Keloids are tumor-like skin scars that grow as a result of the aberrant healing of skin injuries, with no effective treatment. The molecular mechanism underlying keloid pathogenesis is still largely unknown. In this study, we compared microRNA (miRNA) expression profiles between keloidderived fibroblasts and normal fibroblasts (including fetal and adult dermal fibroblasts) by miRNA microarray analysis. We found that the miRNA profiles in keloid-derived fibroblasts are different with those in normal fibroblasts. Nine miRNAs were differentially expressed, six of which were significantly up-regulated in keloid fibroblasts (KFs), including miR-152, miR-23b-3p, miR-31-5p, miR-320c, miR-30a-5p, and hsv1-miR-H7, and three of which were significantly down-regulated, including miR-4328, miR-145-5p, and miR-143-3p. Functional annotations of differentially expressed miRNA targets revealed that they were enriched in several signaling pathways important for scar wound healing. In conclusion, we demonstrate that the miRNA expression profile is altered in KFs compared with in fetal and adult dermal fibroblasts, and the expression profile may provide a useful clue for exploring the pathogenesis of keloids. miRNAs might partially contribute to the etiology of keloids by affecting several signaling pathways relevant to scar wound healing.

With the maturity of genome sequencing technology, huge amounts of sequence reads as well as assembled genomes are generating. With the explosive growth of genomic data, the storage and transmission of genomic data are facing enormous challenges. FASTA, as one of the main storage formats for genome sequences, is widely used in the Gene Bank because it eases sequence analysis and gene research and is easy to be read. Many compression methods for FASTA genome sequences have been proposed, but they still have room for improvement. For example, the compression ratio and speed are not so high and robust enough, and memory consumption is not ideal, etc. Therefore, it is of great significance to improve the efficiency, robustness, and practicability of genomic data compression to reduce the storage and transmission cost of genomic data further and promote the research and development of genomic technology. In this manuscript, a hybrid referential compression method (HRCM) for FASTA genome sequences is proposed. HRCM is a lossless compression method able to compress single sequence as well as large collections of sequences. It is implemented through three stages: sequence information extraction, sequence information matching, and sequence information encoding. A large number of experiments fully evaluated the performance of HRCM. Experimental verification shows that HRCM is superior to the best-known methods in genome batch compression. Moreover, HRCM memory consumption is relatively low and can be deployed on standard PCs.

No abstract