Mutational inactivation of the tumor suppressor tuberous sclerosis complex 2 (TSC2) constitutively activates mTORC1, increases cell proliferation, and induces the pathological manifestations observed in tuberous sclerosis (TS) and in pulmonary lymphangioleiomyomatosis (LAM). While the role of mTORC1 in TSC2-dependent growth has been extensively characterized, little is known about the role of mTORC2. Our data demonstrate that mTORC2 modulates TSC2-null cell proliferation and survival through RhoA GTPase and Bcl2 proteins. TSC2-null cell proliferation was inhibited not only by reexpression of TSC2 or small interfering RNA (siRNA)-induced downregulation of Rheb, mTOR, or raptor, but also by siRNA for rictor. Increased RhoA GTPase activity and P-Ser473 Akt were inhibited by siRNA for rictor. Importantly, constitutively active V14RhoA reversed growth inhibition induced by siRNA for rictor, siRNA TSC1, reexpression of TSC2, or simvastatin. While siRNA for RhoA had a modest effect on growth inhibition, downregulation of RhoA markedly increased TSC2-null cell apoptosis. Inhibition of RhoA activity downregulated antiapoptotic Bcl2 and upregulated proapoptotic Bim, Bok, and Puma. In vitro and in vivo, simvastatin alone or in combination with rapamycin inhibited cell growth and induced TSC2-null cell apoptosis, abrogated TSC2-null tumor growth, improved animal survival, and prevented tumor recurrence by inhibiting cell growth and promoting apoptosis. Our data demonstrate that mTORC2-dependent activation of RhoA is required for TSC2-null cell growth and survival and suggest that targeting both mTORC2 and mTORC1 by a combination of proapoptotic simvastatin and cytostatic rapamycin shows promise for combinational therapeutic intervention in diseases with TSC2 dysfunction.

An additivity-based sequence to reactivity algorithm for the interaction of members of the Kazal family of protein inhibitors with six selected serine proteinases is described. Ten consensus variable contact positions in the inhibitor were identified, and the 19 possible variants at each of these positions were expressed. The free energies of interaction of these variants and the wild type were measured. For an additive system, this data set allows for the calculation of all possible sequences, subject to some restrictions. The algorithm was extensively tested. It is exceptionally fast so that all possible sequences can be predicted. The strongest, the most specific possible, and the least specific inhibitors were designed, and an evolutionary problem was solved.