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Abstract Copy number variants (CNVs) create a major source of variation among individuals and populations. Array-based comparative genomic hybridisation (aCGH) is a powerful method used to detect and compare the copy numbers of DNA sequences at high resolution along the genome. In recent years, several informatics tools for accurate and efficient CNV detection and assessment have been developed. In this paper, most of the well known algorithms, analysis software and the limitations of that software will be briefly reviewed.

Abstract Fetal growth restriction (FGR) is associated with aberrant placentation and accounts for a significant proportion of perinatal deaths. microRNAs have been shown to be dysregulated in FGR. The purpose of this study was to determine microRNA-regulated molecular pathways altered using a caloric restricted mouse model of FGR. Pregnant mice were subjected to a 50% caloric restricted diet beginning at E9. At E18.5, RNA sequencing of placental tissue was performed to identify differences in gene expression between caloric restricted and control placentas. Significant differences in gene expression between caloric restricted and control placentas were observed in 228 of the 1546 (14.7%) microRNAs. Functional analysis of microRNA–mRNA interactions demonstrated enrichment of several biological pathways with oxidative stress, apoptosis, and autophagy pathways upregulated and angiogenesis and signal transduction pathways downregulated. Ingenuity pathway analysis also suggested that ID1 signaling, a pathway integral for trophoblast differentiation, is also dysregulated in caloric restricted placentas. Thus, a maternal caloric restriction mouse model of FGR results in aberrant microRNA-regulated molecular pathways associated with angiogenesis, oxidative stress, signal transduction, apoptosis, and cell differentiation. As several of these pathways are dysregulated in human FGR, our findings suggest that this model may provide an excellent means to study placental microRNA derangements seen in FGR.

Abstract Early detection of cancer is vital for the best chance of successful treatment, but half of all cancers are diagnosed at an advanced stage. A simple and reliable blood screening test applied routinely would therefore address a major unmet medical need. To gain insight into the value of protein biomarkers in early detection and stratification of cancer we determined the time course of changes in the plasma proteome of mice carrying transplanted human lung, breast, colon, or ovarian tumors. For protein measurements we used an aptamer-based assay which simultaneously measures ~ 5000 proteins. Along with tumor lineage-specific biomarkers, we also found 15 markers shared among all cancer types that included the energy metabolism enzymes glyceraldehyde-3-phosphate dehydrogenase, glucose-6-phophate isomerase and dihydrolipoyl dehydrogenase as well as several important biomarkers for maintaining protein, lipid, nucleotide, or carbohydrate balance such as tryptophanyl t-RNA synthetase and nucleoside diphosphate kinase. Using significantly altered proteins in the tumor bearing mice, we developed models to stratify tumor types and to estimate the minimum detectable tumor volume. Finally, we identified significantly enriched common and unique biological pathways among the eight tumor cell lines tested.

The stability of the landfill is an environmental issue. The collapse of the landfill causes environmental pollution and influences human life. In the present study, the crack on the cover of the landfill was simulated. Rankine�s theory and the Phantom Node Method were used for the simulation length of the crack and the mechanism of the crack propagation in the nonlinear extended finite element method (NXFEM). Artificial Neural Networks (ANNs) based on Levenberg-Marquardt Algorithm and Abalone Rings Data Set mode were used to predict displacement in critical points of the model. The vibration mechanism of the landfill was changed in each model. During applying seismic load on the model, the optimized thickness of the clay cover on the landfill was discussed. The thickness of the landfill cover controls the seismic response of the landfill. The numerical simulation shows differential displacement of the landfill impacts on the crack propagation and the need for the appropriate design of the cover thickness of the landfill

