A comparative in vitro study on the effect of SGLT2 inhibitors on chemosensitivity to doxorubicin in MCF-7 breast cancer cells.

Cancer frequently develops resistance to the majority of chemotherapy treatments. This study aimed to examine the synergistic cytotoxic and antitumor effects of SGLT2 inhibitors, specifically Canagliflozin (CAN), Dapagliflozin (DAP), Empagliflozin (EMP), and Doxorubicin (DOX), using in vitro experimentation. The precise combination of CAN+DOX has been found to greatly enhance the cytotoxic effects of doxorubicin (DOX) in MCF-7 cells. Interestingly, it was shown that cancer cells exhibit an increased demand for glucose and ATP in order to support their growth. Notably, when these medications were combined with DOX, there was a considerable inhibition of glucose consumption, as well as reductions in intracellular ATP and lactate levels. Moreover, this effect was found to be dependent on the dosages of the drugs. In addition to effectively inhibiting the cell cycle, the combination of CAN+DOX induces substantial modifications in both cell cycle and apoptotic gene expression. This work represents the initial report on the beneficial impact of SGLT2 inhibitor medications, namely CAN, DAP, and EMP, on the responsiveness to the anticancer properties of DOX. The underlying molecular mechanisms potentially involve the suppression of the function of SGLT2.

Prospective virtual screening combined with bio-molecular simulation enabled identification of new inhibitors for the KRAS drug target.

Lung cancer is a disease with a high mortality rate and it is the number one cause of cancer death globally. Approximately 12-14% of non-small cell lung cancers are caused by mutations in KRASG12C. The KRASG12C is one of the most prevalent mutants in lung cancer patients. KRAS was first considered undruggable. The sotorasib and adagrasib are the recently approved drugs that selectively target KRASG12C, and offer new treatment approaches to enhance patient outcomes however drug resistance frequently arises. Drug development is a challenging, expensive, and time-consuming process. Recently, machine-learning-based virtual screening are used for the development of new drugs. In this study, we performed machine-learning-based virtual screening followed by molecular docking, all atoms molecular dynamics simulation, and binding energy calculations for the identifications of new inhibitors against the KRASG12C mutant. In this study, four machine learning models including, random forest, k-nearest neighbors, Gaussian naïve Bayes, and support vector machine were used. By using an external dataset and 5-fold cross-validation, the developed models were validated. Among all the models the performance of the random forest (RF) model was best on the train/test dataset and external dataset. The random forest model was further used for the virtual screening of the ZINC15 database, in-house database, Pakistani phytochemicals, and South African Natural Products database. A total of 100 ns MD simulation was performed for the four best docking score complexes as well as the standard compound in complex with KRASG12C. Furthermore, the top four hits revealed greater stability and greater binding affinities for KRASG12C compared to the standard drug. These new hits have the potential to inhibit KRASG12C and may help to prevent KRAS-associated lung cancer. All the datasets used in this study can be freely available at ( https://github.com/Amar-Ajmal/Datasets-for-KRAS ).

Deciphering aging-associated molecular mechanisms in bone marrow derived hematopoietic stem cells in the elderly using NGS data.

Aging is a complex process that is not yet fully understood. Despite advancements in research, a deeper understanding of the underlying biological mechanisms is necessary to develop interventions that promote healthy longevity. The aim of this study was to elucidate the complex mechanisms associated with healthy aging and longevity in healthy elderly individuals. The RNA sequencing (RNA-seq) data used in this study was obtained from the Gene Expression Omnibus (GEO) database (accession number GSE104406), which was collected from Fluorescent Activated Cell Sorting (FACS) of human bone marrow derived human hematopoietic stem cells (BM-HSCs) (Lineage-, CD34+, CD38-) young (18-30 years old) and aged (65-75 years old) donors who had no known hematological malignancy, with 10 biological replicates per group. The GEO RNA-seq Experiments Interactive Navigator (GREIN) software was used to obtain raw gene-level counts and filtered metadata for this dataset. Next generation knowledge discovery (NGKD) tools provided by BioJupies were used to obtain differentially regulated pathways, gene ontologies (GO), and gene signatures in the BM-HSCs. Finally, the L1000 Characteristic Direction Signature Search Engine (L1000CDS2) tool was used to identify specific drugs that reverse aging-associated gene signatures in old but healthy individuals. The down-regulation of signaling pathways such as longevity regulation, proteasome, Notch, apoptosis, nuclear factor kappa B (NFkB), and peroxisome proliferator-activated receptors (PPAR) signaling pathways in the BM-HSCs of healthy elderly. GO functions related to negative regulation of bone morphogenetic protein (BMP), telomeric DNA binding, nucleoside binding, calcium -dependent protein binding, chromatin-DNA binding, SMAD binding, and demethylase activity were significantly downregulated in the BM-HSCs of the elderly compared to the healthy young group. Importantly, potential drugs such as salermide, celestrol, cercosporin, dorsomorphin dihydrochloride, and LDN-193189 monohydrochloride that can reverse the aging-associated signatures in HSCs from healthy elderly were identified. The analysis of RNA-seq data based on NGKD techniques revealed a plethora of differentially regulated pathways, gene ontologies, and drugs with anti-aging potential to promote healthspan in the elderly.

