美國的科研人員識別了一個(gè)與人類腎臟老化相關(guān)的基因,。
這項(xiàng)研究結(jié)果發(fā)布在10月16日的PLoS Genetics上,。研究人員聯(lián)合使用了序列轉(zhuǎn)錄譜（sequential transcriptional profiling）和eQTL分析（eQTL mapping）,，他們表示這項(xiàng)技術(shù)能夠用于其他任何類型的遺傳學(xué)識別研究,。
該研究是由Stuart Kim博士主持的,，據(jù)他介紹,，這是首次使用基因組轉(zhuǎn)錄譜對腎臟組織中630個(gè)候選基因進(jìn)行篩選，一共選出了其中的101個(gè)與老化相關(guān)的基因,。接下來他們對兩個(gè)群體的101個(gè)基因進(jìn)行了相關(guān)的腎臟老化檢測,。結(jié)果發(fā)現(xiàn)一個(gè)編碼細(xì)胞外基質(zhì)蛋白（extracellular matrix protein）MMP20的基因與腎臟老化顯著相關(guān)。
由于這項(xiàng)關(guān)于腎臟老化的分析研究是在兩個(gè)群體中進(jìn)行的,，所以研究人員強(qiáng)調(diào)還需要在其他不同的人群中進(jìn)行重復(fù)試驗(yàn),。隨著更多的老化相關(guān)的基因發(fā)現(xiàn)，某些特定的遺傳變異在未來可能用于預(yù)測腎臟的老化軌跡,。（生物谷Bioon.com）
相關(guān)新聞：
Developmental Cell：腎臟發(fā)育的基因表達(dá)圖譜
Nature：腎臟起源于昆蟲,？
生物谷推薦原始出處：
PLoS Genet 5(10): e1000692. doi:10.1371/journal.pgen.1000692
Expression Quantitative Trait Loci Are Highly Sensitive to Cellular Differentiation State
Alice Gerrits1#, Yang Li2#, Bruno M. Tesson2#, Leonid V. Bystrykh1, Ellen Weersing1, Albertina Ausema1, Bert Dontje1, Xusheng Wang3,4, Rainer Breitling2, Ritsert C. Jansen2*, Gerald de Haan1*
1 Department of Cell Biology, Section Stem Cell Biology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands, 2 Groningen Bioinformatics Centre, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Haren, The Netherlands, 3 Department of Anatomy and Neurobiology, University of Tennessee Health Science Center, Memphis, Tennessee, United States of America, 4 Institute of Bioinformatics, Zhejiang University, Hangzhou, China
Genetical genomics is a strategy for mapping gene expression variation to expression quantitative trait loci (eQTLs). We performed a genetical genomics experiment in four functionally distinct but developmentally closely related hematopoietic cell populations isolated from the BXD panel of recombinant inbred mouse strains. This analysis allowed us to analyze eQTL robustness/sensitivity across different cellular differentiation states. Although we identified a large number (365) of “static” eQTLs that were consistently active in all four cell types, we found a much larger number (1,283) of “dynamic” eQTLs showing cell-type–dependence. Of these, 140, 45, 531, and 295 were preferentially active in stem, progenitor, erythroid, and myeloid cells, respectively. A detailed investigation of those dynamic eQTLs showed that in many cases the eQTL specificity was associated with expression changes in the target gene. We found no evidence for target genes that were regulated by distinct eQTLs in different cell types, suggesting that large-scale changes within functional regulatory networks are uncommon. Our results demonstrate that heritable differences in gene expression are highly sensitive to the developmental stage of the cell population under study. Therefore, future genetical genomics studies should aim at studying multiple well-defined and highly purified cell types in order to construct as comprehensive a picture of the changing functional regulatory relationships as possible.