Prostate cancer genomic subtypes that stratify aggressive disease and inform treatment decisions at the primary stage are currently limited. Previously, we functionally validated an aggressive subtype present in 15% of prostate cancer characterized by dual deletion of MAP3K7 and CHD1. Recent studies in the field have focused on deletion of CHD1 and its role in androgen receptor (AR) chromatin distribution and resistance to AR-targeted therapy; however, CHD1 is rarely lost without codeletion of MAP3K7. Here, we show that in the clinically relevant context of co-loss of MAP3K7 and CHD1 there are significant, collective changes to aspects of AR signaling. Although CHD1 loss mainly impacts the expansion of the AR cistrome, loss of MAP3K7 drives increased AR target gene expression. Prostate cancer cell line models engineered to cosuppress MAP3K7 and CHD1 also demonstrated increased AR-v7 expression and resistance to the AR-targeting drug enzalutamide. Furthermore, we determined that low protein expression of both genes is significantly associated with biochemical recurrence (BCR) in a clinical cohort of radical prostatectomy specimens. Low MAP3K7 expression, however, was the strongest independent predictor for risk of BCR over all other tested clinicopathologic factors including CHD1 expression. Collectively, these findings illustrate the importance of MAP3K7 loss in a molecular subtype of prostate cancer that poses challenges to conventional therapeutic approaches. IMPLICATIONS: These findings strongly implicate MAP3K7 loss as a biomarker for aggressive prostate cancer with significant risk for recurrence that poses challenges for conventional androgen receptor-targeted therapies.

Abstract Background Applications of computational methods for predicting protein functional linkages are increasing. In recent years, several bacteria-specific methods for predicting linkages have been developed. The four major genomic context methods are: Gene cluster, Gene neighbor, Rosetta Stone, and Phylogenetic profiles. These methods have been shown to be powerful tools and this paper provides guidelines for when each method is appropriate by exploring different features of each method and potential improvements offered by their combination. We also review many previous treatments of these prediction methods, use the latest available annotations, and offer a number of new observations. Results Using Escherichia coli K12 and Bacillus subtilis, linkage predictions made by each of these methods were evaluated against three benchmarks: functional categories defined by COG and KEGG, known pathways listed in EcoCyc, and known operons listed in RegulonDB. Each evaluated method had strengths and weaknesses, with no one method dominating all aspects of predictive ability studied. For functional categories, as previous studies have shown, the Rosetta Stone method was individually best at detecting linkages and predicting functions among proteins with shared KEGG categories while the Phylogenetic profile method was best for linkage detection and function prediction among proteins with common COG functions. Differences in performance under COG versus KEGG may be attributable to the presence of paralogs. Better function prediction was observed when using a weighted combination of linkages based on reliability versus using a simple unweighted union of the linkage sets. For pathway reconstruction, 99 complete metabolic pathways in E. coli K12 (out of the 209 known, non-trivial pathways) and 193 pathways with 50% of their proteins were covered by linkages from at least one method. Gene neighbor was most effective individually on pathway reconstruction, with 48 complete pathways reconstructed. For operon prediction, Gene cluster predicted completely 59% of the known operons in E. coli K12 and 88% (333/418)in B. subtilis. Comparing two versions of the E. coli K12 operon database, many of the unannotated predictions in the earlier version were updated to true predictions in the later version. Using only linkages found by both Gene Cluster and Gene Neighbor improved the precision of operon predictions. Additionally, as previous studies have shown, combining features based on intergenic region and protein function improved the specificity of operon prediction. Conclusion A common problem for computational methods is the generation of a large number of false positives that might be caused by an incomplete source of validation. By comparing two versions of a database, we demonstrated the dramatic differences on reported results. We used several benchmarks on which we have shown the comparative effectiveness of each prediction method, as well as provided guidelines as to which method is most appropriate for a given prediction task.

Abstract Background Protein-protein interactions networks are most often generated from physical protein-protein interaction data. Co-conservation, also known as phylogenetic profiles, is an alternative source of information for generating protein interaction networks. Co-conservation methods generate interaction networks among proteins that are gained or lost together through evolution. Co-conservation is a particularly useful technique in the compact bacteria genomes. Prior studies in yeast suggest that the topology of protein-protein interaction networks generated from physical interaction assays can offer important insight into protein function. Here, we hypothesize that in bacteria, the topology of protein interaction networks derived via co-conservation information could similarly improve methods for predicting protein function. Since the topology of bacteria co-conservation protein-protein interaction networks has not previously been studied in depth, we first perform such an analysis for co-conservation networks in E. coli K12. Next, we demonstrate one way in which network connectivity measures and global and local function distribution can be exploited to predict protein function for previously uncharacterized proteins. Results Our results showed, like most biological networks, our bacteria co-conserved protein-protein interaction networks had scale-free topologies. Our results indicated that some properties of the physical yeast interaction network hold in our bacteria co-conservation networks, such as high connectivity for essential proteins. However, the high connectivity among protein complexes in the yeast physical network was not seen in the co-conservation network which uses all bacteria as the reference set. We found that the distribution of node connectivity varied by functional category and could be informative for function prediction. By integrating of functional information from different annotation sources and using the network topology, we were able to infer function for uncharacterized proteins. Conclusion Interactions networks based on co-conservation can contain information distinct from networks based on physical or other interaction types. Our study has shown co-conservation based networks to exhibit a scale free topology, as expected for biological networks. We also revealed ways that connectivity in our networks can be informative for the functional characterization of proteins.

