Relevant publications

Sahni N, S Yi, M Taipale, JI Fuxman Bass, J Coulombe-Huntington, F Yang, J Peng, J Weile, GI Karras, Y Wang, IA Kovács, A Kamburov, I Krykbaeva, MH Lam, G Tucker, V Khurana, A Sharma, YY Liu, N Yachie, Q Zhong, Y Shen, A Palagi, A San-Miguel, C Fan, D Balcha, A Dricot, DM Jordan, JM Walsh, AA Shah, X Yang, AK Stoyanova, A Leighton, MA Calderwood, Y Jacob, ME Cusick, K Salehi-Ashtiani, LJ Whitesell, S Sunyaev, B Berger, AL Barabási, B Charloteaux, DE Hill, T Hao, FP Roth, Y Xia, AJ Walhout, S Lindquist, and M Vidal 2015. Widespread macromolecular interaction perturbations in human genetic disorders.  Cell 161(3):647-660.

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Summary: This paper was a result of a long-standing collaboration between Marc Vidal’s lab at the Center for Cancer Systems Biology (DFCI, Boston) and Susan Lindquist’s lab. We generated a collection of almost 3,000 mutant alleles involved in over 1,000 Mendelian diseases. We then systematically characterized how disease-causing point mutations affect protein folding (LUMIER assay), protein/protein interactions (yeast tw0-hybrid assay), and protein/DNA interactions (yeast one-hybrid assay). Our results show that a large fraction of disease mutations are not simple loss-of-function alleles. Rather, they disrupt specific protein/protein and protein/DNA interactions. Such specific disruptions likely explain how different mutations in a single gene can cause completely different diseases (pleiotropy).

Follow-up questions: This paper outlined the general principles of how disease-causing mutations rewire protein/protein and protein/DNA interaction networks. Our next aim is to further phenotype this collection to decipher the principles that govern the folding, localization and degradation of mutant alleles and to identify small molecules that ameliorate these phenotypes.

Taipale, M., G. Tucker, J. Peng, I. Krykbaeva, Z.-Y. Lin, B. Larsen, H. Choi, B. Berger, A.-C. Gingras, and S. Lindquist 2014. A quantitative chaperone interaction network reveals the architecture of cellular protein homeostasis pathways. Cell 158(2):434-448.

Summary: This was a nice collaboration with Anne-Claude Gingras’s lab at the Lunenfeld Tanenbaum Research Institute in Toronto. We characterized the interaction network of about 60 chaperones and co-chaperones by affinity purification followed by mass spectrometry and by a quantitative LUMIER assay. We could connect many poorly characterized co-chaperones to specific biological complexes or processes.

Follow-up questions: This chaperone/client interaction network was large, but there are still many more quality control factors that remain to be characterized. For example, Hsp70 has over 60 co-chaperones that were not included in the analysis. How do these co-factors fit in? More generally, this work presents a static view of the network that undoubtedly looks different in different tissues, organisms, and disease states. Mapping these changes and relating them to the pathobiology of the disease are the next challenges.

Taipale, M., I. Krykbaeva, L. Whitesell, S. Santagata, J. Zhang, Q. Liu, N.S. Gray, and S. Lindquist 2013. Chaperones as thermodynamic sensors of drug::target interactions in living cells. Nature Biotechnology 31(7): 630-637.

Summary: In this paper, we showed that chaperones can be used as “thermodynamic sensors” to detect drug/target interactions. When a small molecule binds its target protein in the cell, the drug/target complex is thermodynamically more stable than the target protein alone. Thus, the target protein’s association with its chaperone is decreased. We exploited this finding to profile the specificities of 30 kinase inhibitors and identified ETV6-NTRK3 as a target of the FDA approved drug Xalkori (crizotinib).

Follow-up questions: This approach worked well for kinases and steroid hormone receptors, two major classes of drug targets. But we have identified thousands of other chaperone/client interactions as well – could the assay be expanded to these too? This is what we are trying to find out now.

Taipale, M., I. Krykbaeva, M. Koeva, C. Kayatekin, K.D. Westover, G. Karras, and S. Lindquist 2012. Quantitative analysis of Hsp90::client interactions reveals modes of substrate recognition. Cell 150(5):987-1001.

