Next generation sequencing reveals limitation of qPCR methods in quantifying emerging antibiotic resistance genes (ARGs) in the environment

Research Trends and Development Dynamics of qPCR-based Biomarkers: A Comprehensive Bibliometric Analysis

Quantitative polymerase chain reaction (qPCR) is a vital molecular technique for biomarker detection; however, its clinical application is impeded by the scarcity of robust biomarkers and the inherent limitations of the technology. This study conducted a bibliometric analysis of 4063 qPCR-based biomarker studies sourced from the Web of Science (WOS) database, employing VOSviewer and CiteSpace to generate multi-dimensional structural insights into this field. The results reveal a growing trend in research within this domain, with gene expression analysis playing a central role in the identification of potential biomarkers. Among these, cancer-related biomarkers are the most prominent, while research on biomarkers for other diseases remains limited. Liquid biopsy biomarkers, including microRNA (miRNA), circulating free DNA (cfDNA), and circulating tumor DNA (ctDNA), are increasingly being explored. The integration of bioinformatics, omics analysis, and high-throughput technologies with qPCR is accelerating biomarker discovery. Furthermore, large-scale parallel sequencing is emerging as a potential alternative to relative quantification and microarray techniques. Nevertheless, qPCR remains essential for validating specific biomarkers, and further standardization of its protocols is necessary to enhance reliability. This study provides a systematic analysis of qPCR-based biomarker research and underscores the need for future technological integration and standardization to facilitate broader clinical applications.

Metagenomic analysis of the dichotomous role of uranium in regulating intracellular and extracellular antibiotic resistance genes in activated sludge

Antibiotic resistance genes (ARGs) in activated sludge include intracellular ARGs (iARGs) and extracellular ARGs (eARGs), both of which are recognized as emerging pollutants. While the activated sludge process has been commonly considered for treating wastewater contaminated with radionuclide, the effects and mechanisms of radioactive heavy metals on the fate of iARGs and eARGs (i/e-ARGs) in activated sludge are largely elusive. Here, the distribution, mobility, and hosts of i/e-ARGs in activated sludge during environmental concentrations (50 μg/L and 5000 μg/L) of radioactive uranium (U) stress were explored via metagenomics. The results revealed that the total relative abundance of iARGs and eARGs decreased by 11.62% and 10.41%, respectively, after 90 days of 50 μg/L of U treatment. In contrast, both i/e-multi- and tetracycline ARGs remarkably increased after being exposed to 5000 μg/L of U. Additionally, exposure to 5000 μg/L of U triggered notable decrease in i/e-insertion sequences and plasmids abundance, but significantly enriched i/e-integrons (p < 0.05). Partial least squares pathway modelling indicated that the prevalence of iARGs and eARGs in activated sludge was primarily driven by bacterial hosts and functional genes, respectively. Our findings revealed the dichotomous variation landscape and mechanisms of i/e-ARGs dynamics in activated sludge during U exposure, offering valuable insights for controlling ARGs risk during radioactive wastewater treatment.

Pioneering Klebsiella Pneumoniae Antibiotic Resistance Prediction With Artificial Intelligence-Clinical Decision Support System-Enhanced Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry: Retrospective Study

BACKGROUND

The rising prevalence and swift spread of multidrug-resistant gram-negative bacteria (MDR-GNB), especially Klebsiella pneumoniae (KP), present a critical global health threat highlighted by the World Health Organization, with mortality rates soaring approximately 50% with inappropriate antimicrobial treatment.

OBJECTIVE

This study aims to advance a novel strategy to develop an artificial intelligence-clinical decision support system (AI-CDSS) that combines machine learning (ML) with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), aiming to significantly improve the accuracy and speed of diagnosing antibiotic resistance, directly addressing the grave health risks posed by the widespread dissemination of pan drug-resistant gram-negative bacteria across numerous countries.

METHODS

A comprehensive dataset comprising 165,299 bacterial specimens and 11,996 KP isolates was meticulously analyzed using MALDI-TOF MS technology. Advanced ML algorithms were harnessed to sculpt predictive models that ascertain resistance to quintessential antibiotics, particularly levofloxacin and ciprofloxacin, by using the amassed spectral data.

RESULTS

Our ML models revealed remarkable proficiency in forecasting antibiotic resistance, with the random forest classifier emerging as particularly effective in predicting resistance to both levofloxacin and ciprofloxacin, achieving the highest area under the curve of 0.95. Performance metrics across different models, including accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1-score, were detailed, underlining the potential of these algorithms in aiding the development of precision treatment strategies.

