Abstract
A decade ago mainstream molecular biologists regarded it impossible or biologically ill-motivated to understand the dynamics of complex biological phenomena, such as cancer genesis and progression, from a network perspective. Indeed, there are numerical difficulties even for those who were determined to explore along this direction. Undeterred, seven years ago a group of Chinese scientists started a program aiming to obtain quantitative connections between tumors and network dynamics. Many interesting results have been obtained. In this paper we wish to test such idea from a different angle: the connection between a normal biological process and the network dynamics. We have taken early myelopoiesis as our biological model. A standard roadmap for the cell-fate diversification during hematopoiesis has already been well established experimentally, yet little was known for its underpinning dynamical mechanisms. Compounding this difficulty there were additional experimental challenges, such as the seemingly conflicting hematopoietic roadmaps and the cell-fate inter-conversion events. With early myeloid cell-fate determination in mind, we constructed a core molecular endogenous network from well-documented gene regulation and signal transduction knowledge. Turning the network into a set of dynamical equations, we found computationally several structurally robust states. Those states nicely correspond to known cell phenotypes. We also found the states connecting those stable states. They reveal the developmental routes—how one stable state would most likely turn into another stable state. Such interconnected network among stable states enabled a natural organization of cell-fates into a multi-stable state landscape. Accordingly, both the myeloid cell phenotypes and the standard roadmap were explained mechanistically in a straightforward manner. Furthermore, recent challenging observations were also explained naturally. Moreover, the landscape visually enables a prediction of a pool of additional cell states and developmental routes, including the non-sequential and cross-branch transitions, which are testable by future experiments. In summary, the endogenous network dynamics provide an integrated quantitative framework to understand the heterogeneity and lineage commitment in myeloid progenitors.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Adolfsson, J., Mansson, R., Buza-Vidas, N., Hultquist, A., Liuba, K., Jensen, C.T., Bryder, D., Yang, L., Borge, O.J., Thoren, L.A.M., Anderson, K., Sitnicka, E., Sasaki, Y., Sigvardsson, M., and Jacobsen, S.E.W. (2005). Identification of Flt3+ lympho-myeloid stem cells lacking erythro-megakaryocytic potential. Cell 121, 295–306.
Akashi, K., Traver, D., Miyamoto, T., and Weissman, I.L. (2000). A clonogenic common myeloid progenitor that gives rise to all myeloid lineages. Nature 404, 193–197.
Alberts, B., Wilson, J.H., and Hunt, T. (2008). Molecular Biology of the Cell, 5th ed. (New York: Garland Science).
Ao, P. (2007). Orders of magnitude change in phenotype rate caused by mutation. Cell Oncol 29, 67–69.
Ao, P. (2009). Global view of bionetwork dynamics: adaptive landscape. J Genets Genomics 36, 63–73.
Ao, P., Galas, D., Hood, L., Yin, L., and Zhu, X.M. (2010). Towards predictive stochastic dynamical modeling of cancer genesis and progression. Interdiscip Sci Comput Life Sci 2, 140–144.
Ao, P., Galas, D., Hood, L., and Zhu, X. (2008). Cancer as robust intrinsic state of endogenous molecular-cellular network shaped by evolution. Med Hypotheses 70, 678–684.
Arnold, V.I., and Levi, M. (1988). Geometrical methods in the theory of ordinary differential equations, 2nd ed. (New York: Springer-Verlag).
Brenner, S. (2010). Sequences and consequences. Philos Trans R Soc B-Biol Sci 365, 207–212.
Bryder, D., Rossi, D.J., and Weissman, I.L. (2006). Hematopoietic stem cells. Am J Pathol 169, 338–346.
Busch, K., Klapproth, K., Barile, M., Flossdorf, M., Holland-Letz, T., Schlenner, S.M., Reth, M., Höfer, T., and Rodewald, H.R. (2015). Fundamental properties of unperturbed haematopoiesis from stem cells in vivo. Nature 518, 542–546.
Cahan, P., Li, H., Morris, S.A., Lummertz da Rocha, E., Daley, G.Q., and Collins, J.J. (2014). CellNet: network biology applied to stem cell engineering. Cell 158, 903–915.
