Anti-PDGFRL (aa 276-375) polyclonal antibody

Contents Ovulation rate and prolificacy are the most important reproductive traits that have major impact on the efficiency of lamb meat production. Here, we compared the whole genomes of the Romanov sheep, known as one of the high prolific breeds, and four other sheep breeds namely Assaf, Awassi, Cambridge and British du cher, to identify genetic mechanisms underlying prolificacy in sheep. Selection signature analysis revealed 637 and 477 protein-coding genes under positive selection from F-ST and nucleotide diversity (Pi) statistics, respectively. Further analysis showed that several candidate genes including LEPR, PDGFRL and KLF5 genes are involved in sheep prolificacy. The identified candidate genes in the selected regions are novel and provide new insights into the genetic mechanisms underlying prolificacy in sheep and can be useful in sheep breeding programmes to develop improved breeds for high reproductive efficiency.

Background: The most common application of microarray technology in disease research is to identify genes differentially expressed in disease versus normal tissues. However, it is known that, in complex diseases, phenotypes are determined not only by genes, but also by the underlying structure of genetic networks. Often, it is the interaction of many genes that causes phenotypic variations. Results: In this work, using cancer as an example, we develop graph-based methods to integrate multiple microarray datasets to discover disease-related co-expression network modules. We propose an unsupervised method that take into account both co-expression dynamics and network topological information to simultaneously infer network modules and phenotype conditions in which they are activated or de-activated. Using our method, we have discovered network modules specific to cancer or subtypes of cancers. Many of these modules are consistent with or supported by their functional annotations or their previously known involvement in cancer. In particular, we identified a module that is predominately activated in breast cancer and is involved in tumor suppression. While individual components of this module have been suggested to be associated with tumor suppression, their coordinated function has never been elucidated. Here by adopting a network perspective, we have identified their interrelationships and, particularly, a hub gene PDGFRL that may play an important role in this tumor suppressor network. Conclusion: Using a network-based approach, our method provides new insights into the complex cellular mechanisms that characterize cancer and cancer subtypes. By incorporating coexpression dynamics information, our approach can not only extract more functionally homogeneous modules than those based solely on network topology, but also reveal pathway coordination beyond co-expression.