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

2019 ◽  
Vol 172 (2) ◽  
pp. 279-291
Author(s):  
Kristopher D Rawls ◽  
Edik M Blais ◽  
Bonnie V Dougherty ◽  
Kalyan C Vinnakota ◽  
Venkat R Pannala ◽  
...  
Keyword(s):  
Experimental Data ◽  
Liver Toxicity ◽  
Rat Hepatocytes ◽  
Metabolomics Data ◽  
Atp Production ◽  
Biomarker Response ◽  
Model Predictions ◽  
Hepatocyte Metabolism ◽  
Genome Scale

Abstract 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.

Carbon tetrachloride induced surface changes in isolated rat hepatocytes

1983 ◽  
Vol 41 ◽  
pp. 682-683
Author(s):  
S.D. Barnard ◽  
W.J. Caldwell ◽  
D.J. Thompson
Keyword(s):  
Free Radicals ◽  
Liver Toxicity ◽  
Smooth Endoplasmic Reticulum ◽  
Rat Hepatocytes ◽  
Laboratory Animals ◽  
Sprague Dawley ◽  
Isolated Rat Hepatocytes ◽  
In Vitro Response ◽  
Surface Changes

The administration of CCl4 to laboratory animals produces liver toxicity detectable by both morphological and biochemical methods. Hepatocellular metabolic cleavage of CCl4 creates free radicals which damage biological membranes through the process of lipid peroxidation; this process produces measurable products of peroxidative decomposition one of which is malondialdehyde (MDA). Many studies of CCl4 -induced toxicity appropriately have focused on the smooth endoplasmic reticulum (SER) where the enzymatic cleavage of CCl4 occurs. Reports that CCl4 is also metabolized to free radicals in the plasma membrane (PM)1 and that in vitro exposure to CCl4 causes PM blebing and loss of microvilli2 indicate a need for further study of this cellular component.A male Sprague-Dawley rat was treated daily, i.p., with 80 mg/kg of phenobarbital in order to induce SER-associated enzymes and thereby enhance the subsequent in vitro response of the cells to CCl4. After 5 days the rat was anesthetized with pentobarbital, and the liver was perfused with collagenase solution.

scholarly journals A DNA-structured mathematical model of cell-cycle progression in cyclic hypoxia

2021 ◽  
Author(s):  
Giulia Laura Celora ◽  
Philip K Maini ◽  
Helen M Byrne ◽  
Ester M Hammond ◽  
Samuel B Bader ◽  
...  
Keyword(s):  
Experimental Data ◽  
Cell Cycle ◽  
Dna Synthesis ◽  
S Phase ◽  
Low Oxygen ◽  
Oxygen Levels ◽  
Wide Range ◽  
Model Predictions ◽  
Periodic Exposure

New experimental data have shown how the periodic exposure of cells to low oxygen levels (i.e., cyclic hypoxia) impacts their progress through the cell-cycle. Cyclic hypoxia has been detected in tumours and linked to poor prognosis and treatment failure. While fluctuating oxygen environments can be reproduced in vitro, the range of oxygen cycles that can be tested is limited. By contrast, mathematical models can be used to predict the response to a wide range of cyclic dynamics. Accordingly, in this paper we develop a mechanistic model of the cell-cycle that can be combined with in vitro experiments, to better understand the link between cyclic hypoxia and cell-cycle dysregulation. A distinguishing feature of our model is the inclusion of impaired DNA synthesis and cell-cycle arrest due to periodic exposure to severely low oxygen levels. Our model decomposes the cell population into four compartments and a time-dependent delay accounts for the variability in the duration of the S phase which increases in severe hypoxia due to reduced rates of DNA synthesis. We calibrate our model against experimental data and show that it recapitulates the observed cell-cycle dynamics. We use the calibrated model to investigate the response of cells to oxygen cycles not yet tested experimentally. When the re-oxygenation phase is sufficiently long, our model predicts that cyclic hypoxia simply slows cell proliferation since cells spend more time in the S phase. On the contrary, cycles with short periods of re-oxygenation are predicted to lead to inhibition of proliferation, with cells arresting from the cell-cycle when they exit the S phase. While model predictions on short time scales (about a day) are fairly accurate (i.e, confidence intervals are small), the predictions become more uncertain over longer periods. Hence, we use our model to inform experimental design that can lead to improved model parameter estimates and validate model predictions.

