13 research outputs found
The effect of 2-day heat stress on the lipid composition of bovine milk and serum
Milk and serum were collected from dairy cows before and during a 2-day heat challenge. The concentrations of free short-chain fatty acids (SCFAs), the fatty acid (FA) profile, and the abundance of the major species of phosphatidylcholine (PC), phosphatidylethanolamine (PE), and sphingomyelin (SM) were measured, and samples collected during heat exposure were compared with those collected prior to heat exposure. It was found that a 2-day heat challenge did not alter the global FA composition of milk fat nor the content of the major phospholipids. Although the concentration of SCFAs C3 and C4 and some lysophosphatidylcholine (LPC) species in milk was found to be associated with the forage type, neither of these lipid molecules can be used as an indicator of acute heat stress. While it is a positive finding that short-term heat stress has no detrimental effect on the FA composition or the nutritive quality of milk fat, this study highlights the complexity of validating a milk lipid biomarker for heat stress in dairy cows
Effects of Surface Geology on Seismic Ground Motion Deduced from Ambient-Noise Measurements in the Town of Avellino, Irpinia Region (Italy)
The effects of surface geology on ground motion
provide an important tool in seismic hazard studies. It is well
known that the presence of soft sediments can cause amplification
of the ground motion at the surface, particularly when there is a
sharp impedance contrast at shallow depth. The town of Avellino is
located in an area characterised by high seismicity in Italy, about
30 km from the epicentre of the 23 November 1980, Irpinia
earthquake (M = 6.9). No earthquake recordings are available in
the area. The local geology is characterised by strong heterogeneity,
with impedance contrasts at depth. We present the results
from seismic noise measurements carried out in the urban area of
Avellino to evaluate the effects of local geology on the seismic
ground motion. We computed the horizontal-to-vertical (H/V)
noise spectral ratios at 16 selected sites in this urban area for which
drilling data are available within the first 40 m of depth. A Rayleigh
wave inversion technique using the peak frequencies of the
noise H/V spectral ratios is then presented for estimating Vs
models, assuming that the thicknesses of the shallow soil layers are
known. The results show a good correspondence between experimental
and theoretical peak frequencies, which are interpreted in
terms of sediment resonance. For one site, which is characterised
by a broad peak in the horizontal-to-vertical spectral-ratio curve,
simple one-dimensional modelling is not representative of the
resonance effects. Consistent variations in peak amplitudes are seen
among the sites. A site classification based on shear-wave velocity
characteristics, in terms of Vs30, cannot explain these data. The
differences observed are better correlated to the impedance contrast
between the sediments and basement. A more detailed investigation
of the physical parameters of the subsoil structure, together with
earthquake data, are desirable for future research, to confirm these
data in terms of site response
Expression of mitochondrial protein genes encoded by nuclear and mitochondrial genomes correlate with energy metabolism in dairy cattle
Background
Mutations in the mitochondrial genome have been implicated in mitochondrial disease, often characterized by impaired cellular energy metabolism. Cellular energy metabolism in mitochondria involves mitochondrial proteins (MP) from both the nuclear (NuMP) and mitochondrial (MtMP) genomes. The expression of MP genes in tissues may be tissue specific to meet varying specific energy demands across the tissues. Currently, the characteristics of MP gene expression in tissues of dairy cattle are not well understood. In this study, we profile the expression of MP genes in 29 adult and six foetal tissues in dairy cattle using RNA sequencing and gene expression analyses: particularly differential gene expression and co-expression network analyses.
Results
MP genes were differentially expressed (DE; over-expressed or under-expressed) across tissues in cattle. All 29 tissues showed DE NuMP genes in varying proportions of over-expression and under-expression. On the other hand, DE of MtMP genes was observed in < 50% of tissues and notably MtMP genes within a tissue was either all over-expressed or all under-expressed. A high proportion of NuMP (up to 60%) and MtMP (up to 100%) genes were over-expressed in tissues with expected high metabolic demand; heart, skeletal muscles and tongue, and under-expressed (up to 45% of NuMP, 77% of MtMP genes) in tissues with expected low metabolic rates; leukocytes, thymus, and lymph nodes. These tissues also invariably had the expression of all MtMP genes in the direction of dominant NuMP genes expression. The NuMP and MtMP genes were highly co-expressed across tissues and co-expression of genes in a cluster were non-random and functionally enriched for energy generation pathway. The differential gene expression and co-expression patterns were validated in independent cow and sheep datasets.
