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[文章编号]1006-2440(2021)03-0213-05[引文格式]周松林,刘畅,谢慧敏,等.miRNA-338促进施万细胞成髓鞘[J ].交通医学,2021,35(3):213-216,231.微小RNA (microRNA ,miRNA )是一类物种之间高度保守、长度为19~25个核苷酸、不编码蛋白质的单链RNA 分子。
miRNA 在RNA 聚合酶Ⅱ作用下形成初级miRNA ,经核糖核酸酶作用得到前体miRNA ,最后在Dicer 酶剪切下成为成熟miRNA 。
miRNA 可以通过抑制翻译或转录水平来调控靶基因的表达,参与细胞分化、生长、增殖调控等重要生命活动过程[1]。
已发现在周围神经损伤后大量miRNAs 存在差异表达,在调节施万细胞表型中发挥重要作用[2]。
施万细胞是周围神经系统主要的胶质细胞,在周围神经发育、功能和再生中起着重要作用,能沿神经突起形成髓鞘。
周围神经损伤后施万细胞发生一系列的表型变化,如脱髓鞘和去分化[3]。
神经再生过程中施万细胞先去分化为施万细胞祖细胞,祖细胞在短时间内分裂、分化、增殖,生成大量施万细胞,参与髓鞘碎屑吞噬和Bunger 带的形成,进而引导神经*[基金项目]国家自然科学基金面上项目(81870975)。
**[作者简介]周松林,男,汉族,湖北黄石人,生于1980年9月,博士研究生,副研究员。
研究方向:神经损伤与修复。
通信作者:于彬,E-mail :*************.cnmiRNA-338促进施万细胞成髓鞘*周松林1**,刘畅1,谢慧敏2,杜明智1,杨述海3,韩笑笑1,于彬1(1南通大学江苏省神经再生重点实验室,江苏226001;2南通大学附属医院口腔科;3南通大学医学院)[摘要]目的:探讨在周围神经损伤后miR-338对大鼠施万细胞髓鞘形成的调控作用及机制。
方法:采用实时荧光定量PCR 检测损伤后坐骨神经中miR-338的表达变化;采用体外背根节神经元与施万细胞共培养实验,MBP 染色检测miR-338模拟物对施万细胞再分化和成髓鞘的作用;芯片检测过表达miR-338对体外培养的施万细胞转录组的影响。
微RNA (microRNA,miRNA)是一类小分子非编码RNA,仅由几十个碱基序列构成,主要调节基因表达,参与了细胞增殖、迁移、凋亡以及癌变等基本细胞生命过程,生物体所患有的很多疾病已被证实与miRNA 的异常表达密切相关[1-2]。
mi-RNA 凭借稳定地存在于人的外周血液中这一优势,被认为是液体活检的重要标志物,临床意义重要。
miRNA 在不同细胞中的表达是异质性的,研究单细胞miRNA 的表达对研究miRNA 介导的调控通路以及miRNA 相关疾病的复杂性和异质性具有重要价值[3-5]。
此外,在面对庞大而复杂的临床样本时,研发出快捷简单、准确有效的miRNA DOI:10.16605/ki.1007-7847.2022.05.0146催化发夹自组装技术用于miRNA 检测的研究进展龙禹同,万里,赵国杰*(中国医科大学生命科学学院,中国辽宁沈阳110122)摘要:微RNA (microRNA,miRNA)是一类小分子RNA,参与了众多的细胞过程,在生命体的生长发育过程中起到了关键作用。
鉴于miRNA 的重要性和结构特殊性,其对于疾病的预测与评估有着深刻的意义。
当前,miRNA 检测技术迅猛发展,其中,催化发夹自组装(catalytic hairpin assembly,CHA)是一项新型核酸恒温扩增技术,具有反应过程无需酶催化、检测灵敏度高特异性强、操作简单方便等优点,在miRNA 的检测领域有着巨大潜力。
本文将着重阐述CHA 技术的检测原理,从靶标识别、信号扩增、信号输出3个方面对基于CHA 技术的miRNA 检测策略进行介绍,并提出该技术当前面临的挑战及前景,旨在为医学、生物信息等相关领域的研究提供进一步参考。
关键词:微RNA (miRNA);催化发夹自组装(CHA);检测中图分类号:Q503文献标志码:A文章编号:1007-7847(2023)01-0086-09收稿日期:2022-05-11;修回日期:2022-08-11;网络首发日期:2022-09-30基金项目:沈阳市中青年科技创新人才支持计划项目(RC190235);中国医科大学大学生创新创业项目(X202210159088)作者简介:龙禹同(2000—),女,辽宁鞍山人,学生;龙禹同和万里对本文的贡献相同,为本文共同第一作者;*通信作者:赵国杰(1978—),男,辽宁沈阳人,博士,中国医科大学教授,主要从事核酸及核苷酸衍生物的生物化学、核酸相关酶学、核酸扩增等方面的研究,E-mail:**************.cn 。
引文格式：白潜, 陈远红, 陈曦, 等. 鸡白痢沙门氏菌感染对雏鸡脾脏miRNA 表达谱的影响[J]. 云南农业大学学报(自然科学), 2023, 38(2): 220−227. DOI: 10.12101/j.issn.1004-390X(n).202205004鸡白痢沙门氏菌感染对雏鸡脾脏miRNA 表达谱的影响*白 潜， 陈远红， 陈 曦， 江康峰， 叶玮琪， 向 斌， 杨亮宇 **， 杨 静 **(云南农业大学 动物医学院，云南 昆明 650201)摘要: 【目的】鉴定感染鸡白痢沙门氏菌后雏鸡脾脏差异表达的miRNA ，为阐明miRNA 在鸡白痢沙门氏菌感染中的调控机制提供理论基础。
【方法】采集感染和未感染鸡白痢沙门氏菌的SPF 雏鸡脾脏，提取脾脏RNA ，构建6个miRNA 文库；利用Illumina Hiseq 2500测序技术和生物信息学分析筛选差异表达miRNA ，通过TargetScan 和miRanda 算法预测差异表达miRNA 的靶基因，并进行GO 功能富集和KEGG 通路富集分析，最后利用RT-qPCR 方法验证测序结果。
【结果】鉴定到29个差异表达miRNAs ，其中15个显著上调、14个显著下调。
GO 分析表明：脂多糖介导的信号通路正调控以及NIK/NF-κB 介导的信号通路负调控等免疫相关生物学过程显著富集；KEGG 分析显示：差异表达的miRNA 主要参与溶酶体和内吞作用等免疫相关通路。
高通量测序结果与RT-qPCR 结果一致。
【结论】成功鉴定到鸡白痢沙门氏菌感染雏鸡脾脏miRNA 表达谱，获得29个鸡白痢沙门氏菌感染潜在相关miRNAs 。
关键词: 鸡白痢沙门氏菌；SPF 雏鸡；脾脏；差异表达miRNA ；免疫中图分类号: S858.312.61 文献标志码: A 文章编号: 1004–390X (2023) 02−0220−08Effects of Salmonella Pullorum Infection on miRNA ExpressionProfiles in Spleen of ChicksBAI Qian ，CHEN Yuanhong ，CHEN Xi ，JIANG Kangfeng ，YE Weiqi ，XIANG Bin ，YANG Liangyu ，YANG Jing(College of Veterinary Medicine, Yunnan Agricultural University, Kunming 650201, China)Abstract: ［Purpose ］To identify the differentially expressed miRNA in spleen of chicken infected with Salmonella Pullorum, providing a theoretical basis for clarifying the regulatory