# | Rank | Similarity | Title + Abs. | Year | PMID |
|---|---|---|---|---|---|
| 0 | 1 | 2 | 3 | 4 | 5 |
| 9603 | 0 | 0.9968 | Resistance signatures manifested in early drug response across cancer types and species. Aim: Growing evidence points to non-genetic mechanisms underlying long-term resistance to cancer therapies. These mechanisms involve pre-existing or therapy-induced transcriptional cell states that confer resistance. However, the relationship between early transcriptional responses to treatment and the eventual emergence of resistant states remains poorly understood. Furthermore, it is unclear whether such early resistance-associated transcriptional responses are evolutionarily conserved. In this study, we examine the similarity between early transcriptional responses and long-term resistant states, assess their clinical relevance, and explore their evolutionary conservation across species. Methods: We integrated datasets on early drug responses and long-term resistance from multiple cancer cell lines, bacteria, and yeast to identify early transcriptional changes predictive of long-term resistance and assess their evolutionary conservation. Using genome-wide CRISPR-Cas9 knockout screens, we evaluated the impact of genes associated with resistant transcriptional states on drug sensitivity. Clinical datasets were analyzed to explore the prognostic value of the identified resistance-associated gene signatures. Results: We found that transcriptional states observed in drug-naive cells and shortly after treatment overlapped with those seen in fully resistant populations. Some of these shared features appear to be evolutionarily conserved. Knockout of genes marking resistant states sensitized ovarian cancer cells to Prexasertib. Moreover, early resistance gene signatures effectively distinguished therapy responders from non-responders in multiple clinical cancer trials and differentiated premalignant breast lesions that progressed to malignancy from those that remained benign. Conclusion: Early cellular transcriptional responses to therapy exhibit key similarities to fully resistant states across different drugs, cancer types, and species. Gene signatures defining these early resistance states have prognostic value in clinical settings. | 2025 | 41019980 |
| 8413 | 1 | 0.9968 | Investigating mechanisms underlying genetic resistance to Salmon Rickettsial Syndrome in Atlantic salmon using RNA sequencing. BACKGROUND: Salmon Rickettsial Syndrome (SRS), caused by Piscirickettsia salmonis, is one of the primary causes of morbidity and mortality in Atlantic salmon aquaculture, particularly in Chile. Host resistance is a heritable trait, and functional genomic studies have highlighted genes and pathways important in the response of salmon to the bacteria. However, the functional mechanisms underpinning genetic resistance are not yet well understood. In the current study, a large population of salmon pre-smolts were challenged with P. salmonis, with mortality levels recorded and samples taken for genotyping. In parallel, head kidney and liver samples were taken from animals of the same population with high and low genomic breeding values for resistance, and used for RNA-Sequencing to compare their transcriptome profile both pre and post infection. RESULTS: A significant and moderate heritability (h(2) = 0.43) was shown for the trait of binary survival. Genome-wide association analyses using 38 K imputed SNP genotypes across 2265 animals highlighted that resistance is a polygenic trait. Several thousand genes were identified as differentially expressed between controls and infected samples, and enriched pathways related to the host immune response were highlighted. In addition, several networks with significant correlation with SRS resistance breeding values were identified, suggesting their involvement in mediating genetic resistance. These included apoptosis, cytoskeletal organisation, and the inflammasome. CONCLUSIONS: While resistance to SRS is a polygenic trait, this study has highlighted several relevant networks and genes that are likely to play a role in mediating genetic resistance. These genes may be future targets for functional studies, including genome editing, to further elucidate their role underpinning genetic variation in host resistance. | 2021 | 33676414 |
| 9604 | 2 | 0.9967 | Extreme Antibiotic Persistence via Heterogeneity-Generating Mutations Targeting Translation. Antibiotic persistence, the noninherited tolerance of a subpopulation of bacteria to high levels of antibiotics, is a bet-hedging phenomenon with broad clinical implications. Indeed, the isolation of bacteria with substantially increased persistence rates from chronic infections suggests that evolution of hyperpersistence is a significant factor in clinical therapy resistance. However, the pathways that lead to hyperpersistence and the underlying cellular states have yet to be systematically studied. Here, we show that laboratory evolution can lead to increase in persistence rates by orders of magnitude for multiple independently evolved populations of Escherichia coli and that the driving mutations are highly enriched in translation-related genes. Furthermore, two