Analytical ultracentrifuge: an ideal tool for characterization of non-coding RNAs

2020 ◽  
Vol 49 (8) ◽  
pp. 809-818 ◽  
Author(s):  
Maulik D. Badmalia ◽  
M. Quadir Siddiqui ◽  
Tyler Mrozowich ◽  
Darren L. Gemmill ◽  
Trushar R. Patel
2020 ◽  
Vol 20 ◽  
Author(s):  
Ammad Ahmad Farooqi ◽  
Evangelia Legaki ◽  
Maria Gazouli ◽  
Silvia Rinaldi ◽  
Rossana Berardi

: Central dogma of molecular biology has remained cornerstone of classical molecular biology but serendipitous discovery of microRNAs (miRNAs) in nematodes paradigmatically shifted our current understanding of the intricate mech-anisms which occur during transitions from transcription to translation. Discovery of miRNA captured tremendous attention and appreciation and we had witnessed an explosion in the field of non-coding RNAs. Ground-breaking discoveries in the field of non-coding RNAs have helped in better characterization of microRNAs and long non-coding RNAs (LncRNAs). There is an ever-increasing list of miRNA targets which are regulated by MALAT1 to stimulate or repress expression of tar-get genes. However, in this review our main focus is to summarize mechanistic insights related to MALAT1-mediated regu-lation of oncogenic signaling pathways. We have discussed how MALAT1 modulated TGF/SMAD and Hippo pathways in various cancers. We have also comprehensively summarized how JAK/STAT and Wnt/β-catenin pathways stimulated MALAT1 expression and consequentially how MALAT1 potentiated these signaling cascades to promote cancer. MALAT1 research has undergone substantial broadening however, there is still a need to identify additional mechanisms. MALAT1 is involved in multi-layered regulation of multiple transduction cascades and detailed analysis of different pathways will be helpful in getting a step closer to individualized medicine.


Genes ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 630
Author(s):  
Yongqing Lan ◽  
Meng Li ◽  
Shuangli Mi

Hematopoietic differentiation is a well-orchestrated process by many regulators such as transcription factor and long non-coding RNAs (lncRNAs). However, due to the large number of lncRNAs and the difficulty in determining their roles, the study of lncRNAs is a considerable challenge in hematopoietic differentiation. Here, through gene co-expression network analysis over RNA-seq data generated from representative types of mouse myeloid cells, we obtained a catalog of potential key lncRNAs in the context of mouse myeloid differentiation. Then, employing a widely used in vitro cell model, we screened a novel lncRNA, named Gdal1 (Granulocytic differentiation associated lncRNA 1), from this list and demonstrated that Gdal1 was required for granulocytic differentiation. Furthermore, knockdown of Cebpe, a principal transcription factor of granulocytic differentiation regulation, led to down-regulation of Gdal1, but not vice versa. In addition, expression of genes involved in myeloid differentiation and its regulation, such as Cebpa, were influenced in Gdal1 knockdown cells with differentiation blockage. We thus systematically identified myeloid differentiation associated lncRNAs and substantiated the identification by investigation of one of these lncRNAs on cellular phenotype and gene regulation levels. This study promotes our understanding of the regulation of myeloid differentiation and the characterization of roles of lncRNAs in hematopoietic system.


Cancers ◽  
2021 ◽  
Vol 13 (23) ◽  
pp. 5944
Author(s):  
Jianfei Tang ◽  
Xiaodan Fang ◽  
Juan Chen ◽  
Haixia Zhang ◽  
Zhangui Tang

Oral squamous cell carcinoma (OSCC) is a type of malignancy with high mortality, leading to poor prognosis worldwide. However, the molecular mechanisms underlying OSCC carcinogenesis have not been fully understood. Recently, the discovery and characterization of long non-coding RNAs (lncRNAs) have revealed their regulatory importance in OSCC. Abnormal expression of lncRNAs has been broadly implicated in the initiation and progress of tumors. In this review, we summarize the functions and molecular mechanisms regarding these lncRNAs in OSCC. In addition, we highlight the crosstalk between lncRNA and tumor microenvironment (TME), and discuss the potential applications of lncRNAs as diagnostic and prognostic tools and therapeutic targets in OSCC. Notably, we also discuss lncRNA-targeted therapeutic techniques including CRISPR-Cas9 as well as immune checkpoint therapies to target lncRNA and the PD-1/PD-L1 axis. Therefore, this review presents the future perspectives of lncRNAs in OSCC therapy, but more research is needed to allow the applications of these findings to the clinic.


Rice Science ◽  
2020 ◽  
Vol 27 (1) ◽  
pp. 21-31
Author(s):  
Qi Weidong ◽  
Chen Hongping ◽  
Yang Zuozhen ◽  
Hu Biaolin ◽  
Luo Xiangdong ◽  
...  

2019 ◽  
Vol 14 (12) ◽  
pp. 1674606 ◽  
Author(s):  
Huiru Jiang ◽  
Zhichao Jia ◽  
Sian Liu ◽  
Beibei Zhao ◽  
Weixing Li ◽  
...  

2019 ◽  
Author(s):  
Rodrigo E. Cáceres ◽  
Marco A. Andonegui ◽  
Diego A. Oliva ◽  
Rodrigo González ◽  
Fernando Luna ◽  
...  

Genes ◽  
2019 ◽  
Vol 10 (7) ◽  
pp. 536 ◽  
Author(s):  
Xiaobo Zhao ◽  
Liming Gan ◽  
Caixia Yan ◽  
Chunjuan Li ◽  
Quanxi Sun ◽  
...  

Long non-coding RNAs (lncRNAs) are involved in various regulatory processes although they do not encode protein. Presently, there is little information regarding the identification of lncRNAs in peanut (Arachis hypogaea Linn.). In this study, 50,873 lncRNAs of peanut were identified from large-scale published RNA sequencing data that belonged to 124 samples involving 15 different tissues. The average lengths of lncRNA and mRNA were 4335 bp and 954 bp, respectively. Compared to the mRNAs, the lncRNAs were shorter, with fewer exons and lower expression levels. The 4713 co-expression lncRNAs (expressed in all samples) were used to construct co-expression networks by using the weighted correlation network analysis (WGCNA). LncRNAs correlating with the growth and development of different peanut tissues were obtained, and target genes for 386 hub lncRNAs of all lncRNAs co-expressions were predicted. Taken together, these findings can provide a comprehensive identification of lncRNAs in peanut.


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