Results for “LIVER” — Page 37

NASA NTRS1980

Effect of excessive saccharose administration on metabolic processes in the liver of rabbits with restricted mobility

The administration of saccharose (3 g per 1 kg for 2 months) intensified changes encountered in hypokinesia. There was a more marked increase in the content of cholesterol, pre-beta and beta-lipoproteins, phospholipids, and glycosaminoglycans in the blood. At the same time, the administration of saccharose improved the course of metabolic processes in the liver of immobilized rabbits, restored to normal levels the reduced glycogen level, the rate of glycolysis and the conversion of cholesterol to bile acids and their discharge in the cystic bile.

Rylnikov, Y. P.↗

NASA NTRS1982

Effects of hypergravity on rat liver regeneration

The effects of centrifugation on liver regrowth were examined by measuring mitotic activity. The results indicate that the increased gravity caused a delay in the onset of mitotic activity and a significant decrease in overall mitotic activity.

Feller, D. D.↗

NASA NTRS1987

Ion-exchange chromatography separates activities synthesizing and degrading fructose 2,6-bisphosphate from C3 and C4 leaves but not from rat liver

Fructose-6-phosphate,2-kinase and fructose-2,6-bisphosphatase were separated on the basis of charge from leaves of C3 (spinach, lettuce, and pea) and C4 (sorghum and amaranthus) plants but not from rat liver--a tissue known to contain a bifunctional enzyme with both activities. [2-32P]Fructose 2,6-bisphosphate binding experiments also suggest that the major forms of these activities reside on different proteins in leaves.

NASA Discipline Exobiology↗

NASA NTRS2007

Toxicity and Radioprotective Effects of DF-1 and Carbon Nanotubes in Human Lung and Liver Cell Lines

The DF-1 compound, a sixty carbon fullerene derivative, has been shown to have antioxidant effects and is thought to possibly help mediate the effects of radiation on cells. While this is potentially useful, it is important to first understand the effect that the DF-1 has on the cells and the growth rate of the cells to determine if the material itself has any innate toxicity. A growth curve was established for both HF-19 cells, human fibroblasts, and HepG2 cells, liver tissue cells in the presence of two different concentrations of DF-1 and for untreated controls. The cells were plated in triplicate in 60mm dishes and were lifted and counted with a hemocytometer daily for one week. The growth curve data for the HF-19 cells show that while the low concentration of DF-1 had no apparent effect on the growth rate, the high concentration of DF-1 appeared to severely inhibit the growth of the HF-19 cells. The growth curve data for the HepG2 cells shows that the DF-1 compound had no significant effect on the rate at which the cells grew. A second growth curve study was performed plain carbon nanotubes, but with only 24 hour exposure to a high and low concentration of material. The carbon nanotubes are another carbon compound similar to DF-1, but in the shape of a tube, rather than a ball. We hypothesize that nanotubes may also mediate the effect of radiation on cells. This time, nanotubes did not showed any significant effect on the growth rate HF-19 or HepG2 cells. A third growth curve study is underway to further determine the effect of DF-1, nanotubes, and a derivatized nanotube (BHT-nanotubes). This derivatized nanotube has been modified with a compound that is known to be very effective at neutralizing free radicals. We expect that the high concentration of DF-1 and possibly the nanotubes and BHT-nanotubes may inhibit the growth of the HF-19 cells while the low concentration will resemble the growth of the control. We also hypothesize that there will be no significant effect on the growth of the HepG2 cells by the nanotubes, and BHT-nanotubes. In order to examine the usefulness of the DF-1, nanotubes, and BHT-nanotubes in mediating the effects of radiation a clonogenic assay is being performed. The HF-19 cells were plated in different concentrations of the various compounds and exposed to varying amounts of radiation. The cells are being allowed to grow in a small enough concentration so that the ability of each cell to divide can be seen by the development of cell clusters. By comparing the irradiated control to the un-irradiated control the effects of radiation alone can be seen. By comparing the compound treated irradiated cells to the irradiated control the usefulness of each compound can be seen. It is thought that Amifostine, the positive control, will have more regularly dividing cells then the irradiated control, as will DF-1 and hopefully both nanotube materials as well.

Burgoyne, Madeline↗

NASA NTRS2011

Changes in Liver Metabolic Gene Expression from Radiation Exposure

Radiation exposure is one of the unique physiological challenges of human spaceflight that is not encountered on earth. While radiation exposure is known to impart physiological stresses and alter normal function, it is unclear how it specifically affects drug metabolism. A major concern is that the actions of medications used in spaceflight may deviate from the expectations formed from terrestrial use. This concern was investigated at the molecular level by analyzing how gamma radiation exposure affected gene expression in the livers of mice. Three different doses of radiation were administered and after various intervals of recovery time, gene expression was measured with RT-qPCR screening arrays for drug metabolism and DNA repair. After examining the results of 192 genes total from each of 72 mice, 65 genes were found to be significantly affected by at least one of the doses of radiation. In general, the genes affected are involved in the metabolism of drugs with lipid or steroid hormone-like structures, as well as the maintenance of redox homeostasis and repair of DNA damage.