mechanism of miRNA in Salmonella Pullorum infection. ［Methods ］Spleens from SPF chicks infected and unin-fected with Salmonella Pullorum were collected, spleen RNA was extracted, and six miRNA libraries were constructed. The differentially expressed miRNA were screened by Illumina Hiseq 2500 high-throughput sequencing technology and bioinformatics technology, the target genes were predicted by TargetScan and miRanda, and the predicted target genes were analyzed using GO functional enrich-ment and KEGG pathway enrichment. Finally, RT-qPCR was used to verify the sequencing results.云南农业大学学报（自然科学），2023，38(2)：220−227Journal of Yunnan Agricultural University (Natural Science)E-mail: ********************收稿日期：2022-05-05 修回日期：2023-03-22 网络首发日期：2023-05-06*基金项目：云南省万人计划-产业技术领军人才(YNWR-CYJS-2019-020)；国家蛋鸡产业技术体系(CARS-40-S25)。
enrichmentscore计算 (1)Enrichment Score CalculationIntroductionEnrichment score calculation is an important method used in bioinformatics and genomics research to analyze gene expression data. It measures the degree of enrichment of a set of genes within a larger gene set, typically based on their association with specific biological processes, pathways, or functional annotations.MethodsThere are several approaches to calculate the enrichment score, but one commonly used method is the Gene Set Enrichment Analysis (GSEA) algorithm developed by Mootha et al. (2003). The GSEA algorithm ranks genes based on their differential expression in a given experiment and then calculates an enrichment score by examining the cumulative distribution of genes within a predefined gene set.The GSEA algorithm consists of the following steps:1. Preprocessing: The gene expression data is normalized to remove any systematic biases or technical variations that may affect the analysis. This ensures that all genes are on a similar scale and allows for meaningful comparison.2. Gene ranking: Genes are ranked based on their differential expression between two or more experimental conditions. This step identifies genes thatshow the most significant changes in expression and are likely to be biologically relevant.3. Enrichment score calculation: The cumulative sum of the ranked gene list is calculated, and the enrichment score is obtained by assessing the degree of over-representation of the predefined gene set at each point in the ranked list. A positive enrichment score indicates an enrichment of the gene set towards the top of the ranked list, while a negative score suggests enrichment towards the bottom.4. Statistical significance assessment: The significance of the enrichment score is assessed through permutation testing. Multiple permutations of the gene labels are generated, and enrichment scores are recalculated for each permutation. This generates a null distribution against which the observed enrichment score is compared to determine its statistical significance.ApplicationsEnrichment score calculation has a wide range of applications in biological research. It can be used to identify pathways or biological processes that are differentially regulated between different experimental conditions, such as disease states or drug treatments. By analyzing the enrichment score, researchers can gain insights into the underlying biological mechanisms