搜索

Synthetic Genetic Interaction (CRISPR-SGI) Profiling in Caenorhabditis elegans
秀丽隐杆线虫中合成遗传相互作用(CRISPR-SGI)分析   

评审
匿名评审
引用 收藏 提问与回复 分享您的反馈 Cited by

本文章节

参见作者原研究论文

本实验方案简略版
eLIFE
Feb 2017

Abstract

Genetic interaction screens are a powerful methodology to establish novel roles for genes and elucidate functional connections between genes. Such studies have been performed to great effect in single-cell organisms such as yeast and E. coli (Schuldiner et al., 2005; Butland et al., 2008; Costanzo et al., 2010), but similar large-scale interaction studies using targeted reverse-genetic deletions in multi-cellular organisms have not been feasible. We developed a CRISPR/Cas9-based method for deleting genes in C. elegans and replacing them with a heterologous fluorescent reporter (Norris et al., 2015). Recently we took advantage of that system to perform a large-scale, reverse genetic screen using null alleles in animals for the first time, focusing on RNA binding protein genes (Norris et al., 2017). This type of approach should be similarly applicable to many other gene classes in C. elegans. Here we detail the protocols involved in generating a library of double mutants and performing medium-throughput competitive fitness assays to test for genetic interactions resulting in fitness changes.

Keywords: C. elegans (秀丽隐杆线虫), Genetics (遗传学), Combinatorial genetics (组合遗传学), RNA binding protein (RNA结合蛋白), Fitness (适应度)

Background

Large-scale genetic interaction screens using reverse-genetic null alleles have not previously been feasible in animals. RNAi has been used to study genetic interactions in C. elegans by knocking down expression of a large number of different genes in the presence of a single mutant background (Baugh et al., 2005; Lehner et al., 2006). However, this strategy is limited by the variable efficacy of RNAi knockdown, thereby complicating the interpretation of the results. We developed a method for efficient editing of the C. elegans genome (Norris et al., 2015) and recently expanded upon that method to enable large-scale genetic interaction profiling in animals using null alleles for the first time. We focused our initial efforts on neuronally-expressed RNA binding protein genes, which have been shown in a number of cases to act combinatorially (Gracida et al., 2016; Norris et al., 2014). We found widespread genetic interactions among the set of RNA binding proteins we studied, and similar strategies should be broadly applicable to other gene classes as well.

Materials and Reagents

  1. Platinum Wire for worm pick, 30 gauge 0.254 mm diameter (e.g., Genesee Scientific, catalog number: 59-30P6 )
  2. Worm pick handle (e.g., Genesee Scientific, catalog number: 59-AWP )
  3. 6 cm Petri dishes (e.g., Fisher Scientific, catalog number: FB0875713A)
    Manufacturer: Corning, catalog number: 431762 .
  4. Wild-type (N2) male and hermaphrodite worms (available from CGC, strain N2)
  5. Monobasic potassium phosphate (KH2PO4) (e.g., Sigma-Aldrich, catalog number: 1551139 )
  6. Solid KOH
  7. Sodium chloride (NaCl) (e.g., Genesee Scientific, catalog number: 18-214 )
  8. Agar (e.g., Genesee Scientific, catalog number: 20-249 )
  9. peptone (e.g., Genesee Scientific, catalog number: 20-261 )
  10. Cholesterol (Sigma-Aldrich, catalog number: C8667 )
  11. 95% ethanol (e.g., Sigma-Aldrich, catalog number: 792799 )
  12. Calcium chloride (CaCl2) (e.g., Sigma-Aldrich, catalog number: C1016 )
  13. Magnesium sulfate (MgSO4) (e.g., Sigma-Aldrich, catalog number: M7506 )
  14. 1 M potassium phosphate (pH 6)
  15. Nematode Growth Media (NGM) (see Recipes)

Equipment

  1. Fluorescent dissecting stereomicroscope with high sensitivity for single-copy fluorescence detection (e.g., ZEISS, model: Axio Zoom.V16 or Leica, model: Leica M165 FC )
  2. 25 °C incubator (e.g., VWR, Sheldon Manufacturing, model: Model 2005 )
  3. Autoclave

