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905500.9864siRNA-AGO2 complex inhibits bacterial gene translation: A promising therapeutic strategy for superbug infection. Silencing resistance genes of pathogenic bacteria by RNA interference (RNAi) is a potential strategy to fight antibiotic-resistant bacterial infections. Currently, RNAi cannot be achieved in bacteria due to the lack of RNA-induced silencing complex machinery and the difficulty of small interfering RNA (siRNA) delivery. Here, we show that exosomal siRNAs can be efficiently delivered into bacterial cells and can silence target genes primarily through translational repression without mRNA degradation. The exosomal Argonaute 2 (AGO2) protein forms a complex with siRNAs, which is essential for bacterial gene silencing. Both in vitro and in vivo-generated exosome-packaged siRNAs resensitize methicillin-resistant Staphylococcus aureus (MRSA) to methicillin treatment by silencing the mecA gene, which is the primary beta-lactam resistance determinant of MRSA. This approach significantly enhances the therapeutic effect in a mouse model of MRSA infection. In summary, our study provides a method for siRNA delivery to bacteria that may facilitate the treatment of antibiotic-resistant bacterial infection.202540054457
907610.9863ResiDB: 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
509820.9863Feature selection and aggregation for antibiotic resistance GWAS in Mycobacterium tuberculosis: a comparative study. INTRODUCTION: Drug resistance (DR) of pathogens remains a global healthcare concern. In contrast to other bacteria, acquiring mutations in the core genome is the main mechanism of drug resistance for Mycobacterium tuberculosis (MTB). For some antibiotics, the resistance of a particular isolate can be reliably predicted by identifying specific mutations, while for other antibiotics the knowledge of resistance mechanisms is limited. Statistical machine learning (ML) methods are used to infer new genes implicated in drug resistance leveraging large collections of isolates with known whole-genome sequences and phenotypic states for different drugs. However, high correlations between the phenotypic states for commonly used drugs complicate the inference of true associations of mutations with drug phenotypes by ML approaches. METHODS: Recently, several new methods have been developed to select a small subset of reliable predictors of the dependent variable, which may help reduce the number of spurious associations identified. In this study, we evaluated several such methods, namely, logistic regression with different regularization penalty functions, a recently introduced algorithm for solving the best-subset selection problem (ABESS) and "Hungry, Hungry SNPos" (HHS) a heuristic algorithm specifically developed to identify resistance-associated genetic variants in the presence of resistance co-occurrence. We assessed their ability to select known causal mutations for resistance to a specific drug while avoiding the selection of mutations in genes associated with resistance to other drugs, thus we compared selected ML models for their applicability for MTB genome wide association studies. RESULTS AND DISCUSSION: In our analysis, ABESS significantly outperformed the other methods, selecting more relevant sets of mutations. Additionally, we demonstrated that aggregating rare mutations within protein-coding genes into markers indicative of changes in PFAM domains improved prediction quality, and these markers were predominantly selected by ABESS, suggesting their high informativeness. However, ABESS yielded lower prediction accuracy compared to logistic regression methods with regularization.202540606161
668930.9861Wastewater-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
908340.9861ARGNet: 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
918450.9860Unlocking the potential of phages: Innovative approaches to harnessing bacteriophages as diagnostic tools for human diseases. Phages, viruses that infect bacteria, have been explored as promising tools for the detection of human disease. By leveraging the specificity of phages for their bacterial hosts, phage-based diagnostic tools can rapidly and accurately detect bacterial infections in clinical samples. In recent years, advances in genetic engineering and biotechnology have enabled the development of more sophisticated phage-based diagnostic tools, including those that express reporter genes or enzymes, or target specific virulence factors or antibiotic resistance genes. However, despite these advancements, there are still challenges and limitations to the use of phage-based diagnostic tools, including concerns over phage safety and efficacy. This review aims to provide a comprehensive overview of the current state of phage-based diagnostic tools, including their advantages, limitations, and potential for future development. By addressing these issues, we hope to contribute to the ongoing efforts to develop safe and effective phage-based diagnostic tools for the detection of human disease.202337770168
