Puberty classifications in beef heifers are moderately to highly heritable and associated with candidate genes related to cyclicity and timing of puberty.

Introduction: Pubertal attainment is critical to reproductive longevity in heifers. Previously, four heifer pubertal classifications were identified according to attainment of blood plasma progesterone concentrations > 1 ng/ml: 1) Early; 2) Typical; 3) Start-Stop; and 4) Non-Cycling. Early and Typical heifers initiated and maintained cyclicity, Start-Stop started and then stopped cyclicity and Non-Cycling never initiated cyclicity. Start-Stop heifers segregated into Start-Stop-Discontinuous (SSD) or Start-Stop-Start (SSS), with SSD having similar phenotypes to Non-Cycling and SSS to Typical heifers. We hypothesized that these pubertal classifications are heritable, and loci associated with pubertal classifications could be identified by genome wide association studies (GWAS). Methods: Heifers (n = 532; 2017 - 2022) genotyped on the Illumina Bovine SNP50 v2 or GGP Bovine 100K SNP panels were used for variant component estimation and GWAS. Heritability was estimated using a univariate Bayesian animal model. Results: When considering pubertal classifications: Early, Typical, SSS, SSD, and Non-Cycling, pubertal class was moderately heritable (0.38 ± 0.08). However, when heifers who initiated and maintained cyclicity were compared to those that did not cycle (Early+Typical vs. SSD+Non-Cycling) heritability was greater (0.59 ± 0.19). A GWAS did not identify single nucleotide polymorphisms (SNPs) significantly associated with pubertal classifications, indicating puberty is a polygenic trait. A candidate gene approach was used, which fitted SNPs within or nearby a set of 71 candidate genes previously associated with puberty, PCOS, cyclicity, regulation of hormone secretion, signal transduction, and methylation. Eight genes/regions were associated with pubertal classifications, and twenty-two genes/regions were associated with whether puberty was attained during the trial. Additionally, whole genome sequencing (WGS) data on 33 heifers were aligned to the reference genome (ARS-UCD1.2) to identify variants in FSHR, a gene critical to pubertal attainment. Fisher's exact test determined if FSHR SNPs segregated by pubertal classification. Two FSHR SNPs that were not on the bovine SNP panel were selected for additional genotyping and analysis, and one was associated with pubertal classifications and whether they cycled during the trial. Discussion: In summary, these pubertal classifications are moderately to highly heritable and polygenic. Consequently, genomic tools to inform selection/management of replacement heifers would be useful if informed by SNPs associated with cyclicity and early pubertal attainment.

Characterization of the Ruminal Microbiome of Water Buffaloes (Bubalus bubalis) Kept in Different Ecosystems in the Eastern Amazon.

Increasing the efficiency of rumen fermentation is one of the main ways to maximize the production of ruminants. It is therefore important to understand the ruminal microbiome, as well as environmental influences on that community. However, there are no studies that describe the ruminal microbiota in buffaloes in the Amazon. The objective of this study was to characterize the rumen microbiome of the water buffalo (Bubalus bubalis) in the eastern Amazon in the dry and rainy seasons in three grazing ecosystems: Baixo Amazonas (BA), Continente do Pará (CP), Ilha do Marajó (IM), and in a confinement system: Tomé-Açu (TA). Seventy-one crossbred male buffaloes (Murrah × Mediterranean) were used, aged between 24 and 36 months, with an average weight of 432 kg in the rainy season and 409 kg in the dry season, and fed on native or cultivated pastures. In the confinement system, the feed consisted of sorghum silage, soybean meal, wet sorghum premix, and commercial feed. Samples of the diet from each ecosystem were collected for bromatological analysis. The collections of ruminal content were carried out in slaughterhouses, with the rumen completely emptied and homogenized, the solid and liquid fractions separated, and the ruminal pH measured. DNA was extracted from the rumen samples, then sequenced using Restriction Enzyme Reduced Representation Sequencing. The taxonomic composition was largely similar between ecosystems. All 61 genera in the reference database were recognized, including members of the domains Bacteria and Archaea. The abundance of 23 bacterial genera differed significantly (p < 0.01) between the Tomé-Açu confinement and other ecosystems. Bacillus, Ruminococcus, and Bacteroides had lower abundance in samples from the Tomé-Açu system. Among the Archaea, the genus Methanomicrobium was less abundant in Tomé-Açu, while Methanosarcina was more abundant. There was a difference caused by all evaluated factors, but the diet (available or offered) was what most influenced the ruminal microbiota.

