Using bioconductor package BiGGR for metabolic flux estimation based on gene expression changes in brain

Metabolic function-based normalization improves transcriptome data-driven reduction of genome-scale metabolic models

Genome-scale metabolic models (GEMs) are extensively used to simulate cell metabolism and predict cell phenotypes. GEMs can also be tailored to generate context-specific GEMs, using omics data integration approaches. To date, many integration approaches have been developed, however, each with specific pros and cons; and none of these algorithms systematically outperforms the others. The key to successful implementation of such integration algorithms lies in the optimal selection of parameters, and thresholding is a crucial component in this process. To improve the predictive accuracy of context-specific models, we introduce a new integration framework that improves the ranking of related genes and homogenizes the expression values of those gene sets using single-sample Gene Set Enrichment Analysis (ssGSEA). In this study, we coupled ssGSEA with GIMME and validated the advantages of the proposed framework to predict the ethanol formation of yeast grown in the glucose-limited chemostats, and to simulate metabolic behaviors of yeast growth in four different carbon sources. This framework enhances the predictive accuracy of GIMME which we demonstrate for predicting the yeast physiology in nutrient-limited cultures.

A chemokine network of T cell exhaustion and metabolic reprogramming in renal cell carcinoma

Renal cell carcinoma (RCC) is frequently infiltrated by immune cells, a process which is governed by chemokines. CD8+ T cells in the RCC tumor microenvironment (TME) may be exhausted which most likely influence therapy response and survival. The aim of this study was to evaluate chemokine-driven T cell recruitment, T cell exhaustion in the RCC TME, as well as metabolic processes leading to their functional anergy in RCC. Eight publicly available bulk RCC transcriptome collectives (n=1819) and a single cell RNAseq dataset (n=12) were analyzed. Immunodeconvolution, semi-supervised clustering, gene set variation analysis and Monte Carlo-based modeling of metabolic reaction activity were employed. Among 28 chemokine genes available, CXCL9/10/11/CXCR3, CXCL13/CXCR5 and XCL1/XCR1 mRNA expression were significantly increased in RCC compared to normal kidney tissue and also strongly associated with tumor-infiltrating effector memory and central memory CD8+ T cells in all investigated collectives. M1 TAMs, T cells, NK cells as well as tumor cells were identified as the major sources of these chemokines, whereas T cells, B cells and dendritic cells were found to predominantly express the cognate receptors. The cluster of RCCs characterized by high chemokine expression and high CD8+ T cell infiltration displayed a strong activation of IFN/JAK/STAT signaling with elevated expression of multiple T cell exhaustion-associated transcripts. Chemokinehigh RCCs were characterized by metabolic reprogramming, in particular by downregulated OXPHOS and increased IDO1-mediated tryptophan degradation. None of the investigated chemokine genes was significantly associated with survival or response to immunotherapy. We propose a chemokine network that mediates CD8+ T cell recruitment and identify T cell exhaustion, altered energy metabolism and high IDO1 activity as key mechanisms of their suppression. Concomitant targeting of exhaustion pathways and metabolism may pose an effective approach to RCC therapy.

Integrated regulatory and metabolic networks of the tumor microenvironment for therapeutic target prioritization

Translation of genomic discovery, such as single-cell sequencing data, to clinical decisions remains a longstanding bottleneck in the field. Meanwhile, computational systems biological models, such as cellular metabolism models and cell signaling pathways, have emerged as powerful approaches to provide efficient predictions in metabolites and gene expression levels, respectively. However, there has been limited research on the integration between these two models. This work develops a methodology for integrating computational models of probabilistic gene regulatory networks with a constraint-based metabolism model. By using probabilistic reasoning with Bayesian Networks, we aim to predict cell-specific changes under different interventions, which are embedded into the constraint-based models of metabolism. Applications to single-cell sequencing data of glioblastoma brain tumors generate predictions about the effects of pharmaceutical interventions on the regulatory network and downstream metabolisms in different cell types from the tumor microenvironment. The model presents possible insights into treatments that could potentially suppress anaerobic metabolism in malignant cells with minimal impact on other cell types' metabolism. The proposed integrated model can guide therapeutic target prioritization, the formulation of combination therapies, and future drug discovery. This model integration framework is also generalizable to other applications, such as different cell types, organisms, and diseases.