955460.9860A multi-label learning framework for predicting antibiotic resistance genes via dual-view modeling. The increasing prevalence of antibiotic resistance has become a global health crisis. For the purpose of safety regulation, it is of high importance to identify antibiotic resistance genes (ARGs) in bacteria. Although culture-based methods can identify ARGs relatively more accurately, the identifying process is time-consuming and specialized knowledge is required. With the rapid development of whole genome sequencing technology, researchers attempt to identify ARGs by computing sequence similarity from public databases. However, these computational methods might fail to detect ARGs due to the low sequence identity to known ARGs. Moreover, existing methods cannot effectively address the issue of multidrug resistance prediction for ARGs, which is a great challenge to clinical treatments. To address the challenges, we propose an end-to-end multi-label learning framework for predicting ARGs. More specifically, the task of ARGs prediction is modeled as a problem of multi-label learning, and a deep neural network-based end-to-end framework is proposed, in which a specific loss function is introduced to employ the advantage of multi-label learning for ARGs prediction. In addition, a dual-view modeling mechanism is employed to make full use of the semantic associations among two views of ARGs, i.e. sequence-based information and structure-based information. Extensive experiments are conducted on publicly available data, and experimental results demonstrate the effectiveness of the proposed framework on the task of ARGs prediction.202235272349
908870.9859Cocrystallizing and Codelivering Complementary Drugs to Multidrugresistant Tuberculosis Bacteria in Perfecting Multidrug Therapy. Bacteria cells exhibit multidrug resistance in one of two ways: by raising the genetic expression of multidrug efflux pumps or by accumulating several drug-resistant components in many genes. Multidrug-resistive tuberculosis bacteria are treated by multidrug therapy, where a few certain antibacterial drugs are administered together to kill a bacterium jointly. A major drawback of conventional multidrug therapy is that the administration never ensures the reaching of different drug molecules to a particular bacterium cell at the same time, which promotes growing drug resistivity step-wise. As a result, it enhances the treatment time. With additional tabletability and plasticity, the formation of a cocrystal of multidrug can ensure administrating the multidrug chemically together to a target bacterium cell. With properly maintaining the basic philosophy of multidrug therapy here, the synergistic effects of drug molecules can ensure killing the bacteria, even before getting the option to raise the drug resistance against them. This can minimize the treatment span, expenditure and drug resistance. A potential threat of epidemic from tuberculosis has appeared after the Covid-19 outbreak. An unwanted loop of finding molecules with the potential to kill tuberculosis, getting their corresponding drug approvals, and abandoning the drug after facing drug resistance can be suppressed here. This perspective aims to develop the universal drug regimen by postulating the principles of drug molecule selection, cocrystallization, and subsequent harmonisation within a short period to address multidrug-resistant bacteria.202337150990
907880.9859MetaCherchant: analyzing genomic context of antibiotic resistance genes in gut microbiota. MOTIVATION: Antibiotic resistance is an important global public health problem. Human gut microbiota is an accumulator of resistance genes potentially providing them to pathogens. It is important to develop tools for identifying the mechanisms of how resistance is transmitted between gut microbial species and pathogens. RESULTS: We developed MetaCherchant-an algorithm for extracting the genomic environment of antibiotic resistance genes from metagenomic data in the form of a graph. The algorithm was validated on a number of simulated and published datasets, as well as applied to new 'shotgun' metagenomes of gut microbiota from patients with Helicobacter pylori who underwent antibiotic therapy. Genomic context was reconstructed for several major resistance genes. Taxonomic annotation of the context suggests that within a single metagenome, the resistance genes can be contained in genomes of multiple species. MetaCherchant allows reconstruction of mobile elements with resistance genes within the genomes of bacteria using metagenomic data. Application of MetaCherchant in differential mode produced specific graph structures suggesting the evidence of possible resistance gene transmission within a mobile element that occurred as a result of the antibiotic therapy. MetaCherchant is a promising tool giving researchers an opportunity to get an insight into dynamics of resistance transmission in vivo basing on metagenomic data. AVAILABILITY AND IMPLEMENTATION: Source code and binaries are freely available for download at https://github.com/ctlab/metacherchant. The code is written in Java and is platform-independent. COTANCT: ulyantsev@rain.ifmo.ru. