Simple oligonucleotide-based multiplexing of single-cell chromatin accessibility.

Microdroplet single-cell ATAC-seq is widely used to measure chromatin accessibility, however, highly scalable and simple sample multiplexing procedures are not available. Here, we present a transposome-assisted single nucleus barcoding approach for ATAC-seq (SNuBar-ATAC) that utilizes a single oligonucleotide adaptor for multiplexing samples during the existing tagmentation step and does not require a pre-labeling procedure. The accuracy and scalability of SNuBar-ATAC was evaluated using cell line mixture experiments. We applied SNuBar-ATAC to investigate treatment-induced chromatin accessibility dynamics by multiplexing 28 mice with lung tumors that received different combinations of chemo, radiation, and targeted immunotherapy. We also applied SNuBar-ATAC to study spatial epigenetic heterogeneity by multiplexing 32 regions from a human breast tissue. Additionally, we show that SNuBar can multiplex single cell ATAC and RNA multiomic assays in cell lines and human breast tissue samples. Our data show that SNuBar is a highly accurate, easy-to-use, and scalable system for multiplexing scATAC-seq and scATAC and RNA co-assay experiments.

RepurposeDrugs: an interactive web-portal and predictive platform for repurposing mono- and combination therapies.

RepurposeDrugs (https://repurposedrugs.org/) is a comprehensive web-portal that combines a unique drug indication database with a machine learning (ML) predictor to discover new drug-indication associations for approved as well as investigational mono and combination therapies. The platform provides detailed information on treatment status, disease indications and clinical trials across 25 indication categories, including neoplasms and cardiovascular conditions. The current version comprises 4314 compounds (approved, terminated or investigational) and 161 drug combinations linked to 1756 indications/conditions, totaling 28 148 drug-disease pairs. By leveraging data on both approved and failed indications, RepurposeDrugs provides ML-based predictions for the approval potential of new drug-disease indications, both for mono- and combinatorial therapies, demonstrating high predictive accuracy in cross-validation. The validity of the ML predictor is validated through a number of real-world case studies, demonstrating its predictive power to accurately identify repurposing candidates with a high likelihood of future approval. To our knowledge, RepurposeDrugs web-portal is the first integrative database and ML-based predictor for interactive exploration and prediction of both single-drug and combination approval likelihood across indications. Given its broad coverage of indication areas and therapeutic options, we expect it accelerates many future drug repurposing projects.

'Against all floods': plant adaptation to flooding stress and combined abiotic stresses.

Current climate change brings with it a higher frequency of environmental stresses, which occur in combination rather than individually leading to massive crop losses worldwide. In addition to, for example, drought stress (low water availability), also flooding (excessive water) can threaten the plant, causing, among others, an energy crisis due to hypoxia, which is responded to by extensive transcriptional, metabolic and growth-related adaptations. While signalling during flooding is relatively well understood, at least in model plants, the molecular mechanisms of combinatorial flooding stress responses, for example, flooding simultaneously with salinity, temperature stress and heavy metal stress or sequentially with drought stress, remain elusive. This represents a significant gap in knowledge due to the fact that dually stressed plants often show unique responses at multiple levels not observed under single stress. In this review, we (i) consider possible effects of stress combinations from a theoretical point of view, (ii) summarize the current state of knowledge on signal transduction under single flooding stress, (iii) describe plant adaptation responses to flooding stress combined with four other abiotic stresses and (iv) propose molecular components of combinatorial flooding (hypoxia) stress adaptation based on their reported dual roles in multiple stresses. This way, more future emphasis may be placed on deciphering molecular mechanisms of combinatorial flooding stress adaptation, thereby potentially stimulating development of molecular tools to improve plant resilience towards multi-stress scenarios.

Innovative pathological network-based multitarget approaches for Alzheimer's disease treatment.