Large-scale analysis of sheep rumen metagenome profiles captured by reduced representation sequencing reveals individual profiles are influenced by the environment and genetics of the host.

BACKGROUND: Producing animal protein while reducing the animal's impact on the environment, e.g., through improved feed efficiency and lowered methane emissions, has gained interest in recent years. Genetic selection is one possible path to reduce the environmental impact of livestock production, but these traits are difficult and expensive to measure on many animals. The rumen microbiome may serve as a proxy for these traits due to its role in feed digestion. Restriction enzyme-reduced representation sequencing (RE-RRS) is a high-throughput and cost-effective approach to rumen metagenome profiling, but the systematic (e.g., sequencing) and biological factors influencing the resulting reference based (RB) and reference free (RF) profiles need to be explored before widespread industry adoption is possible. RESULTS: Metagenome profiles were generated by RE-RRS of 4,479 rumen samples collected from 1,708 sheep, and assigned to eight groups based on diet, age, time off feed, and country (New Zealand or Australia) at the time of sample collection. Systematic effects were found to have minimal influence on metagenome profiles. Diet was a major driver of differences between samples, followed by time off feed, then age of the sheep. The RF approach resulted in more reads being assigned per sample and afforded greater resolution when distinguishing between groups than the RB approach. Normalizing relative abundances within the sampling Cohort abolished structures related to age, diet, and time off feed, allowing a clear signal based on methane emissions to be elucidated. Genus-level abundances of rumen microbes showed low-to-moderate heritability and repeatability and were consistent between diets. CONCLUSIONS: Variation in rumen metagenomic profiles was influenced by diet, age, time off feed and genetics. Not accounting for environmental factors may limit the ability to associate the profile with traits of interest. However, these differences can be accounted for by adjusting for Cohort effects, revealing robust biological signals. The abundances of some genera were consistently heritable and repeatable across different environments, suggesting that metagenomic profiles could be used to predict an individual's future performance, or performance of its offspring, in a range of environments. These results highlight the potential of using rumen metagenomic profiles for selection purposes in a practical, agricultural setting.

Combining host and rumen metagenome profiling for selection in sheep: prediction of methane, feed efficiency, production, and health traits.

BACKGROUND: Rumen microbes break down complex dietary carbohydrates into energy sources for the host and are increasingly shown to be a key aspect of animal performance. Host genotypes can be combined with microbial DNA sequencing to predict performance traits or traits related to environmental impact, such as enteric methane emissions. Metagenome profiles were generated from 3139 rumen samples, collected from 1200 dual purpose ewes, using restriction enzyme-reduced representation sequencing (RE-RRS). Phenotypes were available for methane (CH4) and carbon dioxide (CO2) emissions, the ratio of CH4 to CH4 plus CO2 (CH4Ratio), feed efficiency (residual feed intake: RFI), liveweight at the time of methane collection (LW), liveweight at 8 months (LW8), fleece weight at 12 months (FW12) and parasite resistance measured by faecal egg count (FEC1). We estimated the proportion of phenotypic variance explained by host genetics and the rumen microbiome, as well as prediction accuracies for each of these traits. RESULTS: Incorporating metagenome profiles increased the variance explained and prediction accuracy compared to fitting only genomics for all traits except for CO2 emissions when animals were on a grass diet. Combining the metagenome profile with host genotype from lambs explained more than 70% of the variation in methane emissions and residual feed intake. Predictions were generally more accurate when incorporating metagenome profiles compared to genetics alone, even when considering profiles collected at different ages (lamb vs adult), or on different feeds (grass vs lucerne pellet). A reference-free approach to metagenome profiling performed better than metagenome profiles that were restricted to capturing genera from a reference database. We hypothesise that our reference-free approach is likely to outperform other reference-based approaches such as 16S rRNA gene sequencing for use in prediction of individual animal performance. CONCLUSIONS: This paper shows the potential of using RE-RRS as a low-cost, high-throughput approach for generating metagenome profiles on thousands of animals for improved prediction of economically and environmentally important traits. A reference-free approach using a microbial relationship matrix from log10 proportions of each tag normalized within cohort (i.e., the group of animals sampled at the same time) is recommended for future predictions using RE-RRS metagenome profiles.