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.201829092015
974490.9859PARGT: a software tool for predicting antimicrobial resistance in bacteria. With the ever-increasing availability of whole-genome sequences, machine-learning approaches can be used as an alternative to traditional alignment-based methods for identifying new antimicrobial-resistance genes. Such approaches are especially helpful when pathogens cannot be cultured in the lab. In previous work, we proposed a game-theory-based feature evaluation algorithm. When using the protein characteristics identified by this algorithm, called 'features' in machine learning, our model accurately identified antimicrobial resistance (AMR) genes in Gram-negative bacteria. Here we extend our study to Gram-positive bacteria showing that coupling game-theory-identified features with machine learning achieved classification accuracies between 87% and 90% for genes encoding resistance to the antibiotics bacitracin and vancomycin. Importantly, we present a standalone software tool that implements the game-theory algorithm and machine-learning model used in these studies.202032620856
9060100.9859Targetable nano-delivery vehicles to deliver anti-bacterial small acid-soluble spore protein (SASP) genes. Interest in phage-based therapeutics is increasing, at least in part due to the need for new treatment options for infections caused by antibiotic-resistant bacteria. It is possible to use wild-type (WT) phages to treat bacterial infections, but it is also possible to modify WT phages to generate therapeutics with improved features. Here, we will discuss features of Phico Therapeutics' SASPject technology, which modifies phages for use as targetable nano-delivery vehicles (NDV), to introduce antibacterial Small Acid Soluble Spore Protein (SASP) genes into specific target bacteria.202134723318
8172110.9859From resistance to remedy: the role of clustered regularly interspaced short palindromic repeats system in combating antimicrobial resistance-a review. The growing challenge of antimicrobial resistance (AMR) poses a significant and increasing risk to public health worldwide, necessitating innovative strategies to restore the efficacy of antibiotics. The precise genome-editing abilities of the CRISPR-Cas system have made it a potent instrument for directly targeting and eliminating antibiotic resistance genes. This review explored the mechanisms and applications of CRISPR-Cas systems in combating AMR. The latest developments in CRISPR technology have broadened its potential use, encompassing programmable antibacterial agents and improved diagnostic methods for antibiotic-resistant infections. Nevertheless, several challenges must be overcome for clinical success, including the survival of resistant bacteria, generation of anti-CRISPR proteins that reduce effectiveness, and genetic modifications that change target sequences. Additionally, the efficacy of CRISPR-Cas systems differs across bacterial species, making their universal application challenging. After overcoming these challenges, CRISPR-Cas has the potential to revolutionize AMR treatment, restore antibiotic efficacy, and reshape infection control.202539404843
9174120.9858Developing Phage Therapy That Overcomes the Evolution of Bacterial Resistance. The global rise of antibiotic resistance in bacterial pathogens and the waning efficacy of antibiotics urge consideration of alternative antimicrobial strategies. Phage therapy is a classic approach where bacteriophages (bacteria-specific viruses) are used against bacterial infections, with many recent successes in personalized medicine treatment of intractable infections. However, a perpetual challenge for developing generalized phage therapy is the expectation that viruses will exert selection for target bacteria to deploy defenses against virus attack, causing evolution of phage resistance during patient treatment. Here we review the two main complementary strategies for mitigating bacterial resistance in phage therapy: minimizing the ability for bacterial populations to evolve phage resistance and driving (steering) evolution of phage-resistant bacteria toward clinically favorable outcomes. We discuss future research directions that might further address the phage-resistance problem, to foster widespread development and deployment of therapeutic phage strategies that outsmart evolved bacterial resistance in clinical settings.202337268007
