scholarly journals Transcriptomic Analysis of Coding Genes and Non-Coding RNAs Reveals Complex Regulatory Networks Underlying the Black Back and White Belly Coat Phenotype in Chinese Wuzhishan Pigs

Genes ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 201
Author(s):  
Qiao Xu ◽  
Ximing Liu ◽  
Zhe Chao ◽  
Kejun Wang ◽  
Jue Wang ◽  
...  
Keyword(s):  
Regulatory Networks ◽  
Coat Color ◽  
Mirna Gene ◽  
White Skin ◽  
Melanin Biosynthesis ◽  
Black Skin ◽  
Skin Physiology ◽  
Non Coding Rnas ◽  
Different Levels ◽  
White Belly

Coat color is one of the most important characteristics for distinguishing Chinese indigenous pig breeds. In Wuzhishan pigs, the animals have black on the back and white on the abdomen. However, the molecular genetic basis of this phenotype is unclear. In this study, we used high-throughput RNA sequencing to compare expression profiles of coding and non-coding RNAs from white and black skin samples obtained from individual Wuzhishan pigs. The expression profiling revealed that 194 lncRNAs (long non-coding RNAs), 189 mRNAs (messenger RNAs), and 162 miRNAs (microRNAs) had significantly different levels of expression (|log2 fold change| > 1, p-value < 0.05) in white and black skin. Compared to RNA levels in black skin, white skin had higher levels of expression of 185 lncRNAs, 181 mRNAs, and 23 miRNAs and lower levels of expression of 9 lncRNAs, 8 mRNAs, and 139 miRNAs. Functional analysis suggested that the differentially expressed transcripts are involved in biological processes such as melanin biosynthesis, pigmentation and tyrosine metabolism. Several key genes involved in melanogenesis, including MLANA, PMEL, TYR, TYRP1, DTC, TRPM1 and CAMK2A, had significantly different levels of expression in the two skin tissues. Potential lncRNA–miRNA–gene interactions were also examined. A total of 15 lncRNAs, 11 miRNAs and 7 genes formed 23 lncRNA–miRNA–gene pairs, suggesting that complex regulatory networks of coding and non-coding genes underlie the coat color trait in Wuzhishan pigs. Our study provides a foundation for understanding how lncRNA, miRNA and genes interact to regulate coat color in black-back/white-belly pigs. We also constructed lncRNA–miRNA–gene interaction networks to elucidate the complex molecular mechanisms underlying skin physiology and melanogenesis. The results extend our knowledge about the diversity of coat color among different domestic animals and provide a foundation for studying novel mechanisms that control coat color in Chinese indigenous pigs.

scholarly journals Skin transcriptome profiles associated with coat color in goat

2015 ◽  
Author(s):  
Yongdong Peng ◽  
Xiaohui Liu ◽  
Liying Geng ◽  
Chuansheng Zhang ◽  
Zhengzhu Liu ◽  
...  
Keyword(s):  
Coat Color ◽  
Expression Profiles ◽  
Quantitative Polymerase Chain Reaction ◽  
Capra Hircus ◽  
Rna Seq ◽  
White Skin ◽  
Future Studies ◽  
Skin Physiology ◽  
Significant Difference ◽  
Upregulated Genes

Capra hircus, an economically important livestock, plays an indispensable role in the world animal fiber industry. To identify additional genes that may play important roles in coat color regulation, Illumina/Solexa high throughput sequencing technology was used to catalog the global gene expression profiles in the skin of three different coat colors goat (Lubei white goat (white), Jining gray goat (gray) and Jianyang big ear goat (brown)). The RNA-Seq analysis generated 83174342, 70222592 and 52091212 clean reads in white skin, gray skin and brown skin, respectively, which provided abundant data for further analysis. A total of 91 genes were differentially expressed between the gray skin and white skin libraries, with 74 upregulated and 17 genes downregulated. Between the brown skin and white skin libraries, there were 23 upregulated genes and 44 downregulated genes, while there were 33 upregulated genes and 121 downregulated genes between the brown skin and gray skin libraries. To our surprise, MC1R, MITF, TYR, KIT and KITLG showed no significant difference in the skin of three different coat colors and the expression of ASIP was only detected in white skin and not in gray and brown skins. The expression of PMEL,TRPM1, DCT, TYRP1 and ELOVL3 was validated by real-time quantitative polymerase chain reaction (qPCR) and the results of the qPCR were consistent with the RNA-seq except the expression of TYRP1 between the gray skin and white skin libraries. This study provides several candidate genes that may be associated with the development of diferent coat colors goat skin. More importantly, the fact that the ASIP gene was only detected in the white skin and not in the other dark skins and the MC1R gene showed no significant difference in expression between the three different coat colors goat is of particular interest for future studies that aim to elucidate theirs functional role in the regulation of skin color. These results will expand our understanding of the complex molecular mechanisms of skin physiology and melanogenesis in goat and provide a foundation for future studies.

