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Within the realm of poultry feed mill operations, the persistent concern over microbial feed quality necessitates the establishment of a robust baseline for enhancing and sustaining the standards of commercial feeds. This dual-phase investigation, comprising Parts I, was previously published, and the current study presented here as Part II aimed to illuminate this baseline using 16S rRNA gene sequencing. In Part II, nine distinct commercial poultry feeds formulated as starters, growers, starter/growers, or supplements, the selected feeds underwent genomic DNA extraction, amplification with custom dual-indexed primers, and subsequent Illumina MiSeq sequencing. Through data analysis in QIIME2-2021.4 and R Studio, the study unveils alpha (Kruskal-Wallis) and beta (ANOSIM) diversity, taxonomic differences (ANCOM), and core microbiomes (core&#95;members), deeming main and pairwise effects statistically significant at p  < 0.05 and Q  < 0.05. Notably, the investigation identified 30% common core microbial members across the nine feed types, shedding light on potential foodborne poultry pathogens such as Helicobacter and Campylobacter . Probiotic-associated feeds exhibited distinct microbial communities, emphasizing the need to explore their impact on the early poultry gastrointestinal tract (GIT) further.

Given extensive variability in feed composition, the absence of a dedicated DNA extraction kit for poultry feed underscores the need for an optimized extraction technique for reliable downstream sequencing analyses. This study investigates the impact of five DNA extraction techniques: Qiagen QIAamp DNA Stool Mini Kit (Qiagen), modified Qiagen with Lysing Matrix B (MQ), modified Qiagen with celite purification (MQC), polyethylene glycol (PEG), and 1-Day Direct. Genomic DNA amplification and Illumina MiSeq sequencing were conducted. QIIME2-2021.4 facilitated data analysis, revealing significant diversity and compositional differences influenced by extraction methods. Qiagen exhibited lower evenness and richness compared to other methods. 1-Day Direct and PEG enhanced bacterial diversities by employing bead beating and lysozyme. Despite similar taxonomic resolution, the Qiagen kit provides a rapid, consistent method for assessing poultry feed microbiomes. Modified techniques (MQ and MQC) improve DNA purification, reducing bias in commercial poultry feed samples. PEG and 1-Day Direct methods were effective but may require standardization. Overall, this study underscores the importance of optimized extraction techniques in poultry feed analysis, with potential implications for future standardization of effective methods.

Escherichia coli shows the potential of indicating foodborne pathogens. The purpose of this study is to investigate the association between E. coli and foodborne pathogens such as Campylobacter, Salmonella, and Listeria in pastured poultry farms, as well as in related processing facilities. Five different sample types: (i) feces, (ii) soil, (iii) whole carcass rinse during processing (WCR-P), (iv) whole carcass rinse of final product after chilling and storage (WCR-F), and (v) ceca were measured for E. coli populations. A logistic regression model for pathogen presence was developed for each sample type. The E. coli population significantly increased the predicted probability of Salmonella presence for soil and WCR-P samples (p = 0.0011 and p = 0.0157 respectively). For Campylobacter, the initial prevalence in feces and ceca were high and a decreasing trend of detecting Campylobacter was observed as E. coli concentration increased. In soil and WCR-P models, the probability of the presence of Campylobacter significantly increased as E. coli population increased. These models provide a practical and effective way of evaluating the relationship between E. coli and foodborne pathogens and enable prediction of foodborne pathogen presence based on E. coli prevalence within the pastured poultry farm-to-fork continuum., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Though most strains of E. coli are non-pathogenic components of the intestinal microbiome, certain pathogenic E. coli strains are the cause of diseases and outbreaks. Poultry is identified as a common reservoir for pathogenic E. coli. It is important to identify farm practice factors associated with E. coli in the pastured poultry environment. The objective of this study is to develop models that can predict E. coli levels and to select farm practice factors contributing to E. coli concentration in pastured poultry farms. Five kinds of samples: feces, soil, whole carcass rinse after processing (WCR-P), final product after chilling and storage (WCR-F), and ceca samples were collected for E. coli counts from 11 pastured poultry farms in the southeastern US. The regression tree (RT) and least absolute shrinkage and selection operator (LASSO) methods were applied to data from each sample type. The farm management practices and processing factors such as source of eggs, type of feed used, appearance of other animals on farm, chilling method, and storage time and temperature were all considered as possible explanatory factors in the models. Models were developed to predict the levels of E. coli and to select the most important factors used in predicting E. coli population. Model performances were compared using root mean square error (RMSE). For feces samples, average number of birds and animal source were the two most important variables affecting E. coli population by LASSO. The RT selected animal source, brood feed, day of year, flock age in days, and flock size as the most important variables in predicting E. coli concentration. The RMSE (in log 10 scale) under LASSO was 0.974, while under RT it was 1.032 for feces samples. The predictive models provide practical and effective tools to predict E. coli population and to identify farm practice factors that affect E. coli levels., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