Procedure

  1. Creation of double mutants
    The generation of single mutants using CRISPR/Cas9 has been covered elsewhere (Norris et al., 2015). This protocol begins with two single mutant worms in which the genes of interest have been deleted and replaced by compatible heterologous fluorescent reporters. In this example, the reporters are myo-2::GFP (expressed in pharyngeal muscles) and myo-3::GFP (expressed in body wall muscles).
    1. To create a male strain necessary for crossing, there are two options:
      1. Cross ~5 wild-type (N2) male worms (available from CGC) with ~5 hermaphrodites of mutant strain #1 to create male progeny that are heterozygous for the mutation (verify by ensuring the males are fluorescent).
      2. Obtain homozygous males of mutant strain #1 according to standard protocols (He, 2011a).
    2. Cross males from mutant strain #1 with hermaphrodites from mutant strain #2 (see Figure 1).


      Figure 1. Crossing strategy to create double mutant worms from individual single mutant strains. GFP is used to distinguish homozygous mutants (P0 and F2 panels, brighter fluorescence) from heterozygous mutants (F1 panel, dimmer fluorescence).

    3. Pick a single cross-progeny hermaphrodite containing both GFP markers (both myo-2::GFP and myo-3::GFP) onto a new plate. These worms correspond to double heterozygotes (i.e., mutant1/+; mutant2/+). Allow these hermaphrodites to self-fertilize.
    4. To obtain double-homozygous strains, pick worms with brighter GFP expression coming from both promoters. On a good-quality fluorescence dissection scope, you should be able to tell a difference in brightness between a strain with one copy (heterozygous mutant) and two copies (homozygous mutant) of the GFP transgene (see Figure 2).
      Note: Alternatively, pick a larger number of worms to individual plates and determine double homozygotes as worms with progeny that are 100% fluorescent for both transgenes.


      Figure 2. Fluorescent brightness reveals mutant genotypes. Worms imaged in situ on NGM plates demonstrating that homozygous mutants (left) are noticeably brighter than heterozygous mutants (right).

  2. Competitive fitness assay
    The competitive fitness assay (Figure 3A) provides a simple and relatively high-throughput method for assessing phenotypes of large numbers of mutants and can be performed immediately after strain generation.
    1. Pick equal numbers of wild-type (N2) and mutant worms onto one seeded NGM plate (see Recipes).
      Note: We find that 4 staged L4s of each genotype (i.e., 4 wild-type L4s and 4 mutant L4s) on a 6 cm plate works best to support 2 generations of growth without starving out the plate.
    2. Incubate plate at 25 °C for 5 days.
    3. Under a fluorescent stereomicroscope count the number of fluorescent (mutant) worms versus non-fluorescent (wild-type) worms. We prefer to count from pre-defined locations on each plate (e.g., 50 worms from the middle of the plate, 50 from the periphery, and 50 from in between).
      Notes:
      1. We recommend counting all hatched worms regardless of stage.
      2. For ease of counting, worms can be treated with anesthetic or cooled at 4 °C for an hour or longer.


        Figure 3. The competitive fitness assay. A. Schematic overview of competitive fitness assays; B. Representative visualization of synthetic effect (ɛ) calculation for a genetic interaction between the genes exc-7 and mbl-1.

Data analysis

These data analysis steps are an expansion of those detailed in Norris et al. (2017).

