Network-based pathway enrichment analysis with incomplete network information

Mitochondrial Dysfunction: Unraveling the Elusive Biology Behind Anti-TNF Response During Ulcerative Colitis

BACKGROUND

Recent studies hint at mitochondrial genes influencing UC patient response to anti-TNF treatment. We evaluated this hypothesis by following a targeted strategy to identify gene expression that captures the relationship between mitochondrial dysregulation and response to treatment. Our objective was to initially examine this relationship in colon samples and subsequently assess whether the resulting signal persists in the bloodstream.

METHODS

We analyzed the transcriptome of colon samples from an anti-TNF-treated murine model characterized by impaired mitochondrial activity and treatment resistance. We then transferred the findings that linked mitochondrial dysfunction and compromised treatment response to an anti-TNF-treated UC human cohort. We next matched differential expression in the blood using monocytes from the peripheral blood of controls and IBD patients, and we evaluated a classification process at baseline with whole blood samples from UC patients.

RESULTS

In human colon samples, the derived gene set from the murine model showed differential expression, primarily enriched metabolic pathways, and exhibited similar classification capacity as genes enriching inflammatory pathways. Moreover, the evaluation of the classification signal using blood samples from UC patients at baseline highlighted the involvement of mitochondrial homeostasis in treatment response.

CONCLUSIONS

Our results highlight the involvement of metabolic pathways and mitochondrial homeostasis in determining treatment response and their ability to provide promising classification signals with detection levels in both the colon and the bloodstream.

Bio-primed machine learning to enhance discovery of relevant biomarkers

Precision medicine relies on identifying reliable biomarkers for gene dependencies to tailor individualized therapeutic strategies. The advent of high-throughput technologies presents unprecedented opportunities to explore molecular disease mechanisms but also challenges due to high dimensionality and collinearity among features. Traditional statistical methods often fall short in this context, necessitating novel computational approaches that harness the full potential of big data in bioinformatics. Here, we introduce a novel machine learning approach extending the Least Absolute Shrinkage and Selection Operator (LASSO) regression framework to incorporate biological knowledge, such as protein-protein interaction databases, into the regularization process. This bio-primed approach prioritizes variables that are both statistically significant and biologically relevant. Applying our method to multiple dependency datasets, we identified biomarkers which traditional methods overlooked. Our biologically informed LASSO method effectively identifies relevant biomarkers from high-dimensional collinear data, bridging the gap between statistical rigor and biological insight. This method holds promise for advancing personalized medicine by uncovering novel therapeutic targets and understanding the complex interplay of genetic and molecular factors in disease.

DNEA: an R package for fast and versatile data-driven network analysis of metabolomics data

BACKGROUND

Metabolomics is a high-throughput technology that measures small molecule metabolites in cells, tissues or biofluids. Analysis of metabolomics data is a multi-step process that involves data processing, quality control and normalization, followed by statistical and bioinformatics analysis. The latter step often involves pathway analysis to aid biological interpretation of the data. This approach is limited to endogenous metabolites that can be readily mapped to metabolic pathways. An alternative to pathway analysis that can be used for any classes of metabolites, including unknown compounds that are ubiquitous in untargeted metabolomics data, involves defining metabolite-metabolite interactions using experimental data. Our group has developed several network-based methods that use partial correlations of experimentally determined metabolite measurements. These were implemented in CorrelationCalculator and Filigree, two software tools for the analysis of metabolomics data we developed previously. The latter tool implements the Differential Network Enrichment Analysis (DNEA) algorithm. This analysis is useful for building differential networks from metabolomics data containing two experimental groups and identifying differentially enriched metabolic modules. While Filigree is a user-friendly tool, it has certain limitations when used for the analysis of large-scale metabolomics datasets.

RESULTS

We developed the DNEA R package for the data-driven network analysis of metabolomics data. We present the DNEA workflow and functionality, algorithm enhancements implemented with respect to the package's predecessor, Filigree, and discuss best practices for analyses. We tested the performance of the DNEA R package and illustrated its features using publicly available metabolomics data from the environmental determinants of diabetes in the young. To our knowledge, this package is the only publicly available tool designed for the construction of biological networks and subsequent enrichment testing for datasets containing exogenous, secondary, and unknown compounds. This greatly expands the scope of traditional enrichment analysis tools that can be used to analyze a relatively small set of well-annotated metabolites.

CONCLUSIONS

The DNEA R package is a more flexible and powerful implementation of our previously published software tool, Filigree. The modular structure of the package, along with the parallel processing framework built into the most computationally extensive steps of the algorithm, make it a powerful tool for the analysis of large and complex metabolomics datasets.

DeePathNet: A Transformer-Based Deep Learning Model Integrating Multiomic Data with Cancer Pathways

SIGNIFICANCE

DeePathNet integrates cancer-specific biological pathways using transformer-based deep learning for enhanced cancer analysis. It outperforms existing models in predicting drug responses, cancer types, and subtypes. By enabling pathway-level biomarker discovery, DeePathNet represents a significant advancement in cancer research and could lead to more effective treatments.

