A comparative analysis of the transcriptome profiles of liver and muscle tissue in pigs divergent for feed efficiency.

BACKGROUND: The improvement of feed efficiency is a key economic goal within the pig production industry. The objective of this study was to examine transcriptomic differences in both the liver and muscle of pigs divergent for feed efficiency, thus improving our understanding of the molecular mechanisms influencing feed efficiency and enabling the identification of candidate biomarkers. Residual feed intake (RFI) was calculated for two populations of pigs from two different farms of origin/genotype. The 6 most efficient (LRFI) and 6 least efficient (HRFI) animals from each population were selected for further analysis of Longissimus Dorsi muscle (n = 22) and liver (n = 23). Transcriptomic data were generated from liver and muscle collected post-slaughter. RESULTS: The transcriptomic data segregated based on the RFI value of the pig rather than genotype/farm of origin. A total of 6463 genes were identified as being differentially expressed (DE) in muscle, while 964 genes were identified as being DE in liver. Genes that were commonly DE between muscle and liver (n = 526) were used for the multi-tissue analysis. These 526 genes were associated with protein targeting to membrane, extracellular matrix organisation and immune function. In the muscle-only analysis, genes associated with RNA processing, protein synthesis and energy metabolism were down regulated in the LRFI animals while in the liver-only analysis, genes associated with cell signalling and lipid homeostasis were up regulated in the LRFI animals. CONCLUSIONS: Differences in the transcriptome segregated on pig RFI value rather than the genotype/farm of origin. Multi-tissue analysis identified that genes associated with GO terms protein targeting to membrane, extracellular matrix organisation and a range of terms relating to immune function were over represented in the differentially expressed genes of both liver and muscle.

Refinement of Global Domestic Horse Biogeography Using Historic Landrace Chinese Mongolian Populations.

The Mongolian horse is one of the oldest extant horse populations and although domesticated, most animals are free-ranging and experience minimal human intervention. As an ancient population originating in one of the key domestication centers, the Mongolian horse may play a key role in understanding the origins and recent evolutionary history of horses. Here we describe an analysis of high-density genome-wide single-nucleotide polymorphism (SNP) data in 40 globally dispersed horse populations (n = 895). In particular, we have focused on new results from Chinese Mongolian horses (n = 100) that represent 5 distinct populations. These animals were genotyped for 670K SNPs and the data were analyzed in conjunction with 35K SNP data for 35 distinct breeds. Analyses of these integrated SNP data sets demonstrated that the Chinese Mongolian populations were genetically distinct from other modern horse populations. In addition, compared to other domestic horse breeds, the Chinese Mongolian horse populations exhibited relatively high genomic diversity. These results suggest that, in genetic terms, extant Chinese Mongolian horses may be the most similar modern populations to the animals originally domesticated in this region of Asia. Chinese Mongolian horse populations may therefore retain ancestral genetic variants from the earliest domesticates. Further genomic characterization of these populations in conjunction with archaeogenetic sequence data should be prioritized for understanding recent horse evolution and the domestication process that has led to the wealth of diversity observed in modern global horse breeds.

Equine skeletal muscle adaptations to exercise and training: evidence of differential regulation of autophagosomal and mitochondrial components.

