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

Assessing pharmacokinetics and drug-drug interactions of the combination therapy of myelofibrosis with ruxolitinib and lenalidomide by a new eco-friendly HPLC method for their simultaneous determination in plasma

Ruxolitinib (RUX), a Janus kinase 2 (JAK2) inhibitor, and lenalidomide (LEN), an immunomodulatory agent, have recently been proposed as a combined treatment for myelofibrosis (MF). This combination has demonstrated improved efficacy, safety, and tolerability compared to monotherapy. To further refine these findings, an efficient analytical tool is needed to simultaneously determine RUX and LEN concentrations in blood plasma. This tool would enable the study of their pharmacokinetics, drug-drug interactions, and therapeutic monitoring during MF therapy. Unfortunately, such a method has not been existed in the literature. This study presents the first HPLC method with UV detection for the simultaneous quantitation of RUX and LEN in plasma. The method was validated according to the ICH guidelines for bioanalytical method validation. It exhibited linearity in the concentration ranges of 10 to 3150 ng mL- 1 for RUX and 80 to 5200 ng mL- 1 for LEN. The limits of quantitation were determined to be 25 and 90 ng mL- 1 for RUX and LEN, respectively. All other validation parameters were satisfactory. The HPLC-UV method was successfully employed to study the pharmacokinetics and drug-drug interactions of RUX and LEN in rats following oral administration of single doses. The results demonstrated that the pharmacokinetics of both drugs were changed substantially by their coadministration. LEN exhibited synergistic effects on the maximum plasma concentration (Cmax) and total bioavailability of RUX, meanwhile it exhibited diminishing effect on the values of volume of distribution (Vd) and clearance (CL). Additionally, RUX decreased the Cmax and total bioavailability of LEN, meanwhile it increased its Vd and CL. These data suggest that the use of RUX, as a combination with LEN, is a better therapeutic approach for MF, compared with RUX as a monotherapy. The effects of LEN on the pharmacokinetics of RUX should be considered and can be useful in determining the appropriate RUX dosage and dosing regimen to achieve the desired therapeutic effect when used as a combination therapy with LEN. The method's environmental friendliness was confirmed through three comprehensive tools. This method represents a valuable tool for determining the appropriate dosage and dosing regimen of RUX in combination therapy with LEN to achieve the desired therapeutic effect. Furthermore, it can aid in predicting drug distribution in different patients and assessing the drug accumulation or insufficient drug levels in specific body compartments.

Coating based on chitosan/vancomycin nanoparticles: Patterns of formation in a water-carbon dioxide biphase system and in vivo stability

The patterns of formation of chitosan nanoparticles doped with vancomycin and coatings based on them in carbonate solutions have been investigated for the first time in this study. Using a technique of radioactive indicators, it was found that at a CO2 pressure of 30 MPa, the yield of the nanoparticles was ∼85 %, and a maximum antibiotic encapsulation efficiency of ∼30 % was achieved. By spectrophotometric and high-resolution microscopy, it was found that the coating of stabilized xenopericardial tissue of bioprosthetic heart valve, based on chitosan nanoparticles doped with vancomycin with a zeta potential |ζ| ∼20 mV completely covers collagen fibers by depositing about 60 nm nanoparticles onto them under direct deposition from carbonic acid at a pressure of 30 MPa CO2. The coating preserves the mechanical strength characteristics of collagen tissue and completely suppresses the growth of S. aureus pathogenic biofilm. This is consistent with the observed increase in antibiotic release of 15 % when the medium was acidified. Histological study demonstrated that the structure of pericardial tissues was not significantly altered by the deposition nanoparticles from carbonic acid. It was found that the rate of biodegradation of polymers and vancomycin in the coating differs by half (16 weeks for the rat model). A significantly lower degradation rate of antibiotics (∼50 % of vancomycin total remaining mass and ∼25 % of chitosan) was associated with its reliable encapsulation into nanoparticles.

Effects of PFAS on human liver transporters: implications for health outcomes

Per- and polyfluoroalkyl substances (PFAS) have become internationally recognized over the past three decades as persistent organic pollutants used in the production of various consumer and industrial goods. Research efforts continue to gauge the risk that historically used, and newly produced, PFAS may cause to human health. Numerous studies report toxic effects of PFAS on the human liver as well as increased serum cholesterol levels in adults. A major concern with PFAS, also dubbed "forever chemicals," is that they accumulate in the liver and kidney and persist in serum. The mechanisms responsible for their disposition and excretion in humans are poorly understood. A better understanding of the interaction of PFAS with liver transporters, as it pertains to the disposition of PFAS and other xenobiotics, could provide mechanistic insight into human health effects and guide efforts toward risk assessment of compounds in development. This review summarizes the current state of the literature on the emerging relationships (eg, substrates, inhibitors, modulators of gene expression) between PFAS and specific hepatic transporters. The adaptive and toxicological responses of hepatocytes to PFAS that reveal linkages to pathologies and epidemiological findings are highlighted. The evidence suggests that our understanding of the molecular landscape of PFAS must improve to determine their impact on the expression and function of hepatocyte transporters that play a key role in PFAS or other xenobiotic disposition. From here, we can assess what role these changes may have in documented human health outcomes.

Adding metabolic tasks to human GEM models to improve the study of gene targets and their associated toxicities

Genetic minimal cut sets (gMCS) are genes that must be deactivated simultaneously to avoid unwanted states in a metabolic model. The concept of gMCS can be applied to two different scenarios. First, it can be used to identify potential gene toxicities in generic or healthy cell models. Second, it can be used to develop genetic strategies to target cancer cells and prevent their proliferation. Up to now, gMCS have been evaluated using the traditional procedure of preventing biomass production. This paper proposes an additional way: using essential metabolic tasks, which any human cell should perform, to enlarge the set of unwanted states. Including this addition can significantly improve the study of toxicities and reveal targets that can be used to treat unhealthy cells. Excluding metabolic tasks can cause important information to be overlooked, which could impact the study's success. Regarding toxicities, using the generic Human model, the number of detected generic toxicities with metabolic tasks increases from 106 to 281 (136 gMCSs of length 1 and 49 of length 2). We have used the following context-specific models to evaluate specific toxicities in different healthy tissues: blood, pancreas, liver, heart, and kidney. Again, considering metabolic tasks, we have found new toxicities (lengths 1 and 2) whose inactivation could damage these healthy tissues.Our research strategy has been applied to identify new cancer drug targets in two myeloma cell lines. We obtained new therapeutic targets of lengths 1 and 2 for each cell line. After analyzing the data, we conclude that incorporating metabolic tasks into cancer models can reveal important therapeutic targets previously disregarded by the conventional method of inhibiting biomass production. This approach also improves the evaluation of potential drug toxicities.

Modeling cardiomyocyte signaling and metabolism predicts genotype-to-phenotype mechanisms in hypertrophic cardiomyopathy

Familial hypertrophic cardiomyopathy (HCM) is a significant precursor of heart failure and sudden cardiac death, primarily caused by mutations in sarcomeric and structural proteins. Despite the extensive research on the HCM genotype, the complex and context-specific nature of many signaling and metabolic pathways linking the HCM genotype to phenotype has hindered therapeutic advancements for patients. Here, we have developed a computational model of HCM encompassing cardiomyocyte signaling and metabolic networks and their associated interactions. Utilizing a stochastic logic-based ODE approach, we linked cardiomyocyte signaling to the metabolic network through a gene regulatory network and post-translational modifications. We validated the model against published data on activities of signaling species in the HCM context and transcriptomes of two HCM mouse models (i.e., R403Q-αMyHC and R92W-TnT). Our model predicts that HCM mutation induces changes in metabolic functions such as ATP synthase deficiency and a transition from fatty acids to carbohydrate metabolism. The model indicated major shifts in glutamine-related metabolism and increased apoptosis after HCM-induced ATP synthase deficiency. We predicted that the transcription factors STAT, SRF, GATA4, TP53, and FoxO are the key regulators of cardiomyocyte hypertrophy and apoptosis in HCM in alignment with experiments. Moreover, we identified shared (e.g., activation of PGC1α by AMPK, and FHL1 by titin) and context-specific mechanisms (e.g., regulation of Ca2+ sensitivity by titin in HCM patients) that may control genotype-to-phenotype transition in HCM across different species or mutations. We also predicted potential combination drug targets for HCM (e.g., mavacamten plus ROS inhibitors) preventing or reversing HCM phenotype (i.e., hypertrophic growth, apoptosis, and metabolic remodeling) in cardiomyocytes. This study provides new insights into mechanisms linking genotype to phenotype in familial hypertrophic cardiomyopathy and offers a framework for assessing new treatments and exploring variations in HCM experimental models.

