Flux balance analysis predicts Warburg-like effects of mouse hepatocyte deficient in miR-122a

<div><p>The liver is a vital organ involving in various major metabolic functions in human body. MicroRNA-122 (miR-122) plays an important role in the regulation of liver metabolism, but its intrinsic physiological functions require further clarification. This study integrated the genome-scale metabolic model of hepatocytes and mouse experimental data with germline deletion of <i>Mir122a</i> (<i>Mir122a</i>^{<i>–/–</i>}) to infer Warburg-like effects. Elevated expression of <i>MiR-122a</i> target genes in <i>Mir122a</i>^{<i>–/–</i>}mice, especially those encoding for metabolic enzymes, was applied to analyze the flux distributions of the genome-scale metabolic model in normal and deficient states. By definition of the similarity ratio, we compared the flux fold change of the genome-scale metabolic model computational results and metabolomic profiling data measured through a liquid-chromatography with mass spectrometer, respectively, for hepatocytes of 2-month-old mice in normal and deficient states. The <i>Ddc</i> gene demonstrated the highest similarity ratio of 95% to the biological hypothesis of the Warburg effect, and similarity of 75% to the experimental observation. We also used 2, 6, and 11 months of mir-122 knockout mice liver cell to examined the expression pattern of DDC in the knockout mice livers to show upregulated profiles of DDC from the data. Furthermore, through a bioinformatics (LINCS program) prediction, BTK inhibitors and withaferin A could downregulate DDC expression, suggesting that such drugs could potentially alter the early events of metabolomics of liver cancer cells.</p></div

Metabolomic data in miR-122a deficient mice.

<p>(A) Orthogonal partial least-squares discriminant analysis (OPLS-DA) score plot of metabolite profiles derived from 10 miR-122a deficient mice (green) and the 10 control mice (blue) with corresponding loading plot. The ellipse shown in the model represents the Hotelling T2 with 95% confidence. Each data point represents one mouse liver sample, and the distance between points in the plot indicates the similarity between samples. (B) OPLS-DA loading plot of liver metabolite profiles, each point in represents one feature (or metabolite). (C) Plot of variables importance for the projection (VIP) summarizes the importance of the variables (Fig 1A and 1B). The VIP ranking priority was according to the VIP values, and metabolites with VIP >1 are shown. (D) The 35 metabolites were used for metabolite set enrichment analysis (MSEA). These metabolite sets are ranked according to the Holm P value with hatched lines shown. Liver tissue samples were extracted by the Folch method, and the aqueous (upper) phases were analyzed using LC-TOFMS in the electrospray positive ion mode. Each sample analysis consists of six replicates. A web-based tool (<a href="http://www.metaboanalyst.ca/" target="_blank">www.metaboanalyst.ca</a>) for metabolite set enrichment analysis was used for the analysis. Detail metabolomics differences were revealed by OPLS-DA model (R²X = 0.612, Q2 = 0.984). The axes, t[<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005618#pcbi.1005618.ref001" target="_blank">1</a>], to[<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005618#pcbi.1005618.ref001" target="_blank">1</a>], pq[<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005618#pcbi.1005618.ref001" target="_blank">1</a>], and poso[<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005618#pcbi.1005618.ref001" target="_blank">1</a>] depict the predictive component, the first orthogonal component, the predictive component loadings, and the first orthogonal loadings, respectively, of the OPLS-DA model.</p

Metabolic reprogramming triggered by overexpression of <i>Ddc</i>.

<p>(A) Based on Recon2-hepatocyte model, three enzymatic reactions (red lines) are catalyzed by overexpression of Ddc. As a result of metabolic reprogramming, the fold change (FC) of the concentration of glutamine and glutamate are increased by 4.54 and 2.53, respectively (shown in blue). (B) Elevated expression of Ddc shift liver metabolism toward glycolysis and lactate synthesis in agreement with the hypothesis of the Warburg effect. The (FC) of metabolites in glycolysis, TCA cycle, and glutamine metabolism pathways are indicated in green (decrease) and red (increase).</p

Downregulation of DDC expression by treatment with ibrutinib, LFM-A13, and withaferin-A.

<p>(A) Schematic illustration of similar gene expression signatures between <i>DDC</i> shRNA and chemical compounds. (B) We queried the <i>DDC</i> shRNA gene signature via LINCS database and found BTK inhibitor (LFM-A13), withaferin A, and several compounds shared similar gene expression profiles with DDC shRNA gene signature. Score_best4 and score_best6 are the mean connectivity scores across the four and six cell lines, respectively, in which the perturbagen connected most strongly to the query (<i>DDC</i> shRNA). (C, D, E) DDC, P-BTK, and BTK were upregulated in liver tissues from mir-122 knockout mice. (F) Huh7 cells were treated with various concentrations of ibrutinib, LFM-A13, and withaferin-A for 24–72 hours, respectively. Huh7 cells were treated with (G) 10 and 20 μM ibrutinib or 5 μM sorafenib, (H) 5–20 μM LFM-A13, and (I) 1 and 2 μM withaferin A or 5 μM sorafenib for 24 hours. Cell lysates were subjected to western blot analysis. DDC was downregulated by the treatment of 20 μM ibrutinib and 2 μM withaferin A, but not the treatment of sorafenib. Both DDC and BTK were downregulated by the treatment of LFM-A13 in Huh7 cells.</p

The fold changes of the 35 intracellular metabolites from the LC/MS analysis and 16 miR122a target enzyme computational predictions.

<p>The fold changes of the 35 intracellular metabolites from the LC/MS analysis and 16 miR122a target enzyme computational predictions.</p

Similarity ratio of 20 miR-122a target enzymes to Warburg effect and experimental metabolic profiling observations (LC/MS).

<p>The similarity ratio of each target gene was calculated to indicate that how many percentages for the computational predictions (100% overexpression) are similar to the hypothesis of the Warburg effect (blue line) and experimental metabolic profiling observations (pink line). The Ddc gene received the highest similarity ratio to the Warburg effect (0.952) and experiments (0.75) (a, 54% overexpression; b, downregulation; c, knockout; d, overexpression of forward and reverse reactions).</p