CONCLUSIONS

This investigation highlights the synergy between MALDI-TOF MS and ML as a beacon of hope against the escalating threat of antibiotic resistance. The advent of AI-CDSS heralds a new era in clinical diagnostics, promising a future in which rapid and accurate resistance prediction becomes a cornerstone in combating infectious diseases. Through this innovative approach, we answered the challenge posed by KP and other multidrug-resistant pathogens, marking a significant milestone in our journey toward global health security.

Applications of different forms of nitrogen fertilizers affect soil bacterial community but not core ARGs profile

The objective of this study was to investigate the impact of various chemical nitrogen fertilizers on the profile of antibiotic resistance genes (ARGs) in soil. A microcosm experiment was conducted with four treatments, including CK (control with no nitrogen), AN (ammonium nitrogen), NN (nitrate nitrogen), and ON (urea nitrogen), and the abundance of ARGs was assessed over a 30-day period using a metagenomic sequencing approach. The levels of core ARGs varied between 0.16 and 0.22 copies per cell across different treatments over time. The abundance of core ARGs in the ON treatment closely resembled that of the CK treatment, suggesting that environmentally friendly nitrogen fertilizers, particularly those in controlled release formulations, may be preferable. The core ARG abundance in the AN and NN treatments exhibited noticeable fluctuations over time. Overall, chemical nitrogen fertilizers had minimal effects on the core ARG profile as determined by principal component analysis and clustering analyses. Conversely, distinct and significant changes in bacterial communities were observed with the use of different nitrogen fertilizers. However, the influence of nitrogen fertilizers on the core ARGs is limited due to the unaffected potential bacterial hosts. Nitrogen-cycling-related genes (NCRGs), such as those involved in nitrogen-fixing (nifK, nifD, nifH) and denitrification (narG, napA, nirK, norB, nosZ) processes, exhibit a positive correlation with ARGs (rosA, mexF, bacA, vanS), indicating a potential risk of ARG proliferation during intense denitrification activities. This study indicates that the application of chemical nitrogen has a minimal effect on the abundance of ARGs in soil, thereby alleviating concerns regarding the potential accumulation of ARGs due to the use of chemical nitrogen fertilizers.

Rapid, Multiplexed, and Enzyme-Free Nucleic Acid Detection Using Programmable Aptamer-Based RNA Switches

Rapid, simple, and low-cost diagnostic technologies are crucial tools for combatting infectious disease. We describe a class of aptamer-based RNA switches or aptaswitches that recognize target nucleic acid molecules and initiate folding of a reporter aptamer. Aptaswitches can detect virtually any sequence and provide an intense fluorescent readout without intervening enzymes, generating signals in as little as 5 minutes and enabling detection by eye with minimal equipment. Aptaswitches can be used to regulate folding of seven fluorogenic aptamers, providing a general means of controlling aptamers and an array of multiplexable reporter colors. Coupling isothermal amplification reactions with aptaswitches, we reach sensitivities down to 1 RNA copy/μL in one-pot reactions. Application of multiplexed all-in-one reactions against RNA from clinical saliva samples yields an overall accuracy of 96.67% for detection of SARS-CoV-2 in 30 minutes. Aptaswitches are thus versatile tools for nucleic acid detection that are readily integrated into rapid diagnostic assays.

Horizontal transfer potential of antibiotic resistance genes in wastewater treatment plants unraveled by microfluidic-based mini-metagenomics

Wastewater treatment plants (WWTPs) are known to harbor antibiotic resistance genes (ARGs), which can potentially spread to the environment and human populations. However, the extent and mechanisms of ARG transfer in WWTPs are not well understood due to the high microbial diversity and limitations of molecular techniques. In this study, we used a microfluidic-based mini-metagenomics approach to investigate the transfer potential and mechanisms of ARGs in activated sludge from WWTPs. Our results show that while diverse ARGs are present in activated sludge, only a few highly similar ARGs are observed across different taxa, indicating limited transfer potential. We identified two ARGs, ermF and tla-1, which occur in a variety of bacterial taxa and may have high transfer potential facilitated by mobile genetic elements. Interestingly, genes that are highly similar to the sequences of these two ARGs, as identified in this study, display varying patterns of abundance across geographic regions. Genes similar to ermF found are widely found in Asia and the Americas, while genes resembling tla-1 are primarily detected in Asia. Genes similar to both genes are barely detected in European WWTPs. These findings shed light on the limited horizontal transfer potential of ARGs in WWTPs and highlight the importance of monitoring specific ARGs in different regions to mitigate the spread of antibiotic resistance.