Summary: We quantitatively assayed the interaction of Hsp90 with most kinases, E3 ligases, and transcription factors in human cells. Analyzing the data, we could determine that Hsp90 does not recognize a specific motif in its kinase clients, as was previously thought. Rather, it recognizes the kinase clients in a combinatorial manner. A co-chaperone (Cdc37) recognizes the kinase fold, whereas thermodynamic stability of the kinase determines the degree of association with Hsp90.

Follow-up questions: Kinase clients seem to be distributed almost randomly in the kinase tree. What drives this distribution of clients? Do kinases change their client status rapidly during evolution or do they maintain it for millions of years? Why do kinases associate with chaperones in the first place? Wouldn’t it be better to evolve a more stable fold?

All publications

 

Taipale, M., G. Tucker, J. Peng, I. Krykbaeva, Z.-Y. Lin, B. Larsen, H. Choi, B. Berger, A.-C. Gingras, and S. Lindquist 2014. A quantitative chaperone interaction network reveals the architecture of cellular protein homeostasis pathways. Cell 158(2):434-448.

Taipale, M., I. Krykbaeva, L. Whitesell, S. Santagata, J. Zhang, Q. Liu, N.S. Gray, and S. Lindquist 2013. Chaperones as thermodynamic sensors of drug::target interactions in living cells. Nature Biotechnology 31(7): 630-637.

Taipale, M., I. Krykbaeva, M. Koeva, C. Kayatekin, K.D. Westover, G. Karras, and S. Lindquist 2012. Quantitative analysis of Hsp90::client interactions reveals modes of substrate recognition. Cell 150(5):987-1001.

DeBartolo, J., M. Taipale, and A. Keating 2014. Genome-wide prediction and validation of peptides that bind human prosurvival Bcl-2 receptors. PLOS Computational Biology 10(6):e1003693.

Petschnigg, J., B. Groisman, M. Kotlyar, M. Taipale, Y. Zheng, C.F. Kurat, A. Sayad, J.R. Sierra, M.M. Usaj, J. Snider, A. Nachman, I. Krykbaeva, M.-S. Tsao, J. Moffat, T. Pawson, S. Lindquist, I. Jurisica, and I. Stagljar 2014. Mammalian-Membrane-Two-Hybrid (MaMTH): a novel split-ubiquitin assay for investigation of signaling pathways in human cells. Nature Methods 11(5):585-592.

Lambert, J.-P., G. Ivosev, A.L. Couzens, B. Larsen, M. Taipale, Z.-Y. Lin, Q. Zhong, S. Lindquist, M. Vidal, R. Aebersold, T. Pawson, R. Bonner, S. Tate, and A.-C. Gingras (2013). Mapping differential interactomes by affinity purification coupled with data-independent mass spectrometry acquisition. Nature Methods 10(12):1239-1245.

Shalgi, R., J. A. Hurt, I. Krykbaeva, M. Taipale, S. Lindquist, and C.B. Burge 2013. Widespread regulation of translation by elongation pausing in heat shock. Molecular Cell 49(3):439-452.

Jolma A., J. Yan, T. Whitington, J. Toivonen, K. Nitta, P. Rastas, E. Morgunova, M. Enge, M. Taipale, G. Wei, K. Palin, J.M. Vaquerizas, R. Vincentelli, N.M. Luscombe, T.R. Hughes, P. Lemaire, E. Ukkonen, T. Kivioja, and J. Taipale 2013. Binding specificities of human transcription factors. Cell 150(1-2):327-339.

Taipale, M.*, D.F. Jarosz*, and S. Lindquist 2010. HSP90 at the hub of protein homeostasis: emerging mechanistic insights. Nat. Rev. Mol. Cell Biol. 11(7):515-528.

Jarosz, D.F.*, M. Taipale*, and S. Lindquist 2010. Protein homeostasis and the phenotypic manifestation of genetic diversity: principles and mechanisms. Annu. Rev. Genet. 44:189-216

Wei, G.H., G. Badis, M.F. Berger, T. Kivioja, K. Palin, M. Enge, M. Bonke, A. Jolma, M. Varjosalo, A.R. Gehrke, J. Yan, S. Talukder, M. Turunen, M. Taipale, H.G. Stunnenberg, E. Ukkonen, T.R. Hughes, M.L. Bulyk, and J. Taipale 2010. Genome-wide analysis of ETS-family DNA-binding in vitro and in vivo. EMBO J. 29(13):2147-60.

