The Efficacy of Sunitinib Treatment of Renal Cancer Cells Is Associated with the Protein PHAX In Vitro.

Anti-angiogenic agents, such as the multi-tyrosine kinase inhibitor sunitinib, are key first line therapies for metastatic clear cell renal cell carcinoma (ccRCC), but their mechanism of action is not fully understood. Here, we take steps towards validating a computational prediction based on differential transcriptome network analysis that phosphorylated adapter RNA export protein (PHAX) is associated with sunitinib drug treatment. The regulatory impact factor differential network algorithm run on patient tissue samples suggests PHAX is likely an important regulator through changes in genome-wide network connectivity. Immunofluorescence staining of patient tumours showed strong localisation of PHAX to the microvasculature consistent with the anti-angiogenic effect of sunitinib. In normal kidney tissue, PHAX protein abundance was low but increased with tumour grade (G1 vs. G3/4; p < 0.01), consistent with a possible role in cancer progression. In organ culture, ccRCC cells had higher levels of PHAX protein expression than normal kidney cells, and sunitinib increased PHAX protein expression in a dose dependent manner (untreated vs. 100 µM; p < 0.05). PHAX knockdown in a ccRCC organ culture model impacted the ability of sunitinib to cause cancer cell death (p < 0.0001 untreated vs. treated), suggesting a role for PHAX in mediating the efficacy of sunitinib.

Combining multi-OMICs information to identify key-regulator genes for pleiotropic effect on fertility and production traits in beef cattle.

The identification of biological processes related to the regulation of complex traits is a difficult task. Commonly, complex traits are regulated through a multitude of genes contributing each to a small part of the total genetic variance. Additionally, some loci can simultaneously regulate several complex traits, a phenomenon defined as pleiotropy. The lack of understanding on the biological processes responsible for the regulation of these traits results in the decrease of selection efficiency and the selection of undesirable hitchhiking effects. The identification of pleiotropic key-regulator genes can assist in developing important tools for investigating biological processes underlying complex traits. A multi-breed and multi-OMICs approach was applied to study the pleiotropic effects of key-regulator genes using three independent beef cattle populations evaluated for fertility traits. A pleiotropic map for 32 traits related to growth, feed efficiency, carcass and meat quality, and reproduction was used to identify genes shared among the different populations and breeds in pleiotropic regions. Furthermore, data-mining analyses were performed using the Cattle QTL database (CattleQTLdb) to identify the QTL category annotated in the regions around the genes shared among breeds. This approach allowed the identification of a main gene network (composed of 38 genes) shared among breeds. This gene network was significantly associated with thyroid activity, among other biological processes, and displayed a high regulatory potential. In addition, it was possible to identify genes with pleiotropic effects related to crucial biological processes that regulate economically relevant traits associated with fertility, production and health, such as MYC, PPARG, GSK3B, TG and IYD genes. These genes will be further investigated to better understand the biological processes involved in the expression of complex traits and assist in the identification of functional variants associated with undesirable phenotypes, such as decreased fertility, poor feed efficiency and negative energetic balance.

STAT6, PBX2, and PBRM1 Emerge as Predicted Regulators of 452 Differentially Expressed Genes Associated With Puberty in Brahman Heifers.

The liver plays a central role in metabolism and produces important hormones. Hepatic estrogen receptors and the release of insulin-like growth factor 1 (IGF1) are critical links between liver function and the reproductive system. However, the role of liver in pubertal development is not fully understood. To explore this question, we applied transcriptomic analyses to liver samples of pre- and post-pubertal Brahman heifers and identified differentially expressed (DE) genes and genes encoding transcription factors (TFs). Differential expression of genes suggests potential biological mechanisms and pathways linking liver function to puberty. The analyses identified 452 DE genes and 82 TF with significant contribution to differential gene expression by using a regulatory impact factor metric. Brain-derived neurotrophic factor was observed as the most down-regulated gene (P = 0.003) in post-pubertal heifers and we propose this gene influences pubertal development in Brahman heifers. Additionally, co-expression network analysis provided evidence for three TF as key regulators of liver function during pubertal development: the signal transducer and activator of transcription 6, PBX homeobox 2, and polybromo 1. Pathway enrichment analysis identified transforming growth factor-beta and Wnt signaling pathways as significant annotation terms for the list of DE genes and TF in the co-expression network. Molecular information regarding genes and pathways described in this work are important to further our understanding of puberty onset in Brahman heifers.