  1. For robustness, 3 biological replicates should be performed, each on a different day and different plate.
  2. Relative fitness scores can be calculated by the following formula:

    % mutant/% expected (i.e., 50%) = relative fitness

    This will yield a relative fitness value ranging from 0 (strong loss of fitness) to 2 (strong increase in fitness), with a value of 1 indicating no change of fitness compared to wild-type.
  3. To generate an expected fitness value (see Figure 3B for representative example) for a double mutant (Fexp1,2) based on the null hypothesis of no genetic interaction between mutant 1 (F1) and mutant 2 (F2), use the following formula (Mani et al., 2008; Baryshnikova et al., 2010):

    Fexp1,2 = F1 x F2

  4. To compare the observed fitness of the double mutant (Fobs1,2) to the expected fitness values:

    ɛ = Fobs1,2 - Fexp1,2

    This yields the ‘synthetic effect’ score (ɛ). Negative ɛ values indicate a strain with lower fitness than expected, and positive ɛ values indicate a strain with higher fitness than expected.
  5. To report statistically significant results, we set a conservative threshold of |ɛ| ≥ 0.20. For strains passing the |ɛ| ≥ 0.20 threshold, a Fisher’s exact test is applied to the aggregate observed values and the null-expectation values with a Bonferroni-corrected P-value of < 0.01 used as the significance threshold.

Notes

  1. Ensure that worm strains are treated identically for at least 3 days before they are picked onto competition assay plates (e.g., healthy, unstarved, uncrowded plates grown at the same temperature).
  2. The competition assay can be adapted to a variety of different types of mutants and conditions; the only requirement is that the two strains to be compared have some easily-distinguishable feature (e.g., fluorescence)

Recipes

  1. 1 M potassium phosphate (pH 6) (1 L)
    Dissolve 136.1 g KH2PO4 in about 800 ml dH2O
    Adjust pH to 6.0 with solid KOH (approx. 15 g) before bringing up to volume
    Make 100 ml aliquots and autoclave
  2. Nematode Growth Media (NGM)
    Note: Recipe from ‘Common Worm Media and Buffers’ (He, 2011b).
    For 1 L medium
    3 g NaCl
    17 g agar
    2.5 g peptone
    1 ml cholesterol (5 mg ml-1 in 95% EtOH)
    975 ml ddH2O
    Autoclave, and then add the following sterile solution (autoclaved)
    1 ml 1 M CaCl2
    1 ml 1 M MgSO4
    25 ml 1 M potassium phosphate (pH 6) (to avoid precipitation, mix between addition of MgSO4 and potassium phosphate)
    Typically pour 60 x 6 mm plate (~10 ml of media per plate) and store NGM plates in plastic boxes with covers at room temperature

Acknowledgments

This protocol was adapted from Norris et al., 2017. Support for AN was supplied by the Floyd B. James Endowed Professorship (Southern Methodist University). Support for JC was supplied by NIH Office of the Director (NIH Early Independence Award DP5OD009153) and Natural Sciences and Engineering Research Council of Canada (Discovery Grant RGPIN-2017-06573). There are no conflicts of interest or competing interest.

