@article{SharmaDangSinghetal.2018,
  author    = {Sharma, Niharika and Dang, Trang Minh and Singh, Namrata and Ruzicic, Slobodan and M{\"u}ller-R{\"o}ber, Bernd and Baumann, Ute and Heuer, Sigrid},
  title     = {Allelic variants of OsSUB1A cause differential expression of transcription factor genes in response to submergence in rice},
  series = {Rice},
  volume    = {11},
  journal   = {Rice},
  number    = {2},
  publisher = {Springer Open},
  address   = {London},
  issn      = {1939-8425},
  doi       = {10.1186/s12284-017-0192-z},
  pages     = {19},
  year      = {2018},
  abstract  = {Background: Flooding during seasonal monsoons affects millions of hectares of rice-cultivated areas across Asia. Submerged rice plants die within a week due to lack of oxygen, light and excessive elongation growth to escape the water. Submergence tolerance was first reported in an aus-type rice landrace, FR13A, and the ethylene-responsive transcription factor (TF) gene SUB1A-1 was identified as the major tolerance gene. Intolerant rice varieties generally lack the SUB1A gene but some intermediate tolerant varieties, such as IR64, carry the allelic variant SUB1A-2. Differential effects of the two alleles have so far not been addressed. As a first step, we have therefore quantified and compared the expression of nearly 2500 rice TF genes between IR64 and its derived tolerant near isogenic line IR64-Sub1, which carries the SUB1A-1 allele. Gene expression was studied in internodes, where the main difference in expression between the two alleles was previously shown. Results: Nineteen and twenty-six TF genes were identified that responded to submergence in IR64 and IR64-Sub1, respectively. Only one gene was found to be submergence-responsive in both, suggesting different regulatory pathways under submergence in the two genotypes. These differentially expressed genes (DEGs) mainly included MYB, NAC, TIFY and Zn-finger TFs, and most genes were downregulated upon submergence. In IR64, but not in IR64-Sub1, SUB1B and SUB1C, which are also present in the Sub1 locus, were identified as submergence responsive. Four TFs were not submergence responsive but exhibited constitutive, genotype-specific differential expression. Most of the identified submergence responsive DEGs are associated with regulatory hormonal pathways, i.e. gibberellins (GA), abscisic acid (ABA), and jasmonic acid (JA), apart from ethylene. An in-silico promoter analysis of the two genotypes revealed the presence of allele-specific single nucleotide polymorphisms, giving rise to ABRE, DRE/CRT, CARE and Site II cis-elements, which can partly explain the observed differential TF gene expression. Conclusion: This study identified new gene targets with the potential to further enhance submergence tolerance in rice and provides insights into novel aspects of SUB1A-mediated tolerance.},
  language  = {en}
}
@article{JargoschKroegerGralinskaetal.2016,
  author    = {Jargosch, M. and Kroeger, S. and Gralinska, E. and Klotz, Ulrike and Fang, Z. and Chen, W. and Leser, U. and Selbig, Joachim and Groth, Detlef and Baumgrass, Ria},
  title     = {Data integration for identification of important transcription factors of STAT6-mediated cell fate decisions},
  series = {Genetics and molecular research},
  volume    = {15},
  journal   = {Genetics and molecular research},
  publisher = {FUNPEC},
  address   = {Ribeirao Preto},
  issn      = {1676-5680},
  doi       = {10.4238/gmr.15028493},
  pages     = {17},
  year      = {2016},
  abstract  = {Data integration has become a useful strategy for uncovering new insights into complex biological networks. We studied whether this approach can help to delineate the signal transducer and activator of transcription 6 (STAT6)-mediated transcriptional network driving T helper (Th) 2 cell fate decisions. To this end, we performed an integrative analysis of publicly available RNA-seq data of Stat6-knockout mouse studies together with STAT6 ChIP-seq data and our own gene expression time series data during Th2 cell differentiation. We focused on transcription factors (TFs), cytokines, and cytokine receptors and delineated 59 positively and 41 negatively STAT6-regulated genes, which were used to construct a transcriptional network around STAT6. The network illustrates that important and well-known TFs for Th2 cell differentiation are positively regulated by STAT6 and act either as activators for Th2 cells (e.g., Gata3, Atf3, Satb1, Nfil3, Maf, and Pparg) or as suppressors for other Th cell subpopulations such as Th1 (e.g., Ar), Th17 (e.g., Etv6), or iTreg (e.g., Stat3 and Hifla) cells. Moreover, our approach reveals 11 TFs (e.g., Atf5, Creb3l2, and Asb2) with unknown functions in Th cell differentiation. This fact together with the observed enrichment of asthma risk genes among those regulated by STAT6 underlines the potential value of the data integration strategy used here. Thus, our results clearly support the opinion that data integration is a useful tool to delineate complex physiological processes.},
  language  = {en}
}