mTOR Signaling in Growth, Metabolism, and Disease.

Type I interferons (IFNs) activate intracellular antimicrobial programmes and influence the development of innate and adaptive immune responses. Canonical type I IFN signalling activates the Janus kinase (JAK)-signal transducer and activator of transcription (STAT) pathway, leading to transcription of IFN-stimulated genes (ISGs). Host, pathogen and environmental factors regulate the responses of cells to this signalling pathway and thus calibrate host defences while limiting tissue damage and preventing autoimmunity. Here, we summarize the signalling and epigenetic mechanisms that regulate type I IFN-induced STAT activation and ISG transcription and translation. These regulatory mechanisms determine the biological outcomes of type I IFN responses and whether pathogens are cleared effectively or chronic infection or autoimmune disease ensues.

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Regulation of type I interferon responses

The mechanistic target of rapamycin (mTOR) coordinates eukaryotic cell growth and metabolism with environmental inputs, including nutrients and growth factors. Extensive research over the past two decades has established a central role for mTOR in regulating many fundamental cell processes, from protein synthesis to autophagy, and deregulated mTOR signaling is implicated in the progression of cancer and diabetes, as well as the aging process. Here, we review recent advances in our understanding of mTOR function, regulation, and importance in mammalian physiology. We also highlight how the mTOR signaling network contributes to human disease and discuss the current and future prospects for therapeutically targeting mTOR in the clinic.

mTOR Signaling in Growth, Metabolism, and Disease

On the Dependency of Cellular Protein Levels on mRNA Abundance.

The central role of phosphoinositide 3-kinase (PI3K) activation in tumour cell biology has prompted a sizeable effort to target PI3K and/or downstream kinases such as AKT and mammalian target of rapamycin (mTOR) in cancer. However, emerging clinical data show limited single-agent activity of inhibitors targeting PI3K, AKT or mTOR at tolerated doses. One exception is the response to PI3Kδ inhibitors in chronic lymphocytic leukaemia, where a combination of cell-intrinsic and -extrinsic activities drive efficacy. Here, we review key challenges and opportunities for the clinical development of inhibitors targeting the PI3K-AKT-mTOR pathway. Through a greater focus on patient selection, increased understanding of immune modulation and strategic application of rational combinations, it should be possible to realize the potential of this promising class of targeted anticancer agents.

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PI3K and cancer: lessons, challenges and opportunities

The mammalian target of rapamycin (mTOR) kinase is a master regulator of protein synthesis that couples nutrient sensing to cell growth and cancer. However, the downstream translationally regulated nodes of gene expression that may direct cancer development are poorly characterized. Using ribosome profiling, we uncover specialized translation of the prostate cancer genome by oncogenic mTOR signalling, revealing a remarkably specific repertoire of genes involved in cell proliferation, metabolism and invasion. We extend these findings by functionally characterizing a class of translationally controlled pro-invasion messenger RNAs that we show direct prostate cancer invasion and metastasis downstream of oncogenic mTOR signalling. Furthermore, we develop a clinically relevant ATP site inhibitor of mTOR, INK128, which reprograms this gene expression signature with therapeutic benefit for prostate cancer metastasis, for which there is presently no cure. Together, these findings extend our understanding of how the 'cancerous' translation machinery steers specific cancer cell behaviours, including metastasis, and may be therapeutically targeted.

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The translational landscape of mTOR signalling steers cancer initiation and metastasis

Despite advances in periodontal disease (PD) research and periodontal treatments, 42% of the US population suffer from periodontitis. PD can be prevented if high-risk patients are identified early to provide preventive care. Prediction models can help assess risk for PD before initiation and progression; nevertheless, utilization of existing PD prediction models is seldom because of their suboptimal performance. This study aims to develop and test the PD prediction model using machine learning (ML) and electronic dental record (EDR) data that could provide large sample sizes and up-to-date information. A cohort of 27,138 dental patients and grouped PD diagnoses into: healthy control, mild PD, and severe PD was generated. The ML model (XGBoost) was trained (80% training data) and tested (20% testing data) with a total of 74 features extracted from the EDR. We used a five-fold cross-validation strategy to identify the optimal hyperparameters of the model for this one-vs.-all multi-class classification task. Our prediction model differentiated healthy patients vs. mild PD cases and mild PD vs. severe PD cases with an average area under the curve of 0.72. New associations and features compared to existing models were identified that include patient-level factors such as patient anxiety, chewing problems, speaking trouble, teeth grinding, alcohol consumption, injury to teeth, presence of removable partial dentures, self-image, recreational drugs (Heroin and Marijuana), medications affecting periodontium, and medical conditions such as osteoporosis, cancer, neurological conditions, infectious diseases, endocrine conditions, cardiovascular diseases, and gastroenterology conditions. This pilot study demonstrated promising results in predicting the risk of PD using ML and EDR data. The model may provide new information to the clinicians about the PD risks and the factors responsible for the disease progression to take preventive approaches. Further studies are warned to evaluate the prediction model's performance on the external dataset and determine its usability in clinical settings.

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Evan Y. Shi

Papers

The translational landscape of mTOR signalling steers cancer initiation and metastasis

Developing and testing a prediction model for periodontal disease using machine learning and big electronic dental record data