Metabolic network-based predictions of toxicant-induced metabolite changes in the laboratory rat

Microbiological and metabolic pathways analysing the mechanisms of alfalfa polysaccharide and sulfated alfalfa polysaccharide in alleviating obesity

Alfalfa polysaccharide (AP) and sulfated alfalfa polysaccharide (SAP) exhibit potential for alleviating obesity. This study aimed to analyze the mechanism of action of AP and SAP in alleviating obesity through combined microbiomics and metabolomics. The research selected validated optimal AP and SAP concentration for experiment. The results showed that AP and SAP down-regulated colonic inflammatory gene expression, regulated intestinal pH to normal, and restored intestinal growth. Microbial sequencing showed that AP and SAP altered the microbial composition ratio. AP increased the relative abundance of Muribaculaceae and Romboutsia. SAP increased the relative abundance of Dubosiella, Fecalibaculum and Desulfovibrionaceae. Metabolomic analysis showed that AP regulated steroid hormone biosynthesis, neuroactive ligand-receptor interactions and bile secretion pathways. SAP focuses more on pathways related to amino acid metabolism. Meanwhile, AP and SAP down-regulated the mRNA expression of colonic COX-2, PepT-1 and HK2 and up-regulated the mRNA expression of TPH1. Correlation analysis showed a strong correlation between metabolites and gut bacteria. Dubosiella, Faecalibaculum may be the critical marker flora for polysaccharides to alleviate obesity. This study indicates that AP and SAP alleviate obesity through different pathways and that specific polysaccharide modifications affect characteristic microbial and metabolic pathways, providing new insights into polysaccharide modifications.

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.

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.

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.

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.

Genomics and metabolomics of early-stage thioacetamide-induced liver injury: An interspecies study between guinea pig and rat

To study the complex processes involved in liver injuries, researchers rely on animal investigations, using chemically or surgically induced liver injuries, to extrapolate findings and infer human health risks. However, this presents obvious challenges in performing a detailed comparison and validation between the highly controlled animal models and development of liver injuries in humans. Furthermore, it is not clear whether there are species-dependent and -independent molecular initiating events or processes that cause liver injury before they eventually lead to end-stage liver disease. Here, we present a side-by-side study of rats and guinea pigs using thioacetamide to examine the similarities between early molecular initiating events during an acute-phase liver injury. We exposed Sprague Dawley rats and Hartley guinea pigs to a single dose of 25 or 100 mg/kg thioacetamide and collected blood plasma for metabolomic analysis and liver tissue for RNA-sequencing. The subsequent toxicogenomic analysis identified consistent liver injury trends in both genomic and metabolomic data within 24 and 33 h after thioacetamide exposure in rats and guinea pigs, respectively. In particular, we found species similarities in the key injury phenotypes of inflammation and fibrogenesis in our gene module analysis for liver injury phenotypes. We identified expression of several common genes (e.g., SPP1, TNSF18, SERPINE1, CLDN4, TIMP1, CD44, and LGALS3), activation of injury-specific KEGG pathways, and alteration of plasma metabolites involved in amino acid and bile acid metabolism as some of the key molecular processes that changed early upon thioacetamide exposure and could play a major role in the initiation of acute liver injury.

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.

Synthesizing Systems Biology Knowledge from Omics Using Genome-Scale Models

Omic technologies have enabled the complete readout of the molecular state of a cell at different biological scales. In principle, the combination of multiple omic data types can provide an integrated view of the entire biological system. This integration requires appropriate models in a systems biology approach. Here, genome-scale models (GEMs) are focused upon as one computational systems biology approach for interpreting and integrating multi-omic data. GEMs convert the reactions (related to metabolism, transcription, and translation) that occur in an organism to a mathematical formulation that can be modeled using optimization principles. A variety of genome-scale modeling methods used to interpret multiple omic data types, including genomics, transcriptomics, proteomics, metabolomics, and meta-omics are reviewed. The ability to interpret omics in the context of biological systems has yielded important findings for human health, environmental biotechnology, bioenergy, and metabolic engineering. The authors find that concurrent with advancements in omic technologies, genome-scale modeling methods are also expanding to enable better interpretation of omic data. Therefore, continued synthesis of valuable knowledge, through the integration of omic data with GEMs, are expected.

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

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

Hepatic Transcriptomic Patterns in the Neonatal Rat After Pentabromodiphenyl Ether Exposure

Human exposure to pentabromodiphenyl ether (PBDE) mixture (DE-71) and its PBDE-47 congener can occur both in utero and during lactation. Here, we tested the hypothesis that PBDE-induced neonatal hepatic transcriptomic alterations in Wistar Han rat pups can inform on potential toxicity and carcinogenicity after longer term PBDE exposures. Wistar Han rat dams were exposed to either DE-71 or PBDE-47 daily from gestation day (GD 6) through postnatal day 4 (PND 4). Total plasma thyroxine (T4) was decreased in PND 4 pups. In liver, transcripts for CYPs and conjugation enzymes, Nrf2, and ABC transporters were upregulated. In general, the hepatic transcriptomic alterations after exposure to DE-71 or PBDE-47 were similar and provided early indicators of oxidative stress and metabolic alterations, key characteristics of toxicity processes. The transcriptional benchmark dose lower confidence limits of the most sensitive biological processes were lower for PBDE-47 than for the PBDE mixture. Neonatal rat liver transcriptomic data provide early indicators on molecular pathway alterations that may lead to toxicity and/or carcinogenicity if the exposures continue for longer durations. These early toxicogenomic indicators may be used to help prioritize chemicals for a more complete toxicity and cancer risk evaluation.

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

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

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

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

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

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

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

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

Network Modeling of Liver Metabolism to Predict Plasma Metabolite Changes During Short-Term Fasting in the Laboratory Rat

The liver—a central metabolic organ that integrates whole-body metabolism to maintain glucose and fatty-acid regulation, and detoxify ammonia—is susceptible to injuries induced by drugs and toxic substances. Although plasma metabolite profiles are increasingly investigated for their potential to detect liver injury earlier than current clinical markers, their utility may be compromised because such profiles are affected by the nutritional state and the physiological state of the animal, and by contributions from extrahepatic sources. To tease apart the contributions of liver and non-liver sources to alterations in plasma metabolite profiles, here we sought to computationally isolate the plasma metabolite changes originating in the liver during short-term fasting. We used a constraint-based metabolic modeling approach to integrate central carbon fluxes measured in our study, and physiological flux boundary conditions gathered from the literature, into a genome-scale model of rat liver metabolism. We then measured plasma metabolite profiles in rats fasted for 5–7 or 10–13 h to test our model predictions. Our computational model accounted for two-thirds of the observed directions of change (an increase or decrease) in plasma metabolites, indicating their origin in the liver. Specifically, our work suggests that changes in plasma lipid metabolites, which are reliably predicted by our liver metabolism model, are key features of short-term fasting. Our approach provides a mechanistic model for identifying plasma metabolite changes originating in the liver.