Associations between the K232A polymorphism in the diacylglycerol-O-transferase 1 (DGAT1) gene and performance in Irish Holstein-Friesian dairy cattle

peer-reviewedSelection based on genetic polymorphisms requires accurate quantification of the effect or association of the polymorphisms with all traits of economic importance. The objective of this study was to estimate, using progeny performance data on 848 Holstein-Friesian bulls, the association between a non-conservative alanine to lysine amino acid change (K232A) in exon 8 of the diacylglycerol-O-transferase 1 (DGAT1) gene and milk production and functionality in the Irish Holstein-Friesian population. The DGAT1 gene encodes the diacylglycerol-O-transferase microsomal enzyme necessary to catalyze the final step in triglyceride synthesis. Weighted mixed model methodology, accounting for the additive genetic relationships among animals, was used to evaluate the association between performance and the K232A polymorphism. The minor allele frequency (K allele) was 0.32. One copy of the K allele was associated (P < 0.001) with 77 kg less milk yield, 4.22 kg more fat yield, 0.99 kg less protein yield, and 1.30 and 0.28 g/kg greater milk fat and protein concentration, respectively; all traits were based on predicted 305-day production across the first five lactations. The K232A polymorphism explained 4.8%, 10.3% and 1.0% of the genetic variance in milk yield, fat yield and protein yield, respectively. There was no association between the K232A polymorphism and fertility, functional survival, calving performance, carcass traits, or any conformation trait with the exception of rump width and carcass conformation. Using the current economic values for the milk production traits in the Irish total merit index, one copy of the K allele is worth €5.43 in expected profitability of progeny. Results from this study will be useful in quantifying the cost-benefit of including the K232A polymorphism in the Irish national breeding programme

Raman Spectral Signatures of Cervical Exfoliated Cells from Liquid-Based Cytology Samples

It is widely accepted that cervical screening has significantly reduced the incidence of cervical cancer worldwide. The primary screening test for cervical cancer is the Papanicolaou (Pap) test, which has extremely variable specificity and sensitivity. There is an unmet clinical need for methods to aid clinicians in the early detection of cervical precancer. Raman spectroscopy is a label-free objective method that can provide a biochemical fingerprint of a given sample. Compared with studies on infrared spectroscopy, relatively few Raman spectroscopy studies have been carried out to date on cervical cytology. The aim of this study was to define the Raman spectral signatures of cervical exfoliated cells present in liquid-based cytology Pap test specimens and to compare the signature of high-grade dysplastic cells to each of the normal cell types. Raman spectra were recorded from single exfoliated cells and subjected to multivariate statistical analysis. The study demonstrated that Raman spectroscopy can identify biochemical signatures associated with the most common cell types seen in liquid-based cytology samples; superficial, intermediate, and parabasal cells. In addition, biochemical changes associated with high-grade dysplasia could be identified suggesting that Raman spectroscopy could be used to aid current cervical screening tests

Raman Spectroscopic Detection of High-Grade Cervical Cytology: Using Morphologically Normal Appearing Cells

This study aims to detect high grade squamous intraepithelial cells (HSIL) by investigating HSIL associated biochemical changes in morphologically normal appearing intermediate and superficial cells using Raman spectroscopy. Raman spectra (n = 755) were measured from intermediate and superficial cells from negative cytology ThinPrep specimens (n = 18) and from morphologically normal appearing intermediate and superficial cells from HSIL cytology ThinPrep specimens (n = 17). The Raman data was subjected to multivariate algorithms including the standard principal component analysis (PCA)-linear discriminant analysis (LDA) and partial least squares discriminant analysis (PLS-DA) together with random subsets cross-validation for discriminating negative cytology from HSIL. The PCA-LDA method yielded sensitivities of 74.9%, 72.8%, and 75.6% and specificities of 89.9%, 81.9%, and 84.5%, for HSIL diagnosis based on the dataset obtained from intermediate, superficial and mixed intermediate/superficial cells, respectively. The PLS-DA method provided improved sensitivities of 95.5%, 95.2% and 96.1% and specificities of 92.7%, 94.7% and 93.5% compared to the PCA-LDA method. The results demonstrate that the biochemical signatures of morphologically normal appearing cells can be used to discriminate between negative and HSIL cytology. In addition, it was found that mixed intermediate and superficial cells could be used for HSIL diagnosis as the biochemical differences between negative and HSIL cytology were greater than the biochemical differences between intermediate and superficial cell types

Development and Validation of a Raman Spectroscopic Classification Model for Cervical Intraepithelial Neoplasia (CIN)

The mortality associated with cervical cancer can be reduced if detected at the precancer stage, but current methods are limited in terms of subjectivity, cost and time. Optical spectroscopic methods such as Raman spectroscopy can provide a rapid, label-free and nondestructive measurement of the biochemical fingerprint of a cell, tissue or biofluid. Previous studies have shown the potential of Raman spectroscopy for cervical cancer diagnosis, but most were pilot studies with small sample sizes. The aim of this study is to show the clinical utility of Raman spectroscopy for identifying cervical precancer in a large sample set with validation in an independent test set. Liquid-based cervical cytology samples (n = 662) (326 negative, 200 cervical intraepithelial neoplasia (CIN)1 and 136 CIN2+) were obtained as a training set. Raman spectra were recorded from single-cell nuclei and subjected to a partial least squares discriminant analysis (PLSDA). In addition, the PLSDA classification model was validated using a blinded independent test set (n = 69). A classification accuracy of 91.3% was achieved with only six of the blinded samples misclassified. This study showed the potential clinical utility of Raman spectroscopy with a good classification of negative, CIN1 and CIN2+ achieved in an independent test set

