Differential expression of mRNAs encoding co-receptor (betaglycan) and activin type-I preovulatory and prehierarchical follicles of the putative inhibin and type-II receptors in the laying hen ovary

Ovarian follicle development is primarily regulated by an interplay between the pituitary gonadotrophins, LH and FSH, and ovary-derived steroids. Increasing evidence implicates regulatory roles of transforming growth factor-beta (TGF beta) superfamily members, including inhibins and activins. The aim of this study was to identify the expression of mRNAs encoding key receptors of the inhibin/activin system in ovarian follicles ranging from 4 mm in diameter to the dominant F1 follicle (similar to 40 turn). Ovaries were collected (n=16) from inid-sequence hens maintained on a long-day photoschedule (16h of light:8 h of darkness). All follicles removed were dissected into individual granulosa and thecal layers. RNA was extracted and cDNA synthesized. Real-time quantitative PCR was used to quantify the expression of niRNA encoding betaglycan, activin receptor (ActR) subtypes (type-I, -IIA and -IIB) and glyceraldehyde-3-phosphate dehydrogenase (GAPDH); receptor expression data were normalized to GAPDH expression. Detectable levels of ActRI, -IIA and -IIB and the inhibin co-receptor (betaglycan) expression were found in all granulosa and thecal layers analysed. Granulosa ActRI mRNA peaked (P < 0(.)05) in 8-9(.)9 mm follicles, whereas ActRIIA rose significantly from 6-7(.)9 mm to 8-9(.)9 nun, before filling to F3/2; levels then rose sharply (3-fold) to F1 levels. Granulosa betaglycan niRNA expression rose 3-fold from 4-5(.)9 min to 8-9(.)9 mm, before falling 4-fold to F3/2; levels then rose sharply (4-fold) to F1 levels. ActRIIB levels did not vary significantly during follicular development. Thecal ActRI mRNA expression was similar from 4-7(.)9 mm then decreased significantly to a nadir at the F4 position, before increasing 2-fold to the F1 (P < 0(.)05). Although thecal ActRIIB and -IIA expression did not vary significantly from 4 nim to F3, ActRIIB expression increased significantly (2-fold) from F3 to F1 and ActIIA, increased 22-fold from F2 to F1 (P < 0(.)05). Thecal betaglycan fell to a nadir at F6 after follicle selection; levels then increased significantly to F2, before filling similar to 50% in the F I. In all follicles studied expression of betaglycan and ActRI (granulosa: 1-0(.)65, P < 0-001, n=144/group; theca: r=0(.)49, P < 0-001, n=144/group) was well correlated. No significant correlations were identified between betaglycan and ActRIIA or -IIB. Considering all follicles analysed, granulosa mRNA expression of betaglycan, ActRI ActRIIA and ActRIIB were all significantly lower than in corresponding thecal tissue (betaglycan, 11(.)4-fold; ActRIIB, 5(.)1-fold; ActR(.) 3-8-fold: ActRIIA, 2(.)8-fold). The co-localization of type-I and -II activin receptors and betaglycan on granulosa and thecal cells are consistent with a local auto/paracrine role of inhibins and activins in modulating ovarian follicle development, selection and progression in the domestic fowl.

Rapid parallel expression in E-coli and insect cells: Analysis of five lef gene products of the Autographa californica multiple nuclear polyhedrosis virus (AcMNPV)

A number of strategies are emerging for the high throughput (HTP) expression of recombinant proteins to enable structural and functional study. Here we describe a workable HTP strategy based on parallel protein expression in E. coli and insect cells. Using this system we provide comparative expression data for five proteins derived from the Autographa californica polyhedrosis virus genome that vary in amino acid composition and in molecular weight. Although the proteins are part of a set of factors known to be required for viral late gene expression, the precise function of three of the five, late expression factors (lefs) 6, 7 and 10, is unknown. Rapid expression and characterisation has allowed the determination of their ability to bind DNA and shown a cellular location consistent with their properties. Our data point to the utility of a parallel expression strategy to rapidly obtain workable protein expression levels from many open reading frames (ORFs).