Computational Approaches to Drug Repurposing: Methods, Challenges, and Opportunities

Drug repurposing refers to the inference of therapeutic relationships between a clinical indication and existing compounds. As an emerging paradigm in drug development, drug repurposing enables more efficient treatment of rare diseases, stratified patient populations, and urgent threats to public health. However, prioritizing well-suited drug candidates from among a nearly infinite number of repurposing options continues to represent a significant challenge in drug development. Over the past decade, advances in genomic profiling, database curation, and machine learning techniques have enabled more accurate identification of drug repurposing candidates for subsequent clinical evaluation. This review outlines the major methodologic classes that these approaches comprise, which rely on (a) protein structure, (b) genomic signatures, (c) biological networks, and (d) real-world clinical data. We propose that realizing the full impact of drug repurposing methodologies requires a multidisciplinary understanding of each method's advantages and limitations with respect to clinical practice.

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data

A significant challenge in the analysis of omics data is extracting actionable biological knowledge. Metabolomics is no exception. The general problem of relating changes in levels of individual metabolites to specific biological processes is compounded by the large number of unknown metabolites present in untargeted liquid chromatography-mass spectrometry (LC-MS) studies. Further, secondary metabolism and lipid metabolism are poorly represented in existing pathway databases. To overcome these limitations, our group has developed several tools for data-driven network construction and analysis. These include CorrelationCalculator and Filigree. Both tools allow users to build partial correlation-based networks from experimental metabolomics data when the number of metabolites exceeds the number of samples. CorrelationCalculator supports the construction of a single network, while Filigree allows building a differential network utilizing data from two groups of samples, followed by network clustering and enrichment analysis. We will describe the utility and application of both tools for the analysis of real-life metabolomics data.

Integration of transcriptomics and metabolomics analysis for unveiling the toxicological profile in the liver of mice exposed to uranium in drinking water

Uranium is a contaminate in the underground water in many regions of the world, which poses health risks to the local populations through drinking water. Although the health hazards of natural uranium have been concerned for decades, the controversies about its detrimental effects continue at present since it is still unclear how uranium interacts with molecular regulatory networks to generate toxicity. Here, we integrate transcriptomic and metabolomic methods to unveil the molecular mechanism of lipid metabolism disorder induced by uranium. Following exposure to uranium in drinking water for twenty-eight days, aberrant lipid metabolism and lipogenesis were found in the liver, accompanied with aggravated lipid peroxidation and an increase in dead cells. Multi-omics analysis reveals that uranium can promote the biosynthesis of unsaturated fatty acids through dysregulating the metabolism of arachidonic acid (AA), linoleic acid, and glycerophospholipid. Most notably, the disordered metabolism of polyunsaturated fatty acids (PUFAs) like AA may contribute to lipid peroxidation induced by uranium, which in turn triggers ferroptosis in hepatocytes. Our findings highlight disorder of lipid metabolism as an essential toxicological mechanism of uranium in the liver, offering insight into the health risks of uranium in drinking water.

Deep learning on graphs for multi-omics classification of COPD

Network approaches have successfully been used to help reveal complex mechanisms of diseases including Chronic Obstructive Pulmonary Disease (COPD). However despite recent advances, we remain limited in our ability to incorporate protein-protein interaction (PPI) network information with omics data for disease prediction. New deep learning methods including convolution Graph Neural Network (ConvGNN) has shown great potential for disease classification using transcriptomics data and known PPI networks from existing databases. In this study, we first reconstructed the COPD-associated PPI network through the AhGlasso (Augmented High-Dimensional Graphical Lasso Method) algorithm based on one independent transcriptomics dataset including COPD cases and controls. Then we extended the existing ConvGNN methods to successfully integrate COPD-associated PPI, proteomics, and transcriptomics data and developed a prediction model for COPD classification. This approach improves accuracy over several conventional classification methods and neural networks that do not incorporate network information. We also demonstrated that the updated COPD-associated network developed using AhGlasso further improves prediction accuracy. Although deep neural networks often achieve superior statistical power in classification compared to other methods, it can be very difficult to explain how the model, especially graph neural network(s), makes decisions on the given features and identifies the features that contribute the most to prediction generally and individually. To better explain how the spectral-based Graph Neural Network model(s) works, we applied one unified explainable machine learning method, SHapley Additive exPlanations (SHAP), and identified CXCL11, IL-2, CD48, KIR3DL2, TLR2, BMP10 and several other relevant COPD genes in subnetworks of the ConvGNN model for COPD prediction. Finally, Gene Ontology (GO) enrichment analysis identified glycosaminoglycan, heparin signaling, and carbohydrate derivative signaling pathways significantly enriched in the top important gene/proteins for COPD classifications.