distinct adaptive mutations converge on concordant transcriptional changes, including increased population heterogeneity in the expression of several genes. Cells with extreme expression of these genes showed dramatic differences in persistence rates, enabling isolation of subpopulations in which a substantial fraction of cells are persisters. Expression analysis reveals coherent regulation of specific pathways that may be critical to establishing the hyperpersistence state. Hyperpersister mutants can thus enable the systematic molecular characterization of this unique physiological state, a critical prerequisite for developing antipersistence strategies.IMPORTANCE Bacterial persistence is a fascinating phenomenon in which a small subpopulation of bacteria becomes phenotypically tolerant to lethal antibiotic exposure. There is growing evidence that populations of bacteria in chronic clinical infections develop a hyperpersistent phenotype, enabling a substantially larger subpopulation to survive repeated antibiotic treatment. The mechanisms of persistence and modes of increasing persistence rates remain largely unknown. Here, we utilized experimental evolution to select for Escherichia coli mutants that have more than a thousandfold increase in persistence rates. We discovered that a variety of individual mutations to translation-related processes are causally involved. Furthermore, we found that these mutations lead to population heterogeneity in the expression of specific genes. We show that this can be used to isolate populations in which the majority of bacteria are persisters, thereby enabling systems-level characterization of this fascinating and clinically significant microbial phenomenon. | 2020 | 31964772 |
| 5098 | 3 | 0.9967 | Feature 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. | 2025 | 40606161 |
| 7691 | 4 | 0.9966 | Antimicrobial Chemicals Associate with Microbial Function and Antibiotic Resistance Indoors. Humans purposefully and inadvertently introduce antimicrobial chemicals into buildings, resulting in widespread compounds, including triclosan, triclocarban, and parabens, in indoor dust. Meanwhile, drug-resistant infections continue to increase, raising concerns that buildings function as reservoirs of, or even select for, resistant microorganisms. Support for these hypotheses is limited largely since data describing relationships between antimicrobials and indoor microbial communities are scant. We combined liquid chromatography-isotope dilution tandem mass spectrometry with metagenomic shotgun sequencing of dust collected from athletic facilities to characterize relationships between indoor antimicrobial chemicals and microbial communities. Elevated levels of triclosan and triclocarban, but not parabens, were associated with distinct indoor microbiomes. Dust of high triclosan content contained increased Gram-positive species with diverse drug resistance capabilities, whose pangenomes were enriched for genes encoding osmotic stress responses, efflux pump regulation, lipid metabolism, and material transport across cell membranes; such triclosan-associated functional shifts have been documented in laboratory cultures but not yet from buildings. Antibiotic-resistant bacterial isolates were cultured from all but one facility, and resistance often increased in buildings with very high triclosan levels, suggesting links between human encounters with viable drug-resistant bacteria and local biocide conditions. This characterization uncovers complex relationships between antimicrobials and indoor microbiomes: some chemicals elicit effects, whereas others may not, and no single functional or resistance factor explained chemical-microbe associations. These results suggest that anthropogenic chemicals impact microbial systems in or around buildings and their occupants, highlighting an emergent need to identify the most important indoor, outdoor, and host-associated sources of antimicrobial chemical-resistome interactions. IMPORTANCE The ubiquitous use of antimicrobial chemicals may have undesired consequences, particularly on microbes in buildings. This study shows that the taxonomy and function of microbes in indoor dust are strongly associated with antimicrobial chemicals-more so than any other feature of the buildings. Moreover, we identified links between antimicrobial chemical concentrations in dust and culturable bacteria that are cross-resistant to three clinically relevant antibiotics. These findings suggest that humans may be influencing the microbial species and genes that are found indoors through the addition and removal of particular antimicrobial chemicals. | 2018 | 30574558 |
