Analysis of gene expression profiles to elucidate racial differences in African American and White patients with Triple-negative breast cancer.

Triple-negative breast cancer (TNBC) is the second most diagnosed subtype of breast cancer. It is known to be the most aggressive one that lacks known targetable receptors. One of the concerns in TNBC is the disparities in its prevalence and tumor pathogenesis among women with non-Hispanic African American backgrounds. Despite extensive research, the genetic underpinnings that lead to these disparities remain elusive. The current study aims to provide initiative for further clinical research in the development of targeted therapy for TNBC. Gene expression profiles from African American (AA) and European American (EA) patients with TNBC were collected from Gene Expression Omnibus and performed differential gene expression (DEG)analysis. Candidate genes for a significant correlation between expression and survival rates for breast invasive carcinoma were analyzed using UALCAN. The DAVID annotation tool, Enrichr web server, KEGG database, and Gene Ontology (GO) database were used for functional enrichment analysis of target genes. The Network Analyst server was used to identify ligands with strong affinities, SeamDock server for molecular docking between the biomarkers/associated ligands and examined protein-protein interactions (PPI) from the STRING server. Data from public breast cancer cohorts was utilized to identify expression patterns associated with poor survival outcomes of AA patients with TNBC. Our results showed three genes of interest ( CCT3 , LSM2 , and MRPS16 ) and potential ligands for molecular docking. Molecular docking was performed for the ICG001 ligand to CCT3 (binding affinities of -9.3 kcal/mol and -8.9 kcal/mol) and other interacting proteins ( CDC20 and PPP2CA ) with high degrees of connectivity. The results determined molecular docking of ICG001 to the CDC20 protein resulted in the highest binding affinity. Our results demonstrated that CCT3 and its interacting partners could serve as potential biomarkers due to their association with the survival outcome of AA patients with TNBC and ICG 001 could be the therapeutic lead for these biomarkers.

Bioinformatics analysis of the potential biomarkers of Multiple Sclerosis and Guillain-Barré syndrome.

Recent research emphasizes the intricate interplay of genetics and epigenetics in neurological disorders, notably Multiple Sclerosis (MS) and Guillain-Barre Syndrome (GBS), both of which exhibit cardiovascular dysregulation, with GBS often featuring serious bradyarrhythmias requiring prompt recognition and treatment. While cardiovascular autonomic dysfunction in MS is typically less severe, orthostatic intolerance affects around half of MS patients. Their distinction lies in their autoimmune responses, MS is an autoimmune disease affecting the central nervous system, causes demyelination and axon damage, leading to cognitive, ocular, and musculoskeletal dysfunction. In contrast, GBS primarily affects the peripheral nervous system, resulting in paralysis and respiratory complications. Despite their differences, both diseases share environmental risk factors such as viral infections and Vitamin D deficiency. This study aims to explore shared gene expression pathways, functional annotations, and molecular pathways between MS and GBS to enhance diagnostics, pathogenesis understanding, and treatment strategies through molecular analysis techniques. Through the gene expression analysis, five significant genes were found UTS2, TNFSF10, GBP1, VCAN, FOS. Results shows that Common DEGs are linked to apoptosis, bacterial infections, and atherosclerosis. Molecular docking analysis suggests Aflatoxin B1 as a potential therapeutic compound due to its high binding affinity with common differentially expressed proteins.

Identification of Genes Crucial for Biological Processes in Breast Cancer Liver Metastasis Relapse.

Breast cancer, when advancing to a metastatic stage, involves the liver, impacting over 50% of cases and significantly diminishing survival rates. Presently, a lack of tailored therapeutic protocols for breast cancer liver metastasis (BCLM) underscores the need for a deeper understanding of molecular patterns governing this complication. Therefore, by analyzing differentially expressed genes (DEGs) between primary breast tumors and BCLM lesions, we aimed to shed light on the diversities of this process. This research investigated breast cancer liver metastasis relapse by employing a comprehensive approach that integrated data filtering, gene ontology and KEGG pathway analysis, overall survival analysis, identification of the alteration in the DEGs, visualization of the protein-protein interaction network, Signor 2.0, identification of positively correlated genes, immune cell infiltration analysis, genetic alternation analysis, copy number variant analysis, gene-to-mRNA interaction, transcription factor analysis, molecular docking, and identification of potential treatment targets. This study's integrative approach unveiled metabolic reprogramming, suggesting altered PCK1 and LPL expression as key in breast cancer metastasis recurrence.

