Putative bovine topological association domains and CTCF binding motifs can reduce the search space for causative regulatory variants of complex traits.

BACKGROUND: Topological association domains (TADs) are chromosomal domains characterised by frequent internal DNA-DNA interactions. The transcription factor CTCF binds to conserved DNA sequence patterns called CTCF binding motifs to either prohibit or facilitate chromosomal interactions. TADs and CTCF binding motifs control gene expression, but they are not yet well defined in the bovine genome. In this paper, we sought to improve the annotation of bovine TADs and CTCF binding motifs, and assess whether the new annotation can reduce the search space for cis-regulatory variants. RESULTS: We used genomic synteny to map TADs and CTCF binding motifs from humans, mice, dogs and macaques to the bovine genome. We found that our mapped TADs exhibited the same hallmark properties of those sourced from experimental data, such as housekeeping genes, transfer RNA genes, CTCF binding motifs, short interspersed elements, H3K4me3 and H3K27ac. We showed that runs of genes with the same pattern of allele-specific expression (ASE) (either favouring paternal or maternal allele) were often located in the same TAD or between the same conserved CTCF binding motifs. Analyses of variance showed that when averaged across all bovine tissues tested, TADs explained 14% of ASE variation (standard deviation, SD: 0.056), while CTCF explained 27% (SD: 0.078). Furthermore, we showed that the quantitative trait loci (QTLs) associated with gene expression variation (eQTLs) or ASE variation (aseQTLs), which were identified from mRNA transcripts from 141 lactating cows' white blood and milk cells, were highly enriched at putative bovine CTCF binding motifs. The linearly-furthermost, and most-significant aseQTL and eQTL for each genic target were located within the same TAD as the gene more often than expected (Chi-Squared test P-value < 0.001). CONCLUSIONS: Our results suggest that genomic synteny can be used to functionally annotate conserved transcriptional components, and provides a tool to reduce the search space for causative regulatory variants in the bovine genome.

Putative enhancer sites in the bovine genome are enriched with variants affecting complex traits.

BACKGROUND: Enhancers are non-coding DNA sequences, which when they are bound by specific proteins increase the level of gene transcription. Enhancers activate unique gene expression patterns within cells of different types or under different conditions. Enhancers are key contributors to gene regulation, and causative variants that affect quantitative traits in humans and mice have been located in enhancer regions. However, in the bovine genome, enhancers as well as other regulatory elements are not yet well defined. In this paper, we sought to improve the annotation of bovine enhancer regions by using publicly available mammalian enhancer information. To test if the identified putative bovine enhancer regions are enriched with functional variants that affect milk production traits, we performed genome-wide association studies using imputed whole-genome sequence data followed by meta-analysis and enrichment analysis. RESULTS: We produced a library of candidate bovine enhancer regions by using publicly available bovine ChIP-Seq enhancer data in combination with enhancer data that were identified based on sequence homology with human and mouse enhancer databases. We found that imputed whole-genome sequence variants associated with milk production traits in 16,581 dairy cattle were enriched with enhancer regions that were marked by bovine-liver H3K4me3 and H3K27ac histone modifications from both permutation tests and gene set enrichment analysis. Enhancer regions that were identified based on sequence homology with human and mouse enhancer regions were not as strongly enriched with trait-associated sequence variants as the bovine ChIP-Seq candidate enhancer regions. The bovine ChIP-Seq enriched enhancer regions were located near genes and quantitative trait loci that are associated with pregnancy, growth, disease resistance, meat quality and quantity, and milk quality and quantity traits in dairy and beef cattle. CONCLUSIONS: Our results suggest that sequence variants within enhancer regions that are located in bovine non-coding genomic regions contribute to the variation in complex traits. The level of enrichment was higher in bovine-specific enhancer regions that were identified by detecting histone modifications H3K4me3 and H3K27ac in bovine liver tissues than in enhancer regions identified by sequence homology with human and mouse data. These results highlight the need to use bovine-specific experimental data for the identification of enhancer regions.

Computational recognition for long non-coding RNA (lncRNA): Software and databases.

Since the completion of the Human Genome Project, it has been widely established that most DNA is not transcribed into proteins. These non-protein-coding regions are believed to be moderators within transcriptional and post-transcriptional processes, which play key roles in the onset of diseases. Long non-coding RNAs (lncRNAs) are generally lacking in conserved motifs typically used for detection and thus hard to identify, but nonetheless present certain characteristic features that can be exploited by bioinformatics methods. By combining lncRNA detection with known miRNA, RNA-binding protein and chromatin interaction, current tools are able to recognize and functionally annotate large number of lncRNAs. This review discusses databases and platforms dedicated to cataloging and annotating lncRNAs, as well as tools geared at discovering novel sequences. We emphasize the issues posed by the diversity of lncRNAs and their complex interaction mechanisms, as well as technical issues such as lack of unified nomenclature. We hope that this wide overview of existing platforms and databases might help guide biologists toward the tools they need to analyze their experimental data, while our discussion of limitations and of current lncRNA-related methods may assist in the development of new computational tools.