| 5107 | 5 | 0.9966 | PARMAP: A Pan-Genome-Based Computational Framework for Predicting Antimicrobial Resistance. Antimicrobial resistance (AMR) has emerged as one of the most urgent global threats to public health. Accurate detection of AMR phenotypes is critical for reducing the spread of AMR strains. Here, we developed PARMAP (Prediction of Antimicrobial Resistance by MAPping genetic alterations in pan-genome) to predict AMR phenotypes and to identify AMR-associated genetic alterations based on the pan-genome of bacteria by utilizing machine learning algorithms. When we applied PARMAP to 1,597 Neisseria gonorrhoeae strains, it successfully predicted their AMR phenotypes based on a pan-genome analysis. Furthermore, it identified 328 genetic alterations in 23 known AMR genes and discovered many new AMR-associated genetic alterations in ciprofloxacin-resistant N. gonorrhoeae, and it clearly indicated the genetic heterogeneity of AMR genes in different subtypes of resistant N. gonorrhoeae. Additionally, PARMAP performed well in predicting the AMR phenotypes of Mycobacterium tuberculosis and Escherichia coli, indicating the robustness of the PARMAP framework. In conclusion, PARMAP not only precisely predicts the AMR of a population of strains of a given species but also uses whole-genome sequencing data to prioritize candidate AMR-associated genetic alterations based on their likelihood of contributing to AMR. Thus, we believe that PARMAP will accelerate investigations into AMR mechanisms in other human pathogens. | 2020 | 33193203 |
| 3967 | 6 | 0.9966 | Exploring Post-Treatment Reversion of Antimicrobial Resistance in Enteric Bacteria of Food Animals as a Resistance Mitigation Strategy. Antimicrobial drug use in food animals is associated with an elevation in relative abundance of bacteria resistant to the drug among the animal enteric bacteria. Some of these bacteria are potential foodborne pathogens. Evidence suggests that at least in the enteric nontype-specific Escherichia coli, after treatment the resistance abundance reverts to the background pre-treatment levels, without further interventions. We hypothesize that it is possible to define the distribution of the time period after treatment within which resistance to the administered drug, and possibly other drugs in case of coselection, in fecal bacteria of the treated animals returns to the background pre-treatment levels. Furthermore, it is possible that a novel resistance mitigation strategy for microbiological food safety could be developed based on this resistance reversion phenomenon. The strategy would be conceptually similar to existing antimicrobial drug withdrawal periods, which is a well-established and accepted mitigation strategy for avoiding violative drug residues in the edible products from the treated animals. For developing resistance-relevant withdrawals, a mathematical framework can be used to join the necessary pharmacological, microbiological, and animal production components to project the distributions of the post-treatment resistance reversion periods in the production animal populations for major antimicrobial drug classes in use. The framework can also help guide design of empirical studies into the resistance-relevant withdrawal periods and development of mitigation approaches to reduce the treatment-associated elevation of resistance in animal enteric bacteria. We outline this framework, schematically and through exemplar equations, and how its components could be formulated. | 2016 | 27552491 |
| 9457 | 7 | 0.9966 | Exploring the role of gut microbiota in antibiotic resistance and prevention. BACKGROUND/INTRODUCTION: Antimicrobial resistance (AMR) and the evolution of multiple drug-resistant (MDR) bacteria is of grave public health concern. To combat the pandemic of AMR, it is necessary to focus on novel alternatives for drug development. Within the host, the interaction of the pathogen with the microbiome plays a pivotal role in determining the outcome of pathogenesis. Therefore, microbiome-pathogen interaction is one of the potential targets to be explored for novel antimicrobials. MAIN BODY: This review focuses on how the gut microbiome has evolved as a significant component of the resistome as a source of antibiotic resistance genes (ARGs). Antibiotics alter the composition of the native microbiota of the host by favouring resistant bacteria that can manifest as opportunistic infections. Furthermore, gut dysbiosis has also been linked to low-dosage antibiotic ingestion or subtherapeutic antibiotic treatment (STAT) from food and the environment. DISCUSSION: Colonization by MDR bacteria is potentially acquired and maintained in the gut microbiota. Therefore, it is pivotal to understand microbial diversity and its role in adapting pathogens to AMR. Implementing several strategies to prevent or treat dysbiosis is necessary, including faecal microbiota transplantation, probiotics and prebiotics, phage therapy, drug delivery models, and antimicrobial stewardship regulation. | 2025 | 40096354 |
