Targeting expression of a transforming growth factor beta 1 transgene to the pregnant mammary gland inhibits alveolar development and lactation.

Transforming growth factor-beta 1 (TGF-beta 1) possesses highly potent, diverse and often opposing cell-specific activities, and has been implicated in the regulation of a variety of physiologic and developmental processes. To determine the effects of in vivo overexpression of TGF-beta 1 on mammary gland function, transgenic mice were generated harboring a fusion gene consisting of the porcine TGF-beta 1 cDNA placed under the control of regulatory elements of the pregnancy-responsive mouse whey-acidic protein (WAP) gene. Females from two of four transgenic lines were unable to lactate due to inhibition of the formation of lobuloalveolar structures and suppression of production of endogenous milk protein. In contrast, ductal development of the mammary glands was not overtly impaired. There was a complete concordance in transgenic mice between manifestation of the lactation-deficient phenotype and expression of RNA from the WAP/TGF-beta 1 transgene, which was present at low levels in the virgin gland, but was greatly induced at mid-pregnancy. TGF-beta 1 was localized to numerous alveoli and to the periductal extracellular matrix in the mammary gland of transgenic females late in pregnancy by immunohistochemical analysis. Glands reconstituted from cultured transgenic mammary epithelial cells duplicated the inhibition of lobuloalveolar development observed in situ in the mammary glands of pregnant transgenic mice. Results from this transgenic model strongly support the hypothesis that TGF-beta 1 plays an important in vivo role in regulating the development and function of the mammary gland.     

Study of quercetin and fisetin synergistic effect on breast cancer and potentially involved signaling pathways

Naturally-derived drugs have drawn much attention in recent decades. Efficiency, lower toxicity, and economic reasons are some of their advantages that justify this broad range of administration for different diseases, including cancer. If we can find a specific combination that boosts the effects of their single therapy, leading to synergism effect, increased efficiency, and decreased toxicity, they can act even better. Quercetin and fisetin, two well-known flavonoids, have been used to fight against various cancers. In this study, we investigated their possible synergism quercetin and fisetin on MCF7, MDA-MB-231, BT549, T47D, and 4T1 breast cancer cell lines. Then the optimum combined dose was used to study their impacts on wound healing abilities and clonogenic properties. The real-time qPCR was used to study the expression of their validated downstream effectors in predicted pathways. A significant synergism effect (p < .01, combination index: <1) was observed for all cell lines. Combination therapy was significantly more effective in colony formation (p < .0001) and wound healing assays (p < .001) compared to single therapies. The expression level of potential effectors was also showed a greater change. In vivo study confirmed the in vitro results and showed how significantly (p < .001) their synergism promotes their singular function in inhibiting cancer progression. The breast cancer mouse models receiving combined therapy lived longer with higher average body weight and smaller tumor sizes. These results exhibit that quercetin and fisetin inhibit cancer cell proliferation, migration and colony formation synergistically, and matrix metalloproteinase signaling and apoptotic pathways are relatively responsible for inhibitory activities.     

Anticancer potential of Ferula assa-foetida and its constituents,a powerful plant for cancer therapy

Cancer is one of the main challenges of the health system around the world. This disease is increasing in developing countries and imposes heavy costs on patients and governments. On the other hand, despite various drugs, the death rate among cancer patients is still high and the current treatments have many harmful effects. In the traditional medicine of different countries, there are many medicinal plants that can be effective in the treatment of cancer. Ferula plants are traditionally used as spices and food or for medicinal purposes. Ferula assa-foetida is one of the famous plants of this genus, which has been used for the treatment of various diseases since ancient times. Among the main compounds of this plant, we can mention monoterpenes, sulfide compounds and polyphenols, which can show different therapeutic effects. This article has been compiled with the aim of collecting evidence and articles related to the anti-cancer effects of extracts, derived compounds, essential oils and nanoparticles containing Ferula assa-foetida. This review article was prepared by searching the terms Ferula assa-foetida and cancer, and relevant information was collected through searching electronic databases such as ISI Web of Knowledge, PubMed, and Google Scholar. Fortunately, the results of this review showed that relatively comprehensive studies have been conducted in this field and shown that Ferula assa-foetida can be very promising in the treatment of cancer.     