References

  1. Baryshnikova, A., Costanzo, M., Kim, Y., Ding, H., Koh, J., Toufighi, K., Youn, J. Y., Ou, J., San Luis, B. J., Bandyopadhyay, S., Hibbs, M., Hess, D., Gingras, A. C., Bader, G. D., Troyanskaya, O. G., Brown, G. W., Andrews, B., Boone, C. and Myers, C. L. (2010). Quantitative analysis of fitness and genetic interactions in yeast on a genome scale. Nat Methods 7(12): 1017-1024.
  2. Baugh, L. R., Wen, J. C., Hill, A. A., Slonim, D. K., Brown, E. L. and Hunter, C. P. (2005). Synthetic lethal analysis of Caenorhabditis elegans posterior embryonic patterning genes identifies conserved genetic interactions. Genome Biol 6(5): R45.
  3. Butland, G., Babu, M., Diaz-Mejia, J. J., Bohdana, F., Phanse, S., Gold, B., Yang, W., Li, J., Gagarinova, A. G., Pogoutse, O., Mori, H., Wanner, B. L., Lo, H., Wasniewski, J., Christopolous, C., Ali, M., Venn, P., Safavi-Naini, A., Sourour, N., Caron, S., Choi, J. Y., Laigle, L., Nazarians-Armavil, A., Deshpande, A., Joe, S., Datsenko, K. A., Yamamoto, N., Andrews, B. J., Boone, C., Ding, H., Sheikh, B., Moreno-Hagelseib, G., Greenblatt, J. F. and Emili, A. (2008). eSGA: E. coli synthetic genetic array analysis. Nat Methods 5(9): 789-795.
  4. Costanzo, M., Baryshnikova, A., Bellay, J., Kim, Y., Spear, E. D., Sevier, C. S., Ding, H., Koh, J. L., Toufighi, K., Mostafavi, S., Prinz, J., St Onge, R. P., VanderSluis, B., Makhnevych, T., Vizeacoumar, F. J., Alizadeh, S., Bahr, S., Brost, R. L., Chen, Y., Cokol, M., Deshpande, R., Li, Z., Lin, Z. Y., Liang, W., Marback, M., Paw, J., San Luis, B. J., Shuteriqi, E., Tong, A. H., van Dyk, N., Wallace, I. M., Whitney, J. A., Weirauch, M. T., Zhong, G., Zhu, H., Houry, W. A., Brudno, M., Ragibizadeh, S., Papp, B., Pal, C., Roth, F. P., Giaever, G., Nislow, C., Troyanskaya, O. G., Bussey, H., Bader, G. D., Gingras, A. C., Morris, Q. D., Kim, P. M., Kaiser, C. A., Myers, C. L., Andrews, B. J. and Boone, C. (2010). The genetic landscape of a cell. Science 327(5964): 425-431.
  5. Gracida, X., Norris, A. D. and Calarco, J. A. (2016). Regulation of tissue-specific alternative splicing: C. elegans as a model system. Adv Exp Med Biol 907: 229-261.
  6. He, F. (2011a). Making males of C. elegans. Bio-protocol e58.
  7. He, F. (2011b). Common worm media and buffers. Bio-protocol e55.
  8. Lehner, B., Crombie, C., Tischler, J., Fortunato, A. and Fraser, A. G. (2006). Systematic mapping of genetic interactions in Caenorhabditis elegans identifies common modifiers of diverse signaling pathways. Nat Genet 38(8): 896-903.
  9. Mani, R., St Onge, R. P., Hartman, J. L., Giaever, G. and Roth, F. P. (2008). Defining genetic interaction. Proc Natl Acad Sci U S A 105: 3461-3466.
  10. Norris, A. D., Gao, S., Norris, M. L., Ray, D., Ramani, A. K., Fraser, A. G., Morris, Q., Hughes, T. R., Zhen, M. and Calarco, J. A. (2014). A pair of RNA-binding proteins controls networks of splicing events contributing to specialization of neural cell types. Mol Cell 54(6): 946-959.
  11. Norris, A. D., Gracida, X. and Calarco, J. A. (2017). CRISPR-mediated genetic interaction profiling identifies RNA binding proteins controlling metazoan fitness. Elife 6.
  12. Norris, A. D., Kim, H. M., Colaiacovo, M. P. and Calarco, J. A. (2015). Efficient genome editing in Caenorhabditis elegans with a toolkit of dual-marker selection cassettes. Genetics 201(2): 449-458.
  13. Schuldiner, M., Collins, S. R., Thompson, N. J., Denic, V., Bhamidipati, A., Punna, T., Ihmels, J., Andrews, B., Boone, C., Greenblatt, J. F., Weissman, J. S. and Krogan, N. J. (2005). Exploration of the function and organization of the yeast early secretory pathway through an epistatic miniarray profile. Cell 123(3): 507-519.

