SIRT1, Is It a Tumor Promoter or Tumor Suppressor?

SIRT1 has been considered as a tumor promoter because of its increased expression in some types of cancers and its role in inactivating proteins that are involved in tumor suppression and DNA damage repair. However, recent studies demonstrated that SIRT1 levels are reduced in some other types of cancers, and that SIRT1 deficiency results in genetic instability and tumorigenesis, while overexpression of SIRT1 attenuates cancer formation in mice heterozygous for tumor suppressor p53 or APC. Here, I review these recent findings and discuss the possibility that activation of SIRT1 both extends lifespan and inhibits cancer formation

BRCA1: cell cycle checkpoint, genetic instability, DNA damage response and cancer evolution

Germline mutations of the breast cancer associated gene 1 (BRCA1) predispose women to breast and ovarian cancers. BRCA1 is a large protein with multiple functional domains and interacts with numerous proteins that are involved in many important biological processes/pathways. Mounting evidence indicates that BRCA1 is involved in all phases of the cell cycle and regulates orderly events during cell cycle progression. BRCA1 deficiency, consequently causes abnormalities in the S-phase checkpoint, the G(2)/M checkpoint, the spindle checkpoint and centrosome duplication. The genetic instability caused by BRCA1 deficiency, however, also triggers cellular responses to DNA damage that blocks cell proliferation and induces apoptosis. Thus BRCA1 mutant cells cannot develop further into full-grown tumors unless this cellular defense is broken. Functional analysis of BRCA1 in cell cycle checkpoints, genome integrity, DNA damage response (DDR) and tumor evolution should benefit our understanding of the mechanisms underlying BRCA1 associated tumorigenesis, as well as the development of therapeutic approaches for this lethal disease

Roles of FGF Receptors in Mammalian Development and Congenital Diseases

International audienceFour fibroblast growth factor receptors (FGFR1-4) constitute a family of transmembrane tyrosine kinases that serve as high affinity receptors for at least 22 FGF ligands. Gene targeting in mice has yielded valuable insights into the functions of this important gene family in multiple biological processes. These include mesoderm induction and patterning; cell growth, migration, and differentiation; organ formation and maintenance; neuronal differentiation and survival; wound healing; and malignant transformation. Furthermore, discoveries that mutations in three of the four receptors result in more than a dozen human congenital diseases highlight the importance of these genes in skeletal development. In this review, we will discuss recent progress on the roles of FGF receptors in mammalian development and congenital diseases, with an emphasis on signal transduction pathways

Weakly-supervised Caricature Face Parsing through Domain Adaptation

A caricature is an artistic form of a person's picture in which certain striking characteristics are abstracted or exaggerated in order to create a humor or sarcasm effect. For numerous caricature related applications such as attribute recognition and caricature editing, face parsing is an essential pre-processing step that provides a complete facial structure understanding. However, current state-of-the-art face parsing methods require large amounts of labeled data on the pixel-level and such process for caricature is tedious and labor-intensive. For real photos, there are numerous labeled datasets for face parsing. Thus, we formulate caricature face parsing as a domain adaptation problem, where real photos play the role of the source domain, adapting to the target caricatures. Specifically, we first leverage a spatial transformer based network to enable shape domain shifts. A feed-forward style transfer network is then utilized to capture texture-level domain gaps. With these two steps, we synthesize face caricatures from real photos, and thus we can use parsing ground truths of the original photos to learn the parsing model. Experimental results on the synthetic and real caricatures demonstrate the effectiveness of the proposed domain adaptation algorithm. Code is available at: https://github.com/ZJULearning/CariFaceParsing .Comment: Accepted in ICIP 2019, code and model are available at https://github.com/ZJULearning/CariFaceParsin