Methods for interpreting lists of affected genes obstained in a DNA microarray experiment

Background - The aim of this paper was to describe and compare the methods used and the results obtained by the participants in a joint EADGENE (European Animal Disease Genomic Network of Excellence) and SABRE (Cutting Edge Genomics for Sustainable Animal Breeding) workshop focusing on post analysis of microarray data. The participating groups were provided with identical lists of microarray probes, including test statistics for three different contrasts, and the normalised log-ratios for each array, to be used as the starting point for interpreting the affected probes. The data originated from a microarray experiment conducted to study the host reactions in broilers occurring shortly after a secondary challenge with either a homologous or heterologous species of Eimeria. Results - Several conceptually different analytical approaches, using both commercial and public available software, were applied by the participating groups. The following tools were used: Ingenuity Pathway Analysis, MAPPFinder, LIMMA, GOstats, GOEAST, GOTM, Globaltest, TopGO, ArrayUnlock, Pathway Studio, GIST and AnnotationDbi. The main focus of the approaches was to utilise the relation between probes/genes and their gene ontology and pathways to interpret the affected probes/genes. The lack of a well-annotated chicken genome did though limit the possibilities to fully explore the tools. The main results from these analyses showed that the biological interpretation is highly dependent on the statistical method used but that some common biological conclusions could be reached. Conclusion - It is highly recommended to test different analytical methods on the same data set and compare the results to obtain a reliable biological interpretation of the affected genes in a DNA microarray experimen

Segregation of Regulatory Polymorphisms with Effects on the Gluteus Medius Transcriptome in a Purebred Pig Population

Background: The main goal of the present study was to analyse the genetic architecture of mRNA expression in muscle, a tissue with an outmost economic importance for pig breeders. Previous studies have used F2 crosses to detect porcine expression QTL (eQTL), so they contributed with data that mostly represents the between-breed component of eQTL variation. Herewith, we have analysed eQTL segregation in an outbred Duroc population using two groups of animals with divergent fatness profiles. This approach is particularly suitable to analyse the within-breed component of eQTL variation, with a special emphasis on loci involved in lipid metabolism. Methodology/Principal Findings: GeneChip Porcine Genome arrays (Affymetrix) were used to determine the mRNA expression levels of gluteus medius samples from 105 Duroc barrows. A whole-genome eQTL scan was carried out with a panel of 116 microsatellites. Results allowed us to detect 613 genome-wide significant eQTL unevenly distributed across the pig genome. A clear predominance of trans- over cis-eQTL, was observed. Moreover, 11 trans-regulatory hotspots affecting the expression levels of four to 16 genes were identified. A Gene Ontology study showed that regulatory polymorphisms affected the expression of muscle development and lipid metabolism genes. A number of positional concordances between eQTL and lipid trait QTL were also found, whereas limited evidence of a linear relationship between muscle fat deposition and mRNA levels of eQTL regulated genes was obtained. Conclusions/Significance: Our data provide substantial evidence that there is a remarkable amount of within-breed genetic variation affecting muscle mRNA expression. Most of this variation acts in trans and influences biological processes related with muscle development, lipid deposition and energy balance. The identification of the underlying causal mutations and the ascertainment of their effects on phenotypes would allow gaining a fundamental perspective about how complex traits are built at the molecular level

Transcriptome profiling of sheep granulosa cells and oocytes during early follicular development obtained by Laser Capture Microdissection

<p>Abstract</p> <p>Background</p> <p>Successful achievement of early folliculogenesis is crucial for female reproductive function. The process is finely regulated by cell-cell interactions and by the coordinated expression of genes in both the oocyte and in granulosa cells. Despite many studies, little is known about the cell-specific gene expression driving early folliculogenesis. The very small size of these follicles and the mixture of types of follicles within the developing ovary make the experimental study of isolated follicular components very difficult.</p> <p>The recently developed laser capture microdissection (LCM) technique coupled with microarray experiments is a promising way to address the molecular profile of pure cell populations. However, one main challenge was to preserve the RNA quality during the isolation of single cells or groups of cells and also to obtain sufficient amounts of RNA.</p> <p>Using a new LCM method, we describe here the separate expression profiles of oocytes and follicular cells during the first stages of sheep folliculogenesis.</p> <p>Results</p> <p>We developed a new tissue fixation protocol ensuring efficient single cell capture and RNA integrity during the microdissection procedure. Enrichment in specific cell types was controlled by qRT-PCR analysis of known genes: six oocyte-specific genes (<it>SOHLH2</it>, <it>MAEL</it>, <it>MATER</it>, <it>VASA</it>, <it>GDF9</it>, <it>BMP15</it>) and three granulosa cell-specific genes (<it>KL</it>, <it>GATA4</it>, <it>AMH</it>).</p> <p>A global gene expression profile for each follicular compartment during early developmental stages was identified here for the first time, using a bovine Affymetrix chip. Most notably, the granulosa cell dataset is unique to date. The comparison of oocyte vs. follicular cell transcriptomes revealed 1050 transcripts specific to the granulosa cell and 759 specific to the oocyte.</p> <p>Functional analyses allowed the characterization of the three main cellular events involved in early folliculogenesis and confirmed the relevance and potential of LCM-derived RNA.</p> <p>Conclusions</p> <p>The ovary is a complex mixture of different cell types. Distinct cell populations need therefore to be analyzed for a better understanding of their potential interactions. LCM and microarray analysis allowed us to identify novel gene expression patterns in follicular cells at different stages and in oocyte populations.</p

