SPOROS: A pipeline to analyze DISE/6mer seed toxicity.
SPOROS: A pipeline to analyze DISE/6mer seed toxicity.
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DOI：
10.1371/journal.pcbi.1010022
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发表时间：
2022-03
影响因子：
4.3
通讯作者：
中科院分区：
文献类型：
--
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microRNA (miRNA) 是（18-22nt 长）非编码短 (s)RNA，通过靶向目标 mRNA 的 3' 非翻译区来抑制基因表达。一旦将位于 miRNA 引导链位置 2-7/8 的种子序列加载到 RNA 诱导沉默复合物 (RISC) 中，就会发生这种情况。富含 G 的 6mer 种子序列可以通过靶向位于对细胞生存至关重要的基因中的富含 C 6mer 种子匹配来杀死细胞。这会通过我们称为 6mer 种子毒性的机制诱导生存基因消除诱导死亡 (DISE)。通常通过将小 (sm)RNA 测序的读数与基因组进行比对来定量细胞中的 miRNA。然而，对任何 smRNA Seq 数据集进行预测的 6mer 种子毒性分析需要替代工作流程，仅基于可进入 RISC 的任何短 (s)RNA 的确切位置 2-7。因此，我们开发了 SPOROS，这是一种半自动化管道，可产生多个有用的输出，以预测和比较不同样本之间细胞 sRNA 的 6mer 种子毒性，无论其性质如何。我们提供两个示例来说明 SPOROS 的功能：示例一涉及对癌细胞系（野生型或无法产生大多数 miRNA 的两个突变系）中 RISC 结合 sRNA 的分析。示例二基于来自死后大脑（来自正常人或阿尔茨海默病患者）的公开可用的 smRNA Seq 数据集。我们的方法（可在 https://github.com/ebartom/SPOROS 和 Code Ocean 找到：https://doi.org/10.24433/CO.1732496.v1）旨在用于分析各种正常和疾病环境中的各种 smRNA Seq 数据。我们最近发现了一种嵌入基因组中的杀伤代码，具有强大的抗癌活性。它仅基于 6 个核苷酸（由 A、G、C 或 U 组成），当存在于小双链 RNA 序列中时，它可以像 microRNA (miRNA) 一样发挥作用。 miRNA 是许多细胞功能的重要调节因子。人类基因组中约 2,300 个已知的 miRNA 通过其业务端（种子序列）发挥作用。当这个种子序列有 6 个核苷酸长（6mer 种子）并且主要由 G 组成时，这些小 RNA 可以杀死所有癌细胞。因此，该密码存在于许多具有抗癌活性的 miRNA 中。然而，该代码不仅限于miRNA，在某些条件下也可能影响正常组织。我们现在开发了 SPOROS，这是一种半自动化生物信息学流程，可让人们分析任何已测序小 RNA 的数据集，重点关注其 6mer 种子含量及其杀死细胞的潜力。我们提供了此类分析的两个示例：第一个示例是我们生成的数据集，该数据集是根据人类结肠癌细胞系中所有小 RNA 的表达与不能产生大多数 miRNA 的匹配突变细胞系进行比较而生成的。第二个例子是从正常大脑和阿尔茨海默病患者大脑中分离出的小 RNA 的公开数据集。
microRNAs (miRNAs) are (18-22nt long) noncoding short (s)RNAs that suppress gene expression by targeting the 3’ untranslated region of target mRNAs. This occurs through the seed sequence located in position 2-7/8 of the miRNA guide strand, once it is loaded into the RNA induced silencing complex (RISC). G-rich 6mer seed sequences can kill cells by targeting C-rich 6mer seed matches located in genes that are critical for cell survival. This results in induction of Death Induced by Survival gene Elimination (DISE), through a mechanism we have called 6mer seed toxicity. miRNAs are often quantified in cells by aligning the reads from small (sm)RNA sequencing to the genome. However, the analysis of any smRNA Seq data set for predicted 6mer seed toxicity requires an alternative workflow, solely based on the exact position 2–7 of any short (s)RNA that can enter the RISC. Therefore, we developed SPOROS, a semi-automated pipeline that produces multiple useful outputs to predict and compare 6mer seed toxicity of cellular sRNAs, regardless of their nature, between different samples. We provide two examples to illustrate the capabilities of SPOROS: Example one involves the analysis of RISC-bound sRNAs in a cancer cell line (either wild-type or two mutant lines unable to produce most miRNAs). Example two is based on a publicly available smRNA Seq data set from postmortem brains (either from normal or Alzheimer’s patients). Our methods (found at https://github.com/ebartom/SPOROS and at Code Ocean: https://doi.org/10.24433/CO.1732496.v1) are designed to be used to analyze a variety of smRNA Seq data in various normal and disease settings. We recently discovered a kill code embedded in the genome with powerful anti-cancer activity. It is based on only 6 nucleotides (comprised of A, G, C, or U) that when present in the sequence of a small double stranded RNA allows it to act like a microRNA (miRNA). miRNAs are important regulators of many cell functions. The ~2,300 known miRNAs in the human genome function through their business end, the seed sequence. When this seed sequence is 6 nucleotides long (6mer seed) and is comprised of mostly Gs, then these small RNAs can kill all cancer cells. Hence, this code is found in a number of miRNAs that have anti-cancer activities. However, the code is not limited to miRNAs and may also affect normal tissue under certain conditions. We have now developed SPOROS, a semi-automated bioinformatics pipeline that allows one to analyze any data set of sequenced small RNAs with a focus on their 6mer seed content and their potential to kill cells. We present two examples of such an analysis: the first example is a data set we generated on the expression of all small RNAs in a human colon cancer cell line compared to matching mutant cell lines that cannot produce most miRNAs; the second example is a publicly available data set of small RNAs isolated from normal brains and from brains of patients with Alzheimer’s disease.
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影响因子：
64.8
作者：
Baek, Daehyun;Villen, Judit;Shin, Chanseok;Camargo, Fernando D.;Gygi, Steven P.;Bartel, David P.
通讯作者：
Bartel, David P.
影响因子：
11.2
作者：
Patel M;Wang Y;Bartom ET;Dhir R;Nephew KP;Matei D;Murmann AE;Lengyel E;Peter ME
通讯作者：
Peter ME
DOI：
10.1073/pnas.1506116112
发表时间：
2015-09-22
影响因子：
11.1
作者：
Hauptmann, Judith;Schraivogel, Daniel;Meister, Gunter
通讯作者：
Meister, Gunter
影响因子：
64.5
作者：
Lewis, BP;Shih, IH;Burge, CB
通讯作者：
Burge, CB
影响因子：
10.5
作者：
Han, JJ;Lee, Y;Kim, VN
通讯作者：
Kim, VN