Meta-analysis of pathway enrichment: combining independent and dependent omics data sets

Easy-Amanida: An R Shiny application for the meta-analysis of aggregate results in clinical metabolomics using Amanida and Webchem

Meta-analysis is a useful tool in clinical research, as it combines the results of multiple clinical studies to improve precision when answering a particular scientific question. While there has been a substantial increase in publications using meta-analysis in various clinical research topics, the number of published meta-analyses in metabolomics is significantly lower compared to other omics disciplines. Metabolomics is the study of small chemical compounds in living organisms, which provides important insights into an organism's phenotype. However, the wide variety of compounds and the different experimental methods used in metabolomics make it challenging to perform a thorough meta-analysis. Additionally, there is a lack of consensus on reporting statistical estimates, and the high number of compound naming synonyms further complicates the process. Easy-Amanida is a new tool that combines two R packages, "amanida" and "webchem", to enable meta-analysis of aggregate statistical data, like p-value and fold-change, while ensuring the compounds naming harmonization. The Easy-Amanida app is implemented in Shiny, an R package add-on for interactive web apps, and provides a workflow to optimize the naming combination. This article describes all the steps to perform the meta-analysis using Easy-Amanida, including an illustrative example for interpreting the results. The use of aggregate statistics metrics extends the use of Easy-Amanida beyond the metabolomics field.

Ex vivo metabolomics-A hypothesis-free approach to identify native substrate(s) and product(s) of orphan enzymes

Over the past decade, the number of fully sequenced genomes has increased at an awe-inspiring pace. Similarly, the quality and scope of tools for the prediction of both protein structure and function has seen vast improvements. However, to pinpoint the exact function of a protein, for instance the exact reaction catalyzed by an enzyme, experimental evidence is crucial. At the same time, this step is the main bottleneck when generating a conclusive model for the function of an enzyme and to interpret its function in a physiological context. Hence, a comprehensive experimental strategy for functional annotation of enzymes that is as efficient as possible is required. Ex vivo metabolomics is a powerful non-targeted approach that overcomes several of the challenges inherent to in vitro characterization of enzymes with unknown functions. By incubating the recombinant enzyme of interest in a quasi-native metabolite extract from its tissue of origin under specific environmental and developmental conditions, the complete native substrate range can be tested in a single assay. This unlocks compounds that are commercially unavailable or otherwise difficult to procure. Coupled with non-targeted metabolomics analysis, ex vivo has the capability to test for and identify even unexpected substrates and assign the respective products of the enzymatic reaction.

Metabolite fingerprinting: A powerful metabolomics approach for marker identification and functional gene annotation

Non-targeted metabolome approaches aim to detect metabolite markers related to stress, disease, developmental or genetic perturbation. In the later context, it is also a powerful means for functional gene annotation. A prerequisite for non-targeted metabolome analyses are methods for comprehensive metabolite extraction. We present three extraction protocols for a highly efficient extraction of metabolites from plant material with a very broad metabolite coverage. The presented metabolite fingerprinting workflow is based on liquid chromatography high resolution accurate mass spectrometry (LC-HRAM-MS), which provides suitable separation of the complex sample matrix for the analysis of compounds of different polarity by positive and negative electrospray ionization and mass spectrometry. The resulting data sets are then analyzed with the software suite MarVis and the web-based interface MetaboAnalyst. MarVis offers a straightforward workflow for statistical analysis, data merging as well as visualization of multivariate data, while MetaboAnalyst is used in our hands as complementary software for statistics, correlation networks and figure generation. Finally, MarVis provides access to species-specific metabolite and pathway data bases like KEGG and BioCyc and to custom data bases tailored by the user to connect the identified markers or features with metabolites. In addition, identified marker candidates can be interactively visualized and inspected in metabolic pathway maps by KEGG pathways for a more detailed functional annotation and confirmed by mass spectrometry fragmentation experiments or coelution with authentic standards. Together this workflow is a valuable toolbox to identify novel metabolites, metabolic steps or regulatory principles and pathways.

From SNP to pathway-based GWAS meta-analysis: do current meta-analysis approaches resolve power and replication in genetic association studies?

Genome-wide association studies (GWAS) have benefited greatly from enhanced high-throughput technology in recent decades. GWAS meta-analysis has become increasingly popular to highlight the genetic architecture of complex traits, informing about the replicability and variability of effect estimations across human ancestries. A wealth of GWAS meta-analysis methodologies have been developed depending on the input data and the outcome information of interest. We present a survey of current approaches from SNP to pathway-based meta-analysis by acknowledging the range of resources and methodologies in the field, and we provide a comprehensive review of different categories of Genome-Wide Meta-analysis methods employed. These methods highlight different levels at which GWAS meta-analysis may be done, including Single Nucleotide Polymorphisms, Genes and Pathways, for which we describe their framework outline. We also discuss the strengths and pitfalls of each approach and make suggestions regarding each of them.

