Exploring and targeting potential druggable antimicrobial resistance targets ArgS, SecY, and MurA in Staphylococcus sciuri with TCM inhibitors through a subtractive genomics strategy.

Staphylococcus sciuri (also currently Mammaliicoccus sciuri) are anaerobic facultative and non-motile bacteria that cause significant human pathogenesis such as endocarditis, wound infections, peritonitis, UTI, and septic shock. Methicillin-resistant S. sciuri (MRSS) strains also infects animals that include healthy broilers, cattle, dogs, and pigs. The emergence of MRSS strains thereby poses a serious health threat and thrives the scientific community towards novel treatment options. Herein, we investigated the druggable genome of S. sciuri by employing subtractive genomics that resulted in seven genes/proteins where only three of them were predicted as final targets. Further mining the literature showed that the ArgS (WP_058610923), SecY (WP_058611897), and MurA (WP_058612677) are involved in the multi-drug resistance phenomenon. After constructing and verifying the 3D protein homology models, a screening process was carried out using a library of Traditional Chinese Medicine compounds (consisting of 36,043 compounds). The molecular docking and simulation studies revealed the physicochemical stability parameters of the docked TCM inhibitors in the druggable cavities of each protein target by identifying their druggability potential and maximum hydrogen bonding interactions. The simulated receptor-ligand complexes showed the conformational changes and stability index of the secondary structure elements. The root mean square deviation (RMSD) graph showed fluctuations due to structural changes in the helix-coil-helix and beta-turn-beta changes at specific points where the pattern of the RMSD and root mean square fluctuation (RMSF) (< 1.0 Å) support any major domain shifts within the structural framework of the protein-ligand complex and placement of ligand was well complemented within the binding site. The ß-factor values demonstrated instability at few points while the radius of gyration for structural compactness as a time function for the 100-ns simulation of protein-ligand complexes showed favorable average values and denoted the stability of all complexes. It is assumed that such findings might facilitate researchers to robustly discover and develop effective therapeutics against S. sciuri alongside other enteric infections.

Genomic landscape of the emerging XDR Salmonella Typhi for mining druggable targets clpP, hisH, folP and gpmI and screening of novel TCM inhibitors, molecular docking and simulation analyses.

Typhoid fever is transmitted by ingestion of polluted water, contaminated food, and stool of typhoid-infected individuals, mostly in developing countries with poor hygienic environments. To find novel therapeutic targets and inhibitors, We employed a subtractive genomics strategy towards Salmonella Typhi and the complete genomes of eight strains were primarily subjected to the EDGAR tool to predict the core genome (n = 3207). Human non-homology (n = 2450) was followed by essential genes identification (n = 37). The STRING database predicted maximum protein-protein interactions, followed by cellular localization. The virulent/immunogenic ability of predicted genes were checked to differentiate drug and vaccine targets. Furthermore, the 3D models of the identified putative proteins encoded by the respective genes were constructed and subjected to druggability analyses where only "highly druggable" proteins were selected for molecular docking and simulation analyses. The putative targets ATP-dependent CLP protease proteolytic subunit, Imidazole glycerol phosphate synthase hisH, 7,8-dihydropteroate synthase folP and 2,3-bisphosphoglycerate-independent phosphoglycerate mutase gpmI were screened against a drug-like library (n = 12,000) and top hits were selected based on H-bonds, RMSD and energy scores. Finally, the ADMET properties for novel inhibitors ZINC19340748, ZINC09319798, ZINC00494142, ZINC32918650 were optimized followed by binding free energy (MM/PBSA) calculation for ligand-receptor complexes. The findings of this work are expected to aid in expediting the identification of novel protein targets and inhibitors in combating typhoid Salmonellosis, in addition to the already existing therapies.

Prediction and interpretation of antibiotic-resistance genes occurrence at recreational beaches using machine learning models.

