Considerations and best practices in animal science 16S ribosomal RNA gene sequencing microbiome studies

Microbiome studies in animal science using 16S rRNA gene sequencing have become increasingly common in recent years as sequencing costs continue to fall and bioinformatic tools become more powerful and user-friendly. The combination of molecular biology, microbiology, microbial ecology, computer science, and bioinformatics—in addition to the traditional considerations when conducting an animal science study—makes microbiome studies sometimes intimidating due to the intersection of different fields. The objective of this review is to serve as a jumping-off point for those animal scientists less familiar with 16S rRNA gene sequencing and analyses and to bring up common issues and concerns that arise when planning an animal microbiome study from design through analysis. This review includes an overview of 16S rRNA gene sequencing, its advantages, and its limitations; experimental design considerations such as study design, sample size, sample pooling, and sample locations; wet lab considerations such as field handing, microbial cell lysis, low biomass samples, library preparation, and sequencing controls; and computational considerations such as identification of contamination, accounting for uneven sequencing depth, constructing diversity metrics, assigning taxonomy, differential abundance testing, and, finally, data availability. In addition to general considerations, we highlight some special considerations by species and sample type

Investigating Effects of Tulathromycin Metaphylaxis on the Fecal Resistome and Microbiome of Commercial Feedlot Cattle Early in the Feeding Period

The objective was to examine effects of treating commercial beef feedlot cattle with therapeutic doses of tulathromycin, a macrolide antimicrobial drug, on changes in the fecal resistome and microbiome using shotgun metagenomic sequencing. Two pens of cattle were used, with all cattle in one pen receiving metaphylaxis treatment (800 mg subcutaneous tulathromycin) at arrival to the feedlot, and all cattle in the other pen remaining unexposed to parenteral antibiotics throughout the study period. Fecal samples were collected from 15 selected cattle in each group just prior to treatment (Day 1), and again 11 days later (Day 11). Shotgun sequencing was performed on isolated metagenomic DNA, and reads were aligned to a resistance and a taxonomic database to identify alignments to antimicrobial resistance (AMR) gene accessions and microbiome content. Overall, we identified AMR genes accessions encompassing 9 classes of AMR drugs and encoding 24 unique AMR mechanisms. Statistical analysis was used to identify differences in the resistome and microbiome between the untreated and treated groups at both timepoints, as well as over time. Based on composition and ordination analyses, the resistome and microbiome were not significantly different between the two groups on Day 1 or on Day 11. However, both the resistome and microbiome changed significantly between these two sampling dates. These results indicate that the transition into the feedlot—and associated changes in diet, geography, conspecific exposure, and environment—may exert a greater influence over the fecal resistome and microbiome of feedlot cattle than common metaphylactic antimicrobial drug treatment

Improvement to the Prediction of Fuel Cost Distributions Using ARIMA Model

Availability of a validated, realistic fuel cost model is a prerequisite to the development and validation of new optimization methods and control tools. This paper uses an autoregressive integrated moving average (ARIMA) model with historical fuel cost data in development of a three-step-ahead fuel cost distribution prediction. First, the data features of Form EIA-923 are explored and the natural gas fuel costs of Texas generating facilities are used to develop and validate the forecasting algorithm for the Texas example. Furthermore, the spot price associated with the natural gas hub in Texas is utilized to enhance the fuel cost prediction. The forecasted data is fit to a normal distribution and the Kullback-Leibler divergence is employed to evaluate the difference between the real fuel cost distributions and the estimated distributions. The comparative evaluation suggests the proposed forecasting algorithm is effective in general and is worth pursuing further.Comment: Accepted by IEEE PES 2018 General Meetin

Comparative Genomics of <i>Escherichia coli</i> Serotype O55:H7 Using Complete Closed Genomes

Escherichia coli O55:H7 is a human foodborne pathogen and is recognized as the progenitor strain of E. coli O157:H7. While this strain is important from a food safety and genomic evolution standpoint, much of the genomic diversity of E. coli O55:H7 has been demonstrated using draft genomes. Here, we combine the four publicly available E. coli O55:H7 closed genomes with six newly sequenced closed genomes to provide context to this strain’s genomic diversity. We found significant diversity within the 10 E. coli O55:H7 strains that belonged to three different sequence types. The prophage content was about 10% of the genome, with three prophages common to all strains and seven unique to one strain. Overall, there were 492 insertion sequences identified within the six new sequence strains, with each strain on average containing 75 insertions (range 55 to 114). A total of 31 plasmids were identified between all isolates (range 1 to 6), with one plasmid (pO55) having an identical phylogenetic tree as the chromosome. The release and comparison of these closed genomes provides new insight into E. coli O55:H7 diversity and its ability to cause disease in humans

Increased Prevalence of <i>Salmonella</i> Infantis Isolated from Raw Chicken and Turkey Products in the United States Is Due to a Single Clonal Lineage Carrying the pESI Plasmid

Infantis has recently become one of the most common serotypes of Salmonella isolated in the U.S. from raw meat samples collected in processing facilities and in retail stores. Investigations have determined that the majority of these isolates contain the pESI plasmid, but there has not been a large-scale investigation of the chromosome of these isolates. Here, we investigated 3276 whole-genome sequences of Salmonella Infantis with and without the pESI plasmid to understand chromosomal differences between plasmid carriage groups. S. Infantis genomes arranged into multiple clades with a single clade containing the isolates carrying the plasmid. Fifty-eight SNPs were identified in complete linkage disequilibrium between isolates that did and did not carry the plasmid. However, there were no unique genes present only in the genomes of isolates containing the plasmid. On average, isolates with the plasmid did contain more insertion sequences than those without (p S. Infantis isolates carrying pESI form a single clade, it can be inferred that the increase in carriage of this plasmid in the U.S. is due to rapid clonal expansion of a single strain rather than as a result of multiple transfer events. As this S. Infantis clone does not contain any unique chromosomal genes, its proliferation appears to be due to pESI plasmid-encoded genes that may be advantageous in the chickens and turkeys or in their environment

