Early life socioeconomic factors and genomic DNA methylation in mid-life

Epigenetic modifications may be one mechanism linking early life factors, including parental socioeconomic status (SES), to adult onset disease risk. However, SES influences on DNA methylation patterns remain largely unknown. In a US birth cohort of women, we examined whether indicators of early life and adult SES were associated with white blood cell methylation of repetitive elements (Sat2, Alu and LINE-1) in adulthood. Low family income at birth was associated with higher Sat2 methylation (β = 19.7, 95% CI: 0.4, 39.0 for lowest vs. highest income quartile) and single parent family was associated with higher Alu methylation (β = 23.5, 95% CI: 2.6, 44.4), after adjusting for other early life factors. Lower adult education was associated with lower Sat2 methylation (β = -16.7, 95% CI: -29.0, -4.5). There were no associations between early life SES and LINE-1 methylation. Overall, our preliminary results suggest possible influences of SES across the life-course on genomic DNA methylation in adult women. However, these preliminary associations need to be replicated in larger prospective studies.     

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.     

LNRLMI: Linear neighbour representation for predicting lncRNA‐miRNA interactions

LncRNA and miRNA are key molecules in mechanism of competing endogenous RNAs(ceRNA), and their interactions have been discovered with important roles in gene regulation. As supplementary to the identification of lncRNA‐miRNA interactions from CLIP‐seq experiments, in silico prediction can select the most potential candidates for experimental validation. Although developing computational tool for predicting lncRNA‐miRNA interaction is of great importance for deciphering the ceRNA mechanism, little effort has been made towards this direction. In this paper, we propose an approach based on linear neighbour representation to predict lncRNA‐miRNA interactions (LNRLMI). Specifically, we first constructed a bipartite network by combining the known interaction network and similarities based on expression profiles of lncRNAs and miRNAs. Based on such a data integration, linear neighbour representation method was introduced to construct a prediction model. To evaluate the prediction performance of the proposed model, k‐fold cross validations were implemented. As a result, LNRLMI yielded the average AUCs of 0.8475 ± 0.0032, 0.8960 ± 0.0015 and 0.9069 ± 0.0014 on 2‐fold, 5‐fold and 10‐fold cross validation, respectively. A series of comparison experiments with other methods were also conducted, and the results showed that our method was feasible and effective to predict lncRNA‐miRNA interactions via a combination of different types of useful side information. It is anticipated that LNRLMI could be a useful tool for predicting non‐coding RNA regulation network that lncRNA and miRNA are involved in.     

Genomic imprinting and evolution of insect societies

Reproductive division of labor is a hallmark of social insect societies where individuals follow different developmental pathways resulting in distinct morphological castes. There has been a long controversy over the factors determining caste fate of individuals in social insects. Increasing evidence in the last two decades for heritable influences on division of labor put an end to the assumption that social insect broods are fully totipotent and environmental factors alone determine castes. Nevertheless, the genes that underlie hereditary effects on division of labor have not been identified in any social insects. Studies investigating the hereditary effects on caste determination might have overlooked non-genetic inheritance, while transmission to offspring of factors other than DNA sequences including epigenetic states can also affect offspring phenotype. Genomic imprinting is one of the most informative paradigms for understanding the consequences of interactions between the genome and the epigenome. Recent studies of genomic imprinting show that genes can be differentially marked in egg and sperm and inheritance of these epigenetic marks cause genes to be expressed in a parental-origin-specific manner in the offspring. By reviewing both the eusocial Hymenoptera and termites, I highlight the current theoretical and empirical evidence for genomic imprinting in eusocial insects and discuss how genomic imprinting acts in caste determination and social behavior and challenges for future studies. I also introduce the new idea that genomic imprinting plays an essential role in the origin of eusociality.