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600300.9864Contact Lens Wear Alters Transcriptional Responses to Pseudomonas aeruginosa in Both the Corneal Epithelium and the Bacteria. PURPOSE: Healthy corneas resist colonization by virtually all microbes yet contact lens wear can predispose the cornea to sight-threatening infection with Pseudomonas aeruginosa. Here, we explored how lens wear changes corneal epithelium transcriptional responses to P. aeruginosa and its impact on bacterial gene expression. METHODS: Male and female C57BL/6J mice were fitted with a contact lens on one eye for 24 h. After lens removal, corneas were immediately challenged for 4 h with P. aeruginosa. A separate group of naïve mice were similarly challenged with bacteria. Bacteria-challenged eyes were compared to uninoculated naive controls as was lens wear alone. Total RNA-sequencing determined corneal epithelium and bacterial gene expression. RESULTS: Prior lens wear profoundly altered the corneal response to P. aeruginosa, including: upregulated pattern-recognition receptors (tlr3, nod1), downregulated lectin pathway of complement activation (masp1), amplified upregulation of tcf7, gpr55, ifi205, wfdc2 (immune defense) and further suppression of efemp1 (corneal stromal integrity). Without lens wear, P. aeruginosa upregulated mitochondrial and ubiquinone metabolism genes. Lens wear alone upregulated axl, grn, tcf7, gpr55 (immune defense) and downregulated Ca2(+)-dependent genes necab1, snx31 and npr3. P. aeruginosa exposure to prior lens wearing vs. naïve corneas upregulated bacterial genes of virulence (popD), its regulation (rsmY, PA1226) and antimicrobial resistance (arnB, oprR). CONCLUSION: Prior lens wear impacts corneal epithelium gene expression altering its responses to P. aeruginosa and how P. aeruginosa responds to it favoring virulence, survival and adaptation. Impacted genes and associated networks provide avenues for research to better understand infection pathogenesis.202439677621
600410.9864Contact Lens Wear Alters Transcriptional Responses to Pseudomonas aeruginosa in Both the Corneal Epithelium and the Bacteria. PURPOSE: Healthy corneas resist colonization by virtually all microbes, yet contact lens wear can predispose the cornea to sight-threatening infection with Pseudomonas aeruginosa. Here, we explored how lens wear changes corneal epithelium transcriptional responses to P. aeruginosa and its impact on bacterial gene expression. METHODS: Male and female C57BL/6J mice were fitted with a contact lens on one eye for 24 hours. After lens removal, corneas were immediately challenged for 4 hours with P. aeruginosa. A separate group of naïve mice was similarly challenged with bacteria. Bacteria-challenged eyes were compared to uninoculated naïve controls, as was lens wear alone. Total RNA sequencing determined corneal epithelium and bacterial gene expression. RESULTS: Prior lens wear profoundly altered the corneal response to P. aeruginosa, including upregulated pattern recognition receptors (tlr3, nod1); downregulated lectin pathway of complement activation (masp1); amplified upregulation of tcf7, gpr55, ifi205, and wfdc2 (immune defense); and further suppression of efemp1 (corneal stromal integrity). Without lens wear, P. aeruginosa upregulated mitochondrial and ubiquinone metabolism genes. Lens wear alone upregulated axl, grn, tcf7, and gpr55 (immune defense) and downregulated Ca2+-dependent genes necab1, snx31, and npr3. P. aeruginosa exposure to prior lens wearing versus naïve corneas upregulated bacterial genes of virulence (popD), its regulation (rsmY, PA1226), and antimicrobial resistance (arnB, oprR). CONCLUSIONS: Prior lens wear impacts corneal epithelium gene expression, altering its responses to P. aeruginosa and how P. aeruginosa responds to it favoring virulence, survival, and adaptation. Impacted genes and associated networks provide avenues for research to better understand infection pathogenesis.202539932472
907620.9770ResiDB: An automated database manager for sequence data. The amount of publicly available DNA sequence data is drastically increasing, making it a tedious task to create sequence databases necessary for the design of diagnostic assays. The selection of appropriate sequences is especially challenging in genes affected by frequent point mutations such as antibiotic resistance genes. To overcome this issue, we have designed the webtool resiDB, a rapid and user-friendly sequence database manager for bacteria, fungi, viruses, protozoa, invertebrates, plants, archaea, environmental and whole genome shotgun sequence data. It automatically identifies and curates sequence clusters to create custom sequence databases based on user-defined input sequences. A collection of helpful visualization tools gives the user the opportunity to easily access, evaluate, edit, and download the newly created database. Consequently, researchers do no longer have to manually manage sequence data retrieval, deal with hardware limitations, and run multiple independent software tools, each having its own requirements, input and output formats. Our tool was developed within the H2020 project FAPIC aiming to develop a single diagnostic assay targeting all sepsis-relevant pathogens and antibiotic resistance mechanisms. ResiDB is freely accessible to all users through https://residb.ait.ac.at/.202133495705