A Standardized Brain Molecular Atlas: A Resource for Systems Modeling and Simulation

Accurate molecular concentrations are essential for reliable analyses of biochemical networks and the creation of predictive models for molecular and systems biology, yet protein and metabolite concentrations used in such models are often poorly constrained or irreproducible. Challenges of using data from different sources include conflicts in nomenclature and units, as well as discrepancies in experimental procedures, data processing and implementation of the model. To obtain a consistent estimate of protein and metabolite levels, we integrated and normalized data from a large variety of sources to calculate Adjusted Molecular Concentrations. We found a high degree of reproducibility and consistency of many molecular species across brain regions and cell types, consistent with tight homeostatic regulation. We demonstrated the value of this normalization with differential protein expression analyses related to neurodegenerative diseases, brain regions and cell types. We also used the results in proof-of-concept simulations of brain energy metabolism. The standardized Brain Molecular Atlas overcomes the obstacles of missing or inconsistent data to support systems biology research and is provided as a resource for biomolecular modeling.

A systems biology approach for studying neurodegenerative diseases

Neurodegenerative diseases (NDDs), such as Alzheimer's (AD) and Parkinson's (PD), are among the leading causes of lost years of healthy life and exert a great strain on public healthcare systems. Despite being first described more than a century ago, no effective cure exists for AD or PD. Although extensively characterised at the molecular level, traditional neurodegeneration research remains marred by narrow-sense approaches surrounding amyloid β (Aβ), tau, and α-synuclein (α-syn). A systems biology approach enables the integration of multi-omics data and informs discovery of biomarkers, drug targets, and treatment strategies. Here, we present a comprehensive timeline of high-throughput data collection, and associated biotechnological advancements and computational analysis related to AD and PD. We hereby propose that a philosophical change in the definitions of AD and PD is now needed.

The metaRbolomics Toolbox in Bioconductor and beyond

Metabolomics aims to measure and characterise the complex composition of metabolites in a biological system. Metabolomics studies involve sophisticated analytical techniques such as mass spectrometry and nuclear magnetic resonance spectroscopy, and generate large amounts of high-dimensional and complex experimental data. Open source processing and analysis tools are of major interest in light of innovative, open and reproducible science. The scientific community has developed a wide range of open source software, providing freely available advanced processing and analysis approaches. The programming and statistics environment R has emerged as one of the most popular environments to process and analyse Metabolomics datasets. A major benefit of such an environment is the possibility of connecting different tools into more complex workflows. Combining reusable data processing R scripts with the experimental data thus allows for open, reproducible research. This review provides an extensive overview of existing packages in R for different steps in a typical computational metabolomics workflow, including data processing, biostatistics, metabolite annotation and identification, and biochemical network and pathway analysis. Multifunctional workflows, possible user interfaces and integration into workflow management systems are also reviewed. In total, this review summarises more than two hundred metabolomics specific packages primarily available on CRAN, Bioconductor and GitHub.

Integration of probabilistic regulatory networks into constraint-based models of metabolism with applications to Alzheimer's disease

Background

Mathematical models of biological networks can provide important predictions and insights into complex disease. Constraint-based models of cellular metabolism and probabilistic models of gene regulatory networks are two distinct areas that have progressed rapidly in parallel over the past decade. In principle, gene regulatory networks and metabolic networks underly the same complex phenotypes and diseases. However, systematic integration of these two model systems remains a fundamental challenge.

Results

In this work, we address this challenge by fusing probabilistic models of gene regulatory networks into constraint-based models of metabolism. The novel approach utilizes probabilistic reasoning in BN models of regulatory networks serves as the “glue” that enables a natural interface between the two systems. Probabilistic reasoning is used to predict and quantify system-wide effects of perturbation to the regulatory network in the form of constraints for flux variability analysis. In this setting, both regulatory and metabolic networks inherently account for uncertainty. Applications leverage constraint-based metabolic models of brain metabolism and gene regulatory networks parameterized by gene expression data from the hippocampus to investigate the role of the HIF-1 pathway in Alzheimer’s disease. Integrated models support HIF-1A as effective target to reduce the effects of hypoxia in Alzheimer’s disease. However, HIF-1A activation is far less effective in shifting metabolism when compared to brain metabolism in healthy controls.