Antibiotic-resistant bacteria and antibiotic resistance genes (ARGs) are pollutants of worldwide concern that seriously threaten public health and ecosystems. Machine learning (ML) prediction models have been applied to predict ARGs in beach waters. However, the existing studies were conducted at a single location and had low prediction performance. Moreover, ML models are "black boxes" that do not reveal their predictions' internal nuances and mechanisms. This lack of transparency and trust can result in serious consequences when using these models in high-stakes decisions. In this study, we developed a gradient boosted regression tree based (GBRT) ML model and then described its behavior using six explainable artificial intelligence (XAI) model-agnostic explanation methods. We used hydro-meteorological and qPCR data from the beaches in South Korea and Pakistan and developed ML prediction models for aac (6'-lb-cr), sul1, and tetX with 10-fold time-blocked cross-validation performances of 4.9, 2.06 and 4.4 root mean squared logarithmic error, respectively. We then analyzed the local and global behavior of the developed ML model using four interpretation methods. The developed ML models showed that water temperature, precipitation and tide are the most important predictors for prediction of ARGs at recreational beaches. We show that the model-agnostic interpretation methods not only explain the behavior of the ML model but also provide insights into the behavior of the ML model under new unseen conditions. Moreover, these post-processing techniques can be a debugging tool for ML-based modeling.

Thermodynamics, kinetics and optimization of catalytic behavior of polyacrylamide-entrapped carboxymethyl cellulase (CMCase) for prospective industrial use.

In the current study, kinetic and thermodynamic parameters of free and polyacrylamide-immobilized CMCase were analyzed. The maximum immobilization yield of 34 ± 1.7% was achieved at 11% acrylamide. The enthalpy of activation (ΔH) of free and immobilized enzyme was found to be 13.61 and 0.29 kJ mol-1, respectively. Irreversible inactivation energy of free and immobilized CMCase was 96.43 and 99.01 kJ mol-1, respectively. Similarly, the enthalpy of deactivation (ΔHd) values for free and immobilized enzyme were found to be in the range of 93.51-93.76 kJ mol-1 and 96.08-96.33 kJ mol-1, respectively. Michaelis-Menten constant (Km) increased from 1.267 ± 0.06 to 1.5891 ± 0.07 mg ml-1 and the maximum reaction rate (Vmax) value decreased (8319.47 ± 416 to 5643.34 ± 282 U ml-1 min-1) after immobilization. Due to wide pH and temperature stability profile with sufficient reusing efficiency up to three successive cycles, the immobilized CMCase might be useful for various industrial processes.

Chitosan hydrogel microspheres: an effective covalent matrix for crosslinking of soluble dextranase to increase stability and recycling efficiency.

Dextranase is a unique biocatalyst that has high specificity and stereo-selectivity towards a complex biopolymer known as dextran. Dextranase has wide industrial application, but most of the time harsh environmental conditions adversely affect the functionality and stability of the enzyme. To overcome this issue, a covalent cross-linking immobilization method was adapted in the current study utilizing a nontoxic and biocompatible matrix known as chitosan. Chitosan hydrogel microspheres were synthesized using chitosan which exhibited noteworthy physical and mechanical strength. After treatment with glutaraldehyde, chitosan hydrogel microspheres were used for immobilization of dextranase. The kinetic characteristics of immobilized dextranase were compared with that of the soluble enzyme. A shift in optimum pH and temperature from 7.0 to 7.5 and 50 to 60 °C was observed after immobilization, respectively. Recycling efficiency, thermal stability, and activation energy distinctly improved after immobilization, whereas anchoring of substrate at the active site of the soluble dextranase exhibited an increase in K m with no change in V max after crosslinking. This technique involves the reduction in the size of carrier molecules (microspheres) that provide a larger surface area for improved immobilization efficiency. Therefore, it is concluded that increased stability and reusability of this immobilized biocatalyst makes it a promising aspirant for the utilization at commercial level.

Complex Class 1 Integron Carrying qnrB62 and blaVIM-2 in a Citrobacter freundii Clinical Isolate.

