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

Abstract

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.Abbreviations AIC, akaike information criterion; AMP, adenosine monophosphate; AMPK, adenosine monophosphate-dependent kinase; ATM, ataxiatelangiectasia mutated; ATP, adenosine 5¢-triphosphate; BIC, Bayesian Information Criterion; CaMKK, Ca 2+ -sensitive calmodulin-dependent protein kinase kinase; CV, coefficient of variance; GAPDH, glyceraldehyde-3-phosphate dehydrogenase; HA-myr-Akt, myristoylated version of Akt; HRP, horseradish peroxidase; IR, insulin receptor; IRE1, inositol-requiring enzyme 1; IRS, insulin receptor substrate; LKB1, liver kinase B1; MOTA, Mean Optimal Transformation Approach; mTOR, mammalian target of rapamycin; mTORC1, mammalian target of rapamycin complex 1; mTORC2, mammalian target of rapamycin complex 2; NFL, negative-feedback loop; PDK1, phosphoinositidedependent protein kinase 1; p.i., postinduction; PI3K, phosphatidylinositol 3-kinase; PRAS40, proline-rich Akt substrate of 40 kDa; PTEN, phosphatase and tensin homologue; p70-S6K, S6-kinase; shRaptor, inducible short hairpin Raptor knockdown;TAK1 ⁄ MAP3K7, transforming growth factor-beta-activated kinase-1; TSC1-TSC2, tuberous sclerosis complex 1 ⁄ 2 dimer; 4E-BP, 4E-binding protein.

Dynamic Modelling of Pathways to Cellular Senescence Reveals Strategies for Targeted Interventions

“…The insulin and insulin-like growth factor 1 (IIS)-TOR network was abstracted [from 39], [40] in order to represent as simply as possible (consistent with capturing the functional essence) the dynamics of Akt, mammalian TOR Complex I (mTORC1) and the mTORC1-p70-S6K-induced negative feedback loop. The mammalian TOR Complex II (mTORC2) was not included explicitly but is represented in its contribution to Akt-pS473, which is a readout for both the mTORC1-p70-S6K-negative feedback and insulin-mTORC2 activity, independent of the negative feedback loop [39].…”

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

“…6). We built on our earlier modelling work419 to test whether simulations of the dynamic network response to aa could match the measured time-course data, or which additional inputs would improve the fit between them. Strikingly, a network model assuming only a single aa input to the network via mTORC1 did not reproduce our experimental calibration data.…”

Functional Proteomics Identifies Acinus L as a Direct Insulin- and Amino Acid-Dependent Mammalian Target of Rapamycin Complex 1 (mTORC1) Substrate

“…SILAC Labeling-shRaptor HeLa cells were labeled for at least 11 days in SILAC DMEM (PAA) supplemented with 10% dialyzed FCS (PAA), 1.5% L-glutamine (PAA) and either with "heavy" lysine (146 mg/L; 13 Antibodies-Antibodies for the analysis of mTOR signaling are described elsewhere (49,61). Acinus isoform L and its N-terminal construct (acinus NT-myc-FLAG) were detected with AM26695AF-N of Acris antibodies GmbH, Herford, Germany.…”

“…For immunoblotting, an aliquot of each lysate was diluted in sample buffer (5ϫ buffer: 6 ml glycerol, 0.6 ml beta-mercaptoethanol, 1 g SDS, 3.75 ml 1 M Tris-HCl, pH 6.8, 2 mg of bromphenol blue and 2 ml H 2 O). Immunoblotting was performed as described (61). For MS analyses, equal amounts of protein from each labeling state were pooled (total protein amount of 1-1.3 mg) and digested with sequencing grade trypsin (1:50, Promega, Mannheim, Germany) for 6 h at 37°C.…”

“…All cells were cultivated in DMEM (PAA, Pasching, Austria) supplemented with 10% FCS (PAA), and 1.5% L-glutamine (PAA). Insulin/aa induction was performed as described (61). Knockdown of raptor was induced with 2 g ml Ϫ1 of doxycycline (Calbiochem, Merck, Darmstadt, Germany) for 2 or 3 days.…”