Peters, C. P.↗

NASA NTRS2012

Changes in Liver Metabolic Gene Expression from Radiation Exposure

Increased exposure to radiation is one physiological stressor associated with spaceflight. While known to alter normal physiological function, how radiation affects metabolism of administered medications is unclear. Crew health could be affected if the actions of medications used in spaceflight deviated from expectations formed during terrestrial medication use. Three different doses of gamma radiation (50 mGy - 6.05 Gy) and a sham were administered to groups of 6 mice each, and after various intervals of recovery time, liver gene expression was measured with RT-qPCR arrays for drug metabolism and DNA repair enzymes. Results indicated approx.65 genes of the 190 tested were significantly affected by at least one of the radiation doses. Many of the affected genes are involved in the metabolism of drugs with hydrophobic or steroid-like structures, maintenance of redox homeostasis and repair of DNA damage. Most affected genes returned to near control expression levels by 7 days post-treatment. With 6 Gy exposure, metallothionein expression was 132-fold more than control at the 4 hr time point, and fell at each later time point (11-fold at 24 hrs, and 8-fold at 7 days). In contrast, Cyp17a1 showed a 4-fold elevation at 4 hrs after exposure and remained constant for 7 days.

Peters, C. P.↗

NASA NTRSDate not supplied

Transcriptomics-based Machine Learning Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), has typically limited machine learning (ML) in space studies and further study of radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNAseq) data from 6 mouse liver GeneLab datasets (GLDS) with a total of 113 spaceflight and ground-control samples to determine top features relevant to spaceflight including the effect of radiation exposure. Data was normalized within each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. The top MRMR features were used to predict spaceflight vs. ground-control samples using a Random Forest (RF) classifier with 5-fold cross validation (CV). The ML-based gene sets were further compared against differential gene expression results from individual GLDS. CV training using the top 100 MRMR genes show averages of 86% accuracy and 0.95 AUC value on the validation set over 5 folds (Figure 1A). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 811 or 68 DEGs overlapping between at least 2 or 3 studies, respectively (Figure 1B). Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism. Set analysis between the MRMR features and the DEGs showed 60 or 8 genes overlapping with at least 1 or 2 studies, respectively. MRMR feature selection and ensemble ML methods (e.g. RF) improve performance relative to a Naïve Bayes classifier when NGS data sets are analyzed. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise ratio. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from RNASeq analysis. Non-intersecting sets introduce opportunity to explore spaceflight relevant genes and implementing ML methods across existing NGS datasets may overcome sample size limitations. ML coupled with existing analytical methods enhances understanding of disease by revealing common underlying pathways across datasets.

Machine Learning↗

NASA NTRSDate not supplied

Space Flown Rodent Liver RNA Sequencing Data for Machine Learning in Space Biology Research

High-throughput nucleic acid sequencing (DNA-seq, RNA-seq) has become widespread in biomedical research due to the growing availability and affordability of these assays. Data analysis has been accelerated in recent years by the adoption of artificial intelligence (AI) and machine learning (ML) techniques by biomedical researchers. In space biology research, RNAseq datasets from space-flown experimental samples are critical for characterizing the gene expression aberrations associated with exposure to spaceflight stressors. However, space biological experiments tend to be very low sample size, so identifying proper AI/ML algorithms for sequencing data analysis is an ongoing challenge since these algorithms typically require large sample size. The NASA Science Mission Directorate (SMD) has started the “Benchmark Initiative for AI/ML”, focused on creating datasets meant for three main applications: 1) scientific benchmarking, which finds the best algorithm for a specific problem; 2) application benchmarking, which measures algorithm performance against a set of parameters; and 3) system benchmarking, which evaluates performance of hardware and software architecture. These scientific benchmarks consist of an AI-ready dataset and a reference implementation on a specific scientific question. In this work, we focused on generating standardized datasets to allow the scientific community to benchmark AI/ML algorithms in the domain of space biology. We present here a standardized, AI-ready, publicly available benchmark dataset for space biology RNA-seq data as a collaboration between the NASA AI4LS (Artificial Intelligence for Life Sciences) working group. and NASA’s SMD. This dataset consists of space-flown and ground control mouse liver found in the NASA GeneLab omics database. However, to amplify the small sample number (n=112 samples) for ML purposes, we employ Gaussian noise and a generative adversarial network to extend this dataset to 6,000 synthetic samples, matching the original gene expression characteristics.

