Comprehensive insights into the impact of bacterial indole-3-acetic acid on sensory preferences in Drosophila melanogaster.

Several bacteria of environmental and clinical origins, including some human-associated strains secrete a cross-kingdom signaling molecule indole-3-acetic acid (IAA). IAA is a tryptophan (trp) derivative mainly known for regulating plant growth and development as a hormone. However, the nutritional sources that boost IAA secretion in bacteria and the impact of secreted IAA on non-plant eukaryotic hosts remained less explored. Here, we demonstrate significant trp-dependent IAA production in Pseudomonas juntendi NEEL19 when provided with ethanol as a carbon source in liquid cultures. IAA was further characterized to modulate the odor discrimination, motility and survivability in Drosophila melanogaster. A detailed analysis of IAA-fed fly brain proteome using high-resolution mass spectrometry showed significant (fold change, ± 2; p ≤ 0.05) alteration in the proteins governing neuromuscular features, audio-visual perception and energy metabolism as compared to IAA-unfed controls. Sex-wise variations in differentially regulated proteins were witnessed despite having similar visible changes in chemo perception and psychomotor responses in IAA-fed flies. This study not only revealed ethanol-specific enhancement in trp-dependent IAA production in P. juntendi, but also showed marked behavioral alterations in flies for which variations in an array of proteins governing odor discrimination, psychomotor responses, and energy metabolism are held responsible. Our study provided novel insights into disruptive attributes of bacterial IAA that can potentially influence the eukaryotic gut-brain axis having broad environmental and clinical implications.

Complete genome sequence of Escherichia coli MP1.

Here, we report the complete genome sequence of Escherichia coli strain MP1, consisting of one circular chromosome and one circular plasmid. Long-read assembly was performed using a consensus approach, followed by long- and short-read polishing, and gene annotation.

The Resilient Dairy Genome Project-A general overview of methods and objectives related to feed efficiency and methane emissions.

The Resilient Dairy Genome Project (RDGP) is an international large-scale applied research project that aims to generate genomic tools to breed more resilient dairy cows. In this context, improving feed efficiency and reducing greenhouse gases from dairy is a high priority. The inclusion of traits related to feed efficiency (e.g., dry matter intake [DMI]) or greenhouse gases (e.g., methane emissions [CH4]) relies on available genotypes as well as high quality phenotypes. Currently, 7 countries (i.e., Australia, Canada, Denmark, Germany, Spain, Switzerland, and United States) contribute with genotypes and phenotypes including DMI and CH4. However, combining data are challenging due to differences in recording protocols, measurement technology, genotyping, and animal management across sources. In this study, we provide an overview of how the RDGP partners address these issues to advance international collaboration to generate genomic tools for resilient dairy. Specifically, we describe the current state of the RDGP database, data collection protocols in each country, and the strategies used for managing the shared data. As of February 2022, the database contains 1,289,593 DMI records from 12,687 cows and 17,403 CH4 records from 3,093 cows and continues to grow as countries upload new data over the coming years. No strong genomic differentiation between the populations was identified in this study, which may be beneficial for eventual across-country genomic predictions. Moreover, our results reinforce the need to account for the heterogeneity in the DMI and CH4 phenotypes in genomic analysis.

Hormesis of glyphosate on ferulic acid metabolism and antifungal volatile production in rice root biocontrol endophyte Burkholderia cepacia LS-044.

