Reconciled rat and human metabolic networks for comparative toxicogenomics and biomarker predictions

Genome-scale metabolic network reconstruction of model animals as a platform for translational research

Genome-scale metabolic models (GEMs) are used extensively for analysis of mechanisms underlying human diseases and metabolic malfunctions. However, the lack of comprehensive and high-quality GEMs for model organisms restricts translational utilization of omics data accumulating from the use of various disease models. Here we present a unified platform of GEMs that covers five major model animals, including Mouse1 (Mus musculus), Rat1 (Rattus norvegicus), Zebrafish1 (Danio rerio), Fruitfly1 (Drosophila melanogaster), and Worm1 (Caenorhabditis elegans). These GEMs represent the most comprehensive coverage of the metabolic network by considering both orthology-based pathways and species-specific reactions. All GEMs can be interactively queried via the accompanying web portal Metabolic Atlas. Specifically, through integrative analysis of Mouse1 with RNA-sequencing data from brain tissues of transgenic mice we identified a coordinated up-regulation of lysosomal GM2 ganglioside and peptide degradation pathways which appears to be a signature metabolic alteration in Alzheimer's disease (AD) mouse models with a phenotype of amyloid precursor protein overexpression. This metabolic shift was further validated with proteomics data from transgenic mice and cerebrospinal fluid samples from human patients. The elevated lysosomal enzymes thus hold potential to be used as a biomarker for early diagnosis of AD. Taken together, we foresee that this evolving open-source platform will serve as an important resource to facilitate the development of systems medicines and translational biomedical applications.

Model-based assessment of mammalian cell metabolic functionalities using omics data

Omics experiments are ubiquitous in biological studies, leading to a deluge of data. However, it is still challenging to connect changes in these data to changes in cell functions because of complex interdependencies between genes, proteins, and metabolites. Here, we present a framework allowing researchers to infer how metabolic functions change on the basis of omics data. To enable this, we curated and standardized lists of metabolic tasks that mammalian cells can accomplish. Genome-scale metabolic networks were used to define gene sets associated with each metabolic task. We further developed a framework to overlay omics data on these sets and predict pathway usage for each metabolic task. We demonstrated how this approach can be used to quantify metabolic functions of diverse biological samples from the single cell to whole tissues and organs by using multiple transcriptomic datasets. To facilitate its adoption, we integrated the approach into GenePattern (www.genepattern.org-CellFie).

A Highly Sensitive Nonextraction-Assisted HPLC Method with Fluorescence Detection for Quantification of Duvelisib in Plasma Samples and its Application to Pharmacokinetic Study in Rats

Background

Duvelisib (DUV) is a new oral phosphoinositide-3-kinase (PI3K)-δ and PI3K-γ inhibitor. It has been recently granted an accelerated approval for treatment of adult patients with relapsed or refractory chronic lymphocytic leukemia (CLL) and small lymphocytic lymphoma (SLL). It is also effective in therapy of T-cell lymphoma, solid tumors, and non-Hodgkin’s lymphoma. In literature, there is no method valid for quantitation of DUV in human plasma for its therapeutic monitoring and pharmacokinetic studies.

Purpose

The purpose of this study is the establishment of a highly sensitive HPLC method with fluorescence detection for quantitation of DUV in plasma for its therapeutic monitoring and pharmacokinetic studies of DUV.

Methods

The resolution of DUV and the internal standard (IS) olaparib (OLA) was achieved on Nucleosil CN column, with a mobile phase composed of acetonitrile:water (25:75, v/v) at a flow rate of 1.7 mL min^–1. The fluorescence of both DUV and OLA was detected at 410 nm after excitation at 280 nm. The method was validated according to the guidelines of bioanalytical method validation.

Results

The method was linear in the range of 5–100 ng mL^–1, and its limit of detection (LOD) and limit of quantitation (LOQ) were 2.12 ng mL^–1 and 7 ng mL^–1, respectively. The precisions of the method were ≤ 8.26%, and its accuracies were ≥ 95.32%. All the other validation parameters were satisfactory. The proposed method was successfully employed to the investigation of the pharmacokinetic profile of DUV in rats following a 25 mg/kg single dose of oral administration.

Conclusion

The method is characterized with high sensitivity, accuracy, simple sample pretreatment, rapidity, eco-friendly as it consumes low volumes of organic solvent in the mobile phase and has high analysis throughput as its run time was short (~ 10 min).

Optimization of a modeling platform to predict oncogenes from genome-scale metabolic networks of non-small-cell lung cancers

Cancer cell dysregulations result in the abnormal regulation of cellular metabolic pathways. By simulating this metabolic reprogramming using constraint-based modeling approaches, oncogenes can be predicted, and this knowledge can be used in prognosis and treatment. We introduced a trilevel optimization problem describing metabolic reprogramming for inferring oncogenes. First, this study used RNA-Seq expression data of lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) samples and their healthy counterparts to reconstruct tissue-specific genome-scale metabolic models and subsequently build the flux distribution pattern that provided a measure for the oncogene inference optimization problem for determining tumorigenesis. The platform detected 45 genes for LUAD and 84 genes for LUSC that lead to tumorigenesis. A high level of differentially expressed genes was not an essential factor for determining tumorigenesis. The platform indicated that pyruvate kinase (PKM), a well-known oncogene with a low level of differential gene expression in LUAD and LUSC, had the highest fitness among the predicted oncogenes based on computation. By contrast, pyruvate kinase L/R (PKLR), an isozyme of PKM, had a high level of differential gene expression in both cancers. Phosphatidylserine synthase 1 (PTDSS1), an oncogene in LUAD, was inferred to have a low level of differential gene expression, and overexpression could significantly reduce survival probability. According to the factor analysis, PTDSS1 characteristics were close to those of the template, but they were unobvious in LUSC. Angiotensin-converting enzyme 2 (ACE2) has recently garnered widespread interest as the SARS-CoV-2 virus receptor. Moreover, we determined that ACE2 is an oncogene of LUSC but not of LUAD. The platform developed in this study can identify oncogenes with low levels of differential expression and be used to identify potential therapeutic targets for cancer treatment.

Mechanistic models of microbial community metabolism

Microbial communities affect many facets of human health and well-being. Naturally occurring bacteria, whether in nature or the human body, rarely exist in isolation. A deeper understanding of the metabolic functions of these communities is now possible with emerging computational models. In this review, we summarize frameworks for constructing mechanistic models of microbial community metabolism and discuss available algorithms for model analysis. We highlight essential decision points that greatly influence algorithm selection, as well as model analysis. Polymicrobial metabolic models can be utilized to gain insights into host-pathogen interactions, bacterial engineering, and many more translational applications.

