Architecture of the outbred brown fat proteome defines regulators of metabolic physiology.

Brown adipose tissue (BAT) regulates metabolic physiology. However, nearly all mechanistic studies of BAT protein function occur in a single inbred mouse strain, which has limited the understanding of generalizable mechanisms of BAT regulation over physiology. Here, we perform deep quantitative proteomics of BAT across a cohort of 163 genetically defined diversity outbred mice, a model that parallels the genetic and phenotypic variation found in humans. We leverage this diversity to define the functional architecture of the outbred BAT proteome, comprising 10,479 proteins. We assign co-operative functions to 2,578 proteins, enabling systematic discovery of regulators of BAT. We also identify 638 proteins that correlate with protection from, or sensitivity to, at least one parameter of metabolic disease. We use these findings to uncover SFXN5, LETMD1, and ATP1A2 as modulators of BAT thermogenesis or adiposity, and provide OPABAT as a resource for understanding the conserved mechanisms of BAT regulation over metabolic physiology.

Genetic architecture of 11 organ traits derived from abdominal MRI using deep learning.

Cardiometabolic diseases are an increasing global health burden. While socioeconomic, environmental, behavioural, and genetic risk factors have been identified, a better understanding of the underlying mechanisms is required to develop more effective interventions. Magnetic resonance imaging (MRI) has been used to assess organ health, but biobank-scale studies are still in their infancy. Using over 38,000 abdominal MRI scans in the UK Biobank, we used deep learning to quantify volume, fat, and iron in seven organs and tissues, and demonstrate that imaging-derived phenotypes reflect health status. We show that these traits have a substantial heritable component (8-44%) and identify 93 independent genome-wide significant associations, including four associations with liver traits that have not previously been reported. Our work demonstrates the tractability of deep learning to systematically quantify health parameters from high-throughput MRI across a range of organs and tissues, and use the largest-ever study of its kind to generate new insights into the genetic architecture of these traits.

The promise and reality of therapeutic discovery from large cohorts.

Technological advances in rapid data acquisition have transformed medical biology into a data mining field, where new data sets are routinely dissected and analyzed by statistical models of ever-increasing complexity. Many hypotheses can be generated and tested within a single large data set, and even small effects can be statistically discriminated from a sea of noise. On the other hand, the development of therapeutic interventions moves at a much slower pace. They are determined from carefully randomized and well-controlled experiments with explicitly stated outcomes as the principal mechanism by which a single hypothesis is tested. In this paradigm, only a small fraction of interventions can be tested, and an even smaller fraction are ultimately deemed therapeutically successful. In this Review, we propose strategies to leverage large-cohort data to inform the selection of targets and the design of randomized trials of novel therapeutics. Ultimately, the incorporation of big data and experimental medicine approaches should aim to reduce the failure rate of clinical trials as well as expedite and lower the cost of drug development.

Multimodal microvascular imaging reveals that selective inhibition of class I PI3K is sufficient to induce an antivascular response.

The phosphatidylinositol 3-kinase (PI3K) pathway is a central mediator of vascular endothelial growth factor (VEGF)-driven angiogenesis. The discovery of small molecule inhibitors that selectively target PI3K or PI3K and mammalian target of rapamycin (mTOR) provides an opportunity to pharmacologically determine the contribution of these key signaling nodes in VEGF-A-driven tumor angiogenesis in vivo. This study used an array of micro-vascular imaging techniques to monitor the antivascular effects of selective class I PI3K, mTOR, or dual PI3K/mTOR inhibitors in colorectal and prostate cancer xenograft models. Micro-computed tomography (micro-CT) angiography, dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), vessel size index (VSI) MRI, and DCE ultrasound (DCE-U/S) were employed to quantitatively evaluate the vascular (structural and physiological) response to these inhibitors. GDC-0980, a dual PI3K/mTOR inhibitor, was found to reduce micro-CT angiography vascular density, while VSI MRI demonstrated a significant reduction in vessel density and an increase in mean vessel size, consistent with a loss of small functional vessels and a substantial antivascular response. DCE-MRI showed that GDC-0980 produces a strong functional response by decreasing the vascular permeability/perfusion-related parameter, K (trans). Interestingly, comparable antivascular effects were observed for both GDC-980 and GNE-490 (a selective class I PI3K inhibitor). In addition, mTOR-selective inhibitors did not affect vascular density, suggesting that PI3K inhibition is sufficient to generate structural changes, characteristic of a robust antivascular response. This study supports the use of noninvasive microvascular imaging techniques (DCE-MRI, VSI MRI, DCE-U/S) as pharmacodynamic assays to quantitatively measure the activity of PI3K and dual PI3K/mTOR inhibitors in vivo.

Death receptors DR6 and TROY regulate brain vascular development.

Signaling events that regulate central nervous system (CNS) angiogenesis and blood-brain barrier (BBB) formation are only beginning to be elucidated. By evaluating the gene expression profile of mouse vasculature, we identified DR6/TNFRSF21 and TROY/TNFRSF19 as regulators of CNS-specific angiogenesis in both zebrafish and mice. Furthermore, these two death receptors interact both genetically and physically and are required for vascular endothelial growth factor (VEGF)-mediated JNK activation and subsequent human brain endothelial sprouting in vitro. Increasing beta-catenin levels in brain endothelium upregulate DR6 and TROY, indicating that these death receptors are downstream target genes of Wnt/beta-catenin signaling, which has been shown to be required for BBB development. These findings define a role for death receptors DR6 and TROY in CNS-specific vascular development.

PlGF blockade does not inhibit angiogenesis during primary tumor growth.

It has been recently reported that treatment with an anti-placenta growth factor (PlGF) antibody inhibits metastasis and primary tumor growth. Here we show that, although anti-PlGF treatment inhibited wound healing, extravasation of B16F10 cells, and growth of a tumor engineered to overexpress the PlGF receptor (VEGFR-1), neutralization of PlGF using four novel blocking antibodies had no significant effect on tumor angiogenesis in 15 models. Also, genetic ablation of the tyrosine kinase domain of VEGFR-1 in the host did not result in growth inhibition of the anti-VEGF-A sensitive or resistant tumors tested. Furthermore, combination of anti-PlGF with anti-VEGF-A antibodies did not result in greater antitumor efficacy than anti-VEGF-A monotherapy. In conclusion, our data argue against an important role of PlGF during primary tumor growth in most models and suggest that clinical evaluation of anti-PlGF antibodies may be challenging.