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inMTSCCA: An Integrated Multi-task Sparse Canonical Correlation Analysis for Multi-omic Brain Imaging Genetics inMTSCCA:多组脑成像遗传学的综合多任务稀疏典型相关分析。
IF 9.5 2区 生物学 Q1 Mathematics Pub Date : 2023-04-01 DOI: 10.1016/j.gpb.2023.03.005
Lei Du, Jin Zhang, Ying Zhao, Muheng Shang, Lei Guo, Junwei Han, The Alzheimer's Disease Neuroimaging Initiative

Identifying genetic risk factors for Alzheimer’s disease (AD) is an important research topic. To date, different endophenotypes, such as imaging-derived endophenotypes and proteomic expression-derived endophenotypes, have shown the great value in uncovering risk genes compared to case–control studies. Biologically, a co-varying pattern of different omics-derived endophenotypes could result from the shared genetic basis. However, existing methods mainly focus on the effect of endophenotypes alone; the effect of cross-endophenotype (CEP) associations remains largely unexploited. In this study, we used both endophenotypes and their CEP associations of multi-omic data to identify genetic risk factors, and proposed two integrated multi-task sparse canonical correlation analysis (inMTSCCA) methods, i.e., pairwise endophenotype correlation-guided MTSCCA (pcMTSCCA) and high-order endophenotype correlation-guided MTSCCA (hocMTSCCA). pcMTSCCA employed pairwise correlations between magnetic resonance imaging (MRI)-derived, plasma-derived, and cerebrospinal fluid (CSF)-derived endophenotypes as an additional penalty. hocMTSCCA used high-order correlations among these multi-omic data for regularization. To figure out genetic risk factors at individual and group levels, as well as altered endophenotypic markers, we introduced sparsity-inducing penalties for both models. We compared pcMTSCCA and hocMTSCCA with three related methods on both simulation and real (consisting of neuroimaging data, proteomic analytes, and genetic data) datasets. The results showed that our methods obtained better or comparable canonical correlation coefficients (CCCs) and better feature subsets than benchmarks. Most importantly, the identified genetic loci and heterogeneous endophenotypic markers showed high relevance. Therefore, jointly using multi-omic endophenotypes and their CEP associations is promising to reveal genetic risk factors. The source code and manual of inMTSCCA are available at https://ngdc.cncb.ac.cn/biocode/tools/BT007330.

识别阿尔茨海默病(AD)的遗传危险因素是一个重要的研究课题。到目前为止,与病例对照研究相比,不同的内表型,如成像衍生的内表型和蛋白质组表达衍生的内血型,在揭示风险基因方面显示出巨大的价值。在生物学上,不同组学衍生的内表型的共同变化模式可能是由共同的遗传基础造成的。然而,现有的方法主要集中于内表型单独的影响;交叉内表型(CEP)关联的作用在很大程度上仍未被利用。在这项研究中,我们使用多组数据的内表型及其CEP关联来识别遗传风险因素,并提出了两种集成的多任务稀疏典型相关分析(inMTSCCA)方法,即成对内表型相关引导的MTSCCA(pcMTSCCA。pcMTSCCA采用磁共振成像(MRI)衍生的、血浆衍生的和脑脊液(CSF)衍生的内表型之间的成对相关性作为额外的惩罚。hocMTSCCA使用这些多组数据之间的高阶相关性进行正则化。为了找出个体和群体水平的遗传风险因素,以及改变的内表型标记,我们对两个模型都引入了稀疏性诱导惩罚。我们在模拟和真实数据集(包括神经成像数据、蛋白质组分析和遗传数据)上比较了pcMTSCCA和hocMTSCCA与三种相关方法。结果表明,与基准测试相比,我们的方法获得了更好或可比的正则相关系数和更好的特征子集。最重要的是,已鉴定的遗传位点和异质性内表型标记显示出高度相关性。因此,联合使用多组体内表型及其CEP关联有望揭示遗传风险因素。inMTSCCA的源代码和手册可在https://ngdc.cncb.ac.cn/biocode/tools/BT007330.
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引用次数: 0
NetGO 3.0: Protein Language Model Improves Large-scale Functional Annotations NetGO 3.0:蛋白质语言模型改进了大规模功能注释。
IF 9.5 2区 生物学 Q1 Mathematics Pub Date : 2023-04-01 DOI: 10.1016/j.gpb.2023.04.001
Shaojun Wang , Ronghui You , Yunjia Liu , Yi Xiong , Shanfeng Zhu