Chambers, S.M., Boles, N.C., Lin, K.Y.K., Tierney, M.P., Bowman, T.V., Bradfute, S.B., Chen, A.J., Merchant, A.A., Sirin, O., Weksberg, D.C., Merchant, M.G., Fisk, C.J., Shaw, C.A., and Goodell, M.A. (2007). Hematopoietic fingerprints: an expression database of stem cells and their progeny. Cell Stem Cell 1, 578–591.
Chen, K.C., Csikasz-Nagy, A., Gyorffy, B., Val, J., Novak, B., and Tyson, J.J. (2000). Kinetic analysis of a molecular model of the budding yeast cell cycle. Mol Biol Cell 11, 369–391.
Coussens, L.M., and Werb, Z. (2002). Inflammation and cancer. Nature 420, 860–867.
Enver, T., Pera, M., Peterson, C., and Andrews, P.W. (2009). Stem cell states, fates, and the rules of attraction. Cell Stem Cell 4, 387–397.
Folmes, C.D.L., Dzeja, P.P., Nelson, T.J., and Terzic, A. (2012). Metabolic plasticity in stem cell homeostasis and differentiation. Cell Stem Cell 11, 596–606.
Glass, L., and Kauffman, S.A. (1973). The logical analysis of continuous, non-linear biochemical control networks. J Theor Biol 39, 103–129.
Graf, T. (2002). Differentiation plasticity of hematopoietic cells. Blood 99, 3089–3101.
Graf, T. (2011). Historical origins of transdifferentiation and reprogramming. Cell Stem Cell 9, 504–516.
Grass, J.A., Jing, H., Kim, S.I., Martowicz, M.L., Pal, S., Blobel, G.A., and Bresnick, E.H. (2006). Distinct functions of dispersed GATA factor complexes at an endogenous gene locus. Mol Cellular Biol 26, 7056–7067.
Haas, S., Hansson, J., Klimmeck, D., Loeffler, D., Velten, L., Uckelmann, H., Wurzer, S., Prendergast, Á.M., Schnell, A., Hexel, K., Santarella-Mellwig, R., Blaszkiewicz, S., Kuck, A., Geiger, H., Milsom, M.D., Steinmetz, L.M., Schroeder, T., Trumpp, A., Krijgsveld, J., and Essers, M.A.G. (2015). Inflammation-induced emergency megakaryopoiesis driven by hematopoietic stem cell-like megakaryocyte progenitors. Cell Stem Cell 17, 422–434.
Hanahan, D., and Weinberg, R.A. (2000). The hallmarks of cancer. Cell 100, 57–70.
Hanahan, D., and Weinberg, R.A. (2011). Hallmarks of cancer: the next generation. Cell 144, 646–674.
Hartwell, L.H., and Kastan, M.B. (1994). Cell cycle control and cancer. Science 266, 1821–1828.
Hu, M., Krause, D., Greaves, M., Sharkis, S., Dexter, M., Heyworth, C., and Enver, T. (1997). Multilineage gene expression precedes commitment in the hemopoietic system.. Genes Dev 11, 774–785.
Huang, C.Y.F., and Ferrell, J.E. (1996). Ultrasensitivity in the mitogen-activated protein kinase cascade. Proc Natl Acad Sci USA 93, 10078–10083.
Huang, S., Ernberg, I., and Kauffman, S. (2009). Cancer attractors: A systems view of tumors from a gene network dynamics and developmental perspective. Seminars Cell Dev Biol 20, 869–876.
Iwasaki, H., Mizuno, S., Arinobu, Y., Ozawa, H., Mori, Y., Shigematsu, H., Takatsu, K., Tenen, D.G., and Akashi, K. (2006). The order of expression of transcription factors directs hierarchical specification of hematopoietic lineages. Genes Dev 20, 3010–3021.
Ji, S., Zhang, L., and Hui, L. (2013). Cell fate conversion: Direct induction of hepatocyte-like cells from fibroblasts. J Cell Biochem 114, 256–265.
Klein, A.M., Mazutis, L., Akartuna, I., Tallapragada, N., Veres, A., Li, V., Peshkin, L., Weitz, D.A., and Kirschner, M.W. (2015). Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161, 1187–1201.
