Genotypic characterization of multiple drug resistant Escherichia coli isolates from a pediatric cancer hospital in Egypt

Infections in Children with Cancer Admitted in an Oncology Reference Hospital: A Cross-sectional Study

Pediatric oncology patients are usually immunosuppressed due to factors such as the neoplasm and its treatment, making them more susceptible to infections. This article aims to determine the infection profile of pediatric oncology patients admitted to an oncology reference hospital in Natal, Rio Grande do Norte, Brazil. A retrospective and cross-sectional study was conducted, collecting data from patients hospitalized due to infection in a pediatric oncology unit exclusively for the Brazilian public health system, spanning from 2018 to 2021. A total of 168 episodes of infections were identified in 96 patients, resulting in 157 hospitalizations. Among the patients with infections, 62.4% had hematological malignancies, and out of these cases, 74.6% specifically had Acute Lymphoid Leukemia. The Escherichia coli (31.9%) was the most prevalent microorganism isolated from the samples. Multidrug-resistant microorganisms accounted for 52% of all identified microorganisms. Fluoroquinolones and beta-lactam were the most prevalent antibiotic classes in the analyzed antibiograms. Factors such as Sex, type of cancer, chemotherapy in the last 30 days, were found to be associated with the occurrence of infection (p < 0.05). Conducting epidemiological studies regarding infections in pediatric oncology is crucial to development of empirical protocols, and the implementation of strategies to better control future infections.

Advances in the Applications of Bioinformatics and Chemoinformatics

Chemoinformatics involves integrating the principles of physical chemistry with computer-based and information science methodologies, commonly referred to as "in silico techniques", in order to address a wide range of descriptive and prescriptive chemistry issues, including applications to biology, drug discovery, and related molecular areas. On the other hand, the incorporation of machine learning has been considered of high importance in the field of drug design, enabling the extraction of chemical data from enormous compound databases to develop drugs endowed with significant biological features. The present review discusses the field of cheminformatics and proposes the use of virtual chemical libraries in virtual screening methods to increase the probability of discovering novel hit chemicals. The virtual libraries address the need to increase the quality of the compounds as well as discover promising ones. On the other hand, various applications of bioinformatics in disease classification, diagnosis, and identification of multidrug-resistant organisms were discussed. The use of ensemble models and brute-force feature selection methodology has resulted in high accuracy rates for heart disease and COVID-19 diagnosis, along with the role of special formulations for targeting meningitis and Alzheimer's disease. Additionally, the correlation between genomic variations and disease states such as obesity and chronic progressive external ophthalmoplegia, the investigation of the antibacterial activity of pyrazole and benzimidazole-based compounds against resistant microorganisms, and its applications in chemoinformatics for the prediction of drug properties and toxicity-all the previously mentioned-were presented in the current review.

Genotypic Patterns of Multidrug-Resistant Acinetobacter baumannii: A Systematic Review

Acinetobacter baumannii (A. baumannii) is one of the most common bacteria in nosocomial infections. Inappropriate usage of antibiotics has led to expanding emergence resistance to A. baumannii as a multidrug-resistant (MDR) strain. Empirical antibiotic therapy is necessary to evaluate the resistant gene pattern of MDR A. baumannii. For this purpose, the present study evaluated the resistance genes pattern of MDR A. baumannii collected from hospitalized patients using a genotypic diagnostic technique. To find evidence related to the study objectives, databases were searched such as Google Scholar, Web of Science, Science Direct, PubMed, and Scopus from 2000 to 2022, with specified keywords in the title and text of the articles. Articles were included based on inclusion and exclusion criteria. The mentioned database displayed 284 articles. After screening, 65 eligible articles were included. The results showed that various b-lactamases genes, aminoglycoside-modifying enzymes (AMEs) genes, and pump-expressing genes are resistance gene patterns in MDR A. baumannii isolates. MDR A. baumannii has significantly become resistant to b-lactams, carbapenems, and aminoglycosides.