NetPathMiner: R/Bioconductor package for network path mining through gene expression.

UNLABELLED: NetPathMiner is a general framework for mining, from genome-scale networks, paths that are related to specific experimental conditions. NetPathMiner interfaces with various input formats including KGML, SBML and BioPAX files and allows for manipulation of networks in three different forms: metabolic, reaction and gene representations. NetPathMiner ranks the obtained paths and applies Markov model-based clustering and classification methods to the ranked paths for easy interpretation. NetPathMiner also provides static and interactive visualizations of networks and paths to aid manual investigation. AVAILABILITY: The package is available through Bioconductor and from Github at http://github.com/ahmohamed/NetPathMiner.

Nuclear magnetic resonance metabonomic profiling using tO2PLS.

Blood plasma collected from adult fish (black bream, Sparidae) exposed to a dose of 5 mg kg(-1) 17ß-estradiol underwent metabonomic profiling using nuclear magnetic resonance (NMR). An extension of the orthogonal 2 projection to latent structure (O2PLS) analysis, tO2PLS, was proposed and utilized to classify changes between the control and experimental metabolic profiles. As a bidirectional modeling tool, O2PLS examines the (variable) commonality between two different data blocks, and extracts the joint correlations as well as the unique variations present within each data block. tO2PLS is a proposed matrix transposition of O2PLS to allow for commonality between experiments (spectral profiles) to be observed, rather than between sample variables. tO2PLS analysis highlighted two potential biomarkers, trimethylamine-N-oxide (TMAO) and choline, that distinguish between control and 17ß-estradiol exposed fish. This study presents an alternative way of examining spectroscopic (metabolite) data, providing a method for the visual assessment of similarities and differences between control and experimental spectral features in large data sets.

Identifying pathways of coordinated gene expression.

Methods capable of identifying genetic pathways with coordinated expression signatures are critical to advance our understanding of the functions of biological networks. Currently, the most comprehensive and validated biological networks are metabolic networks. Complete metabolic networks are easily sourced from multiple online databases. These databases reveal metabolic networks to be large, highly complex structures. This complexity is sufficient to hide the specific details on which pathways are interacting to produce an observed network response. In this chapter we will outline a complete framework for identifying the metabolic pathways that relate to an observed phenomenon. To illuminate the functional metabolic pathways, we overlay microarray experiments on top of a complete metabolic network. We then extract the functional components within a metabolic network through a combination of novel pathway ranking, clustering, and classification algorithms. This chapter is designed as a simple tutorial which enables this framework to be applied to any metabolic network and microarray data.

Identifying neighborhoods of coordinated gene expression and metabolite profiles.

In this paper we investigate how metabolic network structure affects any coordination between transcript and metabolite profiles. To achieve this goal we conduct two complementary analyses focused on the metabolic response to stress. First, we investigate the general size of any relationship between metabolic network gene expression and metabolite profiles. We find that strongly correlated transcript-metabolite profiles are sustained over surprisingly long network distances away from any target metabolite. Secondly, we employ a novel pathway mining method to investigate the structure of this transcript-metabolite relationship. The objective of this method is to identify a minimum set of metabolites which are the target of significantly correlated gene expression pathways. The results reveal that in general, a global regulation signature targeting a small number of metabolites is responsible for a large scale metabolic response. However, our method also reveals pathway specific effects that can degrade this global regulation signature and complicates the observed coordination between transcript-metabolite profiles.

Boosted network classifiers for local feature selection.

Like all models, network feature selection models require that assumptions be made on the size and structure of the desired features. The most common assumption is sparsity, where only a small section of the entire network is thought to produce a specific phenomenon. The sparsity assumption is enforced through regularized models, such as the lasso. However, assuming sparsity may be inappropriate for many real-world networks, which possess highly correlated modules. In this paper, we illustrate two novel optimization strategies, namely, boosted expectation propagation (BEP) and boosted message passing (BMP), which directly use the network structure to estimate the parameters of a network classifier. BEP and BMP are ensemble methods that seek to optimize classification performance by combining individual models built upon local network features. Neither BEP nor BMP assumes a sparse solution, but instead they seek a weighted average of all network features where the weights are used to emphasize all features that are useful for classification. In this paper, we compare BEP and BMP with network-regularized logistic regression models on simulated and real biological networks. The results show that, where highly correlated network structure exists, assuming sparsity adversely effects the accuracy and feature selection power of the network classifier.

