Lipidomic profiling in the Strong Heart Study identified American Indians at risk of chronic kidney disease.

Dyslipidemia associates with and usually precedes the onset of chronic kidney disease (CKD), but a comprehensive assessment of molecular lipid species associated with risk of CKD is lacking. Here, we sought to identify fasting plasma lipids associated with risk of CKD among American Indians in the Strong Heart Family Study, a large-scale community-dwelling of individuals, followed by replication in Mexican Americans from the San Antonio Family Heart Study and Caucasians from the Australian Diabetes, Obesity and Lifestyle Study. We also performed repeated measurement analysis to examine the temporal relationship between the change in the lipidome and change in kidney function between baseline and follow-up of about five years apart. Network analysis was conducted to identify differential lipid classes associated with risk of CKD. In the discovery cohort, we found that higher baseline level of multiple lipid species, including glycerophospholipids, glycerolipids and sphingolipids, was significantly associated with increased risk of CKD, independent of age, sex, body mass index, diabetes and hypertension. Many lipid species were replicated in at least one external cohort at the individual lipid species and/or the class level. Longitudinal change in the plasma lipidome was significantly associated with change in the estimated glomerular filtration rate after adjusting for covariates, baseline lipids and the baseline rate. Network analysis identified distinct lipidomic signatures differentiating high from low-risk groups. Thus, our results demonstrated that disturbed lipid metabolism precedes the onset of CKD. These findings shed light on the mechanisms linking dyslipidemia to CKD and provide potential novel biomarkers for identifying individuals with early impaired kidney function at preclinical stages.

Toughening and Healing of CFRPs by Diels-Alder-Based Nano-Modified Resin through Melt Electro-Writing Process Technique.

In the current study, a novel approach in terms of the incorporation of self-healing agent (SHA) into unidirectional (UD) carbon fiber reinforced plastics (CFRPs) has been demonstrated. More precisely, Diels-Alder (DA) mechanism-based resin (Bis-maleimide type) containing or not four layered graphene nanoplatelets (GNPs) at the amount of 1 wt% was integrated locally in the mid-thickness area of CFRPs by melt electro-writing process (MEP). Based on that, CFRPs containing or not SHA were fabricated and further tested under Mode I interlaminar fracture toughness experiments. According to experimental results, modified CFRPs exhibited a considerable enhancement in the interlaminar fracture toughness properties (peak load (Pmax) and fracture toughness energy I (GIC) values). After Mode I interlaminar fracture toughness testing, the damaged samples followed the healing process and then were tested again under identical experimental conditions. The repeating of the tests revealed moderate healing efficiency (H.E.) since part of the interlaminar fracture toughness properties were restored. Furthermore, three-point bending (3PB) experiments were conducted, with the aim of assessing the effect of the incorporated SHA on the in-plane mechanical properties of the final CFRPs. Finally, optical microscopy (OM) examinations were performed to investigate the activated/involved damage mechanisms.

Deep annotation of untargeted LC-MS metabolomics data with Binner.

MOTIVATION: When metabolites are analyzed by electrospray ionization (ESI)-mass spectrometry, they are usually detected as multiple ion species due to the presence of isotopes, adducts and in-source fragments. The signals generated by these degenerate features (along with contaminants and other chemical noise) obscure meaningful patterns in MS data, complicating both compound identification and downstream statistical analysis. To address this problem, we developed Binner, a new tool for the discovery and elimination of many degenerate feature signals typically present in untargeted ESI-LC-MS metabolomics data. RESULTS: Binner generates feature annotations and provides tools to help users visualize informative feature relationships that can further elucidate the underlying structure of the data. To demonstrate the utility of Binner and to evaluate its performance, we analyzed data from reversed phase LC-MS and hydrophilic interaction chromatography (HILIC) platforms and demonstrated the accuracy of selected annotations using MS/MS. When we compared Binner annotations of 75 compounds previously identified in human plasma samples with annotations generated by three similar tools, we found that Binner achieves superior performance in the number and accuracy of annotations while simultaneously minimizing the number of incorrectly annotated principal ions. Data reduction and pattern exploration with Binner have allowed us to catalog a number of previously unrecognized complex adducts and neutral losses generated during the ionization of molecules in LC-MS. In summary, Binner allows users to explore patterns in their data and to efficiently and accurately eliminate a significant number of the degenerate features typically found in various LC-MS modalities. AVAILABILITY AND IMPLEMENTATION: Binner is written in Java and is freely available from http://binner.med.umich.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Renin-angiotensin system inhibition reverses the altered triacylglycerol metabolic network in diabetic kidney disease.