DRAGON: Determining Regulatory Associations using Graphical models on multi-Omic Networks

The increasing quantity of multi-omic data, such as methylomic and transcriptomic profiles collected on the same specimen or even on the same cell, provides a unique opportunity to explore the complex interactions that define cell phenotype and govern cellular responses to perturbations. We propose a network approach based on Gaussian Graphical Models (GGMs) that facilitates the joint analysis of paired omics data. This method, called DRAGON (Determining Regulatory Associations using Graphical models on multi-Omic Networks), calibrates its parameters to achieve an optimal trade-off between the network's complexity and estimation accuracy, while explicitly accounting for the characteristics of each of the assessed omics 'layers.' In simulation studies, we show that DRAGON adapts to edge density and feature size differences between omics layers, improving model inference and edge recovery compared to state-of-the-art methods. We further demonstrate in an analysis of joint transcriptome - methylome data from TCGA breast cancer specimens that DRAGON can identify key molecular mechanisms such as gene regulation via promoter methylation. In particular, we identify Transcription Factor AP-2 Beta (TFAP2B) as a potential multi-omic biomarker for basal-type breast cancer. DRAGON is available as open-source code in Python through the Network Zoo package (netZooPy v0.8; netzoo.github.io).

Metabolomics identifies shared lipid pathways in independent amyotrophic lateral sclerosis cohorts

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease lacking effective treatments. This is due, in part, to a complex and incompletely understood pathophysiology. To shed light, we conducted untargeted metabolomics on plasma from two independent cross-sectional ALS cohorts versus control participants to identify recurrent dysregulated metabolic pathways. Untargeted metabolomics was performed on plasma from two ALS cohorts (cohort 1, n = 125; cohort 2, n = 225) and healthy controls (cohort 1, n = 71; cohort 2, n = 104). Individual differential metabolites in ALS cases versus controls were assessed by Wilcoxon, adjusted logistic regression and partial least squares-discriminant analysis, while group lasso explored sub-pathway level differences. Adjustment parameters included age, sex and body mass index. Metabolomics pathway enrichment analysis was performed on metabolites selected using the above methods. Additionally, we conducted a sex sensitivity analysis due to sex imbalance in the cohort 2 control arm. Finally, a data-driven approach, differential network enrichment analysis (DNEA), was performed on a combined dataset to further identify important ALS metabolic pathways. Cohort 2 ALS participants were slightly older than the controls (64.0 versus 62.0 years, P = 0.009). Cohort 2 controls were over-represented in females (68%, P < 0.001). The most concordant cohort 1 and 2 pathways centred heavily on lipid sub-pathways, including complex and signalling lipid species and metabolic intermediates. There were differences in sub-pathways that were enriched in ALS females versus males, including in lipid sub-pathways. Finally, DNEA of the merged metabolite dataset of both ALS and control cohorts identified nine significant subnetworks; three centred on lipids and two encompassed a range of sub-pathways. In our analysis, we saw consistent and important shared metabolic sub-pathways in both ALS cohorts, particularly in lipids, further supporting their importance as ALS pathomechanisms and therapeutics targets.

Quantitative Framework for Bench-to-Bedside Cancer Research

Bioscience is an interdisciplinary venture. Driven by a quantum shift in the volume of high throughput data and in ready availability of data-intensive technologies, mathematical and quantitative approaches have become increasingly common in bioscience. For instance, a recent shift towards a quantitative description of cells and phenotypes, which is supplanting conventional qualitative descriptions, has generated immense promise and opportunities in the field of bench-to-bedside cancer OMICS, chemical biology and pharmacology. Nevertheless, like any burgeoning field, there remains a lack of shared and standardized framework for quantitative cancer research. Here, in the context of cancer, we present a basic framework and guidelines for bench-to-bedside quantitative research and therapy. We outline some of the basic concepts and their parallel use cases for chemical-protein interactions. Along with several recommendations for assay setup and conditions, we also catalog applications of these quantitative techniques in some of the most widespread discovery pipeline and analytical methods in the field. We believe adherence to these guidelines will improve experimental design, reduce variabilities and standardize quantitative datasets.

Information enhanced model selection for Gaussian graphical model with application to metabolomic data

In light of the low signal-to-noise nature of many large biological data sets, we propose a novel method to learn the structure of association networks using Gaussian graphical models combined with prior knowledge. Our strategy includes two parts. In the first part, we propose a model selection criterion called structural Bayesian information criterion, in which the prior structure is modeled and incorporated into Bayesian information criterion. It is shown that the popular extended Bayesian information criterion is a special case of structural Bayesian information criterion. In the second part, we propose a two-step algorithm to construct the candidate model pool. The algorithm is data-driven and the prior structure is embedded into the candidate model automatically. Theoretical investigation shows that under some mild conditions structural Bayesian information criterion is a consistent model selection criterion for high-dimensional Gaussian graphical model. Simulation studies validate the superiority of the proposed algorithm over the existing ones and show the robustness to the model misspecification. Application to relative concentration data from infant feces collected from subjects enrolled in a large molecular epidemiological cohort study validates that metabolic pathway involvement is a statistically significant factor for the conditional dependence between metabolites. Furthermore, new relationships among metabolites are discovered which can not be identified by the conventional methods of pathway analysis. Some of them have been widely recognized in biological literature.

