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High-throughput RNA sequencing offers broad opportunities to explore the Earth RNA virome. Mining 5,150 diverse metatranscriptomes uncovered >2.5 million RNA virus contigs. Analysis of >330,000 RNA-dependent RNA polymerases (RdRPs) shows that this expansion corresponds to a 5-fold increase of the known RNA virus diversity. Gene content analysis revealed multiple protein domains previously not found in RNA viruses and implicated in virus-host interactions. Extended RdRP phylogeny supports the monophyly of the five established phyla and reveals two putative additional bacteriophage phyla and numerous putative additional classes and orders. The dramatically expanded phylum Lenarviricota, consisting of bacterial and related eukaryotic viruses, now accounts for a third of the RNA virome. Identification of CRISPR spacer matches and bacteriolytic proteins suggests that subsets of picobirnaviruses and partitiviruses, previously associated with eukaryotes, infect prokaryotic hosts.

Manure N from cattle contributes to nitrate leaching, nitrous oxide, and ammonia emissions. Measurement of manure N outputs on commercial beef cattle operations is laborious, expensive, and impractical; therefore, models are needed to predict N excreted in urine and feces. Building robust prediction models requires extensive data from animals under different management systems worldwide. Thus, the study objectives were to 1) collate an international dataset of N excretion in feces and urine based on individual observations from beef cattle; 2) determine the suitability of key variables for predicting fecal, urinary, and total manure N excretion; and 3) develop robust and reliable N excretion prediction models based on individual observation from beef cattle consuming various diets. A meta-analysis based on individual beef data from different experiments was carried out from a raw dataset including 1,004 observations from 33 experiments collected from 5 research institutes in Europe (n = 3), North America (n = 1), and South America (n = 1). A sequential approach was taken in developing models of increasing complexity by incrementally adding significant variables that affected fecal, urinary, or total manure N excretion. Nitrogen excretion was predicted by fitting linear mixed models with experiment as a random effect. Simple models including dry matter intake (DMI) were better at predicting fecal N excretion than those using only dietary nutrient composition or body weight (BW). Simple models based on N intake performed better for urinary and total manure N excretion than those based on DMI. A model including DMI and dietary component concentrations led to the most robust prediction of fecal and urinary N excretion, generating root mean square prediction errors as a percentage of the observed mean values of 25.0% for feces and 25.6% for urine. Complex total manure N excretion models based on BW and dietary component concentrations led to the lowest prediction errors of about 14.6%. In conclusion, several models to predict N excretion already exist, but the ones developed in this study are based on individual observations encompassing larger variability than the previous developed models. In addition, models that include information on DMI or N intake are required for accurate prediction of fecal, urinary, and total manure N excretion. In the absence of intake data, equations have poor performance as compared with equations based on intake and dietary component concentrations.

Changes in the sequence of an organism's genome, i.e., mutations, are the raw material of evolution. The frequency and location of mutations can be constrained by specific molecular mechanisms, such as diversity-generating retroelements (DGRs). DGRs have been characterized from cultivated bacteria and bacteriophages, and perform error-prone reverse transcription leading to mutations being introduced in specific target genes. DGR loci were also identified in several metagenomes, but the ecological roles and evolutionary drivers of these DGRs remain poorly understood. Here, we analyze a dataset of >30,000 DGRs from public metagenomes, establish six major lineages of DGRs including three primarily encoded by phages and seemingly used to diversify host attachment proteins, and demonstrate that DGRs are broadly active and responsible for >10% of all amino acid changes in some organisms. Overall, these results highlight the constraints under which DGRs evolve, and elucidate several distinct roles these elements play in natural communities.

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.