| 4269 | 8 | 0.9965 | Whole-cell modeling of E. coli colonies enables quantification of single-cell heterogeneity in antibiotic responses. Antibiotic resistance poses mounting risks to human health, as current antibiotics are losing efficacy against increasingly resistant pathogenic bacteria. Of particular concern is the emergence of multidrug-resistant strains, which has been rapid among Gram-negative bacteria such as Escherichia coli. A large body of work has established that antibiotic resistance mechanisms depend on phenotypic heterogeneity, which may be mediated by stochastic expression of antibiotic resistance genes. The link between such molecular-level expression and the population levels that result is complex and multi-scale. Therefore, to better understand antibiotic resistance, what is needed are new mechanistic models that reflect single-cell phenotypic dynamics together with population-level heterogeneity, as an integrated whole. In this work, we sought to bridge single-cell and population-scale modeling by building upon our previous experience in "whole-cell" modeling, an approach which integrates mathematical and mechanistic descriptions of biological processes to recapitulate the experimentally observed behaviors of entire cells. To extend whole-cell modeling to the "whole-colony" scale, we embedded multiple instances of a whole-cell E. coli model within a model of a dynamic spatial environment, allowing us to run large, parallelized simulations on the cloud that contained all the molecular detail of the previous whole-cell model and many interactive effects of a colony growing in a shared environment. The resulting simulations were used to explore the response of E. coli to two antibiotics with different mechanisms of action, tetracycline and ampicillin, enabling us to identify sub-generationally-expressed genes, such as the beta-lactamase ampC, which contributed greatly to dramatic cellular differences in steady-state periplasmic ampicillin and was a significant factor in determining cell survival. | 2023 | 37327241 |
| 4394 | 9 | 0.9965 | Signatures of Selection at Drug Resistance Loci in Mycobacterium tuberculosis. Tuberculosis (TB) is the leading cause of death by an infectious disease, and global TB control efforts are increasingly threatened by drug resistance in Mycobacterium tuberculosis. Unlike most bacteria, where lateral gene transfer is an important mechanism of resistance acquisition, resistant M. tuberculosis arises solely by de novo chromosomal mutation. Using whole-genome sequencing data from two natural populations of M. tuberculosis, we characterized the population genetics of known drug resistance loci using measures of diversity, population differentiation, and convergent evolution. We found resistant subpopulations to be less diverse than susceptible subpopulations, consistent with ongoing transmission of resistant M. tuberculosis. A subset of resistance genes ("sloppy targets") were characterized by high diversity and multiple rare variants; we posit that a large genetic target for resistance and relaxation of purifying selection contribute to high diversity at these loci. For "tight targets" of selection, the path to resistance appeared narrower, evidenced by single favored mutations that arose numerous times in the phylogeny and segregated at markedly different frequencies in resistant and susceptible subpopulations. These results suggest that diverse genetic architectures underlie drug resistance in M. tuberculosis and that combined approaches are needed to identify causal mutations. Extrapolating from patterns observed for well-characterized genes, we identified novel candidate variants involved in resistance. The approach outlined here can be extended to identify resistance variants for new drugs, to investigate the genetic architecture of resistance, and when phenotypic data are available, to find candidate genetic loci underlying other positively selected traits in clonal bacteria. IMPORTANCEMycobacterium tuberculosis, the causative agent of tuberculosis (TB), is a significant burden on global health. Antibiotic treatment imposes strong selective pressure on M. tuberculosis populations. Identifying the mutations that cause drug resistance in M. tuberculosis is important for guiding TB treatment and halting the spread of drug resistance. Whole-genome sequencing (WGS) of M. tuberculosis isolates can be used to identify novel mutations mediating drug resistance and to predict resistance patterns faster than traditional methods of drug susceptibility testing. We have used WGS from natural populations of drug-resistant M. tuberculosis to characterize effects of selection for advantageous mutations on patterns of diversity at genes involved in drug resistance. The methods developed here can be used to identify novel advantageous mutations, including new resistance loci, in M. tuberculosis and other clonal pathogens. | 2018 | 29404424 |
| 9661 | 10 | 0.9965 | Pangenomes of human gut microbiota uncover links between genetic diversity and stress response. The genetic diversity of the gut microbiota has a central role in host health. Here, we created pangenomes for 728 human gut prokaryotic species, quadrupling the genes of strain-specific genomes. Each of these species has a core set of a thousand genes, differing even between closely related species, and an accessory set of genes unique to the different strains. Functional analysis shows high strain variability associates with sporulation, whereas low variability is linked with antibiotic resistance. We further map the antibiotic resistome across the human gut population and find 237 cases of extreme resistance even to last-resort antibiotics, with a predominance among Enterobacteriaceae. Lastly, the presence of specific genes in the microbiota relates to host age and sex. Our study underscores the genetic complexity of the human gut microbiota, emphasizing its significant implications for host health. The pangenomes and antibiotic resistance map constitute a valuable resource for further research. | 2024 | 39353429 |
