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BackgroundDifferentiation of lymphocytic-plasmacytic enteropathy (LPE) from small cell lymphoma (SCL) in cats can be challenging.Hypothesis/objectiveHistology-guided mass spectrometry (HGMS) is a suitable method for the differentiation of LPE from SCL in cats.AnimalsForty-one cats with LPE and 52 cats with SCL.MethodsThis is a retrospective clinicopathologic study. Duodenal tissue samples of 17 cats with LPE and 22 cats with SCL were subjected to HGMS, and the acquired data were used to develop a linear discriminate analysis (LDA) machine learning algorithm. The algorithm was subsequently validated using a separate set of 24 cats with LPE and 30 cats with SCL. Cases were classified as LPE or SCL based on a consensus by an expert panel consisting of 5-7 board-certified veterinary specialists. Histopathology, immunohistochemistry, and clonality testing were available for all cats. The panel consensus classification served as a reference for the calculation of test performance parameters.ResultsRelative sensitivity, specificity, and accuracy of HGMS were 86.7% (95% confidence interval [CI]: 74.5%-98.8%), 91.7% (95% CI: 80.6%-100%), and 88.9% (95% CI: 80.5%-97.3%), respectively. Comparatively, the clonality testing had a sensitivity, specificity, and accuracy of 85.7% (95% CI: 72.8%-98.7%), 33.3% (95% CI: 14.5%-52.2%), and 61.5% (95% CI: 48.3%-74.8%) relative to the panel decision.Conclusions and clinical importanceHistology-guided mass spectrometry was a reliable technique for the differentiation of LPE from SCL in duodenal formalin-fixed paraffin-embedded samples of cats and might have advantages over tests currently considered state of the art.

Introduction: The gastrointestinal microbiota profoundly influences the health and productivity of animals. This study aimed to characterize microbial community structures of the mouth, gastrointestinal tract (GIT), and feces of cattle. Methods: Samples were collected from 18 Akaushi crossbred steers at harvest from multiple locations, including the oral cavity, rumen, abomasum, duodenum, jejunum, ileum, cecum, spiral colon, distal colon, and feces. These cattle were raised without exposure to antimicrobial drugs or hormone implants. Total microbial abundance was assessed using qPCR targeting the V3–V4 region of the 16S rRNA gene, and microbial community composition was evaluated through 16S rRNA gene sequencing. Results: Total microbial abundance was lesser in the small intestine than in other GIT regions (p ≤ 0.05). Additionally, microbial communities in the small intestine had lower richness and diversity than other regions (p ≤ 0.05). Microbial community compositions were measurably different along the GIT, with greater relatedness in adjacent GIT sections when progressing from oral to aboral locations. Firmicutes, Bacteroidota, and Actinobacteria were the dominant phyla in all samples. However, variations in composition were evident at lower taxonomic levels within these dominant phyla among samples from different regions. Genera previously associated with healthy gut microbiome communities were observed in low abundance across GIT regions. Taxa historically associated with liver abscesses (e.g., Fusobacterium and Trueperella) were detected in low abundance (≤0.02% relative abundance) throughout the GIT. In contrast, Bacteroides , which recently has been identified as a dominant feature in many liver abscesses, was observed in greater relative abundance (5.2% on average) in the hindgut. Discussion: This study provides an in-depth evaluation of the GIT of harvest-ready Akaushi crossbred cattle of varying growth rates. Clear differences exist in the abundance and composition of microbial populations at different points of the GIT. Unfortunately, no single GIT location can adequately represent the microbial communities of the entire GIT, which has important implications for future research. Additionally, examining microbiome data only at the phylum level likely oversimplifies important complexities of the microbial community structures, and investigations of lower taxonomic ranks should be included. [ABSTRACT FROM AUTHOR]

