Molecular network analysis of phosphotyrosine and lipid metabolism in hepatic PTP1b deletion mice

Abstract

Metabolic syndrome describes a set of obesity-related disorders that increase diabetes, cardiovascular, and mortality risk. Studies of liver-specific protein-tyrosine phosphatase 1b (PTP1b) deletion mice (L-PTP1b−/−) suggest that hepatic PTP1b inhibition would mitigate metabolic-syndrome through amelioration of hepatic insulin resistance, endoplasmic-reticulum stress, and whole-body lipid metabolism. However, the altered molecular-network states underlying these phenotypes are poorly understood. We used mass spectrometry to quantitfy protein-phosphotyrosine network changes in L-PTP1b−/− mouse livers relative to control mice on normal and high-fat diets. We applied a phosphosite-set-enrichment analysis to identify known and novel pathways exhibiting PTP1b- and diet-dependent phosphotyrosine regulation. Detection of a PTP1b-dependent, but functionally uncharacterized, set of phosphosites on lipid-metabolic proteins motivated global lipidomic analyses that revealed altered polyunsaturated-fatty-acid (PUFA) and triglyceride metabolism in L-PTP1b−/− mice. To connect phosphosites and lipid measurements in a unified model, we developed a multivariate-regression framework, which accounts for measurement noise and systematically missing proteomics data. This analysis resulted in quantitative models that predict roles for phosphoproteins involved in oxidation-reduction in altered PUFA and triglyceride metabolism.

Hepatic Dysfunction Caused by Consumption of a High-Fat Diet

“…To date, however, few studies have formally integrated multiple types of omic data in these contexts, with even fewer including metabolomics. Prior studies attempting such joint analyses used correlative statistical routines (Miraldi et al, 2013; Oberbach et al, 2011), methods that overlay proteomic and metabolomic data onto genome-scale metabolic reconstructions (Yizhak et al, 2010), or methods that map metabolomic and transcriptomic data onto known pathway and transcriptional regulatory data without identifying high-confidence sub-networks (e.g., the CircadiOmics resource) (Eckel-Mahan et al, 2013). Our approach goes well beyond these previous methods by incorporating multiple data types from the same samples, allowing for interactions that occur outside well-established signaling or metabolic pathways, and using advanced approaches to reduce the possible interaction space to only the most relevant connections, thus increasing the interpretability of results and providing clear guidance for designing experiments.…”

“…Those that have used simple correlative statistics (Miraldi et al, 2013; Oberbach et al, 2011), overlaid proteomic and metabolomic data onto known pathways with genome-scale metabolic reconstructions (Yizhak et al, 2010), or combined transcriptomic and metabolomic data with known pathway and regulatory data for analysis within local interaction neighborhoods (Eckel-Mahan et al, 2013). By contrast, we integrate matched multi-omic data into a tractable network model that is not biased toward interactions occurring in well-established signaling or metabolic pathways.…”

Hepatic Oxidative Stress Promotes Insulin-STAT-5 Signaling and Obesity by Inactivating Protein Tyrosine Phosphatase N2

“…This is in keeping with previous studies that have shown that PTP1B and TCPTP can function in concert to attenuate hypothalamic JAK-2/STAT-3 signaling in vivo (Loh et al, 2011). Although liver-specific PTP1B deficiency does not promote obesity (Delibegovic et al, 2009), PTP1B knockout mice fed a high fat diet for 19.5 weeks are more steatotic that controls (Miraldi et al, 2013). Thus the oxidation of PTP1B and TCPTP may at least cooperate in the promotion of steatosis.…”

The DIONESUS algorithm provides scalable and accurate reconstruction of dynamic phosphoproteomic networks to reveal new drug targets

“…ŷ is the predicted response trained on all of the data, and y̌ ( i ) is the predicted response variable from a computational model that is not trained on the condition, i . 12,26,62 …”