OBJECTIVE: Dyslipidemia is a significant risk factor for progression of diabetic kidney disease (DKD). Determining the changes in individual lipids and lipid networks across a spectrum of DKD severity may identify lipids that are pathogenic to DKD progression. METHODS: We performed untargeted lipidomic analysis of kidney cortex tissue from diabetic db/db and db/db eNOS-/- mice along with non-diabetic littermate controls. A subset of mice were treated with the renin-angiotensin system (RAS) inhibitors, lisinopril and losartan, which improves the DKD phenotype in the db/db eNOS-/- mouse model. RESULTS: Of the three independent variables in this study, diabetes had the largest impact on overall lipid levels in the kidney cortex, while eNOS expression and RAS inhibition had smaller impacts on kidney lipid levels. Kidney lipid network architecture, particularly of networks involving glycerolipids such as triacylglycerols, was substantially disrupted by worsening kidney disease in the db/db eNOS-/- mice compared to the db/db mice, a feature that was reversed with RAS inhibition. This was associated with decreased expression of the stearoyl-CoA desaturases, Scd1 and Scd2, with RAS inhibition. CONCLUSIONS: In addition to the known salutary effect of RAS inhibition on DKD progression, our results suggest a previously unrecognized role for RAS inhibition on the kidney triacylglycerol lipid metabolic network.

Quantifying heterogeneity of expression data based on principal components.

MOTIVATION: The diversity of biological omics data provides richness of information, but also presents an analytic challenge. While there has been much methodological and theoretical development on the statistical handling of large volumes of biological data, far less attention has been devoted to characterizing their veracity and variability. RESULTS: We propose a method of statistically quantifying heterogeneity among multiple groups of datasets, derived from different omics modalities over various experimental and/or disease conditions. It draws upon strategies from analysis of variance and principal component analysis in order to reduce dimensionality of the variability across multiple data groups. The resulting hypothesis-based inference procedure is demonstrated with synthetic and real data from a cell line study of growth factor responsiveness based on a factorial experimental design. AVAILABILITY AND IMPLEMENTATION: Source code and datasets are freely available at https://github.com/yangzi4/gPCA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Differential network enrichment analysis reveals novel lipid pathways in chronic kidney disease.

MOTIVATION: Functional enrichment testing methods can reduce data comprising hundreds of altered biomolecules to smaller sets of altered biological 'concepts' that help generate testable hypotheses. This study leveraged differential network enrichment analysis methodology to identify and validate lipid subnetworks that potentially differentiate chronic kidney disease (CKD) by severity or progression. RESULTS: We built a partial correlation interaction network, identified highly connected network components, applied network-based gene-set analysis to identify differentially enriched subnetworks, and compared the subnetworks in patients with early-stage versus late-stage CKD. We identified two subnetworks 'triacylglycerols' and 'cardiolipins-phosphatidylethanolamines (CL-PE)' characterized by lower connectivity, and a higher abundance of longer polyunsaturated triacylglycerols in patients with severe CKD (stage ≥4) from the Clinical Phenotyping Resource and Biobank Core. These finding were replicated in an independent cohort, the Chronic Renal Insufficiency Cohort. Using an innovative method for elucidating biological alterations in lipid networks, we demonstrated alterations in triacylglycerols and cardiolipins-phosphatidylethanolamines that precede the clinical outcome of end-stage kidney disease by several years. AVAILABILITY AND IMPLEMENTATION: A complete list of NetGSA results in HTML format can be found at http://metscape.ncibi.org/netgsa/12345-022118/cric_cprobe/022118/results_cric_cprobe/main.html. The DNEA is freely available at https://github.com/wiggie/DNEA. Java wrapper leveraging the cytoscape.js framework is available at http://js.cytoscape.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Elevated lipoxygenase and cytochrome P450 products predict progression of chronic kidney disease.

