Obesity confers an independent risk for carcinogenesis. In the liver, steatosis often proceeds cancer formation; however, the mechanisms by which steatosis promotes carcinogenesis is unknown. We hypothesize that steatosis alters the microenvironment to promote proliferation of tumor initiating cells (TICs) and carcinogenesis. We used several liver cancer models to address the mechanisms underlying the role of obesity in cancer and verified these findings in patient populations. Using bioinformatics analysis and verified by biochemical assays, we identified that hepatosteatosis resulting from either Pten deletion or transgenic expression of HCV core/NS5A proteins, promotes the activation of Wnt/β-catenin. We verified that high fat diet lipid accumulation is also capable of inducing Wnt/β-catenin. Caloric restriction inhibits hepatosteatosis, reduces Wnt/β-catenin activation and blocks the expansion of TICs leading to complete inhibition of tumorigenesis without affecting the phosphatase and tensin homologue deleted on chromosome 10 (PTEN) loss regulated protein kinase B (AKT) activation. Pharmacological inhibition or loss of the Wnt/β-catenin signal represses TIC growth in vitro, and decreases the accumulation of TICs in vivo. In human liver cancers, ontology analysis of gene set enrichment analysis (GSEA)-defined Wnt signature genes indicates that Wnt signaling is significantly induced in tumor samples compared with healthy livers. Indeed, Wnt signature genes predict 90% of tumors in a cohort of 558 patient samples. Selective depletion of macrophages leads to reduction of Wnt and suppresses tumor development, suggesting infiltrating macrophages as a key source for steatosis-induced Wnt expression. These data established Wnt/β-catenin as a novel signal produced by infiltrating macrophages induced by steatosis that promotes growth of tumor progenitor cells, underlying the increased risk of liver tumor development in obese individuals.

SUMMARYEmbryogenesis requires epigenetic information that allows each cell to respond appropriately to developmental cues. Histone modifications are core components of a cell’s epigenome, giving rise to chromatin states that modulate genome function. Here, we systematically profile histone modifications in a diverse panel of mouse tissues at 8 developmental stages from 10.5 days post conception until birth, performing a total of 1,128 ChIP-seq assays across 72 distinct tissue-stages. We combine these histone modification profiles into a unified set of chromatin state annotations, and track their activity across developmental time and space. Through integrative analysis we identify dynamic enhancers, reveal key transcriptional regulators, and characterize the role of chromatin-based repression in developmental gene regulation. We also leverage these data to link enhancers to putative target genes, revealing connections between coding and non-coding sequence variation in disease etiology. Our study provides a compendium of resources for biomedical researchers, and achieves the most comprehensive view of embryonic chromatin states to date.

Correspondence:We introduce a web-enabled small-molecule mass spectrometry (MS) search engine. To date, no tool can query all the public small-molecule tandem MS data in metabolomics repositories, greatly limiting the utility of these resources in clinical, environmental and natural product applications. Therefore, we introduce a Mass Spectrometry Search Tool (MASST) (https://proteosafe-extensions.ucsd.edu/masst/), that enables the discovery of molecular relationships among accessible public metabolomics and natural product tandem mass spectrometry data (MS/MS).The ability to discover related sequences of proteins or genes in publicly accessible sequence data using Basic Local Alignment Search Tool (BLAST), connected to public sequence data repositories through a web interface (WebBLAST, https://blast.ncbi.nlm.nih.gov/Blast.cgi), was introduced in the 1990s. 1 It has garnered more than 138,159 citations according to Google Scholar, placing it among the most widely used bioinformatics tools. WebBLAST enabled detection of the number of sequences in public repositories related to a given query, the organisms in which those sequences occur, and the evolutionary and inferred functional relationships among related sequences. It therefore permitted a broad community to answer simple but scientifically compelling questions such as: Is a protein or DNA sequence common or rare? How is this sequence distributed among different kinds of organisms? What other sequences are related to this sequence (evolutionary variants, or new mutations, or synthetic constructs)? In the early days of making DNA or protein sequence data publicly available, the "metadata" (e.g., contextual information about the sample, population and location the sequence came from, and technical information about how it was produced) in the public repositories was limited and no standards existed. This is a situation similar to the current status of much of the mass spectrometry data in the public domain. However, when publicly deposited data has metadata available, such as organism, location of sampling, host phenotypes such as diseases, etc., it becomes possible to start building higherlevel hypotheses regarding the evolutionary, ecological or functional relationships among these DNA, RNA or protein sequences. The development of the ability to search data with added context continues to have profound impacts on fields including medicine, chemistry, genetics, molecular biology, genomics, microbiology, and ecology.Algorithms developed for mass spectrometry data, including molecular networking 2 and fragmentation trees 3 , enable similarity searches, while powerful metabolomics analysis software infrastructures, such as MS-DIAL 4 , MetaboAnalyst 5 , XCMS Online 6 , HMDB 7 , some of which have been available for over a decade, focus on annotation of MS/MS spectra or finding statistical relationships between molecular features. However, none of the existing tools enable searching against public data in repositories. Finding the distribution of specific data of i...

samples 33 PCD, RK, SM, VN and DS guided experimental design and analysis. 34 MW converted the data in GNPS, developed spectral search and molecular explorer. 35 TT, VN and SM raised animals and guided experimental design. 36 RQ and PD wrote the manuscript 37 38Abstract 39 A mosaic of cross-phyla chemical interactions occurs between all metazoans and their 40 microbiomes. In humans, the gut harbors the heaviest microbial load, but many organs, 41 particularly those with a mucosal surface, associate with highly adapted and evolved 42 microbial consortia 1 . The microbial residents within these organ systems are increasingly well 43 characterized, yielding a good understanding of human microbiome composition, but we have 44 yet to elucidate the full chemical impact the microbiome exerts on an animal and the breadth 45 of the chemical diversity it contributes 2 . A number of molecular families are known to be 46 shaped by the microbiome including short-chain fatty acids, indoles, aromatic amino acid 47 values of the two data types onto the murine 3-D model showed how the gut samples were monosaccharides in all regions of the GI tract, which were absent in SPF animals. Instead, a 132