Strategies to reduce the risks of mRNA drug and vaccine toxicity

mRNA formulated with lipid nanoparticles is a transformative technology that has enabled the rapid development and administration of billions of coronavirus disease 2019 (COVID-19) vaccine doses worldwide. However, avoiding unacceptable toxicity with mRNA drugs and vaccines presents challenges. Lipid nanoparticle structural components, production methods, route of administration and proteins produced from complexed mRNAs all present toxicity concerns. Here, we discuss these concerns, specifically how cell tropism and tissue distribution of mRNA and lipid nanoparticles can lead to toxicity, and their possible reactogenicity. We focus on adverse events from mRNA applications for protein replacement and gene editing therapies as well as vaccines, tracing common biochemical and cellular pathways. The potential and limitations of existing models and tools used to screen for on-target efficacy and de-risk off-target toxicity, including in vivo and next-generation in vitro models, are also discussed.

Distinct neurochemical influences on fMRI response polarity in the striatum

The striatum, known as the input nucleus of the basal ganglia, is extensively studied for its diverse behavioral roles. However, the relationship between its neuronal and vascular activity, vital for interpreting functional magnetic resonance imaging (fMRI) signals, has not received comprehensive examination within the striatum. Here, we demonstrate that optogenetic stimulation of dorsal striatal neurons or their afferents from various cortical and subcortical regions induces negative striatal fMRI responses in rats, manifesting as vasoconstriction. These responses occur even with heightened striatal neuronal activity, confirmed by electrophysiology and fiber-photometry. In parallel, midbrain dopaminergic neuron optogenetic modulation, coupled with electrochemical measurements, establishes a link between striatal vasodilation and dopamine release. Intriguingly, in vivo intra-striatal pharmacological manipulations during optogenetic stimulation highlight a critical role of opioidergic signaling in generating striatal vasoconstriction. This observation is substantiated by detecting striatal vasoconstriction in brain slices after synthetic opioid application. In humans, manipulations aimed at increasing striatal neuronal activity likewise elicit negative striatal fMRI responses. Our results emphasize the necessity of considering vasoactive neurotransmission alongside neuronal activity when interpreting fMRI signal.

Identifying metabolic adaptations characteristic of cardiotoxicity using paired transcriptomics and metabolomics data integrated with a computational model of heart metabolism

Improvements in the diagnosis and treatment of cancer have revealed long-term side effects of chemotherapeutics, particularly cardiotoxicity. Here, we present paired transcriptomics and metabolomics data characterizing in vitro cardiotoxicity to three compounds: 5-fluorouracil, acetaminophen, and doxorubicin. Standard gene enrichment and metabolomics approaches identify some commonly affected pathways and metabolites but are not able to readily identify metabolic adaptations in response to cardiotoxicity. The paired data was integrated with a genome-scale metabolic network reconstruction of the heart to identify shifted metabolic functions, unique metabolic reactions, and changes in flux in metabolic reactions in response to these compounds. Using this approach, we confirm previously seen changes in the p53 pathway by doxorubicin and RNA synthesis by 5-fluorouracil, we find evidence for an increase in phospholipid metabolism in response to acetaminophen, and we see a shift in central carbon metabolism suggesting an increase in metabolic demand after treatment with doxorubicin and 5-fluorouracil.

Transcriptome-based biomarker prediction for Parkinson's disease using genome-scale metabolic modeling

Parkinson's disease (PD) is the second most common neurodegenerative disease in the world. Identification of PD biomarkers is crucial for early diagnosis and to develop target-based therapeutic agents. Integrative analysis of genome-scale metabolic models (GEMs) and omics data provides a computational approach for the prediction of metabolite biomarkers. Here, we applied the TIMBR (Transcriptionally Inferred Metabolic Biomarker Response) algorithm and two modified versions of TIMBR to investigate potential metabolite biomarkers for PD. To this end, we mapped thirteen post-mortem PD transcriptome datasets from the substantia nigra region onto Human-GEM. We considered a metabolite as a candidate biomarker if its production was predicted to be more efficient by a TIMBR-family algorithm in control or PD case for the majority of the datasets. Different metrics based on well-known PD-related metabolite alterations, PD-associated pathways, and a list of 25 high-confidence PD metabolite biomarkers compiled from the literature were used to compare the prediction performance of the three algorithms tested. The modified algorithm with the highest prediction power based on the metrics was called TAMBOOR, TrAnscriptome-based Metabolite Biomarkers by On-Off Reactions, which was introduced for the first time in this study. TAMBOOR performed better in terms of capturing well-known pathway alterations and metabolite secretion changes in PD. Therefore, our tool has a strong potential to be used for the prediction of novel diagnostic biomarkers for human diseases.

Inferring secretory and metabolic pathway activity from omic data with secCellFie

Understanding protein secretion has considerable importance in biotechnology and important implications in a broad range of normal and pathological conditions including development, immunology, and tissue function. While great progress has been made in studying individual proteins in the secretory pathway, measuring and quantifying mechanistic changes in the pathway's activity remains challenging due to the complexity of the biomolecular systems involved. Systems biology has begun to address this issue with the development of algorithmic tools for analyzing biological pathways; however most of these tools remain accessible only to experts in systems biology with extensive computational experience. Here, we expand upon the user-friendly CellFie tool which quantifies metabolic activity from omic data to include secretory pathway functions, allowing any scientist to infer properties of protein secretion from omic data. We demonstrate how the secretory expansion of CellFie (secCellFie) can help predict metabolic and secretory functions across diverse immune cells, hepatokine secretion in a cell model of NAFLD, and antibody production in Chinese Hamster Ovary cells.

Principles of metabolome conservation in animals

Metabolite levels shape cellular physiology and disease susceptibility, yet the general principles governing metabolome evolution are largely unknown. Here, we introduce a measure of conservation of individual metabolite levels among related species. By analyzing multispecies tissue metabolome datasets in phylogenetically diverse mammals and fruit flies, we show that conservation varies extensively across metabolites. Three major functional properties, metabolite abundance, essentiality, and association with human diseases predict conservation, highlighting a striking parallel between the evolutionary forces driving metabolome and protein sequence conservation. Metabolic network simulations recapitulated these general patterns and revealed that abundant metabolites are highly conserved due to their strong coupling to key metabolic fluxes in the network. Finally, we show that biomarkers of metabolic diseases can be distinguished from other metabolites simply based on evolutionary conservation, without requiring any prior clinical knowledge. Overall, this study uncovers simple rules that govern metabolic evolution in animals and implies that most tissue metabolome differences between species are permitted, rather than favored by natural selection. More broadly, our work paves the way toward using evolutionary information to identify biomarkers, as well as to detect pathogenic metabolome alterations in individual patients.

A new metabolic model of Drosophila melanogaster and the integrative analysis of Parkinson's disease

High conservation of the disease-associated genes between flies and humans facilitates the common use of Drosophila melanogaster to study metabolic disorders under controlled laboratory conditions. However, metabolic modeling studies are highly limited for this organism. We here report a comprehensively curated genome-scale metabolic network model of Drosophila using an orthology-based approach. The gene coverage and metabolic information of the draft model derived from a reference human model were expanded via Drosophila-specific KEGG and MetaCyc databases, with several curation steps to avoid metabolic redundancy and stoichiometric inconsistency. Furthermore, we performed literature-based curations to improve gene-reaction associations, subcellular metabolite locations, and various metabolic pathways. The performance of the resulting Drosophila model (8,230 reactions, 6,990 metabolites, and 2,388 genes), iDrosophila1 (https://github.com/SysBioGTU/iDrosophila), was assessed using flux balance analysis in comparison with the other currently available fly models leading to superior or comparable results. We also evaluated the transcriptome-based prediction capacity of iDrosophila1, where differential metabolic pathways during Parkinson's disease could be successfully elucidated. Overall, iDrosophila1 is promising to investigate system-level metabolic alterations in response to genetic and environmental perturbations.

