Treatment with FGFR2-IIIc monoclonal antibody suppresses weight gain and adiposity in KKAy mice

Mammary adipocytes promote breast tumor cell invasion and angiogenesis in the context of menopause and obesity

The mechanism(s) underlying obesity-related postmenopausal (PM) breast cancer (BC) are not clearly understood. We hypothesized that the increased local presence of 'obese' mammary adipocytes within the BC microenvironment promotes the acquisition of an invasive and angiogenic BC cell phenotype and accelerates tumor proliferation and progression. BC cells, treated with primary mammary adipocyte secretome from premenopausal (Pre-M) and PM obese women (ObAdCM; obese adipocyte conditioned-media) upregulated the expression of several pro-tumorigenic factors including VEGF, lipocalin-2 and IL-6. Both Pre-M and PM ObAdCM stimulated endothelial cell recruitment and proliferation and significantly stimulated BC cell proliferation, migration and invasion. IL-6 and LCN2 induced STAT3/Akt signaling in BC cells and STAT3 inhibition abrogated the ObAdCM-stimulated BC cell proliferation and migration. Expression of proangiogenic regulators including VEGF, NRP1, NRP2, IL8RB, TGFβ2, and TSP-1 were found to be differentially regulated in mammary adipocytes from obese PM women. Comparative RNAseq indicated an upregulation of PI3K/Akt signaling, ECM-receptor interactions and lipid/fatty acid metabolism in PM versus Pre-M mammary adipocytes. Our results demonstrate that irrespective of menopausal status, cross-talk between obese mammary adipocytes and BC cells promotes tumor aggressiveness and suggest that targeting the LCN2/IL-6/STAT3 signaling axis may be a useful strategy in obesity-driven breast tumorigenesis.

Exhaustive Variant Interaction Analysis using Multifactor Dimensionality Reduction

One of the main goals of human genetics is to understand the connections between genomic variation and the predisposition to develop a complex disorder. These disease-variant associations are usually studied in a single independent manner, disregarding the possible effect derived from the interaction between genomic variants. In particular, in a background of complex diseases, these interactions can be directly linked to the disorder and may play an important role in disease development. Although their study has been suggested to help to complete the understanding of the genetic bases of complex diseases, this still represents a big challenge due to large computing demands. Here, we have taken advantage of High-Performance Computing technologies to tackle this problem using a combination of machine learning methods and statistical approaches. As a result, we have created a containerized framework that uses Multifactor Dimensionality Reduction to detect pairs of variants associated with Type 2 Diabetes (T2D). This methodology has been tested in the Northwestern University NUgene project cohort using a dataset of 1,883,192 variant pairs with a certain degree of association with T2D. Out of the pairs studied, we have identified 104 significant pairs, two of which exhibit a potential functional relationship with T2D.

Identification and characterization of adipose surface epitopes

Adipose tissue is a central regulator of metabolism and an important pharmacological target to treat the metabolic consequences of obesity, such as insulin resistance and dyslipidemia. Among the various cellular compartments, the adipocyte cell surface is especially appealing as a drug target as it contains various proteins that when activated or inhibited promote adipocyte health, change its endocrine function and eventually maintain or restore whole-body insulin sensitivity. In addition, cell surface proteins are readily accessible by various drug classes. However, targeting individual cell surface proteins in adipocytes has been difficult due to important functions of these proteins outside adipose tissue, raising various safety concerns. Thus, one of the biggest challenges is the lack of adipose selective surface proteins and/or targeting reagents. Here, we discuss several receptor families with an important function in adipogenesis and mature adipocytes to highlight the complexity at the cell surface and illustrate the problems with identifying adipose selective proteins. We then discuss that, while no unique adipocyte surface protein might exist, how splicing, posttranslational modifications as well as protein/protein interactions can create enormous diversity at the cell surface that vastly expands the space of potentially unique epitopes and how these selective epitopes can be identified and targeted.