Translating 'big data': better understanding of host-pathogen interactions to control bacterial foodborne pathogens in poultry

Exploring the predictive power of jejunal microbiome composition in clinical and subclinical necrotic enteritis caused by Clostridium perfringens: insights from a broiler chicken model

BACKGROUND

Necrotic enteritis (NE) is a severe intestinal infection that affects both humans and poultry. It is caused by the bacterium Clostridium perfringens (CP), but the precise mechanisms underlying the disease pathogenesis remain elusive. This study aims to develop an NE broiler chicken model, explore the impact of the microbiome on NE pathogenesis, and study the virulence of CP isolates with different toxin gene combinations.

METHODS

This study established an animal disease model for NE in broiler chickens. The methodology encompassed inducing abrupt protein changes and immunosuppression in the first experiment, and in the second, challenging chickens with CP isolates containing various toxin genes. NE was evaluated through gross and histopathological scoring of the jejunum. Subsequently, jejunal contents were collected from these birds for microbiome analysis via 16S rRNA amplicon sequencing, followed by sequence analysis to investigate microbial diversity and abundance, employing different bioinformatic approaches.

RESULTS

Our findings reveal that CP infection, combined with an abrupt increase in dietary protein concentration and/or infection with the immunosuppressive variant infectious bursal disease virus (vIBDV), predisposed birds to NE development. We observed a significant decrease (p < 0.0001) in the abundance of Lactobacillus and Romboutsia genera in the jejunum, accompanied by a notable increase (p < 0.0001) in Clostridium and Escherichia. Jejunal microbial dysbiosis and severe NE lesions were particularly evident in birds infected with CP isolates containing cpa, netB, tpeL, and cpb2 toxin genes, compared to CP isolates with other toxin gene combinations. Notably, birds that did not develop clinical or subclinical NE following CP infection exhibited a significantly higher (p < 0.0001) level of Romboutsia. These findings shed light on the complex interplay between CP infection, the gut microbiome, and NE pathogenesis in broiler chickens.

CONCLUSION

Our study establishes that dysbiosis within the jejunal microbiome serves as a reliable biomarker for detecting subclinical and clinical NE in broiler chicken models. Additionally, we identify the potential of the genera Romboutsia and Lactobacillus as promising candidates for probiotic development, offering effective alternatives to antibiotics in NE prevention and control.

Antimicrobial Resistance and Recent Alternatives to Antibiotics for the Control of Bacterial Pathogens with an Emphasis on Foodborne Pathogens

Antimicrobial resistance (AMR) is one of the most important global public health problems. The imprudent use of antibiotics in humans and animals has resulted in the emergence of antibiotic-resistant bacteria. The dissemination of these strains and their resistant determinants could endanger antibiotic efficacy. Therefore, there is an urgent need to identify and develop novel strategies to combat antibiotic resistance. This review provides insights into the evolution and the mechanisms of AMR. Additionally, it discusses alternative approaches that might be used to control AMR, including probiotics, prebiotics, antimicrobial peptides, small molecules, organic acids, essential oils, bacteriophage, fecal transplants, and nanoparticles.

Metabolomics insights into the potential of encapsulated essential oils as multifunctional food additives

Growing consumer concern about foodborne disease outbreaks and health risks associated with chemical additives has propelled the usage of essential oils (EOs) as novel food additives, but are limited by instability. In this regard, a series of EOs nano/micro-capsules have been widely used to enhance their stability and improve food quality. However, classical food quality assessment methods are insufficient to fully characterize the effects of encapsulated EOs on food properties, including physical, biochemical, organoleptic, and microbial changes. Recently, the rapid development of high-throughput sequencing is accelerating the application of metabolomics in food safety and quality analysis. This review seeks to present the most recent achievements in the application of non-targeted metabolomics to identify and quantify the overall metabolite profile associated with food quality, which can guide the development of emerging food preservation technologies. The scientific findings confirm that metabolomics opens up exciting prospects for biomarker screening in food preservation and contributes to an in-depth understanding of the mechanisms of action (MoA) of EOs. Future research should focus on constructing food quality assessment criteria based on multi-omics technologies, which will drive the standardization and commercialization of EOs for food industry applications.

