2-isopropylmalic Acid

[Rita Seliba]

Isopropylmalicacid (isopropylmalate) is an intermediate in the biosynthesis of leucine,[1] synthesized from oxoisovalerate by 2-isopropylmalate synthase and converted into isopropyl-3-oxosuccinate by 3-isopropylmalate dehydrogenase. Two isomers are important, the 2- and 3-isopropyl derivatives, and these are interconverted by isopropylmalate dehydratase.

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Abstract: Particulate matters (PMs) increase oxidative stress and inflammatory response in different tissues. PMs disrupt the formation of primary cilia in various skin cells, including keratinocytes and melanocytes. In this study, we found that 2-isopropylmalic acid (2-IPMA) promoted primary ciliogenesis and restored the PM2.5-induced dysgenesis of primary cilia in dermal fibroblasts. Moreover, 2-IPMA inhibited the generation of excessive reactive oxygen species and the activation of stress kinase in PM2.5-treated dermal fibroblasts. Further, 2-IPMA inhibited the production of pro-inflammatory cytokines, including IL-6 and TNF-α, which were upregulated by PM2.5. However, the inhibition of primary ciliogenesis by IFT88 depletion reversed the downregulated cytokines by 2-IPMA. Moreover, we found that PM2.5 treatment increased the MMP-1 expression in dermal fibroblasts and a human 3-D-skin model. The reduced MMP-1 expression by 2-IPMA was further reversed by IFT88 depletion in PM2.5-treated dermal fibroblasts. These findings suggest that 2-IPMA ameliorates PM2.5-induced inflammation by promoting primary ciliogenesis in dermal fibroblasts. Keywords: 2-IPMA; primary cilia; dermal fibroblasts; oxidative stress; inflammation

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Particulate matters (PMs) increase oxidative stress and inflammatory response in different tissues. PMs disrupt the formation of primary cilia in various skin cells, including keratinocytes and melanocytes. In this study, we found that 2-isopropylmalic acid (2-IPMA) promoted primary ciliogenesis and restored the PM2.5-induced dysgenesis of primary cilia in dermal fibroblasts. Moreover, 2-IPMA inhibited the generation of excessive reactive oxygen species and the activation of stress kinase in PM2.5-treated dermal fibroblasts. Further, 2-IPMA inhibited the production of pro-inflammatory cytokines, including IL-6 and TNF-α, which were upregulated by PM2.5. However, the inhibition of primary ciliogenesis by IFT88 depletion reversed the downregulated cytokines by 2-IPMA. Moreover, we found that PM2.5 treatment increased the MMP-1 expression in dermal fibroblasts and a human 3-D-skin model. The reduced MMP-1 expression by 2-IPMA was further reversed by IFT88 depletion in PM2.5-treated dermal fibroblasts. These findings suggest that 2-IPMA ameliorates PM2.5-induced inflammation by promoting primary ciliogenesis in dermal fibroblasts.

The budding yeast Saccharomyces cerevisiae secretes 2-isopropylmalic acid (2-iPMA), an intermediate in leucine biosynthesis. Because 2-iPMA binds Al(III) in the culture medium, it is thought to reduce toxicity by Al(III). The effects of 2-iPMA and malic acid (MA) on Al toxicity were investigated in a medium with a low pH and low concentrations of phosphates and magnesium. The reduction in the growth of S. cerevisiae observed in the presence of 100 μM Al(III) ions was relieved more by the addition of 1.0 mM 2-iPMA than by 1.0 mM MA, indicating that 2-iPMA possesses superior Al(III)-ion detoxification ability. Investigations using the wild type and the Δleu4 and Δleu9 mutant strains indicated that secretion of a sufficient level of 2-iPMA was required to enhance the Al tolerance. It is thought that 2-iPMA secreted from the yeast cells chelates Al ions and prevents them from entering the cells, resulting in Al tolerance.

This research was supported in part by Grants-in-Aid for Scientific Research (numbers 16380067 and 18550082) from the Ministry of Education, Culture, Sports, Science, and Technology and a grant from the Tokyo Ohka Foundation for the Promotion of Science and Technology.

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To examine how omics data of faecal samples can predict IR, we first compared the area under the curve (AUC) of receiver operating characteristic (ROC) curves on the basis of random-forest classifiers. Predictor variables for the models were selected using the minimum-redundancy maximum-relevance algorithm15 from the faecal 16S, metabolome, metagenome and their merged datasets (Supplementary Table 2). We found that the selected features of faecal metabolomic data generally outperformed those of 16S and metagenomics in predicting IR (Fig. 1a), suggesting that faecal metabolomics could be used to study IR pathogenesis.

