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Metabolic rewiring of the hypertensive kidney

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Science Signaling  10 Dec 2019:
Vol. 12, Issue 611, eaax9760
DOI: 10.1126/scisignal.aax9760
  • Fig. 1 Tissue-specific phenotyping and untargeted metabolomic analysis of salt-sensitive rats reveals a distinct glomerular response to hypertension.

    (A) Albuminuria in DSS rats after 1 or 3 weeks on a high-salt diet. Data are generated from n = 5 rats for each group. A significant increase in albuminuria was observed [P < 0.05 by analysis of variance (ANOVA)]. (B) Kidney histology of DSS rats after 1 or 3 weeks on a high-salt diet. Images are representative of n = 3 rats for each group. Scale bar, 100 μm. (C) Overview of highly abundant metabolites in the tubular and glomerular compartments from DSS rats after 1 or 3 weeks on a high-salt diet obtained through untargeted metabolomic analysis as annotated by the MISA (METLIN-guided in-source fragment annotation) algorithm. (D) Pearson’s correlation of XC-MS annotated features with albuminuria in the same rat. The dashed line indicates a Pearson’s correlation coefficient of 0.4 or −0.4. Each dot presents a feature. The dataset of two different chromatographic analyses [hydrophilic interaction liquid chromatography (HILIC) and reversed-phase (RP) chromatography] is presented.

  • Fig. 2 Untargeted metabolomic analysis of the kidney cortex and the kidney glomeruli.

    The indicated amino acids (A), amino acid derivatives (B), sugars (C), lipids (D), and other metabolites (E) obtained through untargeted metabolomic analysis of DSS rats after 1 or 3 weeks on a high-salt diet. For (A) to (E), data are from n = 6 rats for each group. The bars indicate significance based on a Kruskal-Wallis test of signal intensities. Data are presented as mean ratios (fold change over control) ± SEM.

  • Fig. 3 Targeted metabolomic analysis of salt-sensitive hypertensive rats reveals metabolic dysfunction in the glomeruli.

    (A to F) Analysis of TCA cycle metabolites (A), the pyruvate-lactate ratio (B), the NADH/NAD+ ratio (C), the GTP/GDP ratio (D), the ATP/ADP ratio (E), and the oxidized lipids 5-HETE, 9- HETE, and 15-HETE (F). The color-coding scheme in (A) applies to (B) to (F). *P < 0.05 by one-way ANOVA with Dunnett’s post test. For (A) to (F), data are presented as means ± SEM from n = 5 rats for each group.

  • Fig. 4 Phosphoproteomic analysis reveals metabolome-dependent phosphoproteome rewiring after 1 week, but not 3 weeks.

    (A) Overview of the number of phosphosites showing up-regulation (“up”) or down-regulation (“down”). (B) Volcano plot of phosphosite intensity quantification after 1 week of hypertension. n = 5 rats per group. Significance of the comparison day 7 over control (−log P value of a two-tailed t test) is plotted against the log2 ratio of the label-free intensities (LFQ). The dashed lines indicate significance after correcting for multiple testing. The color key indicates activating and inhibiting sites (based on phosphosite.org annotation). (C) Trajectory analysis of high-confidence phosphorylation sites. For (A) to (C), data are from n = 4 rats for each group. “n” in the figures indicate the number of proteins in each cluster.

  • Fig. 5 Modeling of metabolite-dependent phosphoproteomic results using Phonemes.

    Thickness of arrows determines frequency of network observation during modeling. The purple nodes show statistically significantly regulated phosphorylation sites. The green nodes are the source of perturbation (AMPK and MTOR) based on metabolomic results (Fig. 4).

  • Fig. 6 Proteome-metabolome integration reveals increased abundance of enzymes involved in branched-chain amino acid catabolism, explaining decreased abundance of metabolites across models.

    (A) Mapping of protein and metabolite abundance on the KEGG pathway “fatty acid degradation.” Logarithmized and normalized fold changes of abundance of metabolites and protein complexes were mapped on the KEGG pathway “fatty acid degradation.” The left square shows the abundance at day 7, and the right square shows the abundance at day 21 as compared to control. Blue circles represent decreased metabolites. The entire maps are presented in fig. S4. The color code and figure legend also applies to (B). Members of the degrading enzyme complexes are labeled with their respective gene symbol. (B) Mapping of protein and metabolite abundance on the KEGG pathway “leucine, isoleucine and valine degradation.” Panel legend is the same as in (A). (C) Metabolites were fed into an NLP program, and active relationships recognized by the program were depicted as a network.

Supplementary Materials

  • stke.sciencemag.org/cgi/content/full/12/611/eaax9760/DC1

    Fig. S1. Proteomic alterations in DSS rats.

    Fig. S2. Expression analysis of proteins in two canonical pathways.

    Fig. S3. Metabolic network modeling of cross-omics data.

    Fig. S4. Meta-analysis of proteomic results across different models of proteinuria.

    Fig. S5. MS specifics of identified metabolites.

    Data file S1. Untargeted metabolomic results.

    Data file S2. Targeted metabolomic transitions.

    Data file S3. Phosphoproteomic results.

    Data file S4. Proteomic results.

    Data file S5. Tabular data describing metabolite-protein relationships in Fig. 6C.

    Data file S6. Combined proteomic results.

  • The PDF file includes:

    • Fig. S1. Proteomic alterations in DSS rats.
    • Fig. S2. Expression analysis of proteins in two canonical pathways.
    • Fig. S3. Metabolic network modeling of cross-omics data.
    • Fig. S4. Meta-analysis of proteomic results across different models of proteinuria.
    • Fig. S5. MS specifics of identified metabolites.
    • Legends for data files S1 to S6

    [Download PDF]

    Other Supplementary Material for this manuscript includes the following:

    • Data file S1 (Microsoft Excel format). Untargeted metabolomic results.
    • Data file S2 (Microsoft Excel format). Targeted metabolomic transitions.
    • Data file S3 (Microsoft Excel format). Phosphoproteomic results.
    • Data file S4 (Microsoft Excel format). Proteomic results.
    • Data file S5 (Microsoft Excel format). Tabular data describing metabolite-protein relationships in Fig. 6C.
    • Data file S6 (Microsoft Excel format). Combined proteomic results.

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