This study evaluated the effects of Pediococcus pentosaceus (PP) and chitinase combinations on the conservation and nutritive value of alfalfa high-moisture hay (HMH). P. pentosaceus [1012 colony forming unit/g fresh forage] combined with (g/tonne of fresh forage) 1.5 (PP + LC), 7.5 (PP + MC), or 15.0 (PP + HC) g of LANiHay01 chitinase (Exp. 1) or with LANiHay02 (PP + Fe; 1.5 g), LANiHay01 (PP + Pe; 1.5 g), or Sigma (PP + Si; 55 mg) chitinase/tonne (Exp. 2) were used in 2016. In 2017, PP was applied alone or in combination with LANiHay01 at 1.5 g (PP + LC) or 7.5 g (PP + MC) chitinase/tonne (Exp. 3 and 4). Deionized water and propionic acid (4.0 L/tonne of fresh forage in Exp. 1 and 2 and at 6.0 L/tonne of fresh forage in Exp. 3 and 4) were applied as neutral (CON) and positive control (CON+), respectively. The maximum temperature (r2 = 0.66) and NH3-N concentration (r2 = 0.80) of the HMH were positively related to total microbial populations. PP + MC had lower (p ≤ 0.05) yeast and mold counts than CON in Exp. 3 and 4 while the neutral detergent fiber degradability was greater (p < 0.01) for PP + MC and PP + LC than CON in Exp. 1 and 3, respectively. P. pentosaceus in combination with chitinase has the potential in conserving the nutrient quality of alfalfa HMH. [ABSTRACT FROM AUTHOR]

With antimicrobial resistance (AMR) rapidly evolving in pathogens, quick and accurate identification of genetic determinants of phenotypic resistance is essential for improving surveillance, stewardship, and clinical mitigation. Machine learning (ML) models show promise for AMR prediction in diagnostics but require a deep understanding of internal processes to use effectively. Our study utilised AMR gene, pangenomic, and predicted plasmid features from 647 Enterococcus faecium and Enterococcus faecalis genomes across the One Health continuum, along with corresponding resistance phenotypes, to develop interpretive ML classifiers. Vancomycin resistance could be predicted with 99% accuracy with AMR gene features, 98% with pangenome features, and 96% with plasmid clusters. Top pangenome features overlapped with the resistance genes of the vanA operon, which are often laterally transmitted via plasmids. Doxycycline resistance prediction achieved approximately 92% accuracy with pangenome features, with the top feature being elements of Tn916 conjugative transposon, a tet(M) carrier. Erythromycin resistance prediction models achieved about 90% accuracy, but top features were negatively correlated with resistance due to the confounding effect of population structure. This work demonstrates the importance of reviewing ML models' features to discern biological relevance even when achieving high-performance metrics. Our workflow offers the potential to propose hypotheses for experimental testing, enhancing the understanding of AMR mechanisms, which are crucial for combating the AMR crisis. [ABSTRACT FROM AUTHOR]

The rumen is a complex ecosystem composed of anaerobic bacteria, protozoa, fungi, methanogenic archaea and phages. These microbes interact closely to breakdown plant material that cannot be digested by humans, whilst providing metabolic energy to the host and, in the case of archaea, producing methane. Consequently, ruminants produce meat and milk, which are rich in high-quality protein, vitamins and minerals, and therefore contribute to food security. As the world population is predicted to reach approximately 9.7 billion by 2050, an increase in ruminant production to satisfy global protein demand is necessary, despite limited land availability, and whilst ensuring environmental impact is minimized. Although challenging, these goals can be met, but depend on our understanding of the rumen microbiome. Attempts to manipulate the rumen microbiome to benefit global agricultural challenges have been ongoing for decades with limited success, mostly due to the lack of a detailed understanding of this microbiome and our limited ability to culture most of these microbes outside the rumen. The potential to manipulate the rumen microbiome and meet global livestock challenges through animal breeding and introduction of dietary interventions during early life have recently emerged as promising new technologies. Our inability to phenotype ruminants in a high-throughput manner has also hampered progress, although the recent increase in "omic" data may allow further development of mathematical models and rumen microbial gene biomarkers as proxies. Advances in computational tools, high-throughput sequencing technologies and cultivation-independent "omics" approaches continue to revolutionize our understanding of the rumen microbiome. This will ultimately provide the knowledge framework needed to solve current and future ruminant livestock challenges.