Tozasertib Attenuates Neuropathic Pain by Interfering with Aurora Kinase and KIF11 Mediated Nociception

Kinesins are the motor proteins that transport excitatory receptors to the synaptic membrane by forming a complex with receptor cargo leading to central sensitization causing neuropathic pain. Many regulatory proteins govern the transit of receptors by activating kinesin, and Aurora kinases are one of them. In this study, we have performed in silico molecular dynamics simulation to delineate the dynamic interaction of Aurora kinase A with its pharmacological inhibitor, tozasertib. The results from the molecular dynamics study shows that tozasertib-Aurora kinase A complex is stabilized through hydrogen bonding, polar interactions, and water bridges. Findings from the in vitro studies suggest that tozasertib treatment significantly attenuates lipopolysaccharide (LPS)-induced increase in oxidonitrosative stress and kif11 overexpression in C6 glial cell lines. Further, we investigated the regulation of kif11 and its modulation by tozasertib in an animal model of neuropathic pain. Two weeks post-CCI surgery we observed a significant increase in pain hypersensitivity and kif11 overexpression in DRG and spinal cord of nerve-injured rats. Tozasertib treatment significantly attenuates enhanced pain hypersensitivity along with the restoration of kif11 expression in DRG and spinal cord and oxidonitrosative stress in the sciatic nerve of injured rats. Our findings demonstrate the potential role of tozasertib for the management of neuropathic pain.

Modeling Diet-Induced NAFLD and NASH in Rats: A Comprehensive Review

Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease, characterized by hepatic steatosis without any alcohol abuse. As the prevalence of NAFLD is rapidly increasing worldwide, important research activity is being dedicated to deciphering the underlying molecular mechanisms in order to define new therapeutic targets. To investigate these pathways and validate preclinical study, reliable, simple and reproducible tools are needed. For that purpose, animal models, more precisely, diet-induced NAFLD and nonalcoholic steatohepatitis (NASH) models, were developed to mimic the human disease. In this review, we focus on rat models, especially in the current investigation of the establishment of the dietary model of NAFLD and NASH in this species, compiling the different dietary compositions and their impact on histological outcomes and metabolic injuries, as well as external factors influencing the course of liver pathogenesis.

Network model-based screen for FDA-approved drugs affecting cardiac fibrosis

Cardiac fibrosis is a significant component of pathological heart remodeling, yet it is not directly targeted by existing drugs. Systems pharmacology approaches have the potential to provide mechanistic frameworks with which to predict and understand how drugs modulate biological systems. Here, we combine network modeling of the fibroblast signaling network with 36 unique drug-target interactions from DrugBank to predict drugs that modulate fibroblast phenotype and fibrosis. Galunisertib was predicted to decrease collagen and α-SMA expression, which we validated in human cardiac fibroblasts. In vivo fibrosis data from the literature validated predictions for 10 drugs. Further, the model was used to identify network mechanisms by which these drugs work. Arsenic trioxide was predicted to induce fibrosis by AP1-driven TGFβ expression and MMP2-driven TGFβ activation. Entresto (valsartan/sacubitril) was predicted to suppress fibrosis by valsartan suppression of ERK signaling and sacubitril enhancement of PKG activity, both of which decreased Smad3 activity. Overall, this study provides a framework for integrating drug-target mechanisms with logic-based network models, which can drive further studies both in cardiac fibrosis and other conditions.

Systematic investigation of mouse models of Parkinson's disease by transcriptome mapping on a brain-specific genome-scale metabolic network

Genome-scale metabolic networks enable systemic investigation of metabolic alterations caused by diseases by providing interpretation of omics data. Although Mus musculus (mouse) is one of the most commonly used model organisms for neurodegenerative diseases, a brain-specific metabolic network model of mice has not yet been reconstructed. Here we reconstructed the first brain-specific metabolic network model of mice, iBrain674-Mm, by a homology-based approach, which consisted of 992 reactions controlled by 674 genes and distributed over 48 pathways. We validated the newly reconstructed network model by showing that it predicts healthy resting-state metabolic phenotypes of mouse brain compatible with the literature. We later used iBrain674-Mm to interpret various experimental mouse models of Parkinson's Disease (PD) at the transcriptome level. To this end, we applied a constraint-based modelling based biomarker prediction method called TIMBR (Transcriptionally Inferred Metabolic Biomarker Response) to predict altered metabolite production from transcriptomic data. Systemic analysis of seven different PD mouse models by TIMBR showed that the neuronal levels of glutamate, lactate, creatine phosphate, neuronal acetylcholine, bilirubin and formate increased in most of the PD mouse models, whereas the levels of melatonin, epinephrine, astrocytic formate and astrocytic bilirubin decreased. Although most of the predictions were consistent with the literature, there were some inconsistencies among different PD mouse models, signifying that there is no perfect experimental model to reflect PD metabolism. The newly reconstructed brain-specific genome-scale metabolic network model of mice can make important contributions to the interpretation and development of experimental mouse models of PD and other neurodegenerative diseases.

Tryp-ing Up Metabolism: Role of Metabolic Adaptations in Kinetoplastid Disease Pathogenesis

Today, more than a billion people-one-sixth of the world's population-are suffering from neglected tropical diseases. Human African trypanosomiasis, Chagas disease, and leishmaniasis are neglected tropical diseases caused by protozoan parasites belonging to the genera Trypanosoma and Leishmania About half a million people living in tropical and subtropical regions of the world are at risk of contracting one of these three infections. Kinetoplastids have complex life cycles with different morphologies and unique physiological requirements at each life cycle stage. This review covers the latest findings on metabolic pathways impacting disease pathogenesis of kinetoplastids within the mammalian host. Nutrient availability is a key factor shaping in vivo parasite metabolism; thus, kinetoplastids display significant metabolic flexibility. Proteomic and transcriptomic profiles show that intracellular trypanosomatids are able to switch to an energy-efficient metabolism within the mammalian host system. Host metabolic changes can also favor parasite persistence, and contribute to symptom development, in a location-specific fashion. Ultimately, targeted and untargeted metabolomics studies have been a valuable approach to elucidate the specific biochemical pathways affected by infection within the host, leading to translational drug development and diagnostic insights.