The coexistence of qnrB62 and blaVIM-2 was detected in a Citrobacter clinical isolate. The reduced fluoroquinolone susceptibility is attributable to qnrB62, mutations of quinolone-resistance-determining regions, and an efflux pump or pumps. The genetic context surrounding chromosomal qnrB62 was a novel complex class 1 integron (In1184::ISCR1::qnrB62) containing a unique gene array (blaVIM-2-aacA4'-8-gucD). An 18-nucleotide deletion at the 3' end of the pspA gene [pspA(Δ18)], upstream of qnrB62, and an inverted repeat region (IRR2) were detected in In1184::ISCR1::qnrB62, indicating past transposition events.

Why cannot a ß-lactamase gene be detected using an efficient molecular diagnostic method?

OBJECTIVE: Fast detection of ß-lactamase (bla) genes can minimize the spread of antibiotic resistance. Although several molecular diagnostic methods have been developed to detect limited bla gene types, these methods have significant limitations, such as their failure to detect almost all clinically available bla genes. We have evaluated a further refinement of our fast and accurate molecular method, developed to overcome these limitations, using clinical isolates. METHODS: We have recently developed the efficient large-scale bla detection method (large-scaleblaFinder) that can detect bla gene types including almost all clinically available 1,352 bla genes with perfect specificity and sensitivity. Using this method, we have evaluated a further refinement of this method using clinical isolates provided by International Health Management Associates, Inc. (Schaumburg, Illinois, USA). Results were interpreted in a blinded manner by researchers who did not know any information on bla genes harbored by these isolates. RESULTS: With only one exception, the large-scaleblaFinder detected all bla genes identified by the provider using microarray and multiplex PCR. In one of the Escherichia coli test isolates, a blaDHA-1 gene was detected using the multiplex PCR assay but it was not detected using the large-scaleblaFinder. CONCLUSION: The truncation of a blaDHA-1 gene is an important reason for an efficient molecular diagnostic method (large-scaleblaFinder) not to detect the bla gene.

Continuous degradation of maltose: improvement in stability and catalytic properties of maltase (α-glucosidase) through immobilization using agar-agar gel as a support.

Maltose degrading enzyme was immobilized within agar-agar support via entrapment method due to its industrial utilization. The maximum immobilization efficiency (82.77%) was achieved using 4.0% agar-agar keeping the diameter of bead up to 3.0 mm. The matrix entrapment showed maximum catalytic activity at pH 7.0 and temperature 65 °C. Substrate saturation kinetics showed that the K m of immobilized enzyme increased from 1.717 to 2.117 mM ml(-1) where as Vmax decreased from 8,411 to 7,450 U ml(-1 )min(-1) as compared to free enzyme. The immobilization significantly increased the stability of maltase against various temperatures and immobilized maltase retain 100% of its original activity after 2 h at 50 °C, whereas the free maltase only showed 60% residual activity under same condition. The reusability of entrapped maltase showed activity up to 12 cycles and retained 50% of activity even after 5th cycle. Storage stability of agar entrapped maltase retain 73% of its initial activity even after 2 months when stored at 30 °C while free enzyme showed only 37% activity at same storage conditions.

Structural basis for carbapenem-hydrolyzing mechanisms of carbapenemases conferring antibiotic resistance.

Carbapenems (imipenem, meropenem, biapenem, ertapenem, and doripenem) are ß-lactam antimicrobial agents. Because carbapenems have the broadest spectra among all ß-lactams and are primarily used to treat infections by multi-resistant Gram-negative bacteria, the emergence and spread of carbapenemases became a major public health concern. Carbapenemases are the most versatile family of ß-lactamases that are able to hydrolyze carbapenems and many other ß-lactams. According to the dependency of divalent cations for enzyme activation, carbapenemases can be divided into metallo-carbapenemases (zinc-dependent class B) and non-metallo-carbapenemases (zinc-independent classes A, C, and D). Many studies have provided various carbapenemase structures. Here we present a comprehensive and systematic review of three-dimensional structures of carbapenemase-carbapenem complexes as well as those of carbapenemases. We update recent studies in understanding the enzymatic mechanism of each class of carbapenemase, and summarize structural insights about regions and residues that are important in acquiring the carbapenemase activity.