and potentially identify novel therapeutic targets.Enrichment score calculation can also be applied to other types of omics data, such as proteomics or metabolomics. By integrating multiple layers of omics data, researchers can gain a more comprehensive understanding of complex biological systems and unravel the molecular networks that drive normal physiology or disease progression.ConclusionIn conclusion, enrichment score calculation is a powerful tool in bioinformatics and genomics research. It allows for the systematic analysis of gene expression data and provides valuable insights into the functional implications of differentially expressed genes. By accurately quantifying the degree of enrichment of a predefined gene set, researchers can unravel the complex relationships between genes, pathways, and biological processes.。
go富集展示方法Go enrichment display method is an essential technique in the field of bioinformatics. It helps researchers to understand the biological significance of a set of genes or proteins. By analyzing the functions and pathways that are over-represented in a specific gene list, researchers can gain insight into the underlying mechanisms of a biological process or disease.富集分析展示方法是生物信息学领域中的一项重要技术。
它帮助研究人员理解一组基因或蛋白质的生物学意义。
通过分析一个特定基因列表中过度表达的功能和途径，研究人员可以深入了解生物过程或疾病的潜在机制。
There are several different ways to visually display the results of a go enrichment analysis. One common method is to create a scatterplot, where each point represents a gene or protein and is plotted based on its enrichment score and significance. This allows researchers to quickly identify which genes or pathways are most enriched in their dataset.有几种不同的方式来可视化展示富集分析的结果。
microrna靶基因研究MicroRNAs (miRNAs)是一种大小约21—23个碱基的单链小分子RNA，是由具有发夹结构的约70-90个碱基大小的单链RNA前体经过dicer酶加工后生成，不同于siRNA（双链）但是和siRNA密切相关。
据推测，这些非编码小分子RNA（miRNAs）参与调控基因表达，但其机制区别于siRNA介导的mRNA降解。
第一个被确认的miRNA是在线虫中首次发现的lin-4 和let-7，随后多个研究小组在包括人类、果蝇、植物等多种生物物种中鉴别出数百个miRNAs。
最早被发现的两个miRNAs——lin-4 and let-7被认为是通过不完全互补结合到目标靶mRNA 3’非编码区端，以一种未知方式诱发蛋白质翻译抑制，进而抑制蛋白质合成，阻断mRNA的翻译。
多个果蝇miRNAs也被发现和他们的目标靶mRNAs的3’非编码区有部分同源。
由于miRNAs和其潜在的目标靶之间并非完全互补，这使得通过信息学的方法鉴定miRNA的目标靶位点变得困难。
因而也无法确定miRNAs的作用方式是什么，以何种机制影响mRNA的翻译，以何种方式调控基因表达。
miRNAs的作用目标靶和活性机制一直是各地的研究人员的关注热点。
总结了一些microrna靶基因方法的比较重要的文献，与大家共享。
()Combinatorial microRNA target predictionsAbstractMicroRNAs are small noncoding RNAs that recognize and bind to partially complementary sites inthe 3′ untranslated regions of target genes in animals an d, by unknown mechanisms, regulate proteindifferent cell types and may coordinately regulate cell-specific target genes. Here, we present PicTar, a computational method for identifying common targets of microRNAs. Statistical tests usinggenome-wide alignments of eight vertebrate genomes, PicTar's ability to specifically recover published microRNA targets, and experimental validation of seven predicted targets suggest that PicTar has an excellent success rate in predicting targets for single microRNAs and for combinations of microRNAs. We find that vertebrate microRNAs target, on average, roughly 200 transcripts each. Furthermore, our results suggest widespread coordinate control executed by microRNAs. In particular, we experimentally validate common regulation of Mtpn by miR-375, miR-124 and let-7b and thus