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MBio Jun 2024Piperaquine (PPQ) is widely used in combination with dihydroartemisinin as a first-line treatment against malaria. Multiple genetic drivers of PPQ resistance have been...
Piperaquine (PPQ) is widely used in combination with dihydroartemisinin as a first-line treatment against malaria. Multiple genetic drivers of PPQ resistance have been reported, including mutations in the () and increased copies of (). We generated a cross between a Cambodia-derived multidrug-resistant KEL1/PLA1 lineage isolate (KH004) and a drug-susceptible Malawian parasite (Mal31). Mal31 harbors a wild-type (3D7-like) allele and a single copy of , while KH004 has a chloroquine-resistant (Dd2-like) allele with an additional G367C substitution and multiple copies of . We recovered 104 unique recombinant parasites and examined a targeted set of progeny representing all possible combinations of variants at and . We performed a detailed analysis of competitive fitness and a range of PPQ susceptibility phenotypes with these progenies, including PPQ survival assay, area under the dose response curve, and a limited point IC. We find that inheritance of the KH004 allele is required for reduced PPQ sensitivity, whereas copy number variation in further decreases susceptibility but does not confer resistance in the absence of additional mutations in . A deep investigation of genotype-phenotype relationships demonstrates that progeny clones from experimental crosses can be used to understand the relative contributions , , and parasite genetic background to a range of PPQ-related traits. Additionally, we find that the resistance phenotype associated with parasites inheriting the G367C substitution in pfcrt is consistent with previously validated PPQ resistance mutations in this transporter.IMPORTANCEResistance to piperaquine, used in combination with dihydroartemisinin, has emerged in Cambodia and threatens to spread to other malaria-endemic regions. Understanding the causal mutations of drug resistance and their impact on parasite fitness is critical for surveillance and intervention and can also reveal new avenues to limiting the evolution and spread of drug resistance. An experimental genetic cross is a powerful tool for pinpointing the genetic determinants of key drug resistance and fitness phenotypes and has the distinct advantage of quantifying the effects of naturally evolved genetic variation. Our study was strengthened since the full range of copies of KH004 was inherited among the progeny clones, allowing us to directly test the role of the copy number on resistance-related phenotypes in the context of a unique allele. Our multigene model suggests an important role for both loci in the evolution of this multidrug-resistant parasite lineage.
PubMed: 38912775
DOI: 10.1128/mbio.00805-24 -
Frontiers in Cellular and Infection... 2024Widespread opportunistic pathogens pose a serious threat to global health, particularly in susceptible hospital populations. The escalating crisis of antibiotic...
INTRODUCTION
Widespread opportunistic pathogens pose a serious threat to global health, particularly in susceptible hospital populations. The escalating crisis of antibiotic resistance highlights the urgent need for novel antibacterial agents and alternative treatment approaches. Traditional Chinese Medicine (TCM) and its compounds have deep roots in the treatment of infectious diseases. It has a variety of active ingredients and multi-target properties, opening up new avenues for the discovery and development of antimicrobial drugs.
METHODS
This study focuses on assessing the efficacy of the Shensheng-Piwen changed medicinal powder (SPC) extracts against opportunistic pathogen infections by broth microdilution and agar disc diffusion methods. Additionally, biofilm inhibition and eradication assays were performed to evaluate the antibiofilm effects of SPC extracts.
RESULTS
Metabolite profiles were analyzed by LC-MS. Furthermore, the potential synergistic effect between SPC and Metal-Organic Framework (MOF) was investigated by bacterial growth curve analysis. The results indicated that the SPC extracts exhibited antibacterial activity against , with a minimum inhibitory concentration (MIC) of 7.8 mg/mL (crude drug concentration). Notably, at 1/2 MIC, the SPC extracts significantly inhibited biofilm formation, with over 80% inhibition, which was critical in tackling chronic and hospital-acquired infections. Metabolomic analysis of revealed that SPC extracts induced a notable reduction in the levels of various metabolites, including L-proline, L-asparagine. This suggested that the SPC extracts could interfere with the metabolism of . Meanwhile, the growth curve experiment proved that SPC extracts and MOFs had a synergistic antibacterial effect.
