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Metabonomic analysis of potential biomarkers and drug targets involved in diabetic nephropathy mice.

Wei T, Zhao L, Jia J, Xia H, Du Y, Lin Q, Lin X, Ye X, Yan Z, Gao H - Sci Rep (2015)

Bottom Line: Diabetic nephropathy (DN) is one of the lethal manifestations of diabetic systemic microvascular disease.Age-dependent and correlated metabolite analysis identified that cis-aconitate and allantoin could serve as biomarkers for the diagnosis of DN.Further correlative analysis revealed that the enzymes dimethylarginine dimethylaminohydrolase (DDAH), guanosine triphosphate cyclohydrolase I (GTPCH I), and 3-hydroxy-3-methylglutaryl-CoA lyase (HMG-CoA lyase) were involved in dimethylamine metabolism, ketogenesis and GTP metabolism pathways, respectively, and could be potential therapeutic targets for DN.

View Article: PubMed Central - PubMed

Affiliation: School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou 325035 China.

ABSTRACT
Diabetic nephropathy (DN) is one of the lethal manifestations of diabetic systemic microvascular disease. Elucidation of characteristic metabolic alterations during diabetic progression is critical to understand its pathogenesis and identify potential biomarkers and drug targets involved in the disease. In this study, (1)H nuclear magnetic resonance ((1)H NMR)-based metabonomics with correlative analysis was performed to study the characteristic metabolites, as well as the related pathways in urine and kidney samples of db/db diabetic mice, compared with age-matched wildtype mice. The time trajectory plot of db/db mice revealed alterations, in an age-dependent manner, in urinary metabolic profiles along with progression of renal damage and dysfunction. Age-dependent and correlated metabolite analysis identified that cis-aconitate and allantoin could serve as biomarkers for the diagnosis of DN. Further correlative analysis revealed that the enzymes dimethylarginine dimethylaminohydrolase (DDAH), guanosine triphosphate cyclohydrolase I (GTPCH I), and 3-hydroxy-3-methylglutaryl-CoA lyase (HMG-CoA lyase) were involved in dimethylamine metabolism, ketogenesis and GTP metabolism pathways, respectively, and could be potential therapeutic targets for DN. Our results highlight that metabonomic analysis can be used as a tool to identify potential biomarkers and novel therapeutic targets to gain a better understanding of the mechanisms underlying the initiation and progression of diseases.

No MeSH data available.


Related in: MedlinePlus

Pattern recognition analysis of urine samples.(A) PLS trajectory based on the mean 1H NMR spectra of urine samples collected from the db/db mice () at various time points (9-wk, 11-wk, 13-wk, 15-wk and 17-wk), and the age-matched wildtype mice (). (B) The PLS-DA score plot based on 1H-NMR spectra of urine samples from db/db mice 9-wk (), 11-wk (), 13-wk (), 15-wk () and 17-wk (). (C) is the loading plot revealing the metabolites with large intensities responsible for the discrimination of the corresponding score plot shown (A).
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f3: Pattern recognition analysis of urine samples.(A) PLS trajectory based on the mean 1H NMR spectra of urine samples collected from the db/db mice () at various time points (9-wk, 11-wk, 13-wk, 15-wk and 17-wk), and the age-matched wildtype mice (). (B) The PLS-DA score plot based on 1H-NMR spectra of urine samples from db/db mice 9-wk (), 11-wk (), 13-wk (), 15-wk () and 17-wk (). (C) is the loading plot revealing the metabolites with large intensities responsible for the discrimination of the corresponding score plot shown (A).

Mentions: To explore the characteristic alterations in urinary metabolites during the progression of DN, urine samples collected at specific intervals from the db/db mice and their age-matched wildtype controls were analyzed by NMR-based metabonomics. The projection to latent structure discriminant analysis (PLS-DA) mean trajectory showed a characteristic difference in metabolic pattern between the two groups (Fig. 3A). Additionally, results from the db/db mice revealed obvious trajectory space from 9 weeks to 17 weeks, while the wildtype mice occupied a minor position in the plot, suggesting that the observed changes in metabolic profiles were related to the progression of the disease, but not due to the age of mice. To identify the DN-specific metabolic changes, urinary metabolic trajectory from the db/db mice was analyzed (Fig. 3B). As indicated by the arrow, the urine samples of 9-week-old db/db mice were distinguishable from that of the 11-week-old mice and the other three samples. The age-dependent metabolic profiles indicated continuous metabolic modifications throughout the experimental period in db/db mice, which were manifestations of the pathological insult. Figure 3C illustrates the corresponding loading plot with color-coded correlation coefficients (/r/) of metabolites. The plot showed differences in the levels of metabolites, including 3-HB, acetate, acetone, acetoacetate, succinate, citrate, methylamine, creatine, TMAO, glycine, creatinine, hippurate, allantoin, cis-aconitate, fumarate, and 3-indoxylsulfate.