Differential expression of mRNAs encoding co-receptor (betaglycan) and activin type-I preovulatory and prehierarchical follicles of the putative inhibin and type-II receptors in the laying hen ovary

Ovarian follicle development is primarily regulated by an interplay between the pituitary gonadotrophins, LH and FSH, and ovary-derived steroids. Increasing evidence implicates regulatory roles of transforming growth factor-beta (TGF beta) superfamily members, including inhibins and activins. The aim of this study was to identify the expression of mRNAs encoding key receptors of the inhibin/activin system in ovarian follicles ranging from 4 mm in diameter to the dominant F1 follicle (similar to 40 turn). Ovaries were collected (n=16) from inid-sequence hens maintained on a long-day photoschedule (16h of light:8 h of darkness). All follicles removed were dissected into individual granulosa and thecal layers. RNA was extracted and cDNA synthesized. Real-time quantitative PCR was used to quantify the expression of niRNA encoding betaglycan, activin receptor (ActR) subtypes (type-I, -IIA and -IIB) and glyceraldehyde-3-phosphate dehydrogenase (GAPDH); receptor expression data were normalized to GAPDH expression. Detectable levels of ActRI, -IIA and -IIB and the inhibin co-receptor (betaglycan) expression were found in all granulosa and thecal layers analysed. Granulosa ActRI mRNA peaked (P < 0(.)05) in 8-9(.)9 mm follicles, whereas ActRIIA rose significantly from 6-7(.)9 mm to 8-9(.)9 nun, before filling to F3/2; levels then rose sharply (3-fold) to F1 levels. Granulosa betaglycan niRNA expression rose 3-fold from 4-5(.)9 min to 8-9(.)9 mm, before falling 4-fold to F3/2; levels then rose sharply (4-fold) to F1 levels. ActRIIB levels did not vary significantly during follicular development. Thecal ActRI mRNA expression was similar from 4-7(.)9 mm then decreased significantly to a nadir at the F4 position, before increasing 2-fold to the F1 (P < 0(.)05). Although thecal ActRIIB and -IIA expression did not vary significantly from 4 nim to F3, ActRIIB expression increased significantly (2-fold) from F3 to F1 and ActIIA, increased 22-fold from F2 to F1 (P < 0(.)05). Thecal betaglycan fell to a nadir at F6 after follicle selection; levels then increased significantly to F2, before filling similar to 50% in the F I. In all follicles studied expression of betaglycan and ActRI (granulosa: 1-0(.)65, P < 0-001, n=144/group; theca: r=0(.)49, P < 0-001, n=144/group) was well correlated. No significant correlations were identified between betaglycan and ActRIIA or -IIB. Considering all follicles analysed, granulosa mRNA expression of betaglycan, ActRI ActRIIA and ActRIIB were all significantly lower than in corresponding thecal tissue (betaglycan, 11(.)4-fold; ActRIIB, 5(.)1-fold; ActR(.) 3-8-fold: ActRIIA, 2(.)8-fold). The co-localization of type-I and -II activin receptors and betaglycan on granulosa and thecal cells are consistent with a local auto/paracrine role of inhibins and activins in modulating ovarian follicle development, selection and progression in the domestic fowl.

Evolutionary fuzzy clustering of relational data

This paper is concerned with the computational efficiency of fuzzy clustering algorithms when the data set to be clustered is described by a proximity matrix only (relational data) and the number of clusters must be automatically estimated from such data. A fuzzy variant of an evolutionary algorithm for relational clustering is derived and compared against two systematic (pseudo-exhaustive) approaches that can also be used to automatically estimate the number of fuzzy clusters in relational data. An extensive collection of experiments involving 18 artificial and two real data sets is reported and analyzed. (C) 2011 Elsevier B.V. All rights reserved.

Investigation of a new GRASP-based clustering algorithm applied to biological data

A large amount of biological data has been produced in the last years. Important knowledge can be extracted from these data by the use of data analysis techniques. Clustering plays an important role in data analysis, by organizing similar objects from a dataset into meaningful groups. Several clustering algorithms have been proposed in the literature. However, each algorithm has its bias, being more adequate for particular datasets. This paper presents a mathematical formulation to support the creation of consistent clusters for biological data. Moreover. it shows a clustering algorithm to solve this formulation that uses GRASP (Greedy Randomized Adaptive Search Procedure). We compared the proposed algorithm with three known other algorithms. The proposed algorithm presented the best clustering results confirmed statistically. (C) 2009 Elsevier Ltd. All rights reserved.