Utilization of-Omic technologies in cold climate hydrocarbon bioremediation: a text-mining approach

Hydrocarbon spills in cold climates are a prominent and enduring form of anthropogenic contamination. Bioremediation is one of a suite of remediation tools that has emerged as a cost-effective strategy for transforming these contaminants in soil, ideally into less harmful products. However, little is understood about the molecular mechanisms driving these complex, microbially mediated processes. The emergence of -omic technologies has led to a revolution within the sphere of environmental microbiology allowing for the identification and study of so called 'unculturable' organisms. In the last decade, -omic technologies have emerged as a powerful tool in filling this gap in our knowledge on the interactions between these organisms and their environment in vivo. Here, we utilize the text mining software Vosviewer to process meta-data and visualize key trends relating to cold climate bioremediation projects. The results of text mining of the literature revealed a shift over time from optimizing bioremediation experiments on the macro/community level to, in more recent years focusing on individual organisms of interest, interactions within the microbiome and the investigation of novel metabolic degradation pathways. This shift in research focus was made possible in large part by the rise of omics studies allowing research to focus not only what organisms/metabolic pathways are present but those which are functional. However, all is not harmonious, as the development of downstream analytical methods and associated processing tools have outpaced sample preparation methods, especially when dealing with the unique challenges posed when analyzing soil-based samples.

Metagenomic next generation sequencing for studying antibiotic resistance genes in the environment

Bacterial antimicrobial resistance (AMR) is a persisting and growing threat to human health. Characterization of antibiotic resistance genes (ARGs) in the environment is important to understand and control ARG-associated microbial risks. Numerous challenges exist in monitoring ARGs in the environment, due to the extraordinary diversity of ARGs, low abundance of ARGs with respect to the complex environmental microbiomes, difficulties in linking ARGs with bacterial hosts by molecular methods, difficulties in achieving quantification and high throughput simultaneously, difficulties in assessing mobility potential of ARGs, and difficulties in determining the specific AMR determinant genes. Advances in the next generation sequencing (NGS) technologies and related computational and bioinformatic tools are facilitating rapid identification and characterization ARGs in genomes and metagenomes from environmental samples. This chapter discusses NGS-based strategies, including amplicon-based sequencing, whole genome sequencing, bacterial population-targeted metagenome sequencing, metagenomic NGS, quantitative metagenomic sequencing, and functional/phenotypic metagenomic sequencing. Current bioinformatic tools for analyzing sequencing data for studying environmental ARGs are also discussed.

Dual-channel fluorescence detection of antibiotic resistance genes based on DNA-templated silver nanoclusters

The aqueous environment is an ideal site for the generation and transmission of antibiotic resistance genes (ARGs), and has become a sink for multiple ARGs. Detection of multiple ARGs in one-pot by a simple method is essential to control the spread of antibiotic resistance. Herein, we developed a novel fluorescence sensing strategy based on chameleon DNA-templated silver nanoclusters (AgNCs) to achieve simultaneous detection of two ARGs (tet-A and sul-1). A DNA fluorescent probe with AgNCs stabilized at both termini and another DNA probe carried enhancer sequences were designed. The hybridization of the target ARGs and probes can form an infinitely extended linear DNA structure containing multi-branched AgNCs beacons, and the chameleon AgNCs approach the fluorescence enhancer sequence, thereby realizing the transduction and amplification of green and red fluorescence signals. Through this strategy, we successfully achieved highly specific detection of two ARGs with the LOD of 0.45 nM for tet-A and 0.32 nM for sul-1. In addition, the strategy still had good applicability in the detection of actual samples containing complex components. In this study, fluorescent DNA-AgNCs were applied to the rapid, enzyme-free and reliable detection of ARGs for the first time. The excellent performance of the simultaneous detection of two ARGs displayed that this method can be used to simultaneously analyze different types of ARGs, indicating its great potential in rapid screening and quantitative detection of ARGs in various environmental medias.

Wild birds-the sentinel of antibiotic resistance for urban river: Study on egrets and Jinjiang river in Chengdu, China

Antibiotic resistance has become a comprehensive and complicated environmental problem. It is of great importance to effectively determine the abundance of various antibiotic resistance genes (ARGs) in the environment. Here, we attempted to find a practical method for monitoring environmental antibiotic resistance. The results of culture-based analysis of antibiotic resistance and metagenomic sequencing indicate that egrets inhabiting along the urban river (Jinjiang River) can be used as the sentinel of environmental antibiotic resistance. The antibiotic resistance in the environment fluctuated with time, while that in the wild bird was relatively stable. The network analysis based on metagenomic sequencing data gave the co-occurrence pattern of ARGs. The overall situation of the antibiotic resistance in the river was determined by quantifying several module hub genes of the co-occurrence network in river sediments. The temporal and spatial distribution of ARGs in Jinjiang River is highly correlated with that of human gut-specific bacteriophage (crAssphage), which indicates that one main source of the antibiotic resistance in the river is likely to be municipal sewage. The mobility potential of ARGs varying among different niches suggests the transmission direction of antibiotic resistance in the environment.