Predicting causal genes from psychiatric genome-wide association studies using high-level etiological knowledge

Genome-wide association studies have discovered hundreds of genomic loci associated with psychiatric traits, but the causal genes underlying these associations are often unclear, a research gap that has hindered clinical translation. Here, we present a Psychiatric Omnilocus Prioritization Score (PsyOPS) derived from just three binary features encapsulating high-level assumptions about psychiatric disease etiology - namely, that causal psychiatric disease genes are likely to be mutationally constrained, be specifically expressed in the brain, and overlap with known neurodevelopmental disease genes. To our knowledge, PsyOPS is the first method specifically tailored to prioritizing causal genes at psychiatric GWAS loci. We show that, despite its extreme simplicity, PsyOPS achieves state-of-the-art performance at this task, comparable to a prior domain-agnostic approach relying on tens of thousands of features. Genes prioritized by PsyOPS are substantially more likely than other genes at the same loci to have convergent evidence of direct regulation by the GWAS variant according to both DNA looping assays and expression or splicing quantitative trait locus (QTL) maps. We provide examples of genes hundreds of kilobases away from the lead variant, like GABBR1 for schizophrenia, that are prioritized by all three of PsyOPS, DNA looping and QTLs. Our results underscore the power of incorporating high-level knowledge of trait etiology into causal gene prediction at GWAS loci, and comprise a resource for researchers interested in experimentally characterizing psychiatric gene candidates.

An Augmented High-Dimensional Graphical Lasso Method to Incorporate Prior Biological Knowledge for Global Network Learning

Biological networks are often inferred through Gaussian graphical models (GGMs) using gene or protein expression data only. GGMs identify conditional dependence by estimating a precision matrix between genes or proteins. However, conventional GGM approaches often ignore prior knowledge about protein-protein interactions (PPI). Recently, several groups have extended GGM to weighted graphical Lasso (wGlasso) and network-based gene set analysis (Netgsa) and have demonstrated the advantages of incorporating PPI information. However, these methods are either computationally intractable for large-scale data, or disregard weights in the PPI networks. To address these shortcomings, we extended the Netgsa approach and developed an augmented high-dimensional graphical Lasso (AhGlasso) method to incorporate edge weights in known PPI with omics data for global network learning. This new method outperforms weighted graphical Lasso-based algorithms with respect to computational time in simulated large-scale data settings while achieving better or comparable prediction accuracy of node connections. The total runtime of AhGlasso is approximately five times faster than weighted Glasso methods when the graph size ranges from 1,000 to 3,000 with a fixed sample size (n = 300). The runtime difference between AhGlasso and weighted Glasso increases when the graph size increases. Using proteomic data from a study on chronic obstructive pulmonary disease, we demonstrate that AhGlasso improves protein network inference compared to the Netgsa approach by incorporating PPI information.

GPCRs Are Optimal Regulators of Complex Biological Systems and Orchestrate the Interface between Health and Disease

GPCRs arguably represent the most effective current therapeutic targets for a plethora of diseases. GPCRs also possess a pivotal role in the regulation of the physiological balance between healthy and pathological conditions; thus, their importance in systems biology cannot be underestimated. The molecular diversity of GPCR signaling systems is likely to be closely associated with disease-associated changes in organismal tissue complexity and compartmentalization, thus enabling a nuanced GPCR-based capacity to interdict multiple disease pathomechanisms at a systemic level. GPCRs have been long considered as controllers of communication between tissues and cells. This communication involves the ligand-mediated control of cell surface receptors that then direct their stimuli to impact cell physiology. Given the tremendous success of GPCRs as therapeutic targets, considerable focus has been placed on the ability of these therapeutics to modulate diseases by acting at cell surface receptors. In the past decade, however, attention has focused upon how stable multiprotein GPCR superstructures, termed receptorsomes, both at the cell surface membrane and in the intracellular domain dictate and condition long-term GPCR activities associated with the regulation of protein expression patterns, cellular stress responses and DNA integrity management. The ability of these receptorsomes (often in the absence of typical cell surface ligands) to control complex cellular activities implicates them as key controllers of the functional balance between health and disease. A greater understanding of this function of GPCRs is likely to significantly augment our ability to further employ these proteins in a multitude of diseases.