provide evidence for coordinate microRNA control in mammals./ng/journal/v37/n5/full/ng1536.htmlNature Genetics 37, 495 - 500 (2005)Global identification of microRNA–target RNA pairs by parallel analysis of RNA endsMarcelo A German1, Manoj Pillay1, Dong-Hoon Jeong1, Amit Hetawal1, Shujun Luo2, Prakash Janardhanan1, Vimal Kannan1, Linda A Rymarquis1, Kan Nobuta1, Rana German1, Emanuele De Paoli1, Cheng Lu1, Gary Schroth2, Blake C Meyers1 & Pamela J Green1AbstractMicroRNAs (miRNAs) are important regulatory molecules in most eukaryotes and identification of their target mRNAs is essential for their functional analysis. Whereas conventional methods rely on computational prediction and subsequent experimental validation of target RNAs, we directly sequenced >28,000,000 signatures from the 5′ ends of polyadenylat ed products of miRNA-mediated mRNA decay, isolated from inflorescence tissue of Arabidopsis thaliana, to discover novelmiRNA–target RNA pairs. Within the set of ~27,000 transcripts included in the 8,000,000 nonredundant signatures, several previously predicted but nonvalidated targets of miRNAs were found. Like validated targets, most showed a single abundant signature at the miRNA cleavage site, particularly in libraries from a mutant deficient in the 5′-to-3′ exonuclease AtXRN4. Although miRNAs in Arabidopsis have been extensively investigated, working in reverse from the cleavedtargets resulted in the identification and validation of novel miRNAs. This versatile approach will affect the study of other aspects of RNA processing beyond miRNA–target RNA pairs.Nature Biotechnology 26, 941 - 946 (2008)/nbt/journal/v26/n8/full/nbt1417.htmlmicroRNA target predictions in animalsNikolaus RajewskyCenter for Comparative Functional Genomics Department of Biology, 100 Washington Square East, New York, New York 10003, USA. nikolaus.rajewsky@In recent years, microRNAs (miRNAs) have emerged as a major class of regulatory genes, present in most metazoans and important for a diverse range of biological functions.Because experimental identification of miRNA targets is difficult, there has been an explosion of computational target predictions. Although the initial round of predictions resulted in very diverse results, subsequent computational and experimental analyses suggested that at least a certain class of conserved miRNA targets can be confidently predicted and that this class of targets is large, covering, for example, at least 30% of all human genes when considering about 60 conserved vertebrate miRNA gene families. Most recent approaches have also shown that there are correlations between domains of miRNA expression and mRNA levels of their targets. Our understanding of miRNA function is still extremely limited, but it may be that by integrating mRNA and miRNA sequence and expression data with other comparative genomic data, we will be able to gain global and yet specific insights into the function and evolution of a broad layer of post-transcriptional control.Nature Genetics38, S8 - S13 (2006)/ng/journal/v38/n6s/abs/ng1798.htmlMicroRNA Target Recognition and Regulatory FunctionsDavid P. BartelThe publisher's final edited version of this article is available free at CellSee other articles in PMC that cite the published article.Go