This study was conducted to compare aerobic culture, polymerase chain reaction (PCR), lateral flow immunoassay (LFI), and shotgun metagenomics for identification of Salmonella enterica in feces collected from feedlot cattle. Samples were analyzed in parallel using all four tests. Results from aerobic culture and PCR were 100% concordant and indicated low S. enterica prevalence (3/60 samples positive). Although low S. enterica prevalence restricted formal statistical comparisons, LFI and deep metagenomic sequencing results were discordant with these results. Specifically, metagenomic analysis using k-mer-based classification against the RefSeq database indicated that 11/60 of samples contained sequence reads that matched to the S. enterica genome and uniquely identified this species of bacteria within the sample. However, further examination revealed that plasmid sequences were often included with bacterial genomic sequence data submitted to NCBI, which can lead to incorrect taxonomic classification. To circumvent this classification problem, we separated all plasmid sequences included in bacterial RefSeq genomes and reassigned them to a unique taxon so that they would not be uniquely associated with specific bacterial species such as S. enterica. Using this revised database and taxonomic structure, we found that only 6/60 samples contained sequences specific for S. enterica, suggesting increased relative specificity. Reads identified as S. enterica in these six samples were further evaluated using BLAST and NCBI's nr/nt database, which identified that only 2/60 samples contained reads exclusive to S. enterica chromosomal genomes. These two samples were culture- and PCR-negative, suggesting that even deep metagenomic sequencing suffers from lower sensitivity and specificity in comparison to more traditional pathogen detection methods. Additionally, no sample reads were taxonomically classified as S. enterica with two other metagenomic tools, Metagenomic Intra-species Diversity Analysis System (MIDAS) and Metagenomic Phylogenetic Analysis 2 (MetaPhlAn2). This study re-affirmed that the traditional techniques of aerobic culture and PCR provide similar results for S. enterica identification in cattle feces. On the other hand, metagenomic results are highly influenced by the classification method and reference database employed. These results highlight the nuances of computational detection of species-level sequences within short-read metagenomic sequence data, and emphasize the need for cautious interpretation of such results.

Metagenomic investigations have the potential to provide unprecedented insights into microbial ecologies, such as those relating to antimicrobial resistance (AMR). We characterized the microbial resistome in livestock operations raising cattle conventionally (CONV) or without antibiotic exposures (RWA) using shotgun metagenomics. Samples of feces, wastewater from catchment basins, and soil where wastewater was applied were collected from CONV and RWA feedlot and dairy farms. After DNA extraction and sequencing, shotgun metagenomic reads were aligned to reference databases for identification of bacteria (Kraken) and antibiotic resistance genes (ARGs) accessions (MEGARes). Differences in microbial resistomes were found across farms with different production practices (CONV vs. RWA), types of cattle (beef vs. dairy), and types of sample (feces vs. wastewater vs. soil). Feces had the greatest number of ARGs per sample (mean = 118 and 79 in CONV and RWA, respectively), with tetracycline efflux pumps, macrolide phosphotransferases, and aminoglycoside nucleotidyltransferases mechanisms of resistance more abundant in CONV than in RWA feces. Tetracycline and macrolide-lincosamide-streptogramin classes of resistance were more abundant in feedlot cattle than in dairy cow feces, whereas the β-lactam class was more abundant in dairy cow feces. Lack of congruence between ARGs and microbial communities (procrustes analysis) suggested that other factors (e.g., location of farms, cattle source, management practices, diet, horizontal ARGs transfer, and co-selection of resistance), in addition to antimicrobial use, could have impacted resistome profiles. For that reason, we could not establish a cause-effect relationship between antimicrobial use and AMR, although ARGs in feces and effluents were associated with drug classes used to treat animals according to farms' records (tetracyclines and macrolides in feedlots, β-lactams in dairies), whereas ARGs in soil were dominated by multidrug resistance. Characterization of the "resistance potential" of animal-derived and environmental samples is the first step toward incorporating metagenomic approaches into AMR surveillance in agricultural systems. Further research is needed to assess the public-health risk associated with different microbial resistomes.