Experimental infection of horses with Hendra virus/Australia/horse/2008/Redlands.

Hendra virus (HeV) is a highly pathogenic zoonotic paramyxovirus harbored by Australian flying foxes with sporadic spillovers directly to horses. Although the mode and critical control points of HeV spillover to horses from flying foxes, and the risk for transmission from infected horses to other horses and humans, are poorly understood, we successfully established systemic HeV disease in 3 horses exposed to Hendra virus/Australia/Horse/2008/Redlands by the oronasal route, a plausible route for natural infection. In 2 of the 3 animals, HeV RNA was detected continually in nasal swabs from as early as 2 days postexposure, indicating that systemic spread of the virus may be preceded by local viral replication in the nasal cavity or nasopharynx. Our data suggest that a critical factor for reducing HeV exposure risk to humans includes early consideration of HeV in the differential diagnosis and institution of appropriate infection control procedures.

Mining metabolic pathways through gene expression.

MOTIVATION: An observed metabolic response is the result of the coordinated activation and interaction between multiple genetic pathways. However, the complex structure of metabolism has meant that a compete understanding of which pathways are required to produce an observed metabolic response is not fully understood. In this article, we propose an approach that can identify the genetic pathways which dictate the response of metabolic network to specific experimental conditions. RESULTS: Our approach is a combination of probabilistic models for pathway ranking, clustering and classification. First, we use a non-parametric pathway extraction method to identify the most highly correlated paths through the metabolic network. We then extract the defining structure within these top-ranked pathways using both Markov clustering and classification algorithms. Furthermore, we define detailed node and edge annotations, which enable us to track each pathway, not only with respect to its genetic dependencies, but also allow for an analysis of the interacting reactions, compounds and KEGG sub-networks. We show that our approach identifies biologically meaningful pathways within two microarray expression datasets using entire KEGG metabolic networks. AVAILABILITY AND IMPLEMENTATION: An R package containing a full implementation of our proposed method is currently available from http://www.bic.kyoto-u.ac.jp/pathway/timhancock.

Active pathway identification and classification with probabilistic ensembles.

A popular means of modeling metabolic networks is through identifying frequently observed pathways. However the definition of what constitutes an observation of a pathway and how to evaluate the importance of identified pathways remains unclear. In this paper we investigate different methods for defining an observed pathway and evaluate their performance with pathway classification models. We use three methods for defining an observed pathway; a path in gene over-expression, a path in probable gene over-expression and a path of most accurate classification. The performance of each definition is evaluated with three classification models; a probabilistic pathway classifier - HME3M, logistic regression and SVM. The results show that defining pathways using the probability of gene over-expression creates stable and accurate classifiers. Conversely we also show defining pathways of most accurate classification finds a severely biased pathways that are unrepresentative of underlying microarray data structure.

A markov classification model for metabolic pathways.

BACKGROUND: This paper considers the problem of identifying pathways through metabolic networks that relate to a specific biological response. Our proposed model, HME3M, first identifies frequently traversed network paths using a Markov mixture model. Then by employing a hierarchical mixture of experts, separate classifiers are built using information specific to each path and combined into an ensemble prediction for the response. RESULTS: We compared the performance of HME3M with logistic regression and support vector machines (SVM) for both simulated pathways and on two metabolic networks, glycolysis and the pentose phosphate pathway for Arabidopsis thaliana. We use AltGenExpress microarray data and focus on the pathway differences in the developmental stages and stress responses of Arabidopsis. The results clearly show that HME3M outperformed the comparison methods in the presence of increasing network complexity and pathway noise. Furthermore an analysis of the paths identified by HME3M for each metabolic network confirmed known biological responses of Arabidopsis. CONCLUSIONS: This paper clearly shows HME3M to be an accurate and robust method for classifying metabolic pathways. HME3M is shown to outperform all comparison methods and further is capable of identifying known biologically active pathways within microarray data.

A neutralizing human monoclonal antibody protects against lethal disease in a new ferret model of acute nipah virus infection.