to:AbstractMicroRNAs (miRNAs) are endogenous ~23-nt RNAs that can play importantgene-regulatory roles in animals and plants by pairing to the mRNAs ofprotein-coding genes to direct their posttranscriptional repression. This review outlines the current understanding of miRNA target recognition in animals and discusses the widespread impact of miRNAs on both the expression and evolution of protein-coding genes.Cell. Jan 23, 2009; 136(2): 215–233./pmc/articles/PMC3794896/mirWIP: microRNA target prediction based onmicroRNA-containing ribonucleoprotein–enriched transcriptsAbstractTarget prediction for animal microRNAs (miRNAs) has been hindered by the small number ofverified targets available to evaluate the accuracy of predicted miRNA-target interactions. Recently, a dataset of 3,404 miRNA-associated mRNA transcripts was identified by immunoprecipitation of the RNA-induced silencing complex components AIN-1 and AIN-2. Our analysis of this AIN-IP dataset revealed enrichment for defining characteristics of functional miRNA-target interactions, including structural accessibility of target sequences, total free energy of miRNA-target hybridization and topology of base-pairing to the 5' seed region of the miRNA. We used these enriched characteristics as the basis for a quantitative miRNA target prediction method, miRNA targets by weighting immunoprecipitation-enriched parameters (mirWIP), which optimizes sensitivity to verifiedmiRNA-target interactions and specificity to the AIN-IP dataset. MirWIP can be used to capture all known conserved miRNA-mRNA target relationships in Caenorhabditis elegans at a lowerfalse-positive rate than can the current standard methods.Nature Methods 5, 813 - 819 (2008)A guide through present computational approaches for the identification of mammalian microRNA targetsAbstractComputational microRNA (miRNA) target prediction is a field in flux. Here we present a guide through five widely used mammalian target prediction programs. We include an analysis of the performance of these individual programs and of various combinations of these programs. For this analysis we compiled several benchmark data sets of experimentally supported miRNA–target gene interactions. Based on the results, we provide a discussion on the status of target prediction and also suggest a stepwise approach toward predicting and selecting miRNA targets for experimental testing.Nature Methods 3, 881 - 886 (2006)Cell. Apr 2, 2010; 141(1): 129–141.doi: 10.1016/j.cell.2010.03.009PMCID: PMC2861495NIHMSID: NIHMS195398Transcriptome-wide identification of RNA-binding protein and microRNA target sites by PAR-CLIPMarkus Hafner,1,6 Markus Landthaler,1,5,6 Lukas Burger,2 Mohsen Khorshid,2 Jean Hausser,2Philipp Berninger,2Andrea Rothballer,1Manuel Ascano, Jr.,1 Anna-Carina Jungkamp,1,5 Mathias Munschauer,1 Alexander Ulrich,1 Greg S. Wardle,1 Scott Dewell,3 Mihaela Zavolan,2,4 and Thomas Tuschl1,4SummaryRNA transcripts are subject to post-transcriptional gene regulation involving hundreds of RNA-binding proteins (RBPs) and microRNA-containing ribonucleoprotein complexes (miRNPs) expressed in a cell-type dependent fashion. We developed acell-based crosslinking approach to determine at high resolution andtranscriptome-wide the binding sites of cellular RBPs