Progesterone signalling in broiler skeletal muscle is associated with divergent feed efficiency.

BACKGROUND: We contrast the pectoralis muscle transcriptomes of broilers selected from within a single genetic line expressing divergent feed efficiency (FE) in an effort to improve our understanding of the mechanistic basis of FE. RESULTS: Application of a virtual muscle model to gene expression data pointed to a coordinated reduction in slow twitch muscle isoforms of the contractile apparatus (MYH15, TPM3, MYOZ2, TNNI1, MYL2, MYOM3, CSRP3, TNNT2), consistent with diminishment in associated slow machinery (myoglobin and phospholamban) in the high FE animals. These data are in line with the repeated transition from red slow to white fast muscle fibres observed in agricultural species selected on mass and FE. Surprisingly, we found that the expression of 699 genes encoding the broiler mitoproteome is modestly-but significantly-biased towards the high FE group, suggesting a slightly elevated mitochondrial content. This is contrary to expectation based on the slow muscle isoform data and theoretical physiological capacity arguments. Reassuringly, the extreme 40 most DE genes can successfully cluster the 12 individuals into the appropriate FE treatment group. Functional groups contained in this DE gene list include metabolic proteins (including opposing patterns of CA3 and CA4), mitochondrial proteins (CKMT1A), oxidative status (SEPP1, HIG2A) and cholesterol homeostasis (APOA1, INSIG1). We applied a differential network method (Regulatory Impact Factors) whose aim is to use patterns of differential co-expression to detect regulatory molecules transcriptionally rewired between the groups. This analysis clearly points to alterations in progesterone signalling (via the receptor PGR) as the major driver. We show the progesterone receptor localises to the mitochondria in a quail muscle cell line. CONCLUSIONS: Progesterone is sometimes used in the cattle industry in exogenous hormone mixes that lead to a ~20% increase in FE. Because the progesterone receptor can localise to avian mitochondria, our data continue to point to muscle mitochondrial metabolism as an important component of the phenotypic expression of variation in broiler FE.

Systems biology analysis merging phenotype, metabolomic and genomic data identifies Non-SMC Condensin I Complex, Subunit G (NCAPG) and cellular maintenance processes as major contributors to genetic variability in bovine feed efficiency.

Feed efficiency is a paramount factor for livestock economy. Previous studies had indicated a substantial heritability of several feed efficiency traits. In our study, we investigated the genetic background of residual feed intake, a commonly used parameter of feed efficiency, in a cattle resource population generated from crossing dairy and beef cattle. Starting from a whole genome association analysis, we subsequently performed combined phenotype-metabolome-genome analysis taking a systems biology approach by inferring gene networks based on partial correlation and information theory approaches. Our data about biological processes enriched with genes from the feed efficiency network suggest that genetic variation in feed efficiency is driven by genetic modulation of basic processes relevant to general cellular functions. When looking at the predicted upstream regulators from the feed efficiency network, the Tumor Protein P53 (TP53) and Transforming Growth Factor beta 1 (TGFB1) genes stood out regarding significance of overlap and number of target molecules in the data set. These results further support the hypothesis that TP53 is a major upstream regulator for genetic variation of feed efficiency. Furthermore, our data revealed a significant effect of both, the Non-SMC Condensin I Complex, Subunit G (NCAPG) I442M (rs109570900) and the Growth /differentiation factor 8 (GDF8) Q204X (rs110344317) loci, on residual feed intake and feed conversion. For both loci, the growth promoting allele at the onset of puberty was associated with a negative, but favorable effect on residual feed intake. The elevated energy demand for increased growth triggered by the NCAPG 442M allele is obviously not fully compensated for by an increased efficiency in converting feed into body tissue. As a consequence, the individuals carrying the NCAPG 442M allele had an additional demand for energy uptake that is reflected by the association of the allele with increased daily energy intake as observed in our study.