BACKGROUND: The clinical relevance of arachidonic acid (AA) metabolites in chronic kidney disease (CKD) progression is poorly understood. We aimed to compare the concentrations of 85 enzymatic pathway products of AA metabolism in patients with CKD who progressed to end-stage kidney disease (ESKD) versus patients who did not in a subcohort of Chronic Renal Insufficiency Cohort (CRIC) and to estimate the risk of CKD progression and major cardiovascular events by levels of AA metabolites and their link to enzymatic metabolic pathways. METHODS: A total 123 patients in the CRIC study who progressed to ESKD were frequency matched with 177 nonprogressors and serum eicosanoids were quantified by mass spectrometry. We applied serum collected at patients' Year 1 visit and outcome of progression to ESKD was ascertained over the next 10 years. We used logistic regression models for risk estimation. RESULTS: Baseline 15-hydroxyeicosatetraenoate (HETE) and 20-HETE levels were significantly elevated in progressors (false discovery rate Q ≤ 0.026). The median 20-HETE level was 7.6 pmol/mL [interquartile range (IQR) 4.2-14.5] in progressors and 5.4 pmol/mL (IQR 2.8-9.4) in nonprogressors (P < 0.001). In an adjusted model, only 20-HETE independently predicted CKD progression. Each 1 standard deviation increase in 20-HETE was independently associated with 1.45-fold higher odds of progression (95% confidence interval 1.07-1.95; P = 0.017). Principal components of lipoxygenase (LOX) and cytochrome P450 (CYP450) pathways were independently associated with CKD progression. CONCLUSIONS: We found higher odds of CKD progression associated with higher 20-HETE, LOX and CYP450 metabolic pathways. These alterations precede CKD progression and may serve as targets for interventions aimed at halting progression.

A comparative study of topology-based pathway enrichment analysis methods.

BACKGROUND: Pathway enrichment extensively used in the analysis of Omics data for gaining biological insights into the functional roles of pre-defined subsets of genes, proteins and metabolites. A large number of methods have been proposed in the literature for this task. The vast majority of these methods use as input expression levels of the biomolecules under study together with their membership in pathways of interest. The latest generation of pathway enrichment methods also leverages information on the topology of the underlying pathways, which as evidence from their evaluation reveals, lead to improved sensitivity and specificity. Nevertheless, a systematic empirical comparison of such methods is still lacking, making selection of the most suitable method for a specific experimental setting challenging. This comparative study of nine network-based methods for pathway enrichment analysis aims to provide a systematic evaluation of their performance based on three real data sets with different number of features (genes/metabolites) and number of samples. RESULTS: The findings highlight both methodological and empirical differences across the nine methods. In particular, certain methods assess pathway enrichment due to differences both across expression levels and in the strength of the interconnectedness of the members of the pathway, while others only leverage differential expression levels. In the more challenging setting involving a metabolomics data set, the results show that methods that utilize both pieces of information (with NetGSA being a prototypical one) exhibit superior statistical power in detecting pathway enrichment. CONCLUSION: The analysis reveals that a number of methods perform equally well when testing large size pathways, which is the case with genomic data. On the other hand, NetGSA that takes into consideration both differential expression of the biomolecules in the pathway, as well as changes in the topology exhibits a superior performance when testing small size pathways, which is usually the case for metabolomics data.

