scholarly journals Plasma metabolomics profiling and machining learning-driven prediction of nonalcoholic steatohepatitis

2021 ◽  
Author(s):  
Moongi Ji ◽  
Yunju Jo ◽  
Seung Joon Choi ◽  
Seong Min Kim ◽  
Kyoung Kon Kim ◽  
...  
Keyword(s):  
Glutamic Acid ◽  
Aspartic Acid ◽  
Nonalcoholic Steatohepatitis ◽  
Multinomial Logistic Regression ◽  
Plasma Metabolites ◽  
Rna Seq ◽  
Metabolomics Data ◽  
Isocitric Acid ◽  
Aconitic Acid ◽  
Metabolomics Analysis

Rationale: We performed targeted metabolomics with machine learning (ML)-based interpretation to identify metabolites that distinguish the progression of nonalcoholic fatty liver disease (NAFLD) in a cohort. Methods: We conducted plasma metabolomics analysis in healthy control subjects (n=25) and patients with NAFL (n=42) and nonalcoholic steatohepatitis (NASH, n=19) by gas chromatography-tandem mass spectrometry (MS/MS) and liquid chromatography-MS/MS as well as RNA sequencing (RNA-seq) analyses on liver tissues from patients with varying stages of NAFLD (n=12). The resulting metabolomic data were subjected to routine statistical and ML-based analyses and multiomics interpretation with RNA-seq data. Results: We found six metabolites that were significantly altered in NAFLD among 79 detected metabolites. Random-forest and multinomial logistic regression analyses showed that eight metabolites (glutamic acid, cis-aconitic acid, aspartic acid, isocitric acid, α-ketoglutaric acid, oxaloacetic acid, myristoleic acid, and tyrosine) could distinguish the three groups. Then, the recursive partitioning and regression tree algorithm selected three metabolites (glutamic acid, isocitric acid, and aspartic acid) from these eight metabolites. With these three metabolites, we formulated an equation, the MetaNASH score that distinguished NASH with excellent performance. Finally, metabolic map construction and correlation assays integrating metabolomics data into the transcriptome datasets of the liver showed correlations between the concentration of plasma metabolites and the expression of enzymes governing metabolism and specific alterations of these correlations in NASH. Conclusions: We found several metabolites that distinguish NASH from non-NASH via metabolomics analysis and ML approaches, developed the MetaNASH score, and suggested the pathophysiologic implications of metabolite profiles in relation to NAFLD progression.

scholarly journals POS0173 METABOLOMIC PROFILING AND SYMPTOM BURDEN IN BLACK WOMEN WITH SYSTEMIC LUPUS ERYTHEMATOSUS

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 299-300
Author(s):  
L. P. Kimble ◽  
A. Khosroshahi ◽  
R. C. Eldridge ◽  
G. S. Brewster ◽  
N. S. Carlson ◽  
...  
Keyword(s):  
Systemic Lupus Erythematosus ◽  
Black Women ◽  
Lupus Erythematosus ◽  
Energy Production ◽  
Symptom Burden ◽  
Black Female ◽  
Multiple Chronic Conditions ◽  
Systemic Lupus ◽  
Metabolomics Data ◽  
Metabolomics Analysis