Structure of ADC-68, a novel carbapenem-hydrolyzing class C extended-spectrum ß-lactamase isolated from Acinetobacter baumannii.

Outbreaks of multidrug-resistant bacterial infections have become more frequent worldwide owing to the emergence of several different classes of ß-lactamases. In this study, the molecular, biochemical and structural characteristics of an Acinetobacter-derived cephalosporinase (ADC)-type class C ß-lactamase, ADC-68, isolated from the carbapenem-resistant A. baumannii D015 were investigated. The blaADC-68 gene which encodes ADC-68 was confirmed to exist on the chromosome via Southern blot analysis and draft genome sequencing. The catalytic kinetics of ß-lactams and their MICs (minimum inhibitory concentrations) for A. baumannii D015 and purified ADC-68 (a carbapenemase obtained from this strain) were assessed: the strain was resistant to penicillins, narrow-spectrum and extended-spectrum cephalosporins, and carbapenems, which were hydrolyzed by ADC-68. The crystal structure of ADC-68 was determined at a resolution of 1.8 Å. The structure of ADC-68 was compared with that of ADC-1 (a non-carbapenemase); differences were found in the central part of the Ω-loop and the C-loop constituting the edge of the R1 and R2 subsites and are close to the catalytic serine residue Ser66. The ADC-68 C-loop was stabilized in the open conformation of the upper R2 subsite and could better accommodate carbapenems with larger R2 side chains. Furthermore, a wide-open conformation of the R2-loop allowed ADC-68 to bind to and hydrolyze extended-spectrum cephalosporins. Therefore, ADC-68 had enhanced catalytic efficiency against these clinically important ß-lactams (extended-spectrum cephalosporins and carbapenems). ADC-68 is the first reported enzyme among the chromosomal class C ß-lactamases to possess class C extended-spectrum ß-lactamase and carbapenemase activities.

Comparative genomic analysis of Bacillus atrophaeus HAB-5 reveals genes associated with antimicrobial and plant growth-promoting activities.

Bacillus atrophaeus HAB-5 is a plant growth-promoting rhizobacterium (PGPR) that exhibits several biotechnological traits, such as enhancing plant growth, colonizing the rhizosphere, and engaging in biocontrol activities. In this study, we conducted whole-genome sequencing of B. atrophaeus HAB-5 using the single-molecule real-time (SMRT) sequencing platform by Pacific Biosciences (PacBio; United States), which has a circular chromosome with a total length of 4,083,597 bp and a G + C content of 44.21%. The comparative genomic analysis of B. atrophaeus HAB-5 with other strains, Bacillus amyloliquefaciens DSM7, B. atrophaeus SRCM101359, Bacillus velezensis FZB42, B. velezensis HAB-2, and Bacillus subtilis 168, revealed that these strains share 2,465 CDSs, while 599 CDSs are exclusive to the B. atrophaeus HAB-5 strain. Many gene clusters in the B. atrophaeus HAB-5 genome are associated with the production of antimicrobial lipopeptides and polypeptides. These gene clusters comprise distinct enzymes that encode three NRPs, two Transat-Pks, one terpene, one lanthipeptide, one T3PKS, one Ripp, and one thiopeptide. In addition to the likely IAA-producing genes (trpA, trpB, trpC, trpD, trpE, trpS, ywkB, miaA, and nadE), there are probable genes that produce volatile chemicals (acoA, acoB, acoR, acuB, and acuC). Moreover, HAB-5 contained genes linked to iron transportation (fbpA, fetB, feuC, feuB, feuA, and fecD), sulfur metabolism (cysC, sat, cysK, cysS, and sulP), phosphorus solubilization (ispH, pstA, pstC, pstS, pstB, gltP, and phoH), and nitrogen fixation (nif3-like, gltP, gltX, glnR, glnA, nadR, nirB, nirD, nasD, narl, narH, narJ, and nark). In conclusion, this study provides a comprehensive genomic analysis of B. atrophaeus HAB-5, pinpointing the genes and genomic regions linked to the antimicrobial properties of the strain. These findings advance our knowledge of the genetic basis of the antimicrobial properties of B. atrophaeus and imply that HAB-5 may employ a variety of commercial biopesticides and biofertilizers as a substitute strategy to increase agricultural output and manage a variety of plant diseases.

