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

Comparative Proteomics Highlights that GenX Exposure Leads to Metabolic Defects and Inflammation in Astrocytes

Exposure to PFAS such as GenX (HFPO dimer acid) has become increasingly common due to the replacement of older generation PFAS in manufacturing processes. While neurodegenerative and developmental effects of legacy PFAS exposure have been studied in depth, there is a limited understanding specific to the effects of GenX exposure. To investigate the effects of GenX exposure, we exposed Drosophila melanogaster to GenX and assessed the motor behavior and performed quantitative proteomics of fly brains to identify molecular changes in the brain. Additionally, metabolic network-based analysis using the iDrosophila1 model unveiled a potential link between GenX exposure and neurodegeneration. Since legacy PFAS exposure has been linked to Parkinson's disease (PD), we compared the proteome data sets between GenX-exposed flies and a fly model of PD expressing human α-synuclein. Considering the proteomic data- and network-based analyses that revealed GenX may be regulating GABA-associated pathways and the immune system, we next explored the effects of GenX on astrocytes, as astrocytes in the brain can regulate GABA. An array of assays demonstrated GenX exposure may lead to mitochondrial dysfunction and neuroinflammatory response in astrocytes, possibly linking non-cell autonomous neurodegeneration to the motor deficits associated with GenX exposure.

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.

Metabolic modeling of sex-specific liver tissue suggests mechanism of differences in toxicological responses

Male subjects in animal and human studies are disproportionately used for toxicological testing. This discrepancy is evidenced in clinical medicine where females are more likely than males to experience liver-related adverse events in response to xenobiotics. While previous work has shown gene expression differences between the sexes, there is a lack of systems-level approaches to understand the direct clinical impact of these differences. Here, we integrate gene expression data with metabolic network models to characterize the impact of transcriptional changes of metabolic genes in the context of sex differences and drug treatment. We used Tasks Inferred from Differential Expression (TIDEs), a reaction-centric approach to analyzing differences in gene expression, to discover that several metabolic pathways exhibit sex differences including glycolysis, fatty acid metabolism, nucleotide metabolism, and xenobiotics metabolism. When TIDEs is used to compare expression differences in treated and untreated hepatocytes, we find several subsystems with differential expression overlap with the sex-altered pathways such as fatty acid metabolism, purine and pyrimidine metabolism, and xenobiotics metabolism. Finally, using sex-specific transcriptomic data, we create individual and averaged male and female liver models and find differences in the pentose phosphate pathway and other metabolic pathways. These results suggest potential sex differences in the contribution of the pentose phosphate pathway to oxidative stress, and we recommend further research into how these reactions respond to hepatotoxic pharmaceuticals.

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.

Metabolic modeling of sex-specific tissue predicts mechanisms of differences in toxicological responses

Male subjects in animal and human studies are disproportionately used for toxicological testing. This discrepancy is evidenced in clinical medicine where females are more likely than males to experience liver-related adverse events in response to xenobiotics. While previous work has shown gene expression differences between the sexes, there is a lack of systems-level approaches to understand the direct clinical impact effect of these differences. Here, we integrate gene expression data with metabolic network models to characterize the impact of transcriptional changes of metabolic genes in the context of sex differences and drug treatment. We used Tasks Inferred from Differential Expression (TIDEs), a reaction-centric approach to analyzing differences in gene expression, to discover that androgen, ether lipid, glucocorticoid, tryptophan, and xenobiotic metabolism have more activity in the male liver, and serotonin, melatonin, pentose, glucuronate, and vitamin A metabolism have more activity in the female liver. When TIDEs is used to compare expression differences in treated and untreated hepatocytes, we see little response in those sex-altered subsystems, and the largest differences are in subsystems related to lipid metabolism. Finally, using sex-specific transcriptomic data, we create individual and averaged male and female liver models and find differences in the import of bile acids and salts. This result suggests that the sexually dimorphic behavior of the liver may be caused by differences in enterohepatic recirculation, and we suggest an investigation into sex-specific microbiome composition as an avenue of further research.

Author Summary

Male-bias in clinical testing of drugs has led to a disproportionate number of hepatotoxic events in women. Previous works use gene-by-gene differences in biological sex to explain this discrepancy, but there is little focus on the systematic interactions of these differences. To this end, we use a combination of gene expression data and metabolic modeling to compare metabolic activity between the male and female liver and treated and untreated hepatocytes. We find several subsystems with differential activity in each sex; however, when comparing these subsystems with those pathways altered by hepatotoxic agents, we find little overlap. To explore these differences on a reaction-by-reaction basis, we use the same sex-specific transcriptomic data to contextualize the previously published Human1 human cell metabolic model. In these models we find a difference in flux for the import of bile acids and salts, suggesting a potential difference in enterohepatic circulation. These findings can help guide future drug design, toxicological testing, and sex-specific research to better account for the entire human population.