Role of Lactiplantibacillus plantarum strain RD1 (Lpb RD1) in mitochondria-mediated apoptosis: an in vitro analysis.

The purpose of this study was to determine the cytotoxicity of Lactiplantibacillus plantarum strain RD1 (Lpb RD1), which was isolated and identified from the curd by 16 S rRNA sequencing. The probiotic properties of the isolated strain were studied by bile and NaCl tolerance and the ethyl acetate extract of Ea-LpRD1, was used to determine the toxicity against human breast cancer (MCF-7) cell lines and human embryonic kidney (HEK-293) cell lines by MTT assay. DNA fragmentation assay was carried out to study apoptosis induction. Flow cytometry analysis was done to determine the % of a cell population using the FTIC-Annexin V staining method. RT-PCR was used to assess gene expression levels in both cell lines. The IC50 concentration of the Ea-LpRD1 in MCF-7 cells was 0.30 mg/ml and in HEK-293 was 0.47 mg/ml. The expression levels of the BCL-2 gene anti-apoptotic genes in humans were reduced and BAX, caspase-8, caspase-3, and caspase-9 were an increased expression in MCF-7 cell lines.

QSAR modeling, pharmacophore-based virtual screening, and ensemble docking insights into predicting potential epigallocatechin gallate (EGCG) analogs against epidermal growth factor receptor.

Epigallocatechin gallate (EGCG) is a major polyphenols of green tea may have the possibility to inhibit epidermal growth factor receptor (EGFR) activity and lead to reduce non-small cell lung cancer (NSCLC) progression. However, EGCG has some toxic features; moreover, there is a lack of explorations into the molecular interaction mechanisms of EGCG and the EGFR. In this examination, integration of quantitative structure-activity relationship (QSAR) modeling, pharmacophore-based virtual screening, and ensemble docking approaches were used to predict potential novel EGCG analogs as effective EGFR inhibitors. QSAR modeling of logP and logS predictions and toxicity endpoint investigation for a set of 82 compounds were shown good predictive ability and robustness from the applicability domain and confusion matrix elucidations. Virtual screening and docking studies revealed that seven high potential EGCG analogs as strong EGFR binders. Molecular interactions interpretations indicated some insights into the structural features of ligands that efficiently interfere with mutation possible residues (Gly719 and Thr790) of the EGFR. The hydrogen bonds, hydrophobic interactions, atomic π-cation interactions and salt bridges of ligands are contributing additional stability to receptor structure, which can lead to blocking the intracellular protein-tyrosine kinase activity, including EGFR associated pathways activation in NSCLC. Therefore, this can characterize as a block-cluster mechanism between EGCG analogs and EGFR complexes. In silico anti-EGFR and anticancer activity predictions suggested that, ligands could act as promising pharmacological, anticancer, and drug-like templates of EGFR towards moderating the NSCLC progressions. These results and provided pinpoints could be beneficial to recognize probable therapeutic targets for NSCLC therapy.

Analysis of the TP53 Deleterious Single Nucleotide Polymorphisms Impact on Estrogen Receptor Alpha-p53 Interaction: A Machine Learning Approach.

Breast cancer is a leading cancer type and one of the major health issues faced by women around the world. Some of its major risk factors include body mass index, hormone replacement therapy, family history and germline mutations. Of these risk factors, estrogen levels play a crucial role. Among the estrogen receptors, estrogen receptor alpha (ERα) is known to interact with tumor suppressor protein p53 directly thereby repressing its function. Previously, we have studied the impact of deleterious breast cancer-associated non-synonymous single nucleotide polymorphisms (nsnps) rs11540654 (R110P), rs17849781 (P278A) and rs28934874 (P151T) in TP53 gene on the p53 DNA-binding core domain. In the present study, we aimed to analyze the impact of these mutations on p53-ERα interaction. To this end, we, have modelled the full-length structure of human p53 and validated its quality using PROCHECK and subjected it to energy minimization using NOMAD-Ref web server. Three-dimensional structure of ERα activation function-2 (AF-2) domain was downloaded from the protein data bank. Interactions between the modelled native and mutant (R110P, P278A, P151T) p53 with ERα was studied using ZDOCK. Machine learning predictions on the interactions were performed using Weka software. Results from the protein-protein docking showed that the atoms, residues and solvent accessibility surface area (SASA) at the interface was increased in both p53 and ERα for R110P mutation compared to the native complexes indicating that the mutation R110P has more impact on the p53-ERα interaction compared to the other two mutants. Mutations P151T and P278A, on the other hand, showed a large deviation from the native p53-ERα complex in atoms and residues at the surface. Further, results from artificial neural network analysis showed that these structural features are important for predicting the impact of these three mutations on p53-ERα interaction. Overall, these three mutations showed a large deviation in total SASA in both p53 and ERα. In conclusion, results from our study will be crucial in making the decisions for hormone-based therapies against breast cancer.

