Antibiotic resistance: Time of synthesis in a post-genomic age

Identification of potential biogenic chalcones against antibiotic resistant efflux pump (AcrB) via computational study

The cases of bacterial multidrug resistance are increasing every year and becoming a serious concern for human health. Multidrug efflux pumps are key players in the formation of antibiotic resistance, which transfer out a broad spectrum of drugs from the cell and convey resistance to the host. Efflux pumps have significantly reduced the efficacy of the previously available antibiotic armory, thereby increasing the frequency of therapeutic failures. In gram-negative bacteria, the AcrAB-TolC efflux pump is the principal transporter of the substrate and plays a major role in the formation of antibiotic resistance. In the current work, advanced computer-aided drug discovery approaches were utilized to find hit molecules from the library of biogenic chalcones against the bacterial AcrB efflux pump. The results of the performed computational studies via molecular docking, drug-likeness prediction, pharmacokinetic profiling, pharmacophore mapping, density functional theory, and molecular dynamics simulation study provided ZINC000004695648, ZINC000014762506, ZINC000014762510, ZINC000095099506, and ZINC000085510993 as stable hit molecules against the AcrB efflux pumps. Identified hits could successfully act against AcrB efflux pumps after optimization as lead molecules.Communicated by Ramaswamy H. Sarma.

Membrane computing simulation of sexually transmitted bacterial infections in hotspots of individuals with various risk behaviors

The epidemiology of sexually transmitted infections (STIs) is complex due to the coexistence of various pathogens, the variety of transmission modes derived from sexual orientations and behaviors at different ages and genders, and sexual contact hotspots resulting in network transmission. There is also a growing proportion of recreational drug users engaged in high-risk sexual activities, as well as pharmacological self-protection routines fostering non-condom practices. The frequency of asymptomatic patients makes it difficult to develop a comprehensive approach to STI epidemiology. Modeling approaches are required to deal with such complexity. Membrane computing is a natural computing methodology for the virtual reproduction of epidemics under the influence of deterministic and stochastic events with an unprecedented level of granularity. The application of the LOIMOS program to STI epidemiology illustrates the possibility of using it to shape appropriate interventions. Under the conditions of our basic landscape, including sexual hotspots of individuals with various risk behaviors, an increase in condom use reduces STIs in a larger proportion of heterosexuals than in same-gender sexual contacts and is much more efficient for reducing Neisseria gonorrhoeae than Chlamydia and lymphogranuloma venereum infections. Amelioration from diagnostic STI screening could be instrumental in reducing N. gonorrhoeae infections, particularly in men having sex with men (MSM), and Chlamydia trachomatis infections in the heterosexual population; however, screening was less effective in decreasing lymphogranuloma venereum infections in MSM. The influence of STI epidemiology of sexual contacts between different age groups (<35 and ≥35 years) and in bisexual populations was also submitted for simulation.IMPORTANCEThe epidemiology of sexually transmitted infections (STIs) is complex and significantly influences sexual and reproductive health worldwide. Gender, age, sexual orientation, sexual behavior (including recreational drug use and physical and pharmacological protection practices), the structure of sexual contact networks, and the limited application or efficiency of diagnostic screening procedures create variable landscapes in different countries. Modeling techniques are required to deal with such complexity. We propose the use of a simulation technology based on membrane computing, mimicking in silico STI epidemics under various local conditions with an unprecedented level of detail. This approach allows us to evaluate the relative weight of the various epidemic drivers in various populations at risk and the possible outcomes of interventions in particular epidemiological landscapes.

Antibiotic resistance profiles of gut microbiota across various primate species in Guangxi

Introduction

Understanding the gut microbiota and antibiotic resistance gene (ARG) profiles in non-human primates (NHPs) is crucial for evaluating their potential impact on human health and the environment.

Methods

In this study, we performed metagenomic analysis of 203 primate fecal samples, including nine NHP species and humans, to comprehensively characterize their gut microbiota and ARGs.

Results

Our study reveals the prevailing phyla in primates as Firmicutes, Bacteroidetes, Euryarchaeota, and Proteobacteria. The captive NHPs exhibited higher ARG abundance compared to their wild counterparts, with tetracycline and beta-lactam resistance genes prevailing. Notably, ARG subtypes in Trachypithecus leucocephalus (T. leucocephalus) residing in karst limestone habitats displayed a more dispersed distribution compared to other species. Interestingly, ARG profiles of NHPs clustered based on geographic location and captivity status. Co-occurrence network analysis revealed intricate correlations between ARG subtypes and bacterial taxa. Procrustes analysis unveiled a significant correlation between ARGs and microbial phylogenetic community structure. Taxonomic composition analysis further highlighted differences in microbial abundance among NHPs and humans.