908330.9767ARGNet: using deep neural networks for robust identification and classification of antibiotic resistance genes from sequences. BACKGROUND: Emergence of antibiotic resistance in bacteria is an important threat to global health. Antibiotic resistance genes (ARGs) are some of the key components to define bacterial resistance and their spread in different environments. Identification of ARGs, particularly from high-throughput sequencing data of the specimens, is the state-of-the-art method for comprehensively monitoring their spread and evolution. Current computational methods to identify ARGs mainly rely on alignment-based sequence similarities with known ARGs. Such approaches are limited by choice of reference databases and may potentially miss novel ARGs. The similarity thresholds are usually simple and could not accommodate variations across different gene families and regions. It is also difficult to scale up when sequence data are increasing. RESULTS: In this study, we developed ARGNet, a deep neural network that incorporates an unsupervised learning autoencoder model to identify ARGs and a multiclass classification convolutional neural network to classify ARGs that do not depend on sequence alignment. This approach enables a more efficient discovery of both known and novel ARGs. ARGNet accepts both amino acid and nucleotide sequences of variable lengths, from partial (30-50 aa; 100-150 nt) sequences to full-length protein or genes, allowing its application in both target sequencing and metagenomic sequencing. Our performance evaluation showed that ARGNet outperformed other deep learning models including DeepARG and HMD-ARG in most of the application scenarios especially quasi-negative test and the analysis of prediction consistency with phylogenetic tree. ARGNet has a reduced inference runtime by up to 57% relative to DeepARG. CONCLUSIONS: ARGNet is flexible, efficient, and accurate at predicting a broad range of ARGs from the sequencing data. ARGNet is freely available at https://github.com/id-bioinfo/ARGNet , with an online service provided at https://ARGNet.hku.hk . Video Abstract.202438725076
509740.9767Comparing Graph Sample and Aggregation (SAGE) and Graph Attention Networks in the Prediction of Drug-Gene Associations of Extended-Spectrum Beta-Lactamases in Periodontal Infections and Resistance. INTRODUCTION: Gram-negative bacteria exhibit more antibiotic resistance than gram-positive bacteria due to their cell wall structure and composition differences. Porins, or protein channels in these bacteria, can allow small, hydrophilic antibiotics to diffuse, affecting their susceptibility. Mutations in porin protein genes can also impair antibiotic entry. Predicting drug-gene associations of extended-spectrum beta-lactamases (ESBLs) is crucial as they confer resistance to beta-lactam antibiotics, challenging the treatment of infections. This aids clinicians in selecting suitable treatments, optimizing drug usage, enhancing patient outcomes, and controlling antibiotic resistance in healthcare settings. Graph-based neural networks can predict drug-gene associations in periodontal infections and resistance. The aim of the study was to predict drug-gene associations of ESBLs in periodontal infections and resistance. METHODS: The study focuses on analyzing drug-gene associations using probes and drugs. The data was converted into graph language, assigning nodes and edges for drugs and genes. Graph neural networks (GNNs) and similar algorithms were implemented using Google Colab and Python. Cytoscape and CytoHubba are open-source software platforms used for network analysis and visualization. GNNs were used for tasks like node classification, link prediction, and graph-level prediction. Three graph-based models were used: graph convolutional network (GCN), Graph SAGE, and graph attention network (GAT). Each model was trained for 200 epochs using the Adam optimizer with a learning rate of 0.01 and a weight decay of 5e-4. RESULTS: The drug-gene association network has 57 nodes, 79 edges, and a 2.730 characteristic path length. Its structure, organization, and connectivity are analyzed using the GCN and Graph SAGE, which show high accuracy, precision, recall, and an F1-score of 0.94. GAT's performance metrics are lower, with an accuracy of 0.68, precision of 0.47, recall of 0.68, and F1-score of 0.56, suggesting that it may not be as effective in capturing drug-gene relationships. CONCLUSION: Compared to ESBLs, both GCN and Graph SAGE demonstrate excellent performance with accuracy, precision, recall, and an F1-score of 0.94. These results indicate that GCN and Graph SAGE are highly effective in predicting drug-gene associations related to ESBLs. GCN and Graph SAGE outperform GAT in predicting drug-gene associations for ESBLs. Improvements include data augmentation, regularization, and cross-validation. Ethical considerations, fairness, and open-source implementations are crucial for future research in precision periodontal treatment.202439347119