Rapid estimation of \u3ci\u3eSalmonella enterica\u3c/i\u3e contamination level in ground beef – Application of the time-to-positivity method using a combination of molecular detection and direct plating

Little progress has been made in decreasing the incidence rate of salmonellosis in the US over the past decade. Mitigating the contribution of contaminated raw meat to the salmonellosis incidence rate requires rapid methods for quantifying Salmonella, so that highly contaminated products can be removed before entering the food chain. Here we evaluated the use of Time-to-Positivity (TTP) as a rapid, semi-quantitative approach for estimating Salmonella contamination levels in ground beef. Growth rates of 14 Salmonella strains (inoculated at log 1 to −2 CFU/g) were characterized in lean ground beef mTSB enrichments and time-to-detection was determined using culture and molecular detection methods. Enrichments were sampled at five timepoints and results were used to construct a prediction model of estimated contamination level by TTP (superscript indicates time in hours) defined as TTP4: ≥5 CFU/g; TTP6: ≤5, ≥1 CFU/g; TTP8: ≤1, ≥0.01 CFU/g; with samples negative at 8 h estimated ≤0.01 CFU/g. Model performance measures showed high sensitivity (100%) and specificity (83% and 93% for two detection methods) for samples with a TTP4, with false negative rates of 0%

Ground beef microbiome changes with antimicrobial decontamination interventions and product storage.

Ground beef makes up more than half of the beef consumed in the U.S. market. Although numerous studies have been conducted on microbial safety and shelf life of ground beef limited work has been done using a culture-independent approach. While past studies have allowed for the evaluation of a few organisms of interest, there is limited work on the microbial community associated with fresh ground beef. In order to have a more complete picture of the microbial ecology of the product, a culture-independent approach utilizing 16S rRNA gene amplicon sequencing was used. The objectives of this study were to characterize the fresh ground beef microbiome and the effect that antimicrobial interventions and antioxidants, applied to beef trim before grinding, and product storage have on community composition using 16S rRNA gene amplicon sequencing. Beef trimmings were treated with antimicrobials and an antioxidant. Samples were ground, loafed, and overwrapped before being packaged in modified-atmosphere packaging. Samples were in dark storage for 21 days followed by five days in retail display. Periodically during storage, samples were collected for microbiological analysis and DNA isolation. Due to low microbial biomass, only 52 of 210 samples were included in the final analysis. These samples represented two antimicrobial treatments (peroxyacetic acid, and a sulfuric acid and sodium sulfate blend) and a control, from day-15 of dark storage and day-5 of retail display. As sample age increased, so did the number of raw reads (P &lt; 0.001) and aerobic plate counts (P &lt; 0.001), which were correlated (r = 0.94, P = 0.017). Across all samples, lactic acid bacteria were most abundant followed by Enterobacteriaceae; several rare taxa were also identified (namely Geobacillus, Thermus, and Sporosarcina). Antimicrobial treatment altered the bacterial alpha (P &lt; 0.001) and beta (P = 0.001) diversity, while storage day altered alpha (P = 0.001) diversity. Enterobacteriaceae relative abundance differed (P &lt; 0.05) among treatments and was highest in control samples. In addition to confirming previously described dominant microbial differences in culture-dependent results, these data identified genera not typically associated with ground beef and allowed for study of shifts in the entire microbiome and not just a subset of indicator organisms

Ground beef microbiome changes with antimicrobial decontamination interventions and product storage.

Ground beef makes up more than half of the beef consumed in the U.S. market. Although numerous studies have been conducted on microbial safety and shelf life of ground beef limited work has been done using a culture-independent approach. While past studies have allowed for the evaluation of a few organisms of interest, there is limited work on the microbial community associated with fresh ground beef. In order to have a more complete picture of the microbial ecology of the product, a culture-independent approach utilizing 16S rRNA gene amplicon sequencing was used. The objectives of this study were to characterize the fresh ground beef microbiome and the effect that antimicrobial interventions and antioxidants, applied to beef trim before grinding, and product storage have on community composition using 16S rRNA gene amplicon sequencing. Beef trimmings were treated with antimicrobials and an antioxidant. Samples were ground, loafed, and overwrapped before being packaged in modified-atmosphere packaging. Samples were in dark storage for 21 days followed by five days in retail display. Periodically during storage, samples were collected for microbiological analysis and DNA isolation. Due to low microbial biomass, only 52 of 210 samples were included in the final analysis. These samples represented two antimicrobial treatments (peroxyacetic acid, and a sulfuric acid and sodium sulfate blend) and a control, from day-15 of dark storage and day-5 of retail display. As sample age increased, so did the number of raw reads (P < 0.001) and aerobic plate counts (P < 0.001), which were correlated (r = 0.94, P = 0.017). Across all samples, lactic acid bacteria were most abundant followed by Enterobacteriaceae; several rare taxa were also identified (namely Geobacillus, Thermus, and Sporosarcina). Antimicrobial treatment altered the bacterial alpha (P < 0.001) and beta (P = 0.001) diversity, while storage day altered alpha (P = 0.001) diversity. Enterobacteriaceae relative abundance differed (P < 0.05) among treatments and was highest in control samples. In addition to confirming previously described dominant microbial differences in culture-dependent results, these data identified genera not typically associated with ground beef and allowed for study of shifts in the entire microbiome and not just a subset of indicator organisms