scholarly journals Ranking essential bacterial processes by speed of mutant death

2020 ◽  
Vol 117 (30) ◽  
pp. 18010-18017 ◽  
Author(s):  
Larry A. Gallagher ◽  
Jeannie Bailey ◽  
Colin Manoil
Keyword(s):  
Essential Genes ◽  
Insertion Mutant ◽  
Chromosomal Dna ◽  
Atp Production ◽  
Transposon Insertion ◽  
Phenotype Analysis ◽  
Bacterial Mutants ◽  
Bacterial Replication ◽  
Genome Scale

Mutant phenotype analysis of bacteria has been revolutionized by genome-scale screening procedures, but essential genes have been left out of such studies because mutants are missing from the libraries analyzed. Since essential genes control the most fundamental processes of bacterial life, this is a glaring deficiency. To address this limitation, we developed a procedure for transposon insertion mutant sequencing that includes essential genes. The method, called transformation transposon insertion mutant sequencing (TFNseq), employs saturation-level libraries of bacterial mutants generated by natural transformation with chromosomal DNA mutagenized heavily by in vitro transposition. The efficient mutagenesis makes it possible to detect large numbers of insertions in essential genes immediately after transformation and to follow their loss during subsequent growth. It was possible to order 45 essential processes based on how rapidly their inactivation inhibited growth. Inactivating ATP production, deoxyribonucleotide synthesis, or ribosome production blocked growth the fastest, whereas inactivating cell division or outer membrane protein synthesis blocked it the slowest. Individual mutants deleted of essential loci formed microcolonies of nongrowing cells whose sizes were generally consistent with the TFNseq ordering. The sensitivity of essential functions to genetic inactivation provides a metric for ranking their relative importance for bacterial replication and growth. Highly sensitive functions could represent attractive antibiotic targets since even partial inhibition should reduce growth.

scholarly journals Integrated metabolic modeling, culturing and transcriptomics explains enhanced virulence of V. cholerae during co-infection with ETEC

2020 ◽  
Author(s):  
Alyaa M. Abdel-Haleem ◽  
Vaishnavi Ravikumar ◽  
Boyang Ji ◽  
Katsuhiko Mineta ◽  
Xin Gao ◽  
...  
Keyword(s):  
Vibrio Cholerae ◽  
Single Infection ◽  
Gene Essentiality ◽  
E Coli ◽  
Model Predictions ◽  
Metabolic Models ◽  
Genome Scale ◽  
Druggable Targets ◽  
Polymicrobial Infections