A non-targeted metabolomics analysis identifies wound-induced oxylipins in Physcomitrium patens

Plant oxylipins are a class of lipid-derived signaling molecules being involved in the regulation of various biotic and abiotic stress responses. A major class of oxylipins are the circular derivatives to which 12-oxo-phytodienoic acid (OPDA) and its metabolite jasmonic acid (JA) belong. While OPDA and its shorter chain homologue dinor-OPDA (dnOPDA) seem to be ubiquitously found in land plants ranging from bryophytes to angiosperms, the occurrence of JA and its derivatives is still under discussion. The bryophyte Physcomitrium patens has received increased scientific interest as a non-vascular plant model organism over the last decade. Therefore, we followed the metabolism upon wounding by metabolite fingerprinting with the aim to identify jasmonates as well as novel oxylipins in P. patens. A non-targeted metabolomics approach was used to reconstruct the metabolic pathways for the synthesis of oxylipins, derived from roughanic, linoleic, α-linolenic, and arachidonic acid in wild type, the oxylipin-deficient mutants of Ppaos1 and Ppaos2, the mutants of Ppdes being deficient in all fatty acids harboring a Δ⁶-double bond and the C20-fatty acid-deficient mutants of Ppelo. Beside of OPDA, iso-OPDA, dnOPDA, and iso-dnOPDA, three additional C18-compounds and a metabolite being isobaric to JA were identified to accumulate after wounding. These findings can now serve as foundation for future research in determining, which compound(s) will serve as native ligand(s) for the oxylipin-receptor COI1 in P. patens.

Roots drive oligogalacturonide-induced systemic immunity in tomato

Oligogalacturonides (OGs) are fragments of pectin released from the plant cell wall during insect or pathogen attack. They can be perceived by the plant as damage signals, triggering local and systemic defence responses. Here, we analyse the dynamics of local and systemic responses to OG perception in tomato roots or shoots, exploring their impact across the plant and their relevance in pathogen resistance. Targeted and untargeted metabolomics and gene expression analysis in plants treated with purified OGs revealed that local responses were transient, while distal responses were stronger and more sustained. Remarkably, changes were more conspicuous in roots, even upon foliar application of the OGs. The treatments differentially activated the synthesis of defence-related hormones and secondary metabolites including flavonoids, alkaloids and lignans, some of them exclusively synthetized in roots. Finally, the biological relevance of the systemic defence responses activated upon OG perception was confirmed, as the treatment induced systemic resistance to Botrytis cinerea. Overall, this study shows the differential regulation of tomato defences upon OGs perception in roots and shoots and reveals the key role of roots in the coordination of the plant responses to damage sensing.

iODA: An integrated tool for analysis of cancer pathway consistency from heterogeneous multi-omics data

The latest advances in the next generation sequencing technology have greatly facilitated the extensive research of genomics and transcriptomics, thereby promoting the decoding of carcinogenesis with unprecedented resolution. Considering the contribution of analyzing high-throughput multi-omics data to the exploration of cancer molecular mechanisms, an integrated tool for heterogeneous multi-omics data analysis (iODA) is proposed for the systems-level interpretation of multi-omics data, i.e., transcriptomic profiles (mRNA or miRNA expression data) and protein-DNA interactions (ChIP-Seq data). Considering the data heterogeneity, iODA can compare six statistical algorithms in differential analysis for the selected sample data and assist users in choosing the globally optimal one for dysfunctional mRNA or miRNA identification. Since molecular signatures are more consistent at the pathway level than at the gene level, the tool is able to enrich the identified dysfunctional molecules onto the KEGG pathways and extracted the consistent items as key components for further pathogenesis investigation. Compared with other tools, iODA is multi-functional for the systematic analysis of different level of omics data, and its analytical power was demonstrated through case studies of single and cross-level prostate cancer omics data. iODA is open source under GNU GPL and can be downloaded from http://www.sysbio.org.cn/iODA.

Network and Systems Medicine: Position Paper of the European Collaboration on Science and Technology Action on Open Multiscale Systems Medicine

Introduction: Network and systems medicine has rapidly evolved over the past decade, thanks to computational and integrative tools, which stem in part from systems biology. However, major challenges and hurdles are still present regarding validation and translation into clinical application and decision making for precision medicine. Methods: In this context, the Collaboration on Science and Technology Action on Open Multiscale Systems Medicine (OpenMultiMed) reviewed the available advanced technologies for multidimensional data generation and integration in an open-science approach as well as key clinical applications of network and systems medicine and the main issues and opportunities for the future. Results: The development of multi-omic approaches as well as new digital tools provides a unique opportunity to explore complex biological systems and networks at different scales. Moreover, the application of findable, applicable, interoperable, and reusable principles and the adoption of standards increases data availability and sharing for multiscale integration and interpretation. These innovations have led to the first clinical applications of network and systems medicine, particularly in the field of personalized therapy and drug dosing. Enlarging network and systems medicine application would now imply to increase patient engagement and health care providers as well as to educate the novel generations of medical doctors and biomedical researchers to shift the current organ- and symptom-based medical concepts toward network- and systems-based ones for more precise diagnoses, interventions, and ideally prevention. Conclusion: In this dynamic setting, the health care system will also have to evolve, if not revolutionize, in terms of organization and management.

Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources

As researchers are increasingly able to collect data on a large scale from multiple clinical and omics modalities, multi-omics integration is becoming a critical component of metabolomics research. This introduces a need for increased understanding by the metabolomics researcher of computational and statistical analysis methods relevant to multi-omics studies. In this review, we discuss common types of analyses performed in multi-omics studies and the computational and statistical methods that can be used for each type of analysis. We pinpoint the caveats and considerations for analysis methods, including required parameters, sample size and data distribution requirements, sources of a priori knowledge, and techniques for the evaluation of model accuracy. Finally, for the types of analyses discussed, we provide examples of the applications of corresponding methods to clinical and basic research. We intend that our review may be used as a guide for metabolomics researchers to choose effective techniques for multi-omics analyses relevant to their field of study.

