PSF and CMF, autocrine factors that regulate gene expression during growth and early development ofDictyostelium

1995 ◽  
Vol 51 (12) ◽  
pp. 1124-1134 ◽  
Author(s):  
M. Clarke ◽  
R. H. Gomer

2017 ◽  
Author(s):  
Mehmet Ilyas Cosacak ◽  
Hatice Yiğit ◽  
Bünyamin Akgül

ABSTRACTSmall RNAs are known to regulate gene expression during early development. However, the dynamics of interaction between small RNAs and polysomes during this process is largely unknown. 0-1h and 7-8hDrosophilaembryos were fractionated on sucrose density gradients into four fractions based on A254reading (1) translationally inactive messengerribonucleoprotein (mRNP); (2) 60S; (3) monosome; and (4) polysome. Comparative analysis of deep-sequencing reads from fractionated and un-fractionated 0-1h and 8-h embryos revealed development-specific co-sedimentation pattern of small RNAs with the cellular translation machinery. Although most miRNAs did not have a specific preference for any state of the translational machinery, we detected fraction-specific enrichment of some miRNAs such as miR-1-3p, -184-39, 5-5p and 263-5p. More interestingly, we observed dysregulation of a subset of miRNAs in fractionated embryos despite no measurable difference in their amount in unfractionated embryos. Transposon-derived endosiRNAs are over-expressed in 7-8h embryos and are associated mainly with the mRNP fraction. However, transposon-derived piRNAs, which are more abundant in 0-1h embryos, co-sediment primarily with the polysome fractions. These results suggest that there appears to be a complex interplay among the small RNAs with respect to their polysome-cosedimention pattern during early development inDrosophila.



1992 ◽  
Vol 66 (1) ◽  
pp. 95-105 ◽  
Author(s):  
A M Colberg-Poley ◽  
L D Santomenna ◽  
P P Harlow ◽  
P A Benfield ◽  
D J Tenney


2019 ◽  
Vol 70 (19) ◽  
pp. 5355-5374 ◽  
Author(s):  
Dandan Zang ◽  
Jingxin Wang ◽  
Xin Zhang ◽  
Zhujun Liu ◽  
Yucheng Wang

Abstract Plant heat shock transcription factors (HSFs) are involved in heat and other abiotic stress responses. However, their functions in salt tolerance are little known. In this study, we characterized the function of a HSF from Arabidopsis, AtHSFA7b, in salt tolerance. AtHSFA7b is a nuclear protein with transactivation activity. ChIP-seq combined with an RNA-seq assay indicated that AtHSFA7b preferentially binds to a novel cis-acting element, termed the E-box-like motif, to regulate gene expression; it also binds to the heat shock element motif. Under salt conditions, AtHSFA7b regulates its target genes to mediate serial physiological changes, including maintaining cellular ion homeostasis, reducing water loss rate, decreasing reactive oxygen species accumulation, and adjusting osmotic potential, which ultimately leads to improved salt tolerance. Additionally, most cellulose synthase-like (CSL) and cellulose synthase (CESA) family genes were inhibited by AtHSFA7b; some of them were randomly selected for salt tolerance characterization, and they were mainly found to negatively modulate salt tolerance. By contrast, some transcription factors (TFs) were induced by AtHSFA7b; among them, we randomly identified six TFs that positively regulate salt tolerance. Thus, AtHSFA7b serves as a transactivator that positively mediates salinity tolerance mainly through binding to the E-box-like motif to regulate gene expression.



2006 ◽  
Vol 3 (2) ◽  
pp. 109-122 ◽  
Author(s):  
◽  
Christopher H. Bryant ◽  
Graham J.L. Kemp ◽  
Marija Cvijovic

Summary We have taken a first step towards learning which upstream Open Reading Frames (uORFs) regulate gene expression (i.e., which uORFs are functional) in the yeast Saccharomyces cerevisiae. We do this by integrating data from several resources and combining a bioinformatics tool, ORF Finder, with a machine learning technique, inductive logic programming (ILP). Here, we report the challenge of using ILP as part of this integrative system, in order to automatically generate a model that identifies functional uORFs. Our method makes searching for novel functional uORFs more efficient than random sampling. An attempt has been made to predict novel functional uORFs using our method. Some preliminary evidence that our model may be biologically meaningful is presented.



Nature ◽  
2008 ◽  
Vol 453 (7194) ◽  
pp. 534-538 ◽  
Author(s):  
Oliver H. Tam ◽  
Alexei A. Aravin ◽  
Paula Stein ◽  
Angelique Girard ◽  
Elizabeth P. Murchison ◽  
...  




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