Method specific calibration corrects for DNA extraction method effects on relative telomere length measurements by quantitative PCR

Telomere length (TL) is increasingly being used as a biomarker in epidemiological, biomedical and ecological studies. A wide range of DNA extraction techniques have been used in telomere experiments and recent quantitative PCR (qPCR) based studies suggest that the choice of DNA extraction method may influence average relative TL (RTL) measurements. Such extraction method effects may limit the use of historically collected DNA samples extracted with different methods. However, if extraction method effects are systematic an extraction method specific (MS) calibrator might be able to correct for them, because systematic effects would influence the calibrator sample in the same way as all other samples. In the present study we tested whether leukocyte RTL in blood samples from Holstein Friesian cattle and Soay sheep measured by qPCR was influenced by DNA extraction method and whether MS calibration could account for any observed differences. We compared two silica membrane-based DNA extraction kits and a salting out method. All extraction methods were optimized to yield enough high quality DNA for TL measurement. In both species we found that silica membrane-based DNA extraction methods produced shorter RTL measurements than the non-membrane-based method when calibrated against an identical calibrator. However, these differences were not statistically detectable when a MS calibrator was used to calculate RTL. This approach produced RTL measurements that were highly correlated across extraction methods (r > 0.76) and had coefficients of variation lower than 10% across plates of identical samples extracted by different methods. Our results are consistent with previous findings that popular membrane-based DNA extraction methods may lead to shorter RTL measurements than non-membrane-based methods. However, we also demonstrate that these differences can be accounted for by using an extraction method-specific calibrator, offering researchers a simple means of accounting for differences in RTL measurements from samples extracted by different DNA extraction methods within a study

Longitudinal changes in telomere length and associated genetic parameters in dairy cattle analysed using random regression models

Telomeres cap the ends of linear chromosomes and shorten with age in many organisms. In humans short telomeres have been linked to morbidity and mortality. With the accumulation of longitudinal datasets the focus shifts from investigating telomere length (TL) to exploring TL change within individuals over time. Some studies indicate that the speed of telomere attrition is predictive of future disease. The objectives of the present study were to 1) characterize the change in bovine relative leukocyte TL (RLTL) across the lifetime in Holstein Friesian dairy cattle, 2) estimate genetic parameters of RLTL over time and 3) investigate the association of differences in individual RLTL profiles with productive lifespan. RLTL measurements were analysed using Legendre polynomials in a random regression model to describe TL profiles and genetic variance over age. The analyses were based on 1,328 repeated RLTL measurements of 308 female Holstein Friesian dairy cattle. A quadratic Legendre polynomial was fitted to the fixed effect of age in months and to the random effect of the animal identity. Changes in RLTL, heritability and within-trait genetic correlation along the age trajectory were calculated and illustrated. At a population level, the relationship between RLTL and age was described by a positive quadratic function. Individuals varied significantly regarding the direction and amount of RLTL change over life. The heritability of RLTL ranged from 0.36 to 0.47 (SE = 0.05–0.08) and remained statistically unchanged over time. The genetic correlation of RLTL at birth with measurements later in life decreased with the time interval between samplings from near unity to 0.69, indicating that TL later in life might be regulated by different genes than TL early in life. Even though animals differed in their RLTL profiles significantly, those differences were not correlated with productive lifespan (p = 0.954)

Immune traits: descriptive statistics, estimates of variance due to the animal effect and proportion of the total phenotypic variance due to the animal effect (between animal repeatability estimates).

1<p>Coefficient of Variation;</p>2<p>Between animal repeatability of the trait;</p>3<p>Natural Antibodies;</p>4<p>Tumour Necrosis Factor-α;</p>5<p>%Peripheral Blood Mononuclear Cells;</p>6%<p>of PBMC that were CD3, CD4, CD8, CD14, CD21, CD335 and γδ TCR positive;</p>7%<p>of total leukocytes that were lymphocytes, monocytes, neutrophils or eosinophils.</p>*<p>Significantly greater than 0 estimates (P<0.05).</p

Estimated intra-assay repeatability and inter-assay precision for cellular immune trait measurements.

1<p>Coefficient of Variation;</p>2<p>Peripheral Blood Mononuclear Cells;</p>3<p>% of PBMC that were CD3, CD4, CD8, CD14, CD21, CD335 and γδ TCR positive;</p>4<p>% of total leukocytes that were lymphocytes, monocytes, neutrophils or eosinophils.</p

Health event traits: descriptive statistics.

<p>Frequency refers to the proportion of cows with a condition on the week of the immunological analysis; mean, standard deviation and maximum refer to number of distinct episodes per lactation.</p

Details of mouse monoclonal antibodies (mAb) used for flow cytometry.

1<p>AbD Serotec, Kidlington, UK;</p>2<p>VMRD Inc., Pullman, WA;</p>3<p>eBioscience, San Diego, CA;</p>4<p>Moredun Research Institute; n/a = not applicable.</p

Statistically significant (<i>P</i><0.05) post Bonferroni correction animal and phenotypic correlations between immune, health event, reproductive performance and lactation traits.

1<p>expressed as 0/1 on the week of the immune analysis;</p>2<p>expressed as number of distinct episodes in a lactation;</p>3<p>% of PBMC that were CD335, CD8, CD4, CD3, CD14 or γδ TCR positive;</p>4<p>% of total leukocytes that were PBMC or lymphocytes;</p>5<p>Natural Antibodies.</p>a<p>phenotypic correlations between immune and health events traits;</p>b<p>phenotypic correlations between immune and reproductive traits;</p>c<p>animal correlations between immune and lactation traits measured on the same week; ^danimal correlations between immune and lactation traits measured throughout lactation; ^ephenotypic correlations between immune profile and lactation traits measured on the same week;</p>f<p>phenotypic correlations between immune and lactation traits measured throughout lactation.</p