Gab2 deficiency suppresses high-fat diet-induced obesity by reducing adipose tissue inflammation and increasing brown adipose function in mice.

Obesity is caused by a long-term imbalance between energy intake and consumption and is regulated by multiple signals. This study investigated the effect of signaling scaffolding protein Gab2 on obesity and its relevant regulation mechanism. Gab2 knockout (KO) and wild-type (WT) mice were fed with a standard diet (SD) or high-fat diet (HFD) for 12 weeks. The results showed that the a high-fat diet-induced Gab2 expression in adipose tissues, but deletion of Gab2 attenuated weight gain and improved glucose tolerance in mice fed with a high-fat diet. White adipose tissue and systemic inflammations were reduced in HFD-fed Gab2 deficiency mice. Gab2 deficiency increased the expression of Ucp1 and other thermogenic genes in brown adipose tissue. Furthermore, the regulation of Gab2 on the mature differentiation and function of adipocytes was investigated in vitro using primary or immortalized brown preadipocytes. The expression of brown fat-selective genes was found to be elevated in differentiated adipocytes without Gab2. The mechanism of Gab2 regulating Ucp1 expression in brown adipocytes involved with its downstream PI3K (p85)-Akt-FoxO1 signaling pathway. Our research suggests that deletion of Gab2 suppresses diet-induced obesity by multiple pathways and Gab2 may be a novel therapeutic target for the treatment of obesity and associated complications

Tyrosine phosphatase SHP2 in ovarian granulosa cells balances follicular development by inhibiting PI3K/AKT signaling.

In mammals, the growth and maturation of oocytes within growing follicles largely depends on ovarian granulosa cells (GCs) in response to gonadotropin stimulation. Many signals have been shown to regulate GC proliferation and apoptosis. However, whether the tyrosine phosphatase SHP2 is involved remains unclear. In this study, we identified the crucial roles of SHP2 in modulating GC proliferation and apoptosis. The production of both mature oocytes and pups was increased in mice with Shp2 specifically deleted in ovarian GCs via Fshr-Cre. Shp2 deletion simultaneously promoted GC proliferation and inhibited GC apoptosis. Furthermore, Shp2 deficiency promoted, while Shp2 overexpression inhibited, the proliferation of cultured primary mouse ovarian GCs and the human ovarian granulosa-like tumor cell line KGN in vitro. Shp2 deficiency promoted follicule-stimulating hormone (FSH)-activated phosphorylation of AKT in vivo. SHP2 deficiency reversed the inhibitory effect of hydrogen peroxide (H2O2) on AKT activation in KGN cells. H2O2 treatment promoted the interaction between SHP2 and the p85 subunit of PI3K in KGN cells. Therefore, SHP2 in GCs may act as a negative modulator to balance follicular development by suppressing PI3K/AKT signaling. The novel function of SHP2 in modulating proliferation and apoptosis of GCs provides a potential therapeutic target for the clinical treatment of follicle developmental dysfunction

Shp2 in uterine stromal cells critically regulates on time embryo implantation and stromal decidualization by multiple pathways during early pregnancy.

Approximately 75% of failed pregnancies are considered to be due to embryo implantation failure or defects. Nevertheless, the explicit signaling mechanisms governing this process have not yet been elucidated. Here, we found that conditional deletion of the Shp2 gene in mouse uterine stromal cells deferred embryo implantation and inhibited the decidualization of stromal cells, which led to embryonic developmental delay and to the death of numerous embryos mid-gestation, ultimately reducing female fertility. The absence of Shp2 in stromal cells increased the proliferation of endometrial epithelial cells, thereby disturbing endometrial epithelial remodeling. However, Shp2 deletion impaired the proliferation and polyploidization of stromal cells, which are distinct characteristics of decidualization. In human endometrial stromal cells (hESCs), Shp2 expression gradually increased during the decidualization process. Knockout of Shp2 blocked the decidual differentiation of hESCs, while Shp2 overexpression had the opposite effect. Shp2 knockout inhibited the proliferation of hESCs during decidualization. Whole gene expression profiling analysis of hESCs during the decidualization process showed that Shp2 deficiency disrupted many signaling transduction pathways and gene expression. Analyses of hESCs and mouse uterine tissues confirmed that the signaling pathways extracellular regulated protein kinases (ERK), protein kinase B (AKT), signal transducer and activator of transcription 3 (STAT3) and their downstream transcription factors CCAAT/enhancer binding protein β (C/EBPβ) and Forkhead box transcription factor O1 (FOXO-1) were involved in the Shp2 regulation of decidualization. In summary, these results demonstrate that Shp2 plays a crucial role in stromal decidualization by mediating and coordinating multiple signaling pathways in uterine stromal cells. Our discovery possibly provides a novel key regulator of embryo implantation and novel therapeutic target for pregnancy failure

Deletion of the tyrosine phosphatase Shp2 in Sertoli cells causes infertility in mice.