简介

遗传交互筛选是一种强有力的方法,可为基因建立新的作用并阐明基因之间的功能联系。这样的研究已经在单细胞生物如酵母和E中发挥了极大的作用。 (Schuldiner等人,2005; Butland等人,2008; Costanzo等人,2010),但是,在多细胞生物中使用靶向反向遗传缺失的类似大规模相互作用研究尚不可行。我们开发了一种基于CRISPR / Cas9的方法来删除 C中的基因。 elegans 并用异源荧光报道基因取代它们(Norris等人,2015)。最近,我们利用该系统首次在动物中使用无效等位基因进行大规模的反向遗传筛选,重点关注RNA结合蛋白基因(Norris等人,2017)。这种类型的方法应该类似地适用于 C中的许多其他基因类。线虫。在这里,我们详细介绍了生成双突变体库和执行中等通量竞争性健身测定来测试导致适应性变化的遗传相互作用的方案。

【背景】使用反向遗传无效等位基因的大规模遗传相互作用筛选在动物中以前不可行。 RNAi已被用于研究C中的遗传相互作用。通过在存在单一突变背景下敲除大量不同基因的表达(Baugh等人,2005; Lehner等人), ,2006)。然而,这种策略受到RNAi敲低效率的限制,从而使结果的解释变得复杂。我们开发了一种用于高效编辑 C的方法。线虫基因组(Norris et al。,2015),并最近在该方法上进行了扩展,以便首次使用无效等位基因的动物能够进行大规模的基因相互作用分析。我们将我们最初的努力集中在神经元表达的RNA结合蛋白基因上,这些基因已经在许多情况下显示出相互作用(Gracida et al。2016; Norris et al。 ,2014)。我们发现在我们研究的一组RNA结合蛋白中存在广泛的遗传相互作用,类似的策略也应该广泛地适用于其他基因类别。

关键字:秀丽隐杆线虫, 遗传学, 组合遗传学, RNA结合蛋白, 适应度

材料和试剂

  1. 用于蜗杆拾取的铂丝,直径为30号,0.254毫米(例如,Genesee Scientific,目录号:59-30P6)
  2. 蠕虫挑选手柄(例如,Genesee Scientific,目录号:59-AWP)
  3. 6厘米培养皿(,例如,Fisher Scientific,目录号:FB0875713A)
    制造商:康宁,目录号:431762。
  4. 野生型(N2)雄性和雌雄同体蠕虫(可得自CGC,菌株N2)
  5. 磷酸二氢钾(KH 2 PO 4)(例如,Sigma-Aldrich,目录号:1551139)
  6. 固体KOH
  7. 氯化钠(NaCl)(例如,Genesee Scientific,目录号:18-214)
  8. 琼脂(例如,Genesee Scientific,目录号:20-249)
  9. 蛋白胨(例如,Genesee Scientific,目录号:20-261)
  10. 胆固醇(Sigma-Aldrich,目录号:C8667)
  11. 95%乙醇(例如,Sigma-Aldrich,目录号:792799)
  12. 氯化钙(CaCl 2)(例如,Sigma-Aldrich,目录号:C1016)
  13. (MgSO 4)(例如Sigma-Aldrich,目录号:M7506)
  14. 1M磷酸钾(pH 6)
  15. 线虫生长培养基(NGM)(见食谱)

设备

  1. 荧光解剖立体显微镜对于单拷贝荧光检测具有高灵敏度(例如,ZEISS,型号:Axio Zoom.V16或Leica,型号:Leica M165 FC)
  2. 25℃培养箱(例如,,VWR,Sheldon Manufacturing,model:Model 2005)
  3. 高压灭菌器

程序

  1. 创建双重突变体
    使用CRISPR / Cas9的单突变体的产生已经在其他地方进行了报道(Norris等人,2015年)。该协议开始于两个单一的突变蠕虫,其中感兴趣的基因已经被删除并被相容的异源荧光报道基因取代。在这个例子中,记者是myo-2 :: GFP(在咽肌中表达)和myo-3 :: em :: GFP(在体壁肌肉中表达)。
    1. 为了创建穿越所必需的男性应变,有两种选择:
      1. 使用〜5个突变株#1的雌雄同体交叉〜5个野生型(N2)雄性蠕虫(可从CGC获得)以产生突变为杂合的雄性子代(通过确保雄性是荧光的来验证)。
      2. 根据标准方案(He,2011a)获得突变株#1的纯合雄性。