In addition to hydrophilic metabolites, faecal lipid CAGs were also associated with IR (Fig. 1b). Lysophospholipids, bile acids and acylcarnitine were associated with IR and MetS as reported previously19. Among them, a lipid CAG largely consisting of digalactosyl/glucosyldiacylglycerol (DGDG) (lipid CAG 11) came to our attention as DGDG is reportedly derived from bacteria20,21. These lipids contain glucose and/or galactose in their structures, although their biological functions in mammals are largely unclear. Most of the DGDGs in this cluster showed positive correlations with some of the precursor diacylglycerols and monosaccharides (that is, glucose and galactose) (Extended Data Fig. 4a). As diacylglycerols are deeply involved in IR pathogenesis22, the biological functions of this metabolite class are of particular interest. Notably, DGDGs with different acyl chains in lipid CAG 41 showed no association with IR (Supplementary Table 7), implying that the differences in acyl chains of lipids may have a physiological importance as reported previously23.

The IR-associated faecal carbohydrates were also correlated with KEGG pathways relating to carbohydrate metabolism and transportation, such as the phosphotransferase system (PTS), starch and sucrose metabolism, and galactose metabolism, while negatively associated with pathways relating to carbohydrate catabolism, such as glycolysis and pyruvate metabolism (Fig. 2e and Supplementary Tables 14 and 15). These pathways were also distinctly correlated with the participant clusters defined in Fig. 2a and the genera relating to carbohydrates defined in Fig. 2d. Amino acid metabolism was also different, particularly between clusters C and D, whereas lipid metabolism did not show distinct associations with microbiota (Extended Data Fig. 6a,b and Supplementary Table 16). Although carbohydrate pathways such as PTS and starch and sucrose metabolism showed strong positive associations with HbA1c and γ-GTP, the associations with other IR markers were generally sparse (Extended Data Fig. 6c and Supplementary Table 17), suggesting that metabolites are more sensitive to the clinical manifestations as shown in Fig. 1a. PTS is an essential component for bacteria to incorporate sugars into themselves as energy sources31. Detailed analyses of KEGG orthologues revealed that faecal carbohydrates and participant clusters mainly correlated with PTSs relating to disaccharides and amino sugars (Extended Data Fig. 6d,e and Supplementary Table 18), suggesting that the preference of sugar use by microbiota through PTS may affect the metabolite levels. Glycosidases, which catalyse the breakdown of oligo- and disaccharides32, were also associated with faecal monosaccharides (Extended Data Fig. 6f). Extracellular glucosidases such as β-fructofuranosidase (K01193, KEGG Orthology database), amylosucrase (K05341, KEGG Orthology database) and oligo-1,6-glucosidase (K01182, KEGG Orthology database), which were predicted to degrade sucrose and dextrin into glucose and fructose (Extended Data Fig. 6g,h), showed the highest positive correlations, especially with faecal glucose. By contrast, glucosidases relating to starch use such as α-amylases (K01176 and K07405, KEGG Orthology database) were negatively linked with faecal carbohydrates. Importantly, the abundance of these glycosidase genes was significantly different between participant cluster C and the other three clusters, suggesting that taxonomic profiles largely explain the variations of glucosidases (Fig. 2f, Extended Data Fig. 6h and Supplementary Table 18). Consistently, disaccharide-breakdown genes were predominantly conserved in the genomes of Blautia and Dorea abundant in cluster D, whereas they were almost lacking in Bacteroidales abundant in cluster C (Extended Data Fig. 6i). Together, our findings reveal four distinct populations with unique taxonomic profiles and carbohydrate metabolisms characterized by sugar use and degradation, which correlate with IR and its related markers.

The above findings from human multi-omics analyses revealed an association between carbohydrate metabolites and IR pathology. To address the causal relationship between gut microbiota, faecal carbohydrates and metabolic diseases, we first analysed metabolites in the bacterial culture of 22 human faecal IS- and IR-associated bacteria. These bacteria were selected on the basis of the findings from the genus-level co-occurrence (Fig. 2a,b) and the species-level (Extended Data Fig. 5i) profiles. Principal component analysis plots of 198 metabolites indicated that Bacteroidales, a representative IS-associated bacterial order, showed a distinct metabolic profile along PC1 (Extended Data Fig. 8a,b and Supplementary Table 24). The top 10 metabolites contributing to the group separation included several amino acids and fermentation products such as succinate and fumarate, and the majority of these metabolites were preferentially produced by Bacteroidales (Extended Data Fig. 8b,c). We detected 13 out of 15 carbohydrates associated with IR (Fig. 1b) in the bacterial culture (Extended Data Fig. 8b). Most of these carbohydrates were plotted negatively along PC1, suggesting that these metabolites were negatively associated with Bacteroidales. Glucose, mannose and glucosamine were preferentially consumed by Bacteroidales compared with the other orders, whereas lactulose was mainly produced by Eubacteriales (Extended Data Fig. 8d). Alistipes indistinctus was the most potent in consuming a wide variety of carbohydrates (Extended Data Fig. 8e,f). These findings show that Bacteroidales species are potent consumers of several carbohydrates, driving the production of their fermentation products.

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