BACKGROUND: A single bout of exercise induces changes in gene expression in skeletal muscle. Regular exercise results in an adaptive response involving changes in muscle architecture and biochemistry, and is an effective way to manage and prevent common human diseases such as obesity, cardiovascular disorders and type II diabetes. However, the biomolecular mechanisms underlying such responses still need to be fully elucidated. Here we performed a transcriptome-wide analysis of skeletal muscle tissue in a large cohort of untrained Thoroughbred horses (n = 51) before and after a bout of high-intensity exercise and again after an extended period of training. We hypothesized that regular high-intensity exercise training primes the transcriptome for the demands of high-intensity exercise. RESULTS: An extensive set of genes was observed to be significantly differentially regulated in response to a single bout of high-intensity exercise in the untrained cohort (3241 genes) and following multiple bouts of high-intensity exercise training over a six-month period (3405 genes). Approximately one-third of these genes (1025) and several biological processes related to energy metabolism were common to both the exercise and training responses. We then developed a novel network-based computational analysis pipeline to test the hypothesis that these transcriptional changes also influence the contextual molecular interactome and its dynamics in response to exercise and training. The contextual network analysis identified several important hub genes, including the autophagosomal-related gene GABARAPL1, and dynamic functional modules, including those enriched for mitochondrial respiratory chain complexes I and V, that were differentially regulated and had their putative interactions 're-wired' in the exercise and/or training responses. CONCLUSION: Here we have generated for the first time, a comprehensive set of genes that are differentially expressed in Thoroughbred skeletal muscle in response to both exercise and training. These data indicate that consecutive bouts of high-intensity exercise result in a priming of the skeletal muscle transcriptome for the demands of the next exercise bout. Furthermore, this may also lead to an extensive 're-wiring' of the molecular interactome in both exercise and training and include key genes and functional modules related to autophagy and the mitochondrion.

DyNet: visualization and analysis of dynamic molecular interaction networks.

UNLABELLED: : The ability to experimentally determine molecular interactions on an almost proteome-wide scale under different conditions is enabling researchers to move from static to dynamic network analysis, uncovering new insights into how interaction networks are physically rewired in response to different stimuli and in disease. Dynamic interaction data presents a special challenge in network biology. Here, we present DyNet, a Cytoscape application that provides a range of functionalities for the visualization, real-time synchronization and analysis of large multi-state dynamic molecular interaction networks enabling users to quickly identify and analyze the most 'rewired' nodes across many network states. AVAILABILITY AND IMPLEMENTATION: DyNet is available at the Cytoscape (3.2+) App Store (http://apps.cytoscape.org/apps/dynet). CONTACT: david.lynn@sahmri.com SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

HiQuant: Rapid Postquantification Analysis of Large-Scale MS-Generated Proteomics Data.

Recent advances in mass-spectrometry-based proteomics are now facilitating ambitious large-scale investigations of the spatial and temporal dynamics of the proteome; however, the increasing size and complexity of these data sets is overwhelming current downstream computational methods, specifically those that support the postquantification analysis pipeline. Here we present HiQuant, a novel application that enables the design and execution of a postquantification workflow, including common data-processing steps, such as assay normalization and grouping, and experimental replicate quality control and statistical analysis. HiQuant also enables the interpretation of results generated from large-scale data sets by supporting interactive heatmap analysis and also the direct export to Cytoscape and Gephi, two leading network analysis platforms. HiQuant may be run via a user-friendly graphical interface and also supports complete one-touch automation via a command-line mode. We evaluate HiQuant's performance by analyzing a large-scale, complex interactome mapping data set and demonstrate a 200-fold improvement in the execution time over current methods. We also demonstrate HiQuant's general utility by analyzing proteome-wide quantification data generated from both a large-scale public tyrosine kinase siRNA knock-down study and an in-house investigation into the temporal dynamics of the KSR1 and KSR2 interactomes. Download HiQuant, sample data sets, and supporting documentation at http://hiquant.primesdb.eu .

Differential DNA methylation profiles of coding and non-coding genes define hippocampal sclerosis in human temporal lobe epilepsy.