Abstract Background The use of computational methods for predicting protein interaction networks will continue to grow with the number of fully sequenced genomes available. The Co-Conservation method, also known as the Phylogenetic profiles method, is a well-established computational tool for predicting functional relationships between proteins. Results Here, we examined how various aspects of this method affect the accuracy and topology of protein interaction networks. We have shown that the choice of reference genome influences the number of predictions involving proteins of previously unknown function, the accuracy of predicted interactions, and the topology of predicted interaction networks. We show that while such results are relatively insensitive to the E-value threshold used in defining homologs, predicted interactions are influenced by the similarity metric that is employed. We show that differences in predicted protein interactions are biologically meaningful, where judicious selection of reference genomes, or use of a new scoring scheme that explicitly considers reference genome relatedness, produces known protein interactions as well as predicted protein interactions involving coordinated biological processes that are not accessible using currently available databases. Conclusion These studies should prove valuable for future studies seeking to further improve phylogenetic profiling methodologies as well for efforts to efficiently employ such methods to develop new biological insights.

Abstract Background DNA microarrays have proven powerful for functional genomics studies. Several technologies exist for the generation of whole-genome arrays. It is well documented that 25mer probes directed against different regions of the same gene produce variable signal intensity values. However, the extent to which this is true for probes of greater length (60mers) is not well characterized. Moreover, this information has not previously been reported for whole-genome arrays designed against bacteria, whose genomes may differ substantially in characteristics directly affecting microarray performance. Results We report here an analysis of alternative 60mer probe designs for an in-situ synthesized oligonucleotide array for the GC rich, β-proteobacterium Burkholderia cenocepacia. Probes were designed using the ArrayOligoSel3.5 software package and whole-genome microarrays synthesized by Agilent, Inc. using their in-situ, ink-jet technology platform. We first validated the quality of the microarrays as demonstrated by an average signal to noise ratio of >1000. Next, we determined that the variance of replicate probes (1178 total probes examined) of identical sequence was 3.8% whereas the variance of alternative probes (558 total alternative probes examined) designs was 9.5%. We determined that depending upon the definition, about 2.4% of replicate and 7.8% of alternative probes produced outlier conclusions. Finally, we determined none of the probe design subscores (GC content, internal repeat, binding energy and self annealment) produced by ArrayOligoSel3.5 were predictive or probes that produced outlier signals. Conclusion Our analysis demonstrated that the use of multiple probes per target sequence is not essential for in-situ synthesized 60mer oligonucleotide arrays designed against bacteria. Although probes producing outlier signals were identified, the use of ratios results in less than 10% of such outlier conclusions. We also determined that several different measures commonly utilized in probe design were not predictive of outlier probes.

We investigated the extent, biologic characterization, phenotypic specificity, and possible regulation of a β1-adrenergic receptor–linked (β1-AR–linked) gene signaling network (β1-GSN) involved in left ventricular (LV) eccentric pathologic remodeling. A 430-member β1-GSN was identified by mRNA expression in transgenic mice overexpressing human β1-ARs or from literature curation, which exhibited opposite directional behavior in interventricular septum endomyocardial biopsies taken from patients with beta-blocker–treated, reverse remodeled dilated cardiomyopathies. With reverse remodeling, the major biologic categories and percentage of the dominant directional change were as follows: metabolic (19.3%, 81% upregulated); gene regulation (14.9%, 78% upregulated); extracellular matrix/fibrosis (9.1%, 92% downregulated); and cell homeostasis (13.3%, 60% upregulated). Regarding the comparison of β1-GSN categories with expression from 19,243 nonnetwork genes, phenotypic selection for major β1-GSN categories was exhibited for LV end systolic volume (contractility measure), ejection fraction (remodeling index), and pulmonary wedge pressure (wall tension surrogate), beginning at 3 months and persisting to study completion at 12 months. In addition, 121 lncRNAs were identified as possibly involved in cis-acting regulation of β1-GSN members. We conclude that an extensive 430-member gene network downstream from the β1-AR is involved in pathologic ventricular remodeling, with metabolic genes as the most prevalent category.