INsPeCT: INtegrative Platform for Cancer Transcriptomics.

The emergence of transcriptomics, fuelled by high-throughput sequencing technologies, has changed the nature of cancer research and resulted in a massive accumulation of data. Computational analysis, integration, and data visualization are now major bottlenecks in cancer biology and translational research. Although many tools have been brought to bear on these problems, their use remains unnecessarily restricted to computational biologists, as many tools require scripting skills, data infrastructure, and powerful computational facilities. New user-friendly, integrative, and automated analytical approaches are required to make computational methods more generally useful to the research community. Here we present INsPeCT (INtegrative Platform for Cancer Transcriptomics), which allows users with basic computer skills to perform comprehensive in-silico analyses of microarray, ChIP-seq, and RNA-seq data. INsPeCT supports the selection of interesting genes for advanced functional analysis. Included in its automated workflows are (i) a novel analytical framework, RMaNI (regulatory module network inference), which supports the inference of cancer subtype-specific transcriptional module networks and the analysis of modules; and (ii) WGCNA (weighted gene co-expression network analysis), which infers modules of highly correlated genes across microarray samples, associated with sample traits, eg survival time. INsPeCT is available free of cost from Bioinformatics Resource Australia-EMBL and can be accessed at http://inspect.braembl.org.au.

A systems biology approach using metabolomic data reveals genes and pathways interacting to modulate divergent growth in cattle.

BACKGROUND: Systems biology enables the identification of gene networks that modulate complex traits. Comprehensive metabolomic analyses provide innovative phenotypes that are intermediate between the initiator of genetic variability, the genome, and raw phenotypes that are influenced by a large number of environmental effects. The present study combines two concepts, systems biology and metabolic analyses, in an approach without prior functional hypothesis in order to dissect genes and molecular pathways that modulate differential growth at the onset of puberty in male cattle. Furthermore, this integrative strategy was applied to specifically explore distinctive gene interactions of non-SMC condensin I complex, subunit G (NCAPG) and myostatin (GDF8), known modulators of pre- and postnatal growth that are only partially understood for their molecular pathways affecting differential body weight. RESULTS: Our study successfully established gene networks and interacting partners affecting growth at the onset of puberty in cattle. We demonstrated the biological relevance of the created networks by comparison to randomly created networks. Our data showed that GnRH (Gonadotropin-releasing hormone) signaling is associated with divergent growth at the onset of puberty and revealed two highly connected hubs, BTC and DGKH, within the network. Both genes are known to directly interact with the GnRH signaling pathway. Furthermore, a gene interaction network for NCAPG containing 14 densely connected genes revealed novel information concerning the functional role of NCAPG in divergent growth. CONCLUSIONS: Merging both concepts, systems biology and metabolomic analyses, successfully yielded new insights into gene networks and interacting partners affecting growth at the onset of puberty in cattle. Genetic modulation in GnRH signaling was identified as key modifier of differential cattle growth at the onset of puberty. In addition, the benefit of our innovative concept without prior functional hypothesis was demonstrated by data suggesting that NCAPG might contribute to vascular smooth muscle contraction by indirect effects on the NO pathway via modulation of arginine metabolism. Our study shows for the first time in cattle that integration of genetic, physiological and metabolomics data in a systems biology approach will enable (or contribute to) an improved understanding of metabolic and gene networks and genotype-phenotype relationships.

Gene regulatory network inference: evaluation and application to ovarian cancer allows the prioritization of drug targets.