Temporal lobe epilepsy is associated with large-scale, wide-ranging changes in gene expression in the hippocampus. Epigenetic changes to DNA are attractive mechanisms to explain the sustained hyperexcitability of chronic epilepsy. Here, through methylation analysis of all annotated C-phosphate-G islands and promoter regions in the human genome, we report a pilot study of the methylation profiles of temporal lobe epilepsy with or without hippocampal sclerosis. Furthermore, by comparative analysis of expression and promoter methylation, we identify methylation sensitive non-coding RNA in human temporal lobe epilepsy. A total of 146 protein-coding genes exhibited altered DNA methylation in temporal lobe epilepsy hippocampus (n = 9) when compared to control (n = 5), with 81.5% of the promoters of these genes displaying hypermethylation. Unique methylation profiles were evident in temporal lobe epilepsy with or without hippocampal sclerosis, in addition to a common methylation profile regardless of pathology grade. Gene ontology terms associated with development, neuron remodelling and neuron maturation were over-represented in the methylation profile of Watson Grade 1 samples (mild hippocampal sclerosis). In addition to genes associated with neuronal, neurotransmitter/synaptic transmission and cell death functions, differential hypermethylation of genes associated with transcriptional regulation was evident in temporal lobe epilepsy, but overall few genes previously associated with epilepsy were among the differentially methylated. Finally, a panel of 13, methylation-sensitive microRNA were identified in temporal lobe epilepsy including MIR27A, miR-193a-5p (MIR193A) and miR-876-3p (MIR876), and the differential methylation of long non-coding RNA documented for the first time. The present study therefore reports select, genome-wide DNA methylation changes in human temporal lobe epilepsy that may contribute to the molecular architecture of the epileptic brain.

Using biological networks to integrate, visualize and analyze genomics data.

Network biology is a rapidly developing area of biomedical research and reflects the current view that complex phenotypes, such as disease susceptibility, are not the result of single gene mutations that act in isolation but are rather due to the perturbation of a gene's network context. Understanding the topology of these molecular interaction networks and identifying the molecules that play central roles in their structure and regulation is a key to understanding complex systems. The falling cost of next-generation sequencing is now enabling researchers to routinely catalogue the molecular components of these networks at a genome-wide scale and over a large number of different conditions. In this review, we describe how to use publicly available bioinformatics tools to integrate genome-wide 'omics' data into a network of experimentally-supported molecular interactions. In addition, we describe how to visualize and analyze these networks to identify topological features of likely functional relevance, including network hubs, bottlenecks and modules. We show that network biology provides a powerful conceptual approach to integrate and find patterns in genome-wide genomic data but we also discuss the limitations and caveats of these methods, of which researchers adopting these methods must remain aware.

Discovery and visualization of miRNA-mRNA functional modules within integrated data using bicluster analysis.

MicroRNAs (miRNAs) are small non-coding RNA molecules that regulate gene expression at a post-transcriptional level. An miRNA may target many messenger RNA (mRNA) transcripts, and each transcript may be targeted by multiple miRNAs. Our understanding of miRNA regulation is evolving to consider modules of miRNAs that regulate groups of functionally related mRNAs. Here we expand the model of miRNA functional modules and use it to guide the integration of miRNA and mRNA expression and target prediction data. We present evidence of cooperativity between miRNA classes within this integrated miRNA-mRNA association matrix. We then apply bicluster analysis to uncover miRNA functional modules within this integrated data set and develop a novel application to visualize and query these results. We show that this wholly unsupervised approach can discover a network of miRNA-mRNA modules that are enriched for both biological processes and miRNA classes. We apply this method to investigate the interplay of miRNAs and mRNAs in integrated data sets derived from neuroblastoma and human immune cells. This study is the first to apply the technique of biclustering to model functional modules within an integrated miRNA-mRNA association matrix. Results provide evidence of an extensive modular miRNA functional network and enable characterization of miRNA function and dysregulation in disease.

Differential DNA methylation patterns define status epilepticus and epileptic tolerance.

