TY - JOUR A1 - Sharma, Niharika A1 - Dang, Trang Minh A1 - Singh, Namrata A1 - Ruzicic, Slobodan A1 - Müller-Röber, Bernd A1 - Baumann, Ute A1 - Heuer, Sigrid T1 - Allelic variants of OsSUB1A cause differential expression of transcription factor genes in response to submergence in rice JF - Rice N2 - 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. KW - Submergence tolerance KW - SUB1A KW - Rice KW - Transcription factors Y1 - 2018 U6 - https://doi.org/10.1186/s12284-017-0192-z SN - 1939-8425 SN - 1939-8433 VL - 11 IS - 2 PB - Springer Open CY - London ER - TY - JOUR A1 - Jargosch, M. A1 - Kroeger, S. A1 - Gralinska, E. A1 - Klotz, Ulrike A1 - Fang, Z. A1 - Chen, W. A1 - Leser, U. A1 - Selbig, Joachim A1 - Groth, Detlef A1 - Baumgrass, Ria T1 - Data integration for identification of important transcription factors of STAT6-mediated cell fate decisions JF - Genetics and molecular research N2 - 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. KW - Data integration KW - Th2 cells KW - Gene regulatory network KW - STAT6 KW - Transcription factors Y1 - 2016 U6 - https://doi.org/10.4238/gmr.15028493 SN - 1676-5680 VL - 15 PB - FUNPEC CY - Ribeirao Preto ER - TY - THES A1 - Winck, Flavia Vischi T1 - Nuclear proteomics and transcription factor profiling in Chlamydomonas reinhardtii T1 - Nukleare Proteomics und Transkriptionsfaktoren : Profiling in Chlamydomonas reinhardtii N2 - The transcriptional regulation of the cellular mechanisms involves many different components and different levels of control which together contribute to fine tune the response of cells to different environmental stimuli. In some responses, diverse signaling pathways can be controlled simultaneously. One of the most important cellular processes that seem to possess multiple levels of regulation is photosynthesis. A model organism for studying photosynthesis-related processes is the unicellular green algae Chlamydomonas reinhardtii, due to advantages related to culturing, genetic manipulation and availability of genome sequence. In the present study, we were interested in understanding the regulatory mechanisms underlying photosynthesis-related processes. To achieve this goal different molecular approaches were followed. In order to indentify protein transcriptional regulators we optimized a method for isolation of nuclei and performed nuclear proteome analysis using shotgun proteomics. This analysis permitted us to improve the genome annotation previously published and to discover conserved and enriched protein motifs among the nuclear proteins. In another approach, a quantitative RT-PCR platform was established for the analysis of gene expression of predicted transcription factor (TF) and other transcriptional regulator (TR) coding genes by transcript profiling. The gene expression profiles for more than one hundred genes were monitored in time series experiments under conditions of changes in light intensity (200 µE m-2 s-1 to 700 µE m-2 s-1), and changes in concentration of carbon dioxide (5% CO2 to 0.04% CO2). The results indicate that many TF and TR genes are regulated in both environmental conditions and groups of co-regulated genes were found. Our findings also suggest that some genes can be common intermediates of light and carbon responsive regulatory pathways. These approaches together gave us new insights about the regulation of photosynthesis and revealed new candidate regulatory genes, helping to decipher the gene regulatory networks in Chlamydomonas. Further experimental studies are necessary to clarify the function of the candidate regulatory genes and to elucidate how cells coordinately regulate the assimilation of carbon and light responses. N2 - Pflanzen nutzen das Sonnenlicht um Substanzen, sogenannte Kohlenhydrate, zu synthetisieren. Diese können anschließend als Energielieferant für das eigene Wachstum genutzt werden. Der aufbauende Prozess wird als Photosynthese bezeichnet. Ein wichtiges Anliegen ist deshalb zu verstehen, wie Pflanzen äußere Einflüsse wahrnehmen und die Photosynthese dementsprechend regulieren. Ihre Zellen tragen diese Informationen in den Genen. Die Pflanzen nutzen aber in der Regel nicht alle ihre Gene gleichzeitig, die sie zur Anpassung an Umwelteinflüsse besitzen. Zu meist wird nur eine Teilfraktion der gesamten Information benötigt. Wir wollten der Frage nachgehen, welche Gene die Zellen für welche Situation regulieren. Im Zellkern gibt es Proteine, sogenannte Transkriptionsfaktoren, die spezifische Gene finden können und deren Transkription modulieren. Wenn ein Gen gebraucht wird, wird seine Information in andere Moleküle übersetzt (transkribiert), sogenannte Transkripte. Die Information dieser Transkripte wird benutzt um Proteine, Makromoleküle aus Aminsäuren, zu synthetisieren. Aus der Transkription eines Gens kann eine große Zahl des Transkripts entstehen. Es ist wahrscheinlich, dass ein Gen, dass gerade gebraucht wird, mehr Transkriptmoleküle hat als andere Gene. Da die Transkriptionsfaktoren mit der Transkription der Gene interferieren können, entwickelten wir in der vorliegenden Arbeit Strategien zur Identifikation dieser im Zellkern zu findenden Proteine mittels eines „Proteomics“-Ansatzes. Wir entwickelten weiterhin eine Strategie zur Identififikation von Transkripten Transkriptionsfaktor-codierender Gene in der Zelle und in welche Menge sie vorkommen. Dieser Ansatz wird als „Transcript-Profiling“ bezeichnet. Wir fanden Zellkern-lokalisierte Proteine, die als Signalmoleküle funktionieren könnten und Transkripte, die bei unterschiedlichen Umweltbedingungen in der Zelle vorhanden waren. Wir benutzten, die oben genannten Ansätze um die einzellige Grünalge Chlamydomonas zu untersuchen. Die Informationen, die wir erhielten, halfen zu verstehen welche Transkriptionsfaktoren notwendig sind, damit Chlamydomonas bei unterschiedlichen Umweltbedingungen, wie z.B. unterschiedliche Lichtintensitäten und unterschiedlicher Konzentration von Kohlenstoffdioxid, überlebt. KW - Proteomics KW - Transkriptionsfaktoren KW - Pflanzen KW - Chlamydomonas KW - Transcriptomics KW - Proteomics KW - Transcription factors KW - Plants KW - Chlamydomonas KW - Transcriptomics Y1 - 2011 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus-53909 ER -