Glyphosate (GP, N-phosphonomethyl glycine) is one of the most popular organophosphate herbicides widely used in agricultural practices worldwide. There have been extensive reports on the biohazard attributes and hormetic impacts of GP on plant and animal systems. However, the effects of GP on plant growth-promoting microbes and its ecological relevance remain unknown. Here, we show that GP does exert a hormetic impact on Burkholderia cepacia LS-044, a rice (Oryza sativa ssp. japonica cv. Tainung 71) root endophytic isolate. We used increasing doses of ferulic acid (FA, 1-25 mM) and GP (0.5-5 mM) to test the growth and antifungal volatile production in LS-044 by electrochemical, liquid chromatographic, gas chromatographic and spectrophotometric means. GP treatment at a low dose (0.5 mM) increased FA utilization and significantly (P < 0.0001) enhanced antifungal volatile activity in LS-044. Although FA (1 mM) was rapidly utilized by LS-044, no chromatographically detectable utilization of GP was observed at tested doses (0.5-5 mM). LS-044 emitted predominant amounts of tropone in addition to moderate-to-minor amounts of diverse ketones and/or their derivatives (acetone, acetophenone, 2-butanone, 1-propanone, 1-(2-furanyl-ethanone, 1-phenyl-1-propanone and 1-(3-pyridinyl)-1-propanone), d-menthol, 2-methoxy-3-(1-methylethyl)-pyrazine, dimethyl disulfide, pyridine and ammonium carbamate when grown under GP supplement. GP hormesis on LS-044 induced phenotypic variations in O. sativa ssp. japonica cv. Tainan 11 as evident through seed germination assay. Genes involved in the transformation of FA, and a key gene encoding 5-enolpyruvylshikimate 3-phosphate synthase (EPSPS) with Gly-94 and Tyr-95 residues localized at active site most likely rendering EPSPS sensitivity to GP, were detected in LS-044. This is the first report on the GP hormesis influencing morphological and metabolic aspects including volatile emission in a biocontrol bacterium that could modulate rice plant phenotype.

Genomic potential for inorganic carbon sequestration and xenobiotic degradation in marine bacterium Youngimonas vesicularis CC-AMW-ET affiliated to family Paracoccaceae.

Ecological studies on marine microbial communities largely focus on fundamental biogeochemical processes or the most abundant constituents, while minor biological fractions are frequently neglected. Youngimonas vesicularis CC-AMW-ET, isolated from coastal surface seawater in Taiwan, is an under-represented marine Paracoccaceae (earlier Rhodobacteraceae) member. The CC-AMW-ET genome was sequenced to gain deeper insights into its role in marine carbon and sulfur cycles. The draft genome (3.7 Mb) contained 63.6% GC, 3773 coding sequences and 51 RNAs, and displayed maximum relatedness (79.06%) to Thalassobius litoralis KU5D5T, a Roseobacteraceae member. While phototrophic genes were absent, genes encoding two distinct subunits of carbon monoxide dehydrogenases (CoxL, BMS/Form II and a novel form III; CoxM and CoxS), and proteins involved in HCO3- uptake and interconversion, and anaplerotic HCO3- fixation were found. In addition, a gene coding for ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO, form II), which fixes atmospheric CO2 was found in CC-AMW-ET. Genes for complete assimilatory sulfate reduction, sulfide oxidation (sulfide:quinone oxidoreductase, SqrA type) and dimethylsulfoniopropionate (DMSP) cleavage (DMSP lyase, DddL) were also identified. Furthermore, genes that degrade aromatic hydrocarbons such as quinate, salicylate, salicylate ester, p-hydroxybenzoate, catechol, gentisate, homogentisate, protocatechuate, 4-hydroxyphenylacetic acid, N-heterocyclic aromatic compounds and aromatic amines were present. Thus, Youngimonas vesicularis CC-AMW-ET is a potential chemolithoautotroph equipped with genetic machinery for the metabolism of aromatics, and predicted to play crucial roles in the biogeochemical cycling of marine carbon and sulfur.

PHASTEST: faster than PHASTER, better than PHAST.

PHASTEST (PHAge Search Tool with Enhanced Sequence Translation) is the successor to the PHAST and PHASTER prophage finding web servers. PHASTEST is designed to support the rapid identification, annotation and visualization of prophage sequences within bacterial genomes and plasmids. PHASTEST also supports rapid annotation and interactive visualization of all other genes (protein coding regions, tRNA/tmRNA/rRNA sequences) in bacterial genomes. Given that bacterial genome sequencing has become so routine, the need for fast tools to comprehensively annotate bacterial genomes has become progressively more important. PHASTEST not only offers faster and more accurate prophage annotations than its predecessors, it also provides more complete whole genome annotations and much improved genome visualization capabilities. In standardized tests, we found that PHASTEST is 31% faster and 2-3% more accurate in prophage identification than PHASTER. Specifically, PHASTEST can process a typical bacterial genome in 3.2 min (raw sequence) or in 1.3 min when given a pre-annotated GenBank file. Improvements in PHASTEST's ability to annotate bacterial genomes now make it a particularly powerful tool for whole genome annotation. In addition, PHASTEST now offers a much more modern and responsive visualization interface that allows users to generate, edit, annotate and interactively visualize (via zooming, rotating, dragging, panning, resetting), colourful, publication quality genome maps. PHASTEST continues to offer popular options such as an API for programmatic queries, a Docker image for local installations, support for multiple (metagenomic) queries and the ability to perform automated look-ups against thousands of previously PHAST-annotated bacterial genomes. PHASTEST is available online at https://phastest.ca.