Antibiotic resistance genes prevalence prediction and interpretation in beaches affected by urban wastewater discharge.

Background: The annual death toll of over 1.2 million worldwide is attributed to infections caused by resistant bacteria, driven by the significant impact of antibiotic misuse and overuse in spreading these bacteria and their associated antibiotic resistance genes (ARGs). While limited data suggest the presence of ARGs in beach environments, efficient prediction tools are needed for monitoring and detecting ARGs to ensure public health safety. This study aims to develop interpretable machine learning methods for predicting ARGs in beach waters, addressing the challenge of black-box models and enhancing our understanding of their internal mechanisms. Methods: In this study, we systematically collected beach water samples and subsequently isolated bacteria from these samples using various differential and selective media supplemented with different antibiotics. Resistance profiles of bacteria were determined by using Kirby-Bauer disk diffusion method. Further, ARGs were enumerated by using the quantitative polymerase chain reaction (qPCR) to detect and quantify ARGs. The obtained qPCR data and hydro-meteorological were used to create an ML model with high prediction performance and we further used two explainable artificial intelligence (xAI) model-agnostic interpretation methods to describe the internal behavior of ML model. Results: Using qPCR, we detected blaCTX-M, blaNDM, blaCMY, blaOXA, blatetX, blasul1, and blaaac(6'-Ib-cr) in the beach waters. Further, we developed ML prediction models for blaaac(6'-Ib-cr), blasul1, and blatetX using the hydro-metrological and qPCR-derived data and the models demonstrated strong performance, with R2 values of 0.957, 0.997, and 0.976, respectively. Conclusions: Our findings show that environmental factors, such as water temperature, precipitation, and tide, are among the important predictors of the abundance of resistance genes at beaches.

In Vitro Analysis of Cytotoxic Activities of Monotheca buxifolia Targeting WNT/ß-Catenin Genes in Breast Cancer Cells.

Breast cancer (BC) is known to be the most common malignancy among women throughout the world. Plant-derived natural products have been recognized as a great source of anticancer drugs. In this study, the efficacy and anticancer potential of the methanolic extract of Monotheca buxifolia leaves using human breast cancer cells targeting WNT/ß-catenin signaling was evaluated. We used methanolic and other (chloroform, ethyl acetate, butanol, and aqueous) extracts to discover their potential cytotoxicity on breast cancer cells (MCF-7). Among these, the methanol showed significant activity in the inhibition of the proliferation of cancer cells because of the presence of bioactive compounds, including phenols and flavonoids, detected by a Fourier transform infrared spectrophotometer and by gas chromatography mass spectrometry. The cytotoxic effect of the plant extract on the MCF-7 cells was examined by MTT and acid phosphatase assays. Real-time PCR analysis was performed to measure the mRNA expression of WNT-3a and ß-catenin, along with Caspase-1,-3,-7, and -9 in MCF-7 cells. The IC50 value of the extract was found to be 232 µg/mL and 173 µg/mL in the MTT and acid phosphatase assays, respectively. Dose selection (100 and 300 µg/mL) was performed for real-time PCR, Annexin V/PI analysis, and Western blotting using Doxorubicin as a positive control. The extract at 100 µg/mL significantly upregulated caspases and downregulated the WNT-3a and ß-catenin gene in MCF-7 cells. Western blot analysis further confirmed the dysregulations of the WNT signaling component (*** p< 0.0001). The results showed an increase in the number of dead cells in methanolic extract-treated cells in the Annexin V/PI analysis. Our study concludes that M. buxifolia may serve as an effective anticancer mediator through gene modulation that targets WNT/ß-catenin signaling, and it can be further characterized using more powerful experimental and computational tools.