Identifying functional metabolic shifts in heart failure with the integration of omics data and a heart-specific, genome-scale model

In diseased states, the heart can shift to use different carbon substrates, measured through changes in uptake of metabolites by imaging methods or blood metabolomics. However, it is not known whether these measured changes are a result of transcriptional changes or external factors. Here, we explore transcriptional changes in late-stage heart failure using publicly available data integrated with a model of heart metabolism. First, we present a heart-specific genome-scale metabolic network reconstruction (GENRE), iCardio. Next, we demonstrate the utility of iCardio in interpreting heart failure gene expression data by identifying tasks inferred from differential expression (TIDEs), which represent metabolic functions associated with changes in gene expression. We identify decreased gene expression for nitric oxide (NO) and N-acetylneuraminic acid (Neu5Ac) synthesis as common metabolic markers of heart failure. The methods presented here for constructing a tissue-specific model and identifying TIDEs can be extended to multiple tissues and diseases of interest.

Succinate dehydrogenase inhibitors: in silico flux analysis and in vivo metabolomics investigations show no severe metabolic consequences for rats and humans

Succinate dehydrogenase complex II inhibitors (SDHIs) are widely used fungicides since the 1960s. Recently, based on published in vitro cell viability data, potential health effects via disruption of the mitochondrial respiratory chain and tricarboxylic acid cycle have been postulated in mammalian species. As primary metabolic impact of SDH inhibition, an increase in succinate, and compensatory ATP production via glycolysis resulting in excess lactate levels was hypothesized. To investigate these hypotheses, genome-scale metabolic models of Rattus norvegicus and Homo sapiens were used for an in silico analysis of mammalian metabolism. Moreover, plasma samples from 28-day studies with the SDHIs boscalid and fluxapyroxad were subjected to metabolome analyses, to assess in vivo metabolite changes induced by SDHIs. The outcome of in silico analyses indicated that mammalian metabolic networks are robust and able to compensate different types of metabolic perturbation, e.g., partial or complete SDH inhibition. Additionally, the in silico comparison of rat and human responses suggested no noticeable differences between both species, evidencing that the rat is an appropriate testing organism for toxicity of SDHIs. Since no succinate or lactate accumulation were found in rats, such an accumulation is also not expected in humans as a result of SDHI exposure.

Mapping the Metabolic Networks of Tumor Cells and Cancer-Associated Fibroblasts

Metabolism is considered to be the core of all cellular activity. Thus, extensive studies of metabolic processes are ongoing in various fields of biology, including cancer research. Cancer cells are known to adapt their metabolism to sustain high proliferation rates and survive in unfavorable environments with low oxygen and nutrient concentrations. Hence, targeting cancer cell metabolism is a promising therapeutic strategy in cancer research. However, cancers consist not only of genetically altered tumor cells but are interwoven with endothelial cells, immune cells and fibroblasts, which together with the extracellular matrix (ECM) constitute the tumor microenvironment (TME). Cancer-associated fibroblasts (CAFs), which are linked to poor prognosis in different cancer types, are one important component of the TME. CAFs play a significant role in reprogramming the metabolic landscape of tumor cells, but how, and in what manner, this interaction takes place remains rather unclear. This review aims to highlight the metabolic landscape of tumor cells and CAFs, including their recently identified subtypes, in different tumor types. In addition, we discuss various in vitro and in vivo metabolic techniques as well as different in silico computational tools that can be used to identify and characterize CAF-tumor cell interactions. Finally, we provide our view on how mapping the complex metabolic networks of stromal-tumor metabolism will help in finding novel metabolic targets for cancer treatment.

Predicting changes in renal metabolism after compound exposure with a genome-scale metabolic model

The kidneys are metabolically active organs with importance in several physiological tasks such as the secretion of soluble wastes into the urine and synthesizing glucose and oxidizing fatty acids for energy in fasting (non-fed) conditions. Once damaged, the metabolic capability of the kidneys becomes altered. Here, we define metabolic tasks in a computational modeling framework to capture kidney function in an update to the iRno network reconstruction of rat metabolism using literature-based evidence. To demonstrate the utility of iRno for predicting kidney function, we exposed primary rat renal proximal tubule epithelial cells to four compounds with varying levels of nephrotoxicity (acetaminophen, gentamicin, 2,3,7,8-tetrachlorodibenzodioxin, and trichloroethylene) for six and twenty-four hours, and collected transcriptomics and metabolomics data to measure the metabolic effects of compound exposure. For the transcriptomics data, we observed changes in fatty acid metabolism and amino acid metabolism, as well as changes in existing markers of kidney function such as Clu (clusterin). The iRno metabolic network reconstruction was used to predict alterations in these same pathways after integrating transcriptomics data and was able to distinguish between select compound-specific effects on the proximal tubule epithelial cells. Genome-scale metabolic network reconstructions with coupled omics data can be used to predict changes in metabolism as a step towards identifying novel metabolic biomarkers of kidney function and dysfunction.

Experimental models of glaucoma filtration surgery

Glaucoma filtration surgery plays an important role in achieving intraocular pressure (IOP) reduction in patients who have high IOP despite maximum medical therapy. Preclinical experimental models of glaucoma filtration surgery contribute a great deal to our knowledge of the wound healing processes that predispose to scarring and may lead to poor outcomes. However, this research needs to be interpreted in the light of the specific study design, animal model and methods used. We review the existing literature addressing various models of experimental glaucoma filtration surgery, discuss the considerations in assessing these models and describe future steps in evaluating potential therapeutics and bleb characteristics that could impact translational research in this field.

Comparison of Genetically Engineered Immunodeficient Animal Models for Nonclinical Testing of Stem Cell Therapies

For the recovery or replacement of dysfunctional cells and tissue-the goal of stem cell research-successful engraftment of transplanted cells and tissues are essential events. The event is largely dependent on the immune rejection of the recipient; therefore, the immunogenic evaluation of candidate cells or tissues in immunodeficient animals is important. Understanding the immunodeficient system can provide insights into the generation and use of immunodeficient animal models, presenting a unique system to explore the capabilities of the innate immune system. In this review, we summarize various immunodeficient animal model systems with different target genes as valuable tools for biomedical research. There have been numerous immunodeficient models developed by different gene defects, resulting in many different features in phenotype. More important, mice, rats, and other large animals exhibit very different immunological and physiological features in tissue and organs, including genetic background and a representation of human disease conditions. Therefore, the findings from this review may guide researchers to select the most appropriate immunodeficient strain, target gene, and animal species based on the research type, mutant gene effects, and similarity to human immunological features for stem cell research.

Diagnostic Criteria for Metabolic Syndrome in Diet-Induced Rodent Models: A Systematic Review

BACKGROUND

Thousands of publications in recent years have addressed the induction of metabolic syndrome (MetS) in rodents. However, the criteria and the reference values for diagnosing this disease have not been defined.