    2. 来自突变株#1的突变雄性和来自突变株#2的雌雄同体(见图1)。


      图1.从单个单突变株中产生双重突变体蠕虫的交叉策略 GFP用于从杂合突变体(F1组,荧光调节剂)区分纯合突变体(P0和F2组,荧光更明亮)。

    3. 选择一个含有GFP标记(包括myo-2 :: GFP和 myo-3 :: GFP)的单一交叉后代雌雄同体到新的平板上。这些蠕虫对应于双重杂合子(即,突变体1 / +;突变体2 / +)。让这些雌雄同体自我施肥。
    4. 为了获得双纯合菌株,选择来自两种启动子的更明亮的GFP表达的蠕虫。在高质量的荧光解剖范围内,您应该能够分辨出GFP转基因菌株的一个拷贝(杂合突变体)和两个拷贝(纯合突变体)之间的亮度差异(见图2)。
      注意:或者,挑选大量蠕虫到个体平板上,并确定双纯合子为具有对于两种转基因均为100%荧光的后代的蠕虫。


      图2.荧光亮度揭示突变基因型在NGM平板上成像的原虫表明纯合突变体(左)明显比杂合突变体明亮(右)。 >
  2. 竞争性健身测定
    竞争性适应性测定(图3A)提供了用于评估大量突变体的表型的简单且相对高通量的方法,并且可以在菌株产生后立即进行。
    1. 选择相同数量的野生型(N2)和突变虫到一块种子NGM板上(见食谱)。
      注:我们发现在6厘米的平板上每个基因型的4个L4s(即4个野生型L4和4个突变L4)最适合支持2代生长而不会使平板饥饿。

    2. 在25°C孵育板5天。
    3. 在荧光立体显微镜下,计数荧光(突变)蠕虫与非荧光(野生型)蠕虫的数量。我们更愿意从每个平板上的预定义位置(例如,从平板中间的50个蠕虫,从外围的50个蠕虫,以及从中间的50个蠕虫)计数。
      备注:
      1. 我们建议不管阶段如何计算所有孵化的蠕虫。
      2. 为便于计数,蠕虫可用麻醉剂治疗,或在4°C冷却一小时或更长时间。


        图3.竞争性适应性分析。 :一种。竞争性适应性分析的示意图概述; B.代表可视化合成效应(ɛ)计算基因之间的基因相互作用 ex-7 和 mbl-1 。

数据分析

这些数据分析步骤是Norris等人(2017年)详述的那些数据分析步骤的扩展。

  1. 为了鲁棒性,应该进行3次生物学复制,每次在不同的日子和不同的盘子上进行。
  2. 相对健康评分可以通过以下公式计算:

    %变异率/%预期值(即,50%)=相对适应度

    这将产生一个范围从0(强适应性丧失)到2(适应性强增强)的相对适应值,值为1表示与野生型相比适应度没有变化。
  3. 为了基于突变体1(F 1,F 2,F 2,F 3,F 3,F 4,F 4,F 4, (Mani等人,2008; Baryshnikova等人,),使用下列公式计算突变2(F 2 )。 2010):

    Fexp 1,2 = F 1 = F 2

  4. 为了比较观察到的双突变体(Fobs <1,2>)的适应度与预期的适应值:

    ɛ= Fobs 1,2 - Fexp <1,2>

    这产生了“合成效应”分数(ɛ)。负ɛ值表示适应度低于预期的应变,正ɛ值表示适应度高于预期的应变。
  5. 为了报告具有统计意义的结果,我们设置一个保守的阈值|ɛ| ≥0.20。对于通过|ɛ|的菌株≥0.20阈值,将Fisher精确检验应用于总观察值和空值期望值,其中Bonferroni校正的 P 值< 0.01作为显着性阈值。

笔记

  1. 确保蠕虫菌株在被挑选到竞争性测定平板(例如,在相同温度下生长的健康的,未经处理的,非挤压的平板)之前至少处理3天。
  2. 竞争测定可以适应各种不同类型的突变体和条件;唯一的要求是要比较的两个菌株有一些容易区分的特征(例如,荧光)