Computational approaches leveraging integrated connections of multi-omic data toward clinical applications

In line with the advances in high-throughput technologies, multiple omic datasets have accumulated to study biological systems and diseases coherently. No single omics data type is capable of fully representing cellular activity. The complexity of the biological processes arises from the interactions between omic entities such as genes, proteins, and metabolites. Therefore, multi-omic data integration is crucial but challenging. The impact of the molecular alterations in multi-omic data is not local in the neighborhood of the altered gene or protein; rather, the impact diffuses in the network and changes the functionality of multiple signaling pathways and regulation of the gene expression. Additionally, multi-omic data is high-dimensional and has background noise. Several integrative approaches have been developed to accurately interpret the multi-omic datasets, including machine learning, network-based methods, and their combination. In this review, we overview the most recent integrative approaches and tools with a focus on network-based methods. We then discuss these approaches according to their specific applications, from disease-network and biomarker identification to patient stratification, drug discovery, and repurposing.

REHE: Fast variance components estimation for linear mixed models

Linear mixed models are widely used in ecological and biological applications, especially in genetic studies. Reliable estimation of variance components is crucial for using linear mixed models. However, standard methods, such as the restricted maximum likelihood (REML), are computationally inefficient in large samples and may be unstable with small samples. Other commonly used methods, such as the Haseman-Elston (HE) regression, may yield negative estimates of variances. Utilizing regularized estimation strategies, we propose the restricted Haseman-Elston (REHE) regression and REHE with resampling (reREHE) estimators, along with an inference framework for REHE, as fast and robust alternatives that provide nonnegative estimates with comparable accuracy to REML. The merits of REHE are illustrated using real data and benchmark simulation studies.

Integrative Analysis of Next-Generation Sequencing for Next-Generation Cancer Research toward Artificial Intelligence

The rapid improvement of next-generation sequencing (NGS) technologies and their application in large-scale cohorts in cancer research led to common challenges of big data. It opened a new research area incorporating systems biology and machine learning. As large-scale NGS data accumulated, sophisticated data analysis methods became indispensable. In addition, NGS data have been integrated with systems biology to build better predictive models to determine the characteristics of tumors and tumor subtypes. Therefore, various machine learning algorithms were introduced to identify underlying biological mechanisms. In this work, we review novel technologies developed for NGS data analysis, and we describe how these computational methodologies integrate systems biology and omics data. Subsequently, we discuss how deep neural networks outperform other approaches, the potential of graph neural networks (GNN) in systems biology, and the limitations in NGS biomedical research. To reflect on the various challenges and corresponding computational solutions, we will discuss the following three topics: (i) molecular characteristics, (ii) tumor heterogeneity, and (iii) drug discovery. We conclude that machine learning and network-based approaches can add valuable insights and build highly accurate models. However, a well-informed choice of learning algorithm and biological network information is crucial for the success of each specific research question.

netgsa: Fast computation and interactive visualization for topology-based pathway enrichment analysis

Existing software tools for topology-based pathway enrichment analysis are either computationally inefficient, have undesirable statistical power, or require expert knowledge to leverage the methods' capabilities. To address these limitations, we have overhauled NetGSA, an existing topology-based method, to provide a computationally-efficient user-friendly tool that offers interactive visualization. Pathway enrichment analysis for thousands of genes can be performed in minutes on a personal computer without sacrificing statistical power. The new software also removes the need for expert knowledge by directly curating gene-gene interaction information from multiple external databases. Lastly, by utilizing the capabilities of Cytoscape, the new software also offers interactive and intuitive network visualization.

Differential Network Analysis: A Statistical Perspective

Networks effectively capture interactions among components of complex systems, and have thus become a mainstay in many scientific disciplines. Growing evidence, especially from biology, suggest that networks undergo changes over time, and in response to external stimuli. In biology and medicine, these changes have been found to be predictive of complex diseases. They have also been used to gain insight into mechanisms of disease initiation and progression. Primarily motivated by biological applications, this article provides a review of recent statistical machine learning methods for inferring networks and identifying changes in their structures.

Causal Disturbance Analysis: A Novel Graph Centrality Based Method for Pathway Enrichment Analysis

Pathway enrichment analysis models (PEM) are the premier methods for interpreting gene expression profiles from high-throughput experiments. PEM often use a priori background knowledge to infer the underlying biological functions and mechanisms. A shortcoming of standard PEM is their disregarding of interactions for simplicity, which potentially results in partial and inaccurate inference. In this study, we introduce a graph-based PEM, namely Causal Disturbance Analysis (CADIA), that leverages gene interactions to quantify the topological importance of genes' expression profiles in pathways organizations. In particular, CADIA uses a novel graph centrality model, namely Source/Sink, to measure the topological importance. Source/Sink Centrality quantifies a gene's importance as a receiver and a sender of biological information, which allows for prioritizing the genes that are more likely to disturb a pathways functionality. CADIA infers an enrichment score for a pathway by deriving statistical evidence from Source/Sink centrality of the differentially expressed genes and combines it with classical over-representation analysis. Through real-world experimental and synthetic data evaluations, we show that CADIA can uniquely infer critical pathway enrichments that are not observable through other PEM. Our results indicate that CADIA is sensitive towards topologically central gene-level changes that and provides an informative framework for interpreting high-throughput data.

