Development of a loop-mediated isothermal amplification assay targeting lmo0753 gene for detection of Listeria monocytogenes in wastewater

Development of a Novel Phagomagnetic-Assisted Isothermal DNA Amplification System for Endpoint Electrochemical Detection of Listeria monocytogenes

The hitherto implemented Listeria monocytogenes detection techniques are cumbersome or require expensive non-portable instrumentation, hindering their transposition into on-time surveillance systems. The current work proposes a novel integrated system resorting to loop-mediated isothermal amplification (LAMP), assisted by a bacteriophage P100-magnetic platform, coupled to an endpoint electrochemical technique, towards L. monocytogenes expeditious detection. Molybdophosphate-based optimization of the bacterial phagomagnetic separation protocol allowed the determination of the optimal parameters for its execution (pH 7, 25 °C, 32 µg of magnetic particles; 60.6% of specific capture efficiency). The novel LAMP method targeting prfA was highly specific, accomplishing 100% inclusivity (for 61 L. monocytogenes strains) and 100% exclusivity (towards 42 non-target Gram-positive and Gram-negative bacteria). As a proof-of-concept, the developed scheme was successfully validated in pasteurized milk spiked with L. monocytogenes. The phagomagnetic-based approach succeeded in the selective bacterial capture and ensuing lysis, triggering Listeria DNA leakage, which was efficiently LAMP amplified. Methylene blue-based electrochemical detection of LAMP amplicons was accomplished in 20 min with remarkable analytical sensitivity (1 CFU mL^-1). Hence, the combined system presented an outstanding performance and robustness, providing a 2.5 h-swift, portable, cost-efficient detection scheme for decentralized on-field application.

Research and Innovation Opportunities to Improve Epidemiological Knowledge and Control of Environmentally Driven Zoonoses

While zoonotic diseases are defined by transmission processes between animals and humans, for many of these diseases the presence of a contaminated environmental source is the cause of transmission. Most zoonoses depend on complex environmentally driven interactions between humans and animals, which occur along an occupational and recreational environmental continuum, including farming and animal marketing systems, environmental management systems, and community leisure environments. Environmentally driven zoonoses (EDZs) are particularly challenging to diagnose and control as their reservoirs are in the natural environment and thus often escape conventional surveillance systems that rely on host monitoring. Changes in the environment as a result of climate change [1], human population density [2], and intensification of agriculture [3] have been linked to increasing transmission events for this group of infections. As such, there is a recognised need to be able to detect the presence of EDZs in the environment as a means to better anticipate transmission events and improve source attribution investigations. Finally, the recognition that a One Health approach is needed to combat these infections is signalling to governments the need to develop policy that optimises trade-offs across human, animal, and environmental health sectors. In this review, we discuss and critically appraise the main challenges relating to the epidemiology, diagnosis, and control of environmental zoonotic disease. Using a set of exemplar diseases, including avian influenza and antimicrobial resistant pathogens, we explore the epidemiological contexts (risk factors) within which these infections not only impact human health but also contribute to animal health and environmental impacts. We then critically appraise the surveillance challenges of monitoring these infections in the environment and examine the policy trade-offs for a more integrated approach to mitigating their impacts.

Listeria monocytogenes in foods-From culture identification to whole-genome characteristics

Listeria monocytogenes is an important foodborne pathogen, which is able to persist in the food production environments. The presence of these bacteria in different niches makes them a potential threat for public health. In the present review, the current information on the classical and alternative methods used for isolation and identification of L. monocytogenes in food have been described. Although these techniques are usually simple, standardized, inexpensive, and are routinely used in many food testing laboratories, several alternative molecular-based approaches for the bacteria detection in food and food production environments have been developed. They are characterized by the high sample throughput, a short time of analysis, and cost-effectiveness. However, these methods are important for the routine testing toward the presence and number of L. monocytogenes, but are not suitable for characteristics and typing of the bacterial isolates, which are crucial in the study of listeriosis infections. For these purposes, novel approaches, with a high discriminatory power to genetically distinguish the strains during epidemiological studies, have been developed, e.g., whole-genome sequence-based techniques such as NGS which provide an opportunity to perform comparison between strains of the same species. In the present review, we have shown a short description of the principles of microbiological, alternative, and modern methods of detection of L. monocytogenes in foods and characterization of the isolates for epidemiological purposes. According to our knowledge, similar comprehensive papers on such subject have not been recently published, and we hope that the current review may be interesting for research communities.