Methods for interpreting lists of affected genes obtained in a DNA microarray experiment

BACKGROUND: The aim of this paper was to describe and compare the methods used and the results obtained by the participants in a joint EADGENE (European Animal Disease Genomic Network of Excellence) and SABRE (Cutting Edge Genomics for Sustainable Animal Breeding) workshop focusing on post analysis of microarray data. The participating groups were provided with identical lists of microarray probes, including test statistics for three different contrasts, and the normalised log-ratios for each array, to be used as the starting point for interpreting the affected probes. The data originated from a microarray experiment conducted to study the host reactions in broilers occurring shortly after a secondary challenge with either a homologous or heterologous species of Eimeria. RESULTS: Several conceptually different analytical approaches, using both commercial and public available software, were applied by the participating groups. The following tools were used: Ingenuity Pathway Analysis, MAPPFinder, LIMMA, GOstats, GOEAST, GOTM, Globaltest, TopGO, ArrayUnlock, Pathway Studio, GIST and AnnotationDbi. The main focus of the approaches was to utilise the relation between probes/genes and their gene ontology and pathways to interpret the affected probes/genes. The lack of a well-annotated chicken genome did though limit the possibilities to fully explore the tools. The main results from these analyses showed that the biological interpretation is highly dependent on the statistical method used but that some common biological conclusions could be reached. CONCLUSION: It is highly recommended to test different analytical methods on the same data set and compare the results to obtain a reliable biological interpretation of the affected genes in a DNA microarray experimen

RH mapping and real time evaluation of seven porcine candidate genes for meat quality previously identified by means of microarray

Pork is one of the most widely consumed meats worldwide.The selection for efficiency of ham production and quality is of major importance in modern Italian pig production. Gene expression profiles between two pools of six individuals constituting the extreme tails of the meat quality Gaussian distribution from 100 pure breed animals has been previously studied by means of microarray. Among 437 differentially expressed ESTs (Expressed Sequence Tags), seven could be considered candidate genes for meat quality. In fact, these genes were upregulated in animals of the positive tail and they were involved in insulin and muscle metabolism. Therefore, these genes were chosen to be evaluated by quantitative Real-Time PCR. IPP1 and HPRT genes, previously tested in a swine trascriptome analysis, were used as reference genes. These seven genes were also mapped on the swine chromosome using the IMpRH7000 panel. Two-point and multipoint analyses were carried out with assignment of markers on the current IMpRH map with LOD=6.0. At the same time, the PigQTLdb database gave positions of the QTL"s flanking markers. Our candidate genes were located in the same regions of putative QTLs related to meat quality

The GroupHouseNet COST Action: exploiting European synergy to reduce feather pecking in laying hens

Broilers have been selected for growth related characteristics, which are negatively correlated to reproductive traits. This genetic background creates challenges in broiler breeders, as the hens do not make optimal use of the nests provided. This project aims to investigate what factors determine nesting behaviour, i.e. where a broiler breeder hen prefers to lay her eggs. Factors such as genetic background, social interactions, physical characteristics of the nest and climate might interfere with the natural nesting behaviour of the hen. Also fundamental trade-offs between different motivations, such as hunger, comfort and safety, might influence nesting behaviour. Behaviour and use of space will be measured in experimental set-ups in order to gain insight in the importance of different system components. This knowledge will be used to optimise housing conditions and develop strategies that stimulate the hen to lay her egg in the nest. The performance of this improved system will be tested in field experiments to investigate the transferability of results from experimental to field conditions