BACKGROUND: Altered networks of gene regulation underlie many complex conditions, including cancer. Inferring gene regulatory networks from high-throughput microarray expression data is a fundamental but challenging task in computational systems biology and its translation to genomic medicine. Although diverse computational and statistical approaches have been brought to bear on the gene regulatory network inference problem, their relative strengths and disadvantages remain poorly understood, largely because comparative analyses usually consider only small subsets of methods, use only synthetic data, and/or fail to adopt a common measure of inference quality. METHODS: We report a comprehensive comparative evaluation of nine state-of-the art gene regulatory network inference methods encompassing the main algorithmic approaches (mutual information, correlation, partial correlation, random forests, support vector machines) using 38 simulated datasets and empirical serous papillary ovarian adenocarcinoma expression-microarray data. We then apply the best-performing method to infer normal and cancer networks. We assess the druggability of the proteins encoded by our predicted target genes using the CancerResource and PharmGKB webtools and databases. RESULTS: We observe large differences in the accuracy with which these methods predict the underlying gene regulatory network depending on features of the data, network size, topology, experiment type, and parameter settings. Applying the best-performing method (the supervised method SIRENE) to the serous papillary ovarian adenocarcinoma dataset, we infer and rank regulatory interactions, some previously reported and others novel. For selected novel interactions we propose testable mechanistic models linking gene regulation to cancer. Using network analysis and visualization, we uncover cross-regulation of angiogenesis-specific genes through three key transcription factors in normal and cancer conditions. Druggabilty analysis of proteins encoded by the 10 highest-confidence target genes, and by 15 genes with differential regulation in normal and cancer conditions, reveals 75% to be potential drug targets. CONCLUSIONS: Our study represents a concrete application of gene regulatory network inference to ovarian cancer, demonstrating the complete cycle of computational systems biology research, from genome-scale data analysis via network inference, evaluation of methods, to the generation of novel testable hypotheses, their prioritization for experimental validation, and discovery of potential drug targets.

Proteomic analysis of the abomasal mucosal response following infection by the nematode, Haemonchus contortus, in genetically resistant and susceptible sheep.

Sheep have a variable ability to resist gastrointestinal nematode infection, but the key factors mediating this response are poorly defined. Here we report the first large-scale application of quantitative proteomic technologies to define proteins that are differentially abundant between sheep selectively bred to have an enhanced (resistant) or reduced (susceptible) ability to eliminate nematodes. Samples were collected from the abomasal mucosa three days after experimental challenge with the nematode, Haemonchus contortus. This timing reflects the initial interaction of host and parasite, and the tissue represents the immediate interface. We identified and quantified more than 4400 unique proteins, of which 158 proteins showed >1.5 fold difference between the resistant and susceptible sheep. Trefoil factor 2, a member of RAS oncogene family (RAP1A) and ring finger protein 126 were amongst the proteins found to be highly abundant in the abomasal surface of resistant sheep, whereas adenosine deaminase and the gastrokine-3 like precursor were found at higher levels in susceptible sheep. Construction of gut proteome interaction networks identified mitochondrial function and energetic partitioning as important components of an effective nematode eliminating response. The differentially abundant proteins may be useful targets for phenotypic tests that aim to identify sheep with an enhanced ability to resist nematode infection.

Using regulatory and epistatic networks to extend the findings of a genome scan: identifying the gene drivers of pigmentation in merino sheep.

Extending genome wide association analysis by the inclusion of gene expression data may assist in the dissection of complex traits. We examined piebald, a pigmentation phenotype in both human and Merino sheep, by analysing multiple data types using a systems approach. First, a case control analysis of 49,034 ovine SNP was performed which confirmed a multigenic basis for the condition. We combined these results with gene expression data from five tissue types analysed with a skin-specific microarray. Promoter sequence analysis of differentially expressed genes allowed us to reverse-engineer a regulatory network. Likewise, by testing two-loci models derived from all pair-wise comparisons across piebald-associated SNP, we generated an epistatic network. At the intersection of both networks, we identified thirteen genes with insulin-like growth factor binding protein 7 (IGFBP7), platelet-derived growth factor alpha (PDGFRA) and the tetraspanin platelet activator CD9 at the kernel of the intersection. Further, we report a number of differentially expressed genes in regions containing highly associated SNP including ATRN, DOCK7, FGFR1OP, GLI3, SILV and TBX15. The application of network theory facilitated co-analysis of genetic variation with gene expression, recapitulated aspects of the known molecular biology of skin pigmentation and provided insights into the transcription regulation and epistatic interactions involved in piebald Merino sheep.

A Boolean-based systems biology approach to predict novel genes associated with cancer: Application to colorectal cancer.