Alzheimer's disease (AD) is the most prevalent neurodegenerative disease and is a major health threat globally. Its prevalence is forecasted to exponentially increase during the next 30 years due to the global aging population. Currently, approved drugs are merely symptomatic, being ineffective in delaying or blocking the relentless disease advance. Intensive AD research describes this disease as a highly complex multifactorial disease. Disclosure of novel pathological pathways and their interconnections has had a major impact on medicinal chemistry drug development for AD over the last two decades. The complex network of pathological events involved in the onset of the disease has prompted the development of multitarget drugs. These chemical entities combine pharmacological activities toward two or more drug targets of interest. These multitarget-directed ligands are proposed to modify different nodes in the pathological network aiming to delay or even stop disease progression. Here, we review the multitarget drug development strategy for AD during the last decade.

Novel hydrazone compounds with broad-spectrum antiplasmodial activity and synergistic interactions with antimalarial drugs.

The development of novel antiplasmodial compounds with broad-spectrum activity against different stages of Plasmodium parasites is crucial to prevent malaria disease and parasite transmission. This study evaluated the antiplasmodial activity of seven novel hydrazone compounds (referred to as CB compounds: CB-27, CB-41, CB-50, CB-53, CB-58, CB-59, and CB-61) against multiple stages of Plasmodium parasites. All CB compounds inhibited blood stage proliferation of drug-resistant or sensitive strains of Plasmodium falciparum in the low micromolar to nanomolar range. Interestingly, CB-41 exhibited prophylactic activity against hypnozoites and liver schizonts in Plasmodium cynomolgi, a primate model for Plasmodium vivax. Four CB compounds (CB-27, CB-41, CB-53, and CB-61) inhibited P. falciparum oocyst formation in mosquitoes, and five CB compounds (CB-27, CB-41, CB-53, CB-58, and CB-61) hindered the in vitro development of Plasmodium berghei ookinetes. The CB compounds did not inhibit the activation of P. berghei female and male gametocytes in vitro. Isobologram assays demonstrated synergistic interactions between CB-61 and the FDA-approved antimalarial drugs, clindamycin and halofantrine. Testing of six CB compounds showed no inhibition of Plasmodium glutathione S-transferase as a putative target and no cytotoxicity in HepG2 liver cells. CB compounds are promising candidates for further development as antimalarial drugs against multidrug-resistant parasites, which could also prevent malaria transmission.

Two-way pharmacodynamic modeling of drug combinations and its application to pairs of repurposed Ebola and SARS-CoV-2 agents.

Existing pharmacodynamic (PD) mathematical models for drug combinations discriminate antagonistic, additive, multiplicative, and synergistic effects, but fail to consider how concentration-dependent drug interaction effects may vary across an entire dose-response matrix. We developed a two-way pharmacodynamic (TWPD) model to capture the PD of two-drug combinations. TWPD captures interactions between upstream and downstream drugs that act on different stages of viral replication, by quantifying upstream drug efficacy and concentration-dependent effects on downstream drug pharmacodynamic parameters. We applied TWPD to previously published in vitro drug matrixes for repurposed potential anti-Ebola and anti-SARS-CoV-2 drug pairs. Depending on the drug pairing, the model recapitulated combined efficacies as or more accurately than existing models and can be used to infer efficacy at untested drug concentrations. TWPD fits the data slightly better in one direction for all drug pairs, meaning that we can tentatively infer the upstream drug. Based on its high accuracy, TWPD could be used in concert with PK models to estimate the therapeutic effects of drug pairs in vivo.

Combination therapy with novel agents for acute myeloid leukaemia: Insights into treatment of a heterogenous disease.