9086130.9858Emergence and selection of isoniazid and rifampin resistance in tuberculosis granulomas. Drug resistant tuberculosis is increasing world-wide. Resistance against isoniazid (INH), rifampicin (RIF), or both (multi-drug resistant TB, MDR-TB) is of particular concern, since INH and RIF form part of the standard regimen for TB disease. While it is known that suboptimal treatment can lead to resistance, it remains unclear how host immune responses and antibiotic dynamics within granulomas (sites of infection) affect emergence and selection of drug-resistant bacteria. We take a systems pharmacology approach to explore resistance dynamics within granulomas. We integrate spatio-temporal host immunity, INH and RIF dynamics, and bacterial dynamics (including fitness costs and compensatory mutations) in a computational framework. We simulate resistance emergence in the absence of treatment, as well as resistance selection during INH and/or RIF treatment. There are four main findings. First, in the absence of treatment, the percentage of granulomas containing resistant bacteria mirrors the non-monotonic bacterial dynamics within granulomas. Second, drug-resistant bacteria are less frequently found in non-replicating states in caseum, compared to drug-sensitive bacteria. Third, due to a steeper dose response curve and faster plasma clearance of INH compared to RIF, INH-resistant bacteria have a stronger influence on treatment outcomes than RIF-resistant bacteria. Finally, under combination therapy with INH and RIF, few MDR bacteria are able to significantly affect treatment outcomes. Overall, our approach allows drug-specific prediction of drug resistance emergence and selection in the complex granuloma context. Since our predictions are based on pre-clinical data, our approach can be implemented relatively early in the treatment development process, thereby enabling pro-active rather than reactive responses to emerging drug resistance for new drugs. Furthermore, this quantitative and drug-specific approach can help identify drug-specific properties that influence resistance and use this information to design treatment regimens that minimize resistance selection and expand the useful life-span of new antibiotics.201829746491
9187140.9858Recent advances in gene-editing approaches for tackling antibiotic resistance threats: a review. Antibiotic resistance, a known global health challenge, involves the flow of bacteria and their genes among animals, humans, and their surrounding environment. It occurs when bacteria evolve and become less responsive to the drugs designated to kill them, making infections harder to treat. Despite several obstacles preventing the spread of genes and bacteria, pathogens regularly acquire novel resistance factors from other species, which reduces their ability to prevent and treat such bacterial infections. This issue requires coordinated efforts in healthcare, research, and public awareness to address its impact on human health worldwide. This review outlines how recent advances in gene editing technology, especially CRISPR/Cas9, unveil a breakthrough in combating antibiotic resistance. Our focus will remain on the relationship between CRISPR/cas9 and its impact on antibiotic resistance and its related infections. Moreover, the prospects of this new advanced research and the challenges of adopting these technologies against infections will be outlined by exploring its different derivatives and discussing their advantages and limitations over others, thereby providing a corresponding reference for the control and prevention of the spread of antibiotic resistance.202438994001
8400150.9858Transferring 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
8173160.9858Advancing Antibacterial Strategies: CRISPR-Phage-Mediated Gene Therapy Targeting Bacterial Resistance Genes. One of the most significant issues facing the world today is antibiotic resistance, which makes it increasingly difficult to treat bacterial infections. Regular antibiotics no longer work against many bacteria, affecting millions of people. A novel approach known as CRISPR-phage therapy may be beneficial. This technique introduces a technology called CRISPR into resistant bacteria using bacteriophages. The genes that cause bacteria to become resistant to antibiotics can be identified and cut using CRISPR. This enables antibiotics to function by inhibiting the bacteria. This approach is highly precise, unlike conventional antibiotics, so it