Discussion

Our study underscores the impact of lifestyle and geographical location on NHP gut microbiota and ARGs, providing essential insights into the potential risks posed by NHPs to antibiotic resistance dissemination. This comprehensive analysis enhances our understanding of the interplay between NHPs and the gut resistome, offering a critical reference for future research on antibiotic resistance and host-microbe interactions.

Cyanobacterial extracellular antibacterial substances could promote the spread of antibiotic resistance: impacts and reasons

Many studies have shown that antibiotic resistance genes (ARGs) can be facilitated by a variety of antibacterial substances. Cyanobacteria are photosynthetic bacteria that are widely distributed in the ocean. Some extracellular substances produced by marine cyanobacteria have been found to possess antibacterial activity. However, the impact of these extracellular substances on ARGs is unclear. Therefore, we established groups of seawater microcosms that contained different concentrations (1000, 100, 10, 1, 0.1, 0.01, and 0 μg mL-1) of cyanobacterial extracellular substances (CES), and tracked the changes of 17 types of ARGs, the integron gene (intI1), as well as the bacterial community at different time points. The results showed that CES could enrich most ARGs (15/17) in the initial stage, particularly at low concentrations (10 and 100 μg mL-1). The correlation analysis showed a positive correlation between several ARGs and intI1. It is suggested that the abundance of intI1 increased with CES may contribute to the changes of these ARGs, and co-resistance of CES may be the underlying reason for the similar variation pattern of some ARGs. Moreover, the results of qPCR and high-throughput sequencing of 16S rRNA showed that CES had an inhibitory impact on the growth of bacterial communities. High concentrations of CES were found to alter the structure of bacterial communities. Co-occurrence networks showed that bacteria elevated in the high concentration group of CES and might serve as the potential hosts for a variety of ARGs. In general, marine cyanobacteria could play an important role in the global dissemination of ARGs and antibiotic-resistant bacteria (ARBs).

Current Uses and Future Perspectives of Genomic Technologies in Clinical Microbiology

Recent advancements in sequencing technology and data analytics have led to a transformative era in pathogen detection and typing. These developments not only expedite the process, but also render it more cost-effective. Genomic analyses of infectious diseases are swiftly becoming the standard for pathogen analysis and control. Additionally, national surveillance systems can derive substantial benefits from genomic data, as they offer profound insights into pathogen epidemiology and the emergence of antimicrobial-resistant strains. Antimicrobial resistance (AMR) is a pressing global public health issue. While clinical laboratories have traditionally relied on culture-based antimicrobial susceptibility testing, the integration of genomic data into AMR analysis holds immense promise. Genomic-based AMR data can furnish swift, consistent, and highly accurate predictions of resistance phenotypes for specific strains or populations, all while contributing invaluable insights for surveillance. Moreover, genome sequencing assumes a pivotal role in the investigation of hospital outbreaks. It aids in the identification of infection sources, unveils genetic connections among isolates, and informs strategies for infection control. The One Health initiative, with its focus on the intricate interconnectedness of humans, animals, and the environment, seeks to develop comprehensive approaches for disease surveillance, control, and prevention. When integrated with epidemiological data from surveillance systems, genomic data can forecast the expansion of bacterial populations and species transmissions. Consequently, this provides profound insights into the evolution and genetic relationships of AMR in pathogens, hosts, and the environment.

Current and Future Technologies for the Detection of Antibiotic-Resistant Bacteria

Antibiotic resistance is a global public health concern, posing a significant threat to the effectiveness of antibiotics in treating bacterial infections. The accurate and timely detection of antibiotic-resistant bacteria is crucial for implementing appropriate treatment strategies and preventing the spread of resistant strains. This manuscript provides an overview of the current and emerging technologies used for the detection of antibiotic-resistant bacteria. We discuss traditional culture-based methods, molecular techniques, and innovative approaches, highlighting their advantages, limitations, and potential future applications. By understanding the strengths and limitations of these technologies, researchers and healthcare professionals can make informed decisions in combating antibiotic resistance and improving patient outcomes.

What are the missing pieces needed to stop antibiotic resistance?