Background:Black individuals with systemic lupus erythematosus (SLE), who are predominantly women, have disproportionately poorer health outcomes across the trajectory of their disease including increased mortality, higher symptom burden, and poor quality of life than non-Hispanic Whites. The heterogeneity of immunopathology and biochemical complexity of SLE create major knowledge gaps around the mechanisms of disease and differences in SLE symptom expression. Metabolomics may reveal biochemical dysregulation that underlies SLE symptoms and provide novel metabolic targets for precision symptom interventions.Objectives:We conducted untargeted metabolomic plasma profiling of Black females with SLE and Black female non-SLE controls to gain insight into metabolic disturbances associated with SLE.Methods:We analyzed blood specimens collected from 23 Black female patients with diagnosis of SLE during a routine outpatient rheumatology visit and from 21 Black female non-SLE controls whose data were collected as part of another study of obese caregivers. Data collection for both cases and controls was completed with harmonized protocols. Clinical data for cases were obtained via chart review and both cases and controls completed identical, reliable and valid measures of fatigue, depression, anxiety, and sleep disturbance. A commercial metabolomics analysis company within the US conducted untargeted metabolomics on the 44 plasma samples using ultrahigh performance liquid chromatography/tandem mass spectrometry along with metabolite identification and quantification to examine differences between SLE/non-SLE groups.Results:All SLE subjects met 2019 EULAR/ACR criteria (Aringer et al., 2019). SLE subjects were significantly (p < .05) younger (42.5 ± 12.2 vs. 63.2 ± 6.4), had a lower BMI (30.3 ± 9.4 vs. 34.9 ± 4.1), and greater co-morbidities (2.3 ± 1.3 vs. 1.1 ± 1.3) than non-SLE controls. SLE subjects reported higher symptoms than controls across all measures, but differences were not statistically significant. Metabolomics analysis revealed 290 biochemicals that were statistically significant (p ≤ .05) between SLE and non-SLE groups. Random Forest analysis had a predictive accuracy of 91% in differing between the two groups using out-of-bag sampling. Significant metabolic differences between groups were noted in biochemicals associated with glycolysis, the TCA cycle (see Table 1), fatty acid metabolism, branched chain amino acids, sterol levels, heme catabolism, and potential markers of renal impairment. Overall, the differences would suggest reduced energy production among SLE patients compared to controls.Conclusion:Black women with SLE had biochemical profiles consistent with reduced energy production which has implications for the high burden of fatigue and other symptoms in this population. Future work with larger sample sizes should involve integrating symptom and metabolomics data to identify potential biomarkers of symptom burden.References:Aringer, M., Costenbader, K., Daikh, D. et al. (2019). 2019 European League Against Rheumatism/American College of Rheumatology classification criteria for systemic lupus erythematosus. Ann Rheum Dis, 78,1151-1159.Acknowledgements:This work was supported by a research re-entry supplement to L. Kimble under the parent award 1P30NR018090-02S1 Center for the Study of Symptom Science, Metabolomics, and Multiple Chronic Conditions (Song, PI) funded by the National Institute of Nursing Research, National Institutes of Health, USA.Disclosure of Interests:Laura P. Kimble: None declared, Arezou Khosroshahi Consultant of: Have received honorarium for advisory board but has no relationship with this work., Grant/research support from: Have received a research grant from Pfizer; but has no relationship with this work., Ronald C. Eldridge: None declared, Glenna S. Brewster: None declared, Nicole S. Carlson: None declared, Elizabeth J. Corwin: None declared

scholarly journals Dietary glutamic acid and aspartic acid as biomarkers for predicting diabetic retinopathy

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
So Young Park ◽  
Jieun Kim ◽  
Jung Il Son ◽  
Sang Youl Rhee ◽  
Do-Yeon Kim ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Glutamic Acid ◽  
Aspartic Acid ◽  
Education Level ◽  
Screening Rate ◽  
Food Records ◽  
Acid Intake ◽  
Lower Education Level ◽  
Lower Education

AbstractThe screening rate of diabetic retinopathy (DR) is low despite the importance of early diagnosis. We investigated the predictive value of dietary glutamic acid and aspartic acid for diagnosis of DR using the Korea National Diabetes Program cohort study. The 2067 patients with type 2 diabetes without DR were included. The baseline intakes of energy, glutamic acid and aspartic acid were assessed using a 3-day food records. The risk of DR incidence based on intake of glutamic acid and aspartic acid was analyzed. The DR group was older, and had higher HbA1c, longer DM duration, lower education level and income than non-DR group (all p < 0.05). The intake of total energy, glutamic acid and aspartic acid were lower in DR group than non-DR group (p = 0.010, p = 0.025 and p = 0.042, respectively). There was no difference in the risk of developing DR according to the intake of glutamic acid and ascorbic acid. But, aspartic acid intake had a negative correlation with PDR. Hence, the intake of glutamic acid and aspartic acid did not affect in DR incidence. However, lower aspartic acid intake affected the PDR incidence.

Biodegradation of copoly(L-aspartic acid/L-glutamic acid) in vitro

Biopolymers ◽  
1990 ◽  
Vol 29 (3) ◽  
pp. 549-557 ◽  
Author(s):  
Toshio Hayashi ◽  
Makoto Iwatsuki
Keyword(s):  
Glutamic Acid ◽  
Aspartic Acid

scholarly journals Utilization in vivo of glucose and volatile fatty acids by sheep brain for the synthesis of acidic amino acids

1966 ◽  
Vol 101 (3) ◽  
pp. 591-597 ◽  
Author(s):  
R M O'Neal ◽  
R E Koeppe ◽  
E I Williams
Keyword(s):  
Amino Acids ◽  
Fatty Acids ◽  
Glutamic Acid ◽  
Aspartic Acid ◽  
Free Amino Acids ◽  
Free Amino ◽  
Specific Activity ◽  
Tricarboxylic Acid Cycle ◽  
Sheep Brain ◽  
The Brain

