingKdm8gene, encoding a histone demethylase. Our findings provide 2,803 novel lncRNAs and a comprehensive catalog of chromatin-associated lncRNAs across different tissues in mouse.


2017 ◽  
Vol 3 ◽  
pp. 0
Author(s):  
Raheleh Amirkhah

Long noncoding RNAs (lncRNAs) are a heterogeneous class of RNAs with generally longer than 200 nucleotides. It has been proposed that LncRNAs as a piece of paracrine action would control cellular pluripotency, differentiation, maintenance and regulate tissue development, organogenesis and regeneration. Next generation sequencing (RNA-seq) has produced huge data about lncRNAs expression profile in different cell types and condition, but understanding the roles and functions of these novel lncRNAs is poorly understood.


2017 ◽  
Author(s):  
Garth R. Ilsley ◽  
Ritsuko Suyama ◽  
Takeshi Noda ◽  
Nori Satoh ◽  
Nicholas M. Luscombe

AbstractSingle-cell RNA-seq has been established as a reliable and accessible technique enabling new types of analyses, such as identifying cell types and studying spatial and temporal gene expression variation and change at single-cell resolution. Recently, single-cell RNA-seq has been applied to developing embryos, which offers great potential for finding and characterising genes controlling the course of development along with their expression patterns. In this study, we applied single-cell RNA-seq to the 16-cell stage of the Ciona embryo, a marine chordate and performed a computational search for cell-specific gene expression patterns. We recovered many known expression patterns from our single-cell RNA-seq data and despite extensive previous screens, we succeeded in finding new cell-specific patterns, which we validated by in situ and single-cell qPCR.


2019 ◽  
Author(s):  
Mazdak Salavati ◽  
Stephen J. Bush ◽  
Sergio Palma-Vera ◽  
Mary E. B. McCulloch ◽  
David A. Hume ◽  
...  

AbstractPervasive allelic variation at both gene and single nucleotide level (SNV) between individuals is commonly associated with complex traits in humans and animals. Allele-specific expression (ASE) analysis, using RNA-Seq, can provide a detailed annotation of allelic imbalance and infer the existence of cis-acting transcriptional regulation. However, variant detection in RNA-Seq data is compromised by biased mapping of reads to the reference DNA sequence. In this manuscript we describe an unbiased standardised computational pipeline for allele-specific expression analysis using RNA-Seq data, which we have adapted and developed using tools available under open licence. The analysis pipeline we present is designed to minimise reference bias while providing accurate profiling of allele-specific expression across tissues and cell types. Using this methodology, we were able to profile pervasive allelic imbalance across tissues and cell types, at both the gene and SNV level, in Texel x Scottish Blackface sheep, using the sheep gene expression atlas dataset. ASE profiles were pervasive in each sheep and across all tissue types investigated. However, ASE profiles shared across tissues were limited and instead they tended to be highly tissue-specific. These tissue-specific ASE profiles may underlie the expression of economically important traits and could be utilized as weighted SNVs, for example, to improve the accuracy of genomic selection in breeding programmes for sheep. An additional benefit of the pipeline is that it does not require parental genotypes and can therefore be applied to other RNA-Seq datasets for livestock, including those available on the Functional Annotation of Animal Genomes (FAANG) data portal. This study is the first global characterisation of moderate to extreme ASE in tissues and cell types from sheep. We have applied a robust methodology for ASE profiling, to provide both a novel analysis of the multi-dimensional sheep gene expression atlas dataset, and a foundation for identifying the regulatory and expressed elements of the genome that are driving complex traits in livestock.