The treatment landscape of acute myeloid leukaemia (AML) is evolving rapidly. Venetoclax in combination with intensive chemotherapy or doublets or triplets with targeted or immune therapies is the focus of numerous ongoing trials. The development of mutation-targeted therapies has greatly enhanced the treatment armamentarium, with FLT3 inhibitors and isocitrate dehydrogenase inhibitors improving outcomes in frontline and relapsed/refractory (RR) AML, and menin inhibitors showing efficacy in RR NPM1mut and KMT2A-rearranged AML. With so many new drugs approved, the number of potential combinatorial approaches to leverage the maximal benefit of these agents has increased dramatically, while at the same time introducing clinical challenges, such as key preclinical and clinical data supporting the development of combinatorial therapy, how to optimally combine or sequence these novel agents, how to optimise dose and duration to maintain safety while enhancing efficacy, the optimal duration of therapy and the role of measurable residual disease in decision-making in both intensive and low-intensity therapy settings. In this review, we will outline the evidence leading to the approval of key agents in AML, their on-label current approvals and how they may be optimally combined in a safe and deliverable fashion to further improve outcomes in AML.

Sex and survival outcomes in patients with renal cell carcinoma receiving first-line immune-based combinations.

BACKGROUND: There is an ongoing debate as to whether sex could be associated with immune checkpoint inhibitor (ICI) benefit. Existing literature data reveal contradictory results, and data on first-line immune combinations are lacking. METHOD: This was a real-world, multicenter, international, observational study to determine the sex effects on the clinical outcomes in metastatic renal cell carcinoma (mRCC) patients treated with immuno-oncology combinations as first-line therapy. RESULTS: A total of 1827 mRCC patients from 71 cancer centers in 21 countries were included. The median OS was 38.7 months (95% CI 32.7-44.2) in the overall study population: 40.0 months (95% CI 32.7-51.6) in males and 38.7 months (95% CI 26.4-41.0) in females (p = 0.202). The median OS was higher in males vs. females in patients aged 18-49y (36.9 months, 95% CI 29.0-51.6, vs. 24.8 months, 95% CI 16.8-40.4, p = 0.426, with + 19% of 2y-OS rate, 72% vs. 53%, p = 0.006), in the clear cell histology subgroup (44.2 months, 95% CI 35.8-55.7, vs. 38.7 months, 95% CI 26.0-41.0, p = 0.047), and in patients with sarcomatoid differentiation (34.4 months, 95% CI 26.4-59.0, vs. 15.3 months, 95% CI 8.9-41.0, p < 0.001). Sex female was an independent negative prognostic factor in the sarcomatoid population (HR 1.72, 95% CI 1.15 - 2.57, p = 0.008). CONCLUSIONS: Although the female's innate and adaptive immunity has been observed to be more active than the male's, women in the subgroup of clear cell histology, sarcomatoid differentiation, and those under 50 years of age showed shorter OS than males.

Rotini-like MXene@LCE Actuator with Diverse and Programmable Actuation Based on Dual-mode Synergy.

Liquid crystalline elastomer (LCE) exhibits muscle-like actuation upon order-disturbed stimulus, offering ample room for designing soft robotic systems. Multimodal LCE is demonstrated to unleash the potential to perform multitasks. However, each actuation mode is typically isolated. In contrast, coordination between different actuation modes based on an MXene-doped LCE is realized, whose actuation can be triggered either by directly heating/cooling or using near-infrared light due to the photo-thermal effect of MXene. As such, the two activation modes (heat and light) not only can work individually to offer stable actuation under different conditions but also can collaborate synergistically to generate more intelligent motions, such as achieving the brake and turn of an autonomous rolling. The principle therefore can diversify the design principles for multifunctional soft actuators and robotics.

Extended Battery Compatibility Consideration from an Electrolyte Perspective.

The performance of electrochemical batteries is intricately tied to the physicochemical environments established by their employed electrolytes. Traditional battery designs utilizing a single electrolyte often impose identical anodic and cathodic redox conditions, limiting the ability to optimize redox environments for both anode and cathode materials. Consequently, advancements in electrolyte technologies are pivotal for addressing these challenges and fostering the development of next-generation high-performance electrochemical batteries. This review categorizes perspectives on electrolyte technology into three key areas: additives engineering, comprehensive component analysis encompassing solvents and solutes, and the effects of concentration. By summarizing significant studies, the efficacy of electrolyte engineering is highlighted, and the review advocates for further exploration of optimized component combinations. This review primarily focuses on liquid electrolyte technologies, briefly touching upon solid-state electrolytes due to the former greater vulnerability to electrode and electrolyte interfacial effects. The ultimate goal is to generate increased awareness within the battery community regarding the holistic improvement of battery components through optimized combinations.

