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. 2015 Mar;11(3):791.
doi: 10.15252/msb.20145486.

Drugs that reverse disease transcriptomic signatures are more effective in a mouse model of dyslipidemia

Allon Wagner, Noa Cohen, Thomas Kelder, Uri Amit, Elad Liebman, David M Steinberg, Marijana Radonjic, Eytan Ruppin

Drugs that reverse disease transcriptomic signatures are more effective in a mouse model of dyslipidemia

Allon Wagner et al. Mol Syst Biol. 2015 Mar.

Abstract

High-throughput omics have proven invaluable in studying human disease, and yet day-to-day clinical practice still relies on physiological, non-omic markers. The metabolic syndrome, for example, is diagnosed and monitored by blood and urine indices such as blood cholesterol levels. Nevertheless, the association between the molecular and the physiological manifestations of the disease, especially in response to treatment, has not been investigated in a systematic manner. To this end, we studied a mouse model of diet-induced dyslipidemia and atherosclerosis that was subject to various drug treatments relevant to the disease in question. Both physiological data and gene expression data (from the liver and white adipose) were analyzed and compared. We find that treatments that restore gene expression patterns to their norm are associated with the successful restoration of physiological markers to their baselines. This holds in a tissue-specific manner—treatments that reverse the transcriptomic signatures of the disease in a particular tissue are associated with positive physiological effects in that tissue. Further, treatments that introduce large non-restorative gene expression alterations are associated with unfavorable physiological outcomes. These results provide a sound basis to in silico methods that rely on omic metrics for drug repurposing and drug discovery by searching for compounds that reverse a disease’s omic signatures. Moreover, they highlight the need to develop drugs that restore the global cellular state to its healthy norm rather than rectify particular disease phenotypes.

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Figures

Figure 1
Figure 1
The gene expression and physiological spaces

  1. A-C The figure shows the first two principal components of (A) the gene expression space of the liver, (B) the gene expression space of white adipose tissue, and (C) the physiological space. In all panels, each dot represents one animal; color codes denote the different experimental groups. Red squares represent the HFD animals, and blue triangles represent the LFD animals. The dashed arrow connects the HFD centroid (yellow square, circled in red) to the LFD centroid (yellow triangle, circled in blue) and denotes the direction of a reversal of the gene expression or physiological state back to the norm.

Source data are available online for this figure.
Figure 2
Figure 2
Post-treatment deviation from the baseline omic state (e.g. in gene expression) is correlated with a deviation from the baseline physiology

  1. A, B The correlation between the global physiological deviation index (GPDI) and the transcriptomic deviation index (TDI) in (A) white adipose tissue and (B) liver tissue.

  2. C Left panel: the correlation between the liver metabolome deviation index (MDI) and the liver TDI. Right panel: the correlation between the liver MDI and the GPDI.

Data information: Each dot represents one animal; color codes denote the different experimental groups as in Fig1. Pearson correlations and their respective P-values are noted in each panel, as well as a linear regression line. All the results hold when Spearman correlations are used instead (see Supplementary Fig S6). Source data are available online for this figure.
Figure 3
Figure 3
Deviations from the baseline gene expression and deviations from the baseline physiology are correlated in a tissue-specific manner Deviations from the baseline gene expression (TDIs) in liver and white adipose tissues are correlated with deviations from the normal physiology (PDIs) for markers that are known to be associated with those tissues. Bar lengths represent the Spearman correlations between the TDIs of the two tissues and PDIs of 26 different physiological markers. WAT stands for white adipose tissue, ratio visc/sub WAT for ratio of visceral to subcutaneous WAT. Asterisks mark statistically significant correlations (using the Benjamini–Hochberg correction for multiple hypotheses testing with a 5% FDR level). The top part of the figure displays markers whose PDI is more correlated with white adipose TDI than with hepatic TDI. The bottom part shows markers with a reverse tissue-specific pattern. Source data are available online for this figure.
Figure 4
Figure 4
Deviations from the baseline gene expression and deviations from the baseline physiology are correlated across different experimental groups and treatment regimens

  1. A-D White adipose (A, B) and liver (C, D) TDIs (deviations from the baseline gene expression) are correlated with PDIs (deviations from the baseline physiology) across different experimental groups and treatment regimens. PDIs of four physiological markers are shown, two that are highly correlated with adipose transcriptome (A, B) and two that are highly correlated with hepatic transcriptome (C, D).

  2. E Adipose TDIs and plasma cholesterol PDIs are generally correlated, but this does not hold for animals treated with T090137 (purple dots at the top left corner of the figure), probably due to this drug's particular mechanism of action (see main text). The correlation displayed in (E) increases from 0.59 to 0.8 (P < 1.3e-13) when excluding the T090137 animals.

Data information: Each dot represents one animal; color codes denote the different experimental groups. The dashed lines are linear regression lines. The complete data are available in Supplementary Table S7 and in Supplementary Figs S8 and S9. P-values are adjusted to multiple hypotheses testing as described in the main text. Source data are available online for this figure.
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