The use of "omics" has become widespread across poultry production, from breeding to management to bird health to food safety and everywhere in between.  While the conventional poultry industry has become more exposed to the power and utility of "omic" technologies, smaller poultry flock producers typically do not have this same level of experience. Because smaller, nonconventional poultry production is a growing portion of the overall poultry market, it is important that they also have educational access to these research tools and the resultant data. While small flock producers are dedicated and knowledgeable farmers, their knowledge of these newer technologies may be limited at best, and it is the task of academic researchers to communicate the importance of these "omic" tools and how the omic data can improve a variety of different aspects of their operations. This review discusses ways to effectively communicate complex microbiota and microbial genome sequence data to small flock producers and transforming this data into meaningful and applicable information that they can utilize to inform beneficial management decisions., Competing Interests: DISCLOSURES There is no conflict of interest for any of the authors., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Animal manure can be a source of antibiotic-resistant genes (ARGs) and pharmaceutical residues; however, few studies have evaluated the presence of ARG in pasture-raised animal production systems. The objective of this study was to examine changes in microbiome diversity and the presence of antibiotic residues (ABRs) on three farms that contained a diverse range of animal species: pasture-raised poultry (broiler and layer), swine, and beef cattle. Total bacterial communities were determined using 16S rRNA microbiome analysis, while specific ARGs (sulfonamide [Sul; Sul1] and tetracycline [Tet; TetA]) were enumerated by qPCR (real-time PCR). Results indicated that the ARG abundances (Sul1 [P < 0.05] and TetA [P < 0.001]) were higher in layer hen manures (16.5 × 10-4 and 1.4 × 10-4 µg kg-1, respectively) followed by broiler chickens (2.9 × 10-4 and 1.7 × 10-4 µg kg-1, respectively), swine (0.22 × 10-4 and 0.20 × 10-4 µg kg-1, respectively) and beef cattle (0.19 × 10-4 and 0.02 × 10-4 µg kg-1, respectively). Average fecal TetA ABR tended to be greater (P = 0.09) for broiler chickens (11.4 µg kg-1) than for other animal species (1.8 to 0.06 µg kg-1), while chlortetracycline, lincomycin, and oxytetracycline ABRs were similar among animal species. Furthermore, fecal microbial richness and abundances differed significantly (P < 0.01) both among farms and specific species of animal. This study indicated that the microbial diversity, ABR, ARG concentrations, and types in feces varied from farm-to-farm and from animal species-to-animal species. Future studies are necessary to perform detailed investigations of the horizontal transfer mechanism of antibiotic-resistant microorganisms (ARMs) and ARG., (Published by Oxford University Press on behalf of the American Society of Animal Science 2021.)

Recently, there has been a consumer push for natural and organic food products. This has caused alternative poultry production, such as organic, pasture, and free-range systems, to grow in popularity. Due to the stricter rearing practices of alternative poultry production systems, different types of levels of microbiological risks might be present for these systems when compared to conventional production systems. Both conventional and alternative production systems have complex supply chains that present many different opportunities for flocks of birds or poultry meat to be contaminated with foodborne pathogens. As such, it is important to understand the risks involved during each step of production. The purpose of this review is to detail the potential routes of foodborne pathogen transmission throughout the conventional and alternative supply chains, with a special emphasis on the differences in risk between the two management systems, and to identify gaps in knowledge that could assist, if addressed, in poultry risk-based decision making., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

Phenotype microarrays were analyzed for 51 datasets derived from Salmonella enterica. The top 4 serotypes associated with poultry products and one associated with turkey, respectively Typhimurium, Enteritidis, Heidelberg, Infantis and Senftenberg, were represented. Datasets were partitioned initially into two clusters based on ranking by values at pH 4.5 (PM10 A03). Negative control wells were used to establish 90 respiratory units as the point differentiating acid resistance from sensitive strains. Thus, 24 isolates that appeared most acid-resistant were compared initially to 27 that appeared most acid-sensitive (24 × 27 format). Paired cluster analysis was also done and it included the 7 most acid-resistant and -sensitive datasets (7 × 7 format). Statistical analyses of ranked data were then calculated in order of standard deviation, probability value by the Student's t-test and a measure of the magnitude of difference called effect size. Data were reported as significant if, by order of filtering, the following parameters were calculated: i) a standard deviation of 24 respiratory units or greater from all datasets for each chemical, ii) a probability value of less than or equal to 0.03 between clusters and iii) an effect size of at least 0.50 or greater between clusters. Results suggest that between 7.89% and 23.16% of 950 chemicals differentiated acid-resistant isolates from sensitive ones, depending on the format applied. Differences were more evident at the extremes of phenotype using the subset of data in the paired 7 × 7 format. Results thus provide a strategy for selecting compounds for additional research, which may impede the emergence of acid-resistant Salmonella enterica in food., (Published by Elsevier Masson SAS.)

