bims-plasge Biomed news
on Plastid genes
Issue of 2018‒10‒28
three papers selected by
Vera S. Bogdanova
Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences


  1. BMC Plant Biol. 2018 Oct 20. 18(1): 249
    Singh AK, Chaurasia S, Kumar S, Singh R, Kumari J, Yadav MC, Singh N, Gaba S, Jacob SR.
      BACKGROUND: Salinity severely limits wheat production in many parts of the world. Development of salt tolerant varieties represents the most practical option for enhancing wheat production from these areas. Application of marker assisted selection may assist in fast tracking development of salt tolerant wheat varieties. However, SSR markers available in the public domain are not specifically targeted to functional regions of wheat genome, therefore large numbers of these need to be analysed for identification of markers associated with traits of interest. With the availability of a fully annotated wheat genome assembly, it is possible to develop SSR markers specifically targeted to genic regions. We performed extensive analysis to identify candidate gene based SSRs and assessed their utility in characterizing molecular diversity in a panel of wheat genotypes.RESULTS: Our analysis revealed, 161 SSR motifs in 94 salt tolerance candidate genes of wheat. These SSR motifs were nearly equally distributed on the three wheat sub-genomes; 29.8% in A, 35.7% in B and 34.4% in D sub-genome. The maximum number of SSR motifs was present in exons (31.1%) followed by promoters (29.8%), 5'UTRs (21.1%), introns (14.3%) and 3'UTRs (3.7%). Out of the 65 candidate gene based SSR markers selected for validation, 30 were found polymorphic based on initial screening and employed for characterizing genetic diversity in a panel of wheat genotypes including salt tolerant and susceptible lines. These markers generated an average of 2.83 alleles/locus. Phylogenetic analysis revealed four clusters. Salt susceptible genotypes were mainly represented in clusters I and III, whereas high and moderate salt tolerant genotypes were distributed in the remaining two clusters. Population structure analysis revealed two sub-populations, sub-population 1 contained the majority of salt tolerant whereas sub-population 2 contained majority of susceptible genotypes. Moreover, we observed reasonably higher transferability of SSR markers to related wheat species.
    CONCLUSION: We have developed salt responsive gene based SSRs in wheat for the first time. These were highly useful in unravelling functional diversity among wheat genotypes with varying responses to salt stress. The identified gene based SSR markers will be valuable genomic resources for genetic/association mapping of salinity tolerance in wheat.
    Keywords:  Cg-SSRs; Cross-transferability; Genetic diversity; Microsatellite; Salinity tolerance; Salt responsive genes
    DOI:  https://doi.org/10.1186/s12870-018-1476-1
  2. J Plant Physiol. 2018 Oct 06. pii: S0176-1617(18)30514-5. [Epub ahead of print]231 291-296
    Matsui K, Tomatsu T, Kinouchi S, Suzuki T, Sato T.
      Anthocyanins are a group of flavonoids found in buckwheat (Fagopyrum esculentum) and many other plant species; however; little is known about their mechanisms of synthesis and regulation in buckwheat. We previously reported a spontaneous mutant buckwheat line that shows the green stem phenotype; this line does not accumulate anthocyanins but synthesizes flavonol and proanthocyanidin in the stem. Here, we used this line and lines developed by this line to search for genes related to anthocyanin accumulation in buckwheat. The lines with green stem showed flavonoid-3-O-glucosyltransferase activity against UDP-glucose, indicating that the flavonoid-3-O-glucosyltransferase gene was not controlling the green stem trait. We therefore searched the buckwheat genome database for a gene encoding glutathione S-transferase (GST), a flavonoid-binding protein that transports flavonoids to the vacuole, and identified a candidate gene, FeGST1. Expression analysis showed that FeGST1 was expressed in wild type buckwheat but not in the green stem lines. Linkage analysis with an F2 segregating population produced by crossing between the green stem line and a self-compatible line showed that FeGST1 segregated with stem color without any recombination. This indicates that the green stem trait could be caused by homozygous non-functional alleles of the FeGST1 locus.
    Keywords:  Flavonoids; Genome database; Glucosyltransferase; Hemizygous; Stem color; Transport
    DOI:  https://doi.org/10.1016/j.jplph.2018.10.004
  3. Adv Clin Chem. 2018 ;pii: S0065-2423(18)30036-2. [Epub ahead of print]87 1-36
    Zeng Y, Ren K, Zhu X, Zheng Z, Yi G.
      Long noncoding RNAs (lncRNAs) are an important group of pervasive noncoding RNAs (>200nt) proposed to be crucial regulators of numerous physiological and pathological processes. Through interactions with RNA, chromatin, and protein, lncRNAs modulate mRNA stability, chromatin structure, and the function of proteins (including transcription factors). In addition, to their well-known roles in the modulation of cell growth, apoptosis, neurological disease progression and cancer metastasis, these large molecules have also been identified as likely mediators of lipid metabolism. In particular, lncRNAs orchestrate adipogenesis; fatty acid, cholesterol, and phospholipid metabolism and transport; and the formation of high-density and low-density lipoproteins (HDLs and LDLs). LncRNAs also appear to target several transcription factors that play essential roles in the regulation of lipid metabolism, such as liver X receptors (LXRs), sterol regulatory element binding proteins (SREBPs), and peroxisome proliferator-activated receptor γ (PPARγ). Better understanding the regulatory roles of lncRNAs in dyslipidemia, atherosclerosis, and adipogenesis will reveal appropriate strategies to treat these diseases. In this review, we review recent progress in lncRNA-mediated regulation of lipid metabolism, as well as its role in the regulation of adipogenesis.
    Keywords:  Adipogenesis; Dyslipidemias; Lipids metabolism; Long non-coding RNAs; Metabolic disease
    DOI:  https://doi.org/10.1016/bs.acc.2018.07.001