BACKGROUND: Cancer has remarkable complexity at the molecular level, with multiple genes, proteins, pathways and regulatory interconnections being affected. We introduce a systems biology approach to study cancer that formally integrates the available genetic, transcriptomic, epigenetic and molecular knowledge on cancer biology and, as a proof of concept, we apply it to colorectal cancer. RESULTS: We first classified all the genes in the human genome into cancer-associated and non-cancer-associated genes based on extensive literature mining. We then selected a set of functional attributes proven to be highly relevant to cancer biology that includes protein kinases, secreted proteins, transcription factors, post-translational modifications of proteins, DNA methylation and tissue specificity. These cancer-associated genes were used to extract 'common cancer fingerprints' through these molecular attributes, and a Boolean logic was implemented in such a way that both the expression data and functional attributes could be rationally integrated, allowing for the generation of a guilt-by-association algorithm to identify novel cancer-associated genes. Finally, these candidate genes are interlaced with the known cancer-related genes in a network analysis aimed at identifying highly conserved gene interactions that impact cancer outcome. We demonstrate the effectiveness of this approach using colorectal cancer as a test case and identify several novel candidate genes that are classified according to their functional attributes. These genes include the following: 1) secreted proteins as potential biomarkers for the early detection of colorectal cancer (FXYD1, GUCA2B, REG3A); 2) kinases as potential drug candidates to prevent tumor growth (CDC42BPB, EPHB3, TRPM6); and 3) potential oncogenic transcription factors (CDK8, MEF2C, ZIC2). CONCLUSION: We argue that this is a holistic approach that faithfully mimics cancer characteristics, efficiently predicts novel cancer-associated genes and has universal applicability to the study and advancement of cancer research.

Chronic exposure to anabolic steroids induces the muscle expression of oxytocin and a more than fiftyfold increase in circulating oxytocin in cattle.

Molecular mechanisms in skeletal muscle associated with anabolic steroid treatment of cattle are unclear and we aimed to characterize transcriptional changes. Cattle were chronically exposed (68 ± 20 days) to a steroid hormone implant containing 200 mg trenbolone acetate and 20 mg estradiol (Revalor-H). Biopsy samples from 48 cattle (half treated) from longissimus dorsi (LD) muscle under local anesthesia were collected. Gene expression levels were profiled by microarray, covering 16,944 unique bovine genes: 121 genes were differentially expressed (DE) due to the implant (99.99% posterior probability of not being false positives). Among DE genes, a decrease in expression of a number of fat metabolism-associated genes, likely reflecting the lipid storage activity of intramuscular adipocytes, was observed. The expression of IGF1 and genes related to the extracellular matrix, slow twitch fibers, and cell cycle (including SOX8, a satellite cell marker) was increased in the treated muscle. Unexpectedly, a very large 21- (microarray) to 97 (real time quantitative PCR)-fold higher expression of the mRNA encoding the neuropeptide hormone oxytocin was observed in treated muscle. We also observed an ∼50-fold higher level of circulating oxytocin in the plasma of treated animals at the time of biopsy. Using a coexpression network strategy OXTR was identified as more likely than IGF1R to be a major mediator of the muscle response to Revalor-H. A re-investigation of in vivo cattle LD muscle samples during early to mid-fetal development identified a >128-fold increased expression of OXT, coincident with myofiber differentiation and fusion. We propose that oxytocin may be involved in mediating the anabolic effects of Revalor-H treatment.

Association weight matrix for the genetic dissection of puberty in beef cattle.