Methods to rapidly identify serotypes of Salmonella enterica subspecies I are of vital importance for protecting the safety of food. To supplement the serotyping method dkgB-linked intergenic sequence ribotyping (ISR), single-nucleotide polymorphisms were characterized within adenylate cyclase (cyaA). The National Center for Biotechnology Information (NCBI) database had 378 cyaA sequences from S. enterica subspecies I, which included 42 unique DNA sequences and 19 different amino acid sequences. Five representative isolates, namely serotypes Typhimurium, Kentucky, Enteritidis phage type PT4, and two variants of Enteritidis phage type PT13a, were differentiated within a microsphere-based fluidics system in cyaA by allele-specific primer extension. Validation against 25 poultry-related environmental Salmonella isolates representing 11 serotypes yielded a ∼89% success rate at identifying the serotype of the isolate, and a different region could be targeted to achieve 100%. When coupled with ISR, all serotypes were differentiated. Phage lineages of serotype Enteritidis 13a and 4 were identified, and a biofilm-forming strain of PT13a was differentiated from a smooth phenotype within phage type. Comparative ranking of mutation indices to genes such as the tRNA transferases, the diguanylate cyclases, and genes used for multilocus sequence typing indicated that cyaA is an appropriate gene for assessing epidemiological trends of Salmonella because of its relative stability in nucleotide composition.

The efficacy of DNA extraction protocols can be highly dependent upon both the type of sample being investigated and the types of downstream analyses performed. Considering that the use of new bacterial community analysis techniques (e.g., microbiomics, metagenomics) is becoming more prevalent in the agricultural and environmental sciences and many environmental samples within these disciplines can be physiochemically and microbiologically unique (e.g., fecal and litter/bedding samples from the poultry production spectrum), appropriate and effective DNA extraction methods need to be carefully chosen. Therefore, a novel semi-automated hybrid DNA extraction method was developed specifically for use with environmental poultry production samples. This method is a combination of the two major types of DNA extraction: mechanical and enzymatic. A two-step intense mechanical homogenization step (using bead-beating specifically formulated for environmental samples) was added to the beginning of the "gold standard" enzymatic DNA extraction method for fecal samples to enhance the removal of bacteria and DNA from the sample matrix and improve the recovery of Gram-positive bacterial community members. Once the enzymatic extraction portion of the hybrid method was initiated, the remaining purification process was automated using a robotic workstation to increase sample throughput and decrease sample processing error. In comparison to the strict mechanical and enzymatic DNA extraction methods, this novel hybrid method provided the best overall combined performance when considering quantitative (using 16S rRNA qPCR) and qualitative (using microbiomics) estimates of the total bacterial communities when processing poultry feces and litter samples.

Ammonia volatilization from the mineralization of uric acid and urea has a major impact on the poultry industry and the environment. Dry acids are commonly used to reduce ammonia emissions from poultry houses; however, little is known about how acidification affects the litter biologically. The goal of this laboratory incubation was to compare the microbiological and physiochemical effects of dry acid amendments (Al+Clear, Poultry Litter Treatment, Poultry Guard) on poultry litter to an untreated control litter and to specifically correlate uric acid and urea contents of these litters to the microbes responsible for their mineralization. Although all three acidifiers eventually produced similar effects within the litter, there was at least a 2-wk delay in the microbiological responses using Poultry Litter Treatment. Acidification of the poultry litter resulted in >3 log increases in total fungal concentrations, with both uricolytic (uric acid degrading) and ureolytic (urea degrading) fungi increasing by >2 logs within the first 2 to 4 wk of the incubation. Conversely, total, uricolytic, and ureolytic bacterial populations all significantly declined during this same time period. While uric acid and urea mineralization occurred within the first 2 wk in the untreated control litter, acidification resulted in delayed mineralization events for both uric acid and urea (2 and 4 wk delay, respectively) once fungal cell concentrations exceeded a threshold level. Therefore, fungi, and especially uricolytic fungi, appear to have a vital role in the mineralization of organic N in low-pH, high-N environments, and the activity of these fungi should be considered in best management practices to reduce ammonia volatilization from acidified poultry litter.