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XB-ART-54969
Sci Rep January 1, 2018; 8 (1): 8315.

Asymmetric distribution of biomolecules of maternal origin in the Xenopus laevis egg and their impact on the developmental plan.

Sindelka R , Abaffy P , Qu Y , Tomankova S , Sidova M , Naraine R , Kolar M , Peuchen E , Sun L , Dovichi N , Kubista M .


Abstract
Asymmetric cell division is a ubiquitous feature during the development of higher organisms. Asymmetry is achieved by differential localization or activities of biological molecules such as proteins, and coding and non-coding RNAs. Here, we present subcellular transcriptomic and proteomic analyses along the animal-vegetal axis of Xenopus laevis eggs. More than 98% of the maternal mRNAs could be categorized into four localization profile groups: animal, vegetal, extremely vegetal, and a newly described group of mRNAs that we call extremely animal, which are mRNAs enriched in the animal cortex region. 3''UTRs of localized mRNAs were analyzed for localization motifs. Several putative motifs were discovered for vegetal and extremely vegetal mRNAs, while no distinct conserved motifs for the extremely animal mRNAs were identified, suggesting different localization mechanisms. Asymmetric profiles were also found for proteins, with correlation to those of corresponding mRNAs. Based on unexpected observation of the profiles of the homoeologous genes exd2 we propose a possible mechanism of genetic evolution.

PubMed ID: 29844480
Article link: Sci Rep
Grant support: [+]
Genes referenced: acsl6 akt1 ankrd10 armc8 atp1a1 atp2a3 atp5f1a atp6v0a2 bco2 cdk1 cirbp cox6c crem ctdspl ctsc cyb5b dad1 dand5 dazl ddx25 ddx4 eef1b2 eif2a eif3a eif4b epcam exd2 fis1 fn1 foxi2 germes gk glod4 grip2 hnrnpa3 hprt1 hsp90aa1.1 ifrd2 inpp1 itm2a jun lima1 map3k1 mcts1 mrpl1 mrpl11 mrps10 naga nanos1 nox1 nqo1 otx1 pgam1 pgat plin2 ppp2r5a prpsap2 prrg2 psma6 raver1 rbm6 rnf38 rpl14 rras2 sdhc slc13a4l slc18a2 slc66a2.1 sod1 soga1 sox7 sybu thap4 tmem62 trim36 ube3a vat1 vcl vegt velo1 vps35 wnk2 wnt11b zfyve28

GEO Series:
   GSE104848: NCBI

References [+] :
Anders, HTSeq--a Python framework to work with high-throughput sequencing data. 2015, Pubmed


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