As one of the state-of-the-art automated function prediction (AFP) methods, NetGO 2.0 integrates multi-source information to improve the performance. However, it mainly utilizes the proteins with experimentally supported functional annotations without leveraging valuable information from a vast number of unannotated proteins. Recently, protein language models have been proposed to learn informative representations [e.g., Evolutionary Scale Modeling (ESM)-1b embedding] from protein sequences based on self-supervision. Here, we represented each protein by ESM-1b and used logistic regression (LR) to train a new model, LR-ESM, for AFP. The experimental results showed that LR-ESM achieved comparable performance with the best-performing component of NetGO 2.0. Therefore, by incorporating LR-ESM into NetGO 2.0, we developed NetGO 3.0 to improve the performance of AFP extensively. NetGO 3.0 is freely accessible at https://dmiip.sjtu.edu.cn/ng3.0.

作为最先进的自动函数预测(AFP)方法之一,NetGO 2.0集成了多源信息以提高性能。然而,它主要利用具有实验支持的功能注释的蛋白质,而没有利用来自大量未注释蛋白质的有价值信息。最近,蛋白质语言模型被提出来从基于自我监督的蛋白质序列中学习信息表示[例如,进化尺度建模(ESM)-1b嵌入]。在这里,我们用ESM-1b表示每种蛋白质,并使用逻辑回归(LR)来训练AFP的新模型LR-ESM。实验结果表明,LR-ESM的性能与性能最好的NetGO 2.0组件相当。因此,通过将LR-ESM纳入NetGO 2.0,我们开发了NetGO 3.0,以广泛提高AFP的性能。NetGO 3.0可在https://dmiip.sjtu.edu.cn/ng3.0.
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引用次数: 6
TIGER: A Web Portal of Tumor Immunotherapy Gene Expression Resource TIGER:肿瘤免疫治疗基因表达资源门户网站。
IF 9.5 2区 生物学 Q1 Mathematics Pub Date : 2023-04-01 DOI: 10.1016/j.gpb.2022.08.004
Zhihang Chen , Ziwei Luo , Di Zhang , Huiqin Li , Xuefei Liu , Kaiyu Zhu , Hongwan Zhang , Zongping Wang , Penghui Zhou , Jian Ren , An Zhao , Zhixiang Zuo

Immunotherapy is a promising cancer treatment method; however, only a few patients benefit from it. The development of new immunotherapy strategies and effective biomarkers of response and resistance is urgently needed. Recently, high-throughput bulk and single-cell gene expression profiling technologies have generated valuable resources. However, these resources are not well organized and systematic analysis is difficult. Here, we present TIGER, a tumor immunotherapy gene expression resource, which contains bulk transcriptome data of 1508 tumor samples with clinical immunotherapy outcomes and 11,057 tumor/normal samples without clinical immunotherapy outcomes, as well as single-cell transcriptome data of 2,116,945 immune cells from 655 samples. TIGER provides many useful modules for analyzing collected and user-provided data. Using the resource in TIGER, we identified a tumor-enriched subset of CD4+ T cells. Patients with melanoma with a higher signature score of this subset have a significantly better response and survival under immunotherapy. We believe that TIGER will be helpful in understanding anti-tumor immunity mechanisms and discovering effective biomarkers. TIGER is freely accessible at http://tiger.canceromics.org/.