James Casaletto↗

NASA NTRSDate not supplied

Transcriptomics-based Machine Learning Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% was shown on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗

NASA NTRSDate not supplied

Transcriptomics-based Machine Learning (ML) Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗

NASA NTRSDate not supplied

Transcriptomics-based Machine Learning Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% was shown on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗

DOE OSTI2018

Spermidine ameliorates non-alcoholic fatty liver disease through regulating lipid metabolism via AMPK

Highlights: • Spermidine ameliorates high-fat diet-induced body weight increase. • Spermidine improves glucose tolerance and insulin sensitivity. • Spermidine ameliorates high-fat diet-induced hepatic steatosis. • Spermidine inhibits the expression of lipogenic genes dependent on AMPK. In this study, treatment of high-fat diet-induced obesity (DIO) C57BL/6J mice with spermidine decreased body weight and subcutaneous and visceral fat content, reversed the apparent hepatosteatosis, and reduced hepatic intracellular and serum triglyceride and total cholesterol concentrations. Moreover, spermidine treatment improved glucose tolerance and insulin sensitivity in DIO mice. The mechanism studies indicated that spermidine indeed increased the phosphorylation of hepatic AMP-activated protein kinase (AMPK), and inhibited the expression of lipogenic genes in vivo and in vitro. Moreover, these spermidine-mediated molecular effects were also abolished by compound C, an inhibitor of AMPK, in primary hepatocytes. In summary, spermidine protected against DIO-induced hepatosteatosis by decreasing lipogenic genes expression through an AMPK-mediated mechanism.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2012

Application of quantitative time-lapse imaging (QTLI) for evaluation of Mrp2-based drug–drug interaction induced by liver metabolites

We previously reported a quantitative time-lapse imaging (QTLI)-based analysis method to assess drug–drug interactions (DDI) at multidrug resistance-associated protein 2 (Mrp2) in rat sandwich-cultured hepatocyte (SCH) system, utilizing the fluorescent Mrp2 substrate, 5-(and 6)-carboxy-2′,7′-dichlorofluorescein (CDF). Here, we aimed to examine the feasibility of using QTLI to evaluate DDI involving drug metabolite(s) generated in hepatocytes. We used estradiol (E2) and bilirubin as model compounds; both are not substrates of MRP2, whereas their hepatic metabolites, estradiol-17β-glucuronide (E17G) or bilirubin glucuronides, are known to be its substrates as well as inhibitors. When rat SCHs were pre-exposed with E2, fluorescence of CDF accumulated in bile canaliculi decreased depending upon both the duration of pre-exposure and the concentration of extracellular E2. The decrease corresponded with the increase in intracellular concentration of E17G in hepatocytes. Furthermore, cytotoxicity of vinblastine, a substrate of MRP2, was enhanced in SCHs treated with E2. Similarly, CDF accumulated in bile canaliculi was significantly reduced in rat SCHs pre-exposed with bilirubin. In conclusion, these results suggest that phase II biotransformation of a competitor is reflected in alteration of MRP2-mediated CDF transport detected in QTLI. The QTLI might provide a convenient platform to evaluate transporter-based DDIs involving hepatic metabolites of drug candidates without the need to identify the metabolites. -- Highlights: ► Mrp2-mediated CDF transport is inhibited by E2, but not E17G in vesicle study. ► Both E2 and E17G do not compromise CDF formation from CDFDA in hepatocytes. ► CDF accumulation in bile canaliculi is inhibited by E2 or E17G in QTLI. ► Increasing exposure to E2 decreases CDF accumulation in bile canaliculi in QTLI. ► QTLI is feasible to assess Mrp2-based DDI involving drug metabolite in hepatocytes.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2011

Angiographic Evaluation of Feeding Arteries of Hepatocellular Carcinoma in the Caudate Lobe of the Liver

Purpose: To evaluate the origins of feeders of hepatocellular carcinoma (HCC) in the caudate lobe (S1). Materials and Methods: Eighty-eight HCCs (mean diameter 21.4 mm) were treated by chemoembolization. The tumor-feeding caudate artery was confirmed when a tumor stain was demonstrated on angiogram and iodized oil was accumulated into the HCC and S1 on computed tomography (CT). The origins were divided into R{sub 1} (right proximal), R{sub 2} (right distal), L{sub 1} (left proximal), L{sub 2} (left distal), A (anterior segmental), P (posterior segmental), M (middle hepatic or medial segmental), Ph (proper hepatic), Ch (common hepatic), and Ex (extrahepatic). The origins of feeders supplying HCCs in the Spiegel lobe (SP; n = 36), the paracaval portion (PC; n = 38), and the caudate process (CP; n = 14) were also analyzed. Results: One hundred sixteen feeders were identified: 11 (9.5%) arose from R{sub 1}; 21 (18.1%) arose from R{sub 2}; nine arose (0.9%) from L{sub 1}; 15 (12.9%) arose from L{sub 2}; 24 (20.7%) arose from A; 25 (21.6%) arose from P; seven (6.0%) arose from M; one (0.9%) arose from Ph; and three (2.6%) arose from Ex. HCCs in the SP and the PC were fed by feeders from both hepatic arteries (the ratios of right to left were 3:2 and 3:1, respectively), and HCCs in the CP were dominantly fed by feeders from the right hepatic artery. Conclusion: The caudate artery most frequently arises from the right hepatic artery, followed with almost equal frequency by the left hepatic, the anterior segmental, and the posterior segmental artery. The origins of the caudate arteries differ according to the subsegmental locations.