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.

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.

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.

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.

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.

Comprehensive update on the monkeypox outbreak.

Monkeypox (MPX) was first reported in 1970 in humans and outbreaks were restricted and highly localised to endemic regions of western and central Africa. However, after the first reported case in the UK in early May, 2022, the pattern of epidemic spreading in the geographical regions was much larger compared to past, posing a risk MPX might become entrenched beyond endemic areas. This virus is less transmissible than SARS-CoV-2, as it transmitted mainly through personal, close, often skin-to-skin contact with infectious MPX rash, body fluids, or scabs from an individual with MPX. Infections usually present with chills, fever, fatigue, muscle aches, headache, sore throat, skin lesions, and lymphadenopathy. Currently, there are no antivirals approved for MPX. However, an antiviral drug called "tecovirimat," approved for the treatment of smallpox, has been made accessible to treat MPX. Moreover, to prevent MPX, there are two vaccines available which are approved by FDA: Bavarian Nordic JYNNEOS, and ACAM2000 vaccine. Contact tracing is absent in case of MPX outbreak and there is lack of information from the data systems in rapid manner. Additionally, test capacity needs to be increased. Like SARS-CoV-2, global MPX outbreak demand for vaccines far exceeds availability.

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.

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.

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.

Designing Short Peptides to Block the Interaction of SARS-CoV-2 and Human ACE2 for COVID-19 Therapeutics.

To date, the current COVID-19 pandemic caused by SARS-CoV-2 has infected 99.2 million while killed 2.2 million people throughout the world and is still spreading widely. The unavailability of potential therapeutics against this virus urges to search and develop new drugs. SARS-CoV-2 enters human cells by interacting with human angiotensin-converting enzyme 2 (ACE2) receptor expressed on human cell surface through utilizing receptor-binding domain (RBD) of its spike glycoprotein. The RBD is highly conserved and is also a potential target for blocking its interaction with human cell surface receptor. We designed short peptides on the basis of our previously reported truncated ACE2 (tACE2) for increasing the binding affinity as well as the binding interaction network with RBD. These peptides can selectively bind to RBD with much higher affinities than the cell surface receptor. Thus, these can block all the binding residues required for binding to cell surface receptor. We used selected amino acid regions (21-40 and 65-75) of ACE2 as scaffold for the de novo peptide design. Our designed peptide Pep1 showed interactions with RBD covering almost all of its binding residues with significantly higher binding affinity (-13.2 kcal mol-1) than the cell surface receptor. The molecular dynamics (MD) simulation results showed that designed peptides form a stabilized complex with RBD. We suggest that blocking the RBD through de novo designed peptides can serve as a potential candidate for COVID-19 treatment after further clinical investigations.

Mutation-Based Antibiotic Resistance Mechanism in Methicillin-Resistant Staphylococcus aureus Clinical Isolates.

ß-Lactam antibiotics target penicillin-binding proteins and inhibit the synthesis of peptidoglycan, a crucial step in cell wall biosynthesis. Staphylococcus aureus acquires resistance against ß-lactam antibiotics by producing a penicillin-binding protein 2a (PBP2a), encoded by the mecA gene. PBP2a participates in peptidoglycan biosynthesis and exhibits a poor affinity towards ß-lactam antibiotics. The current study was performed to determine the diversity and the role of missense mutations of PBP2a in the antibiotic resistance mechanism. The methicillin-resistant Staphylococcus aureus (MRSA) isolates from clinical samples were identified using phenotypic and genotypic techniques. The highest frequency (60%, 18 out of 30) of MRSA was observed in wound specimens. Sequence variation analysis of the mecA gene showed four amino acid substitutions (i.e., E239K, E239R, G246E, and E447K). The E239R mutation was found to be novel. The protein-ligand docking results showed that the E239R mutation in the allosteric site of PBP2a induces conformational changes in the active site and, thus, hinders its interaction with cefoxitin. Therefore, the present report indicates that mutation in the allosteric site of PBP2a provides a more closed active site conformation than wide-type PBP2a and then causes the high-level resistance to cefoxitin.

Prevalence of clinical malaria and household characteristics of patients in tribal districts of Pakistan.