免疫疗法是一种很有前途的癌症治疗方法;然而,只有少数患者从中受益。迫切需要开发新的免疫治疗策略和有效的反应和耐药性生物标志物。近年来,高通量批量和单细胞基因表达谱技术产生了宝贵的资源。然而,这些资源没有得到很好的组织,很难进行系统的分析。在这里,我们介绍了肿瘤免疫疗法基因表达资源TIGER,它包含1508个具有临床免疫疗法结果的肿瘤样本和11057个没有临床免疫治疗结果的肿瘤/正常样本的大量转录组数据,以及655个样本的2116945个免疫细胞的单细胞转录组数据。TIGER为分析收集的数据和用户提供的数据提供了许多有用的模块。利用TIGER中的资源,我们鉴定了CD4+T细胞的肿瘤富集亚群。该亚群特征得分较高的黑色素瘤患者在免疫治疗下的反应和生存率明显更好。我们相信TIGER将有助于了解抗肿瘤免疫机制和发现有效的生物标志物。TIGER可在http://tiger.canceromics.org/.
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引用次数: 19
Computational Methods for Single-cell DNA Methylome Analysis 单细胞DNA甲基化分析的计算方法
IF 9.5 2区 生物学 Q1 Mathematics Pub Date : 2023-02-01 DOI: 10.1016/j.gpb.2022.05.007
Waleed Iqbal , Wanding Zhou

Dissecting intercellular epigenetic differences is key to understanding tissue heterogeneity. Recent advances in single-cell DNA methylome profiling have presented opportunities to resolve this heterogeneity at the maximum resolution. While these advances enable us to explore frontiers of chromatin biology and better understand cell lineage relationships, they pose new challenges in data processing and interpretation. This review surveys the current state of computational tools developed for single-cell DNA methylome data analysis. We discuss critical components of single-cell DNA methylome data analysis, including data preprocessing, quality control, imputation, dimensionality reduction, cell clustering, supervised cell annotation, cell lineage reconstruction, gene activity scoring, and integration with transcriptome data. We also highlight unique aspects of single-cell DNA methylome data analysis and discuss how techniques common to other single-cell omics data analyses can be adapted to analyze DNA methylomes. Finally, we discuss existing challenges and opportunities for future development.

分析细胞间表观遗传学差异是理解组织异质性的关键。单细胞DNA甲基组分析的最新进展为以最大分辨率解决这种异质性提供了机会。虽然这些进展使我们能够探索染色质生物学的前沿,更好地理解细胞谱系关系,但它们在数据处理和解释方面提出了新的挑战。这篇综述综述了为单细胞DNA甲基组数据分析开发的计算工具的现状。我们讨论了单细胞DNA甲基组数据分析的关键组成部分,包括数据预处理、质量控制、插补、降维、细胞聚类、监督细胞注释、细胞谱系重建、基因活性评分以及与转录组数据的整合。我们还强调了单细胞DNA甲基组数据分析的独特方面,并讨论了如何将其他单细胞组学数据分析中常见的技术应用于分析DNA甲基组。最后,我们讨论了当前的挑战和未来发展的机遇。
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引用次数: 2
Computational Tools and Resources for CRISPR/Cas Genome Editing CRISPR/Cas基因组编辑的计算工具和资源
IF 9.5 2区 生物学 Q1 Mathematics Pub Date : 2023-02-01 DOI: 10.1016/j.gpb.2022.02.006
Chao Li , Wen Chu , Rafaqat Ali Gill , Shifei Sang , Yuqin Shi , Xuezhi Hu , Yuting Yang , Qamar U. Zaman , Baohong Zhang

The past decade has witnessed a rapid evolution in identifying more versatile clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein (Cas) nucleases and their functional variants, as well as in developing precise CRISPR/Cas-derived genome editors. The programmable and robust features of the genome editors provide an effective RNA-guided platform for fundamental life science research and subsequent applications in diverse scenarios, including biomedical innovation and targeted crop improvement. One of the most essential principles is to guide alterations in genomic sequences or genes in the intended manner without undesired off-target impacts, which strongly depends on the efficiency and specificity of single guide RNA (sgRNA)-directed recognition of targeted DNA sequences. Recent advances in empirical scoring algorithms and machine learning models have facilitated sgRNA design and off-target prediction. In this review, we first briefly introduce the different features of CRISPR/Cas tools that should be taken into consideration to achieve specific purposes. Secondly, we focus on the computer-assisted tools and resources that are widely used in designing sgRNAs and analyzing CRISPR/Cas-induced on- and off-target mutations. Thirdly, we provide insights into the limitations of available computational tools that would help researchers of this field for further optimization. Lastly, we suggest a simple but effective workflow for choosing and applying web-based resources and tools for CRISPR/Cas genome editing.