Amelioration of NaCl stress on germination, growth, and nitrogen fixation of Vicia faba at isosmotic Na-Ca combinations and Rhizobium.

MAIN CONCLUSION: The Na+/Ca2+ ratio of 1/5 ameliorated the inhibitory action of NaCl and improved the germination and growth of Vicia faba. Addition of Rhizobium also enhanced nodulation and nitrogen fixation. Casting light upon the impact of salinity stress on growth and nitrogen fixation of Vicia faba supplemented with Rhizobium has been traced in this work. How Ca2+ antagonizes Na+ toxicity and osmotic stress of NaCl was also targeted in isosmotic combinations of NaCl and CaCl2 having various Na+:Ca2+ ratios. Growth of Vicia faba (cultivar Giza 3) was studied at two stages: germination and seedling. At both experiments, seeds or seedlings were exposed to successively increasing salinity levels (0, 50, 100, 150, and 200 mM NaCl) as well as isosmotic combinations of NaCl and CaCl2 (Na+:Ca2+ of 1:1, 1:5, 1:10, 1:15, 1:18, and 1: 20), equivalent to 150 mM NaCl. Inocula of the local nitrogen-fixing bacteria, Rhizobium leguminosarum (OP715892) were supplemented at both stages. NaCl salinity exerted a negative impact on growth and metabolism of Vicia faba; inhibition was proportional with increasing salinity level up to the highest level of 200 mM. Seed germination, shoot and root lengths, fresh and dry weights, chlorophyll content, and nodules (number, weight, leghemoglobin, respiration, and nitrogenase activity) were inhibited by salinity. Ca2+ substitution for Na+, particularly at a Na/Ca ratio of 1:5, was stimulatory to almost all parameters at both stages. Statistical correlations between salinity levels and Na/Ca combinations proved one of the four levels (strong- or weak positive, strong- or weak negative) with most of the investigated parameters, depending on the parameter.

A review of machine learning approaches for drug synergy prediction in cancer.

Combinational pharmacotherapy with the synergistic/additive effect is a powerful treatment strategy for complex diseases such as malignancies. Identifying synergistic combinations with various compounds and structures requires testing a large number of compound combinations. However, in practice, examining different compounds by in vivo and in vitro approaches is costly, infeasible and challenging. In the last decades, significant success has been achieved by expanding computational methods in different pharmacological and bioinformatics domains. As promising tools, computational approaches such as machine learning algorithms (MLAs) are used for prioritizing combinational pharmacotherapies. This review aims to provide the models developed to predict synergistic drug combinations in cancer by MLAs with various information, including gene expression, protein-protein interactions, metabolite interactions, pathways and pharmaceutical information such as chemical structure, molecular descriptor and drug-target interactions.

Computational models, databases and tools for antibiotic combinations.

Antibiotic combination is a promising strategy to extend the lifetime of antibiotics and thereby combat antimicrobial resistance. However, screening for new antibiotic combinations is both time-consuming and labor-intensive. In recent years, an increasing number of researchers have used computational models to predict effective antibiotic combinations. In this review, we summarized existing computational models for antibiotic combinations and discussed the limitations and challenges of these models in detail. In addition, we also collected and summarized available data resources and tools for antibiotic combinations. This study aims to help computational biologists design more accurate and interpretable computational models.

PRODeepSyn: predicting anticancer synergistic drug combinations by embedding cell lines with protein-protein interaction network.

