Dynamic modeling of signal transduction by mTOR complexes in cancer

Decoding clinical biomarker space of COVID-19: Exploring matrix factorization-based feature selection methods

One of the most critical challenges in managing complex diseases like COVID-19 is to establish an intelligent triage system that can optimize the clinical decision-making at the time of a global pandemic. The clinical presentation and patients' characteristics are usually utilized to identify those patients who need more critical care. However, the clinical evidence shows an unmet need to determine more accurate and optimal clinical biomarkers to triage patients under a condition like the COVID-19 crisis. Here we have presented a machine learning approach to find a group of clinical indicators from the blood tests of a set of COVID-19 patients that are predictive of poor prognosis and morbidity. Our approach consists of two interconnected schemes: Feature Selection and Prognosis Classification. The former is based on different Matrix Factorization (MF)-based methods, and the latter is performed using Random Forest algorithm. Our model reveals that Arterial Blood Gas (ABG) O₂ Saturation and C-Reactive Protein (CRP) are the most important clinical biomarkers determining the poor prognosis in these patients. Our approach paves the path of building quantitative and optimized clinical management systems for COVID-19 and similar diseases.

Decoding Clinical Biomarker Space of COVID-19: Exploring Matrix Factorization-based Feature Selection Methods

One of the most critical challenges in managing complex diseases like COVID-19 is to establish an intelligent triage system that can optimize the clinical decision-making at the time of a global pandemic. The clinical presentation and patients’ characteristics are usually utilized to identify those patients who need more critical care. However, the clinical evidence shows an unmet need to determine more accurate and optimal clinical biomarkers to triage patients under a condition like the COVID-19 crisis. Here we have presented a machine learning approach to find a group of clinical indicators from the blood tests of a set of COVID-19 patients that are predictive of poor prognosis and morbidity. Our approach consists of two interconnected schemes: Feature Selection and Prognosis Classification. The former is based on different Matrix Factorization (MF)-based methods, and the latter is performed using Random Forest algorithm. Our model reveals that Arterial Blood Gas (ABG) O 2 Saturation and C-Reactive Protein (CRP) are the most important clinical biomarkers determining the poor prognosis in these patients. Our approach paves the path of building quantitative and optimized clinical management systems for COVID-19 and similar diseases.

A systems pharmacology approach to identify the autophagy-inducing effects of Traditional Persian medicinal plants

Aging is correlated with several complex diseases, including type 2 diabetes, neurodegeneration diseases, and cancer. Identifying the nature of this correlation and treatment of age-related diseases has been a major subject of both modern and traditional medicine. Traditional Persian Medicine (TPM) embodies many prescriptions for the treatment of ARDs. Given that autophagy plays a critical role in antiaging processes, the present study aimed to examine whether the documented effect of plants used in TPM might be relevant to the induction of autophagy? To this end, the TPM-based medicinal herbs used in the treatment of the ARDs were identified from modern and traditional references. The known phytochemicals of these plants were then examined against literature for evidence of having autophagy inducing effects. As a result, several plants were identified to have multiple active ingredients, which indeed regulate the autophagy or its upstream pathways. In addition, gene set enrichment analysis of the identified targets confirmed the collective contribution of the identified targets in autophagy regulating processes. Also, the protein-protein interaction (PPI) network of the targets was reconstructed. Network centrality analysis of the PPI network identified mTOR as the key network hub. Given the well-documented role of mTOR in inhibiting autophagy, our results hence support the hypothesis that the antiaging mechanism of TPM-based medicines might involve autophagy induction. Chemoinformatics study of the phytochemicals using docking and molecular dynamics simulation identified, among other compounds, the cyclo-trijuglone of Juglans regia L. as a potential ATP-competitive inhibitor of mTOR. Our results hence, provide a basis for the study of TPM-based prescriptions using modern tools in the quest for developing synergistic therapies for ARDs.