HS-SPME-GC-MS Untargeted Analysis of Normal Rat Organs Ex Vivo: Differential VOC Discrimination and Fingerprint VOC Identification

The investigation of volatile organic compounds (VOCs) in human metabolites has been a topic of interest as it holds the potential for the development of non-invasive technologies to screen for organ lesions in vivo. However, it remains unclear whether VOCs differ among healthy organs. Consequently, a study was conducted to analyze VOCs in ex vivo organ tissues obtained from 16 Wistar rats, comprising 12 different organs. The VOCs released from each organ tissue were detected by the headspace-solid phase microextraction-gas chromatography-mass spectrometry technique. In the untargeted analysis of 147 chromatographic peaks, the differential volatiles of rat organs were explored based on the Mann-Whitney U test and fold change (FC > 2.0) compared with other organs. It was found that there were differential VOCs in seven organs. A discussion on the possible metabolic pathways and related biomarkers of organ differential VOCs was conducted. Based on the orthogonal partial least squares discriminant analysis and receiver operating characteristic curve, we found that differential VOCs in the liver, cecum, spleen, and kidney can be used as the unique identification of the corresponding organ. In this study, differential VOCs of organs in rats were systematically reported for the first time. Profiles of VOCs produced by healthy organs can serve as a reference or baseline that may indicate the presence of disease or abnormalities in the organ's function. Differential VOCs can be used as the fingerprint of organs, and future integration with metabolic research may contribute to the development of healthcare.

Inferring secretory and metabolic pathway activity from omic data with secCellFie

Understanding protein secretion has considerable importance in the biotechnology industry and important implications in a broad range of normal and pathological conditions including development, immunology, and tissue function. While great progress has been made in studying individual proteins in the secretory pathway, measuring and quantifying mechanistic changes in the pathway's activity remains challenging due to the complexity of the biomolecular systems involved. Systems biology has begun to address this issue with the development of algorithmic tools for analyzing biological pathways; however most of these tools remain accessible only to experts in systems biology with extensive computational experience. Here, we expand upon the user-friendly CellFie tool which quantifies metabolic activity from omic data to include secretory pathway functions, allowing any scientist to infer protein secretion capabilities from omic data. We demonstrate how the secretory expansion of CellFie (secCellFie) can be used to predict metabolic and secretory functions across diverse immune cells, hepatokine secretion in a cell model of NAFLD, and antibody production in Chinese Hamster Ovary cells.

Loss of the Kidney Urate Transporter, Urat1, Leads to Disrupted Redox Homeostasis in Mice

High uric acid is associated with gout, hypertension, metabolic syndrome, cardiovascular disease, and kidney disease. URAT1 (SLC22A12), originally discovered in mice as Rst, is generally considered a very selective uric acid transporter compared to other closely-related kidney uric acid transporters such as OAT1 (SLC22A6, NKT) and OAT3 (SLC22A8). While the role of URAT1 in regulating human uric acid is well-established, in recent studies the gene has been linked to redox regulation in flies as well as progression of renal cell carcinoma. We have now identified over twenty metabolites in the Urat1 knockout that are generally distinct from metabolites accumulating in the Oat1 and Oat3 knockout mice, with distinct molecular properties as revealed by chemoinformatics and machine learning analysis. These metabolites are involved in seemingly disparate aspects of cellular metabolism, including pyrimidine, fatty acid, and amino acid metabolism. However, through integrative systems metabolic analysis of the transcriptomic and metabolomic data using a human metabolic reconstruction to build metabolic genome-scale models (GEMs), the cellular response to loss of Urat1/Rst revealed compensatory processes related to reactive oxygen species handling and maintaining redox state balances via Vitamin C metabolism and cofactor charging reactions. These observations are consistent with the increasingly appreciated role of the antioxidant properties of uric acid. Collectively, the results highlight the role of Urat1/Rst as a transporter strongly tied to maintaining redox homeostasis, with implications for metabolic side effects from drugs that block its function.

Development and validation of an UPLC-ESI-MS/MS method for quantification of duvelisib in plasma: application to pharmacokinetic study in rats

Duvelisib (DUV) is a new oral phosphoinositide-3-kinase (PI3K)-δ and PI3K-γ inhibitor. It is used for the treatment of relapsed or refractory chronic lymphocytic leukemia (CLL) and small lymphocytic lymphoma (SLL). This study describes the development and validation of a new highly sensitive and efficient UPLC-ESI-MS/MS method for quantitation of DUV in plasma samples and its application to the pharmacokinetic study of DUV in rats. The method employed a very simple step for plasma sample pretreatment via precipitation of protein using methanol. DUV and ceritinib (CRB) as an internal standard (IS) were separated on a porous Hypersil BDS-C18 column (125 mm × 2 mm, 3 μm) using a mobile phase consisting of ammonium formate (10 mM, pH 4.2):acetonitrile (42 : 58, v/v), pumped isocratically at a flow rate of 0.3 mL min^-1. DUV and CRB were eluted at 0.58 and 1.10 min, respectively. The mass spectrometric analysis was performed using an ESI in positive mode with multiple reaction monitoring (MRM). The technique was validated in accordance with the standards for validating bioanalytical methods established by the International Conference on Harmonization (ICH). The method's linear range was 5-500 ng mL^-1, and its correlation coefficient was satisfactory as it is almost unity (0.9999). The limit of quantitation (LOQ) was 5 ng mL^-1, while the limit of detection (LOD) was 1.7 ng mL^-1. The recovery of the spiking DUV was between 94.95 and 102.21%, and the relative standard deviation (RSD) was less than 2.70%, confirming the method's accuracy and precision. The specificity/carryover of the method was proved. The robustness and ruggedness of the method was proved as the recovery values were 97.6-101.96% (±01.17-2.20%) and 98.74-102.00 (±1.18-4.02%) for robustness and ruggedness, respectively. The stability of DUV under the different analytical conditions were documented as the recovery values were in the range of 95.89-103.28% and the RSD values did not exceed 7.36%. The method was efficiently used to analyze DUV in human plasma samples that had been spiked with DUV and to conduct pharmacokinetic investigations of DUV in rats after giving them a single oral dosage of 25 mg kg^-1 of the drug. The methodology is distinguished by excellent sensitivity, accuracy, and ease of sample pretreatment. Furthermore, it is efficient and has a short run time, which makes it high throughput and accordingly enables faster processing of many samples in clinical laboratories.

Neuroinflammation, Energy and Sphingolipid Metabolism Biomarkers Are Revealed by Metabolic Modeling of Autistic Brains

Autism spectrum disorders (ASD) are a heterogeneous group of neurodevelopmental disorders generally characterized by repetitive behaviors and difficulties in communication and social behavior. Despite its heterogeneous nature, several metabolic dysregulations are prevalent in individuals with ASD. This work aims to understand ASD brain metabolism by constructing an ASD-specific prefrontal cortex genome-scale metabolic model (GEM) using transcriptomics data to decipher novel neuroinflammatory biomarkers. The healthy and ASD-specific models are compared via uniform sampling to identify ASD-exclusive metabolic features. Noticeably, the results of our simulations and those found in the literature are comparable, supporting the accuracy of our reconstructed ASD model. We identified that several oxidative stress, mitochondrial dysfunction, and inflammatory markers are elevated in ASD. While oxidative phosphorylation fluxes were similar for healthy and ASD-specific models, and the fluxes through the pathway were nearly undisturbed, the tricarboxylic acid (TCA) fluxes indicated disruptions in the pathway. Similarly, the secretions of mitochondrial dysfunction markers such as pyruvate are found to be higher, as well as the activities of oxidative stress marker enzymes like alanine and aspartate aminotransferases (ALT and AST) and glutathione-disulfide reductase (GSR). We also detected abnormalities in the sphingolipid metabolism, which has been implicated in many inflammatory and immune processes, but its relationship with ASD has not been thoroughly explored in the existing literature. We suggest that important sphingolipid metabolites, such as sphingosine-1-phosphate (S1P), ceramide, and glucosylceramide, may be promising biomarkers for the diagnosis of ASD and provide an opportunity for the adoption of early intervention for young children.