Whole genome sequencing of Klebsiella pneumoniae clinical isolates sequence type 627 isolated from Egyptian patients

Klebsiella pneumoniae is considered a threat to public health especially due to multidrug resistance emergence. It is largely oligoclonal based on multi-locus sequence typing (MLST); in Egypt, ST 627 was recently detected. Despites the global dissemination of this ST, there is still paucity of information about it. Herein, we used 4 K. pneumoniae ST627 for whole genome sequencing utilizing an Illumina MiSeq platform. Genome sequences were examined for resistance and virulence determinants, capsular types, plasmids, insertion sequences, phage regions, and Clustered Regularly Interspaced Palindromic Repeats (CRISPR) regions using bioinformatic analysis. The molecular characterization revealed 15 and 65 antimicrobial resistance and virulence genes, respectively. Resistance genes such as tet(D), aph(3’’)-Ib, aph(6)-Id, blaTEM-234, fosA, and fosA6; were mainly responsible for tetracycline, aminoglycoside, and fosfomycin resistance; respectively. The capsular typing revealed that the four strains are KL-24 and O1v1. One plasmid was found in all samples known as pC17KP0052-1 and another plasmid with accession no. NZ_CP032191.1 was found only in K90. IncFIB(K) and IncFII(K) are two replicons found in all samples, while ColRNAI replicon was found only in K90. Entero P88, Salmon SEN5, and Klebsi phiKO2 intact phage regions were identified. All samples harbored CRISPR arrays including CRISPR1 and CRISPR2. Our results shed light on critical tasks of mobile genetic elements in ST 627 in antibiotic resistance spreading.

Genome-Scale Metabolic Models and Machine Learning Reveal Genetic Determinants of Antibiotic Resistance in Escherichia coli and Unravel the Underlying Metabolic Adaptation Mechanisms

Antimicrobial resistance (AMR) is becoming one of the largest threats to public health worldwide, with the opportunistic pathogen Escherichia coli playing a major role in the AMR global health crisis. Unravelling the complex interplay between drug resistance and metabolic rewiring is key to understand the ability of bacteria to adapt to new treatments and to the development of new effective solutions to combat resistant infections. We developed a computational pipeline that combines machine learning with genome-scale metabolic models (GSMs) to elucidate the systemic relationships between genetic determinants of resistance and metabolism beyond annotated drug resistance genes. Our approach was used to identify genetic determinants of 12 AMR profiles for the opportunistic pathogenic bacterium E. coli. Then, to interpret the large number of identified genetic determinants, we applied a constraint-based approach using the GSM to predict the effects of genetic changes on growth, metabolite yields, and reaction fluxes. Our computational platform leads to multiple results. First, our approach corroborates 225 known AMR-conferring genes, 35 of which are known for the specific antibiotic. Second, integration with the GSM predicted 20 top-ranked genetic determinants (including accA, metK, fabD, fabG, murG, lptG, mraY, folP, and glmM) essential for growth, while a further 17 top-ranked genetic determinants linked AMR to auxotrophic behavior. Third, clusters of AMR-conferring genes affecting similar metabolic processes are revealed, which strongly suggested that metabolic adaptations in cell wall, energy, iron and nucleotide metabolism are associated with AMR. The computational solution can be used to study other human and animal pathogens.

IMPORTANCEEscherichia coli is a major public health concern given its increasing level of antibiotic resistance worldwide and extraordinary capacity to acquire and spread resistance via horizontal gene transfer with surrounding species and via mutations in its existing genome. E. coli also exhibits a large amount of metabolic pathway redundancy, which promotes resistance via metabolic adaptability. In this study, we developed a computational approach that integrates machine learning with metabolic modeling to understand the correlation between AMR and metabolic adaptation mechanisms in this model bacterium. Using our approach, we identified AMR genetic determinants associated with cell wall modifications for increased permeability, virulence factor manipulation of host immunity, reduction of oxidative stress toxicity, and changes to energy metabolism. Unravelling the complex interplay between antibiotic resistance and metabolic rewiring may open new opportunities to understand the ability of E. coli, and potentially of other human and animal pathogens, to adapt to new treatments.

