Insulin signaling requires glucose to promote lipid anabolism in adipocytes

Adipose tissue is essential for metabolic homeostasis, balancing lipid storage and mobilization based on nutritional status. This is coordinated by insulin, which triggers kinase signaling cascades to modulate numerous metabolic proteins, leading to increased glucose uptake and anabolic processes like lipogenesis. Given recent evidence that glucose is dispensable for adipocyte respiration, we sought to test whether glucose is necessary for insulin-stimulated anabolism. Examining lipogenesis in cultured adipocytes, glucose was essential for insulin to stimulate the synthesis of fatty acids and glyceride–glycerol. Importantly, glucose was dispensable for lipogenesis in the absence of insulin, suggesting that distinct carbon sources are used with or without insulin. Metabolic tracing studies revealed that glucose was required for insulin to stimulate pathways providing carbon substrate, NADPH, and glycerol 3-phosphate for lipid synthesis and storage. Glucose also displaced leucine as a lipogenic substrate and was necessary to suppress fatty acid oxidation. Together, glucose provided substrates and metabolic control for insulin to promote lipogenesis in adipocytes. This contrasted with the suppression of lipolysis by insulin signaling, which occurred independently of glucose. Given previous observations that signal transduction acts primarily before glucose uptake in adipocytes, these data are consistent with a model whereby insulin initially utilizes protein phosphorylation to stimulate lipid anabolism, which is sustained by subsequent glucose metabolism. Consequently, lipid abundance was sensitive to glucose availability, both during adipogenesis and in Drosophila flies in vivo. Together, these data highlight the importance of glucose metabolism to support insulin action, providing a complementary regulatory mechanism to signal transduction to stimulate adipose anabolism

Identification and characterisation of ABHD15 as a novel regulator of insulin-mediated lipolysis inhibition

Lipolysis, or the breakdown of fat, is a tightly controlled, dynamic process essential for maintaining whole-body energy homeostasis. It is activated during times of energy deprivation and is predominantly inhibited by insulin following nutrient intake. Dysregulation of this dynamic balance, namely disruption of the latter, can lead to the development of a plethora of obesity-associated diseases. Despite this, the mechanism underlying insulin regulation of lipolysis remains controversial. The generally accepted model hinges on the cAMP degrading function of the Akt substrate, phosphodiesterase 3B (PDE3B) in explaining insulin’s anti-lipolytic action. However, several studies have recently challenged this, suggesting that neither Akt, nor its phosphorylation of PDE3B are essential for this process. In this study, I identify and characterise a novel regulator of insulin-mediated lipolysis inhibition, called α/β hydrolase domain-containing protein 15 (ABHD15), and settle controversies regarding the importance of Akt, and PDE3B phosphorylation in this process. Through the generation of three ABHD15 deletion model systems, I demonstrate a significant defect in insulin inhibition of lipolysis that is rescued upon ABHD15 re-expression. Affinity purification coupled mass spectrometry and orthogonal validation further uncover an exciting novel interaction between ABHD15 and Akt, which is essential for their functional roles in this process. Loss of function phosphorylation mutants of PDE3B and ABHD15 also expose a putative PDE3B repressor role for ABHD15, whilst employment of gain of function PDE3B and ABHD15 mutants containing an R18 peptide sequence with high affinity for 14-3-3, demonstrate for the first time the importance of 14-3-3 binding in lipolysis suppression by insulin. Together, my findings lead me to propose an updated model of insulin-mediated lipolysis inhibition, providing novel therapeutic strategies for treating lipolysis dysregulated disorders

How Difficult Is Inference of Mammalian Causal Gene Regulatory Networks?

<div><p>Gene regulatory networks (GRNs) play a central role in systems biology, especially in the study of mammalian organ development. One key question remains largely unanswered: Is it possible to infer mammalian causal GRNs using observable gene co-expression patterns alone? We assembled two mouse GRN datasets (embryonic tooth and heart) and matching microarray gene expression profiles to systematically investigate the difficulties of mammalian causal GRN inference. The GRNs were assembled based on pieces of experimental genetic perturbation evidence from manually reading primary research articles. Each piece of perturbation evidence records the qualitative change of the expression of one gene following knock-down or over-expression of another gene. Our data have thorough annotation of tissue types and embryonic stages, as well as the type of regulation (activation, inhibition and no effect), which uniquely allows us to estimate both sensitivity and specificity of the inference of tissue specific causal GRN edges. Using these unprecedented datasets, we found that gene co-expression does not reliably distinguish true positive from false positive interactions, making inference of GRN in mammalian development very difficult. Nonetheless, if we have expression profiling data from genetic or molecular perturbation experiments, such as gene knock-out or signalling stimulation, it is possible to use the set of differentially expressed genes to recover causal regulatory relationships with good sensitivity and specificity. Our result supports the importance of using perturbation experimental data in causal network reconstruction. Furthermore, we showed that causal gene regulatory relationship can be highly cell type or developmental stage specific, suggesting the importance of employing expression profiles from homogeneous cell populations. This study provides essential datasets and empirical evidence to guide the development of new GRN inference methods for mammalian organ development.</p></div

Fold changes (log) from tooth microarray perturbation experiments that matched the perturbation evidence in the literature show consistency with expected trends.

<p>RTPs that are inhibiting (A), have no effect (B), or are activating (C) trend to have negative, close to zero and positive fold changes respectively. (D) shows the consistency of the literature based RTP type (Lit.) and microarray data (M.A.) as fold change cut-off varies between 0 and 3 (both up- or down-regulation).</p

Summary of tissue and time specific regulatory actions.

<p>RTP: Regulator-Target Pair.</p><p>Summary of tissue and time specific regulatory actions.</p

Comparison of the true positive and false positive rates as determined by different network inference approaches on the tooth dataset: Pearson correlation, Pathway Commons database, protein-protein interactions (PPI), the union of the previous three methods and direct effect on genetic perturbation (log fold change cut-off or 0.5 and 1).

<p>Note: the TP and FP rates for the first 4 methods were calculated based on the subset of 686 RTPs that were represented in the microarray, PPI and pathway data. The TP and FP rates for perturbation data were based on the subset of 39 RTPs with a regulator matching the pathway being perturbed.</p

Evaluation of sensitivity (true positive rate) and specificity (1-false positive rate) of edge discovery by GENIE3 (A–C) and ARANCE (D–F) using the tooth and heart microarray datasets.

<p>To account for the possibility that our literature-curated RTP may represent indirect regulatory interactions, we allow matching of a RTP with a linear path of multiple edges (x-axis). The bar chart above each plot shows the size of the network. Dotted lines shows control background of 1,000 node-label-permuted randomised networks.</p

Spearman correlation of different classes of RTP in the tooth data (A) and the heart data (B).

<p>RTP classes are activating (Act.), no effect (No.) and inhibitory (Inhib.).</p

Scatter plots show the extent of tissue-specific differential expression in dental epithelium (y-axis) and dental mesenchyme (x-axis) as a result of <i>Pax9</i> knockout (A), <i>Msx1</i> knockout (B), <i>Bmp4</i> stimulation (C) and <i>Wnt</i> stimulation (D).

<p>Coloured points represent probes of differentially responsive genes between the two tissues. Pearson correlation is also shown.</p