Low-cost sample preservation methods for high-throughput processing of rumen microbiomes.

BACKGROUND: The use of rumen microbial community (RMC) profiles to predict methane emissions has driven interest in ruminal DNA preservation and extraction protocols that can be processed cheaply while also maintaining or improving DNA quality for RMC profiling. Our standard approach for preserving rumen samples, as defined in the Global Rumen Census (GRC), requires time-consuming pre-processing steps of freeze drying and grinding prior to international transportation and DNA extraction. This impedes researchers unable to access sufficient funding or infrastructure. To circumvent these pre-processing steps, we investigated three methods of preserving rumen samples for subsequent DNA extraction, based on existing lysis buffers Tris-NaCl-EDTA-SDS (TNx2) and guanidine hydrochloride (GHx2), or 100% ethanol. RESULTS: Rumen samples were collected via stomach intubation from 151 sheep at two time-points 2 weeks apart. Each sample was separated into four subsamples and preserved using the three preservation methods and the GRC method (n = 4 × 302). DNA was extracted and sequenced using Restriction Enzyme-Reduced Representation Sequencing to generate RMC profiles. Differences in DNA yield, quality and integrity, and sequencing metrics were observed across the methods (p < 0.0001). Ethanol exhibited poorer quality DNA (A260/A230 < 2) and more failed samples compared to the other methods. Samples preserved using the GRC method had smaller relative abundances in gram-negative genera Anaerovibrio, Bacteroides, Prevotella, Selenomonas, and Succiniclasticum, but larger relative abundances in the majority of 56 additional genera compared to TNx2 and GHx2. However, log10 relative abundances across all genera and time-points for TNx2 and GHx2 were on average consistent (R2 > 0.99) but slightly more variable compared to the GRC method. Relative abundances were moderately to highly correlated (0.68 ± 0.13) between methods for samples collected within a time-point, which was greater than the average correlation (0.17 ± 0.11) between time-points within a preservation method. CONCLUSIONS: The two modified lysis buffers solutions (TNx2 and GHx2) proposed in this study were shown to be viable alternatives to the GRC method for RMC profiling in sheep. Use of these preservative solutions reduces cost and improves throughput associated with processing and sequencing ruminal samples. This development could significantly advance implementation of RMC profiles as a tool for breeding ruminant livestock.

A restriction enzyme reduced representation sequencing approach for low-cost, high-throughput metagenome profiling.

Microbial community profiles have been associated with a variety of traits, including methane emissions in livestock. These profiles can be difficult and expensive to obtain for thousands of samples (e.g. for accurate association of microbial profiles with traits), therefore the objective of this work was to develop a low-cost, high-throughput approach to capture the diversity of the rumen microbiome. Restriction enzyme reduced representation sequencing (RE-RRS) using ApeKI or PstI, and two bioinformatic pipelines (reference-based and reference-free) were compared to bacterial 16S rRNA gene sequencing using repeated samples collected two weeks apart from 118 sheep that were phenotypically extreme (60 high and 58 low) for methane emitted per kg dry matter intake (n = 236). DNA was extracted from freeze-dried rumen samples using a phenol chloroform and bead-beating protocol prior to RE-RRS. The resulting sequences were used to investigate the repeatability of the rumen microbial community profiles, the effect of laboratory and analytical method, and the relationship with methane production. The results suggested that the best method was PstI RE-RRS analyzed with the reference-free approach, which accounted for 53.3±5.9% of reads, and had repeatabilities of 0.49±0.07 and 0.50±0.07 for the first two principal components (PC1 and PC2), phenotypic correlations with methane yield of 0.43±0.06 and 0.46±0.06 for PC1 and PC2, and explained 41±8% of the variation in methane yield. These results were significantly better than for bacterial 16S rRNA gene sequencing of the same samples (p<0.05) except for the correlation between PC2 and methane yield. A Sensitivity study suggested approximately 2000 samples could be sequenced in a single lane on an Illumina HiSeq 2500, meaning the current work using 118 samples/lane and future proposed 384 samples/lane are well within that threshold. With minor adaptations, our approach could be used to obtain microbial profiles from other metagenomic samples.

Identification of factors associated with virus level in tonsils of pigs experimentally infected with porcine reproductive and respiratory syndrome virus.