Insights into Emergence of Antibiotic Resistance in Acid-Adapted Enterohaemorrhagic Escherichia coli

The emergence of multidrug-resistant pathogens presents a global challenge for treating and preventing disease spread through zoonotic transmission. The water and foodborne Enterohaemorrhagic Escherichia coli (EHEC) are capable of causing intestinal and systemic diseases. The root cause of the emergence of these strains is their metabolic adaptation to environmental stressors, especially acidic pH. Acid treatment is desired to kill pathogens, but the protective mechanisms employed by EHECs cross-protect against antimicrobial peptides and thus facilitate opportunities for survival and pathogenesis. In this review, we have discussed the correlation between acid tolerance and antibiotic resistance, highlighting the identification of novel targets for potential production of antimicrobial therapeutics. We have also summarized the molecular mechanisms used by acid-adapted EHECs, such as the two-component response systems mediating structural modifications, competitive inhibition, and efflux activation that facilitate cross-protection against antimicrobial compounds. Moving beyond the descriptive studies, this review highlights low pH stress as an emerging player in the development of cross-protection against antimicrobial agents. We have also described potential gene targets for innovative therapeutic approaches to overcome the risk of multidrug-resistant diseases in healthcare and industry.

High Rates of Genome Rearrangements and Pathogenicity of Shigella spp

Shigella are pathogens originating within the Escherichia lineage but frequently classified as a separate genus. Shigella genomes contain numerous insertion sequences (ISs) that lead to pseudogenisation of affected genes and an increase of non-homologous recombination. Here, we study 414 genomes of E. coli and Shigella strains to assess the contribution of genomic rearrangements to Shigella evolution. We found that Shigella experienced exceptionally high rates of intragenomic rearrangements and had a decreased rate of homologous recombination compared to pathogenic and non-pathogenic E. coli. The high rearrangement rate resulted in independent disruption of syntenic regions and parallel rearrangements in different Shigella lineages. Specifically, we identified two types of chromosomally encoded E3 ubiquitin-protein ligases acquired independently by all Shigella strains that also showed a high level of sequence conservation in the promoter and further in the 5′-intergenic region. In the only available enteroinvasive E. coli (EIEC) strain, which is a pathogenic E. coli with a phenotype intermediate between Shigella and non-pathogenic E. coli, we found a rate of genome rearrangements comparable to those in other E. coli and no functional copies of the two Shigella-specific E3 ubiquitin ligases. These data indicate that the accumulation of ISs influenced many aspects of genome evolution and played an important role in the evolution of intracellular pathogens. Our research demonstrates the power of comparative genomics-based on synteny block composition and an important role of non-coding regions in the evolution of genomic islands.

Occurrence and Antimicrobial Resistance Traits of Escherichia coli from Wild Birds and Rodents in Singapore

Antimicrobial resistance (AMR) in Escherichia coli (E. coli) poses a public health concern worldwide. Wild birds and rodents, due to their mobility, are potential vehicles for transmission of AMR bacteria to humans. Ninety-six wild birds’ faecal samples and 135 rodents’ droppings samples were collected and analysed in 2017. Forty-six E. coli isolates from wild birds and rodents were subjected to AMR phenotypic and genotypic characterisation. The proportion of E. coli isolates resistant to at least one of the antimicrobials tested from wild birds (80.8%) was significantly higher than that of isolates from rodents (40.0%). The proportion of E. coli isolates resistant to each antimicrobial class for wild birds was 3.8% to 73.1% and that for rodents was 5.0% to 35.0%. Six out of 26 E. coli isolates from wild birds (23.1%) and two out of 20 (10.0%) isolates from rodents were multi-drug resistant (MDR) strains. These MDR E. coli isolates were detected with various antimicrobial resistance genes such as bla_TEM-1B and qnrS1 and could be considered as part of the environmental resistome. Findings in this study suggested that wild birds and rodents could play a role in disseminating antimicrobial resistant E. coli, and this underscores the necessity of environment management and close monitoring on AMR bacteria in wild birds and rodents to prevent spreading of resistant organisms to other wildlife animals and humans.