Proksee: in-depth characterization and visualization of bacterial genomes.

Proksee (https://proksee.ca) provides users with a powerful, easy-to-use, and feature-rich system for assembling, annotating, analysing, and visualizing bacterial genomes. Proksee accepts Illumina sequence reads as compressed FASTQ files or pre-assembled contigs in raw, FASTA, or GenBank format. Alternatively, users can supply a GenBank accession or a previously generated Proksee map in JSON format. Proksee then performs assembly (for raw sequence data), generates a graphical map, and provides an interface for customizing the map and launching further analysis jobs. Notable features of Proksee include unique and informative assembly metrics provided via a custom reference database of assemblies; a deeply integrated high-performance genome browser for viewing and comparing analysis results at individual base resolution (developed specifically for Proksee); an ever-growing list of embedded analysis tools whose results can be seamlessly added to the map or searched and explored in other formats; and the option to export graphical maps, analysis results, and log files for data sharing and research reproducibility. All these features are provided via a carefully designed multi-server cloud-based system that can easily scale to meet user demand and that ensures the web server is robust and responsive.

Milk metabolomics analyses of lactating dairy cows with divergent residual feed intake reveals physiological underpinnings and novel biomarkers.

The opportunity to select for feed efficient cows has been limited by inability to cost-effectively record individual feed efficiency on an appropriate scale. This study investigated the differences in milk metabolite profiles between high- and low residual feed intake (RFI) categories and identified biomarkers of residual feed intake and models that can be used to predict residual feed intake in lactating Holsteins. Milk metabolomics analyses were undertaken at early, mid and late lactation stages and residual feed intake was calculated in 72 lactating dairy cows. Cows were ranked and grouped into high residual feed intake (RFI >0.5 SD above the mean, n = 20) and low residual feed intake (RFI <0.5 SD below the mean, n = 20). Milk metabolite profiles were compared between high residual feed intake (least efficient) and low residual feed intake (most efficient) groups. Results indicated that early lactation was predominantly characterized by significantly elevated levels of medium chain acyl carnitines and glycerophospholipids in high residual feed intake cows. Citrate cycle and glycerophospholipid metabolism were the associated pathways enriched with the significantly different metabolites in early lactation. At mid lactation short and medium chain acyl carnitines, glycerophospholipids and amino acids were the main metabolite groups differing according to residual feed intake category. Late lactation was mainly characterized by increased levels of amino acids in high residual feed intake cows. Amino acid metabolism and biosynthesis pathways were enriched for metabolites that differed between residual feed intake groups at the mid and late lactation stages. Receiver operating characteristic curve analysis identified candidate biomarkers: decanoylcarnitine (area under the curve: AUC = 0.81), dodecenoylcarnitine (AUC = 0.81) and phenylalanine (AUC = 0.85) at early, mid and late stages of lactation, respectively. Furthermore, panels of metabolites predicted residual feed intake with validation coefficient of determination (R 2) of 0.65, 0.37 and 0.60 at early, mid and late lactation stages, respectively. The study sheds light on lactation stage specific metabolic differences between high-residual feed intake and low-residual feed intake lactating dairy cows. Candidate biomarkers that distinguished divergent residual feed intake groups and panels of metabolites that predict individual residual feed intake phenotypes were identified. This result supports the potential of milk metabolites to select for more efficient cows given that traditional residual feed intake phenotyping is costly and difficult to conduct in commercial farms.

PlasMapper 3.0-a web server for generating, editing, annotating and visualizing publication quality plasmid maps.