and miRNPs. The crosslinked sites are revealed by thymidine to cytidine transitions in the cDNAs prepared from immunopurified RNPs of 4-thiouridine-treated cells. We determined the binding sites and regulatory consequences for several intensely studied RBPs and miRNPs, including PUM2, QKI, IGF2BP1-3, AGO/EIF2C1-4 and TNRC6A-C. Our study revealed that these factors bind thousands of sites containing defined sequence motifs and have distinct preferences for exonic versus intronic or coding versus untranslated transcript regions. The precise mapping of binding sites across the transcriptome willbe critical to the interpretation of the rapidly emerging data on genetic variation between individuals and how these variations contribute to complex genetic diseases. /pmc/articles/PMC2861495/。
OSTools-GO富集分析⼯具的使⽤与解读详细教程第⼀列为GO term的ID，点击GO ID，可显⽰这个GO term包含的所有基因：再点击这个GO ID，就可以链接到 官⽹，可以查看GO的具体信息。
第⼆列为GO term的功能描述；第三列前⾯的数字为差异表达基因中富集到这个GO term的基因数，后⾯的数字为差异表达基因的总数；第四列前⾯的数字为背景基因中富集到这个GO term的基因数，后⾯的数字为背景基因的总数；第五列为P value，即计算第三列的百分⽐与第四列的百分⽐相⽐，是否有显著差异。
我们将⼩于0.05的P value标红显⽰；第六列为多重检验校正后的Q value，也是把⼩于0.05的Q value标红显⽰。
这些GO term是按照P value从⼩到⼤排列的，⽅便⽼师找差异富集结果。
如在这个例⼦中，microtubule-based process为在差异基因中富集最显著的GO term，说明profile1中的基因显著富集于这个功能。
3. GO有向⽆环图（out.C/P/F.png）从整体上来看，GO注释系统是⼀个有向⽆环图(Directed Acyclic Graphs)，GO各term之间的关系是单向的，GO term之间的分类关系有三种：is a、part of 和 regulates。
具体的解释可看这个帖⼦：。
富集分析结果会分别给出GO三个ontology（细胞组分、分⼦功能、⽣物过程）的有向⽆环图，如下图是⽣物过程的有向⽆环图：在这个图中，越接近根结点的GO term越概括，往下分⽀的GO term为注释到更细层级的term。
我们来看每个GO term⾥的含义：其中，Pvalue 这⼀⾏，如果⼤于0.05，即会显⽰NA，即图中只显⽰显著的P value。
形状的含义：程序默认把显著性最⾼的前10个GO term设置为⽅形，其他的GO term为圆形。
颜⾊的含义：颜⾊越深，代表该GO term越显著。
GO,KEGG, Interproscan, COG的相关知识NR库作为NCBI主要数据库之一其库容较大，通常情况下能够注释到的基因较多，但同时其中未验证的信息过多，且很多基因功能描述模糊，很多时候会影响到基因功能的具体辨识，因此需要结合其他数据注释结果进行确定。
另外，NR库因为在建立之初就包含有物种概念，因此其注释结果中均含有基因的物种来源信息，通过该类信息能够在某种程度上确定所测菌株的物种归属。
GO数据库：注释来源于Interpro数据库中的quick GO数据库，因此，该数据库结果产出会包含与Interpro数据库注释的信息，以x.iprscan.gene.ipr结尾。
Quick GO数据库注释的结果以x.iprscan.go结尾，因为GO数据库三大类之间互有重叠，所以对于同时注释上多个GO分类的基因，可以通过不同大类间的信息来确定其功能。
KEGG数据库：最优的地方在于拥有描绘已知通路的代谢通路图。
其应用举例如下：比如我们关注丙氨酸代谢通路相关基因，这时我们可以通过关键字在x. kegg.list.anno中寻找含有丙氨酸(Alanine)的注释结果。
Interproscan ：是EBI开发的一个继承了蛋白质结构域和功能位点的数据库，其中吧SWISS-PROT,TrEMBL,PROTSITE,PRINTS,PFAM,ProDom等数据库提供的蛋白序列中的各种局与模式，如结构，motif等信息统一起来，提供了一个较为全面的分析工具。
Swiss-Prot 较其他库的优点在于其结果通过了人工验证，可信度较高。
COG:即Clusters of Orthologous Groups of proteins。
构成每个COG的蛋白都是被假定为来自于一个祖先蛋白，并且因此或者是orthologs或是paralogs。
Orthologs是指来自于不同物种的由垂直家系（物种形成）进化而来的蛋白，并且典型的保留与原始蛋白有相同的功能。
唐氏综合征（DS ）是最常见的染色体非整倍体，发病率约为1∶700活产［1］。
该综合征包括80多种影响所有主要器官的临床表现，以及不同程度的认知功能障碍和颅面异常，这是唐氏综合征患者最常见的临床特征。
同时，唐氏综合征常伴发生先天性心脏缺陷、胃肠道异常、儿童白血病或早发性阿尔茨海默病等［2,3］。
唐氏综合征的表型可能是基因失衡所引起，由于21号染色体基因剂量增加了50%且存在与其他基因相互不平衡的作Whole-transcriptome sequencing analysis of placental differential miRNA expression profile in Down syndromeHE Jianping 1,TANG Jian 1,SU Hong 2,SHEN Cuihua 3,LUO Shengjun 1,WANG Haitao 4,QIAN Yuan 1,LÜMengxin 11Department of Medical Genetics and Prenatal Diagnosis,2Genetic Counseling Clinic,3Department of Obstetrics,4Department of Pathology,Kunming Maternal and Child Health Care Hospital,Kunming 650031,China摘要：目的通过筛选唐氏综合征胎盘中表达变化的miRNAs 并分析具体生物学途径来探讨唐氏综合征新的标记物及其分子发病机制。
方法运用全转录组测序分析技术分析唐氏综合征确诊胎盘（DS ，n =3）和产前诊断确诊正常胎盘（n =3）样本，筛选出显著差异表达的miRNA ，通过miRWalk 、Targetscan 、miRDB 软件预测其靶基因并进行GO 和KEGG-Pathway 功能富集分析。