Klimmeck, D., Cabezas-Wallscheid, N., Reyes, A., von Paleske, L., Renders, S., Hansson, J., Krijgsveld, J., Huber, W., and Trumpp, A. (2014). Transcriptome-wide profiling and posttranscriptional analysis of hematopoietic stem/progenitor cell differentiation toward myeloid commitment. Stem Cell Rep 3, 858–875.
Kohli, L., and Passegué, E. (2014). Surviving change: the metabolic journey of hematopoietic stem cells. Trends Cell Biol 24, 479–487.
Kondo, M., Wagers, A.J., Manz, M.G., Prohaska, S.S., Scherer, D.C., Beilhack, G.F., Shizuru, J.A., and Weissman, I.L. (2003). Biology of hematopoietic stem cells and progenitors: Implications for Clinical Application. Annu Rev Immunol 21, 759–806.
Kondo, M., Weissman, I.L., and Akashi, K. (1997). Identification of clonogenic common lymphoid progenitors in mouse bone marrow. Cell 91, 661–672.
Kueh, H.Y., and Rothenberg, E.V. (2012). Regulatory gene network circuits underlying T cell development from multipotent progenitors. WIREs Syst Biol Med 4, 79–102.
Kulessa, H., Frampton, J., and Graf, T. (1995). GATA-1 reprograms avian myelomonocytic cell lines into eosinophils, thromboblasts, and erythroblasts. Genes Dev 9, 1250–1262.
Ladewig, J., Koch, P., and Brustle, O. (2013). Leveling Waddington: the emergence of direct programming and the loss of cell fate hierarchies. Nat Rev Mol Cell Biol 14, 225–236.
Laiosa, C.V., Stadtfeld, M., and Graf, T. (2006). Determinants of lymphoidmyeloid lineage diversification. Annu Rev Immunol 24, 705–738.
Laslo, P., Spooner, C.J., Warmflash, A., Lancki, D.W., Lee, H.J., Sciammas, R., Gantner, B.N., Dinner, A.R., and Singh, H. (2006). Multilineage transcriptional priming and determination of alternate hematopoietic cell fates. Cell 126, 755–766.
Lei, J., Levin, S.A., and Nie, Q. (2014). Mathematical model of adult stem cell regeneration with cross-talk between genetic and epigenetic regulation. Proc Natl Acad Sci USA 111, e880–E887.
Li, C., Hong, T., and Nie, Q. (2016). Quantifying the landscape and kinetic paths for epithelial–mesenchymal transition from a core circuit. Phys Chem Chem Phys 18, 17949–17956.
Li, S., Zhu, X., Liu, B., Wang, G., and Ao, P. (2015). Endogenous molecular network reveals two mechanisms of heterogeneity within gastric cancer. Oncotarget 6, 13607–13627.
Massagué, J. (2004). G1 cell-cycle control and cancer. Nature 432, 298–306.
Naldi, A., Carneiro, J., Chaouiya, C., and Thieffry, D. (2010). Diversity and plasticity of Th cell types predicted from regulatory network modelling. PLoS Comput Biol 6, e1000912.
Nerlov, C., and Graf, T. (1998). PU. 1 induces myeloid lineage commitment in multipotent hematopoietic progenitors. Genes Dev 12, 2403–2412.
Nerlov, C., Querfurth, E., Kulessa, H., and Graf, T. (2000). GATA-1 interacts with the myeloid PU.1 transcription factor and represses PU.1-dependent transcription. Blood 95, 2543–2551.
Notta, F., Zandi, S., Takayama, N., Dobson, S., Gan, O.I., Wilson, G., Kaufmann, K.B., McLeod, J., Laurenti, E., Dunant, C.F., McPherson, J.D., Stein, L.D., Dror, Y., and Dick, J.E. (2016). Distinct routes of lineage development reshape the human blood hierarchy across ontogeny. Science 351, aab2116–aab2116.
Novershtern, N., Subramanian, A., Lawton, L.N., Mak, R.H., Haining, W.N., McConkey, M.E., Habib, N., Yosef, N., Chang, C.Y., Shay, T., Frampton, G.M., Drake, A.C.B., Leskov, I., Nilsson, B., Preffer, F., Dombkowski, D., Evans, J.W., Liefeld, T., Smutko, J.S., Chen, J., Friedman, N., Young, R.A., Golub, T.R., Regev, A., and Ebert, B.L. (2011). Densely interconnected transcriptional circuits control cell states in human hematopoiesis. Cell 144, 296–309.