As recognized by several international agencies, antibiotic resistance is nowadays one of the most relevant problems for human health. While this problem was alleviated with the introduction of new antibiotics into the market in the golden age of antimicrobial discovery, nowadays few antibiotics are in the pipeline. Under these circumstances, a deep understanding on the mechanisms of emergence, evolution and transmission of antibiotic resistance, as well as on the consequences for the bacterial physiology of acquiring resistance is needed to implement novel strategies, beyond the development of new antibiotics or the restriction in the use of current ones, to more efficiently treat infections. There are still several aspects in the field of antibiotic resistance that are not fully understood. In the current article, we make a non-exhaustive critical review of some of them that we consider of special relevance, in the aim of presenting a snapshot of the studies that still need to be done to tackle antibiotic resistance.

Trends in Molecular Diagnosis of Nosocomial Pneumonia Classic PCR vs. Point-of-Care PCR: A Narrative Review

Nosocomial pneumonia is one of the most frequent hospital-acquired infections. One of the types of nosocomial pneumonia is ventilator-associated pneumonia, which occurs in endotracheally intubated patients in intensive care units (ICU). Ventilator-associated pneumonia may be caused by multidrug-resistant pathogens, which increase the risk of complications due to the difficulty in treating them. Pneumonia is a respiratory disease that requires targeted antimicrobial treatment initiated as early as possible to have a good outcome. For the therapy to be as specific and started sooner, diagnostic methods have evolved rapidly, becoming quicker and simpler to perform. Polymerase chain reaction (PCR) is a rapid diagnostic technique with numerous advantages compared to classic plate culture-based techniques. Researchers continue to improve diagnostic methods; thus, the newest types of PCR can be performed at the bedside, in the ICU, so-called point of care testing-PCR (POC-PCR). The purpose of this review is to highlight the benefits and drawbacks of PCR-based techniques in managing nosocomial pneumonia.

The broad antibacterial activity of a small synthetic receptor for cellular phosphatidylglycerol lipids

A small receptor molecule composed of a porphyrin core with tetrakis-ammonium glycine pickets (liptin 3e) appears to target anionic phosphatidylglycerol (PG) lipid head groups through multifunctional binding-pocket complementarity. Although a major component of bacterial cell membranes, PG is not widely found in animal cells, making PG potentially selective for bacterial targeting. Growth of microbial isolates was monitored in liquid cultures treated with liptin 3e by dilution plate counts and turbidity. Inhibition of growth by liptin 3e was observed for the ESKAPE human pathogens (Enterobacter aerogenes, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterococcus faecium), Escherichia coli, Mycobacterium smegmatis, Streptococcus sobrinus, and methicillin-resistant S. aureus (MRSA), with certain species suppressed at <1 μg/mL (sub-μM) concentrations. Prolonged lag phases were observed, although cell viability was mainly unaffected, suggesting that liptin treatment caused bacteriostasis. Cultures treated with liptin 3e eventually recovered, resumed growth, and reached the same final densities as untreated cultures. Growth of the fungus Candida albicans was not appreciably inhibited by liptin 3e. If liptins exhibit bacteriostasis through broad extracellular binding to PG head groups, thereby disrupting cellular processes, liptins may be considered for development into preclinical drug candidates or be useful as a targeting system for molecular beacons or antibacterial drugs.

Dissecting microbial communities and resistomes for interconnected humans, soil, and livestock

A debate is currently ongoing as to whether intensive livestock farms may constitute reservoirs of clinically relevant antimicrobial resistance (AMR), thus posing a threat to surrounding communities. Here, combining shotgun metagenome sequencing, machine learning (ML), and culture-based methods, we focused on a poultry farm and connected slaughterhouse in China, investigating the gut microbiome of livestock, workers and their households, and microbial communities in carcasses and soil. For both the microbiome and resistomes in this study, differences are observed across environments and hosts. However, at a finer scale, several similar clinically relevant antimicrobial resistance genes (ARGs) and similar associated mobile genetic elements were found in both human and broiler chicken samples. Next, we focused on Escherichia coli, an important indicator for the surveillance of AMR on the farm. Strains of E. coli were found intermixed between humans and chickens. We observed that several ARGs present in the chicken faecal resistome showed correlation to resistance/susceptibility profiles of E. coli isolates cultured from the same samples. Finally, by using environmental sensing these ARGs were found to be correlated to variations in environmental temperature and humidity. Our results show the importance of adopting a multi-domain and multi-scale approach when studying microbial communities and AMR in complex, interconnected environments.