Untargeted Metabolomics Differentiates l-Carnitine Treated Septic Shock 1-Year Survivors and Nonsurvivors.

l-Carnitine is a candidate therapeutic for the treatment of septic shock, a condition that carries a ≥40% mortality. Responsiveness to l-carnitine may hinge on unique metabolic profiles that are not evident from the clinical phenotype. To define these profiles, we performed an untargeted metabolomic analysis of serum from 21 male sepsis patients enrolled in a placebo-controlled l-carnitine clinical trial. Although treatment with l-carnitine is known to induce changes in the sepsis metabolome, we found a distinct set of metabolites that differentiated 1-year survivors from nonsurvivors. Following feature alignment, we employed a new and innovative data reduction strategy followed by false discovery correction, and identified 63 metabolites that differentiated carnitine-treated 1-year survivors versus nonsurvivors. Following identification by MS/MS and database search, several metabolite markers of vascular inflammation were determined to be prominently elevated in the carnitine-treated nonsurvivor cohort, including fibrinopeptide A, allysine, and histamine. While preliminary, these results corroborate that metabolic profiles may be useful to differentiate l-carnitine treatment responsiveness. Furthermore, these data show that the metabolic signature of l-carnitine-treated nonsurvivors is associated with a severity of illness (e.g., vascular inflammation) that is not routinely clinically detected.

Impaired ß-Oxidation and Altered Complex Lipid Fatty Acid Partitioning with Advancing CKD.

Studies of lipids in CKD, including ESRD, have been limited to measures of conventional lipid profiles. We aimed to systematically identify 17 different lipid classes and associate the abundance thereof with alterations in acylcarnitines, a metric of ß-oxidation, across stages of CKD. From the Clinical Phenotyping Resource and Biobank Core (CPROBE) cohort of 1235 adults, we selected a panel of 214 participants: 36 with stage 1 or 2 CKD, 99 with stage 3 CKD, 61 with stage 4 CKD, and 18 with stage 5 CKD. Among participants, 110 were men (51.4%), 64 were black (29.9%), and 150 were white (70.1%), and the mean (SD) age was 60 (16) years old. We measured plasma lipids and acylcarnitines using liquid chromatography-mass spectrometry. Overall, we identified 330 different lipids across 17 different classes. Compared with earlier stages, stage 5 CKD associated with a higher abundance of saturated C16-C20 free fatty acids (FFAs) and long polyunsaturated complex lipids. Long-chain-to-intermediate-chain acylcarnitine ratio, a marker of efficiency of ß-oxidation, exhibited a graded decrease from stage 2 to 5 CKD (P<0.001). Additionally, multiple linear regression revealed that the long-chain-to-intermediate-chain acylcarnitine ratio inversely associated with polyunsaturated long complex lipid subclasses and the C16-C20 FFAs but directly associated with short complex lipids with fewer double bonds. We conclude that increased abundance of saturated C16-C20 FFAs coupled with impaired ß-oxidation of FFAs and inverse partitioning into complex lipids may be mechanisms underpinning lipid metabolism changes that typify advancing CKD.

Shared and distinct lipid-lipid interactions in plasma and affected tissues in a diabetic mouse model.

Lipids are ubiquitous metabolites with diverse functions; abnormalities in lipid metabolism appear to be related to complications from multiple diseases, including type 2 diabetes. Through technological advances, the entire lipidome has been characterized and researchers now need computational approaches to better understand lipid network perturbations in different diseases. Using a mouse model of type 2 diabetes with microvascular complications, we examined lipid levels in plasma and in renal, neural, and retinal tissues to identify shared and distinct lipid abnormalities. We used correlation analysis to construct interaction networks in each tissue, to associate changes in lipids with changes in enzymes of lipid metabolism, and to identify overlap of coregulated lipid subclasses between plasma and each tissue to define subclasses of plasma lipids to use as surrogates of tissue lipid metabolism. Lipid metabolism alterations were mostly tissue specific in the kidney, nerve, and retina; no lipid changes correlated between the plasma and all three tissue types. However, alterations in diacylglycerol and in lipids containing arachidonic acid, an inflammatory mediator, were shared among the tissue types, and the highly saturated cholesterol esters were similarly coregulated between plasma and each tissue type in the diabetic mouse. Our results identified several patterns of altered lipid metabolism that may help to identify pathogenic alterations in different tissues and could be used as biomarkers in future research into diabetic microvascular tissue damage.

Influence of the neural microenvironment on prostate cancer.