DISCUSSION
In conclusion, the present study highlights the potential of SPC extracts as a novel antibacterial agent against infections, with promising biofilm inhibition properties. The observed synergistic effect between SPC extracts and MOFs further supports the exploration of this combination as an alternative treatment approach.
Topics: Anti-Bacterial Agents; Biofilms; Microbial Sensitivity Tests; Metal-Organic Frameworks; Drugs, Chinese Herbal; Staphylococcus aureus; Drug Synergism; Powders; Humans; Chromatography, Liquid
PubMed: 38912207
DOI: 10.3389/fcimb.2024.1376312 -
Frontiers in Immunology 2024Infections are common in plasma cell cancer multiple myeloma (MM) due to disease-related immune deficiencies and cancer treatment. Myeloma cells express Toll-like...
Toll-like receptor signaling in multiple myeloma cells promotes the expression of pro-survival genes B-cell lymphoma 2 and MYC and modulates the expression of B-cell maturation antigen.
Infections are common in plasma cell cancer multiple myeloma (MM) due to disease-related immune deficiencies and cancer treatment. Myeloma cells express Toll-like receptors (TLRs), and TLR activation has been shown to induce proliferative and pro-survival signals in cancer cells. MM is a complex and heterogeneous disease, and expression levels of TLRs as well as downstream signaling components are likely to differ between patients. Here, we show that in a large cohort of patients, TLR1, TLR4, TLR6, TLR9, and TLR10 are the most highly expressed in primary CD138 cells. Using an MM cell line expressing TLR4 and TLR9 as a model, we demonstrate that TLR4 and TLR9 activation promoted the expression of well-established pro-survival and oncogenes in MM such as , , , and . TLR4 and TLR9 activation inhibited the efficacy of proteasome inhibitors bortezomib and carfilzomib, drugs used in the treatment of MM. Inhibiting the autophagosome-lysosome protein degradation pathway by hydroxychloroquine (HCQ) diminished the protective effect of TLR activation on proteasome inhibitor-induced cytotoxicity. We also found that TLR signaling downregulated the expression of , the gene encoding for B-cell maturation antigen (BCMA). , , and were upregulated in approximately 50% of primary cells, while the response to TLR signaling in terms of expression was dichotomous, as an equal fraction of patients showed upregulation and downregulation of the gene. While proteasome inhibitors are part of first-line MM treatment, several of the new anti-MM immune therapeutic drugs target BCMA. Thus, TLR activation may render MM cells less responsive to commonly used anti-myeloma drugs.
Topics: Humans; Multiple Myeloma; Signal Transduction; B-Cell Maturation Antigen; Cell Line, Tumor; Toll-Like Receptors; Proto-Oncogene Proteins c-myc; Gene Expression Regulation, Neoplastic; Proto-Oncogene Proteins c-bcl-2; Bortezomib; Male
PubMed: 38911853
DOI: 10.3389/fimmu.2024.1393906 -
One Health (Amsterdam, Netherlands) Jun 2024Livestock associated antimicrobial resistance (AMR) can reduce productivity and cause economic losses, threatening the livelihoods of poor farming communities in...
Livestock associated antimicrobial resistance (AMR) can reduce productivity and cause economic losses, threatening the livelihoods of poor farming communities in low-income settings. We investigated the practices and risk factors for increased antibiotic use, and AMR in including resistance to human critically important antibiotics like cefotaxime and colistin in semi-intensive and free-range poultry farms in Uganda. Samples and farm management data were collected from 402 poultry farms in two districts between October 2021 to March 2022. Samples were processed to isolate and to quantify cefotaxime (CTX) and colistin (COL) resistant coliforms The identification of presumptive isolated on MacConkey agar without antibiotics, was confirmed by matrix-assisted laser desorption/ionization-time of flight mass spectrometry and subjected to antimicrobial susceptibility testing by disk diffusion using EUCAST guidelines. Our models indicated that antibiotic use was associated with production intensity, and type of feed used. Moreover, semi-intensive farmers had better knowledge on antibiotic use compared to farmers in the free-range system. In semi-intensive farms, 52% harbored COL and 57% CTX coliforms. In free-range farms, 54% had COL and 67% CTX coliforms. Resistance to tetracycline, ampicillin and enrofloxacin were more frequent in semi-intensive farms compared to the free-range farms. Multi-drug resistant were identified in both poultry production systems despite different management and antibiotic use practices. There was no significant relationship between antibiotic use and resistance for the six antibiotics tested.