OBJECTIVE

Our main objective was to carry out a systematic review to gather evidence about the criteria for biochemical and anthropometric parameters in which scientific studies have relied on to report that rats developed MetS from a previous dietary manipulation.

METHODS

We compiled characteristics and findings of diet-induced MetS with high-fat, high-carbohydrate, high-fat/high-carbohydrates, and cafeteria diet from PubMed and Science Direct databases published in the last 5 years.

RESULTS

The results on the principal determinants for the syndrome, published in the reviewed articles, were chosen to propose reference values in the rat models of food induction.

CONCLUSION

The values obtained will serve as reference cut-of points in the development of the disease; in addition, the compilation of data will be useful in planning and executing research protocols in animal models.

The contribution of gut bacterial metabolites in the human immune signaling pathway of non-communicable diseases

The interaction disorder between gut microbiota and its host has been documented in different non-communicable diseases (NCDs) such as metabolic syndrome, neurodegenerative disease, and autoimmune disease. The majority of these altered interactions arise through metabolic cross-talk between gut microbiota and host immune system, inducing a low-grade chronic inflammation that characterizes all NCDs. In this review, we discuss the contribution of bacterial metabolites to immune signaling pathways involved in NCDs. We then review recent advances that aid to rationally design microbial therapeutics. A deeper understanding of these intersections between host and gut microbiota metabolism using metabolomics-based system biology platform promises to reveal the fundamental mechanisms that drive metabolic predispositions to disease and suggest new avenues to use microbial therapeutic opportunities for NCDs treatment and prevention. Abbreviations: NCDs: non-communicable disease, IBD: inflammatory bowel disease, IL: interleukin, T2D: type 2 diabetes, SCFAs: short-chain fatty acids, HDAC: histone deacetylases, GPCR: G-protein coupled receptors, 5-HT: 5-hydroxytryptamine receptor signaling, DCs: dendritic cells, IECs: intestinal epithelial cells, T-reg: T regulatory cell, NF-κB: nuclear factor κB, TNF-α: tumor necrosis factor alpha, Th: T helper cell, CNS: central nervous system, ECs: enterochromaffin cells, NSAIDs: non-steroidal anti-inflammatory drugs, AhR: aryl hydrocarbon receptor, IDO: indoleamine 2,3-dioxygenase, QUIN: quinolinic acid, PC: phosphatidylcholine, TMA: trimethylamine, TMAO: trimethylamine N-oxide, CVD: cardiovascular disease, NASH: nonalcoholic steatohepatitis, BAs: bile acids, FXR: farnesoid X receptor, CDCA: chenodeoxycholic acid, DCA: deoxycholic acid, LCA: lithocholic acid, UDCA: ursodeoxycholic acid, CB: cannabinoid receptor, COBRA: constraint-based reconstruction and analysis.

The Human Protein Atlas-Spatial localization of the human proteome in health and disease

For a complete understanding of a system's processes and each protein's role in health and disease, it is essential to study protein expression with a spatial resolution, as the exact location of proteins at tissue, cellular, or subcellular levels is tightly linked to protein function. The Human Protein Atlas (HPA) project is a large-scale initiative aiming at mapping the entire human proteome using antibody-based proteomics and integration of various other omics technologies. The publicly available knowledge resource www.proteinatlas.org is one of the world's most visited biological databases and has been extensively updated during the last few years. The current version is divided into six main sections, each focusing on particular aspects of the human proteome: (a) the Tissue Atlas showing the distribution of proteins across all major tissues and organs in the human body; (b) the Cell Atlas showing the subcellular localization of proteins in single cells; (c) the Pathology Atlas showing the impact of protein levels on survival of patients with cancer; (d) the Blood Atlas showing the expression profiles of blood cells and actively secreted proteins; (e) the Brain Atlas showing the distribution of proteins in human, mouse, and pig brain; and (f) the Metabolic Atlas showing the involvement of proteins in human metabolism. The HPA constitutes an important resource for further understanding of human biology, and the publicly available datasets hold much promise for integration with other emerging efforts focusing on single cell analyses, both at transcriptomic and proteomic level.

Toxicant-Induced Metabolic Alterations in Lipid and Amino Acid Pathways Are Predictive of Acute Liver Toxicity in Rats

Liver disease and disorders associated with aberrant hepatocyte metabolism can be initiated via drug and environmental toxicant exposures. In this study, we tested the hypothesis that gene and metabolic profiling can reveal commonalities in liver response to different toxicants and provide the capability to identify early signatures of acute liver toxicity. We used Sprague Dawley rats and three classical hepatotoxicants: acetaminophen (2 g/kg), bromobenzene (0.4 g/kg), and carbon tetrachloride (0.3 g/kg), to identify early perturbations in liver metabolism after a single acute exposure dose. We measured changes in liver genes and plasma metabolites at two time points (5 and 10 h) and used genome-scale metabolic models to identify commonalities in liver responses across the three toxicants. We found strong correlations for gene and metabolic profiles between the toxicants, indicative of similarities in the liver response to toxicity. We identified several injury-specific pathways in lipid and amino acid metabolism that changed similarly across the three toxicants. Our findings suggest that several plasma metabolites in lipid and amino acid metabolism are strongly associated with the progression of liver toxicity, and as such, could be targeted and clinically assessed for their potential as early predictors of acute liver toxicity.

Approaching Gravity as a Continuum Using the Rat Partial Weight-Bearing Model

For decades, scientists have relied on animals to understand the risks and consequences of space travel. Animals remain key to study the physiological alterations during spaceflight and provide crucial information about microgravity-induced changes. While spaceflights may appear common, they remain costly and, coupled with limited cargo areas, do not allow for large sample sizes onboard. In 1979, a model of hindlimb unloading (HU) was successfully created to mimic microgravity and has been used extensively since its creation. Four decades later, the first model of mouse partial weight-bearing (PWB) was developed, aiming at mimicking partial gravity environments. Return to the Lunar surface for astronauts is now imminent and prompted the need for an animal model closer to human physiology; hence in 2018, our laboratory created a new model of PWB for adult rats. In this review, we will focus on the rat model of PWB, from its conception to the current state of knowledge. Additionally, we will address how this new model, used in conjunction with HU, will help implement new paradigms allowing scientists to anticipate the physiological alterations and needs of astronauts. Finally, we will discuss the outstanding questions and future perspectives in space research and propose potential solutions using the rat PWB model.