Changes in Plasma Metabolomic Profile Following Bariatric Surgery, Lifestyle Intervention or Diet Restriction-Insights from Human and Rat Studies

Although bariatric surgery is known to change the metabolome, it is unclear if this is specific for the intervention or a consequence of the induced bodyweight loss. As the weight loss after Roux-en-Y Gastric Bypass (RYGB) can hardly be mimicked with an evenly effective diet in humans, translational research efforts might be helpful. A group of 188 plasma metabolites of 46 patients from the randomized controlled Würzburg Adipositas Study (WAS) and from RYGB-treated rats (n = 6) as well as body-weight-matched controls (n = 7) were measured using liquid chromatography tandem mass spectrometry. WAS participants were randomized into intensive lifestyle modification (LS, n = 24) or RYGB (OP, n = 22). In patients in the WAS cohort, only bariatric surgery achieved a sustained weight loss (BMI -34.3% (OP) vs. -1.2% (LS), p ≤ 0.01). An explicit shift in the metabolomic profile was found in 57 metabolites in the human cohort and in 62 metabolites in the rodent model. Significantly higher levels of sphingolipids and lecithins were detected in both surgical groups but not in the conservatively treated human and animal groups. RYGB leads to a characteristic metabolomic profile, which differs distinctly from that following non-surgical intervention. Analysis of the human and rat data revealed that RYGB induces specific changes in the metabolome independent of weight loss.

Fate and PPARγ and STATs-driven effects of the mitochondrial complex I inhibitor tebufenpyrad in liver cells revealed with multi-omics

The biological effects of the pesticide and mitochondrial complex I inhibitor tebufenpyrad (TEBU) on liver cells were investigated by combining proteomics and metabolomics. Both cell culture media and cellular lysates were analyzed in dose-response and kinetic experiments on the HepaRG cell line. Responses were compared with those obtained on primary human and rat hepatocytes. A multitude of phase I and II metabolites (>80) mainly common to HepaRG cells and primary hepatocytes and an increase in metabolization enzymes were observed. Synthesis of mitochondrion and oxidative phosphorylation complex constituents, fatty acid oxidation, and cellular uptake of lipids were induced to compensate for complex I inhibition and the decrease in ATP intracellular contents caused by TEBU. Secretion of the 20 S circulating proteasome and overall inhibition of acute inflammation followed by IL-6 secretion in later stages were observed in HepaRG cells. These effects were associated with a decrease in STAT1 and STAT3 transcription factor abundances, but with different kinetics. Based on identified TEBU targets, docking experiments, and nuclear receptor reporter assays, we concluded that liver cell response to TEBU is mediated by its interaction with the PPARγ transcription factor.

Computational Approaches to Assess Abnormal Metabolism in Alzheimer's Disease Using Transcriptomics

Transcriptome-integrated human genome-scale metabolic models (GEMs) have been used widely to assess alterations in metabolism in response to disease. Transcriptome integration leads to identification of metabolic reactions that are differentially inactivated in the tissue of interest. Among the methods available for mapping transcriptome data on GEMs, we focus here on an Integrative Metabolic Analysis Tool (iMAT), which we have recently applied to the analysis of Alzheimer's disease (AD). We provide a detailed protocol for applying iMAT to create models of personalized metabolic networks, which can be further processed to identify reactions associated with abnormal metabolism.

Machine Learning and Hybrid Methods for Metabolic Pathway Modeling

Computational cell metabolism models seek to provide metabolic explanations of cell behavior under different conditions or following genetic alterations, help in the optimization of in vitro cell growth environments, or predict cellular behavior in vivo and in vitro. In the extremes, mechanistic models can include highly detailed descriptions of a small number of metabolic reactions or an approximate representation of an entire metabolic network. To date, all mechanistic models have required details of individual metabolic reactions, either kinetic parameters or metabolic flux, as well as information about extracellular and intracellular metabolite concentrations. Despite the extensive efforts and the increasing availability of high-quality data, required in vivo data are not available for the majority of known metabolic reactions; thus, mechanistic models are based primarily on ex vivo kinetic measurements and limited flux information. Machine learning approaches provide an alternative for derivation of functional dependencies from existing data. The increasing availability of metabolomic and lipidomic data, with growing feature coverage as well as sample set size, is expected to provide new data options needed for derivation of machine learning models of cell metabolic processes. Moreover, machine learning analysis of longitudinal data can lead to predictive models of cell behaviors over time. Conversely, machine learning models trained on steady-state data can provide descriptive models for the comparison of metabolic states in different environments or disease conditions. Additionally, inclusion of metabolic network knowledge in these analyses can further help in the development of models with limited data.This chapter will explore the application of machine learning to the modeling of cell metabolism. We first provide a theoretical explanation of several machine learning and hybrid mechanistic machine learning methods currently being explored to model metabolism. Next, we introduce several avenues for improving these models with machine learning. Finally, we provide protocols for specific examples of the utilization of machine learning in the development of predictive cell metabolism models using metabolomic data. We describe data preprocessing, approaches for training of machine learning models for both descriptive and predictive models, and the utilization of these models in synthetic and systems biology. Detailed protocols provide a list of software tools and libraries used for these applications, step-by-step modeling protocols, troubleshooting, as well as an overview of existing limitations to these approaches.

Guidelines for extracting biologically relevant context-specific metabolic models using gene expression data

Genome-scale metabolic models comprehensively describe an organism's metabolism and can be tailored using omics data to model condition-specific physiology. The quality of context-specific models is impacted by (i) choice of algorithm and parameters and (ii) alternate context-specific models that equally explain the -omics data. Here we quantify the influence of alternate optima on microbial and mammalian model extraction using GIMME, iMAT, MBA, and mCADRE. We find that metabolic tasks defining an organism's phenotype must be explicitly and quantitatively protected. The scope of alternate models is strongly influenced by algorithm choice and the topological properties of the parent genome-scale model with fatty acid metabolism and intracellular metabolite transport contributing much to alternate solutions in all models. mCADRE extracted the most reproducible context-specific models and models generated using MBA had the most alternate solutions. There were fewer qualitatively different solutions generated by GIMME in E. coli, but these increased substantially in the mammalian models. Screening ensembles using a receiver operating characteristic plot identified the best-performing models. A comprehensive evaluation of models extracted using combinations of extraction methods and expression thresholds revealed that GIMME generated the best-performing models in E. coli, whereas mCADRE is better suited for complex mammalian models. These findings suggest guidelines for benchmarking -omics integration algorithms and motivate the development of a systematic workflow to enumerate alternate models and extract biologically relevant context-specific models.

Sexual Dimorphism in the Expression of Cytochrome P450 Enzymes in Rat Heart, Liver, Kidney, Lung, Brain, and Small Intestine

Cytochrome P450 (P450) enzymes are monooxygenases that are expressed hepatically and extrahepatically and play an essential role in xenobiotic metabolism. Substantial scientific evidence indicates sex-specific differences between males and females in disease patterns and drug responses, which could be attributed, even partly, to differences in the expression and/or activity levels of P450 enzymes in different organs. In this study, we compared the mRNA and protein expression of P450 enzymes in different organs of male and female Sprague-Dawley rats by real-time polymerase chain reaction and western blot techniques. We found significant sex- and organ-specific differences in several enzymes. Hepatic Cyp2c11, Cyp2c13, and Cyp4a2 showed male-specific expression, whereas Cyp2c12 showed female-specific expression. Cyp2e1 and Cyp4f enzymes demonstrated higher expression in the female heart and kidneys compared with males; however, they showed no significant sexual dimorphism in the liver. Male rats showed higher hepatic and renal Cyp1b1 levels. All assessed enzymes were found in the liver, but some were not expressed in other organs. At the protein expression level, CYP1A2, CYP3A, and CYP4A1 demonstrated higher expression levels in the females in several organs, including the liver. Elucidating sex-specific differences in P450 enzyme levels could help better understand differences in disease pathogeneses and drug responses between males and females and thus improve treatment strategies. SIGNIFICANCE STATEMENT: This study characterized the differences in the mRNA and protein expression levels of different cytochrome P450 (P450) enzymes between male and female rats in the heart, liver, lung, kidney, brain, and small intestine. It demonstrated unique sex-specific differences in the different organs. This study is considered a big step towards elucidating sex-specific differences in P450 enzyme levels, which is largely important for achieving a better understanding of the differences between males and females in the disease's processes and treatment outcomes.

