Interferon-inducible T-cell alpha chemoattractant (ITAC) induces the melanocytic migration and hypopigmentation through destabilizing p53 via histone deacetylase 5: a possible role of ITAC in pigment-related disorders

Whole Genome Resequencing Reveals Selection Signals Related to Wool Color in Sheep

Wool color is controlled by a variety of genes. Although the gene regulation of some wool colors has been studied in relative depth, there may still be unknown genetic variants and control genes for some colors or different breeds of wool that need to be identified and recognized by whole genome resequencing. Therefore, we used whole genome resequencing data to compare and analyze sheep populations of different breeds by population differentiation index and nucleotide diversity ratios (Fst and θπ ratio) as well as extended haplotype purity between populations (XP-EHH) to reveal selection signals related to wool coloration in sheep. Screening in the non-white wool color group (G1 vs. G2) yielded 365 candidate genes, among which PDE4B, GMDS, GATA1, RCOR1, MAPK4, SLC36A1, and PPP3CA were associated with the formation of non-white wool; an enrichment analysis of the candidate genes yielded 21 significant GO terms and 49 significant KEGG pathways (p < 0.05), among which 17 GO terms and 21 KEGG pathways were associated with the formation of non-white wool. Screening in the white wool color group (G2 vs. G1) yielded 214 candidate genes, including ABCD4, VSX2, ITCH, NNT, POLA1, IGF1R, HOXA10, and DAO, which were associated with the formation of white wool; an enrichment analysis of the candidate genes revealed 9 significant GO-enriched pathways and 19 significant KEGG pathways (p < 0.05), including 5 GO terms and 12 KEGG pathways associated with the formation of white wool. In addition to furthering our understanding of wool color genetics, this research is important for breeding purposes.

Identification of key genes and pathways involved in vitiligo development based on integrated analysis

Vitiligo is a chronic skin condition lack of melanocytes. However, researches on the aetiology and pathogenesis of vitiligo are still under debate. This study aimed to explore the key genes and pathways associated with occurrence and development of vitiligo.Weighted gene coexpression network analysis (WGCNA) was applied to reanalyze the gene expression dataset GSE65127 systematically. Functional enrichments of these modules were carried out at gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), gene set variation analysis (GSVA), and gene set enrichment analysis (GSEA). Then, a map of regulatory network was delineated according to pivot analysis and drug prediction. In addition, hub genes and crucial pathways were validated by an independent dataset GSE75819. The expressions of hub genes in modules were also tested by quantitative real-time polymerase chain reaction (qRT-PCR).Eight coexpressed modules were identified by WGCNA based on 5794 differentially expressed genes of vitiligo. Three modules were found to be significantly correlated with Lesional, Peri-Lesional, and Non-Lesional, respectively. The persistent maladjusted genes included 269 upregulated genes and 82 downregulated genes. The enrichments showed module genes were implicated in immune response, p53 signaling pathway, etc. According to GSEA and GSVA, dysregulated pathways were activated incessantly from Non-Lesional to Peri-Lesional and then to Lesional, 4 of which were verified by an independent dataset GSE75819. Finally, 42 transcription factors and 228 drugs were spotted. Focusing on the persistent maladjusted genes, a map of regulatory network was delineated. Hub genes (CACTIN, DCTN1, GPR143, HADH, MRPL47, NKTR, NUF2) and transcription factors (ITGAV, SYK, PDPK1) were validated by an independent dataset GSE75819. In addition, hub genes (CACTIN, DCTN1, GPR143, MRPL47, NKTR) were also confirmed by qRT-PCR.The present study, at least, might provide an integrated and in-depth insight for exploring the underlying mechanism of vitiligo and predicting potential diagnostic biomarkers and therapeutic targets.

Melanoma exosomes promote mixed M1 and M2 macrophage polarization

Macrophages are key participants in melanoma growth and survival. In general, macrophages can be classified as M1 or M2 activation phenotypes. Increasing evidence demonstrates that melanoma exosomes also facilitate tumor survival and metastasis. However, the role of melanoma exosomes in directly influencing macrophage function is poorly understood. Herein, we investigated the hypothesis that natural melanoma exosomes might directly influence macrophage polarization. To explore this hypothesis, ELISA, RT-qPCR, and macrophage functional studies were performed in vitro using an established source of melanoma exosomes (B16-F10). ELISA results for melanoma exosome induction of common M1 and M2 cytokines in RAW 264.7 macrophages, revealed that melanoma exosomes do not polarize macrophages exclusively in the M1 or M2 direction. Melanoma exosomes induced the M1 and M2 representative cytokines TNF-α and IL-10 respectively. Further assessment, using an RT-qPCR array with RAW 264.7 and primary macrophages, confirmed and extended the ELISA findings. Upregulation of markers common to both M1 and M2 polarization phenotypes included CCL22, IL-12B, IL-1β, IL-6, i-NOS, and TNF-α. The M2 cytokine TGF-β was upregulated in primary but not RAW 264.7 macrophages. Pro-tumor functions have been attributed to each of these markers. Macrophage functional assays demonstrated a trend toward increased i-NOS (M1) to arginase (M2) activity. Collectively, the results provide the first evidence that melanoma exosomes can induce a mixed M1 and M2 pro-tumor macrophage activation phenotype.