PCYT1A suppresses proliferation and migration via inhibiting mTORC1 pathway in lung adenocarcinoma

Lung cancer is one of most common malignant cancer worldwide. It is emerging that PCYT1A, a rate-limiting enzyme required for the biosynthesis of phosphatidylcholine, is associated with cancer progression. However, the biological functions and underlying molecular mechanisms of PCYT1A in lung adenocarcinoma is still unknown. Here we found that PCYT1A suppressed lung adenocarcinoma cancer cell proliferation and migration. Mechanically, PCYT1A served as a novel negative regulator of mTORC1 signaling. PCYT1A knockdown enhanced the malignant proliferation and migration of lung adenocarcinoma cells by activating mTORC1. The promoting effects of PCYT1A silencing on cell proliferation and migration could be abolished when mTORC1 signaling was inhibited by rapamycin or RAPTOR depletion. Importantly, PCYT1A high expression predicted longer survival of lung cancer patients. The expression of PCYT1A was also negatively correlated with mTORC1 activation in the clinical lung cancer samples. We therefore reveal that PCYT1A suppresses proliferation and migration by inhibiting the mTORC1 signaling pathway in lung adenocarcinoma. PCYT1A shows as a potential promising biomarker in lung adenocarcinoma.

AICAR and nicotinamide treatment synergistically augment the proliferation and attenuate senescence-associated changes in mesenchymal stromal cells

Background

Mesenchymal stromal cell (MSC) stemness capacity diminishes over prolonged in vitro culture, which negatively affects their application in regenerative medicine. To slow down the senescence of MSCs, here, we have evaluated the in vitro effects of 5-aminoimidazole-4-carboxamide ribonucleotide (AICAR), an AMPK activator, and nicotinamide (NAM), an activator of sirtuin1 (SIRT1).

Methods

Human adipose-derived MSCs were cultured to passage (P) 5. Subsequently, the cells were grown in either normal medium alone (control group), the medium supplemented with AICAR (1 mM) and NAM (5 mM), or in the presence of both for 5 weeks to P10. Cell proliferation, differentiation capacity, level of apoptosis and autophagy, morphological changes, total cellular reactive oxygen species (ROS), and activity of mTORC1 and AMPK were compared among different treatment groups.

Results

MSCs treated with AICAR, NAM, or both displayed an increase in proliferation and osteogenic differentiation, which was augmented in the group receiving both. Treatment with AICAR or NAM led to decreased expression of β-galactosidase, reduced accumulation of dysfunctional lysosomes, and characteristic morphologic features of young MSCs. Furthermore, while NAM administration could significantly reduce the total cellular ROS in aged MSCs, AICAR treatment did not. Moreover, AICAR-treated cells possess a high proliferation capacity; however, they also show the highest level of cellular apoptosis. The observed effects of AICAR and NAM were in light of the attenuated mTORC1 activity and increased AMPK activity and autophagy.

Conclusions

Selective inhibition of mTORC1 by AICAR and NAM boosts autophagy, retains MSCs’ self-renewal and multi-lineage differentiation capacity, and postpones senescence-associated changes after prolonged in vitro culture. Additionally, co-administration of AICAR and NAM shows an additive or probably a synergistic effect on cellular senescence.

A Systems Biology Roadmap to Decode mTOR Control System in Cancer

Mechanistic target of rapamycin (mTOR) is a critical protein in the regulation of cell fate decision making, especially in cancer cells. mTOR acts as a signal integrator and is one of the main elements of interactions among the pivotal cellular processes such as cell death, autophagy, metabolic reprogramming, cell growth, and cell cycle. The temporal control of these processes is essential for the cellular homeostasis and dysregulation of mTOR signaling pathway results in different phenotypes, including aging, oncogenesis, cell survival, cell growth, senescence, quiescence, and cell death. In this paper, we have proposed a systems biology roadmap to study mTOR control system, which introduces the theoretical and experimental modalities to decode temporal and dynamical characteristics of mTOR signaling in cancer.