PlasMapper 3.0 is a web server that allows users to generate, edit, annotate and interactively visualize publication quality plasmid maps. Plasmid maps are used to plan, design, share and publish critical information about gene cloning experiments. PlasMapper 3.0 is the successor to PlasMapper 2.0 and offers many features found only in commercial plasmid mapping/editing packages. PlasMapper 3.0 allows users to paste or upload plasmid sequences as input or to upload existing plasmid maps from its large database of >2000 pre-annotated plasmids (PlasMapDB). This database can be searched by plasmid names, sequence features, restriction sites, preferred host organisms, and sequence length. PlasMapper 3.0 also supports the annotation of new or never-before-seen plasmids using its own feature database that contains common promoters, terminators, regulatory sequences, replication origins, selectable markers and other features found in most cloning plasmids. PlasMapper 3.0 has several interactive sequence editors/viewers that allow users to select and view plasmid regions, insert genes, modify restriction sites or perform codon optimization. The graphics for PlasMapper 3.0 have also been substantially upgraded. It now offers an interactive, full-color plasmid viewer/editor that allows users to zoom, rotate, re-color, linearize, circularize, edit annotated features and modify plasmid images or labels to improve the esthetic qualities of their plasmid map and textual displays. All the plasmid images and textual displays are downloadable in multiple formats. PlasMapper 3.0 is available online at https://plasmapper.ca.

Characterisation of eight cattle with Swyer syndrome by whole-genome sequencing.

Swyer syndrome is where an individual has the karyotype of a typical male yet is phenotypically a female. The lack of a (functional) SRY gene located on the Y-chromosome is implicated in some cases of the Swyer syndrome, although many Swyer individuals with an apparently fully functional SRY gene have also been documented. The present study undertook whole genome sequence analyses of eight cattle with suspected Swyer syndrome and compared their genome to that of both a control male and female. Sequence analyses coupled with female phenotypes confirmed that all eight individuals had the 60,XY sex reversal Swyer syndrome. Seven of the eight Swyer syndrome individuals had a deletion on the Y chromosome encompassing the SRY gene (i.e., SRY-). The eighth individual had no obvious mutation in the SRY gene (SRY+) or indeed in any reported gene associated with sex reversal in mammals; a necropsy was performed on this individual. No testicles were detected during the necropsy. Histological examination of the reproductive tract revealed an immature uterine body and horns with inactive glandular tissue of normal histological appearance; both gonads were elongated, a characteristic of most reported cases of Swyer in mammals. The flanking sequence of 11 single nucleotide polymorphisms within 10 kb of the SRY gene are provided to help diagnose some cases of Swyer syndrome. These single nucleotide polymorphisms will not, however, detect all cases of Swyer syndrome since, as evidenced from the present study (and other studies), some individuals with the Swyer condition still contain the SRY gene (i.e., SRY+).

Dietary benzoic acid and supplemental enzymes alter fiber-fermenting taxa and metabolites in the cecum of weaned pigs.

Inclusion of enzymes and organic acids in pig diets is an important strategy supporting decreased antibiotic usage in pork production. However, limited knowledge exists about how these additives impact intestinal microbes and their metabolites. To examine the effects of benzoic acid and enzymes on gut microbiota and metabolome, 160 pigs were assigned to one of four diets 7 days after weaning: a control diet or the addition of 0.5% benzoic acid, 0.045% dietary enzymes (phytase, ß-glucanase, xylanase, and α-amylase), or both and fed ad libitum for 21 to 22 d. Individual growth performance and group diarrhea incidence data were collected throughout the experimental period. A decrease of 20% in pen-level diarrhea incidence from days 8 to 14 in pigs-fed both benzoic acid and enzymes compared to the control diet (P = 0.047). Cecal digesta samples were collected at the end of the experimental period from 40 piglets (n = 10 per group) and evaluated for differences using 16S rRNA sequencing and two-dimensional gas chromatography and time-of-flight mass spectrometry (GCxGC-TOFMS). Analysis of cecal microbiota diversity revealed that benzoic acid altered microbiota composition (Unweighted Unifrac, P = 0.047, r2 = 0.07) and decreased α-diversity (Shannon, P = 0.041; Faith's Phylogenetic Diversity, P = 0.041). Dietary enzymes increased fiber-fermenting bacterial taxa such as Prevotellaceae. Two-step feature selection identified 17 cecal metabolites that differed among diets, including increased microbial cross-feeding product 1,2-propanediol in pigs-fed benzoic acid-containing diets. In conclusion, dietary benzoic acid and enzymes affected the gut microbiota and metabolome of weaned pigs and may support the health and resolution of postweaning diarrhea.