Modelling metabolic diseases and drug response using stem cells and organoids

Metabolic diseases, including obesity, diabetes mellitus and cardiovascular disease, are a major threat to health in the modern world, but efforts to understand the underlying mechanisms and develop rational treatments are limited by the lack of appropriate human model systems. Notably, advances in stem cell and organoid technology allow the generation of cellular models that replicate the histological, molecular and physiological properties of human organs. Combined with marked improvements in gene editing tools, human stem cells and organoids provide unprecedented systems for studying mechanisms of metabolic diseases. Here, we review progress made over the past decade in the generation and use of stem cell-derived metabolic cell types and organoids in metabolic disease research, especially obesity and liver diseases. In particular, we discuss the limitations of animal models and the advantages of stem cells and organoids, including their application to metabolic diseases. We also discuss mechanisms of drug action, understanding the efficacy and toxicity of existing therapies, screening for new treatments and pursuing personalized therapies. We highlight the potential of combining stem cell-derived organoids with gene editing and functional genomics to revolutionize the approach to finding treatments for metabolic diseases.

Sex Differences in Dopaminergic Vulnerability to Environmental Toxicants - Implications for Parkinson's Disease

PURPOSE OF REVIEW

Sex dimorphism in Parkinson's disease (PD) is an ostensible feature of the neurological disorder, particularly as men are 1.5-2 times more likely to develop PD than women. Clinical features of the disease, such as presentation at onset, most prevalent symptoms, and response to treatment, are also affected by sex. Despite these well-known sex differences in PD risk and phenotype, the mechanisms that impart sex dimorphisms in PD remain poorly understood.

RECENT FINDINGS

As PD incidence is influenced by environmental factors, an intriguing pattern has recently emerged in research studies suggesting a male-specific vulnerability to dopaminergic neurodegeneration caused by neurotoxicant exposure, with relative protection in females. These new experimental data have uncovered potential mechanisms that provide clues to the source of sex differences in dopaminergic neurodegeneration and other PD pathology such as alpha-synuclein toxicity. In this review, we discuss the emerging evidence of increased male sensitivity to neurodegeneration from environmental exposures. We examine mechanisms underlying dopaminergic neurodegeneration and PD-related pathologies with evidence supporting the roles of estrogen, SRY expression, the vesicular glutamate transporter VGLUT2, and the microbiome as prospective catalysts for male vulnerability. We also highlight the importance of including sex as a biological variable, particularly when evaluating dopaminergic neurotoxicity in the context of PD.

Utility of Human Relevant Preclinical Animal Models in Navigating NAFLD to MAFLD Paradigm

Fatty liver disease is an emerging contributor to disease burden worldwide. The past decades of work established the heterogeneous nature of non-alcoholic fatty liver disease (NAFLD) etiology and systemic contributions to the pathogenesis of the disease. This called for the proposal of a redefinition in 2020 to that of metabolic dysfunction-associated fatty liver disease (MAFLD) to better reflect the current understanding of the disease. To date, several clinical cohort studies comparing NAFLD and MAFLD hint at the relevancy of the new nomenclature in enriching for patients with more severe hepatic injury and extrahepatic comorbidities. However, the underlying systemic pathogenesis is still not fully understood. Preclinical animal models have been imperative in elucidating key biological mechanisms in various contexts, including intrahepatic disease progression, interorgan crosstalk and systemic dysregulation. Furthermore, they are integral in developing novel therapeutics against MAFLD. However, substantial contextual variabilities exist across different models due to the lack of standardization in several aspects. As such, it is crucial to understand the strengths and weaknesses of existing models to better align them to the human condition. In this review, we consolidate the implications arising from the change in nomenclature and summarize MAFLD pathogenesis. Subsequently, we provide an updated evaluation of existing MAFLD preclinical models in alignment with the new definitions and perspectives to improve their translational relevance.

The biological effect of orange (Citrus sinensis L.) by-products on metabolic biomarkers: A systematic review

Industrial processing of citrus fruits generates an important amount of wastes that evoke environmental damage. Orange is the main citrus fruit consumed worldwide, and after its use, approximately more than a half of the fruit remains as by-products, which comprise important bioactive compounds useful for the development of promising nutraceuticals for the treatment of non-communicable diseases. This study aimed to gather scientific evidence about the biological effects of orange by-products using a systematic review. A total of 14 studies that were carried out in rodent models in the last 10 years were retrieved from PubMed and ScienceDirect databases. Studies that used another animal species, another type of citrus, or a combination of orange with other citrus were excluded. The risk of bias was assessed by using the SYRCLE RoB tool, and the results obtained are shown in an informative table, which showed that most of the studies used a pathological model of chronic diseases. We found that the peel is the most used agri-food by-product, and that it has the potential of reducing the levels of triglycerides, total cholesterol, glucose, and systolic blood pressure. However, to clinically assess these effects, these results need to be tested in future in humans. The included studies on the use of orange by-products strengthen the global sustainable food agenda. It is important to consider new research directions about the use of citrus fruit residues since it not only impacts the problem of its disposal but also provides solutions to eliminate the resulting contamination.

Innate metabolic responses against viral infections

Metabolic adaptation to viral infections critically determines the course and manifestations of disease. At the systemic level, a significant feature of viral infection and inflammation that ensues is the metabolic shift from anabolic towards catabolic metabolism. Systemic metabolic sequelae such as insulin resistance and dyslipidaemia represent long-term health consequences of many infections such as human immunodeficiency virus, hepatitis C virus and severe acute respiratory syndrome coronavirus 2. The long-held presumption that peripheral and tissue-specific 'immune responses' are the chief line of defence and thus regulate viral control is incomplete. This Review focuses on the emerging paradigm shift proposing that metabolic engagements and metabolic reconfiguration of immune and non-immune cells following virus recognition modulate the natural course of viral infections. Early metabolic footprints are likely to influence longer-term disease manifestations of infection. A greater appreciation and understanding of how local biochemical adjustments in the periphery and tissues influence immunity will ultimately lead to interventions that curtail disease progression and identify new and improved prognostic biomarkers.

Optimizing rat and human blood cells sampling for in silico morphometric analysis

Measurements of Morphometric Parameters of the Blood Cells (MPBC) are key for the diagnosis of both mental and metabolic diseases. Several manual approaches or computational methodologies are useful to provide reliable clinical diagnosis. The sample processing and data analysis is relevant, however the sample handling on the pre-analytical phase remains scarcely evaluated. The main goal of this study was to favor the preservation of blood smear using a histological resin. This strategy lead us two practical approaches, give a detailed morphometric description of white blood cells and establish reference intervals in male Wistar rats, which are scarcely reported. Blood smears from male Wistar rats (n = 120) and adult men were collected at room temperature. The integrity of Wright-stained cells was evaluated by an in silico image analysis from rat and human blood smear preserved with a toluene-based synthetic resin mounting medium. A single sample of human blood was used as a control of procedure. The reference intervals was established by cell counting. Based on the results of segmentation algorithm followed by an automatic thresholding analysis, the incorporation of resin favor the conservation of cell blood populations, and lead to identify morphologic features such as nucleus/cytoplasmic shape, granules presence and DNA appearance in nucleus of white blood cells. The use of a histological resin could favor a fast and efficient sample handling in silico MPBC measurements both in the species studied as in wild animals.

