Novartis

About: Novartis is a company organization based out in Basel, Switzerland. It is known for research contribution in the topics: Alkyl & Population. The organization has 41930 authors who have published 50566 publications receiving 1978996 citations. The organization is also known as: Novartis International AG.

Fibroblast growth factor 21 regulates energy metabolism by activating the AMPK–SIRT1–PGC-1α pathway

Abstract: Fibroblast growth factor 21 (FGF21) has been identified as a potent metabolic regulator. Administration of recombinant FGF21 protein to rodents and rhesus monkeys with diet-induced or genetic obesity and diabetes exerts strong antihyperglycemic and triglyceride-lowering effects and reduction of body weight. Despite the importance of FGF21 in the regulation of glucose, lipid, and energy homeostasis, the mechanisms by which FGF21 functions as a metabolic regulator remain largely unknown. Here we demonstrate that FGF21 regulates energy homeostasis in adipocytes through activation of AMP-activated protein kinase (AMPK) and sirtuin 1 (SIRT1), resulting in enhanced mitochondrial oxidative function. AMPK phosphorylation levels were increased by FGF21 treatment in adipocytes as well as in white adipose tissue from ob/ob mice. FGF21 treatment increased cellular NAD+ levels, leading to activation of SIRT1 and deacetylation of its downstream targets, peroxisome proliferator-activated receptor-γ coactivator-1α (PGC-1α) and histone 3. Activation of AMPK and SIRT1 by FGF21 in adipocytes enhanced mitochondrial oxidative capacity as demonstrated by increases in oxygen consumption, citrate synthase activity, and induction of key metabolic genes. The effects of FGF21 on mitochondrial function require serine/threonine kinase 11 (STK11/LKB1), which activates AMPK. Inhibition of AMPK, SIRT1, and PGC-1α activities attenuated the effects of FGF21 on oxygen consumption and gene expression, indicating that FGF21 regulates mitochondrial activity and enhances oxidative capacity through an AMPK–SIRT1–PGC1α–dependent mechanism in adipocytes.

A Primary Xenograft Model of Small-Cell Lung Cancer Reveals Irreversible Changes in Gene Expression Imposed by Culture In vitro

Abstract: Traditional approaches to the preclinical investigation of cancer therapies rely on the use of established cell lines maintained in serum-based growth media. This is particularly true of small-cell lung cancer (SCLC), where surgically resected tissue is rarely available. Recent attention has focused on the need for better models that preserve the integrity of cancer stem cell populations, as well as three-dimensional tumor-stromal interactions. Here we describe a primary xenograft model of SCLC in which endobronchial tumor specimens obtained from chemo-naive patients are serially propagated in vivo in immunodeficient mice. In parallel, cell lines grown in conventional tissue culture conditions were derived from each xenograft line, passaged for 6 months, and then reimplanted to generate secondary xenografts. Using the Affymetrix platform, we analyzed gene expression in primary xenograft, xenograft-derived cell line, and secondary xenograft, and compared these data to similar analyses of unrelated primary SCLC samples and laboratory models. When compared with normal lung, primary tumors, xenografts, and cell lines displayed a gene expression signature specific for SCLC. Comparison of gene expression within the xenograft model identified a group of tumor-specific genes expressed in primary SCLC and xenografts that was lost during the transition to tissue culture and that was not regained when the tumors were reestablished as secondary xenografts. Such changes in gene expression may be a common feature of many cancer cell culture systems, with functional implications for the use of such models for preclinical drug development.

A computational framework to explore large-scale biosynthetic diversity

Abstract: Genome mining has become a key technology to exploit natural product diversity. Although initially performed on a single-genome basis, the process is now being scaled up to mine entire genera, strain collections and microbiomes. However, no bioinformatic framework is currently available for effectively analyzing datasets of this size and complexity. In the present study, a streamlined computational workflow is provided, consisting of two new software tools: the ‘biosynthetic gene similarity clustering and prospecting engine’ (BiG-SCAPE), which facilitates fast and interactive sequence similarity network analysis of biosynthetic gene clusters and gene cluster families; and the ‘core analysis of syntenic orthologues to prioritize natural product gene clusters’ (CORASON), which elucidates phylogenetic relationships within and across these families. BiG-SCAPE is validated by correlating its output to metabolomic data across 363 actinobacterial strains and the discovery potential of CORASON is demonstrated by comprehensively mapping biosynthetic diversity across a range of detoxin/rimosamide-related gene cluster families, culminating in the characterization of seven detoxin analogues. Two bioinformatic tools, BiG-SCAPE and CORASON, enable sequence similarity network and phylogenetic analysis of gene clusters and their families across hundreds of strains and in large datasets, leading to the discovery of new natural products.