Jolma A., T. Kivioja, J. Toivonen, L. Cheng, G. Wei, M. Enge, M. Taipale, J.M. Vaquerizas, J. Yan, M.J. Sillanpää, M. Bonke, K. Palin, S. Talukder, T.R. Hughes, N.M. Luscombe, E. Ukkonen, and J. Taipale 2010. Multiplexed massively parallel SELEX for characterization of human transcription factor binding specificities. Genome Res. 20(6):861-73.

Pfister, S., S. Rea, M. Taipale, F. Mendrzyk, B. Straub, C. Ittrich, O. Thueringen, S. Durrinck, H.P. Sinn, P. Lichter, and A. Akhtar 2008. The histone acetyltransferase hMOF is frequently downregulated in primary breast carcinoma and medulloblastoma and constitutes a biomarker for clinical outcome in medulloblastoma. Int. J. Cancer 122(6):1207-1213.

Mendjan, S., M. Taipale*, J. Kind*, H. Holz, P. Gebhardt, M. Schelder, M. Vermeulen, A. Buscaino, K. Duncan, J. Müller, M. Wilm, H. Stunnenberg, H. Saumweber, and A. Akhtar 2006. Nuclear pore components are involved in the transcriptional regulation of dosage compensation in Drosophila. Mol. Cell 21(6): 811-823.

Niggeweg R., T. Kocher, M. Gentzel, A. Buscaino, M. Taipale, A. Akhtar, and M. Wilm 2006. A general precursor ion-like scanning mode on quadrupole-TOF instruments compatible with chromatographic separation. Proteomics 6(1):41-53.

Taipale M., S. Rea, K. Richter, A. Vilar, P. Lichter, A. Imhof, and A. Akhtar 2005. hMOF histone acetyltransferase is required for histone H4 lysine 16 acetylation in mammalian cells. Mol. Cell. Biol. 25(15): 6798-6810.

Taipale M. and A. Akhtar 2005. Chromatin mechanisms in Drosophila dosage compensation. Chapter in: Epigenetics and Chromatin, Philippe Jeanteur (ed), pp. 123-150 (Springer Verlag, Heidelberg, Germany).

Hannula-Jouppi K., N. Kaminen-Ahola, M. Taipale, R. Eklund, J. Nopola-Hemmi, H. Kaariainen, and J. Kere. The axon guidance receptor gene ROBO1 is a candidate gene for developmental dyslexia. PLoS Genet. 1(4): e50.

Taipale M., N. Kaminen, J. Nopola-Hemmi, T. Haltia, B. Myllyluoma, H. Lyytinen, K. Müller, M. Kaaranen, P.J. Lindsberg, K. Hannula-Jouppi, and J. Kere 2003. A candidate gene for developmental dyslexia encodes a nuclear tetratricopeptide repeat domain protein dynamically regulated in brain. Proc. Natl. Acad. Sci. USA 100(20): 11553-11558.

Buscaino A., T. Köcher, J.H. Kind, H. Holz, M. Taipale, K. Wagner, M. Wilm, and A. Akhtar 2003. MOF-regulated acetylation of MSL-3 in the Drosophila dosage compensation complex. Mol. Cell. 11(5): 1265-1277.

Nopola-Hemmi, B. Myllyluoma, T. Haltia, M. Taipale, V. Ollikainen, T. Ahonen, A. Voutilainen A, J. Kere, and E. Widen 2001. A dominant gene for developmental dyslexia on chromosome 3. J. Med. Genet. 38(10): 658-664.

Nopola-Hemmi J., M. Taipale, T. Haltia, A-E. Lehesjoki, A. Voutilainen, and J. Kere 2000. Two translocations of chromosome 15q associated with dyslexia. J. Med. Genet. 37(10): 771-775.

Höglund P., S. Haila, K.H. Gustavson, M. Taipale, K. Hannula, K. Popinska, C. Holmberg, J. Socha, A. de la Chapelle ja J. Kere 1998. Clustering of private mutations in the congenital chloride diarrhea/down-regulated in adenoma gene. Hum. Mutat. 11(4): 321-327.