BACKGROUND: Nerves are key factors in prostate cancer (PCa), but the functional role of innervation in prostate cancer is poorly understood. PCa induced neurogenesis and perineural invasion (PNI), are associated with aggressive disease. METHOD: We denervated rodent prostates chemically and physically, before orthotopically implanting cancer cells. We also performed a human neoadjuvant clinical trial using botulinum toxin type A (Botox) and saline in the same patient, before prostatectomy. RESULT: Bilateral denervation resulted in reduced tumor incidence and size in mice. Botox treatment in humans resulted in increased apoptosis of cancer cells in the Botox treated side. A similar denervation gene array profile was identified in tumors arising in denervated rodent prostates, in spinal cord injury patients and in the Botox treated side of patients. Denervation induced exhibited a signature gene profile, indicating translation and bioenergetic shutdown. Nerves also regulate basic cellular functions of non-neoplastic epithelial cells. CONCLUSION: Nerves play a role in the homeostasis of normal epithelial tissues and are involved in prostate cancer tumor survival. This study confirms that interactions between human cancer and nerves are essential to disease progression. This work may make a major impact in general cancer treatment strategies, as nerve/cancer interactions are likely important in other cancers as well. Targeting the neural microenvironment may represent a therapeutic approach for the treatment of human prostate cancer.

Sparse network modeling and metscape-based visualization methods for the analysis of large-scale metabolomics data.

MOTIVATION: Recent technological advances in mass spectrometry, development of richer mass spectral libraries and data processing tools have enabled large scale metabolic profiling. Biological interpretation of metabolomics studies heavily relies on knowledge-based tools that contain information about metabolic pathways. Incomplete coverage of different areas of metabolism and lack of information about non-canonical connections between metabolites limits the scope of applications of such tools. Furthermore, the presence of a large number of unknown features, which cannot be readily identified, but nonetheless can represent bona fide compounds, also considerably complicates biological interpretation of the data. RESULTS: Leveraging recent developments in the statistical analysis of high-dimensional data, we developed a new Debiased Sparse Partial Correlation algorithm (DSPC) for estimating partial correlation networks and implemented it as a Java-based CorrelationCalculator program. We also introduce a new version of our previously developed tool Metscape that enables building and visualization of correlation networks. We demonstrate the utility of these tools by constructing biologically relevant networks and in aiding identification of unknown compounds. AVAILABILITY AND IMPLEMENTATION: http://metscape.med.umich.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

CAPER is vital for energy and redox homeostasis by integrating glucose-induced mitochondrial functions via ERR-α-Gabpa and stress-induced adaptive responses via NF-κB-cMYC.

Ever since we developed mitochondria to generate ATP, eukaryotes required intimate mito-nuclear communication. In addition, since reactive oxygen species are a cost of mitochondrial oxidative phosphorylation, this demands safeguards as protection from these harmful byproducts. Here we identified a critical transcriptional integrator which eukaryotes share to orchestrate both nutrient-induced mitochondrial energy metabolism and stress-induced nuclear responses, thereby maintaining carbon-nitrogen balance, and preserving life span and reproductive capacity. Inhibition of nutrient-induced expression of CAPER arrests nutrient-dependent cell proliferation and ATP generation and induces autophagy-mediated vacuolization. Nutrient signaling to CAPER induces mitochondrial transcription and glucose-dependent mitochondrial respiration via coactivation of nuclear receptor ERR-α-mediated Gabpa transcription. CAPER is also a coactivator for NF-κB that directly regulates c-Myc to coordinate nuclear transcriptome responses to mitochondrial stress. Finally, CAPER is responsible for anaplerotic carbon flux into TCA cycles from glycolysis, amino acids and fatty acids in order to maintain cellular energy metabolism to counter mitochondrial stress. Collectively, our studies reveal CAPER as an evolutionarily conserved 'master' regulatory mechanism by which eukaryotic cells control vital homeostasis for both ATP and antioxidants via CAPER-dependent coordinated control of nuclear and mitochondrial transcriptomic programs and their metabolisms. These CAPER dependent bioenergetic programs are highly conserved, as we demonstrated that they are essential to preserving life span and reproductive capacity in human cells-and even in C. elegans.

Exponentially time decaying susceptible-informed (SIT) model for information diffusion process on networks.