在过去的十年里,在识别更通用的簇状规则间隔短回文重复序列(CRISPR)/CRISPR相关蛋白(Cas)核酸酶及其功能变体方面,以及在开发精确的CRISPR/Cas衍生基因组编辑器方面,都取得了快速的进展。基因组编辑器的可编程和强大功能为基础生命科学研究和随后在各种场景中的应用提供了一个有效的RNA引导平台,包括生物医学创新和有针对性的作物改良。最基本的原则之一是以预期的方式引导基因组序列或基因的改变,而不会产生不希望的脱靶影响,这在很大程度上取决于单引导RNA(sgRNA)定向识别靶向DNA序列的效率和特异性。经验评分算法和机器学习模型的最新进展促进了sgRNA的设计和脱靶预测。在这篇综述中,我们首先简要介绍了CRISPR/Cas工具的不同特征,这些特征应被考虑以实现特定目的。其次,我们关注广泛用于设计sgRNA和分析CRISPR/Cas诱导的靶上和靶外突变的计算机辅助工具和资源。第三,我们深入了解了现有计算工具的局限性,这将有助于该领域的研究人员进行进一步的优化。最后,我们提出了一个简单但有效的工作流程,用于选择和应用基于网络的资源和工具进行CRISPR/Cas基因组编辑。
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引用次数: 38
The Jasmine (Jasminum sambac) Genome Provides Insight into the Biosynthesis of Flower Fragrances and Jasmonates 茉莉花(Jasminum sambac)基因组深入了解花香和茉莉酸盐的生物合成
IF 9.5 2区 生物学 Q1 Mathematics Pub Date : 2023-02-01 DOI: 10.1016/j.gpb.2022.12.005
Gang Chen , Salma Mostafa , Zhaogeng Lu , Ran Du , Jiawen Cui , Yun Wang , Qinggang Liao , Jinkai Lu , Xinyu Mao , Bang Chang , Quan Gan , Li Wang , Zhichao Jia , Xiulian Yang , Yingfang Zhu , Jianbin Yan , Biao Jin

Jasminum sambac (jasmine flower), a world-renowned plant appreciated for its exceptional flower fragrance, is of cultural and economic importance. However, the genetic basis of its fragrance is largely unknown. Here, we present the first de novo genome assembly of J. sambac with 550.12 Mb (scaffold N50 = 40.10 Mb) assembled into 13 pseudochromosomes. Terpene synthase (TPS) genes associated with flower fragrance are considerably amplified in the form of gene clusters through tandem duplications in the genome. Gene clusters within the salicylic acid/benzoic acid/theobromine (SABATH) and benzylalcohol O-acetyltransferase/anthocyanin O-hydroxycinnamoyltransferases/anthranilate N-hydroxycinnamoyl/benzoyltransferase/deacetylvindoline 4-O-acetyltransferase (BAHD) superfamilies were identified to be related to the biosynthesis of phenylpropanoid/benzenoid compounds. Several key genes involved in jasmonate biosynthesis were duplicated, causing an increase in copy numbers. In addition, multi-omics analyses identified various aromatic compounds and many genes involved in fragrance biosynthesis pathways. Furthermore, the roles of JsTPS3 in β-ocimene biosynthesis, as well as JsAOC1 and JsAOS in jasmonic acid biosynthesis, were functionally validated. The genome assembled in this study for J. sambac offers a basic genetic resource for studying floral scent and jasmonate biosynthesis, and provides a foundation for functional genomic research and variety improvements in Jasminum.

