Current Medicinal Chemistry - Online First

Menopause symptoms may be distressing, especially when they appear at a time when women are expected to play significant responsibilities in society. Numerous biological systems are influenced by the hormonal changes that start during the menopausal transition. This review attempts to decipher the complex relationship between obesity, menopause, and depression, citing some recent longitudinal and cross-sectional studies. Additionally, this study provides a summary of the different phytoestrogens, their sources, and probable mechanisms of action in addition to available therapeutic alternatives.

For this review purpose, the authors have gone through a vast number of articles from various scientific databases like PubMed, Google Scholar, and Web of Science.

It is becoming clear that the physiological basis for these menopausal symptoms is complicated and connected to estrogen deficiency, but not alone. Other hormones like FSH, LH, progesterone, and inhibin B are the major ones that are both directly and indirectly responsible for most of the menopausal symptoms. Numerous longitudinal and cross-sectional studies have found a direct relationship between the incidence of menopause and depression as well as obesity. Phytoestrogens like stilbene, lignans, isoflavone, and coumestan have been reported to be the alternatives to synthetic estrogen with lesser side effects, as reported in various studies.

The complex relationship between depression, menopause, and obesity presents a complex obstacle to women's health and overall well-being. There might be a lot of promising prospects for revolutionary advancements in women's health during the menopausal stage in the future. Promising drug development that targets not just one but also the three conditions -obesity, menopause, and depression - as well as more thorough research are needed to improve the healthcare system for women who suffer from these conditions.

This study seeks to develop a prognostic risk signature for head and neck squamous cell carcinoma (HNSCC) based on cholesterol-related genes (CholRG), aiming to enhance prognostic accuracy in clinical practice.

HNSCC poses significant challenges due to its aggressive behavior and limited response to standard treatments, resulting in elevated morbidity and mortality rates.In order to improve prognostic prediction in HNSCC, our study is inspired by the realization that cholesterol metabolism plays a critical role in accelerating the progression of cancer. To this end, we are developing a unique risk signature using CholRG.

The aim of this study was to create a CholRG-based risk signature to predict HNSCC prognosis, aiding in clinical decision-making accurately.

The TCGA HNSCC dataset, along with GSE41613 and GSE65858, was obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases, respectively. A CholRG-based risk signature was then developed and validated across various independent HNSCC cohorts. Moreover, a nomogram model incorporating CholRG-based risk signature was established. Additionally, functional enrichment analysis was conducted, and the immune landscapes of the high- and low-risk groups were compared. Finally, in vitro experiments were performed using lipid-based transfection to deliver siRNAs targeting ACAT1 to SCC1 and SCC23 cell lines, further examining the effects of ACAT1 knockdown on these cells.

Utilizing RNA-seq, microarray, and clinical data from public databases, we constructed and validated a CholRG-based risk signature that includes key genes such as ACAT1, CYP19A1, CYP27A1, FAXDC2, INSIG2, PRKAA2, and SEC14L2, which can effectively predict the clinical outcome of HNSCC. Additionally, our findings were reinforced by a nomogram model that integrates the risk score with clinical variables for more clinically practical prognostic assessment. In addition, patients at high risk show hypoxia and increased oncogenic pathways such as mTORC1 signaling, as well as a suppressed immune microenvironment marked by a reduction in the infiltration of important immune cells. Notably, in vitro experiments showed that ACAT1 depletion significantly suppressed the proliferation, colony formation, and invasion capabilities of HNSCC cells, confirming ACAT1's role in promoting malignancy.

Collectively, our study not only underscores the importance of cholesterol metabolism in HNSCC pathogenesis but also highlights the CholRG-based risk signature as a promising tool for enhancing prognostic accuracy and personalizing therapeutic strategies.

Non-Nucleoside Reverse Transcriptases Inhibitors (NNRTIs) are among the most extensively studied enzymes for understanding the biology of Human Immunodeficiency Viruses (HIV) and designing inhibitors for managing HIV infections. Indolyl aryl sulfones (IASs), an underexplored class of potent NNRTIs, require further exploration for the development of newer drugs for HIV.