Prolonged seizures (status epilepticus) produce pathophysiological changes in the hippocampus that are associated with large-scale, wide-ranging changes in gene expression. Epileptic tolerance is an endogenous program of cell protection that can be activated in the brain by previous exposure to a non-harmful seizure episode before status epilepticus. A major transcriptional feature of tolerance is gene downregulation. Here, through methylation analysis of 34,143 discrete loci representing all annotated CpG islands and promoter regions in the mouse genome, we report the genome-wide DNA methylation changes in the hippocampus after status epilepticus and epileptic tolerance in adult mice. A total of 321 genes showed altered DNA methylation after status epilepticus alone or status epilepticus that followed seizure preconditioning, with >90% of the promoters of these genes undergoing hypomethylation. These profiles included genes not previously associated with epilepsy, such as the polycomb gene Phc2. Differential methylation events generally occurred throughout the genome without bias for a particular chromosomal region, with the exception of a small region of chromosome 4, which was significantly overrepresented with genes hypomethylated after status epilepticus. Surprisingly, only few genes displayed differential hypermethylation in epileptic tolerance. Nevertheless, gene ontology analysis emphasized the majority of differential methylation events between the groups occurred in genes associated with nuclear functions, such as DNA binding and transcriptional regulation. The present study reports select, genome-wide DNA methylation changes after status epilepticus and in epileptic tolerance, which may contribute to regulating the gene expression environment of the seizure-damaged hippocampus.

MiR-137 functions as a tumor suppressor in neuroblastoma by downregulating KDM1A.

Neuroblastoma is the most common extracranial solid tumor of childhood, and accounts for ∼15% of all childhood cancer deaths. The histone demethylase, lysine-specific demethylase 1 (KDM1A, previously known as LSD1), is strongly expressed in neuroblastomas, and overexpression correlates with poor patient prognosis. Inducing differentiation in neuroblastoma cells has previously been shown to down regulate KDM1A, and siRNA-mediated KDM1A knockdown inhibited neuroblastoma cell viability. The microRNA, miR-137, has been reported to be downregulated in several human cancers, and KDM1A mRNA was reported as a putative target of miR-137 in colon cancer. We hypothesized that miR-137 might have a tumor-suppressive role in neuroblastoma mediated via downregulation of KDM1A. Indeed, low levels of miR-137 expression in primary neuroblastomas correlated with poor patient prognosis. Re-expressing miR-137 in neuroblastoma cell lines increased apoptosis and decreased cell viability and proliferation. KDM1A mRNA was repressed by miR-137 in neuroblastoma cells, and was validated as a direct target of miR-137 using reporter assays in SHEP and HEK293 cells. Furthermore, siRNA-mediated KDM1A knockdown phenocopied the miR-137 re-expression phenotype in neuroblastoma cells. We conclude that miR-137 directly targets KDM1A mRNA in neuroblastoma cells, and activates cell properties consistent with tumor suppression. Therapeutic strategies to re-express miR-137 in neuroblastomas could be useful to reduce tumor aggressiveness.

Expressional alterations in functional ultra-conserved non-coding RNAs in response to all-trans retinoic acid--induced differentiation in neuroblastoma cells.

BACKGROUND: Ultra-conserved regions (UCRs) are segments of the genome (≥ 200 bp) that exhibit 100% DNA sequence conservation between human, mouse and rat. Transcribed UCRs (T-UCRs) have been shown to be differentially expressed in cancers versus normal tissue, indicating a possible role in carcinogenesis. All-trans-retinoic acid (ATRA) causes some neuroblastoma (NB) cell lines to undergo differentiation and leads to a significant decrease in the oncogenic transcription factor MYCN. Here, we examine the impact of ATRA treatment on T-UCR expression and investigate the biological significance of these changes. METHODS: We designed a custom tiling microarray to profile the expression of 481 T-UCRs in sense and anti-sense orientation (962 potential transcripts) in untreated and ATRA-treated neuroblastoma cell lines (SH-SY5Y, SK-N-BE, LAN-5). Following identification of significantly differentially expressed T-UCRs, we carried out siRNA knockdown and gene expression microarray analysis to investigate putative functional roles for selected T-UCRs. RESULTS: Following ATRA-induced differentiation, 32 T-UCRs were differentially expressed (16 up-regulated, 16 down-regulated) across all three cell lines. Further insight into the possible role of T-UC.300A, an independent transcript whose expression is down-regulated following ATRA was achieved by siRNA knockdown, resulting in the decreased viability and invasiveness of ATRA-responsive cell lines. Gene expression microarray analysis following knockdown of T-UC.300A revealed a number of genes whose expression was altered by changing T-UC.300A levels and that might play a role in the increased proliferation and invasion of NB cells prior to ATRA-treatment. CONCLUSIONS: Our results indicate that significant numbers of T-UCRs have altered expression levels in response to ATRA. While the precise roles that T-UCRs might play in cancer or in normal development are largely unknown and an important area for future study, our findings strongly indicate that the function of non-coding RNA T-UC.300A is connected with proliferation, invasion and the inhibition of differentiation of neuroblastoma cell lines prior to ATRA treatment.

