Annika Gwendolin Sonntag

A Dynamic Network Model of mTOR Signaling Reveals TSC-Independent mTORC2 Regulation

Modeling and testing network structures reveal a distinct mechanism of mTORC2 activation by insulin. Computing TOR Regulation Without effective genetic or specific pharmacological tools, it can be challenging to dissect regulatory mechanisms in complex, interconnected pathways. Dalle Pezze et al. combined computational modeling with biochemical analysis to test specific regulatory mechanisms of the mammalian target of rapamycin (mTOR) pathway, in particular the regulation of mTOR complex mTORC2, which has important functions in controlling cell growth. After identifying key network components that could be experimentally monitored to explicitly test specific network structures, they compared the experimental data to the modeled networks to rule out previously suggested regulatory mechanisms and postulated the existence of a previously unknown pathway. Their results led to the proposal of a distinct phosphoinositide 3-kinase–dependent pathway from the insulin receptor to mTORC2 that is independent of various components that participate in the activation of mTORC1 and thus should open new areas of research into mTOR signaling and could provide direction for developing selective regulators of mTORC1 and mTORC2 signaling. The kinase mammalian target of rapamycin (mTOR) exists in two multiprotein complexes (mTORC1 and mTORC2) and is a central regulator of growth and metabolism. Insulin activation of mTORC1, mediated by phosphoinositide 3-kinase (PI3K), Akt, and the inhibitory tuberous sclerosis complex 1/2 (TSC1-TSC2), initiates a negative feedback loop that ultimately inhibits PI3K. We present a data-driven dynamic insulin-mTOR network model that integrates the entire core network and used this model to investigate the less well understood mechanisms by which insulin regulates mTORC2. By analyzing the effects of perturbations targeting several levels within the network in silico and experimentally, we found that, in contrast to current hypotheses, the TSC1-TSC2 complex was not a direct or indirect (acting through the negative feedback loop) regulator of mTORC2. Although mTORC2 activation required active PI3K, this was not affected by the negative feedback loop. Therefore, we propose an mTORC2 activation pathway through a PI3K variant that is insensitive to the negative feedback loop that regulates mTORC1. This putative pathway predicts that mTORC2 would be refractory to Akt, which inhibits TSC1-TSC2, and, indeed, we found that mTORC2 was insensitive to constitutive Akt activation in several cell types. Our results suggest that a previously unknown network structure connects mTORC2 to its upstream cues and clarifies which molecular connectors contribute to mTORC2 activation.

Inhibition of mTORC1 by Astrin and Stress Granules Prevents Apoptosis in Cancer Cells

Mammalian target of rapamycin complex 1 (mTORC1) controls growth and survival in response to metabolic cues. Oxidative stress affects mTORC1 via inhibitory and stimulatory inputs. Whereas downregulation of TSC1-TSC2 activates mTORC1 upon oxidative stress, the molecular mechanism of mTORC1 inhibition remains unknown. Here, we identify astrin as an essential negative mTORC1 regulator in the cellular stress response. Upon stress, astrin inhibits mTORC1 association and recruits the mTORC1 component raptor to stress granules (SGs), thereby preventing mTORC1-hyperactivation-induced apoptosis. In turn, balanced mTORC1 activity enables expression of stress factors. By identifying astrin as a direct molecular link between mTORC1, SG assembly, and the stress response, we establish a unifying model of mTORC1 inhibition and activation upon stress. Importantly, we show that in cancer cells, apoptosis suppression during stress depends on astrin. Being frequently upregulated in tumors, astrin is a potential clinically relevant target to sensitize tumors to apoptosis.

A modelling-experimental approach reveals insulin receptor substrate (IRS)-dependent regulation of adenosine monosphosphate-dependent kinase (AMPK) by insulin

Mammalian target of rapamycin (mTOR) kinase responds to growth factors, nutrients and cellular energy status and is a central controller of cellular growth. mTOR exists in two multiprotein complexes that are embedded into a complex signalling network. Adenosine monophosphate‐dependent kinase (AMPK) is activated by energy deprivation and shuts off adenosine 5′‐triphosphate (ATP)‐consuming anabolic processes, in part via the inactivation of mTORC1. Surprisingly, we observed that AMPK not only responds to energy deprivation but can also be activated by insulin, and is further induced in mTORC1‐deficient cells. We have recently modelled the mTOR network, covering both mTOR complexes and their insulin and nutrient inputs. In the present study we extended the network by an AMPK module to generate the to date most comprehensive data‐driven dynamic AMPK‐mTOR network model. In order to define the intersection via which AMPK is activated by the insulin network, we compared simulations for six different hypothetical model structures to our observed AMPK dynamics. Hypotheses ranking suggested that the most probable intersection between insulin and AMPK was the insulin receptor substrate (IRS) and that the effects of canonical IRS downstream cues on AMPK would be mediated via an mTORC1‐driven negative‐feedback loop. We tested these predictions experimentally in multiple set‐ups, where we inhibited or induced players along the insulin–mTORC1 signalling axis and observed AMPK induction or inhibition. We confirmed the identified model and therefore report a novel connection within the insulin–mTOR–AMPK network: we conclude that AMPK is positively regulated by IRS and can be inhibited via the negative‐feedback loop.

A systems study reveals concurrent activation of AMPK and mTOR by amino acids

Amino acids (aa) are not only building blocks for proteins, but also signalling molecules, with the mammalian target of rapamycin complex 1 (mTORC1) acting as a key mediator. However, little is known about whether aa, independently of mTORC1, activate other kinases of the mTOR signalling network. To delineate aa-stimulated mTOR network dynamics, we here combine a computational–experimental approach with text mining-enhanced quantitative proteomics. We report that AMP-activated protein kinase (AMPK), phosphatidylinositide 3-kinase (PI3K) and mTOR complex 2 (mTORC2) are acutely activated by aa-readdition in an mTORC1-independent manner. AMPK activation by aa is mediated by Ca2+/calmodulin-dependent protein kinase kinase β (CaMKKβ). In response, AMPK impinges on the autophagy regulators Unc-51-like kinase-1 (ULK1) and c-Jun. AMPK is widely recognized as an mTORC1 antagonist that is activated by starvation. We find that aa acutely activate AMPK concurrently with mTOR. We show that AMPK under aa sufficiency acts to sustain autophagy. This may be required to maintain protein homoeostasis and deliver metabolite intermediates for biosynthetic processes.

Response to Comment on "A Dynamic Network Model of mTOR Signaling Reveals TSC-Independent mTORC2 Regulation"

Dalle Pezze et al. respond to criticisms regarding the specificity of phosphorylation of Ser2481 of mTOR as a specific readout of mTORC2 activity. We modeled the mammalian or mechanistic target of rapamycin (mTOR) network and proposed a previously unknown mode of activation of the mTOR-containing complex mTORC2 through a phosphoinositide 3-kinase–dependent, and tuberous sclerosis complex–independent mechanism. Manning questions the validity of using the phosphorylation of Ser2481 of mTOR as a specific readout of mTORC2 activity and suggests an in vitro mTORC2 kinase assay as a more appropriate method to parameterize a dynamic mTOR model. We maintain that our computational-experimental approach in combination with careful selection of the readout and cell system is appropriate for studying mTORC2 regulation by insulin.