Treatment of food-producing animals with antimicrobial drugs (AMD) is controversial because of concerns regarding promotion of antimicrobial resistance (AMR). To investigate this concern, resistance genes in metagenomic bovine fecal samples during a clinical trial were analyzed to assess the impacts of treatment on beef feedlot cattle resistomes. Four groups of cattle were exposed, using a 2-by-2 factorial design, to different regimens of antimicrobial treatment. Injections of ceftiofur crystalline-free acid (a third-generation cephalosporin) were used to treat all cattle in treatment pens or only a single animal, and either chlortetracycline was included in the feed of all cattle in a pen or the feed was untreated. On days 0 and 26, respectively, pre- and posttrial fecal samples were collected, and resistance genes were characterized using shotgun metagenomics. Treatment with ceftiofur was not associated with changes to β-lactam resistance genes. However, cattle fed chlortetracycline had a significant increase in relative abundance of tetracycline resistance genes. There was also an increase of an AMR class not administered during the study, which is a possible indicator of coselection of resistance genes. Samples analyzed in this study had previously been evaluated by culture characterization (Escherichia coli and Salmonella) and quantitative PCR (qPCR) of metagenomic fecal DNA, which allowed comparison of results with this study. In the majority of samples, genes that were selectively enriched through culture and qPCR were not identified through shotgun metagenomic sequencing in this study, suggesting that changes previously documented did not reflect changes affecting the majority of bacterial genetic elements found in the predominant fecal resistome.IMPORTANCE Despite significant concerns about public health implications of AMR in relation to use of AMD in food animals, there are many unknowns about the long- and short-term impact of common uses of AMD for treatment, control, and prevention of disease. Additionally, questions commonly arise regarding how to best measure and quantify AMR genes in relation to public health risks and how to determine which genes are most important. These data provide an introductory view of the utility of using shotgun metagenomic sequencing data as an outcome for clinical trials evaluating the impact of using AMD in food animals.

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.

Extended laboratory culture and antimicrobial susceptibility testing timelines hinder rapid species identification and susceptibility profiling of bacterial pathogens associated with bovine respiratory disease, the most prevalent cause of cattle mortality in the United States. Whole-genome sequencing offers a culture-independent alternative to current bacterial identification methods, but requires a library of bacterial reference genomes for comparison. To contribute new bacterial genome assemblies and evaluate genetic diversity and variation in antimicrobial resistance genotypes, whole-genome sequencing was performed on bovine respiratory disease-associated bacterial isolates (Histophilus somni, Mycoplasma bovis, Mannheimia haemolytica, and Pasteurella multocida) from dairy and beef cattle. One hundred genomically distinct assemblies were added to the NCBI database, doubling the available genomic sequences for these four species. Computer-based methods identified 11 predicted antimicrobial resistance genes in three species, with none being detected in M. bovis While computer-based analysis can identify antibiotic resistance genes within whole-genome sequences (genotype), it may not predict the actual antimicrobial resistance observed in a living organism (phenotype). Antimicrobial susceptibility testing on 64 H. somni, M. haemolytica, and P. multocida isolates had an overall concordance rate between genotype and phenotypic resistance to the associated class of antimicrobials of 72.7% (P < 0.001), showing substantial discordance. Concordance rates varied greatly among different antimicrobial, antibiotic resistance gene, and bacterial species combinations. This suggests that antimicrobial susceptibility phenotypes are needed to complement genomically predicted antibiotic resistance gene genotypes to better understand how the presence of antibiotic resistance genes within a given bacterial species could potentially impact optimal bovine respiratory disease treatment and morbidity/mortality outcomes.