Effects of smoking on the tissue regeneration-associated functions of human endometrial stem cells via a novel target gene SERPINB2

BACKGROUND

Smokers directly inhale mainstream cigarette smoke, which contains numerous known and potential toxic substances, and thus, smoking is expected to have broad harmful effects that cause tissue injury and dysfunction. Interestingly, many studies have suggested that the recent decline in female fertility and increased rate of spontaneous abortion could be associated with increased smoking rates. Indeed, women that smoked for 10 years or more were reported to have a ~ 20% higher infertility rate than women that had never smoked. However, the reasons for the underlying harmful aspects of smoking on female fertility remain a matter of debate. Importantly, a previous study revealed that resident endometrial stem cell deficiency significantly limits the cyclic regeneration potential of endometrium, which, in turn, decreases successful pregnancy outcomes. In this context, we postulated that exposure to mainstream cigarette smoke extracts might decrease female fertility by inhibiting the functions of resident endometrial stem cells.

METHODS

We investigated whether cigarette mainstream smoke exposure directly inhibits various tissue regeneration-associated functions of endometrial stem cells, such as self-renewal, migration, pluripotency, and differentiation capacity in vitro. Next, we determined whether SERPINB2 mediates cigarette smoke-induced suppressive effects on various tissue regeneration-associated functions by depleting SERPINB2 expression with specific shRNA targeting SERPINB2. Mice were injected intraperitoneally with low (0.5 mg/kg) or high (1 mg/kg) doses of cigarette smoke extract (10 times for two weeks), and endometrial stem cells were then isolated from mice uterine tissues.

RESULTS

We found that exposure to cigarette smoke extracts remarkably suppressed various tissue regeneration-associated functions of endometrial stem cells, such as self-renewal, migration, multilineage differentiation ability, and pluripotency in vitro and in vivo by activating the SERPINB2 gene. Indeed, cigarette smoke-induced inhibitory effects on various endometrial stem cell functions were significantly abolished by SERPINB2 knockdown.

CONCLUSIONS

These findings provide valuable information on the harmful effects of cigarette smoking on resident endometrial stem cells and hopefully will facilitate the developments of promising therapeutic strategies for subfertile or infertile women that smoke cigarettes.

The impact of fine particulate matter (PM) on various beneficial functions of human endometrial stem cells through its key regulator SERPINB2

Fine particulate matter (PM) has a small diameter but a large surface area; thus, it may have broad toxic effects that subsequently damage many tissues of the human body. Interestingly, many studies have suggested that the recent decline in female fertility could be associated with increased PM exposure. However, the precise mechanisms underlying the negative effects of PM exposure on female fertility are still a matter of debate. A previous study demonstrated that resident stem cell deficiency limits the cyclic regenerative capacity of the endometrium and subsequently increases the pregnancy failure rate. Therefore, we hypothesized that PM exposure induces endometrial tissue damage and subsequently reduces the pregnancy rate by inhibiting various beneficial functions of local endometrial stem cells. Consistent with our hypothesis, we showed for the first time that PM exposure significantly inhibits various beneficial functions of endometrial stem cells, such as their self-renewal, transdifferentiation, and migratory capacities, in vitro and in vivo through the PM target gene SERPINB2, which has recently been shown to be involved in multiple stem cell functions. In addition, the PM-induced inhibitory effects on the beneficial functions of endometrial stem cells were significantly diminished by SERPINB2 depletion. Our findings may facilitate the development of promising therapeutic strategies for improving reproductive outcomes in infertile women.

Airborne pollutants may reduce female fertility through their debilitating effects on the stem cells that maintain the endometrium, the interior lining of the uterus. Recent evidence suggests that toxic byproducts from fossil fuels known as ‘particulate matter’ represent a danger to women’s reproductive health. South Korean researchers led by Ji-Won Jung, Korea Centers for Disease Control and Prevention, and In-Sun Hong, Gachon University, Incheon, have investigated this risk by exposing cultured human endometrial stem cells to diesel-derived particulate matter. These stem cells normally maintain the endometrium, allowing embryonic implantation to take place, but exposure to particulate matter greatly impaired the cells’ regenerative function. Mice exposed to particulate matter exhibited similar impairments of endometrial maintenance. The researchers identified a molecular pathway associated with this response that could guide development of fertility-restoring treatments.

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.

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.

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.

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.