Identifying Patients with Polycythemia Vera at Risk of Thrombosis after Hydroxyurea Initiation: The Polycythemia Vera-Advanced Integrated Models (PV-AIM) Project.

Patients with polycythemia vera (PV) are at significant risk of thromboembolic events (TE). The PV-AIM study used the Optum® de-identified Electronic Health Record dataset and machine learning to identify markers of TE in a real-world population. Data for 82,960 patients with PV were extracted: 3852 patients were treated with hydroxyurea (HU) only, while 130 patients were treated with HU and then changed to ruxolitinib (HU-ruxolitinib). For HU-alone patients, the annualized incidence rates (IR; per 100 patients) decreased from 8.7 (before HU) to 5.6 (during HU) but increased markedly to 10.5 (continuing HU). Whereas for HU-ruxolitinib patients, the IR decreased from 10.8 (before HU) to 8.4 (during HU) and was maintained at 8.3 (after switching to ruxolitinib). To better understand markers associated with TE risk, we built a machine-learning model for HU-alone patients and validated it using an independent dataset. The model identified lymphocyte percentage (LYP), neutrophil percentage (NEP), and red cell distribution width (RDW) as key markers of TE risk, and optimal thresholds for these markers were established, from which a decision tree was derived. Using these widely used laboratory markers, the decision tree could be used to identify patients at high risk for TE, facilitate treatment decisions, and optimize patient management.

MiRNA-335 suppresses neuroblastoma cell invasiveness by direct targeting of multiple genes from the non-canonical TGF-ß signalling pathway.

Transforming growth factor-ß (TGF-ß) signaling regulates many diverse cellular activities through both canonical (SMAD-dependent) and non-canonical branches, which includes the mitogen-activated protein kinase (MAPK), Rho-like guanosine triphosphatase and phosphatidylinositol-3-kinase/AKT pathways. Here, we demonstrate that miR-335 directly targets and downregulates genes in the TGF-ß non-canonical pathways, including the Rho-associated coiled-coil containing protein (ROCK1) and MAPK1, resulting in reduced phosphorylation of downstream pathway members. Specifically, inhibition of ROCK1 and MAPK1 reduces phosphorylation levels of the motor protein myosin light chain (MLC) leading to a significant inhibition of the invasive and migratory potential of neuroblastoma cells. Additionally, miR-335 targets the leucine-rich alpha-2-glycoprotein 1 (LRG1) messenger RNA, which similarly results in a significant reduction in the phosphorylation status of MLC and a decrease in neuroblastoma cell migration and invasion. Thus, we link LRG1 to the migratory machinery of the cell, altering its activity presumably by exerting its effect within the non-canonical TGF-ß pathway. Moreover, we demonstrate that the MYCN transcription factor, whose coding sequence is highly amplified in a particularly clinically aggressive neuroblastoma tumor subtype, directly binds to a region immediately upstream of the miR-335 transcriptional start site, resulting in transcriptional repression. We conclude that MYCN contributes to neuroblastoma cell migration and invasion, by directly downregulating miR-335, resulting in the upregulation of the TGF-ß signaling pathway members ROCK1, MAPK1 and putative member LRG1, which positively promote this process. Our results provide novel insight into the direct regulation of TGF-ß non-canonical signaling by miR-335, which in turn is downregulated by MYCN.