Gene expression in placentation of farm animals: An overview of gene function during development

Eutherian mammals share a common ancestor that evolved into two main placental types, i.e., hemotrophic (e.g., human and mouse) and histiotrophic (e.g., farm animals), which differ in invasiveness. Pregnancies initiated with assisted reproductive techniques (ART) in farm animals are at increased risk of failure; these losses were associated with placental defects, perhaps due to altered gene expression. Developmentally regulated genes in the placenta seem highly phylogenetically conserved, whereas those expressed later in pregnancy are more species-specific. To elucidate differences between hemotrophic and epitheliochorial placentae, gene expression data were compiled from microarray studies of bovine placental tissues at various stages of pregnancy. Moreover, an in silico subtractive library was constructed based on homology of bovine genes to the database of zebrafish - a nonplacental vertebrate. In addition, the list of placental preferentially expressed genes for the human and mouse were collected using bioinformatics tools (Tissue-specific Gene Expression and Regulation [TiGER] - for humans, and tissue-specific genes database (TiSGeD) - for mice and humans). Humans, mice, and cattle shared 93 genes expressed in their placentae. Most of these were related to immune function (based on analysis of gene ontology). Cattle and women shared expression of 23 genes, mostly related to hormonal activity, whereas mice and women shared 16 genes (primarily sexual differentiation and glycoprotein biology). Because the number of genes expressed by the placentae of both cattle and mice were similar (based on cluster analysis), we concluded that both cattle and mice were suitable models to study the biology of the human placenta. (C) 2011 Elsevier B.V. All rights reserved.

Molecular expression profile reveals potential biomarkers and therapeutic targets in canine endometrial lesions

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

‘Gene shaving’ as a method for identifying distinct sets of genes with similar expression patterns

Background: Large gene expression studies, such as those conducted using DNA arrays, often provide millions of different pieces of data. To address the problem of analyzing such data, we describe a statistical method, which we have called ‘gene shaving’. The method identifies subsets of genes with coherent expression patterns and large variation across conditions. Gene shaving differs from hierarchical clustering and other widely used methods for analyzing gene expression studies in that genes may belong to more than one cluster, and the clustering may be supervised by an outcome measure. The technique can be ‘unsupervised’, that is, the genes and samples are treated as unlabeled, or partially or fully supervised by using known properties of the genes or samples to assist in finding meaningful groupings. Results: We illustrate the use of the gene shaving method to analyze gene expression measurements made on samples from patients with diffuse large B-cell lymphoma. The method identifies a small cluster of genes whose expression is highly predictive of survival. Conclusions: The gene shaving method is a potentially useful tool for exploration of gene expression data and identification of interesting clusters of genes worth further investigation.

Assessing the gain of biological data integration in gene networks inference

Background: A current challenge in gene annotation is to define the gene function in the context of the network of relationships instead of using single genes. The inference of gene networks (GNs) has emerged as an approach to better understand the biology of the system and to study how several components of this network interact with each other and keep their functions stable. However, in general there is no sufficient data to accurately recover the GNs from their expression levels leading to the curse of dimensionality, in which the number of variables is higher than samples. One way to mitigate this problem is to integrate biological data instead of using only the expression profiles in the inference process. Nowadays, the use of several biological information in inference methods had a significant increase in order to better recover the connections between genes and reduce the false positives. What makes this strategy so interesting is the possibility of confirming the known connections through the included biological data, and the possibility of discovering new relationships between genes when observed the expression data. Although several works in data integration have increased the performance of the network inference methods, the real contribution of adding each type of biological information in the obtained improvement is not clear. Methods: We propose a methodology to include biological information into an inference algorithm in order to assess its prediction gain by using biological information and expression profile together. We also evaluated and compared the gain of adding four types of biological information: (a) protein-protein interaction, (b) Rosetta stone fusion proteins, (c) KEGG and (d) KEGG+GO. Results and conclusions: This work presents a first comparison of the gain in the use of prior biological information in the inference of GNs by considering the eukaryote (P. falciparum) organism. Our results indicates that information based on direct interaction can produce a higher improvement in the gain than data about a less specific relationship as GO or KEGG. Also, as expected, the results show that the use of biological information is a very important approach for the improvement of the inference. We also compared the gain in the inference of the global network and only the hubs. The results indicates that the use of biological information can improve the identification of the most connected proteins.

