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Spirohn, Kerstin

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Spirohn

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Kerstin

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Spirohn, Kerstin

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2015 - 20202

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Author's Publications: search.filters.isAuthorOfPublication.1ff198d0-e22e-4593-8182-915942d72fed

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    Discovery of Progenitor Cell Signatures by Time-Series Synexpression Analysis During Drosophila Embryonic Cell Immortalization
    (National Academy of Sciences, 2015-10-20) Dequeant, Mary-Lee; Fagegaltier, Delphine; Hu, Yanhui; Spirohn, Kerstin; Simcox, Amanda; Hannon, Gregory J.; Perrimon, Norbert
    The use of time series profiling to identify groups of functionally related genes (synexpression groups) is a powerful approach for the discovery of gene function. Here we apply this strategy during RasV12 immortalization of Drosophila embryonic cells, a phenomenon not well characterized. Using high-resolution transcriptional time-series datasets, we generated a gene network based on temporal expression profile similarities. This analysis revealed that common immortalized cells are related to adult muscle precursors (AMPs), a stem cell-like population contributing to adult muscles and sharing properties with vertebrate satellite cells. Remarkably, the immortalized cells retained the capacity for myogenic differentiation when treated with the steroid hormone ecdysone. Further, we validated in vivo the transcription factor CG9650, the ortholog of mammalian Bcl11a/b, as a regulator of AMP proliferation predicted by our analysis. Our study demonstrates the power of time series synexpression analysis to characterize Drosophila embryonic progenitor lines and identify stem/progenitor cell regulators.
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    A Reference Map of the Human Binary Protein Interactome
    (Nature Research, 2020-04-08) Luck, Katja; Kim, Dae-Kyum; Lambourne, Luke; Spirohn, Kerstin; Begg, Bridget E; Bian, Wenting; Brignall, Ruth; Cafarelli, Tiziana; Campos-Laborie, Francisco J.; Charloteaux, Benoit; Choi, Dongsic; Coté, Atina; Daley, Meaghan; Deimling, Steven; Desbuleux, Alice; Dricot, Amélie; Gebbia, Marinella; Hardy, Madeleine; Kishore, Nishka; Knapp, Jennifer; Kovács, István A.; Lemmens, Irma; Mee, Miles W.; Mellor, Joseph C.; Pollis, Carl; Pons, Carles; Richardson, Aaron; Schlabach, Sadie; Teeking, Bridget; Yadav, Anupama; Babor, Mariana; Balcha, Dawit; Basha, Omer; Bowman-Colin, Christian; Chin, Suet-Feung; Choi, Soon Gang; Colabella, Claudia; Coppin, Georges; D'Amata, Cassandra; De Ridder, David; De Rouck, Steffi; Duran-Frigola, Miquel; Ennajdaoui, Hanane; Goebels, Florian; Goehring, Liana; Gopal, Anjali; Haddad, Ghazal; Hatchi, Elodie; Helmy, Mohamed; Jacob, Yves; Kassa, Yoseph; Landini, Serena; Li, Roujia; van Lieshout, Natascha; MacWilliams, Andrew; Markey, Dylan; Paulson, Joseph; Rangarajan, Sudharshan; Rasla, John; Rayhan, Ashyad; Rolland, Thomas; San Miguel Delgadillo, Adriana; Shen, Yun; Sheykhkarimli, Dayag; Sheynkman, Gloria; Simonovsky, Eyal; Taşan, Murat; Tejeda, Alexander; Tropepe, Vincent; Twizere, Jean-Claude; Wang, Yang; Weatheritt, Robert; Weile, Jochen; Xia, Yu; Yang, Xinping; Yeger-Lotem, Esti; Zhong, Quan; Aloy, Patrick; Bader, Gary D.; De Las Rivas, Javier; Gaudet, Suzanne; Hao, Tong; Rak, Janusz; Tavernier, Jan; Hill, David; Vidal, Marc; Roth, Frederick P.; Calderwood, Michael
    Global insights into cellular organization and genome function require comprehensive understanding of the interactome networks that mediate genotype-phenotype relationships. Here, we present a human “all-by-all” reference interactome map of human binary protein interactions, or “HuRI”. With ~53,000 high-quality protein-protein interactions (PPIs), HuRI has approximately four times more such interactions than high-quality curated interactions from small-scale studies. Integrating HuRI with genome, transcriptome, and proteome data enables the study of cellular function within most physiological or pathological cellular contexts. We demonstrate the utility of HuRI in identifying specific subcellular roles of PPIs. Inferred tissue-specific networks reveal general principles for the formation of cellular context-specific functions and elucidate potential molecular mechanisms underlying tissue-specific phenotypes of Mendelian diseases. HuRI represents a systematic proteome-wide reference linking genomic variation to phenotypic outcomes.