Genome-wide DNA methylation analysis of neuroblastic tumors reveals clinically relevant epigenetic events and large-scale epigenomic alterations localized to telomeric regions.

The downregulation of specific genes through DNA hypermethylation is a major hallmark of cancer, although the extent and genomic distribution of hypermethylation occurring within cancer genomes is poorly understood. We report on the first genome-wide analysis of DNA methylation alterations in different neuroblastic tumor subtypes and cell lines, revealing higher order organization and clinically relevant alterations of the epigenome. The methylation status of 33,485 discrete loci representing all annotated CpG islands and RefSeq gene promoters was assessed in primary neuroblastic tumors and cell lines. A comparison of genes that were hypermethylated exclusively in the clinically favorable ganglioneuroma/ganglioneuroblastoma tumors revealed that nine genes were associated with poor clinical outcome when overexpressed in the unfavorable neuroblastoma (NB) tumors. Moreover, an integrated DNA methylation and copy number analysis identified 80 genes that were recurrently concomitantly deleted and hypermethylated in NB, with 37 reactivated by 5-aza-deoxycytidine. Lower expression of four of these genes was correlated with poor clinical outcome, further implicating their inactivation in aggressive disease pathogenesis. Analysis of genome-wide hypermethylation patterns revealed 70 recurrent large-scale blocks of contiguously hypermethylated promoters/CpG islands, up to 590 kb in length, with a distribution bias toward telomeric regions. Genome-wide hypermethylation events in neuroblastic tumors are extensive and frequently occur in large-scale blocks with a significant bias toward telomeric regions, indicating that some methylation alterations have occurred in a coordinated manner. Our results indicate that methylation contributes toward the clinicopathological features of neuroblastic tumors, revealing numerous genes associated with poor patient survival in NB.

Dissection of the oncogenic MYCN transcriptional network reveals a large set of clinically relevant cell cycle genes as drivers of neuroblastoma tumorigenesis.

Amplification of the oncogenic transcription factor MYCN plays a major role in the pathogenesis of several pediatric cancers, including neuroblastoma, medulloblastoma, and rhabodomyosarcoma. For neuroblastoma, MYCN amplification is the most powerful genetic predictor of poor patient survival, yet the mechanism by which MYCN drives tumorigenesis is only partially understood. To gain an insight into the distribution of MYCN binding and to identify clinically relevant MYCN target genes, we performed an integrated analysis of MYCN ChIP-chip and mRNA expression using the MYCN repressible SHEP-21N neuroblastoma cell line. We hypothesized that genes exclusively MYCN bound in SHEP-21N cells over-expressing MYCN would be enriched for direct targets which contribute to the process of disease progression. Integrated analysis revealed that MYCN drives tumorigenesis predominantly as a positive regulator of target gene transcription. A high proportion of genes (24%) that are MYCN bound and up-regulated in the SHEP-21N model are significantly associated with poor overall patient survival (OS) in a set of 88 tumors. In contrast, the proportion of genes down-regulated when bound by MYCN in the SHEP-21N model and which are significantly associated with poor overall patient survival when under-expressed in primary tumors was significantly lower (5%). Gene ontology analysis determined a highly statistically significant enrichment for cell cycle related genes within the over-expressed MYCN target group which were also associated with poor OS. We conclude that the over-expression of MYCN leads to aberrant binding and over-expression of genes associated with cell cycle regulation which are significantly correlated with poor OS and MYCN amplification.

MicroRNA-184 inhibits neuroblastoma cell survival through targeting the serine/threonine kinase AKT2.