Triple-negative breast cancer: epidemiology, molecular mechanisms, and modern vaccine-based treatment strategies.

Long-standing scarcity of efficacious treatments and tumor heterogeneity have contributed to triple-negative breast cancer (TNBC), a subtype with a poor prognosis and aggressive behavior that accounts for 10-15% of all new cases of breast cancer. TNBC is characterized by the absence of progesterone and estrogen receptor expression and lacks gene amplification or overexpression of HER2. Genomic sequencing has detected that the unique mutational profile of both the somatic and germline modifications in TNBC is staggeringly dissimilar from other breast tumor subtypes. The clinical utility of sequencing germline BRCA1/2 genes has been well established in TNBC. Nevertheless, reports regarding the penetrance and risk of other susceptibility genes are relatively scarce. Recurring mutations (e.g., TP53 and PI3KCA mutations) occur together with rare mutations in TNBC, and the shared effects of genomic modifications drive its progression. Given the heterogeneity and complexity of this disease, a clinical understanding of the genomic modifications in TNBC can pave an innovative way toward its therapy. In this review, we summarized the most recent discoveries associated with the underlying biology of developmental signaling pathways in TNBC. We also summarize the recent advancements in genetics and epidemiology and discuss state-of-the-art vaccine-based therapeutic strategies for TNBC that will enable tailored therapeutics.

Analysis of the nasopharyngeal microbiome and respiratory pathogens in COVID-19 patients from Saudi Arabia.

BACKGROUND: Infection with SARS-CoV-2 may perturb normal microbiota, leading to secondary infections that can complicate the viral disease. The aim of this study was to probe the alteration of nasopharyngeal (NP) microbiota in the context of SARS-CoV-2 infection and obesity and to identify other respiratory pathogens among COVID-19 cases that may affect patients' health. METHODS: A total of 107 NP swabs, including 22 from control subjects and 85 from COVID-19 patients, were processed for 6S amplicon sequencing. The respiratory pathogens causing secondary infections were identified by RT-PCR assay, using a kit that contained specific primers and probes combinations to amplify 33 known respiratory pathogens. RESULTS: No significant (p > 0.05) difference was observed in the alpha and beta diversity analysis, but specific taxa differed significantly between the control and COVID-19 patient groups. Genera of Sphingomonas, Kurthia, Microbacterium, Methylobacterium, Brevibacillus, Bacillus, Acinetobacter, Lactococcus, and Haemophilus was significantly abundant (p < 0.05) in COVID-19 patients compared with a healthy control group. Staphylococcus was found in relatively high abundance (35.7 %) in the COVID-19 patient groups, mainly those treated with antibiotics. A relatively high percentage of Streptococcus was detected in COVID-19 patient groups with obesity or other comorbidities. Respiratory pathogens, including Staphylococcus aureus, Streptococcus pneumoniae, Haemophilus influenzae, Moraxella catarrhalis, and Salmonella species, along with Pneumocystis jirovecii fungal species were detected by RT-PCR mainly in the COVID-19 patients. Klebsiella pneumoniae was commonly found in most of the samples from the control and COVID-19 patients. Four COVID-19 patients had viral coinfections with human adenovirus, human rhinovirus, enterovirus, and human parainfluenza virus 1. CONCLUSIONS: Overall, no substantial difference was observed in the predominant NP bacterial community, but specific taxa were significantly changed between the healthy control and COVID-19 patients. Comparatively, an increased number of respiratory pathogens were identified in COVID-19 patients, and NP colonization by K. pneumoniae was probably occurring in the local population.