Systems Biology: New Insight into Antibiotic Resistance

Over the past few decades, antimicrobial resistance (AMR) has emerged as an important threat to public health, resulting from the global propagation of multidrug-resistant strains of various bacterial species. Knowledge of the intrinsic factors leading to this resistance is necessary to overcome these new strains. This has contributed to the increased use of omics technologies and their extrapolation to the system level. Understanding the mechanisms involved in antimicrobial resistance acquired by microorganisms at the system level is essential to obtain answers and explore options to combat this resistance. Therefore, the use of robust whole-genome sequencing approaches and other omics techniques such as transcriptomics, proteomics, and metabolomics provide fundamental insights into the physiology of antimicrobial resistance. To improve the efficiency of data obtained through omics approaches, and thus gain a predictive understanding of bacterial responses to antibiotics, the integration of mathematical models with genome-scale metabolic models (GEMs) is essential. In this context, here we outline recent efforts that have demonstrated that the use of omics technology and systems biology, as quantitative and robust hypothesis-generating frameworks, can improve the understanding of antibiotic resistance, and it is hoped that this emerging field can provide support for these new efforts.

reComBat: batch-effect removal in large-scale multi-source gene-expression data integration

Motivation

With the steadily increasing abundance of omics data produced all over the world under vastly different experimental conditions residing in public databases, a crucial step in many data-driven bioinformatics applications is that of data integration. The challenge of batch-effect removal for entire databases lies in the large number of batches and biological variation, which can result in design matrix singularity. This problem can currently not be solved satisfactorily by any common batch-correction algorithm.

Results

We present reComBat, a regularized version of the empirical Bayes method to overcome this limitation and benchmark it against popular approaches for the harmonization of public gene-expression data (both microarray and bulkRNAsq) of the human opportunistic pathogen Pseudomonas aeruginosa. Batch-effects are successfully mitigated while biologically meaningful gene-expression variation is retained. reComBat fills the gap in batch-correction approaches applicable to large-scale, public omics databases and opens up new avenues for data-driven analysis of complex biological processes beyond the scope of a single study.

Availability and implementation

The code is available at https://github.com/BorgwardtLab/reComBat, all data and evaluation code can be found at https://github.com/BorgwardtLab/batchCorrectionPublicData.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Environmental filtering dominated the antibiotic resistome assembly in river networks

River networks play important roles in dissemination of antibiotic resistance genes (ARGs). The occurrence, diversity, and abundance of ARGs in river networks have been widely investigated. However, the assembly processes that shaped ARGs profiles across space and time are largely unknown. Here, the dynamics of ARGs profiles in river networks (Taihu Basin) were revealed by high-throughput quantitative PCR followed by multiple statistical analyses to assess the underlying ecological processes. The results revealed clear variations for ARGs profiles across wet, normal, and dry seasons. Meanwhile, a significant negative correlation (p < 0.01) was observed between the similarity of ARGs profiles and geographic distance, indicating ARGs profiles exhibited distance-decay patterns. Null model analysis showed that ARGs profiles were mainly assembled via deterministic processes. Redundancy analysis followed by hierarchical partitioning revealed that environmental attributes (mainly pH and temperature) were the major factors affecting the dynamics of ARGs profiles. Together, these results indicated that environmental filtering was the dominant ecological process that shaped ARGs profiles. This study enhances our understanding how the antibiotic resistome is assembled in river networks and will be beneficial for the development of management strategies to control ARGs dissemination.

On the Verge of a Catastrophic Collapse? The Need for a Multi-Ecosystem Approach to Microbiome Studies

While the COVID-19 pandemic has led to increased focus on pathogenic microbes that cross the animal-human species barrier, calls to include non-pathogenic interactions in our perspective on public health are gaining traction in the academic community. Over generations, the diversity of the human gut microbiota is being challenged by external perturbations and reduced acquisition of symbiotic species throughout life. When such reduced diversity concerns not only the microbial species, but also the higher taxonomic levels and even the guild level, adequate compensation for possible losses may be lacking. Shifts from a high-abundance to a low-abundance state, known as a tipping point, may result in simultaneous shifts in covarying taxa and ultimately to a catastrophic collapse in which the ecosystem abruptly and possibly irreversibly shifts to an alternative state. Here, we propose that co-occurrence patterns within and between microbial communities across human, animal, soil, water, and other environmental domains should be studied in light of such critical transitions. Improved mechanistic understanding of factors that shape structure and function is needed to understand whether interventions can sustainably remodel disease-prone microbiota compositions to robust and resilient healthy microbiota. Prerequisites for a rational approach are a better understanding of the microbial interaction network, both within and inter-domain, as well as the identification of early warning signs for a catastrophic collapse, warranting a timely response for intervention. We should not forget that mutualism and pathogenicity are two sides of the same coin. Building upon the planetary health concept, we argue that microbiome research should include system level approaches to conserve ecosystem resilience. HIGHLIGHTS 1. Non-pathogenic interactions between ecosystems play a key role in maintaining health. 2. The human gut microbiome may be on the verge of a catastrophic collapse. 3. Research should identify keystone taxa and guilds that interconnect different domains. 4. We should not forget that mutualism and pathogenicity are two sides of the same coin.

