Identification of a Potential Inhibitor (MCULE-8777613195-0-12) of New Delhi Metallo-β-Lactamase-1 (NDM-1) Using In Silico and In Vitro Approaches

Potential drug targets for tumors identified through Mendelian randomization analysis

According to the latest cancer research data, there are a significant number of new cancer cases and a substantial mortality rate each year. Although a substantial number of clinical patients are treated with existing cancer drugs each year, the efficacy is unsatisfactory. The incidence is still high and the effectiveness of most cancer drugs remains unsatisfactory. Therefore, we evaluated the human proteins for their causal relationship to for cancer risk and therefore also their potential as drug targets. We used summary tumors data from the FinnGen and cis protein quantitative trait loci (cis-pQTL) data from a genome-wide association study, and employed Mendelian randomization (MR) to explore the association between potential drug targets and nine tumors, including breast, colorectal, lung, liver, bladder, prostate, kidney, head and neck, pancreatic caners. Furthermore, we conducted MR analysis on external cohort. Moreover, Bidirectional MR, Steiger filtering, and colocalization were employed to validate the main results. The DrugBank database was used to discover potential drugs of tumors. Under the threshold of False discovery rate (FDR) < 0.05, results showed that S100A16 was protective protein and S100A14 was risk protein for human epidermal growth factor receptor 2-positive (HER-positive) breast cancer, phosphodiesterase 5A (PDE5A) was risk protein for colorectal cancer, and melanoma inhibitory activity (MIA) was protective protein for non-small cell lung carcinoma (NSCLC). And there was no reverse causal association between them. Colocalization analysis showed that S100A14 (PP.H4.abf = 0.920) and S100A16 (PP.H4.abf = 0.932) shared causal variation with HER-positive breast cancer, and PDE5A (PP.H4.abf = 0.857) shared causal variation with colorectal cancer (CRC). The MR results of all pQTL of PDE5A and MIA were consistent with main results. In addition, the MR results of MIA and external outcome cohort were consistent with main results. In this study, genetic predictions indicate that circulating S100 calcium binding protein A14 (S100A14) and S100 calcium binding protein A16 (S100A16) are associated with increase and decrease in the risk of HER-positive breast cancer, respectively. Circulating PDE5A is associated with increased risk of CRC, while circulating MIA is associated with decreased risk of NSCLC. These findings suggest that four proteins may serve as biomarkers for cancer prevention and as potential drug targets that could be expected for approval.

Occurrence and characterization of β-lactamase-producing bacteria in biomedical wastewater and in silico enhancement of antibiotic efficacy

Wastewater discharged from hospitals is a recognized contributor to the dissemination of antibiotic-resistant bacteria and their associated genetic traits into the environment. This study focused on the analysis of β-lactamase-producing pathogenic bacteria within untreated biomedical wastewater originating from various hospitals in Dhaka City, Bangladesh, as well as in silico evaluation and structural activity relationship mentioned antibiotics were evaluated. In silico drug design techniques were applied to identify the relationship with how the functional group impacts the binding energy. Out of the 184 isolates obtained from well-established hospital sewage discharge points in Dhaka, 89 were identified as β-lactamase positive. These bacteria were subjected to antimicrobial susceptibility testing using the VITEK-2 assay, and their profiles of extended-spectrum beta-lactamase (ESBL) production were determined through molecular methodologies. Among the β-lactamase-positive isolates, considerable resistance was observed, particularly against ampicillin, Ceftriaxone, Cefuroxime, and Meropenem. The predominant resistant species included Escherichia coli, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter cloacae. The study identified the prevalence of ESBL-producing genes, with blaNDM-1 being the most prevalent, followed by blaOXA-1, blaSHV, blaCTX-M, and blaKPC. None of the isolates carried the blaTEM gene. In addition to characterizing these bacteria, the research explored ways to enhance the binding energy of four existing antibiotics as new inhibitors through computational studies. The findings revealed significant improvements in binding energy. Specifically, Meropenem initially exhibited a binding energy of -7.5 kcal/mol, notably increasing to -8.3 kcal/mol after modification. With an initial binding energy was only -7.9 kcal/mol, Ampicillin experienced an enhancement, reaching -8.0 kcal/mol post-modification. Similarly, Ceftriaxone, with an initial binding energy of -8.2 kcal/mol, increased to -8.5 kcal/mol following structural adjustments. Finally, Cefuroxime, initially registering a binding energy of -7.1 kcal/mol, substantially increased to -8.9 kcal/mol after modification. This finding establishes a foundation for future investigations in the development of modified antibiotics to address the issue of antibiotic resistance. It presents prospective remedies for the persistent problem of antibiotic-resistant bacteria in healthcare and the environment.