Revisiting the use of graph centrality models in biological pathway analysis

The use of graph theory models is widespread in biological pathway analyses as it is often desired to evaluate the position of genes and proteins in their interaction networks of the biological systems. In this article, we argue that the common standard graph centrality measures do not sufficiently capture the informative topological organizations of the pathways, and thus, limit the biological inference. While key pathway elements may appear both upstream and downstream in pathways, standard directed graph centralities attribute significant topological importance to the upstream elements and evaluate the downstream elements as having no importance.We present a directed graph framework, Source/Sink Centrality (SSC), to address the limitations of standard models. SSC separately measures the importance of a node in the upstream and the downstream of a pathway, as a sender and a receiver of biological signals, and combines the two terms for evaluating the centrality. To validate SSC, we evaluate the topological position of known human cancer genes and mouse lethal genes in their respective KEGG annotated pathways and show that SSC-derived centralities provide an effective framework for associating higher positional importance to the genes with higher importance from a priori knowledge. While the presented work challenges some of the modeling assumptions in the common pathway analyses, it provides a straight-forward methodology to extend the existing models. The SSC extensions can result in more informative topological description of pathways, and thus, more informative biological inference.

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.

Plasma metabolomics profiles suggest beneficial effects of a low-glycemic load dietary pattern on inflammation and energy metabolism

BACKGROUND

Low-glycemic load dietary patterns, characterized by consumption of whole grains, legumes, fruits, and vegetables, are associated with reduced risk of several chronic diseases.

METHODS

Using samples from a randomized, controlled, crossover feeding trial, we evaluated the effects on metabolic profiles of a low-glycemic whole-grain dietary pattern (WG) compared with a dietary pattern high in refined grains and added sugars (RG) for 28 d. LC-MS-based targeted metabolomics analysis was performed on fasting plasma samples from 80 healthy participants (n = 40 men, n = 40 women) aged 18-45 y. Linear mixed models were used to evaluate differences in response between diets for individual metabolites. Kyoto Encyclopedia of Genes and Genomes (KEGG)-defined pathways and 2 novel data-driven analyses were conducted to consider differences at the pathway level.

RESULTS

There were 121 metabolites with detectable signal in >98% of all plasma samples. Eighteen metabolites were significantly different between diets at day 28 [false discovery rate (FDR) < 0.05]. Inositol, hydroxyphenylpyruvate, citrulline, ornithine, 13-hydroxyoctadecadienoic acid, glutamine, and oxaloacetate were higher after the WG diet than after the RG diet, whereas melatonin, betaine, creatine, acetylcholine, aspartate, hydroxyproline, methylhistidine, tryptophan, cystamine, carnitine, and trimethylamine were lower. Analyses using KEGG-defined pathways revealed statistically significant differences in tryptophan metabolism between diets, with kynurenine and melatonin positively associated with serum C-reactive protein concentrations. Novel data-driven methods at the metabolite and network levels found correlations among metabolites involved in branched-chain amino acid (BCAA) degradation, trimethylamine-N-oxide production, and β oxidation of fatty acids (FDR < 0.1) that differed between diets, with more favorable metabolic profiles detected after the WG diet. Higher BCAAs and trimethylamine were positively associated with homeostasis model assessment-insulin resistance.

CONCLUSIONS

These exploratory metabolomics results support beneficial effects of a low-glycemic load dietary pattern characterized by whole grains, legumes, fruits, and vegetables, compared with a diet high in refined grains and added sugars on inflammation and energy metabolism pathways. This trial was registered at clinicaltrials.gov as NCT00622661.

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.

Moving beyond the current limits of data analysis in longevity and healthy lifespan studies

Living longer with sustainable quality of life is becoming increasingly important in aging populations. Understanding associative biological mechanisms have proven daunting, because of multigenicity and population heterogeneity. Although Big Data and Artificial Intelligence (AI) could help, naïve adoption is ill advised. We hold the view that model organisms are better suited for big-data analytics but might lack relevance because they do not immediately reflect the human condition. Resolving this hurdle and bridging the human-model organism gap will require some finesse. This includes improving signal:noise ratios by appropriate contextualization of high-throughput data, establishing consistency across multiple high-throughput platforms, and adopting supporting technologies that provide useful in silico and in vivo validation strategies.

