Bone morphogenetic protein (BMP) ligands and receptors in bovine ovarian follicle cells: actions of BMP-4,-6 and-7 on granulosa cells and differential modulation of Smad-1 phosphorylation by follistatin

Given the paucity of information on the potential roles of bone morphogenetic proteins (BMPs) in the ruminant ovary we conducted immunolocalization and functional studies on cells isolated from bovine antral follicles. Immunocytochemistry revealed expression of BMP-4 and -7 in isolated theca cells whereas granulosa cells and oocytes selectively expressed RMP-6. All three cell types expressed a range of BMP-responsive type-I (BMPRIB, ActRI) and type-II (BMPRII, ActRII, ActRIIB) receptors supporting autocrine/paracrine roles within the follicle. This was reinforced by functional experiments on granulosa cells which showed that BMP-4, -6 and -7 promoted cellular accumulation of phosphorylated Smad-1 but not Smad-2 and enhanced 'basal' and IGF-stimulated secretion of oestradiol (E2), inhibin-A, activin-A and follistatin (FS). Concomitantly, each BMP suppressed 'basal' and IGF-stimulated progesterone secretion, consistent with an action to prevent or delay atresia and/or luteinization. BMPs also increased viable cell number under 'basal' (BMP-4 and -7) and IGF-stimulated (BMP-4, -6 and -7) conditions. Since FS, a product of bovine granulosa cells, has been shown to bind several BMPs, we used the Biacore technique to compare its binding affinities for activin-A (prototype FS ligand) and BMP-4, -6 and -7. Compared with activin-A (K-d 0.28 +/- 0.02 nM; 100%), the relative affinities of FS for BMP-4, -6 and -7 were 10, 5 and 1% respectively. Moreover, studies on granulosa cells showed that preincubation of ligand with excess FS abolished activin-A-induced phosphorylation of Smad-2 and BMP-4-induced phosphorylation of Smad-1. However, FS only partially reversed BMP-6-induced Smad-1 phosphorylation and had no inhibitory effect on BMP-7-induced Smad-1 phosphorylation. These findings support functional roles for BMP-4, -6 and -7 as paracrine/autocrine modulators of granulosa cell steroidogenesis, peptide secretion and proliferation in bovine antral follicles. The finding that FS can differentially modulate BMP-induced receptor activation and that this correlates with the relative binding affinity of FS for each BMP type implicates FS as a potential modulator of BMP action in the ovary.

Local roles of TGF-beta superfamily members in the control of ovarian follicle development

Members of the transforming growth factor-beta (TGF-beta) superfamily have wide-ranging influences on many tissue and organ systems including the ovary. Two recently discovered TGF-beta superfamily members, growth/differentiation factor-9 (GDF-9) and bone morphogenetic protein-15 (BMP-15; also designated as GDF-9B) are expressed in an oocyte-specific manner from a very early stage and play a key role in promoting follicle growth beyond the primary stage. Follicle growth to the small antral stage does not require gonadotrophins but appears to be driven by local autocrine/paracrine signals from both somatic cell types (granulosa and theca) and from the oocyte. TGF-beta superfamily members expressed by follicular cells and implicated in this phase of follicle development include TGF-beta, activin, GDF-9/9B and several BMPs. Acquisition of follicle-stimulating hormone (FSH) responsiveness is a pre-requisite for growth beyond the small antral stage and evidence indicates an autocrine role for granulosa-derived activin in promoting granulosa cell proliferation, FSH receptor expression and aromatase activity. Indeed, some of the effects of FSH on granulosa cells may be mediated by endogenous activin. At the same time, activin may act on theca cells to attenuate luteinizing hormone (LH)-dependent androgen production in small to medium-size antral follicles. Dominant follicle selection appears to depend on differential FSH sensitivity amongst a growing cohort of small antral follicles. Activin may contribute to this selection process by sensitizing those follicles with the highest "activin tone" to FSH. Production of inhibin, like oestradiol, increases in selected dominant follicles, in an FSH- and insulin-like growth factor-dependent manner and may exert a paracrine action on theca cells to upregulate LH-induced secretion of androgen, an essential requirement for further oestradiol secretion by the pre-ovulatory follicle. Like activin, BMP-4 and -7 (mostly from theca), and BMP-6 (mostly from oocyte), can enhance oestradiol and inhibin secretion by bovine granulosa cells while suppressing progesterone secretion; this suggests a functional role in delaying follicle luteinization and/or atresia. Follistatin, on the other hand, may favor luteinization and/or atresia by bio-neutralizing intrafollicular activin and BMPs. Activin receptors are expressed by the oocyte and activin may have a further intrafollicular role in the terminal stages of follicle differentiation to promote oocyte maturation and developmental competence. In a reciprocal manner, oocyte-derived GDF-9/9B may act on the surrounding cumulus granulosa cells to attenuate oestradiol output and promote progesterone and hyaluronic acid production, mucification and cumulus expansion.(C) 2003 Elsevier Science B.V. All rights reserved.

