Identification of common factors among fibrosarcoma, rhabdomyosarcoma, and osteosarcoma by network analysis.

Sarcoma cancers are uncommon malignant tumors, and there are many subgroups, including fibrosarcoma (FS), which mainly affects middle-aged and older adults in deep soft tissues. Rhabdomyosarcoma (RMS), on the other hand, is the most common soft-tissue sarcoma in children and is located in the head and neck area. Osteosarcomas (OS) is the predominant form of primary bone cancer among young adults, primarily resulting from sporadically random mutations. This frequently results in the dissemination of cancer cells to the lungs, commonly known as metastasis. Mesodermal cells are the origin of sarcoma cancers. In this study, a rather radical approach has been applied. Instead of comparing homogenous cancer types, we focus on three main subtypes of sarcoma: fibrosarcoma, rhabdomyosarcoma, and osteosarcoma, and compare their gene expression with normal cell groups to identify the differentially expressed genes (DEGs). Next, by applying protein-protein interaction (PPI) network analysis, we determine the hub genes and crucial factors, such as transcription factors (TFs), affected by these types of cancer. Our findings indicate a modification in a range of pathways associated with cell cycle, extracellular matrix, and DNA repair in these three malignancies. Results showed that fibrosarcoma (FS), rhabdomyosarcoma (RMS), and osteosarcoma (OS) had 653, 1270, and 2823 differentially expressed genes (DEGs), respectively. Interestingly, there were 24 DEGs common to all three types. Network analysis showed that the fibrosarcoma network had two sub-networks identified in FS that contributed to the catabolic process of collagen via the G-protein coupled receptor signaling pathway. The rhabdomyosarcoma network included nine sub-networks associated with cell division, extracellular matrix organization, mRNA splicing via spliceosome, and others. The osteosarcoma network has 13 sub-networks, including mRNA splicing, sister chromatid cohesion, DNA repair, etc. In conclusion, the common DEGs identified in this study have been shown to play significant and multiple roles in various other cancers based on the literature review, indicating their significance.

Common factors among three types of cells aged in mice.

The greatest risk factor for the formation of numerous significant chronic disorders is aging. Understanding the core molecular underpinnings of aging can help to slow down the inevitable process. Systematic study of gene expression or DNA methylation data is possible at the transcriptomics and epigenetics levels. DNA methylation and gene expression are both affected by aging. Gene expression is an important element in the aging of Homo sapiens. In this work, we evaluated the expression of differentially expressed genes (DEGs), proteins, and transcription factors (TFs) in three different types of cells in mice: antibody-secreting cells, cardiac mesenchymal stromal cells, and skeletal muscle cells. The goal of this article is to uncover a common cause during aging among these cells in order to increase understanding about establishing complete techniques for preventing aging and improving people's quality of life. We conducted a comprehensive network-based investigation to establish which genes and proteins are shared by the three different types of aged cells. Our findings clearly indicated that aging induces gene dysregulation in immune, pharmacological, and apoptotic pathways. Furthermore, our research developed a list of hub genes with differential expression in aging responses that should be investigated further to discover viable anti-aging treatments.

Guided rational design with scaffold hopping leading to novel histamine H3 receptor ligands.

During the past decades, histamine H3 receptors have received widespread attention in pharmaceutical research due to their involvement in pathophysiology of several diseases such as neurodegenerative disorders. In this context, blocking of these receptors is of paramount importance in progression of such diseases. In the current investigation, novel histamine H3 receptor ligands were designed by exploiting scaffold-hopping drug-design strategy. We inspected the designed molecules in terms of ADME properties, drug-likeness, as well as toxicity profiles. Additionally molecular docking and dynamics simulation studies were performed to predict binding mode and binding free energy calculations, respectively. Among the designed structures, we selected compound d2 and its demethylated derivative as examples for synthesis and affinity measurement. In vitro binding assays of the synthesized molecules demonstrated that d2 has lower binding affinity (Ki = 2.61 µM) in radioligand displacement assay to hH3R than that of demethylated form (Ki = 12.53 µM). The newly designed compounds avoid of any toxicity predictors resulted from extended in silico and experimental studies, can offer another scaffold for histamine H3R antagonists for further structure-activity relationship studies.