Feeding weaned pigs diets containing benzoic acid or supplemental enzymes for 21 d after weaning changed the gut microbiota and metabolome. Benzoic acid increased feed intake, weight gain, and the presence of 1,2-propanediol in cecal digesta, which is an important microbial cross-feeding product. Dietary enzymes altered microbiota composition, increasing the presence of fiber-fermenting microbes including Prevotellaceae. Pigs fed a combination of both benzoic acid and enzymes showed improved resolution of postweaning diarrhea. These differences demonstrate the role of these feed additives in the establishment of gut microbes and metabolic pathways for the degradation of complex dietary components in the weaned pig. This study provides new information about alterations in microbial function and community composition using microbiota sequencing and metabolomic analysis.

Imputation to whole-genome sequence and its use in genome-wide association studies for pork colour traits in crossbred and purebred pigs.

Imputed whole-genome sequence (WGS) has been proposed to improve genome-wide association studies (GWAS), since all causative mutations responsible for phenotypic variation are expected to be present in the data. This approach was applied on a large number of purebred (PB) and crossbred (CB) pigs for 18 pork color traits to evaluate the impact of using imputed WGS relative to medium-density marker panels. The traits included Minolta A*, B*, and L* for fat (FCOL), quadriceps femoris muscle (QFCOL), thawed loin muscle (TMCOL), fresh ham gluteus medius (GMCOL), ham iliopsoas muscle (ICOL), and longissimus dorsi muscle on the fresh loin (FMCOL). Sequence variants were imputed from a medium-density marker panel (61K for CBs and 50K for PBs) in all genotyped pigs using BeagleV5.0. We obtained high imputation accuracy (average of 0.97 for PBs and 0.91 for CBs). GWAS were conducted for three datasets: 954 CBs and 891 PBs, and the combined CBs and PBs. For most traits, no significant associations were detected, regardless of panel density or population type. However, quantitative trait loci (QTL) regions were only found for a few traits including TMCOL Minolta A* and GMCOL Minolta B* (CBs), FMCOL Minolta B*, FMCOL Minolta L*, and ICOL Minolta B* (PBs) and FMCOL Minolta A*, FMCOL Minolta B*, GMCOL Minolta B*, and ICOL Minolta B* (Combined dataset). More QTL regions were identified with WGS (n = 58) relative to medium-density marker panels (n = 22). Most of the QTL were linked to previously reported QTLs or candidate genes that have been previously reported to be associated with meat quality, pH and pork color; e.g., VIL1, PRKAG3, TTLL4, and SLC11A1, USP37. CTDSP1 gene on SSC15 has not been previously associated with meat color traits in pigs. The findings suggest any added value of WGS was only for detecting novel QTL regions when the sample size is sufficiently large as with the Combined dataset in this study. The percentage of phenotypic variance explained by the most significant SNPs also increased with WGS compared with medium-density panels. The results provide additional insights into identification of a number of candidate regions and genes for pork color traits in different pig populations.

Predicting dry matter intake in Canadian Holstein dairy cattle using milk mid-infrared reflectance spectroscopy and other commonly available predictors via artificial neural networks.

Dry matter intake (DMI) is a fundamental component of the animal's feed efficiency, but measuring DMI of individual cows is expensive. Mid-infrared reflectance spectroscopy (MIRS) on milk samples could be an inexpensive alternative to predict DMI. The objectives of this study were (1) to assess if milk MIRS data could improve DMI predictions of Canadian Holstein cows using artificial neural networks (ANN); (2) to investigate the ability of different ANN architectures to predict unobserved DMI; and (3) to validate the robustness of developed prediction models. A total of 7,398 milk samples from 509 dairy cows distributed over Canada, Denmark, and the United States were analyzed. Data from Denmark and the United States were used to increase the training data size and variability to improve the generalization of the prediction models over the lactation. For each milk spectra record, the corresponding weekly average DMI (kg/d), test-day milk yield (MY, kg/d), fat yield (FY, g/d), and protein yield (PY, g/d), metabolic body weight (MBW), age at calving, year of calving, season of calving, days in milk, lactation number, country, and herd were available. The weekly average DMI was predicted with various ANN architectures using 7 predictor sets, which were created by different combinations MY, FY, PY, MBW, and MIRS data. All predictor sets also included age of calving and days in milk. In addition, the classification effects of season of calving, country, and lactation number were included in all models. The explored ANN architectures consisted of 3 training algorithms (Bayesian regularization, Levenberg-Marquardt, and scaled conjugate gradient), 2 types of activation functions (hyperbolic tangent and linear), and from 1 to 10 neurons in hidden layers). In addition, partial least squares regression was also applied to predict the DMI. Models were compared using cross-validation based on leaving out 10% of records (validation A) and leaving out 10% of cows (validation B). Superior fitting statistics of models comprising MIRS information compared with the models fitting milk, fat and protein yields suggest that other unknown milk components may help explain variation in weekly average DMI. For instance, using MY, FY, PY, and MBW as predictor variables produced a predictive accuracy (r) ranging from 0.510 to 0.652 across ANN models and validation sets. Using MIRS together with MY, FY, PY, and MBW as predictors resulted in improved fitting (r = 0.679-0.777). Including MIRS data improved the weekly average DMI prediction of Canadian Holstein cows, but it seems that MIRS predicts DMI mostly through its association with milk production traits and its utility to estimate a measure of feed efficiency that accounts for the level of production, such as residual feed intake, might be limited and needs further investigation. The better predictive ability of nonlinear ANN compared with linear ANN and partial least squares regression indicated possible nonlinear relationships between weekly average DMI and the predictor variables. In general, ANN using Bayesian regularization and scaled conjugate gradient training algorithms yielded slightly better weekly average DMI predictions compared with ANN using the Levenberg-Marquardt training algorithm.