Blood ◽  
2013 ◽  
Vol 121 (24) ◽  
pp. 4842-4846 ◽  
Author(s):  
Vikram R. Paralkar ◽  
Mitchell J. Weiss

Abstract Genome and transcriptome sequencing have revealed a rich assortment of noncoding RNAs in eukaryote cells, including long noncoding RNAs (lncRNAs), which regulate gene expression independent of protein coding potential. LncRNAs modulate protein coding gene expression in many cell types by regulating multiple processes, including epigenetic control of transcription, mRNA stability, and protein localization. Although little is known about lncRNAs in hematopoiesis, they are likely to exert widespread roles in this process.


2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Zhang-Wei Liu ◽  
Nan Zhao ◽  
Yin-Na Su ◽  
Shan-Shan Chen ◽  
Xin-Jian He

An amendment to this paper has been published and can be accessed via a link at the top of the paper.


BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Qi Wu ◽  
Yiming Luo ◽  
Xiaoyong Wu ◽  
Xue Bai ◽  
Xueling Ye ◽  
...  

Abstract Background Night-break (NB) has been proven to repress flowering of short-day plants (SDPs). Long-noncoding RNAs (lncRNAs) play key roles in plant flowering. However, investigation of the relationship between lncRNAs and NB responses is still limited, especially in Chenopodium quinoa, an important short-day coarse cereal. Results In this study, we performed strand-specific RNA-seq of leaf samples collected from quinoa seedlings treated by SD and NB. A total of 4914 high-confidence lncRNAs were identified, out of which 91 lncRNAs showed specific responses to SD and NB. Based on the expression profiles, we identified 17 positive- and 7 negative-flowering lncRNAs. Co-expression network analysis indicated that 1653 mRNAs were the common targets of both types of flowering lncRNAs. By mapping these targets to the known flowering pathways in model plants, we found some pivotal flowering homologs, including 2 florigen encoding genes (FT (FLOWERING LOCUS T) and TSF (TWIN SISTER of FT) homologs), 3 circadian clock related genes (EARLY FLOWERING 3 (ELF3), LATE ELONGATED HYPOCOTYL (LHY) and ELONGATED HYPOCOTYL 5 (HY5) homologs), 2 photoreceptor genes (PHYTOCHROME A (PHYA) and CRYPTOCHROME1 (CRY1) homologs), 1 B-BOX type CONSTANS (CO) homolog and 1 RELATED TO ABI3/VP1 (RAV1) homolog, were specifically affected by NB and competed by the positive and negative-flowering lncRNAs. We speculated that these potential flowering lncRNAs may mediate quinoa NB responses by modifying the expression of the floral homologous genes. Conclusions Together, the findings in this study will deepen our understanding of the roles of lncRNAs in NB responses, and provide valuable information for functional characterization in future.


Plants ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 1465
Author(s):  
Ramon de Koning ◽  
Raphaël Kiekens ◽  
Mary Esther Muyoka Toili ◽  
Geert Angenon

Raffinose family oligosaccharides (RFO) play an important role in plants but are also considered to be antinutritional factors. A profound understanding of the galactinol and RFO biosynthetic gene families and the expression patterns of the individual genes is a prerequisite for the sustainable reduction of the RFO content in the seeds, without compromising normal plant development and functioning. In this paper, an overview of the annotation and genetic structure of all galactinol- and RFO biosynthesis genes is given for soybean and common bean. In common bean, three galactinol synthase genes, two raffinose synthase genes and one stachyose synthase gene were identified for the first time. To discover the expression patterns of these genes in different tissues, two expression atlases have been created through re-analysis of publicly available RNA-seq data. De novo expression analysis through an RNA-seq study during seed development of three varieties of common bean gave more insight into the expression patterns of these genes during the seed development. The results of the expression analysis suggest that different classes of galactinol- and RFO synthase genes have tissue-specific expression patterns in soybean and common bean. With the obtained knowledge, important galactinol- and RFO synthase genes that specifically play a key role in the accumulation of RFOs in the seeds are identified. These candidate genes may play a pivotal role in reducing the RFO content in the seeds of important legumes which could improve the nutritional quality of these beans and would solve the discomforts associated with their consumption.


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