Endpoint Recombinase Polymerase Amplification (RPA) Assay for Enumeration of Thiocyanate-degrading Bacteria

An endpoint recombination amplification reaction (RPA) assay for assessing the abundance of the gene encoding thiocyanate dehydrogenase (TcDH) in Thiohalobacter has been developed. The RPA reaction was performed at 37°C for 30‍ ‍min, terminated by the addition of sodium dodecyl sulfate (SDS) solution, and the DNA concentration of the RPA product was fluorometrically measured. The abundance of TcDH in 22 activated sludge samples and 7 thiocyanate-degrading enrichment cultures ranged between 2.5×10³ and 1.5×10⁶ copies μL^–1, showing a linear relationship (R²=0.83) with those measured using a conventional quantitative PCR assay.

Molecular Methods for Pathogenic Bacteria Detection and Recent Advances in Wastewater Analysis

With increasing concerns about public health and the development of molecular techniques, new detection tools and the combination of existing approaches have increased the abilities of pathogenic bacteria monitoring by exploring new biomarkers, increasing the sensitivity and accuracy of detection, quantification, and analyzing various genes such as functional genes and antimicrobial resistance genes (ARG). Molecular methods are gradually emerging as the most popular detection approach for pathogens, in addition to the conventional culture-based plate enumeration methods. The analysis of pathogens in wastewater and the back-estimation of infections in the community, also known as wastewater-based epidemiology (WBE), is an emerging methodology and has a great potential to supplement current surveillance systems for the monitoring of infectious diseases and the early warning of outbreaks. However, as a complex matrix, wastewater largely challenges the analytical performance of molecular methods. This review synthesized the literature of typical pathogenic bacteria in wastewater, types of biomarkers, molecular methods for bacterial analysis, and their recent advances in wastewater analysis. The advantages and limitation of these molecular methods were evaluated, and their prospects in WBE were discussed to provide insight for future development.

Deep Learning Model for Pathogen Classification Using Feature Fusion and Data Augmentation

Background:

Bacterial pathogens are deadly for animals and humans. The ease of their dissemination, coupled with their high capacity for ailment and death in infected individuals, makes them a threat to society.

Objective:

Due to high similarity among genera and species of pathogens, it is sometimes difficult for microbiologists to differentiate between them. Their automatic classification using deep-learning models can help in reliable, and accurate outcomes.

Method:

Deep-learning models, namely; AlexNet, GoogleNet, ResNet101, and InceptionV3 are used with numerous variations including training model from scratch, fine-tuning without pre-trained weights, fine-tuning along with freezing weights of initial layers, fine-tuning along with adjusting weights of all layers and augmenting the dataset by random translation and reflection. Moreover, as the dataset is small, fine-tuning and data augmentation strategies are applied to avoid overfitting and produce a generalized model. A merged feature vector is produced using two best-performing models and accuracy is calculated by xgboost algorithm on the feature vector by applying cross-validation.

Results:

Fine-tuned models where augmentation is applied produces the best results. Out of these, two-best-performing deep models i.e. (ResNet101, and InceptionV3) selected for feature fusion, produced a similar validation accuracy of 95.83 with a loss of 0.0213 and 0.1066, and a testing accuracy of 97.92 and 93.75, respectively. The proposed model used xgboost to attained a classification accuracy of 98.17% by using 35-folds cross-validation.

Conclusion:

The automatic classification using these models can help experts in the correct identification of pathogens. Consequently, they can help in controlling epidemics and thereby minimizing the socio-economic impact on the community.