Modeling information diffusion on networks is a timely topic due to its significance in massive online social media platforms. Models motivated by disease epidemics, such as the Susceptible-Infected-Removed and Susceptible-Infected-Susceptible (SIS), ones have been used for this task, together with threshold models. A key limitation of these models is that the intrinsic time value of information is not accounted for, an important feature for social media applications, since "old" piece of news does not attract adequate attention. We obtain results pertaining to the diffusion size across the diffusion's evolution over time, as well as for early time points that enable us to calculate the phase transition epoch and the epidemic threshold, using mean field approximations. Further, we explicitly calculate the total probability of getting informed for each node depending on its actual path to the single seed node and then propose a novel approach by constructing a Maximum Weight Tree (MWT) to approximate the final fraction of diffusion, with the weight of each node approximating the total probability of getting informed. The MWT approximation is a novel approach that is exact for tree-like network and is specifically designed for sparse networks. It is also fast to compute and provides another general tool for the analyst to obtain accurate approximations of the "epidemic's" size. Extensive comparisons with results based on Monte Carlo simulation of the information diffusion process show that the derived mean field approximations, as well as that employing the MWT one, provide very accurate estimates of the quantities of interest.

A non-negative matrix factorization method for detecting modules in heterogeneous omics multi-modal data.

MOTIVATION: Recent advances in high-throughput omics technologies have enabled biomedical researchers to collect large-scale genomic data. As a consequence, there has been growing interest in developing methods to integrate such data to obtain deeper insights regarding the underlying biological system. A key challenge for integrative studies is the heterogeneity present in the different omics data sources, which makes it difficult to discern the coordinated signal of interest from source-specific noise or extraneous effects. RESULTS: We introduce a novel method of multi-modal data analysis that is designed for heterogeneous data based on non-negative matrix factorization. We provide an algorithm for jointly decomposing the data matrices involved that also includes a sparsity option for high-dimensional settings. The performance of the proposed method is evaluated on synthetic data and on real DNA methylation, gene expression and miRNA expression data from ovarian cancer samples obtained from The Cancer Genome Atlas. The results show the presence of common modules across patient samples linked to cancer-related pathways, as well as previously established ovarian cancer subtypes. AVAILABILITY AND IMPLEMENTATION: The source code repository is publicly available at https://github.com/yangzi4/iNMF. CONTACT: gmichail@umich.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Network-based pathway enrichment analysis with incomplete network information.

MOTIVATION: Pathway enrichment analysis has become a key tool for biomedical researchers to gain insight into the underlying biology of differentially expressed genes, proteins and metabolites. It reduces complexity and provides a system-level view of changes in cellular activity in response to treatments and/or in disease states. Methods that use existing pathway network information have been shown to outperform simpler methods that only take into account pathway membership. However, despite significant progress in understanding the association amongst members of biological pathways, and expansion of data bases containing information about interactions of biomolecules, the existing network information may be incomplete or inaccurate and is not cell-type or disease condition-specific. RESULTS: We propose a constrained network estimation framework that combines network estimation based on cell- and condition-specific high-dimensional Omics data with interaction information from existing data bases. The resulting pathway topology information is subsequently used to provide a framework for simultaneous testing of differences in expression levels of pathway members, as well as their interactions. We study the asymptotic properties of the proposed network estimator and the test for pathway enrichment, and investigate its small sample performance in simulated and real data settings. AVAILABILITY AND IMPLEMENTATION: The proposed method has been implemented in the R-package netgsa available on CRAN. CONTACT: jinma@upenn.eduSupplementary information: Supplementary data are available at Bioinformatics online.

Integrative Pathway Analysis of Metabolic Signature in Bladder Cancer: A Linkage to The Cancer Genome Atlas Project and Prediction of Survival.