Epigenetic loss of AOX1 expression via EZH2 leads to metabolic deregulations and promotes bladder cancer progression

Advanced Bladder Cancer (BLCA) remains a clinical challenge that lacks effective therapeutic measures. Here, we show that distinct, stage-wise metabolic alterations in BLCA are associated with the loss of function of aldehyde oxidase (AOX1). AOX1 associated metabolites have a high predictive value for advanced BLCA and our findings demonstrate that AOX1 is epigenetically silenced during BLCA progression by the methyltransferase activity of EZH2. Knockdown (KD) of AOX1 in normal bladder epithelial cells re-wires the tryptophan-kynurenine pathway resulting in elevated NADP levels which may increase metabolic flux through the pentose phosphate (PPP) pathway, enabling increased nucleotide synthesis, and promoting cell invasion. Inhibition of NADP synthesis rescues the metabolic effects of AOX1 KD. Ectopic AOX1 expression decreases NADP production, PPP flux and nucleotide synthesis, while decreasing invasion in cell line models and suppressing growth in tumor xenografts. Further gain and loss of AOX1 confirm the EZH2-dependent activation, metabolic deregulation, and tumor growth in BLCA. Our findings highlight the therapeutic potential of AOX1 and provide a basis for the development of prognostic markers for advanced BLCA.

Network approaches to systems biology analysis of complex disease: integrative methods for multi-omics data

In the past decade, significant progress has been made in complex disease research across multiple omics layers from genome, transcriptome and proteome to metabolome. There is an increasing awareness of the importance of biological interconnections, and much success has been achieved using systems biology approaches. However, because of the typical focus on one single omics layer at a time, existing systems biology findings explain only a modest portion of complex disease. Recent advances in multi-omics data collection and sharing present us new opportunities for studying complex diseases in a more comprehensive fashion, and yet simultaneously create new challenges considering the unprecedented data dimensionality and diversity. Here, our goal is to review extant and emerging network approaches that can be applied across multiple biological layers to facilitate a more comprehensive and integrative multilayered omics analysis of complex diseases.

Seeing the wood for the trees: a forest of methods for optimization and omic-network integration in metabolic modelling

Metabolic modelling has entered a mature phase with dozens of methods and software implementations available to the practitioner and the theoretician. It is not easy for a modeller to be able to see the wood (or the forest) for the trees. Driven by this analogy, we here present a 'forest' of principal methods used for constraint-based modelling in systems biology. This provides a tree-based view of methods available to prospective modellers, also available in interactive version at http://modellingmetabolism.net, where it will be kept updated with new methods after the publication of the present manuscript. Our updated classification of existing methods and tools highlights the most promising in the different branches, with the aim to develop a vision of how existing methods could hybridize and become more complex. We then provide the first hands-on tutorial for multi-objective optimization of metabolic models in R. We finally discuss the implementation of multi-view machine learning approaches in poly-omic integration. Throughout this work, we demonstrate the optimization of trade-offs between multiple metabolic objectives, with a focus on omic data integration through machine learning. We anticipate that the combination of a survey, a perspective on multi-view machine learning and a step-by-step R tutorial should be of interest for both the beginner and the advanced user.

KEDDY: a knowledge-based statistical gene set test method to detect differential functional protein-protein interactions

MOTIVATION

Identifying differential patterns between conditions is a popular approach to understanding the discrepancy between different biological contexts. Although many statistical tests were proposed for identifying gene sets with differential patterns based on different definitions of differentiality, few methods were suggested to identify gene sets with differential functional protein networks due to computational complexity.

RESULTS

We propose a method of Knowledge-based Evaluation of Dependency DifferentialitY (KEDDY), which is a statistical test for differential functional protein networks of a set of genes between two conditions with utilizing known functional protein-protein interaction information. Unlike other approaches focused on differential expressions of individual genes or differentiality of individual interactions, KEDDY compares two conditions by evaluating the probability distributions of functional protein networks based on known functional protein-protein interactions. The method has been evaluated and compared with previous methods through simulation studies, where KEDDY achieves significantly improved performance in accuracy and speed than the previous method that does not use prior knowledge and better performance in identifying gene sets with differential interactions than other methods evaluating changes in gene expressions. Applications to cancer data sets show that KEDDY identifies alternative cancer subtype-related differential gene sets compared to other differential expression-based methods, and the results also provide detailed gene regulatory information that drives the differentiality of the gene sets.

AVAILABILITY AND IMPLEMENTATION

The Java implementation of KEDDY is freely available to non-commercial users at https://sites.google.com/site/sjunggsm/keddy.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Enhanced Molecular Appreciation of Psychiatric Disorders Through High-Dimensionality Data Acquisition and Analytics

The initial diagnosis, molecular investigation, treatment, and posttreatment care of major psychiatric disorders (schizophrenia and bipolar depression) are all still significantly hindered by the current inability to define these disorders in an explicit molecular signaling manner. High-dimensionality data analytics, using large datastreams from transcriptomic, proteomic, or metabolomic investigations, will likely advance both the appreciation of the molecular nature of major psychiatric disorders and simultaneously enhance our ability to more efficiently diagnose and treat these debilitating conditions. High-dimensionality data analysis in psychiatric research has been heterogeneous in aims and methods and limited by insufficient sample sizes, poorly defined case definitions, methodological inhomogeneity, and confounding results. All of these issues combine to constrain the conclusions that can be extracted from them. Here, we discuss possibilities for overcoming methodological challenges through the implementation of transcriptomic, proteomic, or metabolomics signatures in psychiatric diagnosis and offer an outlook for future investigations. To fulfill the promise of intelligent high-dimensionality data-based differential diagnosis in mental disease diagnosis and treatment, future research will need large, well-defined cohorts in combination with state-of-the-art technologies.