BACKGROUND: Neuroblastoma is a paediatric cancer of the sympathetic nervous system. The single most important genetic indicator of poor clinical outcome is amplification of the MYCN transcription factor. One of many down-stream MYCN targets is miR-184, which is either directly or indirectly repressed by this transcription factor, possibly due to its pro-apoptotic effects when ectopically over-expressed in neuroblastoma cells. The purpose of this study was to elucidate the molecular mechanism by which miR-184 conveys pro-apoptotic effects. RESULTS: We demonstrate that the knock-down of endogenous miR-184 has the opposite effect of ectopic up-regulation, leading to enhanced neuroblastoma cell numbers. As a mechanism of how miR-184 causes apoptosis when over-expressed, and increased cell numbers when inhibited, we demonstrate direct targeting and degradation of AKT2, a major downstream effector of the phosphatidylinositol 3-kinase (PI3K) pathway, one of the most potent pro-survival pathways in cancer. The pro-apoptotic effects of miR-184 ectopic over-expression in neuroblastoma cell lines is reproduced by siRNA inhibition of AKT2, while a positive effect on cell numbers similar to that obtained by the knock-down of endogenous miR-184 can be achieved by ectopic up-regulation of AKT2. Moreover, co-transfection of miR-184 with an AKT2 expression vector lacking the miR-184 target site in the 3'UTR rescues cells from the pro-apoptotic effects of miR-184. CONCLUSIONS: MYCN contributes to tumorigenesis, in part, by repressing miR-184, leading to increased levels of AKT2, a direct target of miR-184. Thus, two important genes with positive effects on cell growth and survival, MYCN and AKT2, can be linked into a common genetic pathway through the actions of miR-184. As an inhibitor of AKT2, miR-184 could be of potential benefit in miRNA mediated therapeutics of MYCN amplified neuroblastoma and other forms of cancer.

Extensive rewiring of the EGFR network in colorectal cancer cells expressing transforming levels of KRASG13D.

Protein-protein-interaction networks (PPINs) organize fundamental biological processes, but how oncogenic mutations impact these interactions and their functions at a network-level scale is poorly understood. Here, we analyze how a common oncogenic KRAS mutation (KRASG13D) affects PPIN structure and function of the Epidermal Growth Factor Receptor (EGFR) network in colorectal cancer (CRC) cells. Mapping >6000 PPIs shows that this network is extensively rewired in cells expressing transforming levels of KRASG13D (mtKRAS). The factors driving PPIN rewiring are multifactorial including changes in protein expression and phosphorylation. Mathematical modelling also suggests that the binding dynamics of low and high affinity KRAS interactors contribute to rewiring. PPIN rewiring substantially alters the composition of protein complexes, signal flow, transcriptional regulation, and cellular phenotype. These changes are validated by targeted and global experimental analysis. Importantly, genetic alterations in the most extensively rewired PPIN nodes occur frequently in CRC and are prognostic of poor patient outcomes.

Expression Quantitative Trait Loci in Equine Skeletal Muscle Reveals Heritable Variation in Metabolism and the Training Responsive Transcriptome.