Metabonomic analysis of potential biomarkers and drug targets involved in diabetic nephropathy mice.

Wei T, Zhao L, Jia J, Xia H, Du Y, Lin Q, Lin X, Ye X, Yan Z, Gao H - Sci Rep (2015)

Pattern recognition analysis of urine samples.(A) PLS trajectory based on the mean 1H NMR spectra of urine samples collected from the db/db mice () at various time points (9-wk, 11-wk, 13-wk, 15-wk and 17-wk), and the age-matched wildtype mice (). (B) The PLS-DA score plot based on 1H-NMR spectra of urine samples from db/db mice 9-wk (), 11-wk (), 13-wk (), 15-wk () and 17-wk (). (C) is the loading plot revealing the metabolites with large intensities responsible for the discrimination of the corresponding score plot shown (A).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4493693&req=5

f3: Pattern recognition analysis of urine samples.(A) PLS trajectory based on the mean 1H NMR spectra of urine samples collected from the db/db mice () at various time points (9-wk, 11-wk, 13-wk, 15-wk and 17-wk), and the age-matched wildtype mice (). (B) The PLS-DA score plot based on 1H-NMR spectra of urine samples from db/db mice 9-wk (), 11-wk (), 13-wk (), 15-wk () and 17-wk (). (C) is the loading plot revealing the metabolites with large intensities responsible for the discrimination of the corresponding score plot shown (A).
Mentions: To explore the characteristic alterations in urinary metabolites during the progression of DN, urine samples collected at specific intervals from the db/db mice and their age-matched wildtype controls were analyzed by NMR-based metabonomics. The projection to latent structure discriminant analysis (PLS-DA) mean trajectory showed a characteristic difference in metabolic pattern between the two groups (Fig. 3A). Additionally, results from the db/db mice revealed obvious trajectory space from 9 weeks to 17 weeks, while the wildtype mice occupied a minor position in the plot, suggesting that the observed changes in metabolic profiles were related to the progression of the disease, but not due to the age of mice. To identify the DN-specific metabolic changes, urinary metabolic trajectory from the db/db mice was analyzed (Fig. 3B). As indicated by the arrow, the urine samples of 9-week-old db/db mice were distinguishable from that of the 11-week-old mice and the other three samples. The age-dependent metabolic profiles indicated continuous metabolic modifications throughout the experimental period in db/db mice, which were manifestations of the pathological insult. Figure 3C illustrates the corresponding loading plot with color-coded correlation coefficients (/r/) of metabolites. The plot showed differences in the levels of metabolites, including 3-HB, acetate, acetone, acetoacetate, succinate, citrate, methylamine, creatine, TMAO, glycine, creatinine, hippurate, allantoin, cis-aconitate, fumarate, and 3-indoxylsulfate.

Bottom Line: Diabetic nephropathy (DN) is one of the lethal manifestations of diabetic systemic microvascular disease.Age-dependent and correlated metabolite analysis identified that cis-aconitate and allantoin could serve as biomarkers for the diagnosis of DN.Further correlative analysis revealed that the enzymes dimethylarginine dimethylaminohydrolase (DDAH), guanosine triphosphate cyclohydrolase I (GTPCH I), and 3-hydroxy-3-methylglutaryl-CoA lyase (HMG-CoA lyase) were involved in dimethylamine metabolism, ketogenesis and GTP metabolism pathways, respectively, and could be potential therapeutic targets for DN.

View Article: PubMed Central - PubMed

Affiliation: School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou 325035 China.

ABSTRACT
Diabetic nephropathy (DN) is one of the lethal manifestations of diabetic systemic microvascular disease. Elucidation of characteristic metabolic alterations during diabetic progression is critical to understand its pathogenesis and identify potential biomarkers and drug targets involved in the disease. In this study, (1)H nuclear magnetic resonance ((1)H NMR)-based metabonomics with correlative analysis was performed to study the characteristic metabolites, as well as the related pathways in urine and kidney samples of db/db diabetic mice, compared with age-matched wildtype mice. The time trajectory plot of db/db mice revealed alterations, in an age-dependent manner, in urinary metabolic profiles along with progression of renal damage and dysfunction. Age-dependent and correlated metabolite analysis identified that cis-aconitate and allantoin could serve as biomarkers for the diagnosis of DN. Further correlative analysis revealed that the enzymes dimethylarginine dimethylaminohydrolase (DDAH), guanosine triphosphate cyclohydrolase I (GTPCH I), and 3-hydroxy-3-methylglutaryl-CoA lyase (HMG-CoA lyase) were involved in dimethylamine metabolism, ketogenesis and GTP metabolism pathways, respectively, and could be potential therapeutic targets for DN. Our results highlight that metabonomic analysis can be used as a tool to identify potential biomarkers and novel therapeutic targets to gain a better understanding of the mechanisms underlying the initiation and progression of diseases.

No MeSH data available.


Related in: MedlinePlus