AbstractGene essentiality is altered during polymicrobial infections. Nevertheless, most studies rely on single-species infections to assess pathogen gene essentiality. Here, we use genome-scale metabolic models to explore the effect of co-infection of the diarrheagenic pathogen Vibrio cholerae (V. cholerae) with another enteric pathogen, enterotoxigenic E. coli (ETEC). Model predictions showed that V. cholerae metabolic capabilities were increased due to ample cross-feeding opportunities enabled by ETEC. This is in line with increased severity of cholera symptoms known to occur in patients with dual-infections by the two pathogens. In vitro co-culture systems confirmed that V. cholerae growth is enhanced in co-cultures relative to single-cultures. Further, expression levels of several V. cholerae metabolic genes were significantly perturbed as shown by dual RNAseq analysis of its co-cultures with different ETEC strains. A decrease in ETEC growth was also observed, probably mediated by non-metabolic factors. Single gene essentiality analysis predicted conditionally-independent genes that are essential for the pathogen’s growth in both single- and co-infection scenarios. Our results reveal growth differences that are of relevance to drug targeting and efficiency in polymicrobial infections.ImportanceMost studies proposing new strategies to manage and treat infections have been largely focused on identifying druggable targets that can inhibit a pathogen’s growth when it is the single cause of infection. In vivo, however, infections can be caused by multiple species. This is important to take into account when attempting to develop or use current antibacterials since their efficacy can change significantly between single and co-infections. In this study, we used genome-scale metabolic models (GEMs) to interrogate the growth capabilities of Vibrio cholerae (V. cholerae) in single and co-infections with enterotoxigenic E. coli (ETEC), which co-occur in large fraction of diarrheagenic patients. Co-infection model predictions showed that V. cholerae growth capabilities are enhanced in presence of ETEC relative to V. cholerae single-infection, through cross-fed metabolites made available to V. cholerae by ETEC. In vitro, co-cultures of the two enteric pathogens further confirmed model predictions showing an increased growth of V. cholerae in co-culture relative to V. cholerae single-cultures while ETEC growth was suppressed. Dual RNAseq analysis of the co-cultures also confirmed that the transcriptome of V. cholerae is distinct during co-infection compared to single infection scenarios where processes related to metabolism were significantly perturbed. Further, in silico gene-knock out simulations uncovered discrepancies in gene essentiality for V. cholerae growth between single and co-infections. Integrative model-guided analysis thus identified druggable targets that would be critical for V. cholerae growth in both single and co-infections, thus designing inhibitors against those targets would provide a broader spectrum coverage against cholera infections.

Immunoenzymatic demonstration of the simultaneous presence of transferrin, hemopexin and albumin in a same hepatocyte and their ultrastructural localization

1978 ◽  
Vol 36 (2) ◽  
pp. 88-89
Author(s):  
M. Kraemer ◽  
J. Foucrier ◽  
J. Vassy ◽  
M.T. Chalumeau
Keyword(s):  
Plasma Proteins ◽  
Rat Hepatocytes ◽  
Ultrastructural Localization ◽  
Carrier Proteins ◽  
Normal Cells ◽  
Adult Rat ◽  
Adult Rat Hepatocytes ◽  
Monospecific Antisera ◽  
Different Levels

Some authors using immunofluorescent techniques had already suggested that some hepatocytes are able to synthetize several plasma proteins. In vitro studies on normal cells or on cells issued of murine hepatomas raise the same conclusion. These works could be indications of an hepatocyte functionnal non-specialization, meanwhile the authors never give direct topographic proofs suitable with this hypothesis.The use of immunoenzymatic techniques after obtention of monospecific antisera had seemed to us useful to bring forward a better knowledge of this problem. We have studied three carrier proteins (transferrin = Tf, hemopexin = Hx, albumin = Alb) operating at different levels in iron metabolism by demonstrating and localizing the adult rat hepatocytes involved in their synthesis.Immunological, histological and ultrastructural methods have been described in a previous work.

scholarly journals Conifers Phytochemicals: A Valuable Forest with Therapeutic Potential

Molecules ◽  
2021 ◽  
Vol 26 (10) ◽  
pp. 3005
Author(s):  
Kanchan Bhardwaj ◽  
Ana Sanches Silva ◽  
Maria Atanassova ◽  
Rohit Sharma ◽  
Eugenie Nepovimova ◽  
...  
Keyword(s):  
Experimental Data ◽  
Potential Role ◽  
Therapeutic Potential ◽  
Fungal Infections ◽  
Cell Damage ◽  
Cancer Growth ◽  
Naturally Occurring ◽  
Antioxidant Effects

