Characterization of the transcription start point of the trout estrogen receptor-encoding gene: evidence for alternative splicing in the 5′ untranslated region

Gene ◽  
1995 ◽  
Vol 166 (2) ◽  
pp. 243-247 ◽  
Author(s):  
Gwendal Lazennec ◽  
Hervé Huignard ◽  
Yves Valotaire ◽  
Laurence Kern
Gene ◽  
1992 ◽  
Vol 118 (2) ◽  
pp. 205-216 ◽  
Author(s):  
Jean M. Gudas ◽  
Judith L. Fridovich-Keil ◽  
Milton W. Datta ◽  
John Bryan ◽  
Arthur B. Pardee

Gene ◽  
1994 ◽  
Vol 140 (2) ◽  
pp. 273-278 ◽  
Author(s):  
Mourad Kaghad ◽  
Françoise Dessarps ◽  
Hélène Jacquemin-Sablon ◽  
Daniel Caput ◽  
Didier Fradelizi ◽  
...  

Genetics ◽  
2000 ◽  
Vol 154 (1) ◽  
pp. 437-446 ◽  
Author(s):  
Lisa Girard ◽  
Michael Freeling

Abstract Insertions of Mutator transposons into maize genes can generate suppressible alleles. Mu suppression is when, in the absence of Mu activity, the phenotype of a mutant allele reverts to that of its progenitor. Here we present the characterization of five dominant Mu-suppressible alleles of the knox (knotted1-like homeobox) genes liguleless3 and rough sheath1, which exhibit neomorphic phenotypes in the leaves. RNA blot analysis suggests that Mu suppression affects only the neomorphic aspect of the allele, not the wild-type aspect. Additionally, Mu suppression appears to be exerting its effects at the level of transcription or transcript accumulation. We show that truncated transcripts are produced by three alleles, implying a mechanism for Mu suppression of 5′ untranslated region insertion alleles distinct from that which has been described previously. Additionally, it is found that Mu suppression can be caused by at least three different types of Mutator elements. Evidence presented here suggests that whether an allele is suppressible or not may depend upon the site of insertion. We cite previous work on the knox gene kn1, and discuss our results in the context of interactions between Mu-encoded products and the inherently negative regulation of neomorphic liguleless3 and rough sheath1 transcription.


BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Pihua Han ◽  
Jingjun Zhu ◽  
Guang Feng ◽  
Zizhang Wang ◽  
Yanni Ding

Abstract Background Breast cancer (BRCA) is one of the most common cancers worldwide. Abnormal alternative splicing (AS) frequently observed in cancers. This study aims to demonstrate AS events and signatures that might serve as prognostic indicators for BRCA. Methods Original data for all seven types of splice events were obtained from TCGA SpliceSeq database. RNA-seq and clinical data of BRCA cohorts were downloaded from TCGA database. Survival-associated AS events in BRCA were analyzed by univariate COX proportional hazards regression model. Prognostic signatures were constructed for prognosis prediction in patients with BRCA based on survival-associated AS events. Pearson correlation analysis was performed to measure the correlation between the expression of splicing factors (SFs) and the percent spliced in (PSI) values of AS events. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were conducted to demonstrate pathways in which survival-associated AS event is enriched. Results A total of 45,421 AS events in 21,232 genes were identified. Among them, 1121 AS events in 931 genes significantly correlated with survival for BRCA. The established AS prognostic signatures of seven types could accurately predict BRCA prognosis. The comprehensive AS signature could serve as independent prognostic factor for BRCA. A SF-AS regulatory network was therefore established based on the correlation between the expression levels of SFs and PSI values of AS events. Conclusions This study revealed survival-associated AS events and signatures that may help predict the survival outcomes of patients with BRCA. Additionally, the constructed SF-AS networks in BRCA can reveal the underlying regulatory mechanisms in BRCA.


2004 ◽  
Vol 10 (12) ◽  
pp. 853-860 ◽  
Author(s):  
Martin K. Oehler ◽  
Holger Greschik ◽  
Dagmar-C. Fischer ◽  
Xiaowen Tong ◽  
Roland Schuele ◽  
...  

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