It has been proposed that livestock production effluents such as wastewater, airborne dust and manure increase the density of antimicrobial resistant bacteria and genes in the environment. The public health risk posed by this proposed outcome has been difficult to quantify using traditional microbiological approaches. We utilized shotgun metagenomics to provide a first description of the resistome of North American dairy and beef production effluents, and identify factors that significantly impact this resistome. We identified 34 mechanisms of antimicrobial drug resistance within 34 soil, manure and wastewater samples from feedlot, ranch and dairy operations. The majority of resistance-associated sequences found in all samples belonged to tetracycline resistance mechanisms. We found that the ranch samples contained significantly fewer resistance mechanisms than dairy and feedlot samples, and that the resistome of dairy operations differed significantly from that of feedlots. The resistome in soil, manure and wastewater differed, suggesting that management of these effluents should be tailored appropriately. By providing a baseline of the cattle production waste resistome, this study represents a solid foundation for future efforts to characterize and quantify the public health risk posed by livestock effluents.

Foodborne illnesses associated with pathogenic bacteria are a global public health and economic challenge. The diversity of microorganisms (pathogenic and nonpathogenic) that exists within the food and meat industries complicates efforts to understand pathogen ecology. Further, little is known about the interaction of pathogens within the microbiome throughout the meat production chain. Here, a metagenomic approach and shotgun sequencing technology were used as tools to detect pathogenic bacteria in environmental samples collected from the same groups of cattle at different longitudinal processing steps of the beef production chain: cattle entry to feedlot, exit from feedlot, cattle transport trucks, abattoir holding pens, and the end of the fabrication system. The log read counts classified as pathogens per million reads for Salmonella enterica,Listeria monocytogenes,Escherichia coli,Staphylococcus aureus, Clostridium spp. (C. botulinum and C. perfringens), and Campylobacter spp. (C. jejuni,C. coli, and C. fetus) decreased over subsequential processing steps. Furthermore, the normalized read counts for S. enterica,E. coli, and C. botulinumwere greater in the final product than at the feedlots, indicating that the proportion of these bacteria increased (the effect on absolute numbers was unknown) within the remaining microbiome. From an ecological perspective, data indicated that shotgun metagenomics can be used to evaluate not only the microbiome but also shifts in pathogen populations during beef production. Nonetheless, there were several challenges in this analysis approach, one of the main ones being the identification of the specific pathogen from which the sequence reads originated, which makes this approach impractical for use in pathogen identification for regulatory and confirmation purposes.

Antimicrobial resistant determinants (ARDs) can be transmitted from livestock systems through meat products or environmental effluents. The public health risk posed by these two routes is not well understood, particularly in non-pathogenic bacteria. We collected pooled samples from 8 groups of 1741 commercial cattle as they moved through the process of beef production from feedlot entry through slaughter. We recorded antimicrobial drug exposures and interrogated the resistome at points in production when management procedures could potentially influence ARD abundance and/or transmission. Over 300 unique ARDs were identified. Resistome diversity decreased while cattle were in the feedlot, indicating selective pressure. ARDs were not identified in beef products, suggesting that slaughter interventions may reduce the risk of transmission of ARDs to beef consumers. This report highlights the utility and limitations of metagenomics for assessing public health risks regarding antimicrobial resistance, and demonstrates that environmental pathways may represent a greater risk than the food supply.

Increased antimicrobial resistance (AMR), or antibiotic resistance, has resulted in initiatives to reduce the use of antibiotics in food production animals (1,2), but quantification of the public health effects of [...]