doesn't damage our bodies' beneficial bacteria. Preliminary studies and limited clinical trials suggest that this technique can effectively target drug-resistant bacteria such as Klebsiella pneumoniae and Methicillinresistant Staphylococcus aureus (MRSA). However, challenges in phage engineering, host delivery, and the growing threat of bacterial CRISPR resistance demand urgent and strategic innovation. Our perspective underscores that without proactive resolution of these hurdles, the current hopefulness could disappear. Looking ahead, integrating next-generation Cas effectors, non-DSB editors, and resistance monitoring frameworks could transform CRISPR-phage systems from an experimental novelty into a clinical mainstay. This shift will require not only scientific ingenuity but also coordinated advances in regulatory, translational, and manufacturing efforts.202540990280
9189170.9858CRISPR-Cas9 System: A Prospective Pathway toward Combatting Antibiotic Resistance. Antibiotic resistance is rising to dangerously high levels throughout the world. To cope with this problem, scientists are working on CRISPR-based research so that antibiotic-resistant bacteria can be killed and attacked almost as quickly as antibiotic-sensitive bacteria. Nuclease activity is found in Cas9, which can be programmed with a specific target sequence. This mechanism will only attack pathogens in the microbiota while preserving commensal bacteria. This article portrays the delivery methods used in the CRISPR-Cas system, which are both viral and non-viral, along with its implications and challenges, such as microbial dysbiosis, off-target effects, and failure to counteract intracellular infections. CRISPR-based systems have a lot of applications, such as correcting mutations, developing diagnostics for infectious diseases, improving crops productions, improving breeding techniques, etc. In the future, CRISPR-based systems will revolutionize the world by curing diseases, improving agriculture, and repairing genetic disorders. Though all the drawbacks of the technology, CRISPR carries great potential; thus, the modification and consideration of some aspects could result in a mind-blowing technique to attain all the applications listed and present a game-changing potential.202337370394
8262180.9858Advances in CRISPR-Cas systems for human bacterial disease. Prokaryotic adaptive immune systems called CRISPR-Cas systems have transformed genome editing by allowing for precise genetic alterations through targeted DNA cleavage. This system comprises CRISPR-associated genes and repeat-spacer arrays, which generate RNA molecules that guide the cleavage of invading genetic material. CRISPR-Cas is classified into Class 1 (multi-subunit effectors) and Class 2 (single multi-domain effectors). Its applications span combating antimicrobial resistance (AMR), targeting antibiotic resistance genes (ARGs), resensitizing bacteria to antibiotics, and preventing horizontal gene transfer (HGT). CRISPR-Cas3, for example, effectively degrades plasmids carrying resistance genes, providing a precise method to disarm bacteria. In the context of ESKAPE pathogens, CRISPR technology can resensitize bacteria to antibiotics by targeting specific resistance genes. Furthermore, in tuberculosis (TB) research, CRISPR-based tools enhance diagnostic accuracy and facilitate precise genetic modifications for studying Mycobacterium tuberculosis. CRISPR-based diagnostics, leveraging Cas endonucleases' collateral cleavage activity, offer highly sensitive pathogen detection. These advancements underscore CRISPR's transformative potential in addressing AMR and enhancing infectious disease management.202439266183
8171190.9858Advancements in CRISPR-Cas-based strategies for combating antimicrobial resistance. Multidrug resistance (MDR) in bacteria presents a significant global health threat, driven by the widespread dissemination of antibiotic-resistant genes (ARGs). The CRISPR-Cas system, known for its precision and adaptability, holds promise as a tool to combat antimicrobial resistance (AMR). Although previous studies have explored the use of CRISPR-Cas to target bacterial genomes or plasmids harboring resistance genes, the application of CRISPR-Cas-based antimicrobial therapies is still in its early stages. Challenges such as low efficiency and difficulties in delivering CRISPR to bacterial cells remain. This review provides an overview of the CRISPR-Cas system, highlights recent advancements in CRISPR-Cas-based antimicrobials and delivery strategies for combating AMR. The review also discusses potential challenges for the future development of CRISPR-Cas-based antimicrobials. Addressing these challenges would enable CRISPR therapies to become a practical solution for treating AMR infections in the future.202540440869