Although drug combinations in cancer treatment appear to be a promising therapeutic strategy with respect to monotherapy, it is arduous to discover new synergistic drug combinations due to the combinatorial explosion. Deep learning technology holds immense promise for better prediction of in vitro synergistic drug combinations for certain cell lines. In methods applying such technology, omics data are widely adopted to construct cell line features. However, biological network data are rarely considered yet, which is worthy of in-depth study. In this study, we propose a novel deep learning method, termed PRODeepSyn, for predicting anticancer synergistic drug combinations. By leveraging the Graph Convolutional Network, PRODeepSyn integrates the protein-protein interaction (PPI) network with omics data to construct low-dimensional dense embeddings for cell lines. PRODeepSyn then builds a deep neural network with the Batch Normalization mechanism to predict synergy scores using the cell line embeddings and drug features. PRODeepSyn achieves the lowest root mean square error of 15.08 and the highest Pearson correlation coefficient of 0.75, outperforming two deep learning methods and four machine learning methods. On the classification task, PRODeepSyn achieves an area under the receiver operator characteristics curve of 0.90, an area under the precision-recall curve of 0.63 and a Cohen's Kappa of 0.53. In the ablation study, we find that using the multi-omics data and the integrated PPI network's information both can improve the prediction results. Additionally, the case study demonstrates the consistency between PRODeepSyn and previous studies.

Predicting cell line-specific synergistic drug combinations through a relational graph convolutional network with attention mechanism.

Identifying synergistic drug combinations (SDCs) is a great challenge due to the combinatorial complexity and the fact that SDC is cell line specific. The existing computational methods either did not consider the cell line specificity of SDC, or did not perform well by building model for each cell line independently. In this paper, we present a novel encoder-decoder network named SDCNet for predicting cell line-specific SDCs. SDCNet learns common patterns across different cell lines as well as cell line-specific features in one model for drug combinations. This is realized by considering the SDC graphs of different cell lines as a relational graph, and constructing a relational graph convolutional network (R-GCN) as the encoder to learn and fuse the deep representations of drugs for different cell lines. An attention mechanism is devised to integrate the drug features from different layers of the R-GCN according to their relative importance so that representation learning is further enhanced. The common patterns are exploited through partial parameter sharing in cell line-specific decoders, which not only reconstruct the known SDCs but also predict new ones for each cell line. Experiments on various datasets demonstrate that SDCNet is superior to state-of-the-art methods and is also robust when generalized to new cell lines that are different from the training ones. Finally, the case study again confirms the effectiveness of our method in predicting novel reliable cell line-specific SDCs.

Dual action of benzaldehydes: Inhibiting quorum sensing and enhancing antibiotic efficacy for controlling Pseudomonas aeruginosa biofilms.

Quorum sensing (QS) has a central role in biofilm lifestyle and antimicrobial resistance, and disrupting these signaling pathways is a promising strategy to control bacterial pathogenicity and virulence. In this study, the efficacy of three structurally related benzaldehydes (4-hydroxybenzaldehyde, 4-hydroxy-3-methoxybenzaldehyde (vanillin) and 4-hydroxy-3,5-dimethoxybenzaldehyde (syringaldehyde)) in disrupting the las and pqs systems of Pseudomonas aeruginosa was investigated using bioreporter strains and computational simulations. Additionally, these benzaldehydes were combined with tobramycin and ciprofloxacin antibiotics to evaluate their ability to increase antibiotic efficacy in preventing and eradicating P. aeruginosa biofilms. To this end, the total biomass, metabolic activity and culturability of the biofilm cells were determined. In vitro assays results indicated that the aromatic aldehydes have potential to inhibit the las and pqs systems by > 80 %. Molecular docking studies supported these findings, revealing the aldehydes binding in the same pocket as the natural ligands or receptor proteins (LasR, PQSA, PQSE, PQSR). Benzaldehydes were shown to act as virulence factor attenuators, with vanillin achieving a 48 % reduction in pyocyanin production. The benzaldehyde-tobramycin combination led not only to a 60 % reduction in biomass production but also to a 90 % reduction in the metabolic activity of established biofilms. A similar result was observed when benzaldehydes were combined with ciprofloxacin. 4-Hydroxybenzaldehyde demonstrated relevant action in increasing biofilm susceptibility to ciprofloxacin, resulting in a 65 % reduction in biomass. This study discloses, for the first time, that the benzaldehydes studied are potent QS inhibitors and also enhancers of antibiotics antibiofilm activity against P. aeruginosa.