Application of Newly Customized Myeloid NGS Panel in the Diagnosis of Myeloid Malignancies.

Purpose: Genetic mutations are major factors in the diagnosis and prognosis of leukemia, and it is difficult to assess these variants using single-gene analysis. Therefore, this study aimed to develop a fast and cost-effective method for genetic screening of myeloid malignancies using a customized next-generation sequencing (NGS) panel. Patients and Methods: A customized myeloid panel was designed and investigated in 15 acute myeloid leukemia patients. The panel included 11 genes that were most commonly mutated in myeloid malignancies. This panel was designed to sequence the complete genome of CALR, IDH1, IDH2, JAK2, FLT3, NPM1, MPL, TET2, SF3B1, TP53, and MLL. Results: Among the 15 patients, 14 actual pathogenic variants were identified in nine samples, and negative results were found in six samples. Positive findings were observed for JAK2, FLT3, SF3B1, and TET2. Interestingly, non-classical FLT3 mutations (c.1715A>C, c.2513delG, and c.2507dupT) were detected in patients who were negative for FLT3-ITD and TKD by routine molecular results. All identified variants were pathogenic, and the high coverage of the assay allowed us to predict variants at a low frequency (1%) with 1000x coverage. Conclusion: Utilizing a custom panel allowed us to identify variants that were not detected by routine tests or those that were not routinely investigated. Using the costuming panel will enable us to sequence all genes and discover new potential pathogenic variants that are not possible with other commercially available panels that focus only on hotspot regions. This study's strength in utilizing NGS and implanting a customized panel to identify new pathogenic variants that might be common in our population and important in routine diagnosis for providing optimal healthcare for personalized medicine.

Identification of novel STAT3 inhibitors for liver fibrosis, using pharmacophore-based virtual screening, molecular docking, and biomolecular dynamics simulations.

The signal transducer and activator of transcription 3 (STAT3) plays a fundamental role in the growth and regulation of cellular life. Activation and over-expression of STAT3 have been implicated in many cancers including solid blood tumors and other diseases such as liver fibrosis and rheumatoid arthritis. Therefore, STAT3 inhibitors are be coming a growing and interesting area of pharmacological research. Consequently, the aim of this study is to design novel inhibitors of STAT3-SH3 computationally for the reduction of liver fibrosis. Herein, we performed Pharmacophore-based virtual screening of databases including more than 19,481 commercially available compounds and in-house compounds. The hits obtained from virtual screening were further docked with the STAT3 receptor. The hits were further ranked on the basis of docking score and binding interaction with the active site of STAT3. ADMET properties of the screened compounds were calculated and filtered based on drug-likeness criteria. Finally, the top five drug-like hit compounds were selected and subjected to molecular dynamic simulation. The stability of each drug-like hit in complex with STAT3 was determined by computing their RMSD, RMSF, Rg, and DCCM analyses. Among all the compounds Sa32 revealed a good docking score, interactions, and stability during the entire simulation procedure. As compared to the Reference compound, the drug-like hit compound Sa32 showed good docking scores, interaction, stability, and binding energy. Therefore, we identified Sa32 as the best small molecule potent inhibitor for STAT3 that will be helpful in the future for the treatment of liver fibrosis.

Venetoclax-Resistant MV4-11 Leukemic Cells Activate PI3K/AKT Pathway for Metabolic Reprogramming and Redox Adaptation for Survival.

Venetoclax (ABT199) is a selective B-cell lymphoma 2 (BCL-2) inhibitor. The US FDA recently approved it to be used in combination with low-dose cytarabine or hypomethylating agents in acute myeloid leukemia (AML) or elderly patients non-eligible for chemotherapy. However, acquiring resistance to venetoclax in AML patients is the primary cause of treatment failure. To understand the molecular mechanisms inherent in the resistance to BCL-2 inhibitors, we generated a venetoclax-resistant cell line model and assessed the consequences of this resistance on its metabolic pathways. Untargeted metabolomics data displayed a notable impact of resistance on the PI3K/AKT pathway, the Warburg effect, glycolysis, the TCA cycle, and redox metabolism. The resistant cells showed increased NADPH and reduced glutathione levels, switching their energy metabolism towards glycolysis. PI3K/AKT pathway inhibition shifted resistant cells towards oxidative phosphorylation (OXPHOS). Our results provide a metabolic map of resistant cells that can be used to design novel metabolic targets to challenge venetoclax resistance in AML.