Conclusions

The direct integration of probabilistic regulatory networks into constraint-based models of metabolism provides novel insights into how perturbations in the regulatory network may influence metabolic states. Predictive modeling of enzymatic activity can be facilitated using probabilistic reasoning, thereby extending the predictive capacity of the network. This framework for model integration is generalizable to other systems.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2872-8) contains supplementary material, which is available to authorized users.

Recent Advances in the Systems Biology of Aging

SIGNIFICANCE

Reductionist studies have contributed greatly to our understanding of the basic biology of aging in recent years but we still do not understand fundamental mechanisms for many identified drugs and pathways. Use of systems approaches will help us move forward in our understanding of aging. Recent Advances: Recent work described here has illustrated the power of systems biology to inform our understanding of aging through the study of (i) diet restriction, (ii) neurodegenerative disease, and (iii) biomarkers of aging.

CRITICAL ISSUES

Although we do not understand all of the individual genes and pathways that affect aging, as we continue to uncover more of them, we have now also begun to synthesize existing data using systems-level approaches, often to great effect. The three examples noted here all benefit from computational approaches that were unknown a few years ago, and from biological insights gleaned from multiple model systems, from aging laboratories as well as many other areas of biology.

FUTURE DIRECTIONS

Many new technologies, such as single-cell sequencing, advances in epigenetics beyond the methylome (specifically, assay for transposase-accessible chromatin with high throughput sequencing ), and multiomic network studies, will increase the reach of systems biologists. This suggests that approaches similar to those described here will continue to lead to striking findings, and to interventions that may allow us to delay some of the many age-associated diseases in humans; perhaps sooner that we expect. Antioxid. Redox Signal. 29, 973-984.

Computational prediction of changes in brain metabolic fluxes during Parkinson's disease from mRNA expression

Background

Parkinson’s disease is a widespread neurodegenerative disorder which affects brain metabolism. Although changes in gene expression during disease are often measured, it is difficult to predict metabolic fluxes from gene expression data. Here we explore the hypothesis that changes in gene expression for enzymes tend to parallel flux changes in biochemical reaction pathways in the brain metabolic network. This hypothesis is the basis of a computational method to predict metabolic flux changes from post-mortem gene expression measurements in Parkinson’s disease (PD) brain.

Results

We use a network model of central metabolism and optimize the correspondence between relative changes in fluxes and in gene expression. To this end we apply the Least-squares with Equalities and Inequalities algorithm integrated with Flux Balance Analysis (Lsei-FBA). We predict for PD (1) decreases in glycolytic rate and oxygen consumption and an increase in lactate production in brain cortex that correspond with measurements (2) relative flux decreases in ATP synthesis, in the malate-aspartate shuttle and midway in the TCA cycle that are substantially larger than relative changes in glucose uptake in the substantia nigra, dopaminergic neurons and most other brain regions (3) shifts in redox shuttles between cytosol and mitochondria (4) in contrast to Alzheimer’s disease: little activation of the gamma-aminobutyric acid shunt pathway in compensation for decreased alpha-ketoglutarate dehydrogenase activity (5) in the globus pallidus internus, metabolic fluxes are increased, reflecting increased functional activity.

Conclusion

Our method predicts metabolic changes from gene expression data that correspond in direction and order of magnitude with presently available experimental observations during Parkinson’s disease, indicating that the hypothesis may be useful for some biochemical pathways. Lsei-FBA generates predictions of flux distributions in neurons and small brain regions for which accurate metabolic flux measurements are not yet possible.