Predicting methane emission in Canadian Holstein dairy cattle using milk mid-infrared reflectance spectroscopy and other commonly available predictors via artificial neural networks.

Interest in reducing eructed CH4 is growing, but measuring CH4 emissions is expensive and difficult in large populations. In this study, we investigated the effectiveness of milk mid-infrared spectroscopy (MIRS) data to predict CH4 emission in lactating Canadian Holstein cows. A total of 181 weekly average CH4 records from 158 Canadian cows and 217 records from 44 Danish cows were used. For each milk spectra record, the corresponding weekly average CH4 emission (g/d), test-day milk yield (MY, kg/d), fat yield (FY, g/d), and protein yield (PY, g/d) were available. The weekly average CH4 emission was predicted using various artificial neural networks (ANN), partial least squares regression, and different sets of predictors. The ANN architectures consisted of 3 training algorithms, 1 to 10 neurons with hyperbolic tangent activation function in the hidden layer, and 1 neuron with linear (purine) activation function in the hidden layer. Random cross-validation was used to compared the predictor sets: MY (set 1); FY (set 2); PY (set 3); MY and FY (set 4); MY and PY (set 5); MY, FY, and PY (set 6); MIRS (set 7); and MY, FY, PY, and MIRS (set 8). All predictor sets also included age at calving and days in milk, in addition to country, season of calving, and lactation number as categorical effects. Using only MY (set 1), the predictive accuracy (r) ranged from 0.245 to 0.457 and the root mean square error (RMSE) ranged from 87.28 to 99.39 across all prediction models and validation sets. Replacing MY with FY (set 2; r = 0.288-0.491; RMSE = 85.94-98.04) improved the predictive accuracy, but using PY (set 3; r = 0.260-0.468; RMSE = 86.95-98.47) did not. Adding FY to MY (set 4; r = 0.272-0.469; RMSE = 87.21-100.76) led to a negligible improvement compared with sets 1 and 3, but it slightly decreased accuracy compared with set 2. Adding PY to MY (set 5; r = 0.250-0.451; RMSE = 87.66-100.94) did not improve prediction ability. Combining MY, FY, and PY (set 6; r = 0.252-0.455; RMSE = 87.74-101.93) yielded accuracy slightly lower than sets 2 and 3. Using only MIRS data (set 7; r = 0.586-0.717; RMSE = 69.09-96.20) resulted in superior accuracy compared with all previous sets. Finally, the combination of MIRS data with MY, FY, and PY (set 8; r = 0.590-0.727; RMSE = 68.02-87.78) yielded similar accuracy to set 7. Overall, sets including the MIRS data yielded significantly better predictions than the other sets. To assess the predictive ability in a new unseen herd, a limited block cross-validation was performed using 20 cows in the same Canadian herd, which yielded r = 0.229 and RMSE = 154.44, which were clearly much worse than the average r = 0.704 and RMSE = 70.83 when predictions were made by random cross-validation. These results warrant further investigation when more data become available to allow for a more comprehensive block cross-validation before applying the calibrated models for large-scale prediction of CH4 emissions.

