Muhammad Syed Aun, Fatima Nighat, Paracha Rehan Zafar, Ali Amjad, Chen Jake Y
1Institute of Molecular Biology and Biotechnology, Bahauddin Zakariya University, Multan, 60800 Pakistan.
2Department of Pharmacy, COMSATS Institute of Information Technology, Abbottabad, 22060 Pakistan.
J Biol Res (Thessalon). 2019 Apr 4;26:2. doi: 10.1186/s40709-019-0094-x. eCollection 2019 Dec.
Alopecia or hair loss is a complex polygenetic and psychologically devastating disease affecting millions of men and women globally. Since the gene annotation and environmental knowledge is limited for alopecia, a systematic analysis for the identification of candidate biomarkers is required that could provide potential therapeutic targets for hair loss therapy.
We designed an interactive framework to perform a meta-analytical study based on differential expression analysis, systems biology, and functional proteomic investigations. We analyzed eight publicly available microarray datasets and found 12 potential candidate biomarkers including three extracellular proteins from the list of differentially expressed genes with a -value < 0.05. After expression profiling and functional analysis, we studied protein-protein interactions and observed functional associations of source proteins including WIF1, SPON1, LYZ, GPRC5B, PTPRE, ZFP36L2, HBB, PHF15, LMCD1, KRT35 and VAV3 with target proteins including APCDD1, WNT1, WNT3A, SHH, ESRI, TGFB1, and APP. Pathway analysis of these molecules revealed their role in major physiological reactions including protein metabolism, signal transduction, WNT, BMP, EDA, NOTCH and SHH pathways. These pathways regulate hair growth, hair follicle differentiation, pigmentation, and morphogenesis. We studied the regulatory role of β-catenin, Nf-kappa B, cytokines and retinoic acid in the development of hair growth. Therefore, the differential expression of these significant proteins would affect the normal level and could cause aberrations in hair growth.
Our integrative approach helps to prioritize the biomarkers that ultimately lessen the economic burden of experimental studies. It will also be valuable to discover mutants in genomic data in order to increase the identification of new biomarkers for similar problems.
脱发是一种复杂的多基因疾病，对全球数百万男性和女性的心理造成了极大的伤害。由于关于脱发的基因注释和环境知识有限，因此需要进行系统分析以鉴定候选生物标志物，从而为脱发治疗提供潜在的治疗靶点。
我们设计了一个交互式框架，基于差异表达分析、系统生物学和功能蛋白质组学研究进行荟萃分析。我们分析了八个公开可用的微阵列数据集，发现了12种潜在的候选生物标志物，其中包括来自差异表达基因列表中三个细胞外蛋白，其P值<0.05。经过表达谱分析和功能分析后，我们研究了蛋白质-蛋白质相互作用，并观察到源蛋白（包括WIF1、SPON1、LYZ、GPRC5B、PTPRE、ZFP36L2、HBB、PHF15、LMCD1、KRT35和VAV3）与靶蛋白（包括APCDD1、WNT1、WNT3A、SHH、ESRI、TGFB1和APP）之间的功能关联。对这些分子的通路分析揭示了它们在主要生理反应中的作用，包括蛋白质代谢、信号转导、WNT、BMP、EDA、NOTCH和SHH通路。这些通路调节头发生长、毛囊分化、色素沉着和形态发生。我们研究了β-连环蛋白、核因子-κB、细胞因子和视黄酸在头发生长发育中的调节作用。因此，这些重要蛋白质的差异表达会影响正常水平，并可能导致头发生长异常。
我们的综合方法有助于确定生物标志物的优先级，最终减轻实验研究的经济负担。发现基因组数据中的突变体对于增加类似问题新生物标志物的识别也将是有价值的。