In silico and in vitro studies of two non-imidazole multiple targeting agents at histamine H3 receptors and cholinesterase enzymes.

Recently, multi-target directed ligands have been of research interest for multifactorial disorders such as Alzheimer's disease (AD). Since H3 receptors (H3 Rs) and cholinesterases are involved in pathophysiology of AD, identification of dual-acting compounds capable of improving cholinergic neurotransmission is of importance in AD pharmacotherapy. In the present study, H3 R antagonistic activity combined with anticholinesterase properties of two previously computationally identified lead compounds, that is, compound 3 (6-chloro-N-methyl-N-[3-(4-methylpiperazin-1-yl)propyl]-1H-indole-2-carboxamide) and compound 4 (7-chloro-N-[(1-methylpiperidin-3-yl)methyl]-1,2,3,4-tetrahydroisoquinoline-2-carboxamide), was tested. Moreover, molecular docking and binding free energy calculations were conducted for binding mode and affinity prediction of studied ligands toward cholinesterases. Biological evaluations revealed inhibitory activity of ligands in nanomolar (compound 3: H3 R EC50  = 0.73 nM; compound 4: H3 R EC50  = 31 nM) and micromolar values (compound 3: AChE IC50  = 9.09 µM, BuChE IC50  = 21.10 µM; compound 4: AChE IC50  = 8.40 µM, BuChE IC50  = 4.93 µM) for H3 R antagonism and cholinesterase inhibition, respectively. Binding free energies yielded good consistency with cholinesterase inhibitory profiles. The results of this study can be used for lead optimization where dual inhibitory activity on H3 R and cholinesterases is needed. Such ligands can exert their biological activity in a synergistic manner resulting in higher potency and efficacy.

Histamine H3 receptor antagonists/inverse agonists: Where do they go?

Since the discovery of the histamine H3 receptor in 1983, tremendous advances in the pharmacological aspects of H3 receptor antagonists/inverse agonists have been accomplished in preclinical studies. At present, there are several drug candidates that reached clinical trial studies for various indications. However, entrance of these candidates to the pharmaceutical market is not free from challenges, and a variety of difficulties is engaged with their developmental process. In this review, the potential role of H3 receptors in the pathophysiology of various central nervous system, metabolic and allergic diseases is discussed. Thereafter, the current status for H3 receptor antagonists/inverse agonists in ongoing clinical trial studies is reviewed and obstacles in developing these agents are emphasized.

Histamine H3 receptor ligands by hybrid virtual screening, docking, molecular dynamics simulations, and investigation of their biological effects.

Histamine H3 receptors (H3 R), belonging to G-protein coupled receptors (GPCR) class A superfamily, are responsible for modulating the release of histamine as well as of other neurotransmitters by a negative feedback mechanism mainly in the central nervous system (CNS). These receptors have gained increased attention as therapeutic target for several CNS related neurological diseases. In the current study, we aimed to identify novel H3 R ligands using in silico virtual screening methods. To this end, a combination of ligand- and structure-based approaches was utilized for screening of ZINC database on the homology model of human H3 R. Structural similarity- and pharmacophore-based approaches were employed to generate compound libraries. Various molecular modeling methodologies such as molecular docking and dynamics simulation along with different drug likeness filtering criteria were applied to select anti-H3 R ligands as promising candidate molecules based on different known parent lead compounds. In vitro binding assays of the selected molecules demonstrated three of them being active within the micromolar and submicromolar Ki range. The current integrated computational and experimental methods used in this work can provide new general insights for systematic hit identification for novel anti-H3 R agents from large compound libraries.