Porcine reproductive and respiratory syndrome (PRRS) is one of the most important global swine diseases from both an economic and animal welfare standpoint. PRRS has plagued the US swine industry for over 25 yr, and containment of PRRS virus (PRRSV) has been unsuccessful to date. The primary phase of PRRS, tracked by serum viremia, typically clears between 21 and 42 d postinfection (dpi) but tonsils are a main site of PRRSV persistence and PRRSV can be detected in tonsils in excess of 150 dpi. Measuring tonsil virus (TV) levels at late stages of infection (6 to 7 wk postinfection) can be used to assess tonsil persistence, as levels of virus in tonsil at this time likely influence how long the virus will remain in the tissue. TV levels were measured on pigs experimentally infected with either the NVSL-97-7895 (NVSL; n = 524) or KS-2006-72109 (KS06; n = 328) PRRSV type 2 isolates across five trials. The objectives of this study were to (i) estimate the heritability of TV levels at 35 or 42 dpi; (ii) identify factors the affect TV level, including serum viremia; (iii) identify genomic regions associated with TV level; and (iv) compare results for the two PRRSV isolates. TV level was lowly heritable for both isolates (NVSL: 0.05 ± 0.06; KS06: 0.11 ± 0.10). Level of TV was phenotypically associated with traits related to viral clearance from serum: pigs with low TV levels had an earlier and faster rate of maximal serum viral clearance, lower total serum viral load, and lower viremia level at 35 or 42 dpi. Although no genomic regions with major effects on TV level were identified, several showed some association (>0.1% of total genetic variance in the NVSL-infected dataset, the KS06-infected dataset, and the combined dataset). These regions contained the genes CCL1, CCL2, CCL8, HS3ST3B1, GALNT10, TCF7, C1QA/B/C, HPSE, G0S2, and CD34, which are involved in viral infiltration or replication, immune cell migration, and viral clearance from tissue. Results were similar between the two PRRSV isolates. In conclusion, selection for viral clearance traits in serum may reduce PRRSV persistence in the tonsil across PRRSV isolates. However, genetic correlations need to be estimated to determine whether this will be successful.

Genetic relationships of antibody response, viremia level, and weight gain in pigs experimentally infected with porcine reproductive and respiratory syndrome virus1.

Genetic and antigenic variability between Porcine Reproductive and Respiratory Syndrome Virus (PRRSV) isolates has encumbered vaccine development. Here, the genetic basis of PRRSV antibody response was assessed using data from experimental infection trials of commercial crossbred weaner pigs across with one of two distinct PRRSV isolates, NVSL-97-7895 (~750 pigs) and KS-2006-72109 (~450 pigs). Objectives were to estimate the genetic parameters of antibody response, measured as the sample to positive ratio (S:P) of PRRSV N-protein specific IgG in serum at 42 d post infection (dpi); assess the relationship of S:P at 42 dpi with serum viremia and growth under infection; and identify genomic regions associated with S:P at 42 dpi. Estimates of heritability of S:P at 42 dpi for NVSL and KS06 were 0.31 ± 0.09 and 0.40 ± 0.10 and appeared to be under similar genetic control (genetic correlation 0.73 ± 0.39). Estimates of genetic correlations of S:P were generally weak with viral load (NVSL: -0.20 ± 0.18; KS06: -0.69 ± 0.20), measured as area under the curve of log10 serum viremia from 0 to 21 dpi, and with weight gain (WG) from 0 to 42 dpi (NVSL: -0.38 ± 0.19; KS06: -0.08 ± 0.25). However, genetic correlations of S:P at 42 dpi with daily serum viremia and with 3-d WG revealed dynamic relationships, with S:P at 42 dpi having the strongest negative genetic correlations with daily viremia when IgG production starts (10-20 dpi), and negative genetic correlations with WG early after infection but positive later on. This suggests that animals that placed more emphasis on immune response early in infection reaped benefits of that later in infection by more effectively clearing the virus. The WUR10000125 SNP on SSC4, previously associated with response to PRRSV, did not have a significant effect on S:P at 42 dpi (P > 0.05) but genotype-specific genetic correlations of S:P with daily viremia and 3-d WG suggested that the lower WG of pigs with the unfavorable AA WUR10000125 genotype may be due to their utilization of a more energetically costly host response compared to pigs with the favorable genotype. Genome-wide association studies identified three SNPs in the Major Histocompatibility Complex associated with S:P that explained ~10 (NVSL) and 45% (KS06) of the genetic variance but were not associated with viremia or WG. In conclusion, antibody response to PRRSV infection is a possible biomarker for improved host response to PRRSV infection.