We describe a systems biology approach for the genetic dissection of complex traits based on applying gene network theory to the results from genome-wide associations. The associations of single-nucleotide polymorphisms (SNP) that were individually associated with a primary phenotype of interest, age at puberty in our study, were explored across 22 related traits. Genomic regions were surveyed for genes harboring the selected SNP. As a result, an association weight matrix (AWM) was constructed with as many rows as genes and as many columns as traits. Each {i, j} cell value in the AWM corresponds to the z-score normalized additive effect of the ith gene (via its neighboring SNP) on the jth trait. Columnwise, the AWM recovered the genetic correlations estimated via pedigree-based restricted maximum-likelihood methods. Rowwise, a combination of hierarchical clustering, gene network, and pathway analyses identified genetic drivers that would have been missed by standard genome-wide association studies. Finally, the promoter regions of the AWM-predicted targets of three key transcription factors (TFs), estrogen-related receptor gamma (ESRRG), Pal3 motif, bound by a PPAR-gamma homodimer, IR3 sites (PPARG), and Prophet of Pit 1, PROP paired-like homeobox 1 (PROP1), were surveyed to identify binding sites corresponding to those TFs. Applied to our case, the AWM results recapitulate the known biology of puberty, captured experimentally validated binding sites, and identified candidate genes and gene-gene interactions for further investigation.

Regulatory impact factors: unraveling the transcriptional regulation of complex traits from expression data.

MOTIVATION: Although transcription factors (TF) play a central regulatory role, their detection from expression data is limited due to their low, and often sparse, expression. In order to fill this gap, we propose a regulatory impact factor (RIF) metric to identify critical TF from gene expression data. RESULTS: To substantiate the generality of RIF, we explore a set of experiments spanning a wide range of scenarios including breast cancer survival, fat, gonads and sex differentiation. We show that the strength of RIF lies in its ability to simultaneously integrate three sources of information into a single measure: (i) the change in correlation existing between the TF and the differentially expressed (DE) genes; (ii) the amount of differential expression of DE genes; and (iii) the abundance of DE genes. As a result, RIF analysis assigns an extreme score to those TF that are consistently most differentially co-expressed with the highly abundant and highly DE genes (RIF1), and to those TF with the most altered ability to predict the abundance of DE genes (RIF2). We show that RIF analysis alone recovers well-known experimentally validated TF for the processes studied. The TF identified confirm the importance of PPAR signaling in adipose development and the importance of transduction of estrogen signals in breast cancer survival and sexual differentiation. We argue that RIF has universal applicability, and advocate its use as a promising hypotheses generating tool for the systematic identification of novel TF not yet documented as critical.

Inferring the transcriptional landscape of bovine skeletal muscle by integrating co-expression networks.

BACKGROUND: Despite modern technologies and novel computational approaches, decoding causal transcriptional regulation remains challenging. This is particularly true for less well studied organisms and when only gene expression data is available. In muscle a small number of well characterised transcription factors are proposed to regulate development. Therefore, muscle appears to be a tractable system for proposing new computational approaches. METHODOLOGY/PRINCIPAL FINDINGS: Here we report a simple algorithm that asks "which transcriptional regulator has the highest average absolute co-expression correlation to the genes in a co-expression module?" It correctly infers a number of known causal regulators of fundamental biological processes, including cell cycle activity (E2F1), glycolysis (HLF), mitochondrial transcription (TFB2M), adipogenesis (PIAS1), neuronal development (TLX3), immune function (IRF1) and vasculogenesis (SOX17), within a skeletal muscle context. However, none of the canonical pro-myogenic transcription factors (MYOD1, MYOG, MYF5, MYF6 and MEF2C) were linked to muscle structural gene expression modules. Co-expression values were computed using developing bovine muscle from 60 days post conception (early foetal) to 30 months post natal (adulthood) for two breeds of cattle, in addition to a nutritional comparison with a third breed. A number of transcriptional landscapes were constructed and integrated into an always correlated landscape. One notable feature was a 'metabolic axis' formed from glycolysis genes at one end, nuclear-encoded mitochondrial protein genes at the other, and centrally tethered by mitochondrially-encoded mitochondrial protein genes. CONCLUSIONS/SIGNIFICANCE: The new module-to-regulator algorithm complements our recently described Regulatory Impact Factor analysis. Together with a simple examination of a co-expression module's contents, these three gene expression approaches are starting to illuminate the in vivo transcriptional regulation of skeletal muscle development.

A differential wiring analysis of expression data correctly identifies the gene containing the causal mutation.