Orkin, S.H., and Zon, L.I. (2008). Hematopoiesis: an evolving paradigm for stem cell biology. Cell 132, 631–644.
Paul, F., Arkin, Y., Giladi, A., Jaitin, D.A., Kenigsberg, E., Keren-Shaul, H., Winter, D., Lara-Astiaso, D., Gury, M., Weiner, A., David, E., Cohen, N., Lauridsen, F.K.B., Haas, S., Schlitzer, A., Mildner, A., Ginhoux, F., Jung, S., Trumpp, A., Porse, B.T., Tanay, A., and Amit, I. (2015). Transcriptional heterogeneity and lineage commitment in myeloid progenitors. Cell 163, 1663–1677.
Perié, L., Duffy, K.R., Kok, L., de Boer, R.J., and Schumacher, T.N. (2015). The branching point in erythro-myeloid differentiation. Cell 163, 1655–1662.
Qian, H. (2013). Stochastic physics, complex systems and biology. Quant Biol 1, 50–53.
Qu, Z., Weiss, J.N., and MacLellan, W.R. (2003). Regulation of the mammalian cell cycle: a model of the G1-to-S transition. AJP-Cell Physiol 284, C349–C364.
Riddell, J., Gazit, R., Garrison, B.S., Guo, G., Saadatpour, A., Mandal, P.K., Ebina, W., Volchkov, P., Yuan, G.C., Orkin, S.H., and Rossi, D.J. (2014). Reprogramming committed murine blood cells to induced hematopoietic stem cells with defined factors. Cell 157, 549–564.
Rosenbauer, F., and Tenen, D.G. (2007). Transcription factors in myeloid development: balancing differentiation with transformation. Nat Rev Immunol 7, 105–117.
Sanjuan-Pla, A., Macaulay, I.C., Jensen, C.T., Woll, P.S., Luis, T.C., Mead, A., Moore, S., Carella, C., Matsuoka, S., Bouriez Jones, T., Chowdhury, O., Stenson, L., Lutteropp, M., Green, J.C.A., Facchini, R., Boukarabila, H., Grover, A., Gambardella, A., Thongjuea, S., Carrelha, J., Tarrant, P., Atkinson, D., Clark, S.A., Nerlov, C., and Jacobsen, S.E.W. (2013). Platelet-biased stem cells reside at the apex of the haematopoietic stemcell hierarchy. Nature 502, 232–236.
Shin, J.Y., Hu, W., Naramura, M., and Park, C.Y. (2014). High c-Kit expression identifies hematopoietic stem cells with impaired self-renewal and megakaryocytic bias. J Exp Med 211, 217–231.
Starck, J., Cohet, N., Gonnet, C., Sarrazin, S., Doubeikovskaia, Z., Doubeikovski, A., Verger, A., Duterque-Coquillaud, M., and Morle, F. (2003). Functional cross-antagonism between transcription factors FLI-1 and EKLF. Mol Cellular Biol 23, 1390–1402.
Sun, J., Ramos, A., Chapman, B., Johnnidis, J.B., Le, L., Ho, Y.J., Klein, A., Hofmann, O., and Camargo, F.D. (2014). Clonal dynamics of native haematopoiesis. Nature 514, 322–327.
Takahashi, K., and Yamanaka, S. (2015). A developmental framework for induced pluripotency. Development 142, 3274–3285.
Tang, Y., Yuan, R., and Ao, P. (2014a). Nonequilibrium work relation beyond the Boltzmann-Gibbs distribution. Phys Rev E 89, 062112.
Tang, Y., Yuan, R., and Ao, P. (2014b). Summing over trajectories of stochastic dynamics with multiplicative noise. J Chem Phys 141, 044125.
Tang, Y., Yuan, R., Chen, J., and Ao, P. (2014c). Controlling symmetrybreaking states by a hidden quantity in multiplicative noise. Phys Rev E 90, 052121.
Tenen, D.G. (2003). Disruption of differentiation in human cancer: AML shows the way. Nat Rev Cancer 3, 89–101.