Integrating Extracellular Flux Measurements and Genome-Scale Modeling Reveals Differences between Brown and White Adipocytes

White adipocytes are specialized for energy storage, whereas brown adipocytes are specialized for energy expenditure. Explicating this difference can help identify therapeutic targets for obesity. A common tool to assess metabolic differences between such cells is the Seahorse Extracellular Flux (XF) Analyzer, which measures oxygen consumption and media acidification in the presence of different substrates and perturbagens. Here, we integrate the Analyzer's metabolic profile from human white and brown adipocytes with a genome-scale metabolic model to predict flux differences across the metabolic map. Predictions matched experimental data for the metabolite 4-aminobutyrate, the protein ABAT, and the fluxes for glucose, glutamine, and palmitate. We also uncovered a difference in how adipocytes dispose of nitrogenous waste, with brown adipocytes secreting less ammonia and more urea than white adipocytes. Thus, the method and software we developed allow for broader metabolic phenotyping and provide a distinct approach to uncovering metabolic differences.

Genome-Scale Brain Metabolic Networks as Scaffolds for the Systems Biology of Neurodegenerative Diseases: Mapping Metabolic Alterations

Systems-based investigation of diseases requires integrated analysis of cellular networks and high-throughput data of gene products. The use of genome-scale metabolic networks for such integration has led to the elucidation of cellular mechanisms for several cell types from microorganisms to plants. It has become easier and cheaper to generate high-throughput data over years in the form of transcriptome, proteome and metabolome. This has tremendously improved the quality and quantity of information extracted from such data enabling the documentation of active pathways and reactions in cell metabolism. A number of omics-based datasets for several neurodegenerative diseases are now available in public repositories. This increases the potential of using genome-scale brain metabolic networks as a scaffold for this type of data to map metabolic alterations for the purpose of elucidating disease mechanisms and for the diagnosis and treatment of such disorders. This chapter first reviews omics data collected for neurodegenerative diseases to map their effect on metabolism. Later, the potential for genome-scale metabolic modeling of such data is reviewed and discussed in light of recently reconstructed brain metabolic networks at genome-scale.

Evaluation of Metabolic and Synaptic Dysfunction Hypotheses of Alzheimer's Disease (AD): A Meta-Analysis of CSF Markers

BACKGROUND

Alzheimer's disease (AD) is currently incurable and a majority of investigational drugs have failed clinical trials. One explanation for this failure may be the invalidity of hypotheses focusing on amyloid to explain AD pathogenesis. Recently, hypotheses which are centered on synaptic and metabolic dysfunction are increasingly implicated in AD.

OBJECTIVE

Evaluate AD hypotheses by comparing neurotransmitter and metabolite marker concentrations in normal versus AD CSF.

METHODS

Meta-analysis allows for statistical comparison of pooled, existing cerebrospinal fluid (CSF) marker data extracted from multiple publications, to obtain a more reliable estimate of concentrations. This method also provides a unique opportunity to rapidly validate AD hypotheses using the resulting CSF concentration data. Hubmed, Pubmed and Google Scholar were comprehensively searched for published English articles, without date restrictions, for the keywords "AD", "CSF", and "human" plus markers selected for synaptic and metabolic pathways. Synaptic markers were acetylcholine, gamma-aminobutyric acid (GABA), glutamine, and glycine. Metabolic markers were glutathione, glucose, lactate, pyruvate, and 8 other amino acids. Only studies that measured markers in AD and controls (Ctl), provided means, standard errors/deviation, and subject numbers were included. Data were extracted by six authors and reviewed by two others for accuracy. Data were pooled using ratio of means (RoM of AD/Ctl) and random effects meta-analysis using Cochrane Collaboration's Review Manager software.

RESULTS

Of the 435 identified publications, after exclusion and removal of duplicates, 35 articles were included comprising a total of 605 AD patients and 585 controls. The following markers of synaptic and metabolic pathways were significantly changed in AD/controls: acetylcholine (RoM 0.36, 95% CI 0.24-0.53, p<0.00001), GABA (0.74, 0.58-0.94, p<0.01), pyruvate (0.48, 0.24-0.94, p=0.03), glutathione (1.11, 1.01- 1.21, p=0.03), alanine (1.10, 0.98-1.23, p=0.09), and lower levels of significance for lactate (1.2, 1.00-1.47, p=0.05). Of note, CSF glucose and glutamate levels in AD were not significantly different than that of the controls.