Sharing of either phenotypes or genetic variants can increase the accuracy of genomic prediction of feed efficiency.

BACKGROUND: Sharing individual phenotype and genotype data between countries is complex and fraught with potential errors, while sharing summary statistics of genome-wide association studies (GWAS) is relatively straightforward, and thus would be especially useful for traits that are expensive or difficult-to-measure, such as feed efficiency. Here we examined: (1) the sharing of individual cow data from international partners; and (2) the use of sequence variants selected from GWAS of international cow data to evaluate the accuracy of genomic estimated breeding values (GEBV) for residual feed intake (RFI) in Australian cows. RESULTS: GEBV for RFI were estimated using genomic best linear unbiased prediction (GBLUP) with 50k or high-density single nucleotide polymorphisms (SNPs), from a training population of 3797 individuals in univariate to trivariate analyses where the three traits were RFI phenotypes calculated using 584 Australian lactating cows (AUSc), 824 growing heifers (AUSh), and 2526 international lactating cows (OVE). Accuracies of GEBV in AUSc were evaluated by either cohort-by-birth-year or fourfold random cross-validations. GEBV of AUSc were also predicted using only the AUS training population with a weighted genomic relationship matrix constructed with SNPs from the 50k array and sequence variants selected from a meta-GWAS that included only international datasets. The genomic heritabilities estimated using the AUSc, OVE and AUSh datasets were moderate, ranging from 0.20 to 0.36. The genetic correlations (rg) of traits between heifers and cows ranged from 0.30 to 0.95 but were associated with large standard errors. The mean accuracies of GEBV in Australian cows were up to 0.32 and almost doubled when either overseas cows, or both overseas cows and AUS heifers were included in the training population. They also increased when selected sequence variants were combined with 50k SNPs, but with a smaller relative increase. CONCLUSIONS: The accuracy of RFI GEBV increased when international data were used or when selected sequence variants were combined with 50k SNP array data. This suggests that if direct sharing of data is not feasible, a meta-analysis of summary GWAS statistics could provide selected SNPs for custom panels to use in genomic selection programs. However, since this finding is based on a small cross-validation study, confirmation through a larger study is recommended.

Evaluating the potential of third generation metagenomic sequencing for the detection of BRD pathogens and genetic determinants of antimicrobial resistance in chronically ill feedlot cattle.

BACKGROUND: Bovine respiratory disease (BRD) is an important cause of morbidity and mortality and is responsible for most of the injectable antimicrobial use in the feedlot industry. Traditional bacterial culture can be used to diagnose BRD by confirming the presence of causative pathogens and to support antimicrobial selection. However, given that bacterial culture takes up to a week and early intervention is critical for treatment success, culture has limited utility for informing rapid therapeutic decision-making. In contrast, metagenomic sequencing has the potential to quickly resolve all nucleic acid in a sample, including pathogen biomarkers and antimicrobial resistance genes. In particular, third-generation Oxford Nanopore Technology sequencing platforms provide long reads and access to raw sequencing data in real-time as it is produced, thereby reducing the time from sample collection to diagnostic answer. The purpose of this study was to compare the performance of nanopore metagenomic sequencing to traditional culture and sensitivity methods as applied to nasopharyngeal samples from segregated groups of chronically ill feedlot cattle, previously treated with antimicrobials for nonresponsive pneumonia or lameness. RESULTS: BRD pathogens were isolated from most samples and a variety of different resistance profiles were observed across isolates. The sequencing data indicated the samples were dominated by Moraxella bovoculi, Mannheimia haemolytica, Mycoplasma dispar, and Pasteurella multocida, and included a wide range of antimicrobial resistance genes (ARGs), encoding resistance for up to seven classes of antimicrobials. Genes conferring resistance to beta-lactams were the most commonly detected, while the tetH gene was detected in the most samples overall. Metagenomic sequencing detected the BRD pathogens of interest more often than did culture, but there was limited concordance between phenotypic resistance to antimicrobials and the presence of relevant ARGs. CONCLUSIONS: Metagenomic sequencing can reduce the time from sampling to results, detect pathogens missed by bacterial culture, and identify genetically encoded determinants of resistance. Increasing sequencing coverage of target organisms will be an essential component of improving the reliability of this technology, such that it can be better used for the surveillance of pathogens of interest, genetic determinants of resistance, and to inform diagnostic decisions.