516350.9765Multi-omics data elucidate parasite-host-microbiota interactions and resistance to Haemonchus contortus in sheep. BACKGROUND: The integration of molecular data from hosts, parasites, and microbiota can enhance our understanding of the complex biological interactions underlying the resistance of hosts to parasites. Haemonchus contortus, the predominant sheep gastrointestinal parasite species in the tropics, causes significant production and economic losses, which are further compounded by the diminishing efficiency of chemical control owing to anthelmintic resistance. Knowledge of how the host responds to infection and how the parasite, in combination with microbiota, modulates host immunity can guide selection decisions to breed animals with improved parasite resistance. This understanding will help refine management practices and advance the development of new therapeutics for long-term helminth control. METHODS: Eggs per gram (EPG) of feces were obtained from Morada Nova sheep subjected to two artificial infections with H. contortus and used as a proxy to select animals with high resistance or susceptibility for transcriptome sequencing (RNA-seq) of the abomasum and 50 K single-nucleotide genotyping. Additionally, RNA-seq data for H. contortus were generated, and amplicon sequence variants (ASV) were obtained using polymerase chain reaction amplification and sequencing of bacterial and archaeal 16S ribosomal RNA genes from sheep feces and rumen content. RESULTS: The heritability estimate for EPG was 0.12. GAST, GNLY, IL13, MGRN1, FGF14, and RORC genes and transcripts were differentially expressed between resistant and susceptible animals. A genome-wide association study identified regions on chromosomes 2 and 11 that harbor candidate genes for resistance, immune response, body weight, and adaptation. Trans-expression quantitative trait loci were found between significant variants and differentially expressed transcripts. Functional co-expression modules based on sheep genes and ASVs correlated with resistance to H. contortus, showing enrichment in pathways of response to bacteria, immune and inflammatory responses, and hub features of the Christensenellaceae, Bacteroides, and Methanobrevibacter genera; Prevotellaceae family; and Verrucomicrobiota phylum. In H. contortus, some mitochondrial, collagen-, and cuticle-related genes were expressed only in parasites isolated from susceptible sheep. CONCLUSIONS: The present study identified chromosome regions, genes, transcripts, and pathways involved in the elaborate interactions between the sheep host, its gastrointestinal microbiota, and the H. contortus parasite. These findings will assist in the development of animal selection strategies for parasite resistance and interdisciplinary approaches to control H. contortus infection in sheep.202438429820
908260.9763GeneMates: an R package for detecting horizontal gene co-transfer between bacteria using gene-gene associations controlled for population structure. BACKGROUND: Horizontal gene transfer contributes to bacterial evolution through mobilising genes across various taxonomical boundaries. It is frequently mediated by mobile genetic elements (MGEs), which may capture, maintain, and rearrange mobile genes and co-mobilise them between bacteria, causing horizontal gene co-transfer (HGcoT). This physical linkage between mobile genes poses a great threat to public health as it facilitates dissemination and co-selection of clinically important genes amongst bacteria. Although rapid accumulation of bacterial whole-genome sequencing data since the 2000s enables study of HGcoT at the population level, results based on genetic co-occurrence counts and simple association tests are usually confounded by bacterial population structure when sampled bacteria belong to the same species, leading to spurious conclusions. RESULTS: We have developed a network approach to explore WGS data for evidence of intraspecies HGcoT and have implemented it in R package GeneMates ( github.com/wanyuac/GeneMates ). The package takes as input an allelic presence-absence matrix of interested genes and a matrix of core-genome single-nucleotide polymorphisms, performs association tests with linear mixed models controlled for population structure, produces a network of significantly associated alleles, and identifies clusters within the network as plausible co-transferred alleles. GeneMates users may choose to score consistency of allelic physical distances measured in genome assemblies using a novel approach we have developed and overlay scores to the network for further evidence of HGcoT. Validation studies of GeneMates on known acquired antimicrobial resistance genes in Escherichia coli and Salmonella Typhimurium show advantages of our network approach over simple association analysis: (1) distinguishing between allelic co-occurrence driven by HGcoT and that driven by clonal reproduction, (2) evaluating effects of population structure on allelic co-occurrence, and (3) direct links between allele clusters in the network and MGEs when physical distances are incorporated. CONCLUSION: GeneMates offers an effective approach to detection of intraspecies HGcoT using WGS data.202032972363