PubMed: 38910948
DOI: 10.1016/j.onehlt.2024.100762 -
Journal of Global Antimicrobial... Jun 2024The World Health Organization named Stenotrophomonas maltophilia a critical multi-drug resistant threat, necessitating rapid diagnostic strategies. Traditional culturing...
OBJECTIVES
The World Health Organization named Stenotrophomonas maltophilia a critical multi-drug resistant threat, necessitating rapid diagnostic strategies. Traditional culturing methods require up to 96 hours, including 72 hours for bacterial growth, identification with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) through protein profile analysis, and 24 hours for antibiotic susceptibility testing. In this study, we aimed at developing an artificial intelligence-clinical decision support system (AI-CDSS) by integrating MALDI-TOF MS and machine learning to quickly identify levofloxacin and trimethoprim/sulfamethoxazole resistance in S. maltophilia, optimizing treatment decisions.
METHODS
We selected 8,662 S. maltophilia from 165,299 MALDI-TOF MS-analyzed bacterial specimens, collected from a major medical center and four secondary hospitals. We exported mass-to-charge values and intensity spectral profiles from MALDI-TOF MS .mzML files to predict antibiotic susceptibility testing results, obtained with the VITEK-2 system using machine learning algorithms. We optimized the models with GridSearchCV and 5-fold cross-validation.
RESULTS
We identified distinct spectral differences between resistant and susceptible S. maltophilia strains, demonstrating crucial resistance features. The machine learning models, including random forest, light-gradient boosting machine, and XGBoost, exhibited high accuracy. We established an AI-CDSS to offer healthcare professionals swift, data-driven advice on antibiotic use.
CONCLUSIONS
MALDI-TOF MS and machine learning integration into an AI-CDSS significantly improved rapid S. maltophilia resistance detection. This system reduced the identification time of resistant strains from 24 hours to minutes after MALDI-TOF MS identification, providing timely and data-driven guidance. Combining MALDI-TOF MS with machine learning could enhance clinical decision-making and improve S. maltophilia infection treatment outcomes.
PubMed: 38909685
DOI: 10.1016/j.jgar.2024.06.004 -
Scientific Reports Jun 2024This study aimed to investigate the epidemiological characteristics and trends over time of carbapenemase-producing (e.g., KPC, NDM, VIM, IMP, and OXA-48) Gram-negative...
This study aimed to investigate the epidemiological characteristics and trends over time of carbapenemase-producing (e.g., KPC, NDM, VIM, IMP, and OXA-48) Gram-negative bacteria (CPGNB). Non-duplicated multi-drug resistant Gram-negative bacteria (MDRGNB) were collected from the First Affiliated Hospital of Zhengzhou University from April 2019 to February 2023. Species identification of each isolate was performed using the Vitek2 system and confirmed by matrix-assisted laser desorption ionization-time of flight mass spectrometry according to the manufacturer's instructions. PCR detected carbapenem resistance genes in the strains, strains carrying carbapenem resistance genes were categorized as CPGNB strains after validation by carbapenem inactivation assay. A total of 5705 non-repetitive MDRGNB isolates belonging to 78 different species were collected during the study period, of which 1918 CPGNB were validated, with the respiratory tract being the primary source of specimens. Epidemiologic statistics showed a significant predominance of ICU-sourced strains compared to other departments. Klebsiella pneumoniae, Escherichia coli, Acinetobacter baumannii, and Pseudomonas aeruginosa were the significant CPGNB in Henan, and KPC and NDM were the predominant carbapenemases. Carbapenem-resistant infections in Henan Province showed an overall increasing trend, and the carriage of carbapenemase genes by CPGNB has become increasingly prevalent and complicated. The growing prevalence of CPGNB in the post-pandemic era poses a significant challenge to public safety.