NBIA: a network-based integrative analysis framework - applied to pathway analysis

With the explosion of high-throughput data, effective integrative analyses are needed to decipher the knowledge accumulated in biological databases. Existing meta-analysis approaches in systems biology often focus on hypothesis testing and neglect real expression changes, i.e. effect sizes, across independent studies. In addition, most integrative tools completely ignore the topological order of gene regulatory networks that hold key characteristics in understanding biological processes. Here we introduce a novel meta-analysis framework, Network-Based Integrative Analysis (NBIA), that transforms the challenging meta-analysis problem into a set of standard pathway analysis problems that have been solved efficiently. NBIA utilizes techniques from classical and modern meta-analysis, as well as a network-based analysis, in order to identify patterns of genes and networks that are consistently impacted across multiple studies. We assess the performance of NBIA by comparing it with nine meta-analysis approaches: Impact Analysis, GSA, and GSEA combined with classical meta-analysis methods (Fisher’s and the additive method), plus the three MetaPath approaches that employ multiple datasets. The 10 approaches have been tested on 1,737 samples from 27 expression datasets related to Alzheimer’s disease, acute myeloid leukemia (AML), and influenza. For all of the three diseases, NBIA consistently identifies biological pathways relevant to the underlying diseases while the other 9 methods fail to capture the key phenomena. The identified AML signature is also validated on a completely independent cohort of 167 AML patients. In this independent cohort, the proposed signature identifies two groups of patients that have significantly different survival profiles (Cox p-value 2 × 10⁻⁶). The NBIA framework will be included in the next release of BLMA Bioconductor package (http://bioconductor.org/packages/release/bioc/html/BLMA.html).

Identification of disordered metabolic networks in postpartum dairy cows with left displacement of the abomasum through integrated metabolomics and pathway analyses

High-producing dairy cows are easily affected by left displacement of the abomasum (LDA) within 4 weeks postpartum. Although LDA is highly associated with metabolic disturbances, the related information on comprehensive metabolic changes, with the exception of some blood biochemical parameters, remains limited. In this study, the changes in plasma metabolites and in the metabolic profile of postpartum dairy cows with LDA were investigated through liquid chromatography coupled with quadrupole time of flight mass spectrometry (LC-Q/TOF-MS)-based metabolomics, and the metabolic networks related to LDA were constructed through metabolomics pathway analysis (MetPA). An obvious change in the metabolic profile was reflected by significant variations in 68 plasma metabolites in postpartum dairy cows with LDA, and these variations consequently altered 13 metabolic pathways (histidine metabolism, tyrosine metabolism, valine, leucine and isoleucine biosynthesis, phenylalanine, tyrosine and tryptophan biosynthesis, arginine and proline metabolism, tryptophan metabolism, synthesis and degradation of ketone bodies, linoleic acid metabolism, arachidonic acid metabolism, citrate cycle, butanoate metabolism, vitamin B₆ metabolism and pyrimidine metabolism). This study shows that the more detailed information obtained by LC-Q/TOF-MS-based metabolomics and MetPA might contribute to a better understanding of the disordered metabolic networks in postpartum dairy cows with LDA.

MetaBridge: enabling network-based integrative analysis via direct protein interactors of metabolites

Summary

Here, we present MetaBridge, a tool that collates protein interactors (curated metabolite-enzyme interactions) that influence the levels of specific metabolites including both biosynthetic and degradative enzymes. This enables network-based integrative analysis of metabolomics data with other omics data types. MetaBridge is designed to complement a systems-biology approach to analysis, pairing well with network analysis tools such as NetworkAnalyst.ca, but can be used in any bioinformatics workflow.

Availability and implementation

MetaBridge has been implemented as a web tool at https://www.metabridge.org, and the source code is available at https://github.com/samhinshaw/metabridge_shiny (GNU GPLv3).

The glycosyltransferase UGT76E1 significantly contributes to 12-O-glucopyranosyl-jasmonic acid formation in wounded Arabidopsis thaliana leaves

Jasmonoyl-isoleucine (JA-Ile) is a phytohormone that orchestrates plant defenses in response to wounding, feeding insects, or necrotrophic pathogens. JA-Ile metabolism has been studied intensively, but its catabolism as a potentially important mechanism for the regulation of JA-Ile-mediated signaling is not well-understood. Especially the enzyme(s) responsible for specifically glycosylating 12-hydroxy-jasmonic acid (12-OH-JA) and thereby producing 12-O-glucopyranosyl-jasmonic acid (12-O-Glc-JA) is still elusive. Here, we used co-expression analyses of available Arabidopsis thaliana transcriptomic data, identifying four UDP-dependent glycosyltransferase (UGT) genes as wound-induced and 12-OH-JA-related, namely, UGT76E1, UGT76E2, UGT76E11, and UGT76E12 We heterologously expressed and purified the corresponding proteins to determine their individual substrate specificities and kinetic parameters. We then used an ex vivo metabolite-fingerprinting approach to investigate these proteins in conditions as close as possible to their natural environment, with an emphasis on greatly extending the range of potential substrates. As expected, we found that UGT76E1 and UGT76E2 are 12-OH-JA-UGTs, with UGT76E1 contributing a major in vivo UGT activity, as deduced from Arabidopsis mutants with abolished or increased UGT gene expression. In contrast, recombinant UGT76E11 acted on an unidentified compound and also glycosylated two other oxylipins, 11-hydroxy-7,9,13-hexadecatrienoic acid (11-HHT) and 13-hydroxy-9,11,15-octadecatrienoic acid (13-HOT), which were also accepted by recombinant UGT76E1, UGT76E2, and UGT76E12 enzymes. UGT76E12 glycosylated 12-OH-JA only to a low extent, but also accepted an artificial hydroxylated fatty acid and low amounts of kaempferol. In conclusion, our findings have elucidated the missing step in the wound-induced synthesis of 12-O-glucopyranosyl-jasmonic acid in A. thaliana.

