The poultry industry is evolving towards antibiotic-free production to meet market demands and decelerate the increasing spread of the antimicrobial resistance. The growing need for antibiotic free products has challenged producers to decrease or completely stop using antimicrobials as feed supplements in broiler diet to improve feed efficiency, growth rate, and intestinal health. Natural feed additives (e.g., probiotics and phytobiotics) are promising alternatives to substitute antimicrobial growth promoters. The goal of our study was to characterize the effects of a Probiotic and an Essential Oils blend on broilers' performance and perform a time-series analysis to describe their excreta microbiome. A total of 320 Cobb 500 (1-day-old) chicks were raised for 21 d in 32 randomly allocated cages. Treatments consisted of 4 experimental diets: a basal diet, and a basal diet mixed with an Antibiotic (bacitracin methylene disalicylate), an essential oils blend (oregano oil, rosemary, and red pepper), or a Probiotic (Bacillus subtilis). Body weight (on 1, 10, and 21d), and feed intake (10d and 21d) were recorded and feed conversion ratio was calculated. Droppings were collected daily (1-21d) to characterize broilers' excreta microbiota by targeted sequencing of the bacterial 16S rRNA gene. The Probiotic significantly improved feed conversion ratio for starter phase 1 to 10d (P = 0.03), grower phase 10 to 21d (P = 0.05), and total period 1 to 21d (P = 0.01) compared to the Antibiotic. Feed supplements did not affect alpha diversity but did impact microbial beta diversity (P < 0.01). Age also impacted microbiome turnover as differences in alpha and beta diversity were detected. Furthermore, when compared to the basal diet, the probiotic and antibiotic significantly impacted relative abundance of Bifidobacterium (log2 fold change -1.44, P = 0.03), Intestinimonas (log2 fold change 0.560, P < 0.01) and Ligilactobacillus (log2 fold change -1.600, P < 0.01). Overall, Probiotic supplementation but not essential oils supplementation positively impacted broilers' growth performance by directly causing directional shifts in broilers' excreta microbiota structure.

Tracking the bacterial communities present in our food has the potential to inform food safety and product origin. To do so, the entire genetic material present in a sample is extracted using chemical methods or commercially available kits and sequenced using next-generation platforms to provide a snapshot of the microbial composition.

Non-typhoidal Salmonella (NTS) can cause infection in poultry, livestock, and humans. Although the use of antimicrobials as feed additives is prohibited, the previous indiscriminate use and poor regulatory oversight in some parts of the world have resulted in increased bacterial resistance to antimicrobials, including cephalosporins and fluoroquinolones, which are among the limited treatment options available against NTS. This study aimed to isolate potential probiotic lactic acid bacteria (LAB) strains from the poultry gut to inhibit fluoroquinolone and cephalosporin resistant MDR Salmonella Typhimurium and S. Enteritidis. The safety profile of the LAB isolates was evaluated for the hemolytic activity, DNase activity, and antibiotic resistance. Based on the safety results, three possible probiotic LAB candidates for in vitro Salmonella control were chosen. Candidate LAB isolates were identified by 16S rDNA sequencing as Lactobacillus reuteri PFS4, Enterococcus faecium PFS13, and Enterococcus faecium PFS14. These strains demonstrated a good tolerance to gastrointestinal-related stresses, including gastric acid, bile, lysozyme, and phenol. In addition, the isolates that were able to auto aggregate had the ability to co-aggregate with MDR S. Typhimurium and S. Enteritidis. Furthermore, LAB strains competitively reduced the adhesion of pathogens to porcine mucin Type III in co-culture studies. The probiotic combination of the selected LAB isolates inhibited the biofilm formation of S. Typhimurium FML15 and S. Enteritidis FML18 by 90% and 92%, respectively. In addition, the cell-free supernatant (CFS) of the LAB culture significantly reduced the growth of Salmonella in vitro. Thus, L. reuteri PFS4, E. faecium PFS13, and E. faecium PFS 14 are potential probiotics that could be used to control MDR S. Typhimurium and S. Enteritidis in poultry. Future investigations are required to elucidate the in vivo potential of these probiotic candidates as Salmonella control agents in poultry and animal feed.