Conifers have long been recognized for their therapeutic potential in different disorders. Alkaloids, terpenes and polyphenols are the most abundant naturally occurring phytochemicals in these plants. Here, we provide an overview of the phytochemistry and related commercial products obtained from conifers. The pharmacological actions of different phytochemicals present in conifers against bacterial and fungal infections, cancer, diabetes and cardiovascular diseases are also reviewed. Data obtained from experimental and clinical studies performed to date clearly underline that such compounds exert promising antioxidant effects, being able to inhibit cell damage, cancer growth, inflammation and the onset of neurodegenerative diseases. Therefore, an attempt has been made with the intent to highlight the importance of conifer-derived extracts for pharmacological purposes, with the support of relevant in vitro and in vivo experimental data. In short, this review comprehends the information published to date related to conifers’ phytochemicals and illustrates their potential role as drugs.

scholarly journals Integration of machine learning and genome-scale metabolic modeling identifies multi-omics biomarkers for radiation resistance

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Joshua E. Lewis ◽  
Melissa L. Kemp
Keyword(s):  
Machine Learning ◽  
Metabolic Flux ◽  
Metabolic Modeling ◽  
The Cancer Genome Atlas ◽  
Radiation Response ◽  
Metabolomics Data ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Metabolic Biomarkers ◽  
Genome Scale

AbstractResistance to ionizing radiation, a first-line therapy for many cancers, is a major clinical challenge. Personalized prediction of tumor radiosensitivity is not currently implemented clinically due to insufficient accuracy of existing machine learning classifiers. Despite the acknowledged role of tumor metabolism in radiation response, metabolomics data is rarely collected in large multi-omics initiatives such as The Cancer Genome Atlas (TCGA) and consequently omitted from algorithm development. In this study, we circumvent the paucity of personalized metabolomics information by characterizing 915 TCGA patient tumors with genome-scale metabolic Flux Balance Analysis models generated from transcriptomic and genomic datasets. Metabolic biomarkers differentiating radiation-sensitive and -resistant tumors are predicted and experimentally validated, enabling integration of metabolic features with other multi-omics datasets into ensemble-based machine learning classifiers for radiation response. These multi-omics classifiers show improved classification accuracy, identify clinical patient subgroups, and demonstrate the utility of personalized blood-based metabolic biomarkers for radiation sensitivity. The integration of machine learning with genome-scale metabolic modeling represents a significant methodological advancement for identifying prognostic metabolite biomarkers and predicting radiosensitivity for individual patients.

scholarly journals The acute transcriptional responses to dietary methionine restriction are triggered by inhibition of ternary complex formation and linked to Erk1/2, mTOR, and ATF4

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kirsten P. Stone ◽  
Sujoy Ghosh ◽  
Jean Paul Kovalik ◽  
Manda Orgeron ◽  
Desiree Wanders ◽  
...  
Keyword(s):  
Stress Response ◽  
Complex Formation ◽  
Ternary Complex ◽  
Target Genes ◽  
Bioinformatic Analysis ◽  
Transcriptional Responses ◽  
Metabolomics Data ◽  
Ternary Complex Formation ◽  
Methionine Restriction

AbstractThe initial sensing of dietary methionine restriction (MR) occurs in the liver where it activates an integrated stress response (ISR) that quickly reduces methionine utilization. The ISR program is regulated in part by ATF4, but ATF4’s prototypical upstream regulator, eIF2α, is not acutely activated by MR. Bioinformatic analysis of RNAseq and metabolomics data from liver samples harvested 3 h and 6 h after initiating MR shows that general translation is inhibited at the level of ternary complex formation by an acute 50% reduction of hepatic methionine that limits formation of initiator methionine tRNA. The resulting ISR is induced by selective expression of ATF4 target genes that mediate adaptation to reduced methionine intake and return hepatic methionine to control levels within 4 days of starting the diet. Complementary in vitro experiments in HepG2 cells after knockdown of ATF4, or inhibition of mTOR or Erk1/2 support the conclusion that the early induction of genes by MR is partially dependent on ATF4 and regulated by both mTOR and Erk1/2. Taken together, these data show that initiation of dietary MR induces an mTOR- and Erk1/2-dependent stress response that is linked to ATF4 by the sharp, initial drop in hepatic methionine and resulting repression of translation pre-initiation.

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