In Silico Analysis and Immunohistochemical Characterization of NaPi2b Protein Expression in Ovarian Carcinoma With Monoclonal Antibody Mx35

Introduction: Ovarian adenocarcinoma is frequently detected at the late stage, when therapy efficacy is limited and death occurs in up to 50% of the cases. A potential novel treatment for this disease is a monoclonal antibody that recognizes phosphate transporter sodium-dependent phosphate transporter protein 2b (NaPi2b). Materials and Methods: To better understand the expression of this protein in different histologic types of ovarian carcinomas, we immunostained 50 tumor samples with anti-NaPi2b monoclonal antibody MX35 and, in parallel, we assessed the expression of the gene encoding NaPi2b (SCL34A2) by in silico analysis of microarray data. Results: Both approaches detected higher expression of NaPi2b (SCL34A2) in ovarian carcinoma than in normal tissue. Moreover, a comprehensive analysis indicates that SCL34A2 is the only gene of the several phosphate transporters genes whose expression differentiates normal from carcinoma samples, suggesting it might exert a major role in ovarian carcinomas. Immunohistochemical and mRNA expression data have also shown that 2 histologic subtypes of ovarian carcinoma express particularly high levels of NaPi2b: serous and clear cell adenocarcinomas. Serous adenocarcinomas are the most frequent, contrasting with clear cell carcinomas, rare, and with worse prognosis. Conclusion: This identification of subgroups of patients expressing NaPi2b may be important in selecting cohorts who most likely should be included in future clinical trials, as a recently generated humanized version of MX35 has been developed.

Modeling gene expression regulatory networks with the sparse vector autoregressive model

Abstract Background To understand the molecular mechanisms underlying important biological processes, a detailed description of the gene products networks involved is required. In order to define and understand such molecular networks, some statistical methods are proposed in the literature to estimate gene regulatory networks from time-series microarray data. However, several problems still need to be overcome. Firstly, information flow need to be inferred, in addition to the correlation between genes. Secondly, we usually try to identify large networks from a large number of genes (parameters) originating from a smaller number of microarray experiments (samples). Due to this situation, which is rather frequent in Bioinformatics, it is difficult to perform statistical tests using methods that model large gene-gene networks. In addition, most of the models are based on dimension reduction using clustering techniques, therefore, the resulting network is not a gene-gene network but a module-module network. Here, we present the Sparse Vector Autoregressive model as a solution to these problems. Results We have applied the Sparse Vector Autoregressive model to estimate gene regulatory networks based on gene expression profiles obtained from time-series microarray experiments. Through extensive simulations, by applying the SVAR method to artificial regulatory networks, we show that SVAR can infer true positive edges even under conditions in which the number of samples is smaller than the number of genes. Moreover, it is possible to control for false positives, a significant advantage when compared to other methods described in the literature, which are based on ranks or score functions. By applying SVAR to actual HeLa cell cycle gene expression data, we were able to identify well known transcription factor targets. Conclusion The proposed SVAR method is able to model gene regulatory networks in frequent situations in which the number of samples is lower than the number of genes, making it possible to naturally infer partial Granger causalities without any a priori information. In addition, we present a statistical test to control the false discovery rate, which was not previously possible using other gene regulatory network models.

Expression analysis and in silico characterization of intronic long noncoding RNAs in renal cell carcinoma: emerging functional associations

Abstract Background Intronic and intergenic long noncoding RNAs (lncRNAs) are emerging gene expression regulators. The molecular pathogenesis of renal cell carcinoma (RCC) is still poorly understood, and in particular, limited studies are available for intronic lncRNAs expressed in RCC Methods Microarray experiments were performed with custom-designed arrays enriched with probes for lncRNAs mapping to intronic genomic regions. Samples from 18 primary RCC tumors and 11 nontumor adjacent matched tissues were analyzed. Meta-analyses were performed with microarray expression data from three additional human tissues (normal liver, prostate tumor and kidney nontumor samples), and with large-scale public data for epigenetic regulatory marks and for evolutionarily conserved sequences. Results A signature of 29 intronic lncRNAs differentially expressed between RCC and nontumor samples was obtained (false discovery rate (FDR) <5%). A signature of 26 intronic lncRNAs significantly correlated with the RCC five-year patient survival outcome was identified (FDR <5%, p-value ≤0.01). We identified 4303 intronic antisense lncRNAs expressed in RCC, of which 22% were significantly (p <0.05) cis correlated with the expression of the mRNA in the same locus across RCC and three other human tissues. Gene Ontology (GO) analysis of those loci pointed to 'regulation of biological processes’ as the main enriched category. A module map analysis of the protein-coding genes significantly (p <0.05) trans correlated with the 20% most abundant lncRNAs, identified 51 enriched GO terms (p <0.05). We determined that 60% of the expressed lncRNAs are evolutionarily conserved. At the genomic loci containing the intronic RCC-expressed lncRNAs, a strong association (p <0.001) was found between their transcription start sites and genomic marks such as CpG islands, RNA Pol II binding and histones methylation and acetylation. Conclusion Intronic antisense lncRNAs are widely expressed in RCC tumors. Some of them are significantly altered in RCC in comparison with nontumor samples. The majority of these lncRNAs is evolutionarily conserved and possibly modulated by epigenetic modifications. Our data suggest that these RCC lncRNAs may contribute to the complex network of regulatory RNAs playing a role in renal cell malignant transformation.