Database Oriented Big Data Analysis Engine Based on Deep Learning

In recent years, with the development of enterprises to the Internet, the demand for cloud database is also growing, especially how to capture data quickly and efficiently through the database. In order to improve the data structure at all levels in the process of database analysis engine, this paper realizes the accurate construction and rapid analysis of cloud database based on big data analysis engine technology and deep learning wolf pack greedy algorithm. Through the deep learning strategy, a big data analysis engine system based on the deep learning model is constructed. The functions of deep learning technology, wolf greedy algorithm, and data analysis strategy in the cloud database analysis engine system are analyzed, as well as the functions of the whole analysis engine system. Finally, the accuracy and response speed of the cloud database analysis engine system are tested according to the known clustering data. The results show that compared with the traditional data analysis engine system with character search as the core, the database oriented big data analysis engine system based on a deep learning model and wolf swarm greedy algorithm has faster response speed and intelligence. The research application is that the proposed engine system can significantly improve the effect of the analysis engine and greatly improve the retrieval accuracy and analysis efficiency of fixed-point data in the database.

Biological aspects of phage therapy versus antibiotics against Salmonella enterica serovar Typhimurium infection of chickens

Phage therapy is a promising alternative treatment of bacterial infections in human and animals. Nevertheless, despite the appearance of many bacterial strains resistant to antibiotics, these drugs still remain important therapeutics used in human and veterinary medicine. Although experimental phage therapy of infections caused by Salmonella enterica was described previously by many groups, those studies focused solely on effects caused by bacteriophages. Here, we compared the use of phage therapy (employing a cocktail composed of two previously isolated and characterized bacteriophages, vB_SenM-2 and vB_Sen-TO17) and antibiotics (enrofloxacin and colistin) in chickens infected experimentally with S. enterica serovar Typhimurium. We found that the efficacies of both types of therapies (i.e. the use of antibiotics and phage cocktail) were high and very similar to one another when the treatment was applied shortly (one day) after the infection. Under these conditions, S. Typhimurium was quickly eliminated from the gastrointestinal tract (GIT), to the amount not detectable by the used methods. However, later treatment (2 or 4 days after detection of S. Typhimurium in chicken feces) with the phage cocktail was significantly less effective. Bacteriophages remained in the GIT for up to 2-3 weeks, and then were absent in feces and cloaca swabs. Interestingly, both phages could be found in various organs of chickens though with a relatively low abundance. No development of resistance of S. Typhimurium to phages or antibiotics was detected during the experiment. Importantly, although antibiotics significantly changed the GIT microbiome of chickens in a long-term manner, analogous changes caused by phages were transient, and the microbiome normalized a few weeks after the treatment. In conclusion, phage therapy against S. Typhimurium infection in chickens appeared as effective as antibiotic therapy (with either enrofloxacin or colistin), and less invasive than the use the antibiotics as fewer changes in the microbiome were observed.

Omics technologies in poultry health and productivity - part 1: current use in poultry research

In biology, molecular terms with the suffix "-omics" refer to disciplines aiming at the collective characterization of pools of molecules derived from different layers (DNA, RNA, proteins, metabolites) of living organisms using high-throughput technologies. Such omics analyses have been widely implemented in poultry research in recent years. This first part of a bipartite review on omics technologies in poultry health and productivity examines the use of multiple omics and multi-omics techniques in poultry research. More specific present and future applications of omics technologies, not only for the identification of specific diagnostic biomarkers, but also for potential future integration in the daily monitoring of poultry production, are discussed in part 2. Approaches based on omics technologies are particularly used in poultry research in the hunt for genetic markers of economically important phenotypical traits in the host, and in the identification of key bacterial species or functions in the intestinal microbiome. Integrative multi-omics analyses, however, are still scarce. Host physiology is investigated via genomics together with transcriptomics, proteomics and metabolomics techniques, to understand more accurately complex production traits such as disease resistance and fertility. The gut microbiota, as a key player in chicken productivity and health, is also a main subject of such studies, investigating the association between its composition (16S rRNA gene sequencing) or function (metagenomics, metatranscriptomics, metaproteomics, metabolomics) and host phenotypes. Applications of these technologies in the study of other host-associated microbiota and other host characteristics are still in their infancy.