While over ten thousand genetic loci have been associated with phenotypic traits and inherited diseases in genome-wide association studies, in most cases only a relatively small proportion of the trait heritability is explained and biological mechanisms underpinning these traits have not been clearly identified. Expression quantitative trait loci (eQTL) are subsets of genomic loci shown experimentally to influence gene expression. Since gene expression is one of the primary determinants of phenotype, the identification of eQTL may reveal biologically relevant loci and provide functional links between genomic variants, gene expression and ultimately phenotype. Skeletal muscle (gluteus medius) gene expression was quantified by RNA-seq for 111 Thoroughbreds (47 male, 64 female) in race training at a single training establishment sampled at two time-points: at rest (n = 92) and four hours after high-intensity exercise (n = 77); n = 60 were sampled at both time points. Genotypes were generated from the Illumina Equine SNP70 BeadChip. Applying a False Discovery Rate (FDR) corrected P-value threshold (P FDR < 0.05), association tests identified 3,583 cis-eQTL associated with expression of 1,456 genes at rest; 4,992 cis-eQTL associated with the expression of 1,922 genes post-exercise; 1,703 trans-eQTL associated with 563 genes at rest; and 1,219 trans-eQTL associated with 425 genes post-exercise. The gene with the highest cis-eQTL association at both time-points was the endosome-associated-trafficking regulator 1 gene (ENTR1; Rest: P FDR = 3.81 × 10-27, Post-exercise: P FDR = 1.66 × 10-24), which has a potential role in the transcriptional regulation of the solute carrier family 2 member 1 glucose transporter protein (SLC2A1). Functional analysis of genes with significant eQTL revealed significant enrichment for cofactor metabolic processes. These results suggest heritable variation in genomic elements such as regulatory sequences (e.g. gene promoters, enhancers, silencers), microRNA and transcription factor genes, which are associated with metabolic function and may have roles in determining end-point muscle and athletic performance phenotypes in Thoroughbred horses. The incorporation of the eQTL identified with genome and transcriptome-wide association may reveal useful biological links between genetic variants and their impact on traits of interest, such as elite racing performance and adaptation to training.

MetaFIND: a feature analysis tool for metabolomics data.

BACKGROUND: Metabolomics, or metabonomics, refers to the quantitative analysis of all metabolites present within a biological sample and is generally carried out using NMR spectroscopy or Mass Spectrometry. Such analysis produces a set of peaks, or features, indicative of the metabolic composition of the sample and may be used as a basis for sample classification. Feature selection may be employed to improve classification accuracy or aid model explanation by establishing a subset of class discriminating features. Factors such as experimental noise, choice of technique and threshold selection may adversely affect the set of selected features retrieved. Furthermore, the high dimensionality and multi-collinearity inherent within metabolomics data may exacerbate discrepancies between the set of features retrieved and those required to provide a complete explanation of metabolite signatures. Given these issues, the latter in particular, we present the MetaFIND application for 'post-feature selection' correlation analysis of metabolomics data. RESULTS: In our evaluation we show how MetaFIND may be used to elucidate metabolite signatures from the set of features selected by diverse techniques over two metabolomics datasets. Importantly, we also show how MetaFIND may augment standard feature selection and aid the discovery of additional significant features, including those which represent novel class discriminating metabolites. MetaFIND also supports the discovery of higher level metabolite correlations. CONCLUSION: Standard feature selection techniques may fail to capture the full set of relevant features in the case of high dimensional, multi-collinear metabolomics data. We show that the MetaFIND 'post-feature selection' analysis tool may aid metabolite signature elucidation, feature discovery and inference of metabolic correlations.

Extending bicluster analysis to annotate unclassified ORFs and predict novel functional modules using expression data.

BACKGROUND: Microarrays have the capacity to measure the expressions of thousands of genes in parallel over many experimental samples. The unsupervised classification technique of bicluster analysis has been employed previously to uncover gene expression correlations over subsets of samples with the aim of providing a more accurate model of the natural gene functional classes. This approach also has the potential to aid functional annotation of unclassified open reading frames (ORFs). Until now this aspect of biclustering has been under-explored. In this work we illustrate how bicluster analysis may be extended into a 'semi-supervised' ORF annotation approach referred to as BALBOA. RESULTS: The efficacy of the BALBOA ORF classification technique is first assessed via cross validation and compared to a multi-class k-Nearest Neighbour (kNN) benchmark across three independent gene expression datasets. BALBOA is then used to assign putative functional annotations to unclassified yeast ORFs. These predictions are evaluated using existing experimental and protein sequence information. Lastly, we employ a related semi-supervised method to predict the presence of novel functional modules within yeast. CONCLUSION: In this paper we demonstrate how unsupervised classification methods, such as bicluster analysis, may be extended using of available annotations to form semi-supervised approaches within the gene expression analysis domain. We show that such methods have the potential to improve upon supervised approaches and shed new light on the functions of unclassified ORFs and their co-regulation.