CONCLUSION

This study provides proof of concept for the use of meta-analysis validation of AD hypotheses, specifically via robust evidence for the cholinergic hypothesis of AD. Our data disagree with the other synaptic hypotheses of glutamate excitotoxicity and GABAergic resistance to neurodegeneration, given observed unchanged glutamate levels and decreased GABA levels. With regards to metabolic hypotheses, the data supported upregulation of anaerobic glycolysis, pentose phosphate pathway (glutathione), and anaplerosis of the tricarboxylic acid cycle using glutamate. Future applications of meta-analysis indicate the possibility of further in silico evaluation and generation of novel hypotheses in the AD field.

Understanding the physiology of the ageing individual: computational modelling of changes in metabolism and endurance

Ageing and lifespan are strongly affected by metabolism. The maximal possible uptake of oxygen is not only a good predictor of performance in endurance sports, but also of life expectancy. Figuratively speaking, healthy ageing is a competitive sport. Although the root cause of ageing is damage to macromolecules, it is the balance with repair processes that is decisive. Reduced or intermittent nutrition, hormones and intracellular signalling pathways that regulate metabolism have strong effects on ageing. Homeostatic regulatory processes tend to keep the environment of the cells within relatively narrow bounds. On the other hand, the body is constantly adapting to physical activity and food consumption. Spontaneous fluctuations in heart rate and other processes indicate youth and health. A (homeo)dynamic aspect of homeostasis deteriorates with age. We are now in a position to develop computational models of human metabolism and the dynamics of heart rhythm and oxygen transport that will advance our understanding of ageing. Computational modelling of the connections between dietary restriction, metabolism and protein turnover may increase insight into homeostasis of the proteins in our body. In this way, the computational reconstruction of human physiological processes, the Physiome, can help prevent frailty and age-related disease.

BiGG Models: A platform for integrating, standardizing and sharing genome-scale models

Genome-scale metabolic models are mathematically-structured knowledge bases that can be used to predict metabolic pathway usage and growth phenotypes. Furthermore, they can generate and test hypotheses when integrated with experimental data. To maximize the value of these models, centralized repositories of high-quality models must be established, models must adhere to established standards and model components must be linked to relevant databases. Tools for model visualization further enhance their utility. To meet these needs, we present BiGG Models (http://bigg.ucsd.edu), a completely redesigned Biochemical, Genetic and Genomic knowledge base. BiGG Models contains more than 75 high-quality, manually-curated genome-scale metabolic models. On the website, users can browse, search and visualize models. BiGG Models connects genome-scale models to genome annotations and external databases. Reaction and metabolite identifiers have been standardized across models to conform to community standards and enable rapid comparison across models. Furthermore, BiGG Models provides a comprehensive application programming interface for accessing BiGG Models with modeling and analysis tools. As a resource for highly curated, standardized and accessible models of metabolism, BiGG Models will facilitate diverse systems biology studies and support knowledge-based analysis of diverse experimental data.

Forward Individualized Medicine from Personal Genomes to Interactomes

When considering the variation in the genome, transcriptome, proteome and metabolome, and their interaction with the environment, every individual can be rightfully considered as a unique biological entity. Individualized medicine promises to take this uniqueness into account to optimize disease treatment and thereby improve health benefits for every patient. The success of individualized medicine relies on a precise understanding of the genotype-phenotype relationship. Although omics technologies advance rapidly, there are several challenges that need to be overcome: Next generation sequencing can efficiently decipher genomic sequences, epigenetic changes, and transcriptomic variation in patients, but it does not automatically indicate how or whether the identified variation will cause pathological changes. This is likely due to the inability to account for (1) the consequences of gene-gene and gene-environment interactions, and (2) (post)transcriptional as well as (post)translational processes that eventually determine the concentration of key metabolites. The technologies to accurately measure changes in these latter layers are still under development, and such measurements in humans are also mainly restricted to blood and circulating cells. Despite these challenges, it is already possible to track dynamic changes in the human interactome in healthy and diseased states by using the integration of multi-omics data. In this review, we evaluate the potential value of current major bioinformatics and systems biology-based approaches, including genome wide association studies, epigenetics, gene regulatory and protein-protein interaction networks, and genome-scale metabolic modeling. Moreover, we address the question whether integrative analysis of personal multi-omics data will help understanding of personal genotype-phenotype relationships.