Enhancing bactericidal activities of ciprofloxacin by targeting the trans-translation system that is involved in stress responses in Klebsiella pneumoniae.

Although the trans-translation system is a promising target for antcibiotic development, its antibacterial mechanism in Klebsiella pneumoniae (KP) is unclear. Considering that tmRNA was the core component of trans-translation, this study firstly investigated phenotypic changes caused by various environmental stresses in KP lacking trans-translation activities (tmRNA-deleted), and then aimed to evaluate antibacterial activities of the trans-translation-targeting antibiotic combination (tobramycin/ciprofloxacin) in clinical KP isolates based on inhibition activities of aminoglycosides against trans-translation. We found that the tmRNA-deleted strain P4325/ΔssrA was significantly more susceptible than the wild-type KP strain P4325 under environments with hypertonicity (0.5 and 1 M NaCl), hydrogen peroxide (40 mM), and UV irradiation. No significant differences in biofilm formation and survivals under human serum were observed between P4325/ΔssrA and P4325. tmRNA deletion caused twofold lower MIC values for aminoglycosides. As for the membrane permeability, tmRNA deletion increased ethidium bromide (EtBr) uptake of KP in the presence or absence of verapamil and carbonyl cyanide-m-chlorophenylhydrazone (CCCP), decreased EtBr uptake in presence of reserpine in P4325/ΔssrA, and reduced EtBr efflux in P4325/ΔssrA in the presence of CCCP. The time-kill curve and in vitro experiments revealed significant bactericidal activities of the tmRNA-targeting aminoglycoside-based antibiotic combination (tobramycin/ciprofloxacin). Thus, the corresponding tmRNA-targeting antibiotic combinations (aminoglycoside-based) might be effective and promising treatment options against multi-drug resistant KP.

Pichia kurtzmaniana f.a. sp. nov., with the transfer of eight Candida species to Pichia.

Three yeast strains belonging to the ascomycetous yeast genus Pichia were isolated from two soil samples from Yunnan and Guizhou provinces and a marine water sample from Liaoning province, PR China. Phylogenetic analyses based on the sequences of the D1/D2 domains of the large subunit(LSU) rRNA gene and the internal transcribed spacer (ITS) region indicate that these three strains, together with 12 additional strains isolated from various substrates collected in different regions or countries of the world, represent a novel species of the genus Pichia, for which the name Pichia kurtzmaniana sp. nov. (holotype: strain CGMCC 2.7213) is proposed. The novel species differs from its close relatives Candida californica by eight (1.5 %) and 26 (11.1 %) mismatches in the D1/D2 domains and the ITS region, respectively; and from Pichia chibodasensis by 11 (2.1 %) and 20 (8.7 %) mismatches in the D1/D2 domains and the ITS region, respectively. In addition, eight Candida species which belong to the Pichia clade are transferred to the genus Pichia, resulting in the proposal of the following new combinations: Pichia cabralensis comb. nov., Pichia californica comb. nov., Pichia ethanolica comb. nov., Pichia inconspicua comb. nov., Pichia phayaonensis comb. nov., Pichia pseudolambica comb. nov., Pichia rugopelliculosa comb. nov., and Pichia thaimueangensis comb. nov.

In vitro efficacy of combinations of antibiotics used in clinical practice on clinical isolates of Helicobacter pylori.