Deep Learning-Assisted Droplet Digital PCR for Quantitative Detection of Human Coronavirus

Since coronavirus disease 2019 (COVID-19) pandemic rapidly spread worldwide, there is an urgent demand for accurate and suitable nucleic acid detection technology. Although the conventional threshold-based algorithms have been used for processing images of droplet digital polymerase chain reaction (ddPCR), there are still challenges from noise and irregular size of droplets. Here, we present a combined method of the mask region convolutional neural network (Mask R-CNN)-based image detection algorithm and Gaussian mixture model (GMM)-based thresholding algorithm. This novel approach significantly reduces false detection rate and achieves highly accurate prediction model in a ddPCR image processing. We demonstrated that how deep learning improved the overall performance in a ddPCR image processing. Therefore, our study could be a promising method in nucleic acid detection technology.

Establishment of quantitative PCR assays for the rapid detection of Alicyclobacillus spp. that can produce guaiacol in apple juice

Alicyclobacillus species are one of the most significant qualities and safety factors in fruit juice and beverages. The growth of some Alicyclobacillus genus can lead to sour spoilage with the off-odor of medicinal, phenolic or antiseptic, which is mainly caused by the metabolites of guaiacol, dihalophenol and dibromophenol. Especially, guaiacol is regarded as the predominant taint in Alicyclobacillus-spoiled products. In this study, quantitative PCR (qPCR) assays were proposed for the detection of A. acidoterrestris, A. acidiphilus, A. cycloheptanicus and A. herbarius that can produce guaiacol in fruit juice. The 16S rDNA sequences of these four kinds of Alicyclobacillus species were identified and the primers suitable for the qPCR assay were obtained. The sensitivity and specificity of the established methods were evaluated. The results indicated that the developed qPCR approaches were distinctive enough to detect A. acidoterrestris, A. acidiphilus, A. cycloheptanicus and A. herbarius with the sensitivity of 2.6 × 10² CFU/mL, 74 CFU/mL, 2.8 × 10² CFU/mL and 3.1 × 10² CFU/mL, respectively. The correlation coefficients of standard curves were from 0.9807 to 0.9985. Based on the pretreatment of filtration-culture, these bacteria with the initial concentration of 10^-1 CFU/mL, 10⁰ CFU/mL and 10¹ CFU/mL can be effectively detected in 2-20 h, which depended on the target bacteria and their initial concentration. The results displayed that the proposed procedures were effective for the rapid detection of Alicyclobacillus species that can produce guaiacol in apple juice.

Hidden Resistome: Enrichment Reveals the Presence of Clinically Relevant Antibiotic Resistance Determinants in Treated Wastewater-Irrigated Soils

Treated-wastewater (TW) irrigation transfers antibiotic-resistant bacteria (ARB) to soil, but persistence of these bacteria is generally low due to resilience of the soil microbiome. Nonetheless, wastewater-derived bacteria and associated antibiotic resistance genes (ARGs) may persist below detection levels and potentially proliferate under copiotrophic conditions. To test this hypothesis, we exposed soils from microcosm, lysimeter, and field experiments to short-term enrichment in copiotroph-stimulating media. In microcosms, enrichment stimulated growth of multidrug-resistant Escherichia coli up to 2 weeks after falling below detection limits. Lysimeter and orchard soils irrigated in-tandem with either freshwater or TW were subjected to culture-based, qPCR and shotgun metagenomic analyses prior, and subsequent, to enrichment. Although native TW- and freshwater-irrigated soil microbiomes and resistomes were similar to each other, enrichment resulted in higher abundances of cephalosporin- and carbapenem-resistant Enterobacteriaceae and in substantial differences in the composition of microbial communities and ARGs. Enrichment stimulated ARG-harboring Bacillaceae in the freshwater-irrigated soils, whereas in TWW-irrigated soils, ARG-harboring γ-proteobacterial families Enterobacteriaceae and Moraxellaceae were more profuse. We demonstrate that TW-derived ARB and associated ARGs can persist at below detection levels in irrigated soils and believe that similar short-term enrichment strategies can be applied for environmental antimicrobial risk assessment in the future.