Reduction of the resistome risk from cow slurry and manure microbiomes to soil and vegetable microbiomes

In cow farms, the interaction between animal and environmental microbiomes creates hotspots for antibiotic resistance dissemination. A shotgun metagenomic approach was used to survey the resistome risk in five dairy cow farms. To this purpose, 10 environmental compartments were sampled: 3 of them linked to productive cows (fresh slurry, stored slurry, slurry-amended pasture soil); 6 of them to non-productive heifers and dry cows (faeces, fresh manure, aged manure, aged manure-amended orchard soil, vegetables-lettuces and grazed soil); and, finally, unamended control soil. The resistome risk was assessed using MetaCompare, a computational pipeline which scores the resistome risk according to possible links between antibiotic resistance genes (ARGs), mobile genetic elements (MGEs) and human pathogens. The resistome risk decreased from slurry and manure microbiomes to soil and vegetable microbiomes. In total (sum of all the compartments), 18,157 ARGs were detected: 24% related to ansamycins, 21% to multidrugs, 14% to aminoglycosides, 12% to tetracyclines, 9% to β-lactams, and 9% to macrolide-lincosamide-streptogramin B. All but two of the MGE-associated ARGs were only found in the animal dejections (not in soil or vegetable samples). Several ARGs with potential as resistome risk markers (based on their presence in hubs of co-occurrence networks and high dissemination potential) were identified. As a precautionary principle, improved management of livestock dejections is necessary to minimize the risk of antibiotic resistance.

Quantitative biology of survival under antibiotic treatments

The mathematical formulation for the dynamics of growth reduction and/or killing under antibiotic treatments has a long history. Even before the extensive use of antibiotics, attempts to model the killing dynamics of biocides were made [1]. Here, we review relatively simple quantitative formulations of the two main modes of survival under antibiotics, resistance and tolerance, as well as their heterogeneity in bacterial populations. We focus on the two main types of heterogeneity that have been described: heteroresistance and antibiotic persistence, each linked to the variation in a different parameter of the antibiotic response dynamics. Finally, we review the effects on survival of combining resistance and tolerance mutations as well as on the mode and tempo of evolution under antibiotic treatments.

Simulating the impact of non-pharmaceutical interventions limiting transmission in COVID-19 epidemics using a membrane computing model

Epidemics caused by microbial organisms are part of the natural phenomena of increasing biological complexity. The heterogeneity and constant variability of hosts, in terms of age, immunological status, family structure, lifestyle, work activities, social and leisure habits, daily division of time and other demographic characteristics make it extremely difficult to predict the evolution of epidemics. Such prediction is, however, critical for implementing intervention measures in due time and with appropriate intensity. General conclusions should be precluded, given that local parameters dominate the flow of local epidemics. Membrane computing models allows us to reproduce the objects (viruses and hosts) and their interactions (stochastic but also with defined probabilities) with an unprecedented level of detail. Our LOIMOS model helps reproduce the demographics and social aspects of a hypothetical town of 10 320 inhabitants in an average European country where COVID-19 is imported from the outside. The above-mentioned characteristics of hosts and their lifestyle are minutely considered. For the data in the Hospital and the ICU we took advantage of the observations at the Nursery Intensive Care Unit of the Consortium University General Hospital, Valencia, Spain (included as author). The dynamics of the epidemics are reproduced and include the effects on viral transmission of innate and acquired immunity at various ages. The model predicts the consequences of delaying the adoption of non-pharmaceutical interventions (between 15 and 45 days after the first reported cases) and the effect of those interventions on infection and mortality rates (reducing transmission by 20, 50 and 80%) in immunological response groups. The lockdown for the elderly population as a single intervention appears to be effective. This modeling exercise exemplifies the application of membrane computing for designing appropriate multilateral interventions in epidemic situations.