Coat color is a key economic trait in wool-producing species. Color development and pigmentation are controlled by complex mechanisms in animals. Here, we report the first production of an altered coat color by overexpression of miR-137 in transgenic mice. Transgenic mice overexpressing miR-137 developed a range of coat color changes from dark black to light color. Molecular analyses of the transgenic mice showed decreased expression of the major target gene termed MITF and its downstream genes, including TYR, TYRP1, and TYRP2. We also showed that melanogenesis altered by miR-137 is distinct from that affected by UV radiation in transgenic mice. Our study provides the first mouse model for the study of coat color controlled by miRNAs in animals and may have important applications in wool production.
BackgroundPrevious molecular genetic studies of physiology and pigmentation of sheep skin have focused primarily on a limited number of genes and proteins. To identify additional genes that may play important roles in coat color regulation, Illumina sequencing technology was used to catalog global gene expression profiles in skin of sheep with white versus black coat color.ResultsThere were 90,006 and 74,533 unigenes assembled from the reads obtained from white and black sheep skin, respectively. Genes encoding for the ribosomal proteins and keratin associated proteins were most highly expressed. A total of 2,235 known genes were differentially expressed in black versus white sheep skin, with 479 genes up-regulated and 1,756 genes down-regulated. A total of 845 novel genes were differentially expressed in black versus white sheep skin, consisting of 107 genes which were up-regulated (including 2 highly expressed genes exclusively expressed in black sheep skin) and 738 genes that were down-regulated. There was also a total of 49 known coat color genes expressed in sheep skin, from which 13 genes showed higher expression in black sheep skin. Many of these up-regulated genes, such as DCT, MATP, TYR and TYRP1, are members of the components of melanosomes and their precursor ontology category.ConclusionThe white and black sheep skin transcriptome profiles obtained provide a valuable resource for future research to understand the network of gene expression controlling skin physiology and melanogenesis in sheep.
N-staging is a determining factor for prognostic assessment and decision-making for stage-based cancer therapeutic strategies. Visual inspection of whole-slides of intact lymph nodes is currently the main method used by pathologists to calculate the number of metastatic lymph nodes (MLNs). Moreover, even at the same N stage, the outcome of patients varies dramatically. Here, we propose a deep-learning framework for analyzing lymph node whole-slide images (WSIs) to identify lymph nodes and tumor regions, and then to uncover tumor-area-to-MLN-area ratio (T/MLN). After training, our model’s tumor detection performance was comparable to that of experienced pathologists and achieved similar performance on two independent gastric cancer validation cohorts. Further, we demonstrate that T/MLN is an interpretable independent prognostic factor. These findings indicate that deep-learning models could assist not only pathologists in detecting lymph nodes with metastases but also oncologists in exploring new prognostic factors, especially those that are difficult to calculate manually.
Epidermal growth factor-like domain-containing protein 7 (EGFL7) is upregulated in human epithelial tumors and so is a potential biomarker for malignancy. Indeed, previous studies have shown that high EGFL7 expression promotes infiltration and metastasis of gastric carcinoma. The epithelial–mesenchymal transition (EMT) initiates the metastatic cascade and endows cancer cells with invasive and migratory capacity; however, it is not known if EGFL7 promotes metastasis by triggering EMT. We found that EGFL7 was overexpressed in multiple human gastric cancer (GC) cell lines and that overexpression promoted cell invasion and migration as revealed by scratch wound and transwell migration assays. Conversely, shRNA-mediated EGFL7 knockdown reduced invasion and migration. Furthermore, EGFL7-overexpressing cells grew into larger tumors and were more likely to metastasize to the liver compared to underexpressing CG cells following subcutaneous injection in mice. EGFL7 overexpression protected GC cell lines against anoikis, providing a plausible mechanism for this enhanced metastatic capacity. In excised human gastric tumors, expression of EGFL7 was positively correlated with expression levels of the mesenchymal marker vimentin and the EMT-associated transcription repressor Snail, and negatively correlated with expression of the epithelial cell marker E-cadherin. In GC cell lines, EGFL7 knockdown reversed morphological signs of EMT and decreased both vimentin and Snail expression. In addition, EGFL7 overexpression promoted EGF receptor (EGFR) and protein kinase B (AKT) phospho-activation, effects markedly suppressed by the EGFR tyrosine kinase inhibitor AG1478. Moreover, AG1478 also reduced the elevated invasive and migratory capacity of GC cell lines overexpressing EGFL7. Collectively, these results strongly suggest that EGFL7 promotes metastasis by activating EMT through an EGFR−AKT−Snail signaling pathway. Disruption of EGFL7−EGFR−AKT−Snail signaling may a promising therapeutic strategy for gastric cancer.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.