813670.9763Recent progress in CRISPR/Cas9-based genome editing for enhancing plant disease resistance. Nowadays, agricultural production is strongly affected by both climate change and pathogen attacks which seriously threaten global food security. For a long time, researchers have been waiting for a tool allowing DNA/RNA manipulation to tailor genes and their expression. Some earlier genetic manipulation methods such as meganucleases (MNs), zinc finger nucleases (ZFNs), transcription activator-like effector nucleases (TALENs) allowed site directed modification but their successful rate was limited due to lack of flexibility when targeting a 'site-specific nucleic acid'. The discovery of clustered regularly interspaced short palindrome repeats (CRISPR)/CRISPR-associated protein 9 (Cas9) system has revolutionized genome editing domain in different living organisms during the past 9 years. Based on RNA-guided DNA/RNA recognition, CRISPR/Cas9 optimizations have offered an unrecorded scientific opportunity to engineer plants resistant to diverse pathogens. In this report, we describe the main characteristics of the primary reported-genome editing tools ((MNs, ZFNs, TALENs) and evaluate the different CRISPR/Cas9 methods and achievements in developing crop plants resistant to viruses, fungi and bacteria.202336871676
510280.9762Pipeline for Antimicrobial Resistance Gene Quantification from Host Tissue. Antibiotics are frequently used in food production animals to control disease and improve productivity, but this promotes the development of antimicrobial resistance (AMR) and subsequent broader spread of AMR bacteria throughout food chain, endangering the well-being and health of both animals and humans. In humans, the gut microbiome harbors a diverse range of AMR bacteria, known as the resistome. To effectively mitigate AMR in food animals requires first determining the expression and abundance of AMR-related genes in the gut resistome. Currently, such knowledge in regard to food animals is largely lacking. Gut tissue RNA sequencing (GTRS) can capture metabolically active transcripts from both the host and the microbes attached to the gut epithelium. Ideally, AMR genes can be quantified using GTRS data, making it possible to study the relationship between host and microbe. For the majority of these GTRS studies, only host transcriptome changes have been reported, while the microbial AMR remains largely unexamined, mainly due to the lack of easily implementable bioinformatics tools. Here we present a straightforward workflow to accomplish that using common command-line bioinformatics tools. With this pipeline, the host is considered noise, and host data are filtered out from the microbial reads. Transcript quantification of the AMR genes is then performed. The pipeline then continues through AMR transcript quantification, differential gene expression, and SNP analysis. Using open-source tools, we made this analytical pipeline easy to implement and able to generate results ready to be incorporated into publishable reports. Published 2025. This article is a U.S. Government work and is in the public domain in the USA. Basic Protocol: Running the gene quantification pipeline Support Protocol 1: Downloading FASTQ files from the NCBI database Support Protocol 2: Building a genome reference index of the host Support Protocol 3: Differential gene expression analysis Support Protocol 4: Single-nucleotide polymorphism (SNP) analysis.202540145236
907590.9762CamPype: an open-source workflow for automated bacterial whole-genome sequencing analysis focused on Campylobacter. BACKGROUND: The rapid expansion of Whole-Genome Sequencing has revolutionized the fields of clinical and food microbiology. However, its implementation as a routine laboratory technique remains challenging due to the growth of data at a faster rate than can be effectively analyzed and critical gaps in bioinformatics knowledge. RESULTS: To address both issues, CamPype was developed as a new bioinformatics workflow for the genomics analysis of sequencing data of bacteria, especially Campylobacter, which is the main cause of gastroenteritis worldwide making a negative impact on the economy of the public health systems. CamPype allows fully customization of stages to run and tools to use, including read quality control filtering, read contamination, reads extension and assembly, bacterial typing, genome annotation, searching for antibiotic resistance genes, virulence genes and plasmids, pangenome construction and identification of nucleotide variants. All results are processed and resumed in an interactive HTML report for best data visualization and interpretation. CONCLUSIONS: The minimal user intervention of CamPype makes of this workflow an attractive resource for microbiology laboratories with no expertise in bioinformatics as a first line method for bacterial typing and epidemiological analyses, that would help to reduce the costs of disease outbreaks, or for comparative genomic analyses. CamPype is publicly available at https://github.com/JoseBarbero/CamPype .202337474912
