PDB-wide identification of physiological hetero-oligomeric assemblies based on conserved quaternary structure geometry

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Testing prediction accuracy in short-term ecological studies

Applied ecology is based on an assumption that a management action will result in a predicted outcome. Testing the prediction accuracy of ecological models is the most powerful way of evaluating the knowledge implicit in this cause-effect relationship, however, the prevalence of predictive modeling and prediction testing are spreading slowly in ecology. The challenge of prediction testing is particularly acute for small-scale studies, because withholding data for prediction testing (e.g., via k-fold cross validation) can reduce model precision. However, by necessity small-scale studies are common. We use one such study that explored small mammal abundance along an elevational gradient to test prediction accuracy of models with varying degrees of information content. For each of three small mammal species, we conducted 5000 iterations of the following process: (1) randomly selected 75 % of the data to develop generalized linear models of species abundance that used detailed site measurements as covariates, (2) used an information theoretic approach to compare the top model with detailed covariates to habitat type-only and null models constructed with the same data, (3) tested those models’ ability to predict the 25 % of the randomly withheld data, and (4) evaluated prediction accuracy with a quadratic loss function. Detailed models fit the model-evaluation data best but had greater expected prediction error when predicting out-of-sample data relative to the habitat type models. Relationships between species and detailed site variables may be evident only within the framework of explicitly hierarchical analyses. We show that even with a small but relatively typical dataset (n = 28 sampling locations across 125 km over two years), researchers can effectively compare models with different information content and measure models’ predictive power, thus evaluating their own ecological understanding and defining the limits of their inferences. Identifying the appropriate scope of inference through prediction testing is ecologically valuable and is attainable even with small datasets.     

Computational Modeling of Protein Stability: Quantitative Analysis Reveals Solutions to Pervasive Problems

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Kinase–substrate Edge Biomarkers Provide A More Accurate Prognostic Prediction in ER-negative Breast Cancer

The estrogen receptor (ER)-negative breast cancer subtype is aggressive with few treatment options available. To identify specific prognostic factors for ER-negative breast cancer, this study included 705,729 and 1034 breast invasive cancer patients from the Surveillance, Epidemiology, and End Results (SEER) and The Cancer Genome Atlas (TCGA) databases, respectively. To identify key differential kinase–substrate node and edge biomarkers between ER-negative and ER-positive breast cancer patients, we adopted a network-based method using correlation coefficients between molecular pairs in the kinase regulatory network. Integrated analysis of the clinical and molecular data revealed the significant prognostic power of kinase–substrate node and edge features for both subtypes of breast cancer. Two promising kinase–substrate edge features, CSNK1A1–NFATC3 and SRC–OCLN, were identified for more accurate prognostic prediction in ER-negative breast cancer patients.     

AMPH1 functions as a tumour suppressor in ovarian cancer via the inactivation of PI3K/AKT pathway

AMPH1, an abundant protein in nerve terminals, plays a critical role in the recruitment of dynamin to sites of clathrin‐mediated endocytosis. Recently, it is reported to be involved in breast cancer and lung cancer. However, the impact of AMPH1 on ovarian cancer is unclear. In this study, we used gain‐of‐function and loss‐of‐function methods to explore the role of AMPH1 in ovarian cancer cells. AMPH1 inhibited ovarian cancer cell growth and cell migration, and promoted caspase‐3 activity, resulting in the increase of cell apoptosis. In xenograft mice model, AMPH1 prevented tumour progression. The anti‐oncogene effects of AMPH1 on ovarian cancer might be partially due to the inhibition of PI3K/AKT signalling pathway after overexpression of AMPH1. Immunohistochemistry analysis showed that the staining of AMPH1 was remarkably reduced in ovarian cancer tissues compared with normal ovarian tissues. In conclusion, our study identifies AMPH1 as a tumour suppressor in ovarian cancer in vitro and in vivo. This is the first evidence that AMPH1 inhibited cell growth and migration, and induced apoptosis via the inactivation of PI3K/AKT signalling pathway on ovarian cancer, which may be used as an effective strategy.     

TIPE1‐mediated autophagy suppression promotes nasopharyngeal carcinoma cell proliferation via the AMPK/mTOR signalling pathway

Recent studies have shown that tumour necrosis factor‐α–induced protein 8 like‐1(TIPE1) plays distinct roles in different cancers. TIPE1 inhibits tumour proliferation and metastasis in a variety of tumours but acts as an oncogene in cervical cancer. The role of TIPE1 in nasopharyngeal carcinoma (NPC) remains unknown. Interestingly, TIPE1 expression was remarkably increased in NPC tissue samples compared to adjacent normal nasopharyngeal epithelial tissue samples in our study. TIPE1 expression was positively correlated with that of the proliferation marker Ki67 and negatively correlated with patient lifespan. In vitro, TIPE1 inhibited autophagy and induced cell proliferation in TIPE1‐overexpressing CNE‐1 and CNE‐2Z cells. In addition, knocking down TIPE1 expression promoted autophagy and decreased proliferation, whereas overexpressing TIPE1 increased the levels of pmTOR, pS6 and P62 and decreased the level of pAMPK and the LC3B. Furthermore, the decrease in autophagy was remarkably rescued in TIPE1‐overexpressing CNE‐1 and CNE‐2Z cells treated with the AMPK activator AICAR. In addition, TIPE1 promoted tumour growth in BALB/c nude mice. Taken together, results indicate that TIPE1 promotes NPC progression by inhibiting autophagy and inducing cell proliferation via the AMPK/mTOR signalling pathway. Thus, TIPE1 could potentially be used as a valuable diagnostic and prognostic biomarker for NPC.     

Bmp2 regulates Serpinb6b expression via cAMP/PKA/Wnt4 pathway during uterine decidualization

Serpinb6b is a novel member of Serpinb family and found in germ and somatic cells of mouse gonads, but its physiological function in uterine decidualization remains unclear. The present study revealed that abundant Serpinb6b was noted in decidual cells, and advanced the proliferation and differentiation of stromal cells, indicating a creative role of Serpinb6b in uterine decidualization. Further analysis found that Serpinb6b modulated the expression of Mmp2 and Mmp9. Meanwhile, Serpinb6b was identified as a target of Bmp2 regulation in stromal differentiation. Treatment with rBmp2 resulted in an accumulation of intracellular cAMP level whose function in this differentiation program was mediated by Serpinb6b. Addition of PKA inhibitor H89 impeded the Bmp2 induction of Serpinb6b, whereas 8‐Br‐cAMP rescued the defect of Serpinb6b expression elicited by Bmp2 knock‐down. Attenuation of Serpinb6b greatly reduced the induction of constitutive Wnt4 activation on stromal cell differentiation. By contrast, overexpression of Serpinb6b prevented this inhibition of differentiation process by Wnt4 siRNA. Moreover, blockage of Wnt4 abrogated the up‐regulation of cAMP on Serpinb6b. Collectively, Serpinb6b mediates uterine decidualization via Mmp2/9 in response to Bmp2/cAMP/PKA/Wnt4 pathway.