For instance, the best model selected for datasets 2 and 3, NTtRr, overestimates the initial value of p-mTOR on dataset 1, leading to a higher error (see Supplementary material). concomitant decrease in mTOR phosphorylation. We further employed mathematical modelling to investigate hitherto not known relationship of mTOR with NMT1. We SP2509 (HCI-2509) report here for the first time a collection of models and data validating regulation of NMT1 by mTOR. Introduction The hormone receptor status in breast malignancy (BC) is crucial for deciding treatment regimen for BC patients. The presence of estrogen receptor (ER) predicts treatment response to endocrine therapy, primarily due to its role in driving ER positive breast malignancy cells to proliferate1. However, it has been observed that at least 50% of ER positive tumors display de novo resistance to endocrine therapies such as tamoxifen, and many of those initially sensitive acquire resistance despite expressing non-mutated ER2. Earlier studies suggest activation of mTOR potentially plays a role in endocrine resistance3,4. Recently we exhibited that activated mTOR (as measured by phosphorylation at serine (S) 2448 residue) in treatment naive breast tumors is positively associated with overall survival (OS) and recurrence free survival (RFS) in ER positive breast cancer patients who were later treated with tamoxifen5. Also, we exhibited that ER is usually a substrate of mTOR and interacts with it further supporting the crosstalk between ER and mTOR. Therefore, we concluded that in breast tumors where there is usually?an intact estrogen regulated mTOR?signaling, mTOR is associated with an increased likelihood SP2509 (HCI-2509) of responsiveness to endocrine therapy5. Furthermore, very recently, we observed that N-myristoyltransferase (NMT1) is usually a downstream target of mTOR (Jaksic experiments and mathematical modelling approaches. We treated ER positive breast malignancy cells with rapamycin and decided the effect SP2509 (HCI-2509) of mTOR inhibition on NMT1 in a time dependent manner. Signaling pathways involving mTOR have not been extensively studied mathematically or computationally32C35. Most models of mTOR pathway computationally investigate the signaling upstream of mTOR, in particular, the relationship between insulin signaling and mTOR. The complexity of these models is variable, from a few molecules to dozens, allowing to investigate the outcome of potential signaling events, in order to have a better knowledge of the pathway and/or to determine the impact of perturbations such as the effect of drugs32,33,36C38. To the best of our knowledge, the regulation of NMT1 by mTOR has never been mathematically SP2509 (HCI-2509) modelled. In this study, we propose a collection of models of this regulation, including the inhibition of mTOR by rapamycin. The use of a collection of models allowed us to consider a variety of assumptions around the endogenous level of mTOR, the feedback regulation of mTOR by NMT1 and characteristics of the pathway when perturbed by rapamycin. All models were SP2509 (HCI-2509) calibrated and validated by fitting their responses to experimental data; then, the best models were identified. Confronting models predictions to experimental data will help us determine key characteristics that are difficult to obtain experimentally, such as the relevance of the unfavorable feedback of NMT1 on mTOR and the reversibility of the inhibition of mTOR by rapamycin. Results Rapamycin acts as an inhibitor of mTOR and inhibits phosphorylation at S2448 residue of mTOR. In this study, we investigated the effects of rapamycin treatment around the expression of total NMT1 over time. Rapamycin augments NMT1 expression The MCF7 cells were treated with either 100?nM rapamycin or an equivalent volume of DMSO (10?and are considered with rapamycin, thus while without rapamycin only is explicitly described then among the three models considered and its Akaike weight is over 0.9 for these datasets Rabbit Polyclonal to ADCK1 (see Table?2). Moreover, the evidence ratio for model NTt compared to the second best model NT is usually higher than 15 for each dataset. For dataset 4, model NTt is also selected as the best model, however with a lower Akaike weight of 0.6. Furthermore, a feedback regulation of mTOR by NMT1 is usually less likely to occur; the.