2525100.9761Review of antimicrobial resistance surveillance programmes in livestock and meat in EU with focus on humans. OBJECTIVES: In this review, we describe surveillance programmes reporting antimicrobial resistance (AMR) and resistance genes in bacterial isolates from livestock and meat and compare them with those relevant for human health. METHODS: Publications on AMR in European countries were assessed. PubMed was reviewed and AMR monitoring programmes were identified from reports retrieved by Internet searches and by contacting national authorities in EU/European Economic Area (EEA) member states. RESULTS: Three types of systems were identified: EU programmes, industry-funded supranational programmes and national surveillance systems. The mandatory EU-financed programme has led to some harmonization in national monitoring and provides relevant information on AMR and extended-spectrum β-lactamase/AmpC- and carbapenemase-producing bacteria. At the national level, AMR surveillance systems in livestock apply heterogeneous sampling, testing and reporting modalities, resulting in results that cannot be compared. Most reports are not publicly available or are written in a local language. The industry-funded monitoring systems undertaken by the Centre Européen d'Etudes pour la Santé Animale (CEESA) examines AMR in bacteria in food-producing animals. CONCLUSIONS: Characterization of AMR genes in livestock is applied heterogeneously among countries. Most antibiotics of human interest are included in animal surveillance, although results are difficult to compare as a result of lack of representativeness of animal samples. We suggest that EU/EEA countries provide better uniform AMR monitoring and reporting in livestock and link them better to surveillance systems in humans. Reducing the delay between data collection and publication is also important to allow prompt identification of new resistance patterns.201828970159
6691110.9760The antimicrobial resistance monitoring and research (ARMoR) program: the US Department of Defense response to escalating antimicrobial resistance. Responding to escalating antimicrobial resistance (AMR), the US Department of Defense implemented an enterprise-wide collaboration, the Antimicrobial Resistance Monitoring and Research Program, to aid in infection prevention and control. It consists of a network of epidemiologists, bioinformaticists, microbiology researchers, policy makers, hospital-based infection preventionists, and healthcare providers who collaborate to collect relevant AMR data, conduct centralized molecular characterization, and use AMR characterization feedback to implement appropriate infection prevention and control measures and influence policy. A particularly concerning type of AMR, carbapenem-resistant Enterobacteriaceae, significantly declined after the program was launched. Similarly, there have been no further reports or outbreaks of another concerning type of AMR, colistin resistance in Acinetobacter, in the Department of Defense since the program was initiated. However, bacteria containing AMR-encoding genes are increasing. To update program stakeholders and other healthcare systems facing such challenges, we describe the processes and impact of the program.201424795331
2585120.9759A scoping review of the prevalence of antimicrobial-resistant pathogens and signatures in ready-to-eat street foods in Africa: implications for public health. BACKGROUND AND OBJECTIVE: Despite its critical role in individual and societal health, food hygiene remains underexplored. Antibiotic-resistant pathogenic bacteria in ready-to-eat (RTE) food threaten public health. This scoping review collected data on the epidemiological prevalence of RTE food-contaminated pathogens resistant to antimicrobial drugs and resistance genes in Africa. METHOD: Using electronic databases, such as PubMed, Scopus, and Web of Science (WoS), handpicked from references, pre-reviewed published articles were retrieved and analyzed according to the PRISMA-ScR guidelines. RESULTS: The findings indicate 40 previewed published articles qualified for meta-synthesis in the scoping review with a population/case ratio of 11,653/5,338 (45.80%). The most frequently reported RTE foods were meat or beef/beef-soup, chicken or poultry products, salads, vegetable salads, and sandwiches, which harboured pathogens such as E. coli, Salmonella, and Staphylococcus. Antibiotic susceptibility tests revealed the use of 48 antibiotics to manage infections, following CLSI (Clinical and Laboratory Standards Institute) protocols. Moreover, 10 authors reported 54 resistance genes associated with pathogenic resistant bacteria. In addition, only 15 studies received funding or financial support. CONCLUSION: These findings from several researchers indicate that RTE street foods in African and resource-limited nations harbour enteric pathogens and are a significant concern to the public health system and reservoir of the spread of antibiotic resistance. This underscores the necessity of implementing effective control strategies to address challenges and limit the spread of resistant bacteria in RTE foods. The antimicrobial resistance surveillance system in the region is a significant concern. Notably, Africa needs to strengthen the national and international regulatory bodies and a health surveillance system on antimicrobial resistance, particularly among developing nations.202540270817