Dual transcriptome based reconstruction of Salmonella-human integrated metabolic network to screen potential drug targets

Salmonella enterica serovar Typhimurium (S. Typhimurium) is a highly adaptive pathogenic bacteria with a serious public health concern due to its increasing resistance to antibiotics. Therefore, identification of novel drug targets for S. Typhimurium is crucial. Here, we first created a pathogen-host integrated genome-scale metabolic network by combining the metabolic models of human and S. Typhimurium, which we further tailored to the pathogenic state by the integration of dual transcriptome data. The integrated metabolic model enabled simultaneous investigation of metabolic alterations in human cells and S. Typhimurium during infection. Then, we used the tailored pathogen-host integrated genome-scale metabolic network to predict essential genes in the pathogen, which are candidate novel drug targets to inhibit infection. Drug target prioritization procedure was applied to these targets, and pabB was chosen as a putative drug target. It has an essential role in 4-aminobenzoic acid (PABA) synthesis, which is an essential biomolecule for many pathogens. A structure based virtual screening was applied through docking simulations to predict candidate compounds that eliminate S. Typhimurium infection by inhibiting pabB. To our knowledge, this is the first comprehensive study for predicting drug targets and drug like molecules by using pathogen-host integrated genome-scale models, dual RNA-seq data and structure-based virtual screening protocols. This framework will be useful in proposing novel drug targets and drugs for antibiotic-resistant pathogens.

Male rat leukocyte population dynamics predict a window for intervention in aging

Many age-associated changes in the human hematopoietic system have been reproduced in murine models; however, such changes have not been as robustly explored in rats despite the fact these larger rodents are more physiologically similar to humans. We examined peripheral blood of male F344 rats ranging from 3 to 27 months of age and found significant age-associated changes with distinct leukocyte population shifts. We report CD25⁺ CD4⁺ population frequency is a strong predictor of healthy aging, generate a model using blood parameters, and find rats with blood profiles that diverge from chronologic age indicate debility; thus, assessments of blood composition may be useful for non-lethal disease profiling or as a surrogate measure for efficacy of aging interventions. Importantly, blood parameters and DNA methylation alterations, defined distinct juncture points during aging, supporting a non-linear aging process. Our results suggest these inflection points are important considerations for aging interventions. Overall, we present rat blood aging metrics that can serve as a resource to evaluate health and the effects of interventions in a model system physiologically more reflective of humans.

Our blood contains many types of white blood cells, which play important roles in defending the body against infections and other threats to our health. The number of these cells changes with age, and this in turn contributes to many other alterations that happen in the body as we get older. For example, the immune system generally gets weaker at fighting infections and preventing other cells from developing into cancer. On top of that, the white blood cells themselves can become cancerous, resulting in several types of blood cancer that are more likely to happen in older people.

Many previous studies have examined how the number of white blood cells changes with age in humans and mice. However, our understanding of this process in rats is still poor, despite the fact that the way the human body works has more in common with the rat body than the mouse body.

Here, Yanai, Dunn et al. have studied samples of blood from rats between three to 27 months old. The experiments found that it is possible to accurately predict the age of healthy rats by measuring the frequency of populations of white blood cells, especially a certain type known as CD25+ CD4+ cells. If the animals had any form of illness, their predicted age deviated from their actual age. Furthermore, while some changes in the blood were gradual and continuous, others displayed distinct shifts when the rats reached specific ages.

In the future, these findings may be used as a tool to help researchers diagnose illnesses in rats before the animals develop symptoms, or to more easily establish if a treatment is having a positive effect on the rats’ health. The work of Yanai, Dunn et al. also provides new insights into aging that could potentially aid the design of new screening methods to predict cancer and intervene using a model system that is more similar to humans.

Identification of environmental chemicals that activate p53 signaling after in vitro metabolic activation

Currently, approximately 80,000 chemicals are used in commerce. Most have little-to-no toxicity information. The U.S. Toxicology in the 21st Century (Tox21) program has conducted a battery of in vitro assays using a quantitative high-throughput screening (qHTS) platform to gain toxicity information on environmental chemicals. Due to technical challenges, standard methods for providing xenobiotic metabolism could not be applied to qHTS assays. To address this limitation, we screened the Tox21 10,000-compound (10K) library, with concentrations ranging from 2.8 nM to 92 µM, using a p53 beta-lactamase reporter gene assay (p53-bla) alone or with rat liver microsomes (RLM) or human liver microsomes (HLM) supplemented with NADPH, to identify compounds that induce p53 signaling after biotransformation. Two hundred and seventy-eight compounds were identified as active under any of these three conditions. Of these 278 compounds, 73 gave more potent responses in the p53-bla assay with RLM, and 2 were more potent in the p53-bla assay with HLM compared with the responses they generated in the p53-bla assay without microsomes. To confirm the role of metabolism in the differential responses, we re-tested these 75 compounds in the absence of NADPH or with heat-attenuated microsomes. Forty-four compounds treated with RLM, but none with HLM, became less potent under these conditions, confirming the role of RLM in metabolic activation. Further evidence of biotransformation was obtained by measuring the half-life of the parent compounds in the presence of microsomes. Together, the data support the use of RLM in qHTS for identifying chemicals requiring biotransformation to induce biological responses.

Compartmentalization of metabolism between cell types in multicellular organisms: a computational perspective

In multicellular organisms, metabolism is compartmentalized at many levels, including tissues and organs, different cell types, and subcellular compartments. Compartmentalization creates a coordinated homeostatic system where each compartment contributes to the production of energy and biomolecules the organism needs to carrying out specific metabolic tasks. Experimentally studying metabolic compartmentalization and metabolic interactions between cells and tissues in multicellular organisms is challenging at a systems level. However, recent progress in computational modeling provides an alternative approach to this problem. Here we discuss how integrating metabolic network modeling with omics data offers an opportunity to reveal metabolic states at the level of organs, tissues and, ultimately, individual cells. We review the current status of genome-scale metabolic network models in multicellular organisms, methods to study metabolic compartmentalization in silico, and insights gained from computational analyses. We also discuss outstanding challenges and provide perspectives for the future directions of the field.

Systems-based approaches to study immunometabolism

Technical advances at the interface of biology and computation, such as single-cell RNA-sequencing (scRNA-seq), reveal new layers of complexity in cellular systems. An emerging area of investigation using the systems biology approach is the study of the metabolism of immune cells. The diverse spectra of immune cell phenotypes, sparsity of immune cell numbers in vivo, limitations in the number of metabolites identified, dynamic nature of cellular metabolism and metabolic fluxes, tissue specificity, and high dependence on the local milieu make investigations in immunometabolism challenging, especially at the single-cell level. In this review, we define the systemic nature of immunometabolism, summarize cell- and system-based approaches, and introduce mathematical modeling approaches for systems interrogation of metabolic changes in immune cells. We close the review by discussing the applications and shortcomings of metabolic modeling techniques. With systems-oriented studies of metabolism expected to become a mainstay of immunological research, an understanding of current approaches toward systems immunometabolism will help investigators make the best use of current resources and push the boundaries of the discipline.

Liraglutide + PYY3-36 Combination Therapy Mimics Effects of Roux-en-Y Bypass on Early NAFLD Whilst Lacking-Behind in Metabolic Improvements

Background: Treatment options for NAFLD are still limited. Bariatric surgery, such as Roux-en-Y gastric bypass (RYGB), has been shown to improve metabolic and histologic markers of NAFLD. Glucagon-like-peptide-1 (GLP-1) analogues lead to improvements in phase 2 clinical trials. We directly compared the effects of RYGB with a treatment using liraglutide and/or peptide tyrosine tyrosine 3-36 (PYY_3-36) in a rat model for early NAFLD. Methods: Obese male Wistar rats (high-fat diet (HFD)-induced) were randomized into the following treatment groups: RYGB, sham-operation (sham), liraglutide (0.4 mg/kg/day), PYY_3-36 (0.1 mg/kg/day), liraglutide+PYY_3-36, and saline. After an observation period of 4 weeks, liver samples were histologically evaluated, ELISAs and RNA sequencing + RT-qPCRs were performed. Results: RYGB and liraglutide+PYY_3-36 induced a similar body weight loss and, compared to sham/saline, marked histological improvements with significantly less steatosis. However, only RYGB induced significant metabolic improvements (e.g., adiponectin/leptin ratio 18.8 ± 11.8 vs. 2.4 ± 1.2 in liraglutide+PYY_3-36- or 1.4 ± 0.9 in sham-treated rats). Furthermore, RNA sequencing revealed a high number of differentially regulated genes in RYGB treated animals only. Conclusions: The combination therapy of liraglutide+PYY_3-36 partly mimics the positive effects of RYGB on weight reduction and on hepatic steatosis, while its effects on metabolic function lack behind RYGB.

