@article{SchwenderKoenigKlapperstuecketal.2014,
  author    = {Schwender, Joerg and Koenig, Christina and Klapperstueck, Matthias and Heinzel, Nicolas and Munz, Eberhard and Hebbelmann, Inga and Hay, Jordan O. and Denolf, Peter and De Bodt, Stefanie and Redestig, Henning and Caestecker, Evelyne and Jakob, Peter M. and Borisjuk, Ljudmilla and Rolletschek, Hardy},
  title     = {Transcript abundance on its own cannot be used to infer fluxes in central metabolism},
  series = {Frontiers in Plant Science},
  volume    = {5},
  journal   = {Frontiers in Plant Science},
  issn      = {1664-462X},
  doi       = {10.3389/fpls.2014.00668},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-114586},
  year      = {2014},
  abstract  = {An attempt has been made to define the extent to which metabolic flux in central plant metabolism is reflected by changes in the transcriptome and metabolome, based on an analysis of in vitro cultured immature embryos of two oilseed rape (Brassica napus) accessions which contrast for seed lipid accumulation. Metabolic flux analysis (MFA) was used to constrain a flux balance metabolic model which included 671 biochemical and transport reactions within the central metabolism. This highly confident flux information was eventually used for comparative analysis of flux vs. transcript (metabolite). Metabolite profiling succeeded in identifying 79 intermediates within the central metabolism, some of which differed quantitatively between the two accessions and displayed a significant shift corresponding to flux. An RNA-Seq based transcriptome analysis revealed a large number of genes which were differentially transcribed in the two accessions, including some enzymes/proteins active in major metabolic pathways. With a few exceptions, differential activity in the major pathways (glycolysis, TCA cycle, amino acid, and fatty acid synthesis) was not reflected in contrasting abundances of the relevant transcripts. The conclusion was that transcript abundance on its own cannot be used to infer metabolic activity/fluxes in central plant metabolism. This limitation needs to be borne in mind in evaluating transcriptome data and designing metabolic engineering experiments.},
  language  = {en}
}
@article{BocukWolffKrauseetal.2017,
  author    = {Bocuk, Derya and Wolff, Alexander and Krause, Petra and Salinas, Gabriela and Bleckmann, Annalen and Hackl, Christina and Beissbarth, Tim and Koenig, Sarah},
  title     = {The adaptation of colorectal cancer cells when forming metastases in the liver: expression of associated genes and pathways in a mouse model},
  series = {BMC Cancer},
  volume    = {17},
  journal   = {BMC Cancer},
  number    = {342},
  doi       = {10.1186/s12885-017-3342-1},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-170853},
  year      = {2017},
  abstract  = {Background: Colorectal cancer (CRC) is the second leading cause of cancer-related death in men and women. Systemic disease with metastatic spread to distant sites such as the liver reduces the survival rate considerably. The aim of this study was to investigate the changes in gene expression that occur on invasion and expansion of CRC cells when forming metastases in the liver. Methods: The livers of syngeneic C57BL/6NCrl mice were inoculated with 1 million CRC cells (CMT-93) via the portal vein, leading to the stable formation of metastases within 4 weeks. RNA sequencing performed on the Illumina platform was employed to evaluate the expression profiles of more than 14,000 genes, utilizing the RNA of the cell line cells and liver metastases as well as from corresponding tumour-free liver. Results: A total of 3329 differentially expressed genes (DEGs) were identified when cultured CMT-93 cells propagated as metastases in the liver. Hierarchical clustering on heat maps demonstrated the clear changes in gene expression of CMT-93 cells on propagation in the liver. Gene ontology analysis determined inflammation, angiogenesis, and signal transduction as the top three relevant biological processes involved. Using a selection list, matrix metallopeptidases 2, 7, and 9, wnt inhibitory factor, and chemokine receptor 4 were the top five significantly dysregulated genes. Conclusion: Bioinformatics assists in elucidating the factors and processes involved in CRC liver metastasis. Our results support the notion of an invasion-metastasis cascade involving CRC cells forming metastases on successful invasion and expansion within the liver. Furthermore, we identified a gene expression signature correlating strongly with invasiveness and migration. Our findings may guide future research on novel therapeutic targets in the treatment of CRC liver metastasis.},
  language  = {en}
}