Ovarian follicle development in the laying hen is accompanied by divergent changes in inhibin A, inhibin B, activin A and follistatin production in granulosa and theca layers

To study the potential involvement of inhibin A (inhA), inhibin B (inhB), activin A (actA) and follistatin (FS) in the recruitment of follicles into the preovulatory hierarchy, growing follicles (ranging from 1 mm to the largest designated F1) and the three most recent postovulatory follicles (POFs) were recovered from laying hens (n=11). With the exception of <4 mm follicles and POFs, follicle walls were dissected into separate granulosa (G) and theca (T) layers before extraction. Contents of inhA, inhB, actA and FS in tissue extracts were assayed using specific two-site ELISAs and results are expressed per mg DNA. InhB content of both G and T followed a similar developmental pattern, although the content was >4-fold higher in G than in T at all stages. InhB content was very low in follicles <4 nun but increased - 50-fold (P<0.0001) to peak in 7-9 mm follicles, before falling steadily as follicles entered and moved up the follicular hierarchy (40-fold; 8 mm vs F2). In stark contrast, inhA remained very low in prehierarchical follicles (&LE; 9 mm) but then increased progressively as follicles moved up the preovulatory hierarchy to peak in F1 (&SIM; 100-fold increase; P<0.0001); In F1 >97% of inhA was confined to the G layer whereas in 5-9 mm follicles inhA was only. detected in the T layer. Both inhA and inhB contents of POFs were significantly reduced compared with F1. Follicular actA was mainly confined to the T layer although detectable levels were present in G from 9 nun; actA was low between 1 and 9 mm but increased sharply as follicles entered the preovulatory hierarchy (&SIM;6-fold higher in F4; P<0.0001); levels then fell &SIM;2-fold as the follicle progressed to F1. Like actA, FS predominated in the T although significant amounts were also present in the G of prehierarchical follicles (4-9 mm), in contrast to actA, which was absent from the G. The FS content of T rose &SIM;3-fold from 6 mm to a plateau which was sustained until F1. In contrast, the FS content of G was greatest in prehierarchical follicles and fell &SIM;4-fold in F4-F1 follicles. ActA and FS contents of POFs were reduced compared with F1. In vitro studies on follicle wall explants confirmed the striking divergence in the secretion of inhA and inhB during follicle development. These findings of marked stage-dependent differences in the expression of inhA, inhB, actA and FS proteins imply a significant functional role for these peptides in the recruitment and ordered progression of follicles within the avian ovary.

Serum proteomic abnormality predating screen detection of ovarian cancer

Ovarian cancer is characterized by vague, non-specific symptoms, advanced stage at diagnosis and poor overall survival. A nested case control study was undertaken on stored serial serum samples from women who developed ovarian cancer and healthy controls (matched for serum processing and storage conditions as well as attributes such as age) in a pilot randomized controlled trial of ovarian cancer screening. The unique feature of this study is that the women were screened for up to 7 years. The serum samples underwent prefractionation using a reversed-phase batch extraction protocol prior to MALDI-TOF MS data acquisition. Our exploratory analysis shows that combining a single MS peak with CA125 allows statistically significant discrimination at the 5% level between cases and controls up to 12 months in advance of the original diagnosis of ovarian cancer. Such combinations work much better than a single peak or CA125 alone. This paper demonstrates that mass spectra from the low molecular weight serum proteome carry information useful for early detection of ovarian cancer. The next step is to identify the specific biomarkers that make early detection possible.

A study of the effect of heat source location in a ventilated room using multiple regression analysis

Multiple regression analysis is a statistical technique which allows to predict a dependent variable from m ore than one independent variable and also to determine influential independent variables. Using experimental data, in this study the multiple regression analysis is applied to predict the room mean velocity and determine the most influencing parameters on the velocity. More than 120 experiments for four different heat source locations were carried out in a test chamber with a high level wall mounted air supply terminal at air change rates 3-6 ach. The influence of the environmental parameters such as supply air momentum, room heat load, Archimedes number and local temperature ratio, were examined by two methods: a simple regression analysis incorporated into scatter matrix plots and multiple stepwise regression analysis. It is concluded that, when a heat source is located along the jet centre line, the supply momentum mainly influences the room mean velocity regardless of the plume strength. However, when the heat source is located outside the jet region, the local temperature ratio (the inverse of the local heat removal effectiveness) is a major influencing parameter.