Comprehensive LC-MS-Based Metabolite Fingerprinting Approach for Plant and Fungal-Derived Samples

Liquid chromatography-mass spectrometry (LC-MS)-based nontargeted metabolome approaches aim to detect chemotypes as markers for stress, disease, developmental, or genetic perturbation. Herein, we present a metabolite fingerprinting workflow, which is applicable for the analysis of tissues and fluids derived from plants and fungi. This is based on a broad metabolite coverage by a two-phase extraction and the separate analysis of polar, and nonpolar compounds by positive as well as negative electrospray ionization. For analysis of the resulting comprehensive data sets, the interactive and user-friendly data mining software MarVis-Suite is used. It supports statistical analysis, adduct correction, data merging, as well as visualization of multivariate data. Finally, MarVis shapes marker identification to the organism of interest. Therefore, it provides access to the species-specific databases KEGG and BioCyc and to custom databases tailored by the user.

DynSig: Modelling Dynamic Signaling Alterations along Gene Pathways for Identifying Differential Pathways

Although a number of methods have been proposed for identifying differentially expressed pathways (DEPs), few efforts consider the dynamic components of pathway networks, i.e., gene links. We here propose a signaling dynamics detection method for identification of DEPs, DynSig, which detects the molecular signaling changes in cancerous cells along pathway topology. Specifically, DynSig relies on gene links, instead of gene nodes, in pathways, and models the dynamic behavior of pathways based on Markov chain model (MCM). By incorporating the dynamics of molecular signaling, DynSig allows for an in-depth characterization of pathway activity. To identify DEPs, a novel statistic of activity alteration of pathways was formulated as an overall signaling perturbation score between sample classes. Experimental results on both simulation and real-world datasets demonstrate the effectiveness and efficiency of the proposed method in identifying differential pathways.

Granger Causality Analysis of Interictal iEEG Predicts Seizure Focus and Ultimate Resection

BACKGROUND

A critical conceptual step in epilepsy surgery is to locate the causal region of seizures. In practice, the causal region may be inferred from the set of electrodes showing early ictal activity. There would be advantages in deriving information about causal regions from interictal data as well. We applied Granger's statistical approach to baseline interictal data to calculate causal interactions. We hypothesized that maps of the Granger causality network (or GC maps) from interictal data might inform about the seizure network, and set out to see if “causality” in the Granger sense correlated with surgical targets.

OBJECTIVE

To determine whether interictal baseline data could produce GC maps, and whether the regions of high GC would statistically resemble the topography of the ictally active electrode (IAE) set and resection.

METHODS

Twenty-minute interictal baselines obtained from 25 consecutive patients were analyzed. The “GC maps” were quantitatively compared to conventionally constructed surgical plans, by using rank order and Cartesian distance statistics.

RESULTS

In 16 of 25 cases, the interictal GC rankings of the electrodes in the IAE set were lower than predicted by chance (P < .05). The aggregate probability of such a match by chance alone is very small (P < 10⁻²⁰) suggesting that interictal GC maps correlated with ictal networks. The distance of the highest GC electrode to the IAE set and to the resection averaged 4 and 6 mm (Wilcoxon P < .001).

CONCLUSION

GC analysis has the potential to help localize ictal networks from interictal data.

1H nuclear magnetic resonance-based plasma metabolomics provides another perspective of response mechanisms of newborn calves upon the first colostrum feeding

The first meal of a neonatal calf after birth is crucial for survival and health. Blood IgG levels remarkably increase in neonatal calves after the first colostrum feeding. However, there is little comprehensive information on blood small-molecule metabolites in neonatal calves at that time. In this study, the changes in plasma metabolites of neonatal calves after the initial colostrum feeding were first examined with comprehensive 1H nuclear magnetic resonance (NMR). Sixteen plasma samples obtained from 8 calves before and after feeding were analyzed with 1H NMR. Multivariate analyses revealed a significant difference in metabolic profiles. After feeding, acute phase N-acetylated glycoproteins and 13 other plasma metabolites decreased, whereas 19 plasma metabolites increased. Metabolomics pathway analysis of these metabolites revealed that a global metabolic response on the first colostrum feeding was reflected by alterations of 13 metabolic pathways including lipid, carbohydrate, and amino acid metabolism in neonatal calves. These results suggested that besides meeting energy demand, a 4.0 L of high-quality colostrum feeding within 4 h after birth had a positive effect on relieving the postnatal stress in neonatal calves. This study provides another perspective of response mechanisms of newborn calves upon the first colostrum feeding.