Raman spectroscopic detection of high-grade cervical cytology: Using morphologically normal appearing cells

Abstract This study aims to detect high grade squamous intraepithelial cells (HSIL) by investigating HSIL associated biochemical changes in morphologically normal appearing intermediate and superficial cells using Raman spectroscopy. Raman spectra (n = 755) were measured from intermediate and superficial cells from negative cytology ThinPrep specimens (n = 18) and from morphologically normal appearing intermediate and superficial cells from HSIL cytology ThinPrep specimens (n = 17). The Raman data was subjected to multivariate algorithms including the standard principal component analysis (PCA)-linear discriminant analysis (LDA) and partial least squares discriminant analysis (PLS-DA) together with random subsets cross-validation for discriminating negative cytology from HSIL. The PCA-LDA method yielded sensitivities of 74.9%, 72.8%, and 75.6% and specificities of 89.9%, 81.9%, and 84.5%, for HSIL diagnosis based on the dataset obtained from intermediate, superficial and mixed intermediate/superficial cells, respectively. The PLS-DA method provided improved sensitivities of 95.5%, 95.2% and 96.1% and specificities of 92.7%, 94.7% and 93.5% compared to the PCA-LDA method. The results demonstrate that the biochemical signatures of morphologically normal appearing cells can be used to discriminate between negative and HSIL cytology. In addition, it was found that mixed intermediate and superficial cells could be used for HSIL diagnosis as the biochemical differences between negative and HSIL cytology were greater than the biochemical differences between intermediate and superficial cell types

Raman spectral signatures of cervical exfoliated cells from liquid-based cytology samples

International audienceIt is widely accepted that cervical screening has significantly reduced the incidence of cervical cancer worldwide. The primary screening test for cervical cancer is the Papanicolaou (Pap) test, which has extremely variable specificity and sensitivity. There is an unmet clinical need for methods to aid clinicians in the early detection of cervical precancer. Raman spectroscopy is a label-free objective method that can provide a biochemical fingerprint of a given sample. Compared with studies on infrared spectroscopy, relatively few Raman spectroscopy studies have been carried out to date on cervical cytology. The aim of this study was to define the Raman spectral signatures of cervical exfoliated cells present in liquid-based cytology Pap test specimens and to compare the signature of high-grade dysplastic cells to each of the normal cell types. Raman spectra were recorded from single exfoliated cells and subjected to multivariate statistical analysis. The study demonstrated that Raman spectroscopy can identify biochemical signatures associated with the most common cell types seen in liquid-based cytology samples; superficial, intermediate, and parabasal cells. In addition, biochemical changes associated with high-grade dysplasia could be identified suggesting that Raman spectroscopy could be used to aid current cervical screening tests

Processing ThinPrep cervical cytological samples for Raman spectroscopic analysis.

International audienceRaman microspectroscopy has been proven to be a promising technique for diagnosis and early detection of pathologies. The data collected delivers a chemical fingerprint allowing the identification of specific biomarkers indicating the presence of abnormalities. Label free, fast and cost effective, Raman spectroscopy has already been proposed as the new generation of diagnostic tool with a strong potential but has not emerged in the medical field as yet. Notably, it is crucial to improve and adapt the protocols used to reach suitable reproducibility for screening large cohorts of patients. In this study, it is demonstrated that the variability existing in the data sets collected can be limiting. Notably, when working on cervical ThinPrep samples, the presence of blood residue can be detected by Raman spectroscopy swamping the cellular signal. However, combining a washing of the slides using H2O2 and alcohol (70% ethanol and 100% Industrial Methylated Spirits), the blood features are removed from the data without altering either the cell morphology or the spectral features. Ultimately, this work demonstrates the improved potential of Raman spectroscopy for ThinPrep analysis based on improved protocols for sample preparation. Therefore, the screening of cervical cells for the detection of abnormalities and identification of patients with Cervical Intraepithelial Neoplasia (CIN) is achievable

Development and Validation of a Raman Spectroscopic Classification Model for Cervical Intraepithelial Neoplasia (CIN)

The mortality associated with cervical cancer can be reduced if detected at the precancer stage, but current methods are limited in terms of subjectivity, cost and time. Optical spectroscopic methods such as Raman spectroscopy can provide a rapid, label-free and nondestructive measurement of the biochemical fingerprint of a cell, tissue or biofluid. Previous studies have shown the potential of Raman spectroscopy for cervical cancer diagnosis, but most were pilot studies with small sample sizes. The aim of this study is to show the clinical utility of Raman spectroscopy for identifying cervical precancer in a large sample set with validation in an independent test set. Liquid-based cervical cytology samples (n = 662) (326 negative, 200 cervical intraepithelial neoplasia (CIN)1 and 136 CIN2+) were obtained as a training set. Raman spectra were recorded from single-cell nuclei and subjected to a partial least squares discriminant analysis (PLSDA). In addition, the PLSDA classification model was validated using a blinded independent test set (n = 69). A classification accuracy of 91.3% was achieved with only six of the blinded samples misclassified. This study showed the potential clinical utility of Raman spectroscopy with a good classification of negative, CIN1 and CIN2+ achieved in an independent test set