Gene expression in ovarian granulosa and theca cells in cattle selected for double ovulations and twin births

Scope and Method of Study: Components of the IGF system and gonadotropin receptors (FSHR and LHR) might be involved in the control of the development of multiple dominant follicles leading to twinning. Gene expression of FSHR, LHR, IGF2R and the hedgehog system (Ihh and Ptch1) gene expression in granulosa and/or theca cells from cattle not selected (Control) or selected for multiple ovulations (Twinner) were quantitated at two different stages (Day 3, D3; Day 5, D5) of the follicular cycle and in the largest 3 follicles. The effect of IGF2 in small (1-5 mm) and large (8-22 mm) bovine granulosa cell cultures on steroid production, cell proliferation, and gene expression were also investigated.Findings and Conclusions: Granulosa FSHR mRNA was greater (P < 0.05) at D3 versus D5 in healthy estrogen-active (E-A) follicles and in Control versus Twinner cows at D3. Granulosa IGF2R mRNA was lower (P < 0.05) in E-A versus atretic estrogen-inactive (E-I) follicles, and lower (P < 0.05) at D5. Granulosa IGF2R mRNA was greater (P < 0.05) in Control than Twinner cows at D3 and D5; Control F2 had greater (P<0.05) thecal IGF2R mRNA than F1, F2 and F3 of Twinner cows. IGF2 increased granulosa estradiol and progesterone production, stimulated aromatase mRNA and increased proliferation of granulosa cells. IGF1R antibodies reduced the stimulatory effect of IGF2 and IGF1 on estradiol production and cell proliferation. Granulosa Ihh mRNA was two-fold greater (P < 0.05) in healthy than atretic follicles and in D3 than D5; thecal Ptch1 mRNA was lower (P < 0.05) at D3 than D5, and in Twinner versus Control cows. In cultured granulosa cells, IGF1 decreased (P < 0.001) Ihh mRNA abundance, and both IGF1 and IGF2 decreased (P < 0.01) thecal Ptch1 mRNA abundance. Decreased IGF2R in granulosa and theca cells of Twinner cows likely increases bioavailable IGF2, which in turn enhances follicular development of two, rather than one follicle and further supports the idea that IGF2 and its receptor (IGF2R) may play a role in follicular development. For the first time, we demonstrate an interaction between the ovarian IGF and hedgehog systems

Real-time Rt-pcr Quantification of Pregnancy-associated Plasma Protein-A Gene Expression in Granulosa and Theca Cells: Effects of Hormones in Vitro

PAPP-A degrades IGFBPs in the ovary, and increases availability of IGFs. The objective of this study was to determine the effect of IGF-s, insulin, LH, FSH, estradiol, leptin or cortisol on ovarian PAPP-A mRNA in small- (SM) and large- (LG) follicle granulosa (GC) and LG theca (TC) cells. More PAPP-A mRNA was produced by GC than TC, but none of the treatments affected (P&gt;0.10) PAPP-A mRNA in SM-GC and LG-GC. In LG-TC, insulin with or without LH decreased (P&lt;0.05) PAPP-A mRNA; estradiol alone decreased PAPP-A mRNA and amplified the insulin-induced inhibition of PAPP-A mRNA expression. We conclude that PAPP-A gene expression is differentially regulated in granulosa and theca cells. Estradiol and insulin may act as negative feedback regulators to prevent excessive IGF-I - induced androgen production, and prevent excessive estradiol production by GC via decreased thecal PAPP-A production, maintaining desirable levels of IGFBP-4/-5 and subsequently free IGF-I /-II within the follicle.Department of Animal Scienc

Cross-cohort generalizability of deep and conventional machine learning for MRI-based diagnosis and prediction of Alzheimer's disease