Thomas, R., Thieffry, D., and Kaufman, M. (1995). Dynamical behaviour of biological regulatory networks—I. Biological role of feedback loops and practical use of the concept of the loop-characteristic state. Bull Math Biol 57, 247–276.
von Dassow, G., Meir, E., Munro, E.M., and Odell, G.M. (2000). The segment polarity network is a robust developmental module. Nature 406, 188–192.
Waddington, C.H. (1942). Canalization of development and the inheritance of acquired characters. Nature 150, 563–565.
Waddington, C.H. (2014). The strategy of the genes. (New York & London: Routledge).
Wang, G., Su, H., Yu, H., Yuan, R., Zhu, X., and Ao, P. (2016). Endogenous network states predict gain or loss of functions for genetic mutations in hepatocellular carcinoma. J R Soc Interface 13, 20151115.
Wang, G., Zhu, X., Gu, J., and Ao, P. (2014a). Quantitative implementation of the endogenous molecular-cellular network hypothesis in hepatocellular carcinoma. Interface Focus 4, 20130064–20130064.
Wang, G., Zhu, X., Hood, L., and Ao, P. (2013). From Phage lambda to human cancer: endogenous molecular-cellular network hypothesis. Quant Biol 1, 32–49.
Wang, P., Song, C., Zhang, H., Wu, Z., Tian, X.J., and Xing, J. (2014b). Epigenetic state network approach for describing cell phenotypic transitions. Interface Focus 4, 20130068–20130068.
Weissman, I.L. (2000). Stem cells. Cell 100, 157–168.
Wright, S. (1932). The roles of mutation, inbreeding, crossbreeding, and selection in evolution. Proc Sixth Int Congr Genet 1, 356–366.
Yamada, T., Kihara-Negishi, F., Yamamoto, H., Yamamoto, M., Hashimoto, Y., and Oikawa, T. (1998). Reduction of DNA binding activity of the GATA-1 transcription factor in the apoptotic process induced by overexpression of PU.1 in murine erythroleukemia cells. Exp Cell Res 245, 186–194.
Yamamoto, R., Morita, Y., Ooehara, J., Hamanaka, S., Onodera, M., Rudolph, K.L., Ema, H., and Nakauchi, H. (2013). Clonal analysis unveils self-renewing lineage-restricted progenitors generated directly from hematopoietic stem cells. Cell 154, 1112–1126.
Young, N.S., Scheinberg, P., and Calado, R.T. (2008). Aplastic anemia. Curr Opin Hematology 15, 162–168.
Yuan, R., and Ao, P. (2012). Beyond ito versus stratonovich. J Stat Mech: Theory and Exp 2012, P07010.
Yuan, R., Zhu, X., Radich, J.P., and Ao, P. (2016). From molecular interaction to acute promyelocytic leukemia: Calculating leukemogenesis and remission from endogenous molecular-cellular network. Sci Rep 6, 24307.
Yuan, R., Zhu, X., Wang, G., Li, S., and Ao, P. (2017). Cancer as robust intrinsic state shaped by evolution: a key issues review. Rep Prog Phys 80, 042701.
Zhao, M., and Li, L. (2015). Regulation of hematopoietic stem cells in the niche. Sci China Life Sci 58, 1209–1215.
Zhu, X., Yuan, R., Hood, L., and Ao, P. (2015). Endogenous molecularcellular hierarchical modeling of prostate carcinogenesis uncovers robust structure. Prog Biophys Mol Biol 117, 30–42.
Zhu, X.M., Yin, L., Hood, L., and Ao, P. (2004). Robustness, stability and efficiency of phage λ genetic switch: dynamical structure analysis. J Bioinform Comput Biol 02, 785–817.
Acknowledgements
We thank Dr. Qing Nie, Dr. Xiaolong Liu, Dr. Ruibao Ren for insightful comments. We thank our lab members for critical discussions, Haowen Cao for a critical reading of the manuscript, and Tiantian Wu for a drawing artwork. This work was supported by the National Basic Research Program of China (2010CB529200) and National Natural Science Foundation of China (91029738).
Author information
Authors and Affiliations
Corresponding authors
Electronic supplementary material
Rights and permissions
About this article
Cite this article
Su, H., Wang, G., Yuan, R. et al. Decoding early myelopoiesis from dynamics of core endogenous network. Sci. China Life Sci. 60, 627–646 (2017). https://doi.org/10.1007/s11427-017-9059-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11427-017-9059-y