Comparison of host genetic factors influencing pig response to infection with two North American isolates of porcine reproductive and respiratory syndrome virus.

BACKGROUND: Porcine reproductive and respiratory syndrome (PRRS) is one of the most important swine diseases in the world and genetic selection of pigs for increased resistance to PRRS is an attractive method to improve the health status of the swine herd. This study compared phenotypic and genetic responses to infection with one of two genetically distinct type 2 PRRS virus (PRRSV) isolates: NVSL-97-7895 (NVSL) and KS-2006-72109 (KS06), and evaluated whether the single nucleotide polymorphism (SNP) WUR10000125 (WUR) on chromosome 4 that was associated with viral load and weight gain under infection with NVSL also has an effect on response to infection across North American PRRSV isolates. Wood's lactation curve was fitted to repeated viremia measurements to derive five curve characteristics that were evaluated. RESULTS: Infection with NVSL was characterized by reaching a 14 ± 2 % higher peak viremia (PV) 2.5 ± 0.6 days earlier (time to peak; TP) than KS06, followed by 36 ± 1 % faster virus clearance, which occurred 3.9 ± 0.7 days sooner. Weight gain from 0 to 42 days post-infection (WG) tended to be higher under infection with KS06 than NVSL (3.7 ± 1.5 kg). Estimates of heritability were moderate for both PRRSV isolates for viral load from 0 to 21 days post-infection (VL) (NVSL: 0.31 ± 0.06; KS06: 0.51 ± 0.09) and WG (NVSL: 0.33 ± 0.06; KS06: 0.31 ± 0.09). Strong negative genetic correlations were observed between VL and WG for both NVSL (-0.74 ± 0.10) and KS06 (-0.52 ± 0.17) infected pigs. Pigs with genotype AB at the WUR SNP had a more desirable phenotype than AA pigs for all traits under infection with NVSL, but only for VL and PV with KS06; effects on other traits were smaller and not significantly different from zero (P > 0.05). Genetic correlations of host response between isolates were strong for VL, WG and PV. Accounting for WUR genotype had little impact on these correlations, suggesting that response to PRRSV infection has a substantial polygenic component that is common between these two isolates. CONCLUSIONS: These results suggest that the KS06 PRRSV isolate is less virulent than NVSL but that genetic selection for increased resistance to either of these genetically distinct isolates is expected to increase resistance to the other isolate.

The Effect of Calf Gender on Milk Production in Seasonal Calving Cows and Its Impact on Genetic Evaluations.

Gender of the calf whose birth initiates lactation could influence whole lactation milk yield of the dam due to hormonal influences on mammary gland development, or through calf gender effects on gestation length. Fetal gender could influence late lactation yields because cows become pregnant at peak lactation. The effects of calf gender sequences in parities 1-3 were assessed by separately fitting animal models to datasets from New Zealand comprising 274 000 Holstein Friesian and 85 000 Jersey cows, decreasing to 12 000 and 4 000 cows by parity 3. The lactation initiated by the birth of a female rather than a male calf was associated with a 0.33-1.1% (p≤0.05) higher milk yield. Female calf gender had carryover effects associated with higher milk yield in second lactations for Holstein Friesians (0.24%; p = 0.01) and third lactations for Jerseys (1.1%; p = 0.01). Cows giving birth to bull calves have 2 day longer gestations, which reduces lactation length in seasonal calving herds. Adding a covariate for lactation length to the animal model eroded some of these calf gender effects, such that calving a female led to higher milk yield only for second lactation Holstein Friesians (1.6%; p = 0.002). The interval centering method generates lower estimates of whole lactation yield when Wood's lactation curves are shifted to the right by 2 days for male calves and this explained the higher yield in female calves when differences in lactation length were considered. Correlations of estimated breeding values between models including or excluding calf gender sequence were 1.00 for bulls or cows. Calf gender primarily influences milk yield through increased gestation length of male calves, and bias associated with the interval centering method used to estimate whole lactation milk yields. Including information on calf gender is unlikely to have an effect on selection response in New Zealand dairy cattle.