Regression, developmental trajectory and associated problems in disorders in the autism spectrum: the SNAP study

We report rates of regression and associated findings in a population derived group of 255 children aged 9-14 years, participating in a prevalence study of autism spectrum disorders (ASD); 53 with narrowly defined autism, 105 with broader ASD and 97 with non-ASD neurodevelopmental problems, drawn from those with special educational needs within a population of 56,946 children. Language regression was reported in 30% with narrowly defined autism, 8% with broader ASD and less than 3% with developmental problems without ASD. A smaller group of children were identified who underwent a less clear setback. Regression was associated with higher rates of autistic symptoms and a deviation in developmental trajectory. Regression was not associated with epilepsy or gastrointestinal problems.

A sparse kernel density estimation algorithm using forward constrained regression

Using the classical Parzen window (PW) estimate as the target function, the sparse kernel density estimator is constructed in a forward constrained regression manner. The leave-one-out (LOO) test score is used for kernel selection. The jackknife parameter estimator subject to positivity constraint check is used for the parameter estimation of a single parameter at each forward step. As such the proposed approach is simple to implement and the associated computational cost is very low. An illustrative example is employed to demonstrate that the proposed approach is effective in constructing sparse kernel density estimators with comparable accuracy to that of the classical Parzen window estimate.

Probability density function estimation using orthogonal forward regression

Using the classical Parzen window estimate as the target function, the kernel density estimation is formulated as a regression problem and the orthogonal forward regression technique is adopted to construct sparse kernel density estimates. The proposed algorithm incrementally minimises a leave-one-out test error score to select a sparse kernel model, and a local regularisation method is incorporated into the density construction process to further enforce sparsity. The kernel weights are finally updated using the multiplicative nonnegative quadratic programming algorithm, which has the ability to reduce the model size further. Except for the kernel width, the proposed algorithm has no other parameters that need tuning, and the user is not required to specify any additional criterion to terminate the density construction procedure. Two examples are used to demonstrate the ability of this regression-based approach to effectively construct a sparse kernel density estimate with comparable accuracy to that of the full-sample optimised Parzen window density estimate.

Fully complex-valued radial basis function networks for orthogonal least squares regression

We consider a fully complex-valued radial basis function (RBF) network for regression application. The locally regularised orthogonal least squares (LROLS) algorithm with the D-optimality experimental design, originally derived for constructing parsimonious real-valued RBF network models, is extended to the fully complex-valued RBF network. Like its real-valued counterpart, the proposed algorithm aims to achieve maximised model robustness and sparsity by combining two effective and complementary approaches. The LROLS algorithm alone is capable of producing a very parsimonious model with excellent generalisation performance while the D-optimality design criterion further enhances the model efficiency and robustness. By specifying an appropriate weighting for the D-optimality cost in the combined model selecting criterion, the entire model construction procedure becomes automatic. An example of identifying a complex-valued nonlinear channel is used to illustrate the regression application of the proposed fully complex-valued RBF network.

Sparse kernel density estimator using orthogonal regression based on D-optimality experimental design

A novel sparse kernel density estimator is derived based on a regression approach, which selects a very small subset of significant kernels by means of the D-optimality experimental design criterion using an orthogonal forward selection procedure. The weights of the resulting sparse kernel model are calculated using the multiplicative nonnegative quadratic programming algorithm. The proposed method is computationally attractive, in comparison with many existing kernel density estimation algorithms. Our numerical results also show that the proposed method compares favourably with other existing methods, in terms of both test accuracy and model sparsity, for constructing kernel density estimates.

Sparse kernel regression modeling using combined locally regularized orthogonal least squares and D-optimality experimental design

The note proposes an efficient nonlinear identification algorithm by combining a locally regularized orthogonal least squares (LROLS) model selection with a D-optimality experimental design. The proposed algorithm aims to achieve maximized model robustness and sparsity via two effective and complementary approaches. The LROLS method alone is capable of producing a very parsimonious model with excellent generalization performance. The D-optimality design criterion further enhances the model efficiency and robustness. An added advantage is that the user only needs to specify a weighting for the D-optimality cost in the combined model selecting criterion and the entire model construction procedure becomes automatic. The value of this weighting does not influence the model selection procedure critically and it can be chosen with ease from a wide range of values.