Evaluation of the utility of the Beta Human Liver Emulation System (BHLES) for CFSAN's regulatory toxicology program

Microphysiological systems (MPS), such as organ-on-a-chip platforms, are an emerging alternative model that may be useful for predicting human physiology and/or toxicity. Due to the interest in these platforms, the Center for Food Safety and Applied Nutrition partnered with Emulate to evaluate the utility of the Beta Human Liver Emulation System (BHLES) for its regulatory science program. Using known hepatotoxic compounds (usnic acid, benzbromarone, tamoxifen, and acetaminophen) and compounds that have no reported human cases of liver toxicity (dimethyl sulfoxide, theophylline, and aminohippurate) the platforms' performance was evaluated. Chemical toxicity was assessed by albumin secretion, urea and LDH release, nuclei number, mitochondrial membrane potential, and apoptosis. System/platform performance was evaluated in terms of sensitivity and specificity, power, and variability and repeatability. Chemical interactions with the Chip material were also assessed. Preliminary findings suggested that for the model test compounds selected, the BHLES was able to accurately predict toxicity, demonstrated high sensitivity and specificity, high power, and low variability. However, some compounds interacted with the Chip material indicating variable exposure levels that should be accounted for when planning experimentation. The details of the evaluation are presented herein.

OUP accepted manuscript

Recessive mutations in the CTNS gene encoding the lysosomal transporter cystinosin cause cystinosis, a lysosomal storage disease leading to kidney failure and multisystem manifestations. A Ctns knockout mouse model recapitulates features of cystinosis, but the delayed onset of kidney manifestations, phenotype variability and strain effects limit its use for mechanistic and drug development studies. To provide a better model for cystinosis, we generated a Ctns knockout rat model using CRISPR/Cas9 technology. The Ctns^−/− rats display progressive cystine accumulation and crystal formation in multiple tissues including kidney, liver and thyroid. They show an early onset and progressive loss of urinary solutes, indicating generalized proximal tubule dysfunction, with development of typical swan-neck lesions, tubulointerstitial fibrosis and kidney failure, and decreased survival. The Ctns^−/− rats also present crystals in the cornea, and bone and liver defects, as observed in patients. Mechanistically, the loss of cystinosin induces a phenotype switch associating abnormal proliferation and dedifferentiation, loss of apical receptors and transporters, and defective lysosomal activity and autophagy in the cells. Primary cultures of proximal tubule cells derived from the Ctns^−/− rat kidneys confirmed the key changes caused by cystine overload, including reduced endocytic uptake, increased proliferation and defective lysosomal dynamics and autophagy. The novel Ctns^−/− rat model and derived proximal tubule cell system provide invaluable tools to investigate the pathogenesis of cystinosis and to accelerate drug discovery.

Directing Cholangiocyte Morphogenesis in Natural Biomaterial Scaffolds

Patients with Alagille syndrome carry monogenic mutations in the Notch signaling pathway and face complications such as jaundice and cholestasis. Given the presence of intrahepatic ductopenia in these patients, Notch2 receptor signaling is implicated in driving normal biliary development and downstream branching morphogenesis. As a result, in vitro model systems of liver epithelium are needed to further mechanistic insight of biliary tissue assembly. Here, primary human intrahepatic cholangiocytes as a candidate population for such a platform are systematically evaluated, and conditions that direct their branching morphogenesis are described. It is found that extracellular matrix presentation, coupled with mitogen stimulation, promotes biliary branching in a Notch-dependent manner. These results demonstrate the utility of using 3D scaffolds for mechanistic investigation of cholangiocyte branching and provide a gateway to integrate biliary architecture in additional in vitro models of liver tissue.

Systems Biology Approaches to Decipher the Underlying Molecular Mechanisms of Glioblastoma Multiforme

Glioblastoma multiforme (GBM) is one of the most malignant central nervous system tumors, showing a poor prognosis and low survival rate. Therefore, deciphering the underlying molecular mechanisms involved in the progression of the GBM and identifying the key driver genes responsible for the disease progression is crucial for discovering potential diagnostic markers and therapeutic targets. In this context, access to various biological data, development of new methodologies, and generation of biological networks for the integration of multi-omics data are necessary for gaining insights into the appearance and progression of GBM. Systems biology approaches have become indispensable in analyzing heterogeneous high-throughput omics data, extracting essential information, and generating new hypotheses from biomedical data. This review provides current knowledge regarding GBM and discusses the multi-omics data and recent systems analysis in GBM to identify key biological functions and genes. This knowledge can be used to develop efficient diagnostic and treatment strategies and can also be used to achieve personalized medicine for GBM.

Age-related changes in the energy of human mesenchymal stem cells

Aging is a physiological process that leads to a higher risk for the most devastating diseases. There are a number of theories of human aging proposed, and many of them are directly or indirectly linked to mitochondria. Here, we used mesenchymal stem cells (MSCs) from young and older donors to study age-related changes in mitochondrial metabolism. We have found that aging in MSCs is associated with a decrease in mitochondrial membrane potential and lower NADH levels in mitochondria. Mitochondrial DNA content is higher in aged MSCs, but the overall mitochondrial mass is decreased due to increased rates of mitophagy. Despite the higher level of ATP in aged cells, a higher rate of ATP consumption renders them more vulnerable to energy deprivation compared to younger cells. Changes in mitochondrial metabolism in aged MSCs activate the overproduction of reactive oxygen species in mitochondria which is compensated by a higher level of the endogenous antioxidant glutathione. Thus, energy metabolism and redox state are the drivers for the aging of MSCs/mesenchymal stromal cells.

Targeting hippocampal phospholipid and tryptophan metabolism for antidepressant-like effects of albiflorin

BACKGROUND

Current antidepressant therapy remains unsatisfactory due to its delayed clinical onset of action and the heterogeneity of depression. Targeting disturbed neurometabolic pathways could provide a novel therapeutic approach for the treatment of depression. Albiflorin is a phytomedicine isolated from the root of Peony (Paeonia albiflora Pall) with excellent clinical tolerance. Until now, the antidepressant-like activities of albiflorin in different subtypes of depression and its effects on neurometabolism are unknown.

PURPOSE

The objective of this study was to investigate the rapid antidepressant-like effects of albiflorin in three common animal models of depression and elucidate the pharmaco-metabolic mechanisms of its action using a multi-omics approach.

RESULTS

We found that albiflorin produces rapid antidepressant-like effects in chronic unpredictable mild stress (CUMS), olfactory bulbectomy (OBX), and lipopolysaccharide (LPS)-induced murine models of depression. Using a system-wide approach combining metabolomics, lipidomics, and transcriptomics, we showed that the therapeutic effects of albiflorin are highly associated with the rapid restoration of a set of common metabolic abnormities in the hippocampus across all three depression models, including phospholipid and tryptophan metabolism. Further mechanistic analysis revealed that albiflorin normalized the metabolic dysregulation in phospholipid metabolism by suppressing hippocampal cytosolic phospholipases A2 (cPLA2). Additionally, inhibition of cPLA2 overexpression by albiflorin corrects abnormal kynurenine pathway of tryptophan metabolism via the cPLA2-protein kinase B (Akt1)-indoleamine 2,3-dioxygenase 1(IDO1) regulatory loop and directs tryptophan catabolism towards more hippocampal serotonin biosynthesis.

CONCLUSION

Our study contributed to a better understanding of the homogeneity in the metabolic mechanisms of depression and established a proof-of-concept for rapid treatment of depression through targeting dysregulated neurometabolic pathways.