| 9605 | 11 | 0.9965 | Gene Expression Variability Underlies Adaptive Resistance in Phenotypically Heterogeneous Bacterial Populations. The root cause of the antibiotic resistance crisis is the ability of bacteria to evolve resistance to a multitude of antibiotics and other environmental toxins. The regulation of adaptation is difficult to pinpoint due to extensive phenotypic heterogeneity arising during evolution. Here, we investigate the mechanisms underlying general bacterial adaptation by evolving wild-type Escherichia coli populations to dissimilar chemical toxins. We demonstrate the presence of extensive inter- and intrapopulation phenotypic heterogeneity across adapted populations in multiple traits, including minimum inhibitory concentration, growth rate, and lag time. To search for a common response across the heterogeneous adapted populations, we measured gene expression in three stress-response networks: the mar regulon, the general stress response, and the SOS response. While few genes were differentially expressed, clustering revealed that interpopulation gene expression variability in adapted populations was distinct from that of unadapted populations. Notably, we observed both increases and decreases in gene expression variability upon adaptation. Sequencing select genes revealed that the observed gene expression trends are not necessarily attributable to genetic changes. To further explore the connection between gene expression variability and adaptation, we propagated single-gene knockout and CRISPR (clustered regularly interspaced short palindromic repeats) interference strains and quantified impact on adaptation to antibiotics. We identified significant correlations that suggest genes with low expression variability have greater impact on adaptation. This study provides evidence that gene expression variability can be used as an indicator of bacterial adaptive resistance, even in the face of the pervasive phenotypic heterogeneity underlying adaptation. | 2015 | 27623410 |
| 4200 | 12 | 0.9965 | Antibiotic resistance: are we all doomed? Antibiotic resistance is a growing and worrying problem associated with increased deaths and suffering for people. Overall, there are only two factors that drive antimicrobial resistance, and both can be controlled. These factors are the volumes of antimicrobials used and the spread of resistant micro-organisms and/or the genes encoding for resistance. The One Health concept is important if we want to understand better and control antimicrobial resistance. There are many things we can do to better control antimicrobial resistance. We need to prevent infections. We need to have better surveillance with good data on usage patterns and resistance patterns available across all sectors, both human and agriculture, locally and internationally. We need to act on these results when we see either inappropriate usage or resistance levels rising in bacteria that are of concern for people. We need to ensure that food and water sources do not spread multi-resistant micro-organisms or resistance genes. We need better approaches to restrict successfully what and how antibiotics are used in people. We need to restrict the use of 'critically important' antibiotics in food animals and the entry of these drugs into the environment. We need to ensure that 'One Health' concept is not just a buzz word but implemented. We need to look at all sectors and control not only antibiotic use but also the spread and development of antibiotic resistant bacteria - both locally and internationally. | 2015 | 26563691 |
| 6668 | 13 | 0.9965 | Complexities in understanding antimicrobial resistance across domesticated animal, human, and environmental systems. Antimicrobial resistance (AMR) is a significant threat to both human and animal health. The spread of AMR bacteria and genes across systems can occur through a myriad of pathways, both related and unrelated to agriculture, including via wastewater, soils, manure applications, direct exchange between humans and animals, and food exposure. Tracing origins and drivers of AMR bacteria and genes is challenging due to the array of contexts and the complexity of interactions overlapping health practice, microbiology, genetics, applied science and engineering, as well as social and human factors. Critically assessing the diverse and sometimes contradictory AMR literature is a valuable step in identifying tractable mitigation options to stem AMR spread. In this article we review research on the nonfoodborne spread of AMR, with a focus on domesticated animals and the environment and possible exposures to humans. Attention is especially placed on delineating possible sources and causes of AMR bacterial phenotypes, including underpinning the genetics important to human and animal health. | 2019 | 30924539 |