BACKGROUND: The main antibiotics used against Helicobacter pylori have been chosen empirically over time, with few preclinical studies to provide support. The rise in resistance to some of these antibiotics is prompting a reassessment of their use. This work aimed to evaluate the in vitro efficacy of 2 × 2 combinations of the most widely used antibiotics against H. pylori. MATERIALS AND METHODS: J99 reference strains and 19 clinical isolates of H. pylori with various antibiotic resistance phenotypes were used. Minimum inhibitory concentrations were carried out using the microdilution method in 96-well plates. The activity of 15 possible combinations of two antibiotics including amoxicillin, clarithromycin (CLA), levofloxacin, rifampicin, tetracycline, and metronidazole was determined for all strains by the checkerboard method. A mean fractional inhibitory concentration index (FICmean) was calculated for each combination and strain and the type of pharmacodynamic interaction was considered as synergic if FICmean ≤ 0.5, additive if 0.5 < FICmean ≤ 1, indifferent if 1 < FICmean < 4 or antagonistic if FICmean ≥ 4. RESULTS: Most of the 285 pharmacodynamic interactions tested with clinical strains were close to additivity (average FICmean = 0.89 [0.38-1.28]). No interaction was found to be antagonistic. When two antibiotics to which a strain was resistant were combined, the concentrations required to inhibit bacterial growth were higher than their respective breakpoints. CONCLUSION: The present results have shown that in vitro, the different antibiotics used in therapeutics have additive effects. The addition of the effects of two antibiotics to which a strain was resistant was not sufficient to inhibit bacterial growth. In probabilistic treatment, the choice of antibiotics to combine should therefore be based on the local epidemiology of resistance, and on susceptibility testing in the case of CLA therapy, so that at least one antibiotic to which the strain is susceptible is used.

Phenotypic and molecular characterization of extended spectrum- and metallo- beta lactamase producing Pseudomonas aeruginosa clinical isolates from Egypt.

BACKGROUND: Antimicrobial resistance among Pseudomonas aeruginosa (P. aeruginosa), a leading cause of nosocomial infections worldwide, is escalating. This study investigated the prevalence of extended-spectrum ß-lactamases (ESBLs) and metallo-ß-lactamases (MBLs) among 104 P. aeruginosa clinical isolates from Alexandria Main University Hospital, Alexandria, Egypt. METHODS: Antimicrobial susceptibility testing was performed using agar dilution technique, or broth microdilution method in case of colistin. ESBL and MBL prevalence was assessed phenotypically and genotypically using polymerase chain reaction (PCR). The role of plasmids in mediating resistance to extended-spectrum ß-lactams was studied via transformation technique using plasmids isolated from ceftazidime-resistant isolates. RESULTS: Antimicrobial susceptibility testing revealed alarming resistance rates to carbapenems, cephalosporins, and fluoroquinolones. Using PCR as the gold standard, phenotypic methods underestimated ESBL production while overestimating MBL production. Eighty-five isolates (81.7%) possessed only ESBL encoding genes, among which 69 isolates harbored a single ESBL gene [blaOXA-10 (n = 67) and blaPER (n = 2)]. Four ESBL-genotype combinations were detected: blaPER + blaOXA-10 (n = 8), blaVEB-1 + blaOXA-10 (n = 6), blaPSE + blaOXA-10 (n = 1), and blaPER + blaVEB-1 + blaOXA-10 (n = 1). Three isolates (2.9%) possessed only the MBL encoding gene blaVIM. Three ESBL + MBL- genotype combinations: blaOXA-10 + blaAIM, blaOXA-10 + blaVIM, and blaPER + blaOXA-10 + blaAIM were detected in 2, 1 and 1 isolate(s), respectively. Five plasmid preparations harboring blaVEB-1 and blaOXA-10 were successfully transformed into chemically competent Escherichia coli DH5α with transformation efficiencies ranging between 6.8 × 10 3 and 3.7 × 10 4 CFU/µg DNA plasmid. Selected tested transformants were ceftazidime-resistant and harbored plasmids carrying blaOXA-10. CONCLUSIONS: The study highlights the importance of the expeditious characterization of ESBLs and MBLs using genotypic methods among P. aeruginosa clinical isolates to hinder the development and dissemination of multidrug resistant strains.