6686130.9759The Impact of Wastewater on Antimicrobial Resistance: A Scoping Review of Transmission Pathways and Contributing Factors. BACKGROUND/OBJECTIVES: Antimicrobial resistance (AMR) is a global issue driven by the overuse of antibiotics in healthcare, agriculture, and veterinary settings. Wastewater and treatment plants (WWTPs) act as reservoirs for antibiotic-resistant bacteria (ARB) and antibiotic resistance genes (ARGs). The One Health approach emphasizes the interconnectedness of human, animal, and environmental health in addressing AMR. This scoping review analyzes wastewater's role in the AMR spread, identifies influencing factors, and highlights research gaps to guide interventions. METHODS: This scoping review followed the PRISMA-ScR guidelines. A comprehensive literature search was conducted across the PubMed and Web of Science databases for articles published up to June 2024, supplemented by manual reference checks. The review focused on wastewater as a source of AMR, including hospital effluents, industrial and urban sewage, and agricultural runoff. Screening and selection were independently performed by two reviewers, with conflicts resolved by a third. RESULTS: Of 3367 studies identified, 70 met the inclusion criteria. The findings indicated that antibiotic residues, heavy metals, and microbial interactions in wastewater are key drivers of AMR development. Although WWTPs aim to reduce contaminants, they often create conditions conducive to horizontal gene transfer, amplifying resistance. Promising interventions, such as advanced treatment methods and regulatory measures, exist but require further research and implementation. CONCLUSIONS: Wastewater plays a pivotal role in AMR dissemination. Targeted interventions in wastewater management are essential to mitigate AMR risks. Future studies should prioritize understanding AMR dynamics in wastewater ecosystems and evaluating scalable mitigation strategies to support global health efforts.202540001375
8400140.9758Transferring knowledge of bacterial protein interaction networks to predict pathogen targeted human genes and immune signaling pathways: a case study on M. tuberculosis. BACKGROUND: Bacterial invasive infection and host immune response is fundamental to the understanding of pathogen pathogenesis and the discovery of effective therapeutic drugs. However, there are very few experimental studies on the signaling cross-talks between bacteria and human host to date. METHODS: In this work, taking M. tuberculosis H37Rv (MTB) that is co-evolving with its human host as an example, we propose a general computational framework that exploits the known bacterial pathogen protein interaction networks in STRING database to predict pathogen-host protein interactions and their signaling cross-talks. In this framework, significant interlogs are derived from the known pathogen protein interaction networks to train a predictive l(2)-regularized logistic regression model. RESULTS: The computational results show that the proposed method achieves excellent performance of cross validation as well as low predicted positive rates on the less significant interlogs and non-interlogs, indicating a low risk of false discovery. We further conduct gene ontology (GO) and pathway enrichment analyses of the predicted pathogen-host protein interaction networks, which potentially provides insights into the machinery that M. tuberculosis H37Rv targets human genes and signaling pathways. In addition, we analyse the pathogen-host protein interactions related to drug resistance, inhibition of which potentially provides an alternative solution to M. tuberculosis H37Rv drug resistance. CONCLUSIONS: The proposed machine learning framework has been verified effective for predicting bacteria-host protein interactions via known bacterial protein interaction networks. For a vast majority of bacterial pathogens that lacks experimental studies of bacteria-host protein interactions, this framework is supposed to achieve a general-purpose applicability. The predicted protein interaction networks between M. tuberculosis H37Rv and Homo sapiens, provided in the Additional files, promise to gain applications in the two fields: (1) providing an alternative solution to drug resistance; (2) revealing the patterns that M. tuberculosis H37Rv genes target human immune signaling pathways.201829954330