茉莉花是世界著名的植物,以其独特的花香而闻名,具有重要的文化和经济意义。然而,其香味的遗传基础在很大程度上是未知的。在这里,我们提出了桑巴克的第一个从头基因组组装,550.12Mb(支架N50=40.10Mb)组装成13个假染色体。与花香相关的萜烯合成酶(TPS)基因通过基因组中的串联重复以基因簇的形式被显著扩增。水杨酸/苯甲酸/可可碱(SABATH)和苄醇O-乙酰转移酶/花青素O-羟基肉桂酰转移酶/邻氨基苯甲酸酯N-羟基肉桂酰/苯甲酰转移酶-脱乙酰吲哚啉-4-乙酰转移酶(BAHD)超家族中的基因簇被鉴定与苯丙素/类苯化合物的生物合成有关。参与茉莉酸生物合成的几个关键基因被复制,导致拷贝数增加。此外,多组学分析确定了各种芳香化合物和许多参与香料生物合成途径的基因。此外,JsTPS3在β-ocimene生物合成中的作用,以及JsAOC1和JsAOS在茉莉酸生物合成中的功能得到了验证。本研究中组装的茉莉花基因组为研究茉莉花的花香和茉莉酸生物合成提供了基本的遗传资源,并为茉莉花的功能基因组研究和品种改良奠定了基础。
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引用次数: 5
Computational Approaches and Challenges in Spatial Transcriptomics 空间转录组学的计算方法和挑战
IF 9.5 2区 生物学 Q1 Mathematics Pub Date : 2023-02-01 DOI: 10.1016/j.gpb.2022.10.001
Shuangsang Fang , Bichao Chen , Yong Zhang , Haixi Sun , Longqi Liu , Shiping Liu , Yuxiang Li , Xun Xu

The development of spatial transcriptomics (ST) technologies has transformed genetic research from a single-cell data level to a two-dimensional spatial coordinate system and facilitated the study of the composition and function of various cell subsets in different environments and organs. The large-scale data generated by these ST technologies, which contain spatial gene expression information, have elicited the need for spatially resolved approaches to meet the requirements of computational and biological data interpretation. These requirements include dealing with the explosive growth of data to determine the cell-level and gene-level expression, correcting the inner batch effect and loss of expression to improve the data quality, conducting efficient interpretation and in-depth knowledge mining both at the single-cell and tissue-wide levels, and conducting multi-omics integration analysis to provide an extensible framework toward the in-depth understanding of biological processes. However, algorithms designed specifically for ST technologies to meet these requirements are still in their infancy. Here, we review computational approaches to these problems in light of corresponding issues and challenges, and present forward-looking insights into algorithm development.

空间转录组学(ST)技术的发展将遗传学研究从单细胞数据水平转变为二维空间坐标系,并促进了对不同环境和器官中各种细胞亚群的组成和功能的研究。这些ST技术生成的包含空间基因表达信息的大规模数据,引发了对空间分辨方法的需求,以满足计算和生物数据解释的要求。这些要求包括处理数据的爆炸性增长以确定细胞水平和基因水平的表达,纠正内部批量效应和表达损失以提高数据质量,在单细胞和组织范围内进行有效的解释和深入的知识挖掘,以及进行多组学整合分析,为深入理解生物过程提供可扩展的框架。然而,专门为ST技术设计的满足这些要求的算法仍处于初级阶段。在这里,我们根据相应的问题和挑战回顾了解决这些问题的计算方法,并对算法开发提出了前瞻性的见解。
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引用次数: 13
KinasePhos 3.0: Redesign and Expansion of the Prediction on Kinase-specific Phosphorylation Sites KinasePhos 3.0:激酶特异性磷酸化位点预测的重新设计和扩展
IF 9.5 2区 生物学 Q1 Mathematics Pub Date : 2023-02-01 DOI: 10.1016/j.gpb.2022.06.004
Renfei Ma , Shangfu Li , Wenshuo Li , Lantian Yao , Hsien-Da Huang , Tzong-Yi Lee