Extruded flaxseed (25%) and ground hay (75%) were each fed (DM basis) either together in a total mixed ration (TMR) or as flaxseed first followed by hay (non-TMR) to three pens of eight crossbred steers (n = 24 per diet) for 240 days. Compared to TMR, feeding non-TMR enriched subcutaneous fat with α-linolenic acid (ALA, 18:3n-3) and its biohydrogenation intermediates including vaccenic acid [trans(t)11-18:1], rumenic acid [cis(c)9,t11-conjugated linoleic acid] and conjugated linolenic acid (CLnA). Rumen microbial analysis using QIIME indicated that 14 genera differed (P ≤ 0.05) between TMR and the non-TMR. Azoarcus and Streptococcus were the only genera which increased in relative abundance in the TMR fed steers, whereas Methanimicrococcus, Moryella, Prevotella, Succiniclasticum, Succinivibrio, Suttenella, and TG5 decreased as compared to steers fed the non-TMR. Among these, Moryella, Succiniclasticum, and Succinivibrio, spp. were correlated with fatty acid profiles, specifically intermediates believed to be components of the major biohydrogenation pathway for ALA (i.e., t11, c15-18:2, c9, t11, c15-18:3, and total CLnA). In addition, negative correlations were found between the less abundant Ruminoccocus-like OTU60 and major ALA biohydrogenation intermediates, as well as positive correlations with several intermediates from alternative pathways that did not involve the formation of trans 11 double bonds. The present results suggest a number of pathways for ALA biohydrogenation are operating concurrently in the rumen, with their balance being influenced by diet and driven by less abundant species rather than members of the core bacterial population.

The rapid increase of antimicrobial-resistant bacteria in humans and livestock is concerning. Antimicrobials are essential for the treatment of disease in modern day medicine, and their misuse in humans and food animals has contributed to an increase in the prevalence of antimicrobial-resistant bacteria. Globally, antimicrobial resistance is recognized as a One Health problem affecting humans, animals, and environment. Enterococcal species are Gram-positive bacteria that are widely distributed in nature. Their occurrence, prevalence, and persistence across the One Health continuum make them an ideal candidate to study antimicrobial resistance from a One Health perspective. The objective of this review was to summarize the role of enterococci as an indicator of antimicrobial resistance across One Health sectors. We also briefly address the prevalence of enterococci in human, animal, and environmental settings. In addition, a 16S RNA gene-based phylogenetic tree was constructed to visualize the evolutionary relationship among enterococcal species and whether they segregate based on host environment. We also review the genomic basis of antimicrobial resistance in enterococcal species across the One Health continuum. [ABSTRACT FROM AUTHOR]

An increase in chronic, non-responsive bovine respiratory disease (BRD) infections in North American feedlot cattle is observed each fall, a time when cattle are administered multiple antimicrobial treatments for BRD. A number of factors are responsible for BRD antimicrobial treatment failure, with formation of biofilms possibly being one. It is widely accepted that biofilms play a role in chronic infections in humans and it has been hypothesized that they are the default lifestyle of most bacteria. However, research on bacterial biofilms associated with livestock is scarce and significant knowledge gaps exist in our understanding of their role in AMR of the bacterial BRD complex. The four main bacterial species of the BRD complex, Mannheimia haemolytica, Pasteurella multocida, Histophilus somni, and Mycoplasma bovis are able to form biofilms in vitro and there is evidence that at least H. somni retains this ability in vivo. However, there is a need to elucidate whether their biofilm-forming ability contributes to pathogenicity and antimicrobial treatment failure of BRD. Overall, a better understanding of the possible role of BRD bacterial biofilms in clinical disease and AMR could assist in the prevention and management of respiratory infections in feedlot cattle. We review and discuss the current knowledge of BRD bacteria biofilm biology, study methodologies, and their possible relationship to AMR. [ABSTRACT FROM AUTHOR]

This study assessed the environmental impacts of beef cattle production and their effects on the overall sustainability of Canadian beef production. Cradle to farm gate, cradle to processor's gate, and cradle to consumer plate life cycle assessments were carried out to quantify greenhouse gases (GHG), resource use (i.e., water, land, and fuel), and potential water and air pollution (i.e., freshwater eutrophication, terrestrial acidification, and photochemical oxidants formation). Across the production chain, feed production had the greatest impact on most environmental indicators. The GHG intensity without dairy meat was estimated as 10.4 kg CO2-eq per kg of live weight (LW), corresponding to 32.8 kg CO2-eq per kg of consumed boneless beef. Including dairy meat reduced GHG intensity by 5.8% (0.6 kg CO2-eq kg LW–1) compared to when it was excluded. Other environmental metrics per kg of LW were 657 L, 38.7 m2 annual crop-eq, 0.4 kg oil-eq, 2.6 kg P-eq, 115.9 kg SO2-eq, and 8.7 kg NOx-eq for water use, land use, fossil fuel use, freshwater eutrophication, terrestrial acidification, and photochemical oxidants, respectively. Data provide benchmarks for use in future regional and national assessments that are designed to encourage the adoption of sustainable management practices that can lower the environmental footprint of Canadian beef production. [ABSTRACT FROM AUTHOR]

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.

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.