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During early mammalian embryo development, a small number of cells make robust fate decisions at particular spatial locations in a tight time window to form inner cell mass (ICM), and later epiblast (Epi) and primitive endoderm (PE). While recent single-cell transcriptomics data allows scrutinization of heterogeneity of individual cells, consistent spatial and temporal mechanisms the early embryo utilize to robustly form the Epi/PE layers from ICM remain elusive. Here we build a multiscale three-dimensional model for mammalian embryo to recapitulate the observed patterning process from zygote to late blastocyst. By integrating the spatiotemporal information reconstructed from multiple single-cell transcriptomic datasets, the data-informed modeling analysis suggests two major processes critical to the formation of Epi/PE layers: a selective cell-cell adhesion mechanism (via EphA4/EphrinB2) for fate-location coordination and a temporal attenuation mechanism of cell signaling (via Fgf). Spatial imaging data and distinct subsets of single-cell gene expression data are then used to validate the predictions. Together, our study provides a multiscale framework that incorporates single-cell gene expression datasets to analyze gene regulations, cell-cell communications, and physical interactions among cells in complex geometries at single-cell resolution, with direct application to late-stage development of embryogenesis., Competing Interests: The authors have declared that no competing interests exist.

This work introduces a number of algebraic topology approaches, including multi-component persistent homology, multi-level persistent homology, and electrostatic persistence for the representation, characterization, and description of small molecules and biomolecular complexes. In contrast to the conventional persistent homology, multi-component persistent homology retains critical chemical and biological information during the topological simplification of biomolecular geometric complexity. Multi-level persistent homology enables a tailored topological description of inter- and/or intra-molecular interactions of interest. Electrostatic persistence incorporates partial charge information into topological invariants. These topological methods are paired with Wasserstein distance to characterize similarities between molecules and are further integrated with a variety of machine learning algorithms, including k-nearest neighbors, ensemble of trees, and deep convolutional neural networks, to manifest their descriptive and predictive powers for protein-ligand binding analysis and virtual screening of small molecules. Extensive numerical experiments involving 4,414 protein-ligand complexes from the PDBBind database and 128,374 ligand-target and decoy-target pairs in the DUD database are performed to test respectively the scoring power and the discriminatory power of the proposed topological learning strategies. It is demonstrated that the present topological learning outperforms other existing methods in protein-ligand binding affinity prediction and ligand-decoy discrimination.

Although deep learning approaches have had tremendous success in image, video and audio processing, computer vision, and speech recognition, their applications to three-dimensional (3D) biomolecular structural data sets have been hindered by the geometric and biological complexity. To address this problem we introduce the element-specific persistent homology (ESPH) method. ESPH represents 3D complex geometry by one-dimensional (1D) topological invariants and retains important biological information via a multichannel image-like representation. This representation reveals hidden structure-function relationships in biomolecules. We further integrate ESPH and deep convolutional neural networks to construct a multichannel topological neural network (TopologyNet) for the predictions of protein-ligand binding affinities and protein stability changes upon mutation. To overcome the deep learning limitations from small and noisy training sets, we propose a multi-task multichannel topological convolutional neural network (MM-TCNN). We demonstrate that TopologyNet outperforms the latest methods in the prediction of protein-ligand binding affinities, mutation induced globular protein folding free energy changes, and mutation induced membrane protein folding free energy changes., Availability: weilab.math.msu.edu/TDL/.

Objective: Previous studies have revealed the association of lead (Pb) exposure with obesity. DNA methylation alteration has been suggested to be one of the regulatory mechanisms of obesity. We aimed to explore whether Pb exposure is related with weight gain and DNA methylation alteration., Methods: Male adult 8 week Wistar rats were divided into 5 groups: the normal chow diet (NCD); the NCD+0.05%Pb; the NCD+0.15%Pb; the NCD+0.45%Pb and the high fat diet. Rats were exposed to different dosages of Pb through drinking water for 21 weeks. Body weight, fasted blood glucose level, fasted insulin level, homeostasis assessment of insulin resistance (HOMA-IR) index and lipid profile were detected. Intra-peritoneal glucose tolerance test (IPGTT) was constructed to evaluate the glucose tolerance. Lipid accumulation of liver was detected and liver DNA underwent whole genome bisulfite sequencing., Results: The NCD+0.05%Pb group had significantly greater weight, HOMA-IR and triglycerides, and lower glucose intolerance than the NCD group (P <0.05). This group also showed hepatic lipid accumulation. These metabolic changes were not observed in the other two Pb dosage groups. Furthermore, DNA hypermethylation extended along pathways related to glucose and lipid metabolism in NCD+0.05%Pb group., Conclusion: Pb exposure resulted in dose-specific weight gain in adult Wistar rats, accompanied by alteration of DNA methylation., Competing Interests: The authors have declared that no competing interests exist.

Objective: The association ns between prediabetes and androgens have been rarely reported, especially in Chinese men. We aimed to investigate whether androgens were associated with the prevalence of prediabetes diagnosed with new American Diabetes Association criteria in Chinese men and then to assess which androgen value was the most relevant factor., Methods: A total of 2654 men (52.6±13.4 years old) were selected. Serum total testosterone (TT), sex hormone-binding globulin (SHBG) and free testosterone (FT) were measured. Covariance analysis of different androgen values were performed in age subgroups. Multinomial logistic regression was used for the association of TT, SHBG and FT with prediabetes and diabetes, as well as prediabetes in age subgroups., Results: According to ADA new criteria, normoglycemia, prediabetes, and diabetes were diagnosed in 1405, 907 and 342 men, respectively. In covariance analysis, SHBG of prediabetes were found lower than that of normoglycemia but higher than that of diabetes (P <0.05). In multinomial logistic regression, serum TT and SHBG were inversely associated with prediabetes and diabetes. While, after full adjustment for age, residence area, economic status, waist circumference, metabolic factors, other two androgen values and HOMA-IR, only the associations of SHBG with prevalence of prediabetes and diabetes persisted statistically significant, especially in the elderly with prediabetes (all P for trend <0.05)., Conclusions: Serum androgen was inversely associated with prediabetes and diabetes in Chinese men. Low serum SHBG was the most relevant factor for prediabetes and diabetes. Whether it is an independent predictor for incident prediabetes in Chinese men needs further explorations., Competing Interests: The authors have declared that no competing interests exist.