The males ability to reproduce is completely dependent on Sertoli cells. However, the mechanisms governing the functional integrity of Sertoli cells have remained largely unexplored. Here, we demonstrate that deletion of Shp2 in Sertoli cells results in infertility in mice. In Shp2 knockout mice (SCSKO), a normal population of Sertoli cells was observed, but the blood-testis barrier (BTB) was not formed. Shp2 ablation initiated the untimely and excessive differentiation of spermatogonial stem cells (SSCs) by disturbing the expression of paracrine factors. As a consequence, the process of spermatogenesis was disrupted, and the germ cells were depleted. Furthermore, Shp2 deletion impaired the cell junctions of the primary Sertoli cells and failed to support the clonal formation of SSCs co-cultured with SCSKO Sertoli cells. As expected, Shp2 restoration largely restores the cell junctions of the primary Sertoli cells and the clonal formation of SSCs. To identify the underlying mechanism, we further demonstrated that the absence of Shp2 suppressed Erk phosphorylation, and thus, the expression of follicle-stimulating hormone (FSH)- and testosterone-induced target genes. These results collectively suggest that Shp2 is a critical signaling protein that is required to maintain Sertoli cell function and could serve as a novel target for male infertility therapies

Deciphering a critical role of uterine epithelial SHP2 in parturition initiation at single cell resolution.

The timely onset of female parturition is a critical determinant for pregnancy success. The highly heterogenous maternal decidua has been increasingly recognized as a vital factor in setting the timing of labor. Despite the cell type specific roles in parturition, the role of the uterine epithelium in the decidua remains poorly understood. This study uncovers the critical role of epithelial SHP2 in parturition initiation via COX1 and COX2 derived PGF2α leveraging epithelial specific Shp2 knockout mice, whose disruption contributes to delayed parturition initiation, dystocia and fetal deaths. Additionally, we also show that there are distinct types of epithelium in the decidua approaching parturition at single cell resolution accompanied with profound epithelium reformation via proliferation. Meanwhile, the epithelium maintains the microenvironment by communicating with stromal cells and macrophages. The epithelial microenvironment is maintained by a close interaction among epithelial, stromal and macrophage cells of uterine stromal cells. In brief, this study provides a previously unappreciated role of the epithelium in parturition preparation and sheds lights on the prevention of preterm birth

The Genomes of Oryza sativa: A History of Duplications

We report improved whole-genome shotgun sequences for the genomes of indica and japonica rice, both with multimegabase contiguity, or almost 1,000-fold improvement over the drafts of 2002. Tested against a nonredundant collection of 19,079 full-length cDNAs, 97.7% of the genes are aligned, without fragmentation, to the mapped super-scaffolds of one or the other genome. We introduce a gene identification procedure for plants that does not rely on similarity to known genes to remove erroneous predictions resulting from transposable elements. Using the available EST data to adjust for residual errors in the predictions, the estimated gene count is at least 38,000–40,000. Only 2%–3% of the genes are unique to any one subspecies, comparable to the amount of sequence that might still be missing. Despite this lack of variation in gene content, there is enormous variation in the intergenic regions. At least a quarter of the two sequences could not be aligned, and where they could be aligned, single nucleotide polymorphism (SNP) rates varied from as little as 3.0 SNP/kb in the coding regions to 27.6 SNP/kb in the transposable elements. A more inclusive new approach for analyzing duplication history is introduced here. It reveals an ancient whole-genome duplication, a recent segmental duplication on Chromosomes 11 and 12, and massive ongoing individual gene duplications. We find 18 distinct pairs of duplicated segments that cover 65.7% of the genome; 17 of these pairs date back to a common time before the divergence of the grasses. More important, ongoing individual gene duplications provide a never-ending source of raw material for gene genesis and are major contributors to the differences between members of the grass family

A Prognosis Classifier for Breast Cancer Based on Conserved Gene Regulation between Mammary Gland Development and Tumorigenesis: A Multiscale Statistical Model