Importance of the biomass formulation for cancer metabolic modeling and drug prediction

Genome-scale metabolic reconstructions include all known biochemical reactions occurring in a cell. A typical application is the prediction of potential drug targets for cancer treatment. The precision of these predictions relies on the definition of the objective function. Generally, the biomass reaction is used to illustrate the growth capacity of a cancer cell. Today, seven human biomass reactions can be identified in published metabolic models. The impact of these differences on the metabolic model predictions has not been explored in detail. We explored this impact on cancer metabolic model predictions and showed that the metabolite composition and the associated coefficients had a large impact on the growth rate prediction accuracy, whereas gene essentiality predictions were mainly affected by the metabolite composition. Our results demonstrate the importance of defining a consensus biomass reaction compatible with most human models, which would contribute to ensuring the reproducibility and consistency of the results.

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The definition of the biomass reaction is of utmost importance for model predictions

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Growth rate predictions are affected by metabolite composition and their coefficients

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Gene essentiality predictions are mainly affected by the metabolite composition

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Need to find a standard biomass reaction for reproducibility and consistency purposes

Machine learning applications for therapeutic tasks with genomics data

Thanks to the increasing availability of genomics and other biomedical data, many machine learning algorithms have been proposed for a wide range of therapeutic discovery and development tasks. In this survey, we review the literature on machine learning applications for genomics through the lens of therapeutic development. We investigate the interplay among genomics, compounds, proteins, electronic health records, cellular images, and clinical texts. We identify 22 machine learning in genomics applications that span the whole therapeutics pipeline, from discovering novel targets, personalizing medicine, developing gene-editing tools, all the way to facilitating clinical trials and post-market studies. We also pinpoint seven key challenges in this field with potentials for expansion and impact. This survey examines recent research at the intersection of machine learning, genomics, and therapeutic development.

Advances in Genome-Scale Metabolic Modeling toward Microbial Community Analysis of the Human Microbiome

A genome-scale metabolic model (GEM) represents metabolic pathways of an organism in a mathematical form and can be built using biochemistry and genome annotation data. GEMs are invaluable for understanding organisms since they analyze the metabolic capabilities and behaviors quantitatively and can predict phenotypes. The development of high-throughput data collection techniques led to an immense increase in omics data such as metagenomics, which expand our knowledge on the human microbiome, but this also created a need for systematic analysis of these data. In recent years, GEMs have also been reconstructed for microbial species, including human gut microbiota, and methods for the analysis of microbial communities have been developed to examine the interaction between the organisms or the host. The purpose of this review is to provide a comprehensive guide for the applications of GEMs in microbial community analysis. Starting with GEM repositories, automatic GEM reconstruction tools, and quality control of models, this review will give insights into microbe-microbe and microbe-host interaction predictions and optimization of microbial community models. Recent studies that utilize microbial GEMs and personalized models to infer the influence of microbiota on human diseases such as inflammatory bowel diseases (IBD) or Parkinson's disease are exemplified. Being powerful system biology tools for both species-level and community-level analysis of microbes, GEMs integrated with omics data and machine learning techniques will be indispensable for studying the microbiome and their effects on human physiology as well as for deciphering the mechanisms behind human diseases.

Metabolomics as a Truly Translational Tool for Precision Medicine

Metabolomics is unique among omics technologies in being applicable to metabolism and toxicity studies broadly across organisms (e.g., humans, other mammals, model organisms, and even bacteria) and across biological materials (e.g., blood, urine, saliva, biopsy, and stool), including cultured cells and subcellular fractions. Metabolomics can be used to characterize biologic response patterns in humans as well as to support mechanistic studies in model systems and ex vivo studies. A broad range of resources are available, including publicly accessible data repositories (e.g., Metabolomics Workbench), tools for biostatistics and bioinformatics (e.g., MetaboAnalyst), metabolite identification (e.g., Metlin), and pathway analysis (e.g., Kyoto Encyclopedia of Genes and Genomes). Thus, metabolomics is more than a promise of the future; metabolomics is already available as a translational approach to facilitate precision medicine. This ACT Symposium review will contain an introduction to metabolomics in toxicity studies followed by sections on translational metabolic networks, translational metabolite biomarkers of acetaminophen-induced acute liver injury, translational framework using high-resolution metabolomics for integrated pharmacokinetics and pharmacodynamics, and precision medicine applications: extracting actionable targets from untargeted metabolomics data following one year in space.

A Review on Chromatography-Mass Spectrometry Applications on Anthocyanin and Ellagitannin Metabolites of Blackberries and Raspberries

Berries have been widely assessed for their beneficial health effects, predominately due to their high (poly)phenol content of anthocyanins and ellagitannins. After ellagitannins and ellagic acid are metabolized by the gut microbiome, a class of compounds known as urolithins are produced, which exert potential advantageous health effects. Anthocyanins, on the other hand, undergo a complex metabolic pathway after their interaction with microbial and endogenous enzymes, forming a broad range of metabolites and catabolic products. In most cases, in vitro models and cell lines are used to generate metabolites, whereas their assessment in vivo is currently limited. Thus far, several analytical methods have been developed for the qualitative and quantitative analysis of phenolic metabolites in berries, including liquid chromatography, mass spectrometry, and other hyphenated techniques, and have been undoubtedly valuable tools for the detailed metabolite characterization and profiling. In this review, a compilation of studies providing information on the qualitative and quantitative analysis of (poly)phenol metabolites in blackberries and raspberries after the utilization of in vitro and in vivo methods is presented. The different analytical techniques employed are assessed, focusing on the fate of the produced metabolic compounds in order to provide evidence on their characteristics, formation, and beneficial effects.

Multi-omics approaches for revealing the complexity of cardiovascular disease

The development and progression of cardiovascular disease (CVD) can mainly be attributed to the narrowing of blood vessels caused by atherosclerosis and thrombosis, which induces organ damage that will result in end-organ dysfunction characterized by events such as myocardial infarction or stroke. It is also essential to consider other contributory factors to CVD, including cardiac remodelling caused by cardiomyopathies and co-morbidities with other diseases such as chronic kidney disease. Besides, there is a growing amount of evidence linking the gut microbiota to CVD through several metabolic pathways. Hence, it is of utmost importance to decipher the underlying molecular mechanisms associated with these disease states to elucidate the development and progression of CVD. A wide array of systems biology approaches incorporating multi-omics data have emerged as an invaluable tool in establishing alterations in specific cell types and identifying modifications in signalling events that promote disease development. Here, we review recent studies that apply multi-omics approaches to further understand the underlying causes of CVD and provide possible treatment strategies by identifying novel drug targets and biomarkers. We also discuss very recent advances in gut microbiota research with an emphasis on how diet and microbial composition can impact the development of CVD. Finally, we present various biological network analyses and other independent studies that have been employed for providing mechanistic explanation and developing treatment strategies for end-stage CVD, namely myocardial infarction and stroke.

Effects of quinolone and poloxamer otic suspension on rat tympanic membranes

OBJECTIVES

Commercial quinolone ear drops (0.3%) delivered twice daily for 10 days cause tympanic membrane perforations (TMPs) in rats. We aimed to evaluate if a single application of 6% quinolone in poloxamer causes TMPs in rats.

METHODS

Rats were randomized to 5 groups (10/group), with one ear receiving a single otic instillation of 16% poloxamer 407 or 188 (as found in a commercial otic preparation and a wound dressing), or ofloxacin, ciprofloxacin, or neomycin at 6% in suspension with 16% poloxamer 407. The contralateral ear received saline. Rats were assessed over 42 days.

RESULTS

No TMPs were seen in ears treated with saline, poloxamer 407 or 188, or in ears treated with ofloxacin-, ciprofloxacin-, or neomycin-poloxamer suspension. White precipitates were observed on the canal or tympanic membrane of ciprofloxacin and ofloxacin-treated ears. Precipitates were more common in ciprofloxacin-treated ears until day 10 (p < 0.0001 to p = 0.0004). Tympanic membrane surface irregularities, were also observed mostly in the ciprofloxacin-treated ears from day 3-42 (p = 0.03 to p = 0.0033).

CONCLUSIONS

Quinolone in poloxamer otic preparations may be a safer therapeutic alternative to conventional quinolone ear drops in ears with intact TMs, particularly those felt to be at risk for developing TMPs.

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).