Temporal Variation of Meropenem Resistance in E. coli Isolated from Sewage Water in Islamabad, Pakistan.

The WHO has classified carbapenem-resistant Enterobacteriaceae in most critical priority pathogens that pose a threat to human health. The present study investigated the prevalence of meropenem-resistant Escherichia coli (E. coli) in relation to its temporal variation in different seasons along with its resistance markers in sewage water. E. coli was selected on MacConkey agar containing meropenem (3 µg/mL). There were 27% of sites/sewage samples carrying meropenem-resistant E. coli. All E. coli were confirmed through the amplification of the uidA gene. All isolated E. coli were multidrug-resistant (MDR), and among them, 51% were extensively drug-resistant (XDR). An antibiogram determined against 15 antibiotics showed the highest resistance to ampicillin and cefotaxime (98% each) and lowest resistance to fosfomycin (2%). Phylogenetic groups and resistance gene analysis through PCR showed a significant co-occurrence of carbapenemases with extended spectrum beta lactamases (ESBLs), plasmid encoded quinolone, and colistin resistance genes. The higher number of resistance genes in E. coli isolates in community sewage indirectly indicate that these isolates circulate abundantly in the community.

Prediction of antimicrobial minimal inhibitory concentrations for Neisseria gonorrhoeae using machine learning models.

The lowest concentration of an antimicrobial agent that can inhibit the visible growth of a microorganism after overnight incubation is called as minimum inhibitory concentration (MIC) and the drug prescriptions are made on the basis of MIC data to ensure successful treatment outcomes. Therefore, reliable antimicrobial susceptibility data is crucial, and it will help clinicians about which drug to prescribe. Although few prediction studies based on strategies have been conducted, however, no single machine learning (ML) modelling has been carried out to predict MICs in N. gonorrhoeae. In this study, we propose a ML based approach that can predict MICs of a specific antibiotic using unitigs sequences data. We retrieved N. gonorrhoeae genomes from European Nucleotide Archive and NCBI and analysed them combined with their respective MIC data for cefixime, ciprofloxacin, and azithromycin and then we constructed unitigs by using de Brujin graphs. We built and compared 35 different ML regression models to predict MICs. Our results demonstrate that RandomForest and CATBoost models showed best performance in predicting MICs of the three antibiotics. The coefficient of determination, R2, (a statistical measure of how well the regression predictions approximate the real data points) for cefixime, ciprofloxacin, and azithromycin was 0.75787, 0.77241, and 0.79009 respectively using RandomForest. For CATBoost model, the R2 value was 0.74570, 0.77393, and 0.79317 for cefixime, ciprofloxacin, and azithromycin respectively. Lastly, using feature importance, we explore the important genomic regions identified by the models for predicting MICs. The major mutations which are responsible for resistance against these three antibiotics were chosen by ML models as a top feature in case of each antibiotics. CATBoost, DecisionTree, GradientBoosting, and RandomForest regression models chose the same unitigs which are responsible for resistance. This unitigs-based strategy for developing models for MIC prediction, clinical diagnostics, and surveillance can be applicable for other critical bacterial pathogens.

Application of Decision-Tree-Based Machine Learning Algorithms for Prediction of Antimicrobial Resistance.

Timely and efficacious antibiotic treatment depends on precise and quick in silico antimicrobial-resistance predictions. Limited treatment choices due to antimicrobial resistance (AMR) highlight the necessity to optimize the available diagnostics. AMR can be explicitly anticipated on the basis of genome sequence. In this study, we used transcriptomes of 410 multidrug-resistant isolates of Pseudomonas aeruginosa. We trained 10 machine learning (ML) classifiers on the basis of data on gene expression (GEXP) information and generated predictive models for meropenem, ciprofloxacin, and ceftazidime drugs. Among all the used ML models, four models showed high F1-score, accuracy, precision, and specificity compared with the other models. However, RandomForestClassifier showed a moderate F1-score (0.6), precision (0.61), and specificity (0.625) for ciprofloxacin. In the case of ceftazidime, RidgeClassifier performed well and showed F1-score (0.652), precision (0.654), and specificity (0.652) values. For meropenem, KNeighborsClassifier exhibited moderate F1-score (0.629), precision (0.629), and specificity (0.629). Among these three antibiotics, GEXP data on meropenem and ceftazidime improved diagnostic performance. The findings will pave the way for the establishment of a resistance profiling tool that can predict AMR on the basis of transcriptomic markers.