Capturing context-specific regulation in molecular interaction networks

BACKGROUND

Molecular profiles change in response to perturbations. These changes are coordinated into functional modules via regulatory interactions. The genes and their products within a functional module are expected to be differentially expressed in a manner coherent with their regulatory network. This perspective presents a promising approach to increase precision in detecting differential signals as well as for describing differential regulatory signals within the framework of a priori knowledge about the underlying network, and so from a mechanistic point of view.

RESULTS

We present Coherent Network Expression (CoNE), an effective procedure for identifying differentially activated functional modules in molecular interaction networks. Differential gene expression is chosen as example, and differential signals coherent with the regulatory nature of the network are identified. We apply our procedure to systematically simulated data, comparing its performance to alternative methods. We then take the example case of a transcription regulatory network in the context of particle-induced pulmonary inflammation, recapitulating and proposing additional candidates to previously obtained results. CoNE is conveniently implemented in an R-package along with simulation utilities.

CONCLUSION

Combining coherent interactions with error control on differential gene expression results in uniformly greater specificity in inference than error control alone, ensuring that captured functional modules constitute real findings.

piMGM: incorporating multi-source priors in mixed graphical models for learning disease networks

Motivation

Learning probabilistic graphs over mixed data is an important way to combine gene expression and clinical disease data. Leveraging the existing, yet imperfect, information in pathway databases for mixed graphical model (MGM) learning is an understudied problem with tremendous potential applications in systems medicine, the problems of which often involve high-dimensional data.

Results

We present a new method, piMGM, which can learn with accuracy the structure of probabilistic graphs over mixed data by appropriately incorporating priors from multiple experts with different degrees of reliability. We show that piMGM accurately scores the reliability of prior information from a given expert even at low sample sizes. The reliability scores can be used to determine active pathways in healthy and disease samples. We tested piMGM on both simulated and real data from TCGA, and we found that its performance is not affected by unreliable priors. We demonstrate the applicability of piMGM by successfully using prior information to identify pathway components that are important in breast cancer and improve cancer subtype classification.

Availability and implementation

http://www.benoslab.pitt.edu/manatakisECCB2018.html.

Supplementary information

Supplementary data are available at Bioinformatics online.

Gene set analysis methods: a systematic comparison

Background

Gene set analysis is a valuable tool to summarize high-dimensional gene expression data in terms of biologically relevant sets. This is an active area of research and numerous gene set analysis methods have been developed. Despite this popularity, systematic comparative studies have been limited in scope.

Methods

In this study we present a semi-synthetic simulation study using real datasets in order to test and compare commonly used methods.

Results

A software pipeline, Flexible Algorithm for Novel Gene set Simulation (FANGS) develops simulated data based on a prostate cancer dataset where the KRAS and TGF-β pathways were differentially expressed. The FANGS software is compatible with other datasets and pathways. Comparisons of gene set analysis methods are presented for Gene Set Enrichment Analysis (GSEA), Significance Analysis of Function and Expression (SAFE), sigPathway, and Correlation Adjusted Mean RAnk (CAMERA) methods. All gene set analysis methods are tested using gene sets from the MSigDB knowledge base. The false positive rate and power are estimated and presented for comparison. Recommendations are made for the utility of the default settings of methods and each method’s sensitivity towards various effect sizes.

Conclusions

The results of this study provide empirical guidance to users of gene set analysis methods. The FANGS software is available for researchers for continued methods comparisons.

Electronic supplementary material

The online version of this article (10.1186/s13040-018-0166-8) contains supplementary material, which is available to authorized users.

Functional network community detection can disaggregate and filter multiple underlying pathways in enrichment analyses

Differential expression experiments or other analyses often end in a list of genes. Pathway enrichment analysis is one method to discern important biological signals and patterns from noisy expression data. However, pathway enrichment analysis may perform suboptimally in situations where there are multiple implicated pathways - such as in the case of genes that define subtypes of complex diseases. Our simulation study shows that in this setting, standard overrepresentation analysis identifies many false positive pathways along with the true positives. These false positives hamper investigators' attempts to glean biological insights from enrichment analysis. We develop and evaluate an approach that combines community detection over functional networks with pathway enrichment to reduce false positives. Our simulation study demonstrates that a large reduction in false positives can be obtained with a small decrease in power. Though we hypothesized that multiple communities might underlie previously described subtypes of high-grade serous ovarian cancer and applied this approach, our results do not support this hypothesis. In summary, applying community detection before enrichment analysis may ease interpretation for complex gene sets that represent multiple distinct pathways.