Nipah virus is a broadly tropic and highly pathogenic zoonotic paramyxovirus in the genus Henipavirus whose natural reservoirs are several species of Pteropus fruit bats. Nipah virus has repeatedly caused outbreaks over the past decade associated with a severe and often fatal disease in humans and animals. Here, a new ferret model of Nipah virus pathogenesis is described where both respiratory and neurological disease are present in infected animals. Severe disease occurs with viral doses as low as 500 TCID(50) within 6 to 10 days following infection. The underlying pathology seen in the ferret closely resembles that seen in Nipah virus infected humans, characterized as a widespread multisystemic vasculitis, with virus replicating in highly vascular tissues including lung, spleen and brain, with recoverable virus from a variety of tissues. Using this ferret model a cross-reactive neutralizing human monoclonal antibody, m102.4, targeting the henipavirus G glycoprotein was evaluated in vivo as a potential therapeutic agent. All ferrets that received m102.4 ten hours following a high dose oral-nasal Nipah virus challenge were protected from disease while all controls died. This study is the first successful post-exposure passive antibody therapy for Nipah virus using a human monoclonal antibody.

Semi-supervised graph partitioning with decision trees.

In this paper we investigate a new framework for graph partitioning using decision trees to search for sub-graphs within a graph adjacency matrix. Graph partitioning by a decision tree seeks to optimize a specified graph partitioning index such as ratio cut by recursively applying decision rules found within nodes of the graph. Key advantages of tree models for graph partitioning are they provide a predictive framework for evaluating the quality of the solution, determining the number of sub-graphs and assessing overall variable importance. We evaluate the performance of tree based graph partitioning on a benchmark dataset for multiclass classification of tumor diagnosis based on gene expression. Three graph cut indices will be compared, ratio cut, normalized cut and network modularity and assessed in terms of their classification accuracy, power to estimate the optimal number of sub-graphs and ability to extract known important variables within the dataset.

A portable handpump is effective in the evacuation of hemothorax in a swine model of penetrating chest injury.

BACKGROUND: Standard pleural evacuation devices are not practical for use on the battlefield. A small, portable, easy-to-use handpump (HP) that does not require continuous suction for treating hemopneumothorax would offer a major logistical advantage. In addition, using endotracheal tubes instead of regular pleural tubes would help minimize supplies carried on the battlefield. A swine model of penetrating chest injury was designed to test this concept. Our hypothesis was that an HP would be as effective as the standard of care for the evacuation of a large hemopneumothorax. METHODS: A 2-cm lung laceration was created in 18 Yorkshire swine (35-51 kg) under inhaled anesthesia and 1.4 L of blood was infused into the pleural space (200 mL every 15 minutes). Fluid resuscitation (2,000 mL of 0.9% saline) was started 15 minutes after injury, and animals were randomized into one of three groups: group 1, 36-Fr Argyle pleural tube and Pleur-Evac chest drainage unit with 20-cm H2O suction (control); group 2, 36-Fr pleural tube attached to the HP; and group 3, a No. 8 endotracheal tube in the pleural space attached to the HP. After 120 minutes, a thoracotomy was performed to determine the amount of residual blood in the pleural space. RESULTS: Effectiveness of the three methods as a percentage of total blood (evacuated and retained) removed was measured over 2 hours. The handpump (group 2) performed better than the standard of care (group 1) at numerous time points and evacuated significantly (p < 0.05) more blood at the end of the experiment. CONCLUSION: Using the handpump with a pleural tube was more effective than the standard of care in treating traumatic hemothorax. The use of an endotracheal instead of a conventional pleural tube had no adverse impact on efficacy of the pump in evacuating blood from the chest cavity.

Ketone and pyruvate Ringer's solutions decrease pulmonary apoptosis in a rat model of severe hemorrhagic shock and resuscitation.

BACKGROUND: Resuscitation fluids containing beta-hydroxybutyrate (BHB) have been shown to decrease cellular injury after hemorrhagic shock and resuscitation through an unknown mechanism. We tested whether this effect was related to BHB-induced metabolic modulations. METHODS: Male Sprague Dawley rats (n=30) were subjected to volume-controlled hemorrhage (27 mL/kg during 10 minutes followed by 75 minutes of shock during which another 8 mL/kg of blood was withdrawn). Experimental groups included the following: (1) sham, (2) no resuscitation (NR), (3) racemic lactated Ringer's (DL-LR) solution, (4) LR containing L-isomer only (L-LR), (5) ketone Ringer's solution with lactate substituted by BHB (KR), and (6) pyruvate Ringer's solution with lactate substituted by pyruvate (PR). The resuscitation fluids were infused during 45 minutes simultaneously with additional hemorrhage of 8 mL/kg. Hemodynamic and physiologic parameters and the plasma levels of BHB were serially measured. The animals were killed 2 hours after resuscitation, and tissues were frozen instantaneously for cellular adenylate extraction and adenosine triphosphate (ATP) and adenosine diphosphate analysis. Pulmonary apoptosis was studied using Western blotting, immunohistochemistry, and reverse transcriptase-polymerase chain reaction. Expression of enzymes involved in ketogenesis and ketolysis was analyzed by reverse transcriptase-polymerase chain reaction. RESULTS: NR and resuscitation with DL-LR increased the expression of apoptotic markers, whereas resuscitation with KR and PR significantly decreased the expression of apoptotic markers in rat lungs. Resuscitation with KR was followed by a profound increase in plasma BHB levels; however, the expression levels of ketolytic enzymes were essentially unaffected. KR infusion did not induce significant improvements in tissue ATP levels. CONCLUSIONS: Resuscitation with KR and PR protects against pulmonary apoptosis without improving tissue ATP content. Therefore, metabolic modulation is unlikely to be the major mechanism by which BHB exerts its protective effects during reperfusion.