1. Free glutamic acid, aspartic acid, glutamic acid from glutamine and, in some instances, the glutamic acid from glutathione and the aspartic acid from N-acetyl-aspartic acid were isolated from the brains of sheep and assayed for radioactivity after intravenous injection of [2-(14)C]glucose, [1-(14)C]acetate, [1-(14)C]butyrate or [2-(14)C]propionate. These brain components were also isolated and analysed from rats that had been given [2-(14)C]propionate. The results indicate that, as in rat brain, glucose is by far the best precursor of the free amino acids of sheep brain. 2. Degradation of the glutamate of brain yielded labelling patterns consistent with the proposal that the major route of pyruvate metabolism in brain is via acetyl-CoA, and that the short-chain fatty acids enter the brain without prior metabolism by other tissue and are metabolized in brain via the tricarboxylic acid cycle. 3. When labelled glucose was used as a precursor, glutamate always had a higher specific activity than glutamine; when labelled fatty acids were used, the reverse was true. These findings add support and complexity to the concept of the metabolic; compartmentation' of the free amino acids of brain. 4. The results from experiments with labelled propionate strongly suggest that brain metabolizes propionate via succinate and that this metabolic route may be a limited but important source of dicarboxylic acids in the brain.

Nanocomposites of hydroxyapatite with aspartic acid and glutamic acid and their interaction with osteoblast-like cells

Biomaterials ◽  
2006 ◽  
Vol 27 (25) ◽  
pp. 4428-4433 ◽  
Author(s):  
E BOANINI ◽  
P TORRICELLI ◽  
M GAZZANO ◽  
R GIARDINO ◽  
A BIGI
Keyword(s):  
Glutamic Acid ◽  
Aspartic Acid

scholarly journals The Content of Non-essential Amino Acids in the Grains of Winter and Spring Varieties of Oats (Avena sativa L.) under the Conditions of Central Southern Bulgaria

2013 ◽  
Vol 14 (1) ◽  
pp. 105
Author(s):  
T. Georgieva ◽  
P. Zorovski
Keyword(s):  
Amino Acids ◽  
Glutamic Acid ◽  
G Protein ◽  
Aspartic Acid ◽  
Avena Sativa ◽  
Essential Amino Acids

The purpose of this survey is to study the content of non-essential amino acids in four winter (Dunav 1, Ruse 8, Resor 1, Line M-K) and five spring (Obraztsov chiflik 4, Mina, HiFi, Novosadski golozarnest and Prista 2) cultivars of oats grown in Central Southern Bulgaria within the period from 2007 to 2009. The tested cultivars have different contents of non-essential amino acids. Dunav 1 has the highest quantity of glicine (5.12 g/100 g protein) of all the winter cultivars, Ruse 8 has the highest quantity of alanine (5.69 g/100 g protein) and Resor 1 – the highest quantity of arginine (6.14 g/100 g protein). Generally speaking, the spring cultivars have a larger quantity of glutamic acid (from 25.86 to 26.07 g/100 g protein) and proline (from 6.15 to 8.21 g/100 g protein) but a smaller quantity of glycine (from 4.68 to 4.99 g/100 g protein) compared to the winter cultivars. The naked cultivar Mina has the highest quantity of cystine (2.14 g/100 g protein), cultivar Prista 2 has the highest quantity of proline (8.21 g/100 g protein) and glutamic acid (26.07 g/100g protein) and HiFi ranks first in terms of aspartic acid (9.05 g/100 g protein), serine (5.02 g/100 g protein) and tyrosine (2.09 g/100 g protein). In the study we have also established certain relations between non-essential amino acids.

USE OF GLUTAMIC ACID-U-C14 TO DETERMINE NUTRITIONALLY ESSENTIAL AMINO ACIDS FOR LARVAE OF THE BLOW FLY, PHORMIA REGINA

1960 ◽  
Vol 38 (11) ◽  
pp. 1229-1234 ◽  
Author(s):  
R. Kasting ◽  
A. J. McGinnis
Keyword(s):  
Amino Acids ◽  
Glutamic Acid ◽  
Aspartic Acid ◽  
Essential Amino Acids ◽  
Phormia Regina ◽  
Blow Fly ◽  
Isoleucine Leucine ◽  
Low Level ◽  
Third Instar Larvae

The production of C14O2 by third-instar larvae of the blow fly, Phormia regina Meig., after it was injected with glutamic acid-U-C14, indicates that this substrate was metabolized under these conditions. However, the nutritionally essential amino acids lysine, phenylalanine, valine, isoleucine, leucine, and threonine, isolated from the injected larvae, contained little radioactivity. A low level of radioactivity in arginine, histidine, and methionine suggests that they were slowly synthesized. The nutritionally non-essential amino acids alanine, serine, aspartic acid, and proline contained large quantities of radioactivity; tyrosine and glycine were exceptions. These results, in agreement with earlier work that used glucose-U-C14, show that radioactivity data are useful for determining certain of the nutritionally essential amino acids.

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