| 3965 | 14 | 0.9965 | Antimicrobial resistance associated with the use of antimicrobial processing aids during poultry processing operations: cause for concern? Antimicrobial resistance has become a global issue and a threat to human and animal health. Contamination of poultry carcasses with meat-borne pathogens represents both an economic and a public health concern. The use of antimicrobial processing aids (APA) during poultry processing has contributed to an improvement in the microbiological quality of poultry carcasses. However, the extensive use of these decontaminants has raised concerns about their possible role in the co-selection of antibiotic-resistant bacteria. This topic is presented in the current review to provide an update on the information related to bacterial adaptation to APA used in poultry processing establishments, and to discuss the relationship between APA bacterial adaptation and the acquisition of a new resistance phenotype to therapeutic antimicrobials by bacteria. Common mechanisms such as active efflux and changes in membrane fluidity are the most documented mechanisms responsible for bacterial cross-resistance to APA and antimicrobials. Although most studies reported a bacterial resistance to antibiotics not reaching a clinical level, the under-exposure of bacteria to APA remains a concern in the poultry industry. Further research is needed to determine if APA used during poultry processing and therapeutic antimicrobials share common sites of action in bacteria and encounter similar mechanisms of resistance. | 2021 | 32744054 |
| 8339 | 15 | 0.9965 | Dynamical model of antibiotic responses linking expression of resistance genes to metabolism explains emergence of heterogeneity during drug exposures. Antibiotic responses in bacteria are highly dynamic and heterogeneous, with sudden exposure of bacterial colonies to high drug doses resulting in the coexistence of recovered and arrested cells. The dynamics of the response is determined by regulatory circuits controlling the expression of resistance genes, which are in turn modulated by the drug's action on cell growth and metabolism. Despite advances in understanding gene regulation at the molecular level, we still lack a framework to describe how feedback mechanisms resulting from the interdependence between expression of resistance and cell metabolism can amplify naturally occurring noise and create heterogeneity at the population level. To understand how this interplay affects cell survival upon exposure, we constructed a mathematical model of the dynamics of antibiotic responses that links metabolism and regulation of gene expression, based on the tetracycline resistancetetoperon inE. coli. We use this model to interpret measurements of growth and expression of resistance in microfluidic experiments, both in single cells and in biofilms. We also implemented a stochastic model of the drug response, to show that exposure to high drug levels results in large variations of recovery times and heterogeneity at the population level. We show that stochasticity is important to determine how nutrient quality affects cell survival during exposure to high drug concentrations. A quantitative description of how microbes respond to antibiotics in dynamical environments is crucial to understand population-level behaviors such as biofilms and pathogenesis. | 2024 | 38412523 |
| 6583 | 16 | 0.9965 | Impact of international travel and diarrhea on gut microbiome and resistome dynamics. International travel contributes to the global spread of antimicrobial resistance. Travelers' diarrhea exacerbates the risk of acquiring multidrug-resistant organisms and can lead to persistent gastrointestinal disturbance post-travel. However, little is known about the impact of diarrhea on travelers' gut microbiomes, and the dynamics of these changes throughout travel. Here, we assembled a cohort of 159 international students visiting the Andean city of Cusco, Peru and applied next-generation sequencing techniques to 718 longitudinally-collected stool samples. We find that gut microbiome composition changed significantly throughout travel, but taxonomic diversity remained stable. However, diarrhea disrupted this stability and resulted in an increased abundance of antimicrobial resistance genes that can remain high for weeks. We also identified taxa differentially abundant between diarrheal and non-diarrheal samples, which were used to develop a classification model that distinguishes between these disease states. Additionally, we sequenced the genomes of 212 diarrheagenic Escherichia coli isolates and found those from travelers who experienced diarrhea encoded more antimicrobial resistance genes than those who did not. In this work, we find the gut microbiomes of international travelers' are resilient to dysbiosis; however, they are also susceptible to colonization by multidrug-resistant bacteria, a risk that is more pronounced in travelers with diarrhea. | 2022 | 36470885 |