In this context, we synthesized a series of novels by Indolyl Aryl Sulfones with a hydrazone moiety at the carboxylate site of the indole nucleus. A 2D-QSAR model was developed to predict Reverse Transcriptase inhibitory activity against wild-type RT (WT-RT) enzyme.

The model was successfully applied to predict the HIV-1 inhibitory activity of known Indolyl Aryl Sulfones. Considering the reliability, robustness, and reproducibility of the 2D-QSAR model, we made an in-silico prediction of the RT inhibition for our synthesized compounds (1-14).

Molecular docking and dynamics simulations established our synthesized Indolyl Aryl Sulfones, particularly compounds 23, 24, and 28, as effective NNRTIs by stabilizing HIV reverse transcriptase's structure. Binding energy calculations revealed compound 28 as the strongest inhibitor (-43.21 ± 0.09 kcal/mol), followed by 23 (-40.94 ± 0.10 kcal/mol) and 24 (-39.18±0.08 kcal/mol), emphasizing their binding affinity towards HIV reverse transcriptase.

In summary, the synthesized Indolyl Aryl Sulfones, particularly compounds 23, 24, and 28, demonstrate significant potential as Non-Nucleoside Reverse Transcriptase Inhibitors (NNRTIs) against HIV. These results highlight the promising role of these compounds in developing novel NNRTIs for managing HIV infections.

Both coronavirus disease 2019 (COVID-19) and idiopathic pulmonary fibrosis (IPF) could cause severe pulmonary injury and have extremely dismal prognoses with a high risk of mortality. Resveratrol (RSV), a natural polyphenol, has promising potential in the treatment of viral infection and pulmonary fibrosis.

The purpose of this research was to investigate the unclear mechanism of RSV as an anti-COVID-19 and IPF therapy.

Utilizing relevant databases, the intersection of genes related to IPF, COVID-19, and possible RSV targets was discovered. Then the obtained targets were investigated using GO and KEGG analysis, TP and PPI network analysis. Furthermore, the binding affinities between core targets and RSV were calculated using molecular docking.

The 1101 COVID-19 targets, 2166 IPF targets, and 341 RSV targets intersected with 21 overlapping targets. PPI network reveals the interactions among targets and TP network reveals interactions between targets and pathways. Five targets including JUN, CCL2, CXCL8, IL6, and SERPINE1 were identified as the core targets through two network analyses. GO analysis demonstrated chemotaxis, inflammatory response and angiogenesis were the significant pathophysiological processes. Combing TP network analysis and KEGG analysis, IL-17 signaling pathway was considered as the significant pathway. Except for JUN, molecular docking showed the binding energies of other four targets were lower than -5 kcal/mol indicating intimate interactions between RSV and other targets.

Our research elucidate the targets, pathways and pathophysiological processes of RSV involved in effects of anti-COVID-19 and IPF, suggesting RSV could be a therapeutic candidate for reducing infection and fibrosis.

Atherosclerosis is a complex cardiovascular disease often associated with mitochondrial dysfunction, which can lead to various cellular and metabolic abnormalities. Within the mitochondrial genome, specific mutations have been implicated in contributing to mitochondrial dysfunction. Atherosclerosis-associated m.15059G>A mutation has been of particular interest due to its potential role in altering mitochondrial function and cellular health.

This study aims to investigate the role of the atherosclerosis-associated m.15059G>A mutation in the development of mitochondrial dysfunction in monocyte-like cells.

Monocyte-like cytoplasmic hybrid cell line TC-HSMAM1, which contains the m.15059G>A mutation in mtDNA, was used. The MitoCas9 vector was utilized to eliminate mtDNA copies carrying the m.15059G>A mutation from TC-HSMAM1 cybrids. Mitochondrial membrane potential, generation of reactive oxygen species, and lipid peroxidation levels were assessed using flow cytometry. Cellular reduced glutathione levels were assessed using the confocal microscopy. The oxygen consumption rate was measured using polarographic oxygen respirometry.

The elimination of the m.15059G>A mutation resulted in a significant increase in mitochondrial membrane potential and improved mitochondrial efficiency while also causing a decrease in the generation of reactive oxygen species, lipid peroxidation, as well as cellular bioenergetic parameters, such as proton leak and non-mitochondrial oxygen consumption. At the same time, no changes were found in the intracellular antioxidant system after the mitochondrial genome editing.