食谱

  1. 1M磷酸钾(pH 6)(1 L)
    在约800ml dH 2 O中溶解136.1g KH 2 PO 4 4 使用固体KOH(约15克)将pH调节至6.0,然后调至量
    制作100毫升等分试样和高压灭菌器
  2. 线虫生长培养基(NGM)
    注意:“Common Worm Media and Buffers”的配方(He,2011b)。
    对于1 L培养基
    3克NaCl
    17克琼脂
    2.5克蛋白胨
    1ml胆固醇(5mg / ml,在95%EtOH中)
    975ml ddH 2 O
    高压灭菌器,然后添加以下无菌溶液(高压灭菌)
    1毫升1M氯化钙2
    1毫升1M MgSO 4
    25ml 1M磷酸钾(pH 6)(以避免沉淀,加入MgSO 4和磷酸钾之间混合)
    通常倒入60 x 6毫米板(每板大约10毫升培养基),并将NGM板存放在室温带盖的塑料盒中。

致谢

该协议是从Norris等人改编的,2017年。对AN的支持由Floyd B. James捐赠的教授(南卫理公会大学)提供。对JC的支持由NIH办公室主任(NIH早期独立奖DP5OD009153)和加拿大自然科学和工程研究委员会(Discovery Grant RGPIN-2017-06573)提供。没有利益冲突或利益冲突。

参考

  1. Baryshnikova,A.,Costanzo,M.,Kim,Y.,Ding,H.,Koh,J.,Toufighi,K.,Youn,JY,Ou,J.,San Luis,BJ,Bandyopadhyay,S.,Hibbs ,M.,Hess,D.,Gingras,AC,Bader,GD,Troyanskaya,OG,Brown,GW,Andrews,B.,Boone,C.和Myers,CL(2010)。 定量分析酵母在基因组中的适应性和遗传相互作用 Nat Methods 7(12):1017-1024。
  2. Baugh,L.R.,Wen,J.C.,Hill,A.A.,Slonim,D.K.,Brown,E.L。和Hunter,C.P.(2005)。 秀丽隐杆线虫的合成致死分析后胚胎模式基因识别保守的遗传相互作用。 Genome Biol 6(5):R45。
  3. Butland,G.,Babu,M.,Diaz-Mejia,JJ,Bohdana,F.,Phanse,S.,Gold,B.,Yang,W.,Li,J.,Gagarinova,AG,Pogoutse,O。, Mori,H.,Wanner,BL,Lo,H.,Wasniewski,J.,Christopolous,C.,Ali,M.,Venn,P.,Safavi-Naini,A.,Sourour,N.,Caron,S. ,Choi,JY,Laigle,L.,Nazarians-Armavil,A.,Deshpande,A.,Joe,S.,Datsenko,KA,Yamamoto,N.,Andrews,BJ,Boone,C.,Ding,H., Sheikh,B.,Moreno-Hagelseib,G.,Greenblatt,JF和Emili,A。(2008)。 eSGA:大肠杆菌合成基因阵列分析。 Nat Methods 5(9):789-795。
  4. Costanzo,M.,Baryshnikova,A.,Bellay,J.,Kim,Y.,Spear,ED,Sevier,CS,Ding,H.,Koh,JL,Toufighi,K.,Mostafavi,S.,Prinz,J 。,St Onge,RP,VanderSluis,B.,Makhnevych,T.,Vizeacoumar,FJ,Alizadeh,S.,Bahr,S.,Brost,RL,Chen,Y.,Cokol,M.,Deshpande,R., Li,Z.,Lin,ZY,Liang,W.,Marback,M.,Paw,J.,San Luis,BJ,Shuteriqi,E.,Tong,AH,van Dyk,N.,Wallace,IM,Whitney, JA,Weirauch,MT,Zhong,G.,Zhu,H.,Houry,WA,Brudno,M.,Ragibizadeh,S.,Papp,B.,Pal,C.,Roth,FP,Giaever,G.,Nislow ,C.,Troyanskaya,OG,Bussey,H.,Bader,GD,Gingras,AC,Morris,QD,Kim,PM,Kaiser,CA,Myers,CL,Andrews,BJ和Boone,C.(2010)。 细胞的遗传景观 科学 327( 5964):425-431。
  5. Gracida,X.,Norris,A.D。和Calarco,J.A。(2016)。 调节组织特异性选择性剪接:线虫作为模型系统。 Med Exp Biol 907:229-261。
  6. 他,楼(2011a)。 制作 C的男性。 elegans 。 Bio-protocol e58。
  7. 他,楼(2011b)。 常见的蠕虫介质和缓冲区。 Bio-protocol e55。 br />
  8. Lehner,B.,Crombie,C.,Tischler,J.,Fortunato,A。和Fraser,A.G。(2006)。 秀丽隐杆线虫中遗传相互作用的系统图谱标识了不同信号传导的常见修饰因子路径。 Nat Genet 38(8):896-903。
  9. Mani,R.,St Onge,R.P.,Hartman,J.L.,Giaever,G.and Roth,F.P。(2008)。 定义遗传相互作用 美国国家科学院院刊