Systems Biology Approaches to Understanding the Human Immune System

Systems biology is an approach to interrogate complex biological systems through large-scale quantification of numerous biomolecules. The immune system involves >1,500 genes/proteins in many interconnected pathways and processes, and a systems-level approach is critical in broadening our understanding of the immune response to vaccination. Changes in molecular pathways can be detected using high-throughput omics datasets (e.g., transcriptomics, proteomics, and metabolomics) by using methods such as pathway enrichment, network analysis, machine learning, etc. Importantly, integration of multiple omic datasets is becoming key to revealing novel biological insights. In this perspective article, we highlight the use of protein-protein interaction (PPI) networks as a multi-omics integration approach to unravel information flow and mechanisms during complex biological events, with a focus on the immune system. This involves a combination of tools, including: InnateDB, a database of curated interactions between genes and protein products involved in the innate immunity; NetworkAnalyst, a visualization and analysis platform for InnateDB interactions; and MetaBridge, a tool to integrate metabolite data into PPI networks. The application of these systems techniques is demonstrated for a variety of biological questions, including: the developmental trajectory of neonates during the first week of life, mechanisms in host-pathogen interaction, disease prognosis, biomarker discovery, and drug discovery and repurposing. Overall, systems biology analyses of omics data have been applied to a variety of immunology-related questions, and here we demonstrate the numerous ways in which PPI network analysis can be a powerful tool in contributing to our understanding of the immune system and the study of vaccines.

Challenges and Opportunities in the Design of Liver-on-Chip Microdevices

The liver is the central hub of xenobiotic metabolism and consequently the organ most prone to cosmetic- and drug-induced toxicity. Failure to detect liver toxicity or to assess compound clearance during product development is a major cause of postmarketing product withdrawal, with disastrous clinical and financial consequences. While small animals are still the preferred model in drug development, the recent ban on animal use in the European Union created a pressing need to develop precise and efficient tools to detect human liver toxicity during cosmetic development. This article includes a brief review of liver development, organization, and function and focuses on the state of the art of long-term cell culture, including hepatocyte cell sources, heterotypic cell-cell interactions, oxygen demands, and culture medium formulation. Finally, the article reviews emerging liver-on-chip devices and discusses the advantages and pitfalls of individual designs. The goal of this review is to provide a framework to design liver-on-chip devices and criteria with which to evaluate this emerging technology.

Sexual Dimorphism in Energy Metabolism of Wistar Rats Using Data Analysis

The prevalence of some chronic diseases, such as cancer or neurodegenerative disorders, differs between sexes. Animal models provide an important tool to adopt potential therapies from preclinical studies to humans. Laboratory rats are the most popular animals in toxicology, neurobehavioral, or cancer research. Our study aimed to reveal the basic differences in blood metabolome (amino acids, biogenic amines, and acylcarnitines) of the adult male (n = 10) and female (n = 10) Wistar rats. Partial least square-discrimination analysis (PLS-DA) and a variance im portance in projection (VIP) score was used to identify the key sex-specific metabolites. All groups of metabolites, as the main markers of energy metabolism, showed a significant sex-dependent pattern. The most important features calculated in PLS-DA according to VIP score were free carnitine (C0), tyrosine (Tyr), and acylcarnitine C5-OH. While aromatic amino acids, such as Tyr and phenylalanine (Phe), were significantly elevated in the blood plasma of males, tryptophan (Trp) was found in higher levels in the blood plasma of females. Besides, significant sex-related changes in urea cycle were found. Our study provides an important insight into sex-specific differences in energy metabolism in rats and indicates that further studies should consider sex as the main aspect in design and data interpretation.

Comparative Analyses of mTOR/Akt and Muscle Atrophy-Related Signaling in Aged Respiratory and Gastrocnemius Muscles

Sarcopenia is the degenerative loss of skeletal muscle mass and function associated with aging and occurs in the absence of any underlying disease or condition. A comparison of the age-related molecular signaling signatures of different muscles has not previously been reported. In this study, we compared the age-related molecular signaling signatures of the intercostal muscles, the diaphragm, and the gastrocnemii using 6-month and 20-month-old rats. The phosphorylation of Akt, ribosomal S6, and Forkhead box protein O1 (FoxO1) in diaphragms significantly increased with age, but remained unchanged in the intercostal and gastrocnemius muscles. In addition, ubiquitin-proteasome degradation, characterized by the levels of MuRF1 and Atrogin-1, did not change with age in all rat muscles. Interestingly, an increase in LC3BII and p62 levels marked substantial blockage of autophagy in aged gastrocnemii but not in aged respiratory muscles. These changes in LC3BII and p62 levels were also associated with a decrease in markers of mitochondrial quality control. Therefore, our results suggest that the age-related signaling events in respiratory muscles differ from those in the gastrocnemii, most likely to preserve the vital functions played by the respiratory muscles.

Transcriptome-guided parsimonious flux analysis improves predictions with metabolic networks in complex environments

The metabolic responses of bacteria to dynamic extracellular conditions drives not only the behavior of single species, but also entire communities of microbes. Over the last decade, genome-scale metabolic network reconstructions have assisted in our appreciation of important metabolic determinants of bacterial physiology. These network models have been a powerful force in understanding the metabolic capacity that species may utilize in order to succeed in an environment. Increasingly, an understanding of context-specific metabolism is critical for elucidating metabolic drivers of larger phenotypes and disease. However, previous approaches to use network models in concert with omics data to better characterize experimental systems have met challenges due to assumptions necessary by the various integration platforms or due to large input data requirements. With these challenges in mind, we developed RIPTiDe (Reaction Inclusion by Parsimony and Transcript Distribution) which uses both transcriptomic abundances and parsimony of overall flux to identify the most cost-effective usage of metabolism that also best reflects the cell’s investments into transcription. Additionally, in biological samples where it is difficult to quantify specific growth conditions, it becomes critical to develop methods that require lower amounts of user intervention in order to generate accurate metabolic predictions. Utilizing a metabolic network reconstruction for the model organism Escherichia coli str. K-12 substr. MG1655 (iJO1366), we found that RIPTiDe correctly identifies context-specific metabolic pathway activity without supervision or knowledge of specific media conditions. We also assessed the application of RIPTiDe to in vivo metatranscriptomic data where E. coli was present at high abundances, and found that our approach also effectively predicts metabolic behaviors of host-associated bacteria. In the setting of human health, understanding metabolic changes within bacteria in environments where growth substrate availability is difficult to quantify can have large downstream impacts on our ability to elucidate molecular drivers of disease-associated dysbiosis across the microbiota. Our results indicate that RIPTiDe may have potential to provide understanding of context-specific metabolism of bacteria within complex communities.