Conclusions
The results of this study support the concept that there are biological interaction of MP genes from the mitochondrial and nuclear genomes given their over-expression in tissues with high energy demand and co-expression in tissues. This highlights the importance of considering MP genes from both genomes in future studies related to mitochondrial functions and traits related to energy metabolism
Correction to:Expression of mitochondrial protein genes encoded by nuclear and mitochondrial genomes correlate with energy metabolism in dairy cattle
Use of dry-matter intake recorded at multiple time periods during lactation increases the accuracy of genomic prediction for dry-matter intake and residual feed intake in dairy cattle
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
Metagenomic predictions: from microbiome to complex health and environmental phenotypes in humans and cattle
Mammals have a large cohort of endo- and ecto- symbiotic microorganisms (the microbiome) that potentially influence host phenotypes. There have been numerous exploratory studies of these symbiotic organisms in humans and other animals, often with the aim of relating the microbiome to a complex phenotype such as body mass index (BMI) or disease state. Here, we describe an efficient methodology for predicting complex traits from quantitative microbiome profiles. The method was demonstrated by predicting inflammatory bowel disease (IBD) status and BMI from human microbiome data, and enteric greenhouse gas production from dairy cattle rumen microbiome profiles. The method uses unassembled massively parallel sequencing (MPS) data to form metagenomic relationship matrices (analogous to genomic relationship matrices used in genomic predictions) to predict IBD, BMI and methane production phenotypes with useful accuracies (r = 0.423, 0.422 and 0.466 respectively). Our results show that microbiome profiles derived from MPS can be used to predict complex phenotypes of the host. Although the number of biological replicates used here limits the accuracy that can be achieved, preliminary results suggest this approach may surpass current prediction accuracies that are based on the host genome. This is especially likely for traits that are largely influenced by the gut microbiota, for example digestive tract disorders or metabolic functions such as enteric methane production in cattle
Economic Threshold Analysis of Supplementing Dairy Cow Diets with Betaine and Fat during a Heat Challenge: A Pre- and Post-Experimental Comparison
Ex ante economic analysis can be used to establish the production threshold for a proposed experimental diet to be as profitable as the control treatment. This study reports (1) a pre-experimental economic analysis to estimate the milk production thresholds for an experiment where dietary supplements were fed to dairy cows experiencing a heat challenge, and (2) comparison of these thresholds to the milk production results of the subsequent animal experiment. The pre-experimental thresholds equated to a 1% increase in milk production for the betaine supplement, 9% increase for the fat supplement, and 11% increase for fat and betaine in combination, to achieve the same contribution to farm profit as the control diet. For the post-experimental comparison, previously modelled climate predictions were used to extrapolate the milk production results from the animal experiment over the annual hot-weather period for the dairying region in northern Victoria, Australia. Supplementing diets with fat or betaine had the potential to produce enough extra milk to exceed the production thresholds, making either supplement a profitable alternative to feeding the control diet during the hot-weather period. Feeding fat and betaine in combination failed to result in the extra milk required to justify the additional cost when compared to the control diet
Dietary Fat and Betaine Supplements Offered to Lactating Cows Affect Dry Matter Intake, Milk Production and Body Temperature Responses to an Acute Heat Challenge
Supplementing the diet of lactating cows with ingredients that increase energy density, or reduce internal heat production, may reduce some of the negative impacts of hot weather on milk yield. Thirty-two dairy cows were assigned either: (1) basal diet only, (2) basal diet plus canola oil, (3) basal diet plus betaine, or (4) basal diet plus canola oil and betaine. The basal diet was lucerne hay, pasture silage, and grain. Cows were exposed to a four-day heat challenge (temperature-humidity index 74 to 84) in controlled-environment chambers. Canola oil supplementation increased milk production (22.0 vs. 18.7 kg/d) across all periods of our experiment and increased body temperature (39.6 vs. 39.0 °C) during the heat challenge. Betaine supplementation reduced maximum body temperature during the pre-challenge period (39.2 vs. 39.6 °C) but not during the heat challenge (40.3 °C). Cows fed canola oil had greater declines in dry matter intake (5.4 vs 2.7 kg DM) and energy corrected milk (1.3 vs. 1.0 kg) from the pre-challenge to the heat challenge than other cows. Contrary to our expectations, the combination of fat and betaine supplements did not result in a clear benefit in terms of milk production or body temperature. Further work is warranted to understand the interactions between diet and hot weather
Reference population characteristics effect on metagenomic prediction accuracy.
<p>Prediction of residual enteric methane production from cattle (Red in panels a-c), and body mass index (BMI) from humans (Blue in panels a-c). Bovine predictions all use bovGMC as the reference population and bovFCE as the validation population. A) Lines: effect of reference population size on prediction accuracy. Line indicates the average accuracy of prediction from 20 random replicate populations sampled from the whole dataset. Squares: Accuracy of prediction when the most extreme phenotypes were used in the reference. Triangles: Accuracy of prediction when least extreme samples were used in the reference. B) Comparison of prediction accuracy using the BLUP and randomForests methods. The same reference and validation populations were used in the BLUP and randomForest methods. The randomForest predictions were performed with default settings, and the average correlation of 100 replicate runs is reported. C) Prediction accuracies under different sequence depths in the bovine dataset, phenotype is residual methane production, reference population is bovGMC, validation population is bovFCE. D) Prediction accuracy when different sized contig databases were used. Phenotype is residual methane production, reference population is bovGMC, and validation population is bovFCE. Blue diamonds: N contigs were randomly selected from the whole dataset. Red triangles: Contigs were randomly assigned to 4 groups of 100,000 contigs (no overlap between contig groups).</p