Erratum to < Distinctive roles between rumen epimural and content bacterial communities on beef cattle feed efficiency: a combined analysis>.

[This corrects the article DOI: 10.1016/j.crmicr.2021.100085.].

Neural transcriptomic signature of chronic wasting disease in white-tailed deer.

BACKGROUND: The increasing prevalence and expanding geographical range of the chronic wasting disease (CWD) panzootic in cervids is threatening human, animal, environmental and economic health. The pathogenesis of CWD in cervids is, however, not well understood. We used RNA sequencing (RNA-seq) to compare the brain transcriptome from white-tailed deer (WTD; Odocoileus virginianus) clinically affected with CWD (n = 3) to WTD that tested negative (n = 8) for CWD. In addition, one preclinical CWD+ brain sample was analyzed by RNA-seq. RESULTS: We found 255 genes that were significantly deregulated by CWD, 197 of which were upregulated. There was a high degree of overlap in differentially expressed genes (DEGs) identified when using either/both the reference genome assembly of WTD for mapping sequenced reads to or the better characterized genome assembly of a closely related model species, Bos taurus. Quantitative PCR of a subset of the DEGs confirmed the RNA-seq data. Gene ontology term enrichment analysis found a majority of genes involved in immune activation, consistent with the neuroinflammatory pathogenesis of prion diseases. A metagenomic analysis of the RNA-seq data was conducted to look for the presence of spiroplasma and other bacteria in CWD infected deer brain tissue. CONCLUSIONS: The gene expression changes identified highlight the role of innate immunity in prion infection, potential disease associated biomarkers and potential targets for therapeutic agents. An association between CWD and spiroplasma infection was not found.

Subfertility in young male mice mutant for chromatin remodeller CECR2.

Defects in spermatogenesis are an important cause of male infertility. Multiple aspects of spermatogenesis are controlled by chromatin remodellers, including regulating transcription. We previously described mutations in chromatin remodelling gene Cecr2 that resulted in the lethal neural tube defect exencephaly in most mutant mice and subfertility in mice that were non-penetrant for exencephaly. Here, we show that the severity of male subfertility is dependent on age. Cecr2GT/Del males contain two mutant alleles, one of which is hypomorphic and therefore produces a small amount of protein. These males sire the fewest pups just after sexual maturity (88% fewer than Cecr2+/+ at P42-60) but improve with age (49% fewer than Cecr2+/+ at P81-100), although never completely recovering to Cecr2+/+(wild type) levels. When young, they also have defects in testis histology, in vivo fertilization frequency, sperm number and motility, and testis weight that show similar improvement with age. Immunostaining of staged seminiferous tubules showed CECR2 in type A, intermediate and B spermatogonia, and less in preleptotene and leptotene spermatocytes. Histological defects were first apparent in Cecr2GT/Del testes at P24, and RNA-seq analysis revealed 387 differentially expressed genes. This included 66 genes on the X chromosome (almost double the number on any other chromosome), all more highly expressed in Cecr2GT/Del testes. This inappropriate expression of X chromosome genes could be caused by a failure of effective meiotic sex chromosome inactivation. We identify several abnormally expressed genes that may contribute to defects in spermatogenesis at P24. Our results support a role for Cecr2 in juvenile spermatogenesis.

Distinctive roles between rumen epimural and content bacterial communities on beef cattle feed efficiency: A combined analysis.

Rumen content-associated (RC) and epithelial tissue-attached (RT) bacterial communities are composed of different phylotypes and play distinctive roles. This study aimed to compare the composition of the RT and RC bacterial communities of steers differing in feed efficiency. The microbiota of RT and RC samples collected from sixteen beef steers with high or low residual feed intake (RFI) were analyzed through sequencing of partial 16S rRNA gene amplicons. Bacteroidetes, Proteobacteria and Firmicutes were the predominant phyla and Prevotella was the most abundant genus in both RC and RT bacterial communities. In total, 19 OTUs of the RC samples and 19 OTUs of the RT samples were differentially abundant (DA) between H-RFI and l-RFI steers. Among them, a common DA OTU belonged to Prevotella genus was identified in both RC and RT samples, making it the potential key microbial marker for indicating feed efficiency of steers. The co-occurrence of the DA OTUs among RT and RC samples suggest the importance of these two communities function as a complete system in influencing host feed efficiency.