PURPOSE: We used targeted mass spectrometry to study the metabolic fingerprint of urothelial cancer and determine whether the biochemical pathway analysis gene signature would have a predictive value in independent cohorts of patients with bladder cancer. MATERIALS AND METHODS: Pathologically evaluated, bladder derived tissues, including benign adjacent tissue from 14 patients and bladder cancer from 46, were analyzed by liquid chromatography based targeted mass spectrometry. Differential metabolites associated with tumor samples in comparison to benign tissue were identified by adjusting the p values for multiple testing at a false discovery rate threshold of 15%. Enrichment of pathways and processes associated with the metabolic signature were determined using the GO (Gene Ontology) Database and MSigDB (Molecular Signature Database). Integration of metabolite alterations with transcriptome data from TCGA (The Cancer Genome Atlas) was done to identify the molecular signature of 30 metabolic genes. Available outcome data from TCGA portal were used to determine the association with survival. RESULTS: We identified 145 metabolites, of which analysis revealed 31 differential metabolites when comparing benign and tumor tissue samples. Using the KEGG (Kyoto Encyclopedia of Genes and Genomes) Database we identified a total of 174 genes that correlated with the altered metabolic pathways involved. By integrating these genes with the transcriptomic data from the corresponding TCGA data set we identified a metabolic signature consisting of 30 genes. The signature was significant in its prediction of survival in 95 patients with a low signature score vs 282 with a high signature score (p = 0.0458). CONCLUSIONS: Targeted mass spectrometry of bladder cancer is highly sensitive for detecting metabolic alterations. Applying transcriptome data allows for integration into larger data sets and identification of relevant metabolic pathways in bladder cancer progression.

Metabolomic profiling identifies biochemical pathways associated with castration-resistant prostate cancer.

Despite recent developments in treatment strategies, castration-resistant prostate cancer (CRPC) is still the second leading cause of cancer-associated mortality among American men, the biological underpinnings of which are not well understood. To this end, we measured levels of 150 metabolites and examined the rate of utilization of 184 metabolites in metastatic androgen-dependent prostate cancer (AD) and CRPC cell lines using a combination of targeted mass spectrometry and metabolic phenotyping. Metabolic data were used to derive biochemical pathways that were enriched in CRPC, using Oncomine concept maps (OCM). The enriched pathways were then examined in-silico for their association with treatment failure (i.e., prostate specific antigen (PSA) recurrence or biochemical recurrence) using published clinically annotated gene expression data sets. Our results indicate that a total of 19 metabolites were altered in CRPC compared to AD cell lines. These altered metabolites mapped to a highly interconnected network of biochemical pathways that describe UDP glucuronosyltransferase (UGT) activity. We observed an association with time to treatment failure in an analysis employing genes restricted to this pathway in three independent gene expression data sets. In summary, our studies highlight the value of employing metabolomic strategies in cell lines to derive potentially clinically useful predictive tools.

OKVAR-Boost: a novel boosting algorithm to infer nonlinear dynamics and interactions in gene regulatory networks.

MOTIVATION: Reverse engineering of gene regulatory networks remains a central challenge in computational systems biology, despite recent advances facilitated by benchmark in silico challenges that have aided in calibrating their performance. A number of approaches using either perturbation (knock-out) or wild-type time-series data have appeared in the literature addressing this problem, with the latter using linear temporal models. Nonlinear dynamical models are particularly appropriate for this inference task, given the generation mechanism of the time-series data. In this study, we introduce a novel nonlinear autoregressive model based on operator-valued kernels that simultaneously learns the model parameters, as well as the network structure. RESULTS: A flexible boosting algorithm (OKVAR-Boost) that shares features from L2-boosting and randomization-based algorithms is developed to perform the tasks of parameter learning and network inference for the proposed model. Specifically, at each boosting iteration, a regularized Operator-valued Kernel-based Vector AutoRegressive model (OKVAR) is trained on a random subnetwork. The final model consists of an ensemble of such models. The empirical estimation of the ensemble model's Jacobian matrix provides an estimation of the network structure. The performance of the proposed algorithm is first evaluated on a number of benchmark datasets from the DREAM3 challenge and then on real datasets related to the In vivo Reverse-Engineering and Modeling Assessment (IRMA) and T-cell networks. The high-quality results obtained strongly indicate that it outperforms existing approaches. AVAILABILITY: The OKVAR-Boost Matlab code is available as the archive: http://amis-group.fr/sourcecode-okvar-boost/OKVARBoost-v1.0.zip. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.