Bacterial diversity and the antimicrobial resistome in the southwestern highlands of Saudi Arabia.

Soil is a reservoir of microbial diversity and the most supportive habitat for acquiring and transmitting antimicrobial resistance. Resistance transfer usually occurs from animal to soil and vice versa, and it may ultimately appear in clinical pathogens. In this study, the southwestern highlands of Saudi Arabia were studied to assess the bacterial diversity and antimicrobial resistance that could be affected by the continuous development of tourism in the region. Such effects could have a long-lasting impact on the local environment and community. Culture-dependent, quantitative polymerase chain reaction (qPCR), and shotgun sequencing-based metagenomic approaches were used to evaluate the diversity, functional capabilities, and antimicrobial resistance of bacteria isolated from collected soil samples. Bacterial communities in the southwestern highlands were mainly composed of Proteobacteria, Bacteroidetes, and Actinobacteria. A total of 102 antimicrobial resistance genes (ARGs) and variants were identified in the soil microbiota and were mainly associated with multidrug resistance, followed by macrolide, tetracycline, glycopeptide, bacitracin, and beta-lactam antibiotic resistance. The mechanisms of resistance included efflux, antibiotic target alteration, and antibiotic inactivation. qPCR confirmed the detection of 18 clinically important ARGs. In addition, half of the 49 identified isolates were phenotypically resistant to at least one of the 15 antibiotics tested. Overall, ARGs and indicator genes of anthropogenic activities (human-mitochondrial [hmt] gene and integron-integrase [int1]) were found in relatively lower abundance. Along with a high diversity of bacterial communities, variation was observed in the relative abundance of bacterial taxa among sampling sites in the southwestern highlands of Saudi Arabia.

Insights into genome evolution, pan-genome, and phylogenetic implication through mitochondrial genome sequence of Naegleria fowleri species.

In the current study, we have systematically analysed the mitochondrial DNA (mtDNA) sequence of Naegleria fowleri (N. fowleri) isolate AY27, isolated from Karachi, Pakistan. The N. fowleri isolate AY27 has a circular mtDNA (49,541 bp), which harbours 69 genes (46 protein-coding genes, 21 tRNAs and 2 rRNAs). The pan-genome analysis of N. fowleri species showed a Bpan value of 0.137048, which implies that the pan-genome is open. KEGG classified core, accessory and unique gene clusters for human disease, metabolism, environmental information processing, genetic information processing and organismal system. Similarly, COG characterization of protein showed that core and accessory genes are involved in metabolism, information storages and processing, and cellular processes and signaling. The Naegleria species (n = 6) formed a total of 47 gene clusters; 42 single-copy gene clusters and 5 orthologous gene clusters. It was noted that 100% genes of Naegleria species were present in the orthogroups. We identified 44 single nucleotide polymorphisms (SNP) in the N. fowleri isolate AY27 mtDNA using N. fowleri strain V511 as a reference. Whole mtDNA phylogenetic tree analysis showed that N. fowleri isolates AY27 is closely related to N. fowleri (Accession no. JX174181.1). The ANI (Average Nucleotide Identity) values presented a much clear grouping of the Naegleria species compared to the whole mtDNA based phylogenetic analysis. The current study gives a comprehensive understanding of mtDNA architecture as well as a comparison of Naegleria species (N. fowleri and N. gruberi species) at the mitochondrial genome sequence level.