The purpose of this work is to enhance KinasePhos, a machine learning-based kinase-specific phosphorylation site prediction tool. Experimentally verified kinase-specific phosphorylation data were collected from PhosphoSitePlus, UniProtKB, the GPS 5.0, and Phospho.ELM. In total, 41,421 experimentally verified kinase-specific phosphorylation sites were identified. A total of 1380 unique kinases were identified, including 753 with existing classification information from KinBase and the remaining 627 annotated by building a phylogenetic tree. Based on this kinase classification, a total of 771 predictive models were built at the individual, family, and group levels, using at least 15 experimentally verified substrate sites in positive training datasets. The improved models demonstrated their effectiveness compared with other prediction tools. For example, the prediction of sites phosphorylated by the protein kinase B, casein kinase 2, and protein kinase A families had accuracies of 94.5%, 92.5%, and 90.0%, respectively. The average prediction accuracy for all 771 models was 87.2%. For enhancing interpretability, the SHapley Additive exPlanations (SHAP) method was employed to assess feature importance. The web interface of KinasePhos 3.0 has been redesigned to provide comprehensive annotations of kinase-specific phosphorylation sites on multiple proteins. Additionally, considering the large scale of phosphoproteomic data, a downloadable prediction tool is available at https://awi.cuhk.edu.cn/KinasePhos/download.html or https://github.com/tom-209/KinasePhos-3.0-executable-file.

这项工作的目的是增强KinasePhos,一种基于机器学习的激酶特异性磷酸化位点预测工具。从PhosphoSitePlus、UniProtKB、GPS 5.0和Phospho.ELM收集了实验验证的激酶特异性磷酸化数据。总共鉴定了41421个实验验证的磷酸化位点。共鉴定出1380种独特的激酶,其中753种具有来自KinBase的现有分类信息,其余627种通过构建系统发育树进行注释。基于这种激酶分类,使用阳性训练数据集中至少15个实验验证的底物位点,在个体、家族和组水平上总共建立了771个预测模型。与其他预测工具相比,改进后的模型显示了其有效性。例如,蛋白激酶B、酪蛋白激酶2和蛋白激酶A家族磷酸化位点的预测准确率分别为94.5%、92.5%和90.0%。771个模型的平均预测准确率为87.2%。为了提高可解释性,采用了SHapley加性预测(SHAP)方法来评估特征重要性。KinasePhos 3.0的网络界面已被重新设计,以提供多种蛋白质上激酶特异性磷酸化位点的全面注释。此外,考虑到大规模的磷酸蛋白质组学数据,可下载的预测工具可在https://awi.cuhk.edu.cn/KinasePhos/download.html或https://github.com/tom-209/KinasePhos-3.0-executable-file.
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引用次数: 11
What Has Genomics Taught An Evolutionary Biologist? 基因组学教会进化生物学家什么?
IF 9.5 2区 生物学 Q1 Mathematics Pub Date : 2023-02-01 DOI: 10.1016/j.gpb.2023.01.005
Jianzhi Zhang

Genomics, an interdisciplinary field of biology on the structure, function, and evolution of genomes, has revolutionized many subdisciplines of life sciences, including my field of evolutionary biology, by supplying huge data, bringing high-throughput technologies, and offering a new approach to biology. In this review, I describe what I have learned from genomics and highlight the fundamental knowledge and mechanistic insights gained. I focus on three broad topics that are central to evolutionary biology and beyond—variation, interaction, and selection—and use primarily my own research and study subjects as examples. In the next decade or two, I expect that the most important contributions of genomics to evolutionary biology will be to provide genome sequences of nearly all known species on Earth, facilitate high-throughput phenotyping of natural variants and systematically constructed mutants for mapping genotype–phenotype–fitness landscapes, and assist the determination of causality in evolutionary processes using experimental evolution.

