Ovarian Cancer Cell-Conditioning Medium Induces Cancer-Associated Fibroblast Phenoconversion through Glucose-Dependent Inhibition of Autophagy.

One aspect of ovarian tumorigenesis which is still poorly understood is the tumor-stroma interaction, which plays a major role in chemoresistance and tumor progression. Cancer-associated fibroblasts (CAFs), the most abundant stromal cell type in the tumor microenvironment, influence tumor growth, metabolism, metastasis, and response to therapy, making them attractive targets for anti-cancer treatment. Unraveling the mechanisms involved in CAFs activation and maintenance is therefore crucial for the improvement of therapy efficacy. Here, we report that CAFs phenoconversion relies on the glucose-dependent inhibition of autophagy. We show that ovarian cancer cell-conditioning medium induces a metabolic reprogramming towards the CAF-phenotype that requires the autophagy-dependent glycolytic shift. In fact, 2-deoxy-D-glucose (2DG) strongly hampers such phenoconversion and, most importantly, induces the phenoreversion of CAFs into quiescent fibroblasts. Moreover, pharmacological inhibition (by proline) or autophagy gene knockdown (by siBECN1 or siATG7) promotes, while autophagy induction (by either 2DG or rapamycin) counteracts, the metabolic rewiring induced by the ovarian cancer cell secretome. Notably, the nutraceutical resveratrol (RV), known to inhibit glucose metabolism and to induce autophagy, promotes the phenoreversion of CAFs into normal fibroblasts even in the presence of ovarian cancer cell-conditioning medium. Overall, our data support the view of testing autophagy inducers for targeting the tumor-promoting stroma as an adjuvant strategy to improve therapy success rates, especially for tumors with a highly desmoplastic stroma, like ovarian cancer.

Isolation, Characterization, and Autophagy Function of BECN1-Splicing Isoforms in Cancer Cells.

Alternative splicing allows the synthesis of different protein variants starting from a single gene. Human Beclin 1 (BECN1) is a key autophagy regulator that acts as haploinsufficient tumor suppressor since its decreased expression correlates with tumorigenesis and poor prognosis in cancer patients. Recent studies show that BECN1 mRNA undergoes alternative splicing. Here, we report on the isolation and molecular and functional characterization of three BECN1 transcript variants (named BECN1-α, -ß and -γ) in human cancer cells. In ovarian cancer NIHOVCAR3, these splicing variants were found along with the canonical wild-type. BECN1-α lacks 143 nucleotides at its C-terminus and corresponds to a variant previously described. BECN1-ß and -γ lack the BCL2 homology 3 domain and other regions at their C-termini. Following overexpression in breast cancer cells MDA-MB231, we found that BECN1-α stimulates autophagy. Specifically, BECN1-α binds to Parkin and stimulates mitophagy. On the contrary, BECN1-ß reduces autophagy with a dominant negative effect over the endogenous wild-type isoform. BECN1-γ maintains its ability to interact with the vacuolar protein sorting 34 and only has a slight effect on autophagy. It is possible that cancer cells utilize the alternative splicing of BECN1 for modulating autophagy and mitophagy in response to environmental stresses.

Calorie Restriction for Cancer Prevention and Therapy: Mechanisms, Expectations, and Efficacy.

Cancer is one of the most frequently diagnosed diseases, and despite the continuous efforts in searching for new and more effective treatments, its morbidity and mortality remain a significant health problem worldwide. Calorie restriction, a dietary manipulation that consists in a reduction of the calorie intake, is gaining attention as a potential adjuvant intervention for preventing and/or fighting cancer. Several forms of energy reduction intake, which includes caloric restriction tout-court, dietary restrictions, and intermittent fasting, are being explored for their ability to prevent or slow down cancer progression. Additionally, another anti-cancer approach being under investigation relies on the use of nutraceuticals known as "Caloric Restriction Mimetics" that can provide caloric restriction-mediated benefits without subjecting the patients to a strict diet. Preclinical in vitro and in vivo studies consistently show that diet modifiers reducing the calorie have impact on tumor microenvironment and cancer metabolism, resulting in reduced growth and progression of cancer. Preliminary clinical studies show that patients subjected to a reduced nutrient/energy intake experience improved outcomes from chemo- and radiotherapy while better tolerating the side effects. Here, we review the state of the art on the therapeutic potential of calorie restriction and of caloric restriction mimetics in preventing or retarding tumor development by modulating a subset of cellular processes. The most recent clinical progresses with caloric restriction mimetics in the clinical practice are also discussed.

Machine Learning Enabled Computational Screening of Inorganic Solid Electrolytes for Suppression of Dendrite Formation in Lithium Metal Anodes.

Next generation batteries based on lithium (Li) metal anodes have been plagued by the dendritic electrodeposition of Li metal on the anode during cycling, resulting in short circuit and capacity loss. Suppression of dendritic growth through the use of solid electrolytes has emerged as one of the most promising strategies for enabling the use of Li metal anodes. We perform a computational screening of over 12 000 inorganic solids based on their ability to suppress dendrite initiation in contact with Li metal anode. Properties for mechanically isotropic and anisotropic interfaces that can be used in stability criteria for determining the propensity of dendrite initiation are usually obtained from computationally expensive first-principles methods. In order to obtain a large data set for screening, we use machine-learning models to predict the mechanical properties of several new solid electrolytes. The machine-learning models are trained on purely structural features of the material, which do not require any first-principles calculations. We train a graph convolutional neural network on the shear and bulk moduli because of the availability of a large training data set with low noise due to low uncertainty in their first-principles-calculated values. We use gradient boosting regressor and kernel ridge regression to train the elastic constants, where the choice of the model depends on the size of the training data and the noise that it can handle. The material stiffness is found to increase with an increase in mass density and ratio of Li and sublattice bond ionicity, and decrease with increase in volume per atom and sublattice electronegativity. Cross-validation/test performance suggests our models generalize well. We predict over 20 mechanically anisotropic interfaces between Li metal and four solid electrolytes which can be used to suppress dendrite growth. Our screened candidates are generally soft and highly anisotropic, and present opportunities for simultaneously obtaining dendrite suppression and high ionic conductivity in solid electrolytes.

Understanding the Differences Between Cocrystal and Salt Aqueous Solubilities.

This work challenges the popular notion that pharmaceutical salts are more soluble than cocrystals. There are cocrystals that are more soluble than salt forms of a drug and vice-versa. It all depends on the interplay between the chemistry of both the solid and solution phases. Aqueous solubility, pHmax, and supersaturation index (SA = SCC/SD or Ssalt/SD) of cocrystals and salts of a basic drug, lamotrigine (LTG), were determined, and mathematical models that predict the influence of cocrystal/salt Ksp and Ka were derived. Ksp and SA followed the order LTG-nicotinamide cocrystal (18) > LTG-HCl salt (12) > LTG-saccharin salt (5) > LTG-methylparaben cocrystal (1) > LTG-phenobarbital cocrystal (0.2). The values in parenthesis represent SA under nonionizing conditions. Cocrystal/salt solubility and thermodynamic stability are determined by pH and will drastically change with a single unit change in pH. pHmax values ranged from 5.0 (saccharin salt) to 6.4 (methylparaben cocrystal) to 9.0 (phenobarbital cocrystal). Cocrystal/salt pHmax dependence on pKsp and pKa shows that cocrystals and salts exhibit different behavior. Solubility and pHmax are as important as supersaturation index in assessing the stability and risks associated with conversions of supersaturating forms.