The use of heavy metals in economic and social development can create an accumulation of toxic waste in the environment. High concentrations of heavy metals can damage human and animal health, lead to the development of antibiotic resistance, and possibly change in bovine microbiota. It is important to investigate the influence of heavy metals in food systems to determine potential harmful effects environmental heavy metal contamination on human health. Because of a mining dam rupture, 43 million cubic meters of iron ore waste flowed into the Doce river basin surrounding Mariana City, Brazil, in 2015. Following this environmental disaster, we investigated the consequences of long-term exposure to contaminated drinking water on the microbiome and resistome of dairy cattle. We identified bacterial antimicrobial resistance (AMR) genes in the feces, rumen fluid, and nasopharynx of 16 dairy cattle 4 years after the environmental disaster. Cattle had been continuously exposed to heavy metal contaminated water until sample collection (A) and compared them to analogous samples from 16 dairy cattle in an unaffected farm, 356 km away (B). The microbiome and resistome of farm A and farm B differed in many aspects. The distribution of genes present in the cattle’s nasopharynx, rumen, and feces conferring AMR was highly heterogeneous, and most genes were present in only a few samples. The relative abundance and prevalence (presence/absence) of AMR genes were higher in farm A than in farm B. Samples from farm A had a higher prevalence (presence) of genes conferring resistance to multiple drugs, metals, biocides, and multi-compound resistance. Fecal samples had a higher relative abundance of AMR genes, followed by rumen fluid samples, and the nasopharynx had the lowest relative abundance of AMR genes detected. Metagenome functional annotation suggested that selective pressures of heavy metal exposure potentially skewed pathway diversity toward fewer, more specialized functions. This is the first study that evaluates the consequences of a Brazilian environmental accident with mining ore dam failure in the microbiome of dairy cows. Our findings suggest that the long-term persistence of heavy metals in the environment may result in differences in the microbiota and enrichment of antimicrobial-resistant bacteria. Our results also suggest that AMR genes are most readily detected in fecal samples compared to rumen and nasopharyngeal samples which had relatively lower bacterial read counts. Since heavy metal contamination has an effect on the animal microbiome, environmental management is warranted to protect the food system from hazardous consequences.

Introduction: Nutritional and environmental stressors can disturb the gut microbiome of horses which may ultimately decrease their health and performance. We hypothesized that supplementation with a yeast-derived postbiotic (Saccharomyces cerevisiae fermentation product-SCFP) would benefit horses undergoing an established model of stress due to prolonged transportation.Methods: Quarter horses (n = 20) were blocked based on sex, age (22 ± 3 mo) and body weight (439 ± 3 kg) and randomized to receive either a basal diet of 60% hay and 40% concentrate (CON) or the basal diet supplemented with 21 g/d Diamond V TruEquine C (SCFP; Diamond V, Cedar Rapids, IA) for 60 days. On day 57, horses were tethered with their heads elevated 35cm above wither height for 12 h to induce mild upper respiratory tract inflammation. Fecal samples were collected at days 0, 28, and 56 before induction of stress, and at 0, 12, 24, and 72 h post-stress and subjected to DNA extraction and Nanopore shotgun metagenomics. Within sample (alpha) diversity was evaluated by fitting a linear model and between sample (beta) diversity was tested with permutational ANOVA.Results: The SCFP stabilized alpha diversity across all time points, whereas CON horses had more fluctuation (P < 0.05) at 12, 24, and 72 h post-challenge compared to d 56. A significant difference between CON and SCFP was observed at 0 and 12 h. There was no difference in beta-diversity between SCFP and CON on d 56.Discussion: Taken together, these observations led us to conclude that treatment with SCFP resulted in more robust and stable microbial profiles in horses after stress challenge.

The increasing knowledge of microbial ecology in food products relating to quality and safety and the established usefulness of machine learning algorithms for anomaly detection in multiple scenarios suggests that the application of microbiome data in food production systems for anomaly detection could be a valuable approach to be used in food systems. These methods could be used to identify ingredients that deviate from their typical microbial composition, which could indicate food fraud or safety issues. The objective of this study was to assess the feasibility of using shotgun sequencing data as input into anomaly detection algorithms using fluid milk as a model system. Contrastive principal component analysis (PCA), cluster-based methods, and explainable artificial intelligence (AI) were evaluated for the detection of two anomalous sample classes using longitudinal metagenomic profiling of fluid milk compared to baseline (BL) samples collected under comparable circumstances. Traditional methods (alpha and beta diversity, clustering-based contrastive PCA, multidimensional scaling, and dendrograms) failed to differentiate anomalous sample classes; however, explainable AI was able to classify anomalous vs baseline samples and indicate microbial drivers in association with antibiotic use. We validated the potential for explainable AI to classify different milk sources using larger publicly available fluid milk 16S rDNA sequencing data sets and demonstrated that explainable AI is able to differentiate between milk storage methods, processing stages, and seasons. Our results indicate that the application of artificial intelligence continues to hold promise in the realm of microbiome data analysis and could present further opportunities for downstream analytic automation to aid in food safety and quality. IMPORTANCE: We evaluated the feasibility of using untargeted metagenomic sequencing of raw milk for detecting anomalous food ingredient content with artificial intelligence methods in a study specifically designed to test this hypothesis. We also show through analysis of publicly available fluid milk microbial data that our artificial intelligence approach is able to successfully predict milk in different stages of processing. The approach could potentially be applied in the food industry for safety and quality control.