Structural Probing, Screening and Structure-Based Drug Repositioning Insights into the Identification of Potential Cox-2 Inhibitors from Selective Coxibs.

The rate-limiting enzyme cyclooxygenase-2 (COX-2) is considered as an insightful prognostic target for non-small cell lung cancer (NSCLC) therapy. Now, administration and prolonged utilization of selective COX-2 inhibitors (COXIBs) towards moderating the NSCLC has been associated with different side effects. In the present study, we focused on the structure-based drug repositioning approaches for predicting therapeutic potential de novo candidates for human COX-2. Due to discrepancies in the eminence of x-ray diffraction structures, creates a big barrier in drug discovery approach. Hence, the adaptable COX-2 structure was investigated using multi-template modeling method. Next, a dataset of twenty-six celebrex-associated optimized scaffolds were screened from ZINC database. Comparative docking approaches were then utilized to identify five compounds as best binders to the active site of COX-2 structures and strongly agree with enormous experimental consequences. MD simulations of regarded protein-ligand complexes reveals that lead molecules were stabilized dynamically in inside the cyclooxygenase site by forming potential salt bridges with Tyr348, Tyr385 and Ser530 residues. These significant results revealed that, identified druggables could prevent the tyrosyl radicals and prostaglandin production that reduces NSCLC progression. Furthermore, pharmacokinetics assets of respected ligands were analyzed, which incorporates similarity ensemble approach, druglikeness and ADMET properties. Finally, the identified novel candidates could serve as COX-2 inhibitors for NSCLC therapy, and coxibs are the best choices for designing new scaffolds to treat cyclooxygenases regard disorders.

In silico insights into prediction and analysis of potential novel pyrrolopyridine analogs against human MAPKAPK-2: a new SAR-based hierarchical clustering approach.

In the present study, we have focused on to elucidate potential bioactive pyrrolopyridine (PYP23) analogs against human mitogen-activated protein kinase-activated protein kinase-2 (MK-2). Here, in silico methods and computational systems biology tools were used as rational strategies to predict novel PYP23 analogs against the MK-2. Initially, crystal structure (PDB-ID: 2P3G) consists steriochemical conflicts were rectified by structure-optimization approaches using the Modeller program, and a new optimized-high resolution model was generated. The stereochemical qualities of the predicted MK-2 model were judged; these showed that the model was reliable for docking assessments. SAR-based bioactivity analysis showed that among the 197 datasets only 15 candidates contained bioactivity data and were accepted as probable MK-2 inhibitors. Virtual screening and docking strategies of dataset compounds against the ligand-binding domain of MK-2 recognized 13 composites containing high binding affinity than known compounds. Furthermore, the comparative structure clustering, in silico toxicogenomics and QSAR-based anticancer properties prediction approaches were successful in the recognition of five best potential compounds such as 60118340, 60118338, 60117736, 60118473 and 60118322, which have great anticancer and drug-likeness with non-toxicity class indices. Leu70, Glu139, Leu141, Glu145, Glu190, Thr206 and Asp207 were found to be novel hotspot residues prominently involved in H-bonds framing with ligands. Interestingly, they have shown better molecular similarity with known bioactive PYP inhibitors. Thus, predicted five compounds can useful as possible chemotherapeutic agents for MK-2 and show similar molecular actions like known PYP inhibitors. Overall, these streamlined new methods may have great potential to reveal possible ligands toward other molecular targets and biomarkers.

In silico screening and identification of potential GSK3ß inhibitors.