Microarray meta-analysis to explore abiotic stress-specific gene expression patterns in Arabidopsis

BACKGROUND

Abiotic stresses are the major limiting factors that affect plant growth, development, yield and final quality. Deciphering the underlying mechanisms of plants' adaptations to stresses using few datasets might overlook the different aspects of stress tolerance in plants, which might be simultaneously and consequently operated in the system. Fortunately, the accumulated microarray expression data offer an opportunity to infer abiotic stress-specific gene expression patterns through meta-analysis. In this study, we propose to combine microarray gene expression data under control, cold, drought, heat, and salt conditions and determined modules (gene sets) of genes highly associated with each other according to the observed expression data.

RESULTS

By analyzing the expression variations of the Eigen genes from different conditions, we had identified two, three, and five gene modules as cold-, heat-, and salt-specific modules, respectively. Most of the cold- or heat-specific modules were differentially expressed to a particular degree in shoot samples, while most of the salt-specific modules were differentially expressed to a particular degree in root samples. A gene ontology (GO) analysis on the stress-specific modules suggested that the gene modules exclusively enriched stress-related GO terms and that different genes under the same GO terms may be alternatively disturbed in different conditions. The gene regulatory events for two genes, DREB1A and DEAR1, in the cold-specific gene module had also been validated, as evidenced through the literature search.

CONCLUSIONS

Our protocols study the specificity of the gene modules that were specifically activated under a particular type of abiotic stress. The biplot can also assist to visualize the stress-specific gene modules. In conclusion, our approach has the potential to further elucidate mechanisms in plants and beneficial for future experiments design under different abiotic stresses.

Extracting replicable associations across multiple studies: Empirical Bayes algorithms for controlling the false discovery rate

In almost every field in genomics, large-scale biomedical datasets are used to report associations. Extracting associations that recur across multiple studies while controlling the false discovery rate is a fundamental challenge. Here, we propose a new method to allow joint analysis of multiple studies. Given a set of p-values obtained from each study, the goal is to identify associations that recur in at least k > 1 studies while controlling the false discovery rate. We propose several new algorithms that differ in how the study dependencies are modeled, and compare them and extant methods under various simulated scenarios. The top algorithm, SCREEN (Scalable Cluster-based REplicability ENhancement), is our new algorithm that works in three stages: (1) clustering an estimated correlation network of the studies, (2) learning replicability (e.g., of genes) within clusters, and (3) merging the results across the clusters. When we applied SCREEN to two real datasets it greatly outperformed the results obtained via standard meta-analysis. First, on a collection of 29 case-control gene expression cancer studies, we detected a large set of consistently up-regulated genes related to proliferation and cell cycle regulation. These genes are both consistently up-regulated across many cancer studies, and are well connected in known gene networks. Second, on a recent pan-cancer study that examined the expression profiles of patients with and without mutations in the HLA complex, we detected a large active module of up-regulated genes that are both related to immune responses and are well connected in known gene networks. This module covers thrice more genes as compared to the original study at a similar false discovery rate, demonstrating the high power of SCREEN. An implementation of SCREEN is available in the supplement.

When analyzing results from multiple studies, extracting replicated associations is the first step towards making new discoveries. The standard approach for this task is to use meta-analysis methods, which usually make an underlying null hypothesis that a gene has no effect in all studies. On the other hand, in replicability analysis we explicitly require that the gene will manifest a recurring pattern of effects. In this study we develop new algorithms for replicability analysis that are both scalable (i.e., can handle many studies) and allow controlling the false discovery rate. We show that our main algorithm called SCREEN (Scalable Cluster-based REplicability ENhancement) outperforms the other methods in simulated scenarios. Moreover, when applied to real datasets, SCREEN greatly extended the results of the meta-analysis, and can even facilitate detection of new biological results.

DANUBE: Data-driven meta-ANalysis using UnBiased Empirical distributions-applied to biological pathway analysis

Identifying the pathways and mechanisms that are significantly impacted in a given phenotype is challenging. Issues include patient heterogeneity and noise. Many experiments do not have a large enough sample size to achieve the statistical power necessary to identify significantly impacted pathways. Meta-analysis based on combining p-values from individual experiments has been used to improve power. However, all classical meta-analysis approaches work under the assumption that the p-values produced by experiment-level statistical tests follow a uniform distribution under the null hypothesis. Here we show that this assumption does not hold for three mainstream pathway analysis methods, and significant bias is likely to affect many, if not all such meta-analysis studies. We introduce DANUBE, a novel and unbiased approach to combine statistics computed from individual studies. Our framework uses control samples to construct empirical null distributions, from which empirical p-values of individual studies are calculated and combined using either a Central Limit Theorem approach or the additive method. We assess the performance of DANUBE using four different pathway analysis methods. DANUBE is compared with five meta-analysis approaches, as well as with a pathway analysis approach that employs multiple datasets (MetaPath). The 25 approaches have been tested on 16 different datasets related to two human diseases, Alzheimer's disease (7 datasets) and acute myeloid leukemia (9 datasets). We demonstrate that DANUBE overcomes bias in order to consistently identify relevant pathways. We also show how the framework improves results in more general cases, compared to classical meta-analysis performed with common experiment-level statistical tests such as Wilcoxon and t-test.