基因组学是一个关于基因组结构、功能和进化的跨学科生物学领域,它通过提供大量数据、带来高通量技术和提供一种新的生物学方法,彻底改变了生命科学的许多分支学科,包括我的进化生物学领域。在这篇综述中,我描述了我从基因组学中学到的东西,并强调了所获得的基本知识和机制见解。我专注于三个广泛的主题,这三个主题是进化生物学的核心,超越了变异、相互作用和选择,并主要以我自己的研究和研究主题为例。在未来的一二十年里,我预计基因组学对进化生物学最重要的贡献将是提供地球上几乎所有已知物种的基因组序列,促进自然变体的高通量表型分型,并系统构建用于绘制基因型-表型适应度景观的突变体,并使用实验进化来帮助确定进化过程中的因果关系。
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引用次数: 1
LDHA Desuccinylase Sirtuin 5 as A Novel Cancer Metastatic Stimulator in Aggressive Prostate Cancer LDHA去丁二酰酶Sirtuin 5作为侵袭性癌症癌症转移刺激因子
IF 9.5 2区 生物学 Q1 Mathematics Pub Date : 2023-02-01 DOI: 10.1016/j.gpb.2022.02.004
Oh Kwang Kwon , In Hyuk Bang , So Young Choi , Ju Mi Jeon , Ann-Yae Na , Yan Gao , Sam Seok Cho , Sung Hwan Ki , Youngshik Choe , Jun Nyung Lee , Yun-Sok Ha , Eun Ju Bae , Tae Gyun Kwon , Byung-Hyun Park , Sangkyu Lee

Prostate cancer (PCa) is the most commonly diagnosed genital cancer in men worldwide. Around 80% of the patients who developed advanced PCa suffered from bone metastasis, with a sharp drop in the survival rate. Despite great efforts, the detailed mechanisms underlying castration-resistant PCa (CRPC) remain unclear. Sirtuin 5 (SIRT5), an NAD+-dependent desuccinylase, is hypothesized to be a key regulator of various cancers. However, compared to other SIRTs, the role of SIRT5 in cancer has not been extensively studied. Here, we revealed significantly decreased SIRT5 levels in aggressive PCa cells relative to the PCa stages. The correlation between the decrease in the SIRT5 level and the patient’s reduced survival rate was also confirmed. Using quantitative global succinylome analysis, we characterized a significant increase in the succinylation at lysine 118 (K118su) of lactate dehydrogenase A (LDHA), which plays a role in increasing LDH activity. As a substrate of SIRT5, LDHA-K118su significantly increased the migration and invasion of PCa cells and LDH activity in PCa patients. This study reveals the reduction of SIRT5 protein expression and LDHA-K118su as a novel mechanism involved in PCa progression, which could serve as a new target to prevent CPRC progression for PCa treatment.

癌症(PCa)是全世界男性最常见的生殖器癌症。大约80%的晚期前列腺癌患者患有骨转移,生存率急剧下降。尽管付出了巨大的努力,但去势抗性前列腺癌(CRPC)的详细机制仍不清楚。Sirtuin 5(SIRT5)是一种NAD+依赖性去琥珀酸酶,被认为是各种癌症的关键调节因子。然而,与其他SIRT相比,SIRT5在癌症中的作用尚未得到广泛研究。在这里,我们揭示了与前列腺癌阶段相比,侵袭性前列腺癌细胞中SIRT5水平显著降低。SIRT5水平下降与患者生存率下降之间的相关性也得到了证实。使用定量全局琥珀酰组分析,我们表征了乳酸脱氢酶a(LDHA)在赖氨酸118(K118su)的琥珀酰化显著增加,其在增加LDH活性中起作用。LDHA-K118su作为SIRT5的底物,显著增加前列腺癌患者前列腺癌细胞的迁移和侵袭以及LDH活性。本研究揭示了SIRT5蛋白表达的减少和LDHA-K118su作为一种参与前列腺癌进展的新机制,可作为预防前列腺癌进展治疗的新靶点。
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引用次数: 12
期刊
Genomics, Proteomics & Bioinformatics
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