Glycogen synthase kinase-3ß (GSK3ß) has been reported for its impact on multitude biological processes from cell proliferation to apoptosis. The increase in the ratio of active/inactive GSK3ß is the major factor associated in the etiology of several psychiatric diseases, diabetes, muscle hypertrophy, neurodegenerative diseases, and some cancers. These findings made GSK3ß a promising therapeutic target, and the interest in the discovery, synthesis of novel drugs to effectively attenuate its function with probably no side effects has been increasing in the chronology of GSK3ß drug discovery. In the present study, we applied a combination of computational tools on a chemical library for the virtual discovery of their potency to inhibit GSK3ß. The chemical library was screened against a set of filters at different levels. Finally, five compounds in the chemical library were found to potentially inhibit GSK3ß with no toxic effects. Furthermore, binding mode analysis revealed that all the compounds bound to the ATP site and most of the hydrogen bonding interactions are conserved as in GSK3ß structures deposited in PDB.

Computational screening, ensemble docking and pharmacophore analysis of potential gefitinib analogues against epidermal growth factor receptor.

The observable mutated isoforms of epidermal growth factor receptor (EGFR) are important considerable therapeutic benchmarks in moderating the non-small cell lung cancer (NSCLC). Recently, quinazoline-based ATP competitive inhibitors have been developed against the EGFR; however, these imply the mutation probabilities, which contribute to the discovery of high probable novel inhibitors for EGFR mutants. Therefore, SAR-based bioactivity analysis, molecular docking and computational toxicogenomics approaches were performed to identify and evaluate new analogs of gefitinib against the ligand-binding domain of the EGFR double-mutated model. From the diverse groups of molecular clustering and molecular screening strategies, top high-binding gefitinib-analogues were identified and studied against EGFR core cavity through three-phase ensemble docking approach. Resulted high possible leads showed good binding orientations than gefitinib (positive control) thus they were subjected to pharmacophore analysis that possesses possible molecular assets to tight binding with EGFR domain. Residues Ser720, Arg841 and Trp880 were observed as novel hot spots and involved in H-bonds, pi-stacking and π-cation interactions that contribute additional electrostatic potency to sustain stability and complexity of protein-ligand complexes, thus they have ability to profoundly adopted by pharmacophoric features. Furthermore, lead molecules have an inhibition percent probability, anticancer potency, toxic impacts, flexible pharmacokinetics, potential gene-chemical interactions towards EGFR were revealed by computational systems biology tools. Our multiple screening strategies confirmed that the druggable sub-pocket was crucial to strong EGFR-ligand binding. The essential pharmacophoric features of ligands provided viewpoints for new inhibitors envisaging, and predicted scaffolds could used as anticancer agents against selected EGFR mutated isoforms.

Expression, purification and biological characterisation of recombinant human irisin (12.5 kDa).

Fibronectin type III domain containing 5 (FNDC5) is a transmembrane protein. Upon cleavage, it yields a peptide called irisin that is supposedly bind to an unknown receptor and facilitates browning of white adipose tissue (WAT). Increased levels of irisin are associated with increased levels of energy expenditure markers PGC-1α, UCP-1, besides abundance of beige adipocytes in WAT. Though varied sizes of irisin were reported in humans and rodents it is not yet clear about the actual size of the irisin produced physiologically. Hence, we cloned and expressed human irisin (32-143 aa of FNDC5) in Escherichia coli based on the proposed cleavage site that yields 12.5 kDa peptide to study its antigenicity and other biological functions in vitro. We purified recombinant human irisin (rh-irisin) to 95% homogeneity with simple purification method with a yield of 25 mg/g wet cell pellet. rh-irisin has been detected by commercially available antibodies from different sources with similar antigenicity. Biological activity of the rh-irisin was confirmed by using 3T3-L1 pre-adipocyte differentiation by Oil red O staining. Further, rh-irisin treatment on pre-adipocytes showed increased expression of markers associated with energy expenditure. As it is involved in energy expenditure process, it could be considered as potential therapeutic option for various metabolic diseases.

Ligand-based virtual screening, molecular docking, QSAR and pharmacophore analysis of quercetin-associated potential novel analogs against epidermal growth factor receptor.