Expression profiling of stem cell-related genes in neoadjuvant-treated gastric cancer: a NOTCH2, GSK3B and β-catenin gene signature predicts survival

Cancer stem cell (CSC) based gene expression signatures are associated with prognosis in various tumour types and CSCs are suggested to be particularly drug resistant. The aim of our study was first, to determine the prognostic significance of CSC-related gene expression in residual tumour cells of neoadjuvant-treated gastric cancer (GC) patients. Second, we wished to examine, whether expression alterations between pre- and post-therapeutic tumour samples exist, consistent with an enrichment of drug resistant tumour cells. The expression of 44 genes was analysed in 63 formalin-fixed, paraffin embedded tumour specimens with partial tumour regression (10-50% residual tumour) after neoadjuvant chemotherapy by quantitative real time PCR low-density arrays. A signature of combined GSK3B(high), β-catenin (CTNNB1)(high) and NOTCH2(low) expression was strongly correlated with better patient survival (p<0.001). A prognostic relevance of these genes was also found analysing publically available gene expression data. The expression of 9 genes was compared between pre-therapeutic biopsies and post-therapeutic resected specimens. A significant post-therapeutic increase in NOTCH2, LGR5 and POU5F1 expression was found in tumours with different tumour regression grades. No significant alterations were observed for GSK3B and CTNNB1. Immunohistochemical analysis demonstrated a chemotherapy-associated increase in the intensity of NOTCH2 staining, but not in the percentage of NOTCH2. Taken together, the GSK3B, CTNNB1 and NOTCH2 expression signature is a novel, promising prognostic parameter for GC. The results of the differential expression analysis indicate a prominent role for NOTCH2 and chemotherapy resistance in GC, which seems to be related to an effect of the drugs on NOTCH2 expression rather than to an enrichment of NOTCH2 expressing tumour cells.

Classification and selection of biomarkers in genomic data using LASSO

High-throughput gene expression technologies such as microarrays have been utilized in a variety of scientific applications. Most of the work has been on assessing univariate associations between gene expression with clinical outcome (variable selection) or on developing classification procedures with gene expression data (supervised learning). We consider a hybrid variable selection/classification approach that is based on linear combinations of the gene expression profiles that maximize an accuracy measure summarized using the receiver operating characteristic curve. Under a specific probability model, this leads to consideration of linear discriminant functions. We incorporate an automated variable selection approach using LASSO. An equivalence between LASSO estimation with support vector machines allows for model fitting using standard software. We apply the proposed method to simulated data as well as data from a recently published prostate cancer study.

An introduction to low-level analysis methods of DNA microarray data

This article gives an overview over the methods used in the low--level analysis of gene expression data generated using DNA microarrays. This type of experiment allows to determine relative levels of nucleic acid abundance in a set of tissues or cell populations for thousands of transcripts or loci simultaneously. Careful statistical design and analysis are essential to improve the efficiency and reliability of microarray experiments throughout the data acquisition and analysis process. This includes the design of probes, the experimental design, the image analysis of microarray scanned images, the normalization of fluorescence intensities, the assessment of the quality of microarray data and incorporation of quality information in subsequent analyses, the combination of information across arrays and across sets of experiments, the discovery and recognition of patterns in expression at the single gene and multiple gene levels, and the assessment of significance of these findings, considering the fact that there is a lot of noise and thus random features in the data. For all of these components, access to a flexible and efficient statistical computing environment is an essential aspect.