TY  - JOUR
A1  - Caliskan, Aylin
A1  - Crouch, Samantha A. W.
A1  - Giddins, Sara
A1  - Dandekar, Thomas
A1  - Dangwal, Seema
T1  - Progeria and aging — Omics based comparative analysis
JF  - Biomedicines
N2  - Since ancient times aging has also been regarded as a disease, and humankind has always strived to extend the natural lifespan. Analyzing the genes involved in aging and disease allows for finding important indicators and biological markers for pathologies and possible therapeutic targets. An example of the use of omics technologies is the research regarding aging and the rare and fatal premature aging syndrome progeria (Hutchinson-Gilford progeria syndrome, HGPS). In our study, we focused on the in silico analysis of differentially expressed genes (DEGs) in progeria and aging, using a publicly available RNA-Seq dataset (GEO dataset GSE113957) and a variety of bioinformatics tools. Despite the GSE113957 RNA-Seq dataset being well-known and frequently analyzed, the RNA-Seq data shared by Fleischer et al. is far from exhausted and reusing and repurposing the data still reveals new insights. By analyzing the literature citing the use of the dataset and subsequently conducting a comparative analysis comparing the RNA-Seq data analyses of different subsets of the dataset (healthy children, nonagenarians and progeria patients), we identified several genes involved in both natural aging and progeria (KRT8, KRT18, ACKR4, CCL2, UCP2, ADAMTS15, ACTN4P1, WNT16, IGFBP2). Further analyzing these genes and the pathways involved indicated their possible roles in aging, suggesting the need for further in vitro and in vivo research. In this paper, we (1) compare “normal aging” (nonagenarians vs. healthy children) and progeria (HGPS patients vs. healthy children), (2) enlist genes possibly involved in both the natural aging process and progeria, including the first mention of IGFBP2 in progeria, (3) predict miRNAs and interactomes for WNT16 (hsa-mir-181a-5p), UCP2 (hsa-mir-26a-5p and hsa-mir-124-3p), and IGFBP2 (hsa-mir-124-3p, hsa-mir-126-3p, and hsa-mir-27b-3p), (4) demonstrate the compatibility of well-established R packages for RNA-Seq analysis for researchers interested but not yet familiar with this kind of analysis, and (5) present comparative proteomics analyses to show an association between our RNA-Seq data analyses and corresponding changes in protein expression.
KW  - progeria
KW  - aging
KW  - omics
KW  - RNA sequencing
KW  - bioinformatics
KW  - sun exposure
KW  - HGPS
KW  - IGFBP2
KW  - ACKR4
KW  - WNT
Y1  - 2022
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-289868
SN  - 2227-9059
VL  - 10
IS  - 10
ER  - 
TY  - JOUR
A1  - Peindl, Matthias
A1  - Göttlich, Claudia
A1  - Crouch, Samantha
A1  - Hoff, Niklas
A1  - Lüttgens, Tamara
A1  - Schmitt, Franziska
A1  - Pereira, Jesús Guillermo Nieves
A1  - May, Celina
A1  - Schliermann, Anna
A1  - Kronenthaler, Corinna
A1  - Cheufou, Danjouma
A1  - Reu-Hofer, Simone
A1  - Rosenwald, Andreas
A1  - Weigl, Elena
A1  - Walles, Thorsten
A1  - Schüler, Julia
A1  - Dandekar, Thomas
A1  - Nietzer, Sarah
A1  - Dandekar, Gudrun
T1  - EMT, stemness, and drug resistance in biological context: a 3D tumor tissue/in silico platform for analysis of combinatorial treatment in NSCLC with aggressive KRAS-biomarker signatures
JF  - Cancers
N2  - Epithelial-to-mesenchymal transition (EMT) is discussed to be centrally involved in invasion, stemness, and drug resistance. Experimental models to evaluate this process in its biological complexity are limited. To shed light on EMT impact and test drug response more reliably, we use a lung tumor test system based on a decellularized intestinal matrix showing more in vivo-like proliferation levels and enhanced expression of clinical markers and carcinogenesis-related genes. In our models, we found evidence for a correlation of EMT with drug resistance in primary and secondary resistant cells harboring KRAS\(^{G12C}\) or EGFR mutations, which was simulated in silico based on an optimized signaling network topology. Notably, drug resistance did not correlate with EMT status in KRAS-mutated patient-derived xenograft (PDX) cell lines, and drug efficacy was not affected by EMT induction via TGF-β. To investigate further determinants of drug response, we tested several drugs in combination with a KRAS\(^{G12C}\) inhibitor in KRAS\(^{G12C}\) mutant HCC44 models, which, besides EMT, display mutations in P53, LKB1, KEAP1, and high c-MYC expression. We identified an aurora-kinase A (AURKA) inhibitor as the most promising candidate. In our network, AURKA is a centrally linked hub to EMT, proliferation, apoptosis, LKB1, and c-MYC. This exemplifies our systemic analysis approach for clinical translation of biomarker signatures.
KW  - EMT
KW  - drug resistance
KW  - invasion
KW  - stemness
KW  - 3D lung tumor tissue models
KW  - KRAS biomarker signatures
KW  - boolean in silico models
KW  - targeted combination therapy
Y1  - 2022
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-270744
SN  - 2072-6694
VL  - 14
IS  - 9
ER  -