The present study was to explore expectation and examination of therapeutic potential quercetin analogs as efficient anticancer agents against human epidermal growth factor receptor (EGFR), which is a consistent hallmark for moderating the non-small-cell lung carcinoma (NSCLC). Here, ligand-based virtual screening, pharmacophore approach and molecular docking were established as rational strategies for recognition of small analogs against the ligand binding domain of EGFR (PDB code: 1XKK). Adverse effects, toxicogenomics and pharmacokinetics reported that 10 candidates showed reliable consequences with less side effects and more efficient for target receptor. Protein-ligand interaction profiles revealed that the probable H-bonds, atomic-π contacts, salt bridges and van der Waals interactions sustain the complexity and stability of receptor structure; thus, they could complicate to generate single alteration acquired for drug resistance. In silico anticancer properties explain the lead scaffolds which are assumed to be flexible and experimentally proved chemicals. The overall consequences indicated that recognized leads could be utilized as reference skeletons for new inhibitors envisaging toward EGFR to ameliorate NSCLC and other malignant disorders.

Computational prediction and analysis of breast cancer targets for 6-methyl-1, 3, 8-trichlorodibenzofuran.

Breast cancer is one of the most known cancer types caused to the women around the world. Dioxins on the other hand are a wide range of chemical compounds known to cause the effects on human health. Among them, 6-Methyl-1,3,8-trichlorodibenzofuran (MCDF) is a relatively non toxic prototypical alkyl polychlorinated dibenzofuran known to act as a highly effective agent for inhibiting hormone-responsive breast cancer growth in animal models. In this study, we have developed a multi-level computational approach to identify possible new breast cancer targets for MCDF. We used PharmMapper Server to predict breast cancer target proteins for MCDF. Search results showed crystal Structure of the Antagonist Form of Glucocorticoid Receptor with highest fit score and AutoLigand analysis showed two potential binding sites, site-A and site-B for MCDF. A molecular docking was performed on these two sites and based on binding energy site-B was selected. MD simulation studies on Glucocorticoid receptor-MCDF complex revealed that MCDF conformation was stable at site-B and the intermolecular interactions were maintained during the course of simulation. In conclusion, our approach couples reverse pharmacophore analysis, molecular docking and molecular dynamics simulations to identify possible new breast cancer targets for MCDF.

Computational screening and molecular dynamic simulation of breast cancer associated deleterious non-synonymous single nucleotide polymorphisms in TP53 gene.

Breast cancer is one of the most common cancers among the women around the world. Several genes are known to be responsible for conferring the susceptibility to breast cancer. Among them, TP53 is one of the major genetic risk factor which is known to be mutated in many of the breast tumor types. TP53 mutations in breast cancer are known to be related to a poor prognosis and chemo resistance. This renders them as a promising molecular target for the treatment of breast cancer. In this study, we present a computational based screening and molecular dynamic simulation of breast cancer associated deleterious non-synonymous single nucleotide polymorphisms in TP53. We have predicted three deleterious coding non-synonymous single nucleotide polymorphisms rs11540654 (R110P), rs17849781 (P278A) and rs28934874 (P151T) in TP53 with a phenotype in breast tumors using computational tools SIFT, Polyphen-2 and MutDB. We have performed molecular dynamics simulations to study the structural and dynamic effects of these TP53 mutations in comparison to the wild-type protein. Results from our simulations revealed a detailed consequence of the mutations on the p53 DNA-binding core domain that may provide insight for therapeutic approaches in breast cancer.

Ligand-based virtual screening to predict inhibitors against metastatic lymph node 64.

Conversion of cholesterol to pregnenolone is the rate-limiting step in steroidogenesis, which is mediated by StAR protein. The mammalian genome contains 15 START domain proteins (StARD1-StARD15) of which C-terminal cytosolic START domain of metastatic lymph node 64 (MLN64 or StARD3), is known to mobilize cholesterol and proposed to participate in steroidogenesis. Being a key in steroidogenesis, it is of interest to identify new inhibitors that are able to bind MLN64 protein. In the present study, we used ligand-based virtual screening approach to identify ligands from the ZINC database with D(-)-Tartaric Acid (TAR) serving as a template.

Prediction and analysis of ligands against estrogen related receptor alpha.

Breast cancer is one of the most common malignancies in women around the world. Among the various hormonal types of breast cancer, those that are estrogen receptor (ER) positive account for the majority. Among the estrogen related receptors, estrogen related receptor α is known to have a potential role in breast cancer and is one of the therapeutic target. Hence, prediction of novel ligands interact with estrogen related receptor alpha is therapeutically important. The present study, aims at prediction and analysis of ligands from the KEGG COMPOUND database (containing 10,739 entries) able to interact against estrogen receptor alpha using a similarity search and molecular docking approach.