Network meta-analysis of antibiotic resistance patterns in gram-negative bacterial infections: a comparative study of carbapenems, fluoroquinolones, and aminoglycosides

Introduction

Antimicrobial resistance poses a grave global threat, particularly with the emergence of multidrug-resistant gram-negative bacterial infections, which severely limit treatment options. The increasing global threat of antimicrobial resistance demands rigorous investigation, particularly concerning multidrug-resistant gram-negative bacterial infections that present limited therapeutic options. This study employed a network meta-analysis, a powerful tool for comparative effectiveness assessment of diverse antibiotics. The primary aim of this study was to comprehensively evaluate and compare resistance patterns among widely used antibiotic classes, namely carbapenems, fluoroquinolones, and aminoglycosides, for combating gram-negative pathogens.

Methods

We searched PubMed, Web of Sciences, Scopus, Scholarly, Medline, Embase, and Cochrane databases up to August 27, 2023. Studies showing antibiotic resistance in clinical isolates of Enterobacteriaceae, Pseudomonas aeruginosa, and Acinetobacter baumannii exposed to carbapenems, fluoroquinolones, and aminoglycosides were included. This study determined treatment-specific resistance percentages and ranked these treatments based on resistance using a random-effects network meta-analysis technique. To investigate the impact of the study and pathogen features, subgroup and meta-regression analyses were performed. Risk ratios and 95% confidence intervals (CIs) were calculated using a network meta-analysis (NMA) incorporating both direct and indirect evidence. Clinical improvement, cure, microbiological eradication, and death from any cause were the primary outcomes. Nephrotoxicity was a secondary result.

Results

The analysis included 202 publications and 365,782 gram-negative isolates. The NMA included data from 20 studies and 4,835 patients. Carbapenems had the lowest resistance rates throughout the pathogen spectrum, with resistance percentages of 17.1, 22.4, and 33.5% for Enterobacteriaceae, P. aeruginosa, and A. baumannii, respectively. For the same infections, aminoglycosides showed resistance rates of 28.2, 39.1, and 50.2%, respectively. Fluoroquinolones had the highest resistance rates at 43.1, 57.3, and 65.7%, respectively. Unexpectedly, resistance to all three antibiotic classes has increased over time, with multidrug resistance being the most prevalent.

Conclusion

This extensive network meta-analysis provides an overview of the patterns of resistance throughout the world and how they are changing. The most effective choice is still carbapenems, but the increasing resistance highlights the critical need for multimodal therapies to protect antibiotic effectiveness against these powerful gram-negative infections.

A Shortcut from Genome to Drug: The Employment of Bioinformatic Tools to Find New Targets for Gastric Cancer Treatment

Gastric cancer (GC) is a highly heterogeneous, complex disease and the fifth most common cancer worldwide (about 1 million cases and 784,000 deaths worldwide in 2018). GC has a poor prognosis (the 5-year survival rate is less than 20%), but there is an effort to find genes highly expressed during tumor establishment and use the related proteins as targets to find new anticancer molecules. Data were collected from the Gene Expression Omnibus (GEO) bank to obtain three dataset matrices analyzing gastric tumor tissue versus normal gastric tissue and involving microarray analysis performed using the GPL570 platform and different sources. The data were analyzed using the GEPIA tool for differential expression and KMPlot for survival analysis. For more robustness, GC data from the TCGA database were used to corroborate the analysis of data from GEO. The genes found in in silico analysis in both GEO and TCGA were confirmed in several lines of GC cells by RT-qPCR. The AlphaFold Protein Structure Database was used to find the corresponding proteins. Then, a structure-based virtual screening was performed to find molecules, and docking analysis was performed using the DockThor server. Our in silico and RT-qPCR analysis results confirmed the high expression of the AJUBA, CD80 and NOLC1 genes in GC lines. Thus, the corresponding proteins were used in SBVS analysis. There were three molecules, one molecule for each target, MCULE-2386589557-0-6, MCULE-9178344200-0-1 and MCULE-5881513100-0-29. All molecules had favorable pharmacokinetic, pharmacodynamic and toxicological properties. Molecular docking analysis revealed that the molecules interact with proteins in critical sites for their activity. Using a virtual screening approach, a molecular docking study was performed for proteins encoded by genes that play important roles in cellular functions for carcinogenesis. Combining a systematic collection of public microarray data with a comparative meta-profiling, RT-qPCR, SBVS and molecular docking analysis provided a suitable approach for finding genes involved in GC and working with the corresponding proteins to search for new molecules with anticancer properties.