Functional Characterization of CYP94-Genes and Identification of a Novel Jasmonate Catabolite in Flowers

Over the past decades much research focused on the biosynthesis of the plant hormone jasmonyl-isoleucine (JA-Ile). While many details about its biosynthetic pathway as well about its physiological function are established nowadays, knowledge about its catabolic fate is still scarce. Only recently, the hormonal inactivation mechanisms became a stronger research focus. Two major pathways have been proposed to inactivate JA-Ile: i) The cleavage of the jasmonyl-residue from the isoleucine moiety, a reaction that is catalyzed by specific amido-hydrolases, or ii), the sequential oxidation of the ω-end of the pentenyl side-chain. This reaction is catalyzed by specific members of the cytochrome P450 (CYP) subfamily CYP94: CYP94B1, CYP94B3 and CYP94C1. In the present study, we further investigated the oxidative fate of JA-Ile by expanding the analysis on Arabidopsis thaliana mutants, lacking only one (cyp94b1, cyp94b2, cyp94b3, cyp94c1), two (cyp94b1xcyp94b2, cyp94b1xcyp94b3, cyp94b2xcyp94b3), three (cyp94b1xcyp94b2xcyp94b3) or even four (cyp94b1xcyp94b2xcyp94b3xcyp94c1) CYP94 functionalities. The results obtained in the present study show that CYP94B1, CYP94B2, CYP94B3 and CYP94C1 are responsible for catalyzing the sequential ω-oxidation of JA-Ile in a semi-redundant manner. While CYP94B-enzymes preferentially hydroxylate JA-Ile to 12-hydroxy-JA-Ile, CYP94C1 catalyzes primarily the subsequent oxidation, yielding 12-carboxy-JA-Ile. In addition, data obtained from investigating the triple and quadruple mutants let us hypothesize that a direct oxidation of unconjugated JA to 12-hydroxy-JA is possible in planta. Using a non-targeted metabolite fingerprinting analysis, we identified unconjugated 12-carboxy-JA as novel jasmonate derivative in floral tissues. Using the same approach, we could show that deletion of CYP94-genes might not only affect JA-homeostasis but also other signaling pathways. Deletion of CYP94B1, for example, led to accumulation of metabolites that may be characteristic for plant stress responses like systemic acquired resistance. Evaluation of the in vivo function of the different CYP94-enzymes on the JA-sensitivity demonstrated that particularly CYP94B-enzymes might play an essential role for JA-response, whereas CYP94C1 might only be of minor importance.

An enhanced plant lipidomics method based on multiplexed liquid chromatography-mass spectrometry reveals additional insights into cold- and drought-induced membrane remodeling

Within the lipidome of plants a few bulk molecular species hamper the detection of the rest, which are present at relatively low levels. In addition, low-abundance species are often masked by numerous isobaric interferences, such as those caused by isoelemental species and isotopologues. This scenario not only means that minor species are underrepresented, but also leads to potential misidentifications and limits the structural information gathered by lipidomics approaches. In order to overcome these limitations we have developed a multiplexed liquid chromatography-mass spectrometry lipidomics platform able to achieve an enhanced coverage of plant lipidomes. The platform is based on a single extraction step followed by a series of ultra-performance liquid chromatography separations. Post-column flow is then directed to both a triple quadrupole analyzer for targeted profiling and a time-of-flight analyzer for accurate mass analysis. As a proof of concept, plants were subjected to cold or drought, which are known to trigger widespread remodeling events in plant cell membranes. Analysis of the leaf lipidome yielded 393 molecular species within 23 different lipid classes. This enhanced coverage allowed us to identify lipid molecular species and even classes that are altered upon stress, allowing hypotheses on role of glycosylinositolphosphoceramides (GIPC), steryl glycosides (SG) and acylated steryl glycosides (ASG) in drought stress to be addressed and confirming the findings from numerous previous studies with a single, wide-ranging lipidomics approach. This extended our knowledge on membrane remodeling during the drought response, integrating sphingolipids and sterol lipids into the current glycerolipid-based model.

Transcriptomic and metabolomic data integration

Many studies now produce parallel data sets from different omics technologies; however, the task of interpreting the acquired data in an integrated fashion is not trivial. This review covers those methods that have been used over the past decade to statistically integrate and interpret metabolomics and transcriptomic data sets. It defines four categories of approaches, correlation-based integration, concatenation-based integration, multivariate-based integration and pathway-based integration, into which all existing statistical methods fit. It also explores the choices in study design for generating samples for analysis by these omics technologies and the impact that these technical decisions have on the subsequent data analysis options.

A novel bi-level meta-analysis approach: applied to biological pathway analysis

MOTIVATION

The accumulation of high-throughput data in public repositories creates a pressing need for integrative analysis of multiple datasets from independent experiments. However, study heterogeneity, study bias, outliers and the lack of power of available methods present real challenge in integrating genomic data. One practical drawback of many P-value-based meta-analysis methods, including Fisher's, Stouffer's, minP and maxP, is that they are sensitive to outliers. Another drawback is that, because they perform just one statistical test for each individual experiment, they may not fully exploit the potentially large number of samples within each study.

RESULTS

We propose a novel bi-level meta-analysis approach that employs the additive method and the Central Limit Theorem within each individual experiment and also across multiple experiments. We prove that the bi-level framework is robust against bias, less sensitive to outliers than other methods, and more sensitive to small changes in signal. For comparative analysis, we demonstrate that the intra-experiment analysis has more power than the equivalent statistical test performed on a single large experiment. For pathway analysis, we compare the proposed framework versus classical meta-analysis approaches (Fisher's, Stouffer's and the additive method) as well as against a dedicated pathway meta-analysis package (MetaPath), using 1252 samples from 21 datasets related to three human diseases, acute myeloid leukemia (9 datasets), type II diabetes (5 datasets) and Alzheimer's disease (7 datasets). Our framework outperforms its competitors to correctly identify pathways relevant to the phenotypes. The framework is sufficiently general to be applied to any type of statistical meta-analysis.