Transcription factor (TF) regulation is often post-translational. TF modifications such as reversible phosphorylation and missense mutations, which can act independent of TF expression level, are overlooked by differential expression analysis. Using bovine Piedmontese myostatin mutants as proof-of-concept, we propose a new algorithm that correctly identifies the gene containing the causal mutation from microarray data alone. The myostatin mutation releases the brakes on Piedmontese muscle growth by translating a dysfunctional protein. Compared to a less muscular non-mutant breed we find that myostatin is not differentially expressed at any of ten developmental time points. Despite this challenge, the algorithm identifies the myostatin 'smoking gun' through a coordinated, simultaneous, weighted integration of three sources of microarray information: transcript abundance, differential expression, and differential wiring. By asking the novel question "which regulator is cumulatively most differentially wired to the abundant most differentially expressed genes?" it yields the correct answer, "myostatin". Our new approach identifies causal regulatory changes by globally contrasting co-expression network dynamics. The entirely data-driven 'weighting' procedure emphasises regulatory movement relative to the phenotypically relevant part of the network. In contrast to other published methods that compare co-expression networks, significance testing is not used to eliminate connections.

Simultaneous identification of differential gene expression and connectivity in inflammation, adipogenesis and cancer.

MOTIVATION: Biological differences between classes are reflected in transcriptional changes which in turn affect the levels by which essential genes are individually expressed and collectively connected. The purpose of this communication is to introduce an analytical procedure to simultaneously identify genes that are differentially expressed (DE) as well as differentially connected (DC) in two or more classes of interest. RESULTS: Our procedure is based on a two-step approach: First, mixed-model equations are applied to obtain the normalized expression levels of each gene in each class treatment. These normalized expressions form the basis to compute a measure of (possible) DE as well as the correlation structure existing among genes. Second, a two-component mixture of bi-variate distributions is fitted to identify the component that encapsulates those genes that are DE and/or DC. We demonstrate our approach using three distinct datasets including a human systemic inflammation oligonucleotide data; a spotted cDNA data dealing with bovine in vitro adipogenesis and SAGE database on cancerous and normal tissue samples.

Gene expression profiling of bovine in vitro adipogenesis using a cDNA microarray.

The gene expression profile of bovine bone marrow stromal cells undergoing adipogenesis was established using a custom cDNA microarray. Cells that were treated with adipogenic stimulants and those that were not were collected at each of the six time points, and gene expression differences between the treated and untreated samples within each time point were compared using a microarray. Statistical analyses revealed that 158 genes showed a minimum fold change of 2 in at least one of the five post-differentiation time points. These genes are involved in various cellular pathways and functions, including lipogenesis, glycolysis, cytoskeleton remodelling, extracellular matrix, transcription as well as various signalling pathways such as insulin, calcium and wingless signalling. The experiment also identified 17 differentially expressed (DE) microarray elements with no assigned function. Quantitative real-time PCR was employed to validate eight DE genes, and the PCR data were found to reproduce the microarray data for these eight genes. Subsequent gene ontology annotation was able to provide a global overview of the molecular function of DE genes during adipogenesis. This analysis was able to indicate the importance of different gene categories at various stages of adipogenic conversion, thereby providing further insights into the molecular changes during bovine adipogenesis.

Transcriptional profiling of skeletal muscle tissue from two breeds of cattle.

We used a 9.6 K cattle muscle/fat cDNA microarray to study gene expression differences between the longuissimus dorsi (LD) muscle of Japanese Black (JB) and Holstein (HOL) cattle. JB cattle exhibit an unusual ability to accumulate intramuscular adipose tissue with fat melting points lower than that in other breeds. The LD biopsies from three JB (Tajima strain) and three HOL animals were used in this breed comparison. Seventeen genes were identified as preferentially expressed in LD samples from JB and seven genes were found to be expressed more highly in HOL. The expression of six selected differentially expressed genes was confirmed by quantitative real-time PCR. The genes more highly expressed in JB are associated with unsaturated fatty acid synthesis, fat deposition, and the thyroid hormone pathway. These results are consistent with the increased amounts and proportions of monounsaturated fatty acids observed in the muscle of JB animals. By discovering as yet uncharacterized genes that are differentially regulated in this comparison, the work may lead us to a better understanding of the regulatory pathways involved in the development of intramuscular adipose tissue.