活细胞中Bax与Bcl-XL相互作用的FRET成像研究

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"这篇文章主要探讨了Bax和Bcl-XL两种关键的Bcl-2家族蛋白质之间的相互作用机制,并通过部分受体光漂白基于荧光共振能量转移(FRET)的定量测量方法——PbFRET,在活细胞中进行成像分析。研究人员在单个活的HepG2细胞中同时表达CFP-Bax和YFP-Bcl-XL,以映射FRET效率(E)图像,结果显示细胞质中的FRET效率为4.59%,而线粒体上的FRET效率为11.31%。" 这篇报告详述了Bax和Bcl-XL这两种蛋白质之间的相互作用,它们是Bcl-2蛋白家族中的重要成员,对细胞凋亡过程有着至关重要的影响。Bcl-2家族的蛋白质在细胞的生存与死亡决定中扮演着关键角色,Bax通常被认为是促凋亡蛋白,而Bcl-XL则被归类为抗凋亡蛋白。两者的精确交互机制一直是研究的热点,也是争议的焦点。 文中提到的PbFRET(部分受体光漂白FRET)是一种在活细胞中广泛应用的FRET量化技术。FRET(荧光共振能量转移)是一种非辐射能量传递现象,当两个荧光分子间距离足够近时,一个荧光分子(供体)可以将其吸收的能量转移到另一个分子(受体),导致受体发光。PbFRET通过部分接受器的光漂白来定量这种能量转移的效率,从而揭示蛋白质之间的相互作用距离和强度。 在实验中,研究者利用宽场显微镜进行了像素级别的PbFRET成像,观察了在HepG2细胞中同时表达的CFP(蓝色荧光蛋白)标记的Bax和YFP(黄色荧光蛋白)标记的Bcl-XL。通过这种方式,他们能够绘制出细胞内不同区域的FRET效率图。结果显示,在细胞质中,Bax和Bcl-XL之间的FRET效率为4.59%,这表明两者之间存在一定的相互作用。而在线粒体上,这一效率提高到11.31%,这可能意味着在特定细胞结构中,Bax和Bcl-XL的相互作用更为紧密,可能涉及线粒体依赖的凋亡途径。 这项工作提供了有关Bax和Bcl-XL动态相互作用的直接证据,对于理解细胞凋亡调控的分子机制具有重要意义。此外,它也展示了PbFRET技术在活细胞中监测蛋白质相互作用的强大潜力,为未来研究细胞生物学过程中蛋白质网络的动态变化提供了有力工具。

Rab GTPases serve as master regulators of membrane trafficking. They can be activated by guanine nucleotide exchange factors (GEF) and be inactivated by GTPase-activating proteins (GAPs). The roles of some GAPs have been explored in Saccharomyces cerevisiae, but are largely unknown in filamentous fungi. Here, we investigated the role of GAP Gyp3 gene, an ortholog of S. cerevisiae Gyp3, in an entomopathogenic fungus, Metarhizium acridum. We found that MaGyp3 is mainly localized to the endoplasmic reticulum (ER) of vegetative hyphae, nuclei of mature conidia, and both ER and nuclei in invasive hyphae. Lack of MaGyp3 caused a decreased tolerance to hyperosmotic stress, heat-shock and UV-B radiation. Moreover, the ΔMaGyp3 mutant showed a significantly decreased pathogenicity owing to delayed germination, reduced appressorium-mediated penetration and impaired invasive growth. Loss of MaGyp3 also caused impaired fungal growth, advanced conidiation and defects in utilization of carbon and nitrogen sources, while overexpression of MaGyp3 exhibited delayed conidiation on nutrient-rich medium and conidiation pattern shift from microcycle conidiation to normal conidiation on nutrient-limited medium. Mavib-1, a tanscription factor invloved in conidiation by affecting nutrient utilizaiton, can directly bind to the promoter of MaGyp3. ΔMaGyp3 and ΔMavib-1 mutants shared similar phenotypes, and overexpression mutants of MaGyp3 and Mavib-1 (Mavib-1-OE) exhibited similar phenotypes in growth, conidiation and pathogenicity. Reintroduction of the Magyp3 driven by strong promoter gpd in ΔMavib-1 mutant recovered the defects in growth and conidiation for dysfunction of Mavib1. Taken together, our findings uncovered the role of GAP3 in a filamentous pathogenic fungus and and illustrated the upstream regulatory mechanism by direct interaction with Mavib-1.请用nature杂志的风格润色成学术论文的形式。

2023-02-10 上传

3.4 Pair Interaction Feature The interaction pattern between two individuals is encoded by a spatial descriptor with view invariant relative pose encoding. Given the 3D locations of two individual detec- tions zi,zj and two pose features pi,pj, we represent the pairwise relationship using view normalization, pose co-occurrence encoding, semantic compression and a spatial histogram (see Fig. 5 for illustration). The view normalization is performed by rotating the two people in 3D space by θ with respect to their midpoint, making their connecting line perpendicular to the cam- era view point. In this step, the pose features are also shifted accordingly (e.g. if θ = 45‘, shift 1 dimension with a cycle). Then, the co-occurrence feature is obtained by building a 2-dimensional matrix in which each element (r, c) corresponds to min(pi(r), pj (c)). Although the feature is view invariant, there are still elements in the matrix that deliver the same semantic concepts (e.g. left-left and right-right). To reduce such unnecessary variance and obtain a compact representation, we perform another transformation by multiplying a semantic compression matrix Sc to the vector form of the co-occurrence feature. The matrix Sc is learned offline by enumerating all possible configurations of view points and grouping the pairs that are equivalent when rotated by 180 degrees. Finally, we obtain the pair interaction descriptor by building a spatial histogram based on the 3D distance between the two (bin centers at 0.2, 0.6, 2.0 and 6.5 m). Here, we use linear interpolation similarly to contextual feature in Sec. 3.3. Given the interac- tion descriptor for each pair, we represent the interaction feature φxx(xi,xj) using the confidence value from an SVM classifier trained on a dictionary of interaction labels Y.什么意思

2023-07-17 上传