This work validates the generalizability of MRI-based classification of Alzheimer’s disease (AD) patients and controls (CN) to an external data set and to the task of prediction of conversion to AD in individuals with mild cognitive impairment (MCI).We used a conventional support vector machine (SVM) and a deep convolutional neural network (CNN) approach based on structural MRI scans that underwent either minimal pre-processing or more extensive pre-processing into modulated gray matter (GM) maps. Classifiers were optimized and evaluated using cross-validation in the Alzheimer’s Disease Neuroimaging Initiative (ADNI; 334 AD, 520 CN). Trained classifiers were subsequently applied to predict conversion to AD in ADNI MCI patients (231 converters, 628 non-converters) and in the independent Health-RI Parelsnoer Neurodegenerative Diseases Biobank data set. From this multi-center study representing a tertiary memory clinic population, we included 199 AD patients, 139 participants with subjective cognitive decline, 48 MCI patients converting to dementia, and 91 MCI patients who did not convert to dementia.AD-CN classification based on modulated GM maps resulted in a similar area-under-the-curve (AUC) for SVM (0.940; 95%CI: 0.924–0.955) and CNN (0.933; 95%CI: 0.918–0.948). Application to conversion prediction in MCI yielded significantly higher performance for SVM (AUC = 0.756; 95%CI: 0.720-0.788) than for CNN (AUC = 0.742; 95%CI: 0.709-0.776) (p<0.01 for McNemar’s test). In external validation, performance was slightly decreased. For AD-CN, it again gave similar AUCs for SVM (0.896; 95%CI: 0.855–0.932) and CNN (0.876; 95%CI: 0.836–0.913). For prediction in MCI, performances decreased for both SVM (AUC = 0.665; 95%CI: 0.576-0.760) and CNN (AUC = 0.702; 95%CI: 0.624-0.786). Both with SVM and CNN, classification based on modulated GM maps significantly outperformed classification based on minimally processed images (p=0.01).Deep and conventional classifiers performed equally well for AD classification and their performance decreased only slightly when applied to the external cohort. We expect that this work on external validation contributes towards translation of machine learning to clinical practice

The Dutch Parelsnoer Institute - Neurodegenerative diseases; methods, design and baseline results

Background: The is a collaboration between 8 Dutch University Medical Centers in which clinical data and biomaterials from patients suffering from chronic diseases (so called "Pearls") are collected according to harmonized protocols. The Pearl Neurodegenerative Diseases focuses on the role of biomarkers in the early diagnosis, differential diagnosis and in monitoring the course of neurodegenerative diseases, in particular Alzheimer's disease. Methods: The Pearl Neurodegenerative Diseases is a 3-year follow-up study of patients referred to a memory clinic with cognitive complaints. At baseline, all patients are subjected to a standardized examination, including clinical data and biobank materials, e.g. blood samples, MRI and cerebrospinal fluid. At present, in total more than 1000 patients have been included, of which cerebrospinal fluid and DNA samples are available of 211 and 661 patients, respectively. First descriptives of a subsample of the data (n = 665) shows that patients are diagnosed with dementia (45%), mild cognitive impairment (31%), and subjective memory complaints (24%). Discussion: The Pearl Neurodegenerative Diseases is an ongoing large network collecting clinical data and biomaterials of more than 1000 patients with cognitive impairments. The project has started with data analyses of the baseline characteristics and biomarkers, which will be the starting point of future specific research questions that can be answered by this unique dataset

Genetic and lifestyle risk factors for MRI-defined brain infarcts in a population-based setting.

OBJECTIVE: To explore genetic and lifestyle risk factors of MRI-defined brain infarcts (BI) in large population-based cohorts. METHODS: We performed meta-analyses of genome-wide association studies (GWAS) and examined associations of vascular risk factors and their genetic risk scores (GRS) with MRI-defined BI and a subset of BI, namely, small subcortical BI (SSBI), in 18 population-based cohorts (n = 20,949) from 5 ethnicities (3,726 with BI, 2,021 with SSBI). Top loci were followed up in 7 population-based cohorts (n = 6,862; 1,483 with BI, 630 with SBBI), and we tested associations with related phenotypes including ischemic stroke and pathologically defined BI. RESULTS: The mean prevalence was 17.7% for BI and 10.5% for SSBI, steeply rising after age 65. Two loci showed genome-wide significant association with BI: FBN2, p = 1.77 × 10-8; and LINC00539/ZDHHC20, p = 5.82 × 10-9. Both have been associated with blood pressure (BP)-related phenotypes, but did not replicate in the smaller follow-up sample or show associations with related phenotypes. Age- and sex-adjusted associations with BI and SSBI were observed for BP traits (p value for BI, p [BI] = 9.38 × 10-25; p [SSBI] = 5.23 × 10-14 for hypertension), smoking (p [BI] = 4.4 × 10-10; p [SSBI] = 1.2 × 10-4), diabetes (p [BI] = 1.7 × 10-8; p [SSBI] = 2.8 × 10-3), previous cardiovascular disease (p [BI] = 1.0 × 10-18; p [SSBI] = 2.3 × 10-7), stroke (p [BI] = 3.9 × 10-69; p [SSBI] = 3.2 × 10-24), and MRI-defined white matter hyperintensity burden (p [BI] = 1.43 × 10-157; p [SSBI] = 3.16 × 10-106), but not with body mass index or cholesterol. GRS of BP traits were associated with BI and SSBI (p ≤ 0.0022), without indication of directional pleiotropy. CONCLUSION: In this multiethnic GWAS meta-analysis, including over 20,000 population-based participants, we identified genetic risk loci for BI requiring validation once additional large datasets become available. High BP, including genetically determined, was the most significant modifiable, causal risk factor for BI