    105:3461-3466。
  10. Mani,R.,St Onge,R.P.,Hartman,J.L.,Giaever,G.and Roth,F.P。(2008)。 定义遗传相互作用 美国国家科学院院刊


    105:3461-3466。
  11. Norris,A. D.,Gao,S.,Norris,M.L.,Ray,D.,Ramani,A.K.,Fraser,A.G.,Morris,Q.,Hughes,T.R.,Zhen,M.and Calarco,J.A。(2014)。 一对RNA结合蛋白控制有助于神经细胞类型专业化的剪接事件的网络。 / a> Mol Cell 54(6):946-959。
  12. Norris,A.D。,Gracida,X。和Calarco,J.A。(2017)。 CRISPR介导的基因相互作用分析可鉴定控制后生动物健康的RNA结合蛋白。 Elife 6。
  13. Norris,A.D.,Kim,H.M.,Colaiacovo,M.P。和Calarco,J.A。(2015)。 秀丽隐杆线虫的高效基因组编辑带有双标记选择工具包盒子。 Genetics 201(2):449-458。
  14. Schuldiner,M.,Collins,SR,Thompson,NJ,Denic,V.,Bhamidipati,A.,Punna,T.,Ihmels,J.,Andrews,B.,Boone,C.,Greenblatt,JF,Weissman,JS和新泽西州的Krogan(2005)。 通过上位性小基因谱探索酵母早期分泌途径的功能和组织。 Cell 123(3):507-519。
  • English
  • 中文翻译
免责声明 × 为了向广大用户提供经翻译的内容,www.bio-protocol.org 采用人工翻译与计算机翻译结合的技术翻译了本文章。基于计算机的翻译质量再高,也不及 100% 的人工翻译的质量。为此,我们始终建议用户参考原始英文版本。 Bio-protocol., LLC对翻译版本的准确性不承担任何责任。
Copyright Calarco and Norris. This article is distributed under the terms of the Creative Commons Attribution License (CC BY 4.0).
引用: Readers should cite both the Bio-protocol article and the original research article where this protocol was used:
  1. Calarco, J. A. and Norris, A. D. (2018). Synthetic Genetic Interaction (CRISPR-SGI) Profiling in Caenorhabditis elegans. Bio-protocol 8(5): e2756. DOI: 10.21769/BioProtoc.2756.
  2. Norris, A. D., Gracida, X. and Calarco, J. A. (2017). CRISPR-mediated genetic interaction profiling identifies RNA binding proteins controlling metazoan fitness. Elife 6.
提问与回复

(提问前,请先登录)bio-protocol作为媒介平台,会将您的问题转发给作者,并将作者的回复发送至您的邮箱(在bio-protocol注册时所用的邮箱)。为了作者与用户间沟通流畅(作者能准确理解您所遇到的问题并给与正确的建议),我们鼓励用户用图片的形式来说明遇到的问题。

当遇到任何问题时,强烈推荐您通过上传图片的形式提交相关数据。