The presence of the m.15059G>A mutation contributes to mitochondrial dysfunction by reducing mitochondrial membrane potential, increasing the generation of reactive oxygen species and lipid peroxidation, and altering mitochondrial bioenergetics. Elimination of the mtDNA containing atherogenic mutation leads to an improvement in mitochondrial function.

Silent information regulator two homologue one (SIRT1) is an emerging target for managing metabolic disorders. This study aimed to synthesize novel 5-(substituted phenyl)-2-aryl benzimidazole derivatives and evaluate them for SIRT1 activation.

The compounds were designed according to the findings of the QSAR models framed in our previous studies. Molecular docking and dynamics studies were also performed to explore the interactions of designed compounds with the active site of the SIRT1 enzyme using AutoDock Vina and Schrödinger Maestro version 11.8.012, respectively. Compounds with good binding affinity were synthesized by Suzuki-Miyaura cross-coupling and spectrally characterized. The molecules were evaluated for their in vitro SIRT1 activation properties using a fluorescent screening kit. Based on the results of in vitro assay, a structure-activity relationship was established. SwissADME was employed to calculate the pharmacokinetics characteristics of the synthesized molecules.

The molecular docking studies revealed that all the activators were effectively docked in the catalytic active site. All compounds demonstrated interactions with important amino acids like Glu230 and Arg446. In molecular dynamics simulations, the root mean square deviation (RMSD) of compound 5m and protein SIRT1 remained stable, i.e., below 3mm. Compound 5m, 4-(2-(3,4-dihydroxy-5-nitrophenyl)-1H-benzo[d]imidazol-5-yl)benzaldehyde, was the most potent compound with an EC50 value of 0.006 mM (±0.001) and maximum activation of 240.5%. All the synthesized compounds had acceptable theoretical ADME profiles, and drug-likeness properties complied with Lipinski’s rule.

According to the findings, synthesized compounds may be viable leads for SIRT1 activators and may be used to advance preclinical in vivo research utilizing animal models.

Mitochondrial fission and fusion play important roles in tumorigenesis, progression and therapy. Dysregulation of these processes may lead to tumor progression, and regulation of these processes may provide novel strategies for cancer therapy. The involvement of genes related to mitochondrial fission and fusion (MD) in gastric cancer (GC) remains poorly understood.

The aim of this study was to establish an MD gene signature for GC patients and to investigate its association with prognosis, tumor microenvironment and treatment response in GC.

We use the TCGA-GC database as the cohort, focusing specifically on genes associated with MD. We conducted identification and consistency clustering analysis of differentially expressed genes in MD, conducted MD gene mutation and copy number variation analysis, as well as correlation and functional enrichment analysis between MD gene cluster classification and immune infiltration. TCGA-GC and GSE15459 were used to construct training and validation cohorts for the model. We used various statistical methods, including Cox and Lasso regression, to develop the model. We validated the model using bulk transcriptome and single-cell transcriptome datasets (GSE13861, GSE26901, GSE66229, and GSE13450). We used GSEA enrichment, CIBERSORT algorithm, ESTIMATE, and TIDE to gain insight into the annotation of MD signature and the characterization of the tumor microenvironment. OncoPredict was used to analyze the relationship between the PRG signature and the drug sensitivity. We validated the expression of several key genes in MD signature on GC cell lines using quantitative real-time PCR (qRT-PCR).

These MDs-related subtypes exhibited different prognosis and immune filtration patterns. A five-gene signature, comprising AGT, HCFC1, KIFC3, NOX4, and RIN1, was developed. There was a clear distinction in overall survival between low- and high-risk patients. The analyses showed further confirmation of the independent prognostic value of the gene signature. There was a notable correlation between the MD signature, immune infiltration and drug susceptibility. The expression levels of AGT, HCFC1, KIFC3, NOX4 and RIN1 mRNA were all increased in these GC cells.

The MD signature has the capacity to significantly contribute to the prediction of personalized outcomes and the advancement of novel therapeutic strategies tailored for GC patients.