scholarly journals Molecular Mediators of RNA Loading into Extracellular Vesicles

Cells ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 3355
Author(s):  
Chiara Corrado ◽  
Maria Magdalena Barreca ◽  
Chiara Zichittella ◽  
Riccardo Alessandro ◽  
Alice Conigliaro
Keyword(s):  
Gene Expression ◽  
Extracellular Vesicles ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Translation Regulation ◽  
Circular Rnas ◽  
Non Coding Rna ◽  
Gene Regulatory ◽  
Non Coding Rnas ◽  
Different Levels

In the last decade, an increasing number of studies have demonstrated that non-coding RNA (ncRNAs) cooperate in the gene regulatory networks with other biomolecules, including coding RNAs, DNAs and proteins. Among them, microRNAs (miRNAs), long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs) are involved in transcriptional and translation regulation at different levels. Intriguingly, ncRNAs can be packed in vesicles, released in the extracellular space, and finally internalized by receiving cells, thus affecting gene expression also at distance. This review focuses on the mechanisms through which the ncRNAs can be selectively packaged into extracellular vesicles (EVs).

Radio-Susceptibility of Nasopharyngeal Carcinoma: Focus on Epstein- Barr Virus, MicroRNAs, Long Non-Coding RNAs and Circular RNAs

2020 ◽  
Vol 13 (3) ◽  
pp. 192-205 ◽  
Author(s):  
Fanghong Lei ◽  
Tongda Lei ◽  
Yun Huang ◽  
Mingxiu Yang ◽  
Mingchu Liao ◽  
...  
Keyword(s):  
Nasopharyngeal Carcinoma ◽  
Regulatory Networks ◽  
Epstein Barr Virus ◽  
Molecular Mechanisms ◽  
Acquired Resistance ◽  
Treatment Strategies ◽  
Circular Rnas ◽  
Barr Virus ◽  
Non Coding Rnas ◽  
Epstein Barr

Nasopharyngeal carcinoma (NPC) is a type of head and neck cancer. As a neoplastic disorder, NPC is a highly malignant squamous cell carcinoma that is derived from the nasopharyngeal epithelium. NPC is radiosensitive; radiotherapy or radiotherapy combining with chemotherapy are the main treatment strategies. However, both modalities are usually accompanied by complications and acquired resistance to radiotherapy is a significant impediment to effective NPC therapy. Therefore, there is an urgent need to discover effective radio-sensitization and radio-resistance biomarkers for NPC. Recent studies have shown that Epstein-Barr virus (EBV)-encoded products, microRNAs (miRNAs), long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs), which share several common signaling pathways, can function in radio-related NPC cells or tissues. Understanding these interconnected regulatory networks will reveal the details of NPC radiation sensitivity and resistance. In this review, we discuss and summarize the specific molecular mechanisms of NPC radio-sensitization and radio-resistance, focusing on EBV-encoded products, miRNAs, lncRNAs and circRNAs. This will provide a foundation for the discovery of more accurate, effective and specific markers related to NPC radiotherapy. EBVencoded products, miRNAs, lncRNAs and circRNAs have emerged as crucial molecules mediating the radio-susceptibility of NPC. This understanding will improve the clinical application of markers and inform the development of novel therapeutics for NPC.

Label-free proteomic analysis reveals large dynamic changes to the cellular proteome upon expression of the miRNA-23a-27a-24-2 microRNA cluster

2020 ◽  
Vol 98 (1) ◽  
pp. 61-69 ◽  
Author(s):  
Ramanaguru S. Piragasam ◽  
S. Faraz Hussain ◽  
Steven G. Chaulk ◽  
Zaeem A. Siddiqi ◽  
Richard P. Fahlman
Keyword(s):  
Gene Expression ◽  
Proteomic Analysis ◽  
Small Rnas ◽  
Regulatory Networks ◽  
Mirna Cluster ◽  
Label Free ◽  
Dynamic Changes ◽  
Mrna Targets ◽  
Order Of Magnitude ◽  
Non Coding Rnas

In deciphering the regulatory networks of gene expression controlled by the small non-coding RNAs known as microRNAs (miRNAs), a major challenge has been with the identification of the true mRNA targets by these RNAs within the context of the enormous numbers of predicted targets for each of these small RNAs. To facilitate the system-wide identification of miRNA targets, a variety of system wide methods, such as proteomics, have been implemented. Here we describe the utilization of quantitative label-free proteomics and bioinformatics to identify the most significant changes to the proteome upon expression of the miR-23a-27a-24-2 miRNA cluster. In light of recent work leading to the hypothesis that only the most pronounced regulatory events by miRNAs may be physiologically relevant, our data reveal that label-free analysis circumvents the limitations of proteomic labeling techniques that limit the maximum differences that can be quantified. The result of our analysis identifies a series of novel candidate targets that are reduced in abundance by more than an order of magnitude upon the expression of the miR-23a-27a-24-2 cluster.