AVAILABILITY AND IMPLEMENTATION

The R scripts are available on demand from the authors.

CONTACT

sorin@wayne.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Metabolomic profiling for the identification of novel diagnostic markers in prostate cancer

Metabolomic profiling offers a powerful methodology for understanding the perturbations of biochemical systems occurring during a disease process. During neoplastic transformation, prostate cells undergo metabolic reprogramming to satisfy the demands of growth and proliferation. An early event in prostate cell transformation is the loss of capacity to accumulate zinc. This change is associated with a higher energy efficiency and increased lipid biosynthesis for cellular proliferation, membrane formation and cell signaling. Moreover, recent studies have shown that sarcosine, an N-methyl derivative of glycine, was significantly increased during disease progression from normal to localized to metastatic prostate cancer. Mapping the metabolomic profiles to their respective biochemical pathways showed an upregulation of androgen-induced protein synthesis, an increased amino acid metabolism and a perturbation of nitrogen breakdown pathways, along with high total choline-containing compounds and phosphocholine levels. In this review, the role of emerging biomarkers is summarized, based on the current understanding of the prostate cancer metabolome.

ONION: Functional Approach for Integration of Lipidomics and Transcriptomics Data

To date, the massive quantity of data generated by high-throughput techniques has not yet met bioinformatics treatment required to make full use of it. This is partially due to a mismatch in experimental and analytical study design but primarily due to a lack of adequate analytical approaches. When integrating multiple data types e.g. transcriptomics and metabolomics, multidimensional statistical methods are currently the techniques of choice. Typical statistical approaches, such as canonical correlation analysis (CCA), that are applied to find associations between metabolites and genes are failing due to small numbers of observations (e.g. conditions, diet etc.) in comparison to data size (number of genes, metabolites). Modifications designed to cope with this issue are not ideal due to the need to add simulated data resulting in a lack of p-value computation or by pruning of variables hence losing potentially valid information. Instead, our approach makes use of verified or putative molecular interactions or functional association to guide analysis. The workflow includes dividing of data sets to reach the expected data structure, statistical analysis within groups and interpretation of results. By applying pathway and network analysis, data obtained by various platforms are grouped with moderate stringency to avoid functional bias. As a consequence CCA and other multivariate models can be applied to calculate robust statistics and provide easy to interpret associations between metabolites and genes to leverage understanding of metabolic response. Effective integration of lipidomics and transcriptomics is demonstrated on publically available murine nutrigenomics data sets. We are able to demonstrate that our approach improves detection of genes related to lipid metabolism, in comparison to applying statistics alone. This is measured by increased percentage of explained variance (95% vs. 75–80%) and by identifying new metabolite-gene associations related to lipid metabolism.

Systems biology analysis of the proteomic alterations induced by MPP(+), a Parkinson's disease-related mitochondrial toxin

Parkinson's disease (PD) is a complex neurodegenerative disease whose etiology has not been completely characterized. Many cellular processes have been proposed to play a role in the neuronal damage and loss: defects in the proteosomal activity, altered protein processing, increased reactive oxygen species burden. Among them, the involvement of a decreased activity and an altered disposal of mitochondria is becoming more and more evident. The mitochondrial toxin 1-methyl-4-phenylpyridinium (MPP(+)), an inhibitor of complex I, has been widely used to reproduce biochemical alterations linked to PD in vitro and its precursor, 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine hydrochloride (MPTP), to induce a Parkinson-like syndrome in vivo. Therefore, we performed a meta-analysis of the literature of all the proteomic investigations of neuronal alterations due to MPP(+) treatment and compared it with our results obtained with a mitochondrial proteomic analysis of SH-SY5Y cells treated with MPP(+). By using open-source bioinformatics tools, we identified the biochemical pathways and the molecular functions mostly affected by MPP(+), i.e., ATP production, the mitochondrial unfolded stress response, apoptosis, autophagy, and, most importantly, the synapse funcionality. Eventually, we generated protein networks, based on physical or functional interactions, to highlight the relationships among the molecular actors involved. In particular, we identified the mitochondrial protein HSP60 as the central hub in the protein-protein interaction network. As a whole, this analysis clarified the cellular responses to MPP(+), the specific mitochondrial proteome alterations induced and how this toxic model can recapitulate some pathogenetic events of PD.

MarVis-Pathway: integrative and exploratory pathway analysis of non-targeted metabolomics data

A central aim in the evaluation of non-targeted metabolomics data is the detection of intensity patterns that differ between experimental conditions as well as the identification of the underlying metabolites and their association with metabolic pathways. In this context, the identification of metabolites based on non-targeted mass spectrometry data is a major bottleneck. In many applications, this identification needs to be guided by expert knowledge and interactive tools for exploratory data analysis can significantly support this process. Additionally, the integration of data from other omics platforms, such as DNA microarray-based transcriptomics, can provide valuable hints and thereby facilitate the identification of metabolites via the reconstruction of related metabolic pathways. We here introduce the MarVis-Pathway tool, which allows the user to identify metabolites by annotation of pathways from cross-omics data. The analysis is supported by an extensive framework for pathway enrichment and meta-analysis. The tool allows the mapping of data set features by ID, name, and accurate mass, and can incorporate information from adduct and isotope correction of mass spectrometry data. MarVis-Pathway was integrated in the MarVis-Suite (http://marvis.gobics.de), which features the seamless highly interactive filtering, combination, clustering, and visualization of omics data sets. The functionality of the new software tool is illustrated using combined mass spectrometry and DNA microarray data. This application confirms jasmonate biosynthesis as important metabolic pathway that is upregulated during the wound response of Arabidopsis plants.