Comparative analysis of hemostatic agents in a swine model of lethal groin injury.

BACKGROUND: Techniques for better hemorrhage control after injury could change outcome. A large-animal model of lethal, uncontrolled hemorrhage was developed to test whether the use of various hemostatic agents would decrease bleeding and improve early survival. METHODS: A complex groin injury was created in 30 Yorkshire swine (42-55 kg) to produce uncontrolled hemorrhage. This injury included semitransection of the proximal thigh and complete division of the femoral artery and vein. After 5 minutes, the animals were randomized to (n = 6 animals per group) no dressing (ND), standard dressing (SD), SD and Rapid Deployment Hemostat (RDH) bandage, SD and QuikClot hemostatic agent (QC), or SD and TraumaDEX (TDEX). Limited volume 0.9% saline (1,000 mL over 30 minutes) resuscitation was started 30 minutes after injury. We measured blood loss, early mortality (180 minutes), and physiologic markers of hemorrhagic shock (e.g., cardiac output, blood pressure, hemoglobin, metabolic acidosis). RESULTS: Application of wound dressing decreased mortality in all groups compared with the ND group (83% mortality). However, this difference was significant (p < 0.05) only for the QuikClot hemostatic agent (0% mortality). Before the application of dressing (first 5 minutes), there were no differences in blood loss between the groups. After application of dressings, the QC group had the lowest blood loss (4.4 +/- 1.4 mL/kg). CONCLUSION: Of the hemostatic agents tested, QuikClot improved survival and decreased bleeding in a swine model of lethal vascular and soft tissue injury.

Learning and memory is preserved after induced asanguineous hyperkalemic hypothermic arrest in a swine model of traumatic exsanguination.

BACKGROUND: Induced asanguineous hypothermic metabolic arrest (suspended animation) could provide valuable time to repair major vascular injuries if safely induced in patients with trauma. We report a novel method of doing this in a swine model of uncontrolled lethal hemorrhage (ULH) that resulted in preservation of learning ability and memory. METHODS: Yorkshire swine (100 to 125 lb) underwent ULH before rapid intra-aortic infusion of a hypothermic (4 degrees C), hyperkalemic (70 mEq/L) organ preservation solution by a left thoracotomy. Cooling continued until core temperature reached 10 degrees C, and this was maintained for 60 minutes using low-flow cardiopulmonary bypass. Vascular injuries were repaired during this state of suspended animation, which was then reversed, and the animals were observed for 6 weeks. Cognitive functions were tested by training animals to retrieve food from color-coded boxes. Postoperatively, the ability to remember this task and a 75-point objective neurologic scale were used to test neurologic function. In experiment I, ULH was caused by lacerating thoracic aorta (n = 9). Five preoperatively untrained animals were trained to perform the task and compared with control animals (n = 15), and 4 preoperatively trained animals were tested for memory retention postoperatively. In experiment II, ULH was induced by creating an iliac artery and vein injury (n = 15). Animals were kept in shock for 15, 30, and 60 minutes before the induction of hypothermia. RESULTS: In experiment I, surviving animals (7/9) were neurologically intact, and their capacity to learn new skills was no different than for control animals. All pretrained animals demonstrated complete memory retention. In experiment II, survival with 15, 30, and 60 minutes of shock were 80%, 60%, and 80%, respectively. All animals (except 1) in the 60-minute group were neurologically intact and displayed normal learning capacity. CONCLUSIONS: Induction of hypothermic metabolic arrest (by thoracotomy) for repair of complex traumatic injuries is feasible with preservation of normal neurologic function, even after extended periods of shock from an intra-abdominal source of uncontrolled hemorrhage.