Topics: beta-Lactamases; China; Bacterial Proteins; Humans; Gram-Negative Bacteria; Gram-Negative Bacterial Infections; Male; Female; Microbial Sensitivity Tests; Adult; Middle Aged; Carbapenems; Anti-Bacterial Agents; Aged; Drug Resistance, Multiple, Bacterial; Child; Adolescent; Child, Preschool; Young Adult; Klebsiella pneumoniae; Acinetobacter baumannii; Infant
PubMed: 38909136
DOI: 10.1038/s41598-024-65106-0 -
Nature Communications Jun 2024Determining the balance between DNA double strand break repair (DSBR) pathways is essential for understanding treatment response in cancer. We report a method for...
Determining the balance between DNA double strand break repair (DSBR) pathways is essential for understanding treatment response in cancer. We report a method for simultaneously measuring non-homologous end joining (NHEJ), homologous recombination (HR), and microhomology-mediated end joining (MMEJ). Using this method, we show that patient-derived glioblastoma (GBM) samples with acquired temozolomide (TMZ) resistance display elevated HR and MMEJ activity, suggesting that these pathways contribute to treatment resistance. We screen clinically relevant small molecules for DSBR inhibition with the aim of identifying improved GBM combination therapy regimens. We identify the ATM kinase inhibitor, AZD1390, as a potent dual HR/MMEJ inhibitor that suppresses radiation-induced phosphorylation of DSBR proteins, blocks DSB end resection, and enhances the cytotoxic effects of TMZ in treatment-naïve and treatment-resistant GBMs with TP53 mutation. We further show that a combination of G2/M checkpoint deficiency and reliance upon ATM-dependent DSBR renders TP53 mutant GBMs hypersensitive to TMZ/AZD1390 and radiation/AZD1390 combinations. This report identifies ATM-dependent HR and MMEJ as targetable resistance mechanisms in TP53-mutant GBM and establishes an approach for simultaneously measuring multiple DSBR pathways in treatment selection and oncology research.
Topics: Humans; Ataxia Telangiectasia Mutated Proteins; Glioblastoma; Tumor Suppressor Protein p53; DNA Breaks, Double-Stranded; Temozolomide; Cell Line, Tumor; Mutation; Drug Resistance, Neoplasm; DNA Repair; Brain Neoplasms; Animals; DNA End-Joining Repair; Mice; Phosphorylation
PubMed: 38906885
DOI: 10.1038/s41467-024-49316-8 -
PloS One 2024Lung cancer, a relentless and challenging disease, demands unwavering attention in drug design research. Single-target drugs have yielded limited success, unable to...
Unrevealing the multitargeted potency of 3-1-BCMIYPPA against lung cancer structural maintenance and suppression proteins through pharmacokinetics, QM-DFT, and multiscale MD simulation studies.
Lung cancer, a relentless and challenging disease, demands unwavering attention in drug design research. Single-target drugs have yielded limited success, unable to effectively address this malignancy's profound heterogeneity and often developed resistance. Consequently, the clarion call for lung cancer drug design echoes louder than ever, and multitargeted drug design emerges as an imperative approach in this landscape, which is done by concurrently targeting multiple proteins and pathways and offering a beacon of hope. This study is focused on the multitargeted drug designing approach by identifying drug candidates against human cyclin-dependent kinase-2, SRC-2 domains of C-ABL, epidermal growth factor and receptor extracellular domains, and insulin-like growth factor-1 receptor kinase. We performed the multitargeted molecular docking studies of Drug Bank compounds using HTVS, SP and XP algorithms and poses filter with MM\GBSA against all proteins and identified DB02504, namely [3-(1-Benzyl-3-Carbamoylmethyl-2-Methyl-1h-Indol-5-Yloxy)-Propyl-]-Phosphonic Acid (3-1-BCMIYPPA) as multitargeted lead with docking and MM\GBSA score range from -8.242 to -6.274 and -28.2 and -44.29 Kcal/mol, respectively. Further, the QikProp-based pharmacokinetic computations and QM-based DFT showed acceptance results against standard values, and interaction fingerprinting reveals that THR, MET, GLY, VAL, LEU, GLU and ASP were among the most interacting residues. The NPT ensemble-based 100ns MD simulation in a neutralised state with an SPC water model has also shown a stable performance and produced deviation and fluctuations <2Å with huge interactions, making it a promising multitargeted drug candidate-however, experimental studies are suggested.