Collaborative mining and interpretation of large-scale data for biomedical research insights

Biomedical research becomes increasingly interdisciplinary and collaborative in nature. Researchers need to efficiently and effectively collaborate and make decisions by meaningfully assembling, mining and analyzing available large-scale volumes of complex multi-faceted data residing in different sources. In line with related research directives revealing that, in spite of the recent advances in data mining and computational analysis, humans can easily detect patterns which computer algorithms may have difficulty in finding, this paper reports on the practical use of an innovative web-based collaboration support platform in a biomedical research context. Arguing that dealing with data-intensive and cognitively complex settings is not a technical problem alone, the proposed platform adopts a hybrid approach that builds on the synergy between machine and human intelligence to facilitate the underlying sense-making and decision making processes. User experience shows that the platform enables more informed and quicker decisions, by displaying the aggregated information according to their needs, while also exploiting the associated human intelligence.

Integrating Genome-Wide Association Study and Brain Expression Data Highlights Cell Adhesion Molecules and Purine Metabolism in Alzheimer's Disease

Alzheimer's disease (AD) is the most common neurodegenerative disease in the elderly. Recently, genome-wide association studies (GWAS) have been used to investigate AD pathogenesis. However, a large proportion of AD heritability has yet to be explained. We previously identified the cell adhesion molecule (CAM) pathway as a consistent signal in two AD GWAS. However, it is unclear whether CAM is present in the Genetic and Environmental Risk for Alzheimer's Disease Consortium (GERAD) GWAS and brain expression GWAS. Meanwhile, we think integrating AD GWAS and AD brain expression datasets may provide complementary information to identify important pathways involved in AD. Here, we conducted a systems analysis using (1) KEGG pathways, (2) large-scale AD GWAS from GERAD (n = 11,789), (3) two brain expression GWAS datasets (n = 399) from the AD cerebellum and temporal cortex, and (4) previous results from pathway analysis of AD GWAS. Our results indicate that (1) CAM is a consistent signal in five AD GWAS; (2) CAM is the most significant signal in AD; (3) we confirmed previous AD risk pathways related to immune system and diseases, and cardiovascular disease, etc.; and (4) we highlighted the purine metabolism pathway in AD for the first time. We believe that our results may advance our understanding of AD mechanisms and will be very informative for future genetic studies in AD.

Proteomics for systems toxicology

Current toxicology studies frequently lack measurements at molecular resolution to enable a more mechanism-based and predictive toxicological assessment. Recently, a systems toxicology assessment framework has been proposed, which combines conventional toxicological assessment strategies with system-wide measurement methods and computational analysis approaches from the field of systems biology. Proteomic measurements are an integral component of this integrative strategy because protein alterations closely mirror biological effects, such as biological stress responses or global tissue alterations. Here, we provide an overview of the technical foundations and highlight select applications of proteomics for systems toxicology studies. With a focus on mass spectrometry-based proteomics, we summarize the experimental methods for quantitative proteomics and describe the computational approaches used to derive biological/mechanistic insights from these datasets. To illustrate how proteomics has been successfully employed to address mechanistic questions in toxicology, we summarized several case studies. Overall, we provide the technical and conceptual foundation for the integration of proteomic measurements in a more comprehensive systems toxicology assessment framework. We conclude that, owing to the critical importance of protein-level measurements and recent technological advances, proteomics will be an integral part of integrative systems toxicology approaches in the future.

Interactive XCMS Online: simplifying advanced metabolomic data processing and subsequent statistical analyses

XCMS Online (xcmsonline.scripps.edu) is a cloud-based informatic platform designed to process and visualize mass-spectrometry-based, untargeted metabolomic data. Initially, the platform was developed for two-group comparisons to match the independent, "control" versus "disease" experimental design. Here, we introduce an enhanced XCMS Online interface that enables users to perform dependent (paired) two-group comparisons, meta-analysis, and multigroup comparisons, with comprehensive statistical output and interactive visualization tools. Newly incorporated statistical tests cover a wide array of univariate analyses. Multigroup comparison allows for the identification of differentially expressed metabolite features across multiple classes of data while higher order meta-analysis facilitates the identification of shared metabolic patterns across multiple two-group comparisons. Given the complexity of these data sets, we have developed an interactive platform where users can monitor the statistical output of univariate (cloud plots) and multivariate (PCA plots) data analysis in real time by adjusting the threshold and range of various parameters. On the interactive cloud plot, metabolite features can be filtered out by their significance level (p-value), fold change, mass-to-charge ratio, retention time, and intensity. The variation pattern of each feature can be visualized on both extracted-ion chromatograms and box plots. The interactive principal component analysis includes scores, loadings, and scree plots that can be adjusted depending on scaling criteria. The utility of XCMS functionalities is demonstrated through the metabolomic analysis of bacterial stress response and the comparison of lymphoblastic leukemia cell lines.