Transcriptomic analyses of bacteria have become instrumental to our understanding of their responses to changes in their environment. While traditional analyses have been informative, leveraging these datasets within genome-scale metabolic network reconstructions (GENREs) can provide greatly improved context for shifts in pathway utilization and downstream/upstream ramifications for changes in metabolic regulation. Many previous techniques for GENRE transcript integration have focused on creating maximum consensus with input datasets, but these approaches were recently shown to generate less accurate metabolic predictions than a transcript-agnostic method of flux minimization (pFBA), which identifies the most efficient/economic patterns of metabolism given certain growth constraints. Despite this success, growth conditions are not always easily quantifiable and highlights the need for novel platforms that build from these findings. Our new method, RIPTiDe, combines these concepts and utilizes overall minimization of flux weighted by transcriptomic analysis to identify the most energy efficient pathways to achieve growth that include more highly transcribed enzymes, without previous insight into extracellular conditions. Utilizing a well-studied GENRE from Escherichia coli, we demonstrate that this new approach correctly predicts patterns of metabolism utilizing a variety of both in vitro and in vivo transcriptomes. This platform could be important for revealing context-specific bacterial phenotypes in line with governing principles of adaptive evolution, that drive disease manifestation or interactions between microbes.

iMM1865: A New Reconstruction of Mouse Genome-Scale Metabolic Model

Since the first in silico generation of a genome-scale metabolic (GSM) model for Haemophilus influenzae in 1999, the GSM models have been reconstructed for various organisms including human and mouse. There are two important strategies for generating a GSM model: in the bottom-up approach, individual genomic and biochemical components are integrated to build a GSM model. Alternatively, the orthology-based strategy uses a previously reconstructed model of a reference organism to infer a GSM model of a target organism. Following the update and development of the metabolic network of reference organism, the model of the target organism can also be updated to eliminate defects. Here, we presented iMM1865 model as an orthology-based reconstruction of a GSM model for Mus musculus based on the last flux-consistent version of the human metabolic network, Recon3D. We proposed two versions of the new mouse model, iMM1865 and min-iMM1865, with the same number of gene-associated reactions but different subsets of non-gene-associated reactions. A third extended but flux-inconsistent model (iMM3254) was also created based on the extended version of Recon3D. Compared to the previously published mouse models, both versions of iMM1865 include more comprehensive annotations of metabolites and reactions with no dead-end metabolites and blocked reactions. We evaluated functionality of the models using 431 metabolic objective functions. iMM1865 and min-iMM1865 passed 93% and 87% of the tests, respectively, while iMM1415 and MMR (another available mouse GSM) passed 80% and 84% of the tests, respectively. Three versions of tissue-specific embryo heart models were also reconstructed from each of iMM1865 and min-iMM1865 using mCADRE algorithm with different thresholds on expression-based scores. The ability of corresponding GSM and embryo heart models to predict essential genes was assessed across experimentally derived lethal and viable gene sets. Our analysis revealed that tissue-specific models render much better predictions than GSM models.

An atlas of human metabolism

Genome-scale metabolic models (GEMs) are valuable tools to study metabolism and provide a scaffold for the integrative analysis of omics data. Researchers have developed increasingly comprehensive human GEMs, but the disconnect among different model sources and versions impedes further progress. We therefore integrated and extensively curated the most recent human metabolic models to construct a consensus GEM, Human1. We demonstrated the versatility of Human1 through the generation and analysis of cell- and tissue-specific models using transcriptomic, proteomic, and kinetic data. We also present an accompanying web portal, Metabolic Atlas (https://www.metabolicatlas.org/), which facilitates further exploration and visualization of Human1 content. Human1 was created using a version-controlled, open-source model development framework to enable community-driven curation and refinement. This framework allows Human1 to be an evolving shared resource for future studies of human health and disease.

Insulin stimulated GLUT4 translocation - Size is not everything!

Insulin-regulated trafficking of the facilitative glucose transporter GLUT4 has been studied in many cell types. The translocation of GLUT4 from intracellular membranes to the cell surface is often described as a highly specialised form of membrane traffic restricted to certain cell types such as fat and muscle, which are the major storage depots for insulin-stimulated glucose uptake. Here, we discuss evidence that favours the argument that rather than being restricted to specialised cell types, the machinery through which insulin regulates GLUT4 traffic is present in all cell types. This is an important point as it provides confidence in the use of experimentally tractable model systems to interrogate the trafficking itinerary of GLUT4.

Oncogene inference optimization using constraint-based modelling incorporated with protein expression in normal and tumour tissues

Cancer cells are known to exhibit unusual metabolic activity, and yet few metabolic cancer driver genes are known. Genetic alterations and epigenetic modifications of cancer cells result in the abnormal regulation of cellular metabolic pathways that are different when compared with normal cells. Such a metabolic reprogramming can be simulated using constraint-based modelling approaches towards predicting oncogenes. We introduced the tri-level optimization problem to use the metabolic reprogramming towards inferring oncogenes. The algorithm incorporated Recon 2.2 network with the Human Protein Atlas to reconstruct genome-scale metabolic network models of the tissue-specific cells at normal and cancer states, respectively. Such reconstructed models were applied to build the templates of the metabolic reprogramming between normal and cancer cell metabolism. The inference optimization problem was formulated to use the templates as a measure towards predicting oncogenes. The nested hybrid differential evolution algorithm was applied to solve the problem to overcome solving difficulty for transferring the inner optimization problem into the single one. Head and neck squamous cells were applied as a case study to evaluate the algorithm. We detected 13 of the top-ranked one-hit dysregulations and 17 of the top-ranked two-hit oncogenes with high similarity ratios to the templates. According to the literature survey, most inferred oncogenes are consistent with the observation in various tissues. Furthermore, the inferred oncogenes were highly connected with the TP53/AKT/IGF/MTOR signalling pathway through PTEN, which is one of the most frequently detected tumour suppressor genes in human cancer.

Translating preclinical models to humans

Computational models for cross-species translation could improve drug development

Genome-Scale Model-Based Identification of Metabolite Indicators for Early Detection of Kidney Toxicity

Identifying early indicators of toxicant-induced organ damage is critical to provide effective treatment. To discover such indicators and the underlying mechanisms of toxicity, we used gentamicin as an exemplar kidney toxicant and performed systematic perturbation studies in Sprague Dawley rats. We obtained high-throughput data 7 and 13 h after administration of a single dose of gentamicin (0.5 g/kg) and identified global changes in genes in the liver and kidneys, metabolites in the plasma and urine, and absolute fluxes in central carbon metabolism. We used these measured changes in genes in the liver and kidney as constraints to a rat multitissue genome-scale metabolic network model to investigate the mechanism of gentamicin-induced kidney toxicity and identify metabolites associated with changes in tissue gene expression. Our experimental analysis revealed that gentamicin-induced metabolic perturbations could be detected as early as 7 h postexposure. Our integrated systems-level analyses suggest that changes in kidney gene expression drive most of the significant metabolite alterations in the urine. The analyses thus allowed us to identify several significantly enriched injury-specific pathways in the kidney underlying gentamicin-induced toxicity, as well as metabolites in these pathways that could serve as potential early indicators of kidney damage.