Topics: Humans; Lung Neoplasms; Molecular Dynamics Simulation; Molecular Docking Simulation; Antineoplastic Agents; Drug Design; Indoles; Density Functional Theory
PubMed: 38905286
DOI: 10.1371/journal.pone.0303784 -
PloS One 2024The battle against viral drug resistance highlights the need for innovative approaches to replace time-consuming and costly traditional methods. Deep generative models...
The battle against viral drug resistance highlights the need for innovative approaches to replace time-consuming and costly traditional methods. Deep generative models offer automation potential, especially in the fight against Human immunodeficiency virus (HIV), as they can synthesize diverse molecules effectively. In this paper, an application of an LSTM-based deep generative model named "LSTM-ProGen" is proposed to be tailored explicitly for the de novo design of drug candidate molecules that interact with a specific target protein (HIV-1 protease). LSTM-ProGen distinguishes itself by employing a long-short-term memory (LSTM) architecture, to generate novel molecules target specificity against the HIV-1 protease. Following a thorough training process involves fine-tuning LSTM-ProGen on a diverse range of compounds sourced from the ChEMBL database. The model was optimized to meet specific requirements, with multiple iterations to enhance its predictive capabilities and ensure it generates molecules that exhibit favorable target interactions. The training process encompasses an array of performance evaluation metrics, such as drug-likeness properties. Our evaluation includes extensive silico analysis using molecular docking and PCA-based visualization to explore the chemical space that the new molecules cover compared to those in the training set. These evaluations reveal that a subset of 12 de novo molecules generated by LSTM-ProGen exhibit a striking ability to interact with the target protein, rivaling or even surpassing the efficacy of native ligands. Extended versions with further refinement of LSTM-ProGen hold promise as versatile tools for designing efficacious and customized drug candidates tailored to specific targets, thus accelerating drug development and facilitating the discovery of new therapies for various diseases.
Topics: HIV Protease Inhibitors; Drug Design; Humans; HIV Protease; HIV-1; Acquired Immunodeficiency Syndrome; Molecular Docking Simulation
PubMed: 38905197
DOI: 10.1371/journal.pone.0303597 -
Briefings in Bioinformatics May 2024The inherent heterogeneity of cancer contributes to highly variable responses to any anticancer treatments. This underscores the need to first identify precise...
The inherent heterogeneity of cancer contributes to highly variable responses to any anticancer treatments. This underscores the need to first identify precise biomarkers through complex multi-omics datasets that are now available. Although much research has focused on this aspect, identifying biomarkers associated with distinct drug responders still remains a major challenge. Here, we develop MOMLIN, a multi-modal and -omics machine learning integration framework, to enhance drug-response prediction. MOMLIN jointly utilizes sparse correlation algorithms and class-specific feature selection algorithms, which identifies multi-modal and -omics-associated interpretable components. MOMLIN was applied to 147 patients' breast cancer datasets (clinical, mutation, gene expression, tumor microenvironment cells and molecular pathways) to analyze drug-response class predictions for non-responders and variable responders. Notably, MOMLIN achieves an average AUC of 0.989, which is at least 10% greater when compared with current state-of-the-art (data integration analysis for biomarker discovery using latent components, multi-omics factor analysis, sparse canonical correlation analysis). Moreover, MOMLIN not only detects known individual biomarkers such as genes at mutation/expression level, most importantly, it correlates multi-modal and -omics network biomarkers for each response class. For example, an interaction between ER-negative-HMCN1-COL5A1 mutations-FBXO2-CSF3R expression-CD8 emerge as a multimodal biomarker for responders, potentially affecting antimicrobial peptides and FLT3 signaling pathways. In contrast, for resistance cases, a distinct combination of lymph node-TP53 mutation-PON3-ENSG00000261116 lncRNA expression-HLA-E-T-cell exclusions emerged as multimodal biomarkers, possibly impacting neurotransmitter release cycle pathway. MOMLIN, therefore, is expected advance precision medicine, such as to detect context-specific multi-omics network biomarkers and better predict drug-response classifications.
Topics: Humans; Breast Neoplasms; Female; Machine Learning; Biomarkers, Tumor; Algorithms; Antineoplastic Agents; Computational Biology; Genomics
PubMed: 38904542
DOI: 10.1093/bib/bbae300