Synthesis,Biological Evaluation,and Molecular Modeling Studies of New 1,3,4-Thiadiazole Derivatives as Potent Antimicrobial Agents

In this work, the synthesis, characterization, and biological activities of a new series of 1,3,4-thiadiazole derivatives were investigated. The structures of final compounds were identified using ¹H-NMR, ¹³C-NMR, elemental analysis, and HRMS. All the new synthesized compounds were then screened for their antimicrobial activity against four types of pathogenic bacteria and one fungal strain, by application of the MIC assays, using Ampicilin, Gentamycin, Vancomycin, and Fluconazole as standards. Among the compounds, the MIC values of 4 and 8 μg/mL of the compounds 3f and 3g , respectively, are remarkable and indicate that these compounds are good candidates for antifungal activity. The docking experiments were used to identify the binding forms of produced ligands with sterol 14-demethylase to acquire insight into relevant proteins. The MD performed about 100 ns simulations to validate selected compounds’ theoretical studies. Finally, using density functional theory (DFT) to predict reactivity, the chemical characteristics and quantum factors of synthesized compounds were computed. These results were then correlated with the experimental data. Furthermore, computational estimation was performed to predict the ADME properties of the most active compound 3f .     

Design,Synthesis, and Biological Evaluation of Novel Indanone Derivatives as Cholinesterase Inhibitors for Potential Use in Alzheimer's Disease

Indanone derivatives containing meta/para-substituted aminopropoxy benzyl/benzylidene moieties were designed based on the structures of donepezil and ebselen analogs as the cholinesterase inhibitors. The designed compounds were synthesized and their acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) inhibitory activities were measured. Inhibitory potencies (IC₅₀ values) for the synthesized compounds ranged from 0.12 to 11.92 μM and 0.04 to 24.36 μM against AChE and BChE, respectively. Compound 5 c showed the highest AChE inhibitory potency with IC₅₀ value of 0.12 μM, whereas the highest BChE inhibition was achieved by structure 7 b (IC₅₀=0.04 μM). Structure-activity relationship (SAR) analysis revealed that there is no significant difference between meta and para-substituted derivatives in AChE and BChE inhibition. However, the most potent AChE inhibitor 5 c belongs to meta-substituted compounds, while the most active BChE inhibitor is para-substituted derivative 7 b . The order of enzyme inhibition potency based on the substituted amine group is dimethyl amine>piperidine>morpholine. Compounds containing C=C linkage are more potent AChE inhibitors than the corresponding saturated structures. Molecular docking studies indicated that 5 c interacts with AChE in a very similar way to that observed experimentally for donepezil. The introduced indanone-aminopropoxy benzylidenes could be used in drug-discovery against Alzheimer's disease.     

Removal of heavy metals from industrial wastewater using microbial fuel cell

Removal efficiency of gold from a solution of pure tetrachloroaurate ions was investigated using microbial fuel cell (MFC) technology. The effects of type of catholyte solution and initial gold concentration on the removal efficiency were considered. Due to its presence at high levels in the gold wastewater, the effect of copper ions on the removal efficiency of the gold ions was also studied. The effects of pH and initial biomass concentration on the gold removal efficiency was also determined. The results showed that after 5 h contact time, 95% of gold removal efficiency from a wastewater containing 250 ppm of initial gold ions at ambient temperature using 80 g/L yeast concentration was achieved. After 48 h of the cell''s operation under the same condition, 98.86% of AuCl₄ ^– ions were successfully removed from the solution. At initial gold concentration in the waste solution of 250 ppm, pH 2, and initial yeast concentration of 80 g/L, 100% removal efficiency of the gold was achieved. On the other hand, the most suitable condition for copper removal was found at a pH of 5.2, where 53% removal efficiency from the waste solution was accomplished.     

Strain design optimization using reinforcement learning

Engineered microbial cells present a sustainable alternative to fossil-based synthesis of chemicals and fuels. Cellular synthesis routes are readily assembled and introduced into microbial strains using state-of-the-art synthetic biology tools. However, the optimization of the strains required to reach industrially feasible production levels is far less efficient. It typically relies on trial-and-error leading into high uncertainty in total duration and cost. New techniques that can cope with the complexity and limited mechanistic knowledge of the cellular regulation are called for guiding the strain optimization.In this paper, we put forward a multi-agent reinforcement learning (MARL) approach that learns from experiments to tune the metabolic enzyme levels so that the production is improved. Our method is model-free and does not assume prior knowledge of the microbe’s metabolic network or its regulation. The multi-agent approach is well-suited to make use of parallel experiments such as multi-well plates commonly used for screening microbial strains.We demonstrate the method’s capabilities using the genome-scale kinetic model of Escherichia coli, k-ecoli457, as a surrogate for an in vivo cell behaviour in cultivation experiments. We investigate the method’s performance relevant for practical applicability in strain engineering i.e. the speed of convergence towards the optimum response, noise tolerance, and the statistical stability of the solutions found. We further evaluate the proposed MARL approach in improving L-tryptophan production by yeast Saccharomyces cerevisiae, using publicly available experimental data on the performance of a combinatorial strain library.Overall, our results show that multi-agent reinforcement learning is a promising approach for guiding the strain optimization beyond mechanistic knowledge, with the goal of faster and more reliably obtaining industrially attractive production levels.