Abstract
Genetic and biochemical screening approaches often fail to identify functionally relevant pathway networks because many signaling proteins contribute to multiple gene ontology pathways. We developed a DRUGPATH-approach to predict pathway-interactomes from high-content drug screen data. DRUGPATH is based upon combining z-scores of effective inhibitors with their corresponding and validated targets. We test DRUGPATH by comparing homeostatic pathways in human embryonic stem cells (hESCs), human induced pluripotent stem cells (hiPSCs) and human amniotic fluid stem cells (hAFSCs). We show that hAFSCs utilize distinct interactomes compared to hESCs/hiPSCs and that pathways orchestrating cell cycle and apoptosis are strongly interconnected, while pathways regulating survival and size are not. Interestingly, hESCs/hiPSCs regulate their size by growing exact additional sizes during each cell cycle. Chemical and genetic perturbation studies show that this “adder-model” is dependent on the DNA-damage pathway. In the future, the DRUGPATH-approach may help to predict novel pathway interactomes from high-content drug screens.
Highlights
Human ESCs have no expansion limit and represent a source for differentiated cells stemming from all three embryonic germ layers
We hypothesized that homeostatic processes may be differentially regulated in human embryonic stem cells (hESCs), human induced pluripotent stem cells (hiPSCs) and human amniotic fluid stem cells (hAFSCs)
We found that the Gaussian distribution of all added sizes in hAFSCs is much higher compared to hESCs or hiPSCs (Fig. S15E) – which is compatible with the sizer model in which the cells grow different volumes between G1 and G2 in order to end up with one constant size at the end of G2
Summary
Human ESCs have no expansion limit and represent a source for differentiated cells stemming from all three embryonic germ layers. Drug-screens are suitable for the analysis of homeostatic pathways because small-molecule inhibitors may act potently and instantaneously on their specific targets. When screening, it is recommendable to use multiple inhibitors targeting the same on-target pathways. Distinct functional responses arising from inhibitors targeting the same specific targets www.nature.com/scientificreports/. STEP 5 - calculation of significant hits from z-scores: paired t-test STEP 7 - calculation of functional enrichment clusters from Δz-scores of significant hits Pearson correlation STEP 8 - merging calculated Δz-scores with known inhibitor targets e.g. SB 202190 - inhibits p38α and p38β (gene names: MAPK14 and MAPK11) - Table S4
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