National Basic Research Program of China [2010CB945004]; National Natural Science Foundation of China [30772546]Identification of novel cancer genes for molecular therapy and diagnosis is a current focus of breast cancer research. Although a few small gene sets were identified as prognosis classifiers, more powerful models are still needed for the definition of effective gene sets for the diagnosis and treatment guidance in breast cancer. In the present study, we have developed a novel statistical approach for systematic analysis of intrinsic correlations of gene expression between development and tumorigenesis in mammary gland. Based on this analysis, we constructed a predictive model for prognosis in breast cancer that may be useful for therapy decisions. We first defined developmentally associated genes from a mouse mammary gland epithelial gene expression database. Then, we found that the cancer modulated genes were enriched in this developmentally associated genes list. Furthermore, the developmentally associated genes had a specific expression profile, which associated with the molecular characteristics and histological grade of the tumor. These result suggested that the processes of mammary gland development and tumorigenesis share gene regulatory mechanisms. Then, the list of regulatory genes both on the developmental and tumorigenesis process was defined an 835-member prognosis classifier, which showed an exciting ability to predict clinical outcome of three groups of breast cancer patients (the predictive accuracy 64 similar to 72%) with a robust prognosis prediction (hazard ratio 3.3 similar to 3.8, higher than that of other clinical risk factors (around 2.0-2.8)). In conclusion, our results identified the conserved molecular mechanisms between mammary gland development and neoplasia, and provided a unique potential model for mining unknown cancer genes and predicting the clinical status of breast tumors. These findings also suggested that developmental roles of genes may be important criteria for selecting genes for prognosis prediction in breast cancer

A Prognosis Classifier for Breast Cancer Based on Conserved Gene Regulation between Mammary Gland Development and Tumorigenesis: A Multiscale Statistical Model

<div><p>Identification of novel cancer genes for molecular therapy and diagnosis is a current focus of breast cancer research. Although a few small gene sets were identified as prognosis classifiers, more powerful models are still needed for the definition of effective gene sets for the diagnosis and treatment guidance in breast cancer. In the present study, we have developed a novel statistical approach for systematic analysis of intrinsic correlations of gene expression between development and tumorigenesis in mammary gland. Based on this analysis, we constructed a predictive model for prognosis in breast cancer that may be useful for therapy decisions. We first defined developmentally associated genes from a mouse mammary gland epithelial gene expression database. Then, we found that the cancer modulated genes were enriched in this developmentally associated genes list. Furthermore, the developmentally associated genes had a specific expression profile, which associated with the molecular characteristics and histological grade of the tumor. These result suggested that the processes of mammary gland development and tumorigenesis share gene regulatory mechanisms. Then, the list of regulatory genes both on the developmental and tumorigenesis process was defined an 835-member prognosis classifier, which showed an exciting ability to predict clinical outcome of three groups of breast cancer patients (the predictive accuracy 64∼72%) with a robust prognosis prediction (hazard ratio 3.3∼3.8, higher than that of other clinical risk factors (around 2.0–2.8)). In conclusion, our results identified the conserved molecular mechanisms between mammary gland development and neoplasia, and provided a unique potential model for mining unknown cancer genes and predicting the clinical status of breast tumors. These findings also suggested that developmental roles of genes may be important criteria for selecting genes for prognosis prediction in breast cancer.</p> </div

The 835 prognosis classifier acts as a powerful predictor of clinical outcome in 78 breast cancer patients.

<p><b>A.</b> 78 breast cancer samples were first classified by the expression profiles of the 835 prognosis classifier, using unsupervised classification. The survival curve of the two groups was then compared with Kaplan Meier analysis to define clinical outcome (lower panel). Accuracy of classification was assessed with Fisher exact test (upper table). <b>B.</b> The distribution of tumors risk factors in the four groups classified by clinical metastasis and by the 835 prognosis classifier. <b>C.</b> The prognostic value of the 835 prognosis classifier and tumor risk factors.</p

The 835 prognosis classifier could predict clinical outcome in a large set of breast cancer patients.

<p>The intrinsic dataset was applied to 144 node positive and 151 node negative primary breast tumors. The accuracy of prediction (<b>A</b>) or the prognosis value (<b>B</b>) of 835 prognosis classifier and tumor risk factors was assessed by the same approach as described in the legend of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0060131#pone-0060131-g005" target="_blank">Fig. 5B</a>.</p