| 6652 | 17 | 0.9965 | Strategic measures for the control of surging antimicrobial resistance in Hong Kong and mainland of China. Antimicrobial-resistant bacteria are either highly prevalent or increasing rapidly in Hong Kong and China. Treatment options for these bacteria are generally limited, less effective and more expensive. The emergence and dynamics of antimicrobial resistance genes in bacteria circulating between animals, the environment and humans are not entirely known. Nonetheless, selective pressure by antibiotics on the microbiomes of animal and human, and their associated environments (especially farms and healthcare institutions), sewage systems and soil are likely to confer survival advantages upon bacteria with antimicrobial-resistance genes, which may be further disseminated through plasmids or transposons with integrons. Therefore, antibiotic use must be tightly regulated to eliminate such selective pressure, including the illegalization of antibiotics as growth promoters in animal feed and regulation of antibiotic use in veterinary practice and human medicine. Heightened awareness of infection control measures to reduce the risk of acquiring resistant bacteria is essential, especially during antimicrobial use or institutionalization in healthcare facilities. The transmission cycle must be interrupted by proper hand hygiene, environmental cleaning, avoidance of undercooked or raw food and compliance with infection control measures by healthcare workers, visitors and patients, especially during treatment with antibiotics. In addition to these routine measures, proactive microbiological screening of hospitalized patients with risk factors for carrying resistant bacteria, including history of travel to endemic countries, transfer from other hospitals, and prolonged hospitalization; directly observed hand hygiene before oral intake of drugs, food and drinks; and targeted disinfection of high-touch or mutual-touch items, such as bed rails and bed curtains, are important. Transparency of surveillance data from each institute for public scrutiny provides an incentive for controlling antimicrobial resistance in healthcare settings at an administrative level. | 2015 | 26038766 |
| 6580 | 18 | 0.9965 | Transmission of antimicrobial resistance (AMR) during animal transport. The transmission of antimicrobial resistance (AMR) between food-producing animals (poultry, cattle and pigs) during short journeys (< 8 h) and long journeys (> 8 h) directed to other farms or to the slaughterhouse lairage (directly or with intermediate stops at assembly centres or control posts, mainly transported by road) was assessed. Among the identified risk factors contributing to the probability of transmission of antimicrobial-resistant bacteria (ARB) and antimicrobial resistance genes (ARGs), the ones considered more important are the resistance status (presence of ARB/ARGs) of the animals pre-transport, increased faecal shedding, hygiene of the areas and vehicles, exposure to other animals carrying and/or shedding ARB/ARGs (especially between animals of different AMR loads and/or ARB/ARG types), exposure to contaminated lairage areas and duration of transport. There are nevertheless no data whereby differences between journeys shorter or longer than 8 h can be assessed. Strategies that would reduce the probability of AMR transmission, for all animal categories include minimising the duration of transport, proper cleaning and disinfection, appropriate transport planning, organising the transport in relation to AMR criteria (transport logistics), improving animal health and welfare and/or biosecurity immediately prior to and during transport, ensuring the thermal comfort of the animals and animal segregation. Most of the aforementioned measures have similar validity if applied at lairage, assembly centres and control posts. Data gaps relating to the risk factors and the effectiveness of mitigation measures have been identified, with consequent research needs in both the short and longer term listed. Quantification of the impact of animal transportation compared to the contribution of other stages of the food-production chain, and the interplay of duration with all risk factors on the transmission of ARB/ARGs during transport and journey breaks, were identified as urgent research needs. | 2022 | 36304831 |
| 9809 | 19 | 0.9965 | The gut microbiome: an emerging epicenter of antimicrobial resistance? The human gut is one of the most densely populated microbial environments, home to trillions of microorganisms that live in harmony with the body. These microbes help with digestion and play key roles in maintaining a balanced immune system and protecting us from harmful pathogens. However, the crowded nature of this ecosystem makes it easier for harmful bacteria to acquire antimicrobial resistance (AMR) genes, which can lead to multidrug-resistant (MDR) infections. The rise of MDR infections makes treatments harder, leading to more extended hospital stays, relapses, and worse outcomes for patients, ultimately increasing healthcare costs and environmental strain. Since many MDR infections are challenging to treat, nosocomial infection control protocols and infection prevention programmes are frequently the only measures in our hands to stop the spread of these bacteria. New approaches are therefore urgently required to prevent the colonization of MDR infections. This review aims to explore the current understanding of antimicrobial resistance pathways, focusing on how the gut microbiota contributes to AMR. We have also emphasized the potential strategies to prevent the spread and colonization of MDR infections. | 2025 | 40463440 |