Metabolic Network Reconstructions to Predict Drug Targets and Off-Target Effects

The drug development pipeline has stalled because of the difficulty in identifying new drug targets while minimizing off-target effects. Computational methods, such as the use of metabolic network reconstructions, may provide a cost-effective platform to test new hypotheses for drug targets and prevent off-target effects. Here, we summarize available methods to identify drug targets and off-target effects using either reaction-centric, gene-centric, or metabolite-centric approaches with genome-scale metabolic network reconstructions.

Genome-Scale Characterization of Toxicity-Induced Metabolic Alterations in Primary Hepatocytes

Context-specific GEnome-scale metabolic Network REconstructions (GENREs) provide a means to understand cellular metabolism at a deeper level of physiological detail. Here, we use transcriptomics data from chemically-exposed rat hepatocytes to constrain a GENRE of rat hepatocyte metabolism and predict biomarkers of liver toxicity using the Transcriptionally Inferred Metabolic Biomarker Response algorithm. We profiled alterations in cellular hepatocyte metabolism following in vitro exposure to four toxicants (acetaminophen, carbon tetrachloride, 2,3,7,8-tetrachlorodibenzodioxin, and trichloroethylene) for six hour. TIMBR predictions were compared with paired fresh and spent media metabolomics data from the same exposure conditions. Agreement between computational model predictions and experimental data led to the identification of specific metabolites and thus metabolic pathways associated with toxicant exposure. Here, we identified changes in the TCA metabolites citrate and alpha-ketoglutarate along with changes in carbohydrate metabolism and interruptions in ATP production and the TCA Cycle. Where predictions and experimental data disagreed, we identified testable hypotheses to reconcile differences between the model predictions and experimental data. The presented pipeline for using paired transcriptomics and metabolomics data provides a framework for interrogating multiple omics datasets to generate mechanistic insight of metabolic changes associated with toxicological responses.

Agricultural Animals as Biomedical Models: Occupational Health and Safety Considerations

The use of agricultural animals in biomedical research is increasing. Their overall size and metabolic rate, organ size, longer gestation period, and other physiological similarities make them good candidates for animal models of human disease. There are a number of special considerations for use of traditional farm animals for biomedical research. Differences in physical plant infrastructure, handling equipment, training of personnel, and potential zoonoses are some of the important considerations when traditional farm animals are used in biomedical research. This article provides an overview of some of the special considerations for using traditional agricultural animals in biomedical research. With the growing need for improved translational research, it is reasonable to predict significant growth in these animal models.

Mining Public Toxicogenomic Data Reveals Insights and Challenges in Delineating Liver Steatosis Adverse Outcome Pathways

Exposure to chemicals contributes to the development and progression of fatty liver, or steatosis, a process characterized by abnormal accumulation of lipids within liver cells. However, lack of knowledge on how chemicals cause steatosis has prevented any large-scale assessment of the 80,000+ chemicals in current use. To address this gap, we mined a large, publicly available toxicogenomic dataset associated with 18 known steatogenic chemicals to assess responses across assays (in vitro and in vivo) and species (i.e., rats and humans). We identified genes that were differentially expressed (DEGs) in rat in vivo, rat in vitro, and human in vitro studies in which rats or in vitro primary cell lines were exposed to the chemicals at different doses and durations. Using these DEGs, we performed pathway enrichment analysis, analyzed the molecular initiating events (MIEs) of the steatosis adverse outcome pathway (AOP), and predicted metabolite changes using metabolic network analysis. Genes indicative of oxidative stress were among the DEGs most frequently observed in the rat in vivo studies. Nox4, a pro-fibrotic gene, was down-regulated across these chemical exposure conditions. We identified eight genes (Cyp1a1, Egr1, Ccnb1, Gdf15, Cdk1, Pdk4, Ccna2, and Ns5atp9) and one pathway (retinol metabolism), associated with steatogenic chemicals and whose response was conserved across the three in vitro and in vivo systems. Similarly, we found the predicted metabolite changes, such as increases of saturated and unsaturated fatty acids, conserved across the three systems. Analysis of the target genes associated with the MIEs of the current steatosis AOP did not provide a clear association between these 18 chemicals and the MIEs, underlining the multi-factorial nature of this disease. Notably, our overall analysis implicated mitochondrial toxicity as an important and overlooked MIE for chemical-induced steatosis. The integrated toxicogenomics approach to identify genes, pathways, and metabolites based on known steatogenic chemicals, provide an important mean to assess development of AOPs and gauging the relevance of new testing strategies.

Predicting Metabolism from Gene Expression in an Improved Whole-Genome Metabolic Network Model of Danio rerio

Zebrafish is a useful modeling organism for the study of vertebrate development, immune response, and metabolism. Metabolic studies can be aided by mathematical reconstructions of the metabolic network of zebrafish. These list the substrates and products of all biochemical reactions that occur in the zebrafish. Mathematical techniques such as flux-balance analysis then make it possible to predict the possible metabolic flux distributions that optimize, for example, the turnover of food into biomass. The only available genome-scale reconstruction of zebrafish metabolism is ZebraGEM. In this study, we present ZebraGEM 2.0, an updated and validated version of ZebraGEM. ZebraGEM 2.0 is extended with gene-protein-reaction associations (GPRs) that are required to integrate genetic data with the metabolic model. To demonstrate the use of these GPRs, we performed an in silico genetic screening for knockouts of metabolic genes and validated the results against published in vivo genetic knockout and knockdown screenings. Among the single knockout simulations, we identified 74 essential genes, whose knockout stopped growth completely. Among these, 11 genes are known have an abnormal knockout or knockdown phenotype in vivo (partial), and 41 have human homologs associated with metabolic diseases. We also added the oxidative phosphorylation pathway, which was unavailable in the published version of ZebraGEM. The updated model performs better than the original model on a predetermined list of metabolic functions. We also determined a minimal feed composition. The oxidative phosphorylation pathways were validated by comparing with published experiments in which key components of the oxidative phosphorylation pathway were pharmacologically inhibited. To test the utility of ZebraGEM2.0 for obtaining new results, we integrated gene expression data from control and Mycobacterium marinum-infected zebrafish larvae. The resulting model predicts impeded growth and altered histidine metabolism in the infected larvae.