6507150.9757What Are the Drivers Triggering Antimicrobial Resistance Emergence and Spread? Outlook from a One Health Perspective. Antimicrobial resistance (AMR) has emerged as a critical global public health threat, exacerbating healthcare burdens and imposing substantial economic costs. Currently, AMR contributes to nearly five million deaths annually worldwide, surpassing mortality rates of any single infectious disease. The economic burden associated with AMR-related disease management is estimated at approximately $730 billion per year. This review synthesizes current research on the mechanisms and multifaceted drivers of AMR development and dissemination through the lens of the One Health framework, which integrates human, animal, and environmental health perspectives. Intrinsic factors, including antimicrobial resistance genes (ARGs) and mobile genetic elements (MGEs), enable bacteria to evolve adaptive resistance mechanisms such as enzymatic inactivation, efflux pumps, and biofilm formation. Extrinsic drivers span environmental stressors (e.g., antimicrobials, heavy metals, disinfectants), socioeconomic practices, healthcare policies, and climate change, collectively accelerating AMR proliferation. Horizontal gene transfer and ecological pressures further facilitate the spread of antimicrobial-resistant bacteria across ecosystems. The cascading impacts of AMR threaten human health and agricultural productivity, elevate foodborne infection risks, and impose substantial economic burdens, particularly in low- and middle-income countries. To address this complex issue, the review advocates for interdisciplinary collaboration, robust policy implementation (e.g., antimicrobial stewardship), and innovative technologies (e.g., genomic surveillance, predictive modeling) under the One Health paradigm. Such integrated strategies are essential to mitigate AMR transmission, safeguard global health, and ensure sustainable development.202540558133
8393160.9756The draft genome of whitefly Bemisia tabaci MEAM1, a global crop pest, provides novel insights into virus transmission, host adaptation, and insecticide resistance. BACKGROUND: The whitefly Bemisia tabaci (Hemiptera: Aleyrodidae) is among the 100 worst invasive species in the world. As one of the most important crop pests and virus vectors, B. tabaci causes substantial crop losses and poses a serious threat to global food security. RESULTS: We report the 615-Mb high-quality genome sequence of B. tabaci Middle East-Asia Minor 1 (MEAM1), the first genome sequence in the Aleyrodidae family, which contains 15,664 protein-coding genes. The B. tabaci genome is highly divergent from other sequenced hemipteran genomes, sharing no detectable synteny. A number of known detoxification gene families, including cytochrome P450s and UDP-glucuronosyltransferases, are significantly expanded in B. tabaci. Other expanded gene families, including cathepsins, large clusters of tandemly duplicated B. tabaci-specific genes, and phosphatidylethanolamine-binding proteins (PEBPs), were found to be associated with virus acquisition and transmission and/or insecticide resistance, likely contributing to the global invasiveness and efficient virus transmission capacity of B. tabaci. The presence of 142 horizontally transferred genes from bacteria or fungi in the B. tabaci genome, including genes encoding hopanoid/sterol synthesis and xenobiotic detoxification enzymes that are not present in other insects, offers novel insights into the unique biological adaptations of this insect such as polyphagy and insecticide resistance. Interestingly, two adjacent bacterial pantothenate biosynthesis genes, panB and panC, have been co-transferred into B. tabaci and fused into a single gene that has acquired introns during its evolution. CONCLUSIONS: The B. tabaci genome contains numerous genetic novelties, including expansions in gene families associated with insecticide resistance, detoxification and virus transmission, as well as numerous horizontally transferred genes from bacteria and fungi. We believe these novelties likely have shaped B. tabaci as a highly invasive polyphagous crop pest and efficient vector of plant viruses. The genome serves as a reference for resolving the B. tabaci cryptic species complex, understanding fundamental biological novelties, and providing valuable genetic information to assist the development of novel strategies for controlling whiteflies and the viruses they transmit.201627974049
6689170.9756Wastewater-Based Epidemiology as a Complementary Tool for Antimicrobial Resistance Surveillance: Overcoming Barriers to Integration. This commentary highlights the potential of wastewater-based epidemiology (WBE) as a complementary tool for antimicrobial resistance (AMR) surveillance. WBE can support the early detection of resistance trends at the population level, including in underserved communities. However, several challenges remain, including technical variability, complexities in data interpretation, and regulatory gaps. An additional limitation is the uncertainty surrounding the origin of resistant bacteria and their genes in wastewater, which may derive not only from human sources but also from industrial, agricultural, or infrastructural contributors. Therefore, effective integration of WBE into public health systems will require standardized methods, sustained investment, and cross-sector collaboration. This could be achieved through joint monitoring initiatives that combine hospital wastewater data with agricultural and municipal surveillance to inform antibiotic stewardship policies. Overcoming these barriers could position WBE as an innovative tool for AMR monitoring, enhancing early warning systems and supporting more responsive, equitable, and preventive public health strategies.202540522150