BACKGROUND: Malaria, disproportionately affects poor people more than any other disease of public health concern in developing countries. In resource-constrained environments, monitoring the occurrence of malaria is essential for the success of national malaria control programs. Militancy and military conflicts have been a major challenge in monitoring the incidence and controlling malaria and other emerging infectious diseases. The conflicts and instability in Afghanistan have resulted in the migration of refugees into the war-torn tribal districts of Pakistan's Khyber Pakhtunkhwa (KPK) province and the possible introduction of many contagious epidemics. Although malaria is very common in all tribal districts, molecular, clinical and epidemiological data are scarce in these high-burden districts. Therefore, for the proper surveillance, detection, and control of malaria, obtaining and analyzing reliable data in these districts is essential. METHODOLOGY/PRINCIPAL FINDINGS: All 1,127 malaria-suspected patients were sampled within the transmission season in the tribal districts of KPK province between March 2016 to December 2018. After a detailed demographic and clinical investigation of malaria-suspected patients, the data were recorded. The data of the control group was collected simultaneously at the same site. They were considered as uncomplicated cases for statistical analyses. Blood samples were collected from malaria-suspected patients for the detection of Plasmodium species using microscopy and nested PCR (nPCR). Microscopy and nPCR examination detected 78% (n = 882) and 38% (n = 429) Plasmodium-positive patients, respectively. Among1,127 of 429nPCR detected cases with both species of malaria, the frequency of complications was as follows: anemia (n = 71; 16.5%), decompensated shock (n = 40; 9%), hyperpyrexia (n = 117; 27%), hyperparasitaemia (n = 49; 11%) hypoglycemia (n = 45; 10.5%), jaundice (n = 54; 13%), multiple convulsions (n = 37; 9%), and petechia (n = 16; 4%). We observed that 37% (n = 157 out of 429) of those patients infected by both Plasmodium species were children between the ages of 1 and 15 years old. The results revealed that Bajaur (24%), Kurram (20%), and Khyber (18%) districtshada higher proportion of P. vivax than P. falciparum cases. Most of the malaria cases were males (74%). Patients infected by both Plasmodium species tended to less commonly have received formal education and ownership of wealth indicators (e.g., fridge, TV set) was lower. CONCLUSIONS/SIGNIFICANCE: Malaria in tribal districts of the KPK province largely affects young males. P. vivax is a major contributor to the spread of malaria in the area, including severe malaria. We observed a high prevalence of P. vivax in the Bajaur district. Children were the susceptible population to malaria infections whereas they were the least expected to use satisfactory prevention strategies. A higher level of education, a possession of TV sets, the use of bed nets, the use of repellent fluids, and fridges were all associated with protection from malaria. An increased investment in socio-economic development, a strong health infrastructure, and malaria education are key interventions to reduce malaria in the tribal districts.

Dual activity of PNGM-1 pinpoints the evolutionary origin of subclass B3 metallo-ß-lactamases: a molecular and evolutionary study.

Resistance to ß-lactams is one of the most serious problems associated with Gram-negative infections. ß-Lactamases are able to hydrolyze ß-lactams such as cephalosporins and/or carbapenems. Evolutionary origin of metallo-ß-lactamases (MBLs), conferring critical antibiotic resistance threats, remains unknown. We discovered PNGM-1, the novel subclass B3 MBL, in deep-sea sediments that predate the antibiotic era. Here, our phylogenetic analysis suggests that PNGM-1 yields insights into the evolutionary origin of subclass B3 MBLs. We reveal the structural similarities between tRNase Zs and PNGM-1, and demonstrate that PNGM-1 has both MBL and tRNase Z activities, suggesting that PNGM-1 is thought to have evolved from a tRNase Z. We also show kinetic and structural comparisons between PNGM-1 and other proteins including subclass B3 MBLs and tRNase Zs. These comparisons revealed that the B3 MBL activity of PNGM-1 is a promiscuous activity and subclass B3 MBLs are thought to have evolved through PNGM-1 activity.

The novel metallo-ß-lactamase PNGM-1 from a deep-sea sediment metagenome: crystallization and X-ray crystallographic analysis.

Metallo-ß-lactamases (MBLs) are present in major Gram-negative pathogens and environmental species, and pose great health risks because of their ability to hydrolyze the ß-lactam rings of antibiotics such as carbapenems. PNGM-1 was the first reported case of a subclass B3 MBL protein that was identified from a metagenomic library from deep-sea sediments that predate the antibiotic era. In this study, PNGM-1 was overexpressed, purified and crystallized. Crystals of native and selenomethionine-substituted PNGM-1 diffracted to 2.10 and 2.30 Šresolution, respectively. Both the native and the selenomethionine-labelled PNGM-1 crystals belonged to the monoclinic space group P21, with unit-cell parameters a = 122, b = 83, c = 163 Å, ß = 110°. Matthews coefficient (VM) calculations suggested the presence of 6-10 molecules in the asymmetric unit, corresponding to a solvent content of ∼31-58%. Structure determination is currently in progress.

PNGM-1, a novel subclass B3 metallo-ß-lactamase from a deep-sea sediment metagenome.

OBJECTIVES: In order to find antimicrobial resistance gene(s) pre-dating the use of antibiotics through metagenomics, functional screening of a metagenomic library from the deep-seep sediments of Edison Seamount (ca. 10000 years old) was performed. METHODS: Among 60 antimicrobial-resistant clones, a single clone with the highest minimum inhibitory concentration (MIC) for ampicillin was selected. Sequence analysis revealed a new metallo-ß-lactamase (MBL) gene, designated as blaPNGM-1. PNGM-1 retains a zinc ion-binding motif (H116XH118XD120H121, H196 and H263), conserved in subclass B3 MBLs. The catalytic parameters of purified PNGM-1 and the MICs of ß-lactams for Escherichia coli TOP10 transformants harbouring the blaPNGM-1 gene were assessed. RESULTS: Antimicrobial susceptibility testing indicated reduced susceptibility to penicillins, narrow- and extended-spectrum cephalosporins, and carbapenems in E. coli TOP10 transformants harbouring the blaPNGM-1 gene. In addition, kinetic analyses revealed that PNGM-1 hydrolysed almost all ß-lactams. CONCLUSIONS: The PNGM-1 enzyme is the first case of a subclass B3 MBL derived from a functional metagenomic library of a deep-sea sediment that pre-dates the antibiotic era.

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.