Use cases, best practice and reporting standards for metabolomics in regulatory toxicology

Metabolomics is a widely used technology in academic research, yet its application to regulatory science has been limited. The most commonly cited barrier to its translation is lack of performance and reporting standards. The MEtabolomics standaRds Initiative in Toxicology (MERIT) project brings together international experts from multiple sectors to address this need. Here, we identify the most relevant applications for metabolomics in regulatory toxicology and develop best practice guidelines, performance and reporting standards for acquiring and analysing untargeted metabolomics and targeted metabolite data. We recommend that these guidelines are evaluated and implemented for several regulatory use cases.

Genome-scale metabolic model of the rat liver predicts effects of diet restriction

Mapping network analysis in cells and tissues can provide insights into metabolic adaptations to changes in external environment, pathological conditions, and nutrient deprivation. Here, we reconstructed a genome-scale metabolic network of the rat liver that will allow for exploration of systems-level physiology. The resulting in silico model (iRatLiver) contains 1,882 reactions, 1,448 metabolites, and 994 metabolic genes. We then used this model to characterize the response of the liver’s energy metabolism to a controlled perturbation in diet. Transcriptomics data were collected from the livers of Sprague Dawley rats at 4 or 14 days of being subjected to 15%, 30%, or 60% diet restriction. These data were integrated with the iRatLiver model to generate condition-specific metabolic models, allowing us to explore network differences under each condition. We observed different pathway usage between early and late time points. Network analysis identified several highly connected “hub” genes (Pklr, Hadha, Tkt, Pgm1, Tpi1, and Eno3) that showed differing trends between early and late time points. Taken together, our results suggest that the liver’s response varied with short- and long-term diet restriction. More broadly, we anticipate that the iRatLiver model can be exploited further to study metabolic changes in the liver under other conditions such as drug treatment, infection, and disease.

Current status and applications of genome-scale metabolic models

Genome-scale metabolic models (GEMs) computationally describe gene-protein-reaction associations for entire metabolic genes in an organism, and can be simulated to predict metabolic fluxes for various systems-level metabolic studies. Since the first GEM for Haemophilus influenzae was reported in 1999, advances have been made to develop and simulate GEMs for an increasing number of organisms across bacteria, archaea, and eukarya. Here, we review current reconstructed GEMs and discuss their applications, including strain development for chemicals and materials production, drug targeting in pathogens, prediction of enzyme functions, pan-reactome analysis, modeling interactions among multiple cells or organisms, and understanding human diseases.

Human Systems Biology and Metabolic Modelling: A Review-From Disease Metabolism to Precision Medicine

In cell and molecular biology, metabolism is the only system that can be fully simulated at genome scale. Metabolic systems biology offers powerful abstraction tools to simulate all known metabolic reactions in a cell, therefore providing a snapshot that is close to its observable phenotype. In this review, we cover the 15 years of human metabolic modelling. We show that, although the past five years have not experienced large improvements in the size of the gene and metabolite sets in human metabolic models, their accuracy is rapidly increasing. We also describe how condition-, tissue-, and patient-specific metabolic models shed light on cell-specific changes occurring in the metabolic network, therefore predicting biomarkers of disease metabolism. We finally discuss current challenges and future promising directions for this research field, including machine/deep learning and precision medicine. In the omics era, profiling patients and biological processes from a multiomic point of view is becoming more common and less expensive. Starting from multiomic data collected from patients and N-of-1 trials where individual patients constitute different case studies, methods for model-building and data integration are being used to generate patient-specific models. Coupled with state-of-the-art machine learning methods, this will allow characterizing each patient's disease phenotype and delivering precision medicine solutions, therefore leading to preventative medicine, reduced treatment, and in silico clinical trials.

Mechanistic identification of biofluid metabolite changes as markers of acetaminophen-induced liver toxicity in rats

Acetaminophen (APAP) is the most commonly used analgesic and antipyretic drug in the world. Yet, it poses a major risk of liver injury when taken in excess of the therapeutic dose. Current clinical markers do not detect the early onset of liver injury associated with excess APAP-information that is vital to reverse injury progression through available therapeutic interventions. Hence, several studies have used transcriptomics, proteomics, and metabolomics technologies, both independently and in combination, in an attempt to discover potential early markers of liver injury. However, the casual relationship between these observations and their relation to the APAP mechanism of liver toxicity are not clearly understood. Here, we used Sprague-Dawley rats orally gavaged with a single dose of 2 g/kg of APAP to collect tissue samples from the liver and kidney for transcriptomic analysis and plasma and urine samples for metabolomic analysis. We developed and used a multi-tissue, metabolism-based modeling approach to integrate these data, characterize the effect of excess APAP levels on liver metabolism, and identify a panel of plasma and urine metabolites that are associated with APAP-induced liver toxicity. Our analyses, which indicated that pathways involved in nucleotide-, lipid-, and amino acid-related metabolism in the liver were most strongly affected within 10 h following APAP treatment, identified a list of potential metabolites in these pathways that could serve as plausible markers of APAP-induced liver injury. Our approach identifies toxicant-induced changes in endogenous metabolism, is applicable to other toxicants based on transcriptomic data, and provides a mechanistic framework for interpreting metabolite alterations.

Increasing consensus of context-specific metabolic models by integrating data-inferred cell functions

Genome-scale metabolic models provide a valuable context for analyzing data from diverse high-throughput experimental techniques. Models can quantify the activities of diverse pathways and cellular functions. Since some metabolic reactions are only catalyzed in specific environments, several algorithms exist that build context-specific models. However, these methods make differing assumptions that influence the content and associated predictive capacity of resulting models, such that model content varies more due to methods used than cell types. Here we overcome this problem with a novel framework for inferring the metabolic functions of a cell before model construction. For this, we curated a list of metabolic tasks and developed a framework to infer the activity of these functionalities from transcriptomic data. We protected the data-inferred tasks during the implementation of diverse context-specific model extraction algorithms for 44 cancer cell lines. We show that the protection of data-inferred metabolic tasks decreases the variability of models across extraction methods. Furthermore, resulting models better capture the actual biological variability across cell lines. This study highlights the potential of using biological knowledge, inferred from omics data, to obtain a better consensus between existing extraction algorithms. It further provides guidelines for the development of the next-generation of data contextualization methods.