5115180.9756Search Engine for Antimicrobial Resistance: A Cloud Compatible Pipeline and Web Interface for Rapidly Detecting Antimicrobial Resistance Genes Directly from Sequence Data. BACKGROUND: Antimicrobial resistance remains a growing and significant concern in human and veterinary medicine. Current laboratory methods for the detection and surveillance of antimicrobial resistant bacteria are limited in their effectiveness and scope. With the rapidly developing field of whole genome sequencing beginning to be utilised in clinical practice, the ability to interrogate sequencing data quickly and easily for the presence of antimicrobial resistance genes will become increasingly important and useful for informing clinical decisions. Additionally, use of such tools will provide insight into the dynamics of antimicrobial resistance genes in metagenomic samples such as those used in environmental monitoring. RESULTS: Here we present the Search Engine for Antimicrobial Resistance (SEAR), a pipeline and web interface for detection of horizontally acquired antimicrobial resistance genes in raw sequencing data. The pipeline provides gene information, abundance estimation and the reconstructed sequence of antimicrobial resistance genes; it also provides web links to additional information on each gene. The pipeline utilises clustering and read mapping to annotate full-length genes relative to a user-defined database. It also uses local alignment of annotated genes to a range of online databases to provide additional information. We demonstrate SEAR's application in the detection and abundance estimation of antimicrobial resistance genes in two novel environmental metagenomes, 32 human faecal microbiome datasets and 126 clinical isolates of Shigella sonnei. CONCLUSIONS: We have developed a pipeline that contributes to the improved capacity for antimicrobial resistance detection afforded by next generation sequencing technologies, allowing for rapid detection of antimicrobial resistance genes directly from sequencing data. SEAR uses raw sequencing data via an intuitive interface so can be run rapidly without requiring advanced bioinformatic skills or resources. Finally, we show that SEAR is effective in detecting antimicrobial resistance genes in metagenomic and isolate sequencing data from both environmental metagenomes and sequencing data from clinical isolates.201526197475
5236190.9755Genome characterization of a multi-drug resistant Escherichia coli strain, L1PEag1, isolated from commercial cape gooseberry fruits (Physalis peruviana L.). INTRODUCTION: Foodborne infections, which are frequently linked to bacterial contamination, are a serious concern to public health on a global scale. Whether agricultural farming practices help spread genes linked to antibiotic resistance in bacteria associated with humans or animals is a controversial question. METHODS: This study applied a long-read Oxford Nanopore MinION-based sequencing to obtain the complete genome sequence of a multi-drug resistant Escherichia coli strain (L1PEag1), isolated from commercial cape gooseberry fruits (Physalis peruviana L.) in Ecuador. Using different genome analysis tools, the serotype, Multi Locus Sequence Typing (MLST), virulence genes, and antimicrobial resistance (AMR) genes of the L1PEag1 isolate were determined. Additionally, in vitro assays were performed to demonstrate functional genes. RESULTS: The complete genome sequence of the L1PEag1 isolate was assembled into a circular chromosome of 4825.722 Kbp and one plasmid of 3.561 Kbp. The L1PEag1 isolate belongs to the B2 phylogroup, sequence type ST1170, and O1:H4 serotype based on in silico genome analysis. The genome contains 4,473 genes, 88 tRNA, 8 5S rRNA, 7 16S rRNA, and 7 23S rRNA. The average GC content is 50.58%. The specific annotation consisted of 4,439 and 3,723 genes annotated with KEEG and COG respectively, 3 intact prophage regions, 23 genomic islands (GIs), and 4 insertion sequences (ISs) of the ISAs1 and IS630 families. The L1PEag1 isolate carries 25 virulence genes, and 4 perfect and 51 strict antibiotic resistant gene (ARG) regions based on VirulenceFinder and RGI annotation. Besides, the in vitro antibiotic profile indicated resistance to kanamycin (K30), azithromycin (AZM15), clindamycin (DA2), novobiocin (NV30), amikacin (AMK30), and other antibiotics. The L1PEag1 isolate was predicted as a human pathogen, matching 464 protein families (0.934 likelihood). CONCLUSION: Our work emphasizes the necessity of monitoring environmental antibiotic resistance, particularly in commercial settings to contribute to develop early mitigation techniques for dealing with resistance diffusion.202439104589