The mechanical behaviour of Fibre-reinforced sands (FRS) has been extensively studied, presenting improved mechanical properties compared to unreinforced soils. Many models have been developed to predict its general stress-strain behaviour. However, the use of double-phase models in FRS is still incipient. Double-phase models are advantageous because they can simulate the whole FRS and the behaviour of its individual components, soil skeleton and reinforcement. This paper uses a modified model for Municipal Solid Waste to reproduce the FRS mechanical response. Introducing a new hardening parameter and a dilatant zone allowed the model to reproduce FRS dilatancy. The model's variables are easily understood, allowing the reproduction of the mechanical behaviour of FRS formed by sands with void ratios ranging from 0.610 to 0.917 and mean grain size from 0.29 to 0.83 mm. The fibres' lengths varied from 12.5 to 51 mm. The results of triaxial and hollow cylinder torsional tests under different stress paths had their main characteristics (peak strength, post-peak behaviour, dilatancy and reinforcement effectiveness) well captured by the model. Predicted and experimental FRS's deviator stress usually differ by less than 15% and the model performance is equivalent or superior to other available models, even requiring fewer input parameters. [ABSTRACT FROM AUTHOR]

microRNAs (miRs) are small non-coding single-stranded RNAs that regulate gene expression. We previously evaluated expression of miRs in the cardiac tissue of children with dilated cardiomyopathy (DCM) using miRNA-seq. However, a comparative analysis of serum and cardiac miRs has not been performed in this population. The current study aimed to evaluate miR levels in the serum of pediatric DCM patients compared to healthy non-failing (NF) donor controls and investigate the association between miR levels in tissue and sera from the same pediatric DCM patients. Defining the relationship between serum and tissue miRs may allow the use of circulating miRs as surrogate markers of cardiac miRs. miR levels were investigated through miR-array in sera [n = 10 NF, n = 12 DCM] and miR-seq in tissue (n = 10 NF, n = 12 DCM). Pathway analysis was investigated using the miR enrichment analysis and annotation tool (miEAA) for the five miRs commonly dysregulated in the sera and tissue of pediatric DCM patients. Functional analysis of miRs commonly dysregulated in the sera and tissue of pediatric DCM patients suggests altered pathways related to cell growth, differentiation and proliferation, inflammation, mitochondrial function, and metabolism. These findings suggest that circulating miRs could reflect altered levels of cardiac tissue miRs.

Dilated cardiomyopathy (DCM) is the most common form of cardiomyopathy and main indication for heart transplantation in children. Therapies specific to pediatric DCM remain limited due to lack of a disease model. Our previous study showed that treatment of neonatal rat ventricular myocytes (NRVMs) with serum from nonfailing or DCM pediatric patients activates the fetal gene program (FGP). Here we show that serum treatment with proteinase K prevents activation of the FGP, whereas RNase treatment exacerbates it, suggesting that circulating proteins, but not circulating miRNAs, promote these pathological changes. Evaluation of the protein secretome showed that midkine (MDK) is upregulated in DCM serum, and NRVM treatment with MDK activates the FGP. Changes in gene expression in serum-treated NRVMs, evaluated by next-generation RNA-Seq, indicated extracellular matrix remodeling and focal adhesion pathways were upregulated in pediatric DCM serum and in DCM serum–treated NRVMs, suggesting alterations in cellular stiffness. Cellular stiffness was evaluated by Atomic Force Microscopy, which showed an increase in stiffness in DCM serum–treated NRVMs. Of the proteins increased in DCM sera, secreted frizzled-related protein 1 (sFRP1) was a potential candidate for the increase in cellular stiffness, and sFRP1 treatment of NRVMs recapitulated the increase in cellular stiffness observed in response to DCM serum treatment. Our results show that serum circulating proteins promoted pathological changes in gene expression and cellular stiffness, and circulating miRNAs were protective against pathological changes.