[Keywords: TEAD; YAP; CTGF; Hippo; transcription, cancer] Supplemental material is available at http://www.genesdev.org.

Systematic mapping of protein-protein interactions, or 'interactome' mapping, was initiated in model organisms, starting with defined biological processes and then expanding to the scale of the proteome. Although far from complete, such maps have revealed global topological and dynamic features of interactome networks that relate to known biological properties, suggesting that a human interactome map will provide insight into development and disease mechanisms at a systems level. Here we describe an initial version of a proteome-scale map of human binary protein-protein interactions. Using a stringent, high-throughput yeast two-hybrid system, we tested pairwise interactions among the products of approximately 8,100 currently available Gateway-cloned open reading frames and detected approximately 2,800 interactions. This data set, called CCSB-HI1, has a verification rate of approximately 78% as revealed by an independent co-affinity purification assay, and correlates significantly with other biological attributes. The CCSB-HI1 data set increases by approximately 70% the set of available binary interactions within the tested space and reveals more than 300 new connections to over 100 disease-associated proteins. This work represents an important step towards a systematic and comprehensive human interactome project.

To initiate studies on how protein-protein interaction (or “interactome”) networks relate to multicellular functions, we have mapped a large fraction of the Caenorhabditis elegans interactome network. Starting with a subset of metazoan-specific proteins, more than 4000 interactions were identified from high-throughput, yeast two-hybrid (HT=Y2H) screens. Independent coaffinity purification assays experimentally validated the overall quality of this Y2H data set. Together with already described Y2H interactions and interologs predicted in silico , the current version of the Worm Interactome (WI5) map contains ∼5500 interactions. Topological and biological features of this interactome network, as well as its integration with phenome and transcriptome data sets, lead to numerous biological hypotheses.

Highlights d Heterogeneity and plasticity of non-parenchymal cells in healthy and NASH liver d Landscape of intrahepatic ligand-receptor signaling at single-cell resolution d Emergence of Trem2+ NASH-associated macrophages (NAMs) in mouse and human NASH d Stellakine secretion and contractile response to vasoactive hormones by HSCs

Brown fat activates uncoupled respiration to defend against cold and contributes to systemic metabolic homeostasis. To date, the metabolic action of brown fat has been primarily attributed to its role in fuel oxidation and uncoupling protein 1 (UCP1)-mediated thermogenesis. Whether brown fat engages other tissues through secreted factors remains largely unexplored. Here we show that Neuregulin 4 (Nrg4), a member of the EGF family of extracellular ligands, is highly expressed in adipose tissues, enriched in brown fat, and markedly increased during brown adipocyte differentiation. Adipose tissue Nrg4 expression was reduced in rodent and human obesity. Gain- and loss-of-function studies in mice demonstrated that Nrg4 protects against diet-induced insulin resistance and hepatic steatosis through attenuating hepatic lipogenic signaling. Mechanistically, Nrg4 activates ErbB3/ErbB4 signaling in hepatocytes and negatively regulates de novo lipogenesis mediated by LXR/SREBP1c in a cell-autonomous manner. These results establish Nrg4 as a brown fat-enriched endocrine factor with therapeutic potential for the treatment of obesity-associated disorders, including type 2 diabetes and non-alcoholic fatty liver disease.

The PGC-1 family of coactivators stimulates the activity of certain transcription factors and nuclear receptors. Transcription factors in the sterol responsive element binding protein (SREBP) family are key regulators of the lipogenic genes in the liver. We show here that high-fat feeding, which induces hyperlipidemia and atherogenesis, stimulates the expression of both PGC-1beta and SREBP1c and 1a in liver. PGC-1beta coactivates the SREBP transcription factor family and stimulates lipogenic gene expression. Further, PGC-1beta is required for SREBP-mediated lipogenic gene expression. However, unlike SREBP itself, PGC-1beta reduces fat accumulation in the liver while greatly increasing circulating triglycerides and cholesterol in VLDL particles. The stimulation of lipoprotein transport upon PGC-1beta expression is likely due to the simultaneous coactivation of the liver X receptor, LXRalpha, a nuclear hormone receptor with known roles in hepatic lipid transport. These data suggest a mechanism through which dietary saturated fats can stimulate hyperlipidemia and atherogenesis.

The mammalian clock regulates major aspects of energy metabolism, including glucose and lipid homeostasis and mitochondrial oxidative metabolism. The biochemical basis for coordinated control of the circadian clock and diverse metabolic pathways is not well understood. Here we show that PGC-1alpha (Ppargc1a), a transcriptional coactivator that regulates energy metabolism, is rhythmically expressed in the liver and skeletal muscle of mice. PGC-1alpha stimulates the expression of clock genes, notably Bmal1 (Arntl) and Rev-erbalpha (Nr1d1), through coactivation of the ROR family of orphan nuclear receptors. Mice lacking PGC-1alpha show abnormal diurnal rhythms of activity, body temperature and metabolic rate. The disruption of physiological rhythms in these animals is correlated with aberrant expression of clock genes and those involved in energy metabolism. Analyses of PGC-1alpha-deficient fibroblasts and mice with liver-specific knockdown of PGC-1alpha indicate that it is required for cell-autonomous clock function. We have thus identified PGC-1alpha as a key component of the circadian oscillator that integrates the mammalian clock and energy metabolism.

We posit that visually descriptive language offers computer vision researchers both information about the world, and information about how people describe the world. The potential benefit from this source is made more significant due to the enormous amount of language data easily available today. We present a system to automatically generate natural language descriptions from images that exploits both statistics gleaned from parsing large quantities of text data and recognition algorithms from computer vision. The system is very effective at producing relevant sentences for images. It also generates descriptions that are notably more true to the specific image content than previous work.