This study aims to compare rumen microbiome and metabolites between second lactation dairy cows in the 75th percentile (n = 12; 57.2 ± 5.08 kg/d) of production according to genomic predicted transmitting ability for milk (GPTAM) and their counterparts in the 25th percentile (n = 12; 47.2 ± 8.61 kg/d). It was hypothesized that the metagenome and metabolome would differ between production levels. Cows were matched by days in milk (DIM), sire, occurrence of disease, and days open in previous lactation. For an additional comparison, the cows were also divided by phenotype into high (n = 6; 61.3 ± 2.8 kg/d), medium (n = 10; 55 ± 1.2 kg/d), and low (n = 8; 41.9 ± 5.6 kg/d) based on their milk production. Samples were collected 65 ± 14 DIM. Rumen content was collected using an oro-gastric tube and serum samples were collected from the coccygeal vessels. High-resolution liquid chromatography-mass spectrometry (LC-MS) was used for rumen and serum metabolite profiling. Shotgun metagenomics was used for rumen microbiome profiling. Microbiome sample richness and diversity were used to determine alpha and Bray-Curtis dissimilarity index was used to estimate beta diversity. Differences in metabolites were determined using t-tests or ANOVA. Pearson correlations were used to consider associations between serum metabolites and milk production. There was no evidence of a difference in rumen metabolites or microbial communities by GPTAM or phenotype. Cows in the phenotypic low group had greater serum acetate to propionate ratio and acetate proportion compared to the cows in the phenotypic medium group. Likewise, serum propionate proportion was greater in the medium compared to the low phenotypic group. Serum acetate, butyrate, and propionate concentrations had a weak positive correlation with milk production. When investigating associations between rumen environment and milk production, future studies must consider the impact of the ruminal epithelium absorption and post-absorption processes in relation to milk production. [ABSTRACT FROM AUTHOR]

Plague is rare in dogs, even in areas to which Yersinia pestis is endemic (1,2). We describe a case of canine pneumonic plague that resulted in [greater than or equal [...]

Zoonotic diseases are infectious diseases that are shared between humans and other vertebrate animals. Extension professionals often serve as consultants and educators to individuals at high risk of zoonotic diseases, such as participants in 4-H livestock projects. Effective education about zoonotic diseases begins with an awareness of the multitude of challenges that health care professionals face in diagnosing zoonotic disease. This review describes the factors that influence diagnosis of these diseases, as well as potential methods that the Extension professional can use to convert those challenges into effective educational messages.

The American College of Veterinary Internal Medicine (ACVIM) is proposing the development of ACVIM-Endorsed Statements (AES) to provide veterinarians with high-quality information on important topics. These statements will use specific methodologies, including consensus statements, evidence-based practice guidelines, and systematic reviews. The text explains the importance of systematic reviews in providing objective assessments of existing data and outlines different methodologies for developing AES. [Extracted from the article]

Introduction: The objectives of this study were to evaluate the impacts of two modified-live virus (MLV) vaccination protocols and respiratory disease (BRD) occurrence on the microbial community composition of the nasopharynx in feedlot cattle. Methods: The treatment groups included in this randomized controlled trial included: 1) no viral respiratory vaccination (CON), 2) intranasal, trivalent, MLV respiratory vaccine in addition to a parenteral BVDV type I and II vaccine (INT), and 3) parenteral, pentavalent, MLV respiratory vaccination against the same agents (INJ). Calves (n = 525) arrived in 5 truckload blocks and were stratified by body weight, sex, and presence of a pre-existing identification ear-tag. A total of 600 nasal swab samples were selected for DNA extraction and subsequent 16S rRNA gene sequencing to characterize the microbiome of the upper respiratory tract. Nasal swabs collected on d 28 fromhealthy cattle were used to evaluate the impact of vaccination on upper respiratory tract (URT) microbial communities. Results: Firmicutes were less abundant in INT calves (n = 114; P < 0.05) and this difference was attributed to decreased relative abundance (RA) of Mycoplasma spp. (P = 0.04). Mannheimia and Pasteurella had lower RA in INT (P < 0.05). The microbiome in healthy animals on d 28 had increased Proteobacteria (largely Moraxella spp.) and decreased Firmicutes (comprised almost exclusively of Mycoplasma spp.) compared to animals that were treated for or died from BRD (P < 0.05). Cattle that died had a greater RA of Mycoplasma spp. in their respiratory microbiome on d 0 (P < 0.02). Richness was similar on d 0 and 28, but diversity increased for all animals on d 28 (P > 0.05). [ABSTRACT FROM AUTHOR]