scholarly journals Integrating non-coding RNAs in JAK-STAT regulatory networks

JAK-STAT ◽  
2014 ◽  
Vol 3 (1) ◽  
pp. e28055 ◽  
Author(s):  
Steven Witte ◽  
Stefan A Muljo
Keyword(s):  
Regulatory Networks ◽  
Non Coding Rnas

scholarly journals The PARP Inhibitor Olaparib Modulates the Transcriptional Regulatory Networks of Long Non-Coding RNAs during Vasculogenic Mimicry

Cells ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2690
Author(s):  
Mónica Fernández-Cortés ◽  
Eduardo Andrés-León ◽  
Francisco Javier Oliver
Keyword(s):  
Transcription Factors ◽  
Binding Sites ◽  
Regulatory Networks ◽  
Target Genes ◽  
Parp Inhibitor ◽  
Vasculogenic Mimicry ◽  
Transcriptome Profiling ◽  
Key Species ◽  
Non Coding Rnas ◽  
The Impact

In highly metastatic tumors, vasculogenic mimicry (VM) involves the acquisition by tumor cells of endothelial-like traits. Poly-(ADP-ribose) polymerase (PARP) inhibitors are currently used against tumors displaying BRCA1/2-dependent deficient homologous recombination, and they may have antimetastatic activity. Long non-coding RNAs (lncRNAs) are emerging as key species-specific regulators of cellular and disease processes. To evaluate the impact of olaparib treatment in the context of non-coding RNA, we have analyzed the expression of lncRNA after performing unbiased whole-transcriptome profiling of human uveal melanoma cells cultured to form VM. RNAseq revealed that the non-coding transcriptomic landscape differed between olaparib-treated and non-treated cells: olaparib significantly modulated the expression of 20 lncRNAs, 11 lncRNAs being upregulated, and 9 downregulated. We subjected the data to different bioinformatics tools and analysis in public databases. We found that copy-number variation alterations in some olaparib-modulated lncRNAs had a statistically significant correlation with alterations in some key tumor suppressor genes. Furthermore, the lncRNAs that were modulated by olaparib appeared to be regulated by common transcription factors: ETS1 had high-score binding sites in the promoters of all olaparib upregulated lncRNAs, while MZF1, RHOXF1 and NR2C2 had high-score binding sites in the promoters of all olaparib downregulated lncRNAs. Finally, we predicted that olaparib-modulated lncRNAs could further regulate several transcription factors and their subsequent target genes in melanoma, suggesting that olaparib may trigger a major shift in gene expression mediated by the regulation lncRNA. Globally, olaparib changed the lncRNA expression landscape during VM affecting angiogenesis-related genes.

scholarly journals Non-Coding RNAs in the Brain-Heart Axis: The Case of Parkinson’s Disease

2020 ◽  
Vol 21 (18) ◽  
pp. 6513 ◽  
Author(s):  
Shubhra Acharya ◽  
Antonio Salgado-Somoza ◽  
Francesca Maria Stefanizzi ◽  
Andrew I. Lumley ◽  
Lu Zhang ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
High Throughput ◽  
Regulatory Networks ◽  
Current Knowledge ◽  
Circular Rnas ◽  
Clinical Markers ◽  
Wide Range ◽  
Non Coding Rnas ◽  
The Brain

Parkinson’s disease (PD) is a complex and heterogeneous disorder involving multiple genetic and environmental influences. Although a wide range of PD risk factors and clinical markers for the symptomatic motor stage of the disease have been identified, there are still no reliable biomarkers available for the early pre-motor phase of PD and for predicting disease progression. High-throughput RNA-based biomarker profiling and modeling may provide a means to exploit the joint information content from a multitude of markers to derive diagnostic and prognostic signatures. In the field of PD biomarker research, currently, no clinically validated RNA-based biomarker models are available, but previous studies reported several significantly disease-associated changes in RNA abundances and activities in multiple human tissues and body fluids. Here, we review the current knowledge of the regulation and function of non-coding RNAs in PD, focusing on microRNAs, long non-coding RNAs, and circular RNAs. Since there is growing evidence for functional interactions between the heart and the brain, we discuss the benefits of studying the role of non-coding RNAs in organ interactions when deciphering the complex regulatory networks involved in PD progression. We finally review important concepts of harmonization and curation of high throughput datasets, and we discuss the potential of systems biomedicine to derive and evaluate RNA biomarker signatures from high-throughput expression data.

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