Integrating Multi-Omics for Uncovering the Architecture of Cross-Talking Pathways in Breast Cancer

Cross-talk among abnormal pathways widely occurs in human cancer and generally leads to insensitivity to cancer treatment. Moreover, alterations in the abnormal pathways are not limited to single molecular level. Therefore, we proposed a strategy that integrates a large number of biological sources at multiple levels for systematic identification of cross-talk among risk pathways in cancer by random walk on protein interaction network. We applied the method to multi-Omics breast cancer data from The Cancer Genome Atlas (TCGA), including somatic mutation, DNA copy number, DNA methylation and gene expression profiles. We identified close cross-talk among many known cancer-related pathways with complex change patterns. Furthermore, we identified key genes (linkers) bridging these cross-talks and showed that these genes carried out consistent biological functions with the linked cross-talking pathways. Through identification of leader genes in each pathway, the architecture of cross-talking pathways was built. Notably, we observed that linkers cooperated with leaders to form the fundamentation of cross-talk of pathways which play core roles in deterioration of breast cancer. As an example, we observed that KRAS showed a direct connection to numerous cancer-related pathways, such as MAPK signaling pathway, suggesting that it may be a central communication hub. In summary, we offer an effective way to characterize complex cross-talk among disease pathways, which can be applied to other diseases and provide useful information for the treatment of cancer.     

Optimal design of feedback control by inhibition

The local stability of unbranched biosynthetic pathways is examined by mathematical analysis and computer simulation using a novel nonlinear formalism that appears to accurately describe biochemical systems. Four factors affecting the stability are examined: strength of feedback inhibition, equalization of the values among the corresponding kinetic parameters for the reactions of the pathway, pathway length, and alternative patterns of feedback interactions. The strength of inhibition and the pattern of feedback interactions are important determinants of steady-state behavior. The simple pattern of end-product inhibition in unbranched pathways may have evolved because it optimizes the steady-state behavior and is temporally most responsive to change. Stability in these simple systems is achieved by shortening pathway length either physically or, in the case of necessarily long pathways, kinetically by a wide devergence in the values of the corresponding kinetic parameters for the reactions of the pathway. These conclusions are discussed in the light of available experimental evidence.     

MicroRNA调控动物脂肪细胞分化研究进展

脂肪组织不仅在维持机体能量代谢和稳态上发挥重要作用,同时也是重要的内分泌器官。脂肪细胞分化是由间充质干细胞(Mesenchymal stem cells, MSC)向成熟脂肪细胞分化的复杂生理过程,该过程由大量转录因子、激素、信号通路分子协同调控。miRNA作为内源性非编码RNA,主要通过抑制转录后翻译等机制来调控基因表达。近年来越来越多的证据表明miRNA通过调控脂肪细胞分化相关的转录因子和重要信号分子进而影响动物脂肪细胞的分化和脂肪形成。本文对miRNA影响动物白色、棕色和米色脂肪细胞分化的作用机制及其相关调控通路和关键因子进行了归纳总结,以期为肥胖等代谢性疾病的治疗提供一定的理论指导和新的治疗思路。