Genomics, Proteomics & Bioinformatics最新文献

{"title":"inMTSCCA: An Integrated Multi-task Sparse Canonical Correlation Analysis for Multi-omic Brain Imaging Genetics","authors":"Lei Du,&nbsp;Jin Zhang,&nbsp;Ying Zhao,&nbsp;Muheng Shang,&nbsp;Lei Guo,&nbsp;Junwei Han,&nbsp;The Alzheimer's Disease Neuroimaging Initiative","doi":"10.1016/j.gpb.2023.03.005","DOIUrl":"10.1016/j.gpb.2023.03.005","url":null,"abstract":"<div><p>Identifying <strong>genetic risk factors</strong> 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 <strong>cross-endophenotype</strong> (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, <em>i.e.</em>, pairwise endophenotype correlation-guided MTSCCA (<em>pc</em>MTSCCA) and high-order endophenotype correlation-guided MTSCCA (<em>hoc</em>MTSCCA). <em>pc</em>MTSCCA employed pairwise correlations between magnetic resonance imaging (MRI)-derived, plasma-derived, and cerebrospinal fluid (CSF)-derived endophenotypes as an additional penalty. <em>hoc</em>MTSCCA 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 <em>pc</em>MTSCCA and <em>hoc</em>MTSCCA 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 <strong>multi-omic endophenotypes</strong> and their CEP associations is promising to reveal genetic risk factors. The source code and manual of inMTSCCA are available at <span>https://ngdc.cncb.ac.cn/biocode/tools/BT007330</span><svg><path></path></svg>.</p></div>","PeriodicalId":12528,"journal":{"name":"Genomics, Proteomics & Bioinformatics","volume":null,"pages":null},"PeriodicalIF":9.5,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10126781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"NetGO 3.0: Protein Language Model Improves Large-scale Functional Annotations","authors":"Shaojun Wang ,&nbsp;Ronghui You ,&nbsp;Yunjia Liu ,&nbsp;Yi Xiong ,&nbsp;Shanfeng Zhu","doi":"10.1016/j.gpb.2023.04.001","DOIUrl":"10.1016/j.gpb.2023.04.001","url":null,"abstract":"<div><p>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, <strong>protein language models</strong> have been proposed to learn informative representations [<em>e.g.</em>, 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 <span>https://dmiip.sjtu.edu.cn/ng3.0</span><svg><path></path></svg>.</p></div>","PeriodicalId":12528,"journal":{"name":"Genomics, Proteomics & Bioinformatics","volume":null,"pages":null},"PeriodicalIF":9.5,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10021973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TIGER: A Web Portal of Tumor Immunotherapy Gene Expression Resource","authors":"Zhihang Chen ,&nbsp;Ziwei Luo ,&nbsp;Di Zhang ,&nbsp;Huiqin Li ,&nbsp;Xuefei Liu ,&nbsp;Kaiyu Zhu ,&nbsp;Hongwan Zhang ,&nbsp;Zongping Wang ,&nbsp;Penghui Zhou ,&nbsp;Jian Ren ,&nbsp;An Zhao ,&nbsp;Zhixiang Zuo","doi":"10.1016/j.gpb.2022.08.004","DOIUrl":"10.1016/j.gpb.2022.08.004","url":null,"abstract":"<div><p><strong>Immunotherapy</strong> is a promising cancer treatment method; however, only a few patients benefit from it. The development of new immunotherapy strategies and effective <strong>biomarkers</strong> of response and resistance is urgently needed. Recently, high-throughput bulk and single-cell <strong>gene expression</strong> 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 <span>http://tiger.canceromics.org/</span><svg><path></path></svg>.</p></div>","PeriodicalId":12528,"journal":{"name":"Genomics, Proteomics & Bioinformatics","volume":null,"pages":null},"PeriodicalIF":9.5,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10410423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computational Methods for Single-cell DNA Methylome Analysis","authors":"Waleed Iqbal ,&nbsp;Wanding Zhou","doi":"10.1016/j.gpb.2022.05.007","DOIUrl":"10.1016/j.gpb.2022.05.007","url":null,"abstract":"<div><p>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 <strong>computational tools</strong> 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.</p></div>","PeriodicalId":12528,"journal":{"name":"Genomics, Proteomics & Bioinformatics","volume":null,"pages":null},"PeriodicalIF":9.5,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10372927/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9939249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computational Tools and Resources for CRISPR/Cas Genome Editing","authors":"Chao Li ,&nbsp;Wen Chu ,&nbsp;Rafaqat Ali Gill ,&nbsp;Shifei Sang ,&nbsp;Yuqin Shi ,&nbsp;Xuezhi Hu ,&nbsp;Yuting Yang ,&nbsp;Qamar U. Zaman ,&nbsp;Baohong Zhang","doi":"10.1016/j.gpb.2022.02.006","DOIUrl":"10.1016/j.gpb.2022.02.006","url":null,"abstract":"<div><p>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 <strong>efficiency and specificity</strong> of single guide RNA (<strong>sgRNA</strong>)-directed recognition of targeted DNA sequences. Recent advances in empirical scoring <strong>algorithms</strong> 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 <strong>computational tools</strong> 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 <strong>genome editing</strong>.</p></div>","PeriodicalId":12528,"journal":{"name":"Genomics, Proteomics & Bioinformatics","volume":null,"pages":null},"PeriodicalIF":9.5,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10372911/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9884374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Jasmine (Jasminum sambac) Genome Provides Insight into the Biosynthesis of Flower Fragrances and Jasmonates","authors":"Gang Chen ,&nbsp;Salma Mostafa ,&nbsp;Zhaogeng Lu ,&nbsp;Ran Du ,&nbsp;Jiawen Cui ,&nbsp;Yun Wang ,&nbsp;Qinggang Liao ,&nbsp;Jinkai Lu ,&nbsp;Xinyu Mao ,&nbsp;Bang Chang ,&nbsp;Quan Gan ,&nbsp;Li Wang ,&nbsp;Zhichao Jia ,&nbsp;Xiulian Yang ,&nbsp;Yingfang Zhu ,&nbsp;Jianbin Yan ,&nbsp;Biao Jin","doi":"10.1016/j.gpb.2022.12.005","DOIUrl":"10.1016/j.gpb.2022.12.005","url":null,"abstract":"<div><p><strong><em>Jasminum sambac</em></strong> (<strong>jasmine flower</strong>), a world-renowned plant appreciated for its exceptional <strong>flower fragrance</strong>, is of cultural and economic importance. However, the genetic basis of its fragrance is largely unknown. Here, we present the first <em>de novo</em> <strong>genome</strong> assembly of <em>J. sambac</em> 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 <em>O</em>-acetyltransferase/anthocyanin <em>O</em>-hydroxycinnamoyltransferases/anthranilate <em>N</em>-hydroxycinnamoyl/benzoyltransferase/deacetylvindoline 4-<em>O</em>-acetyltransferase (BAHD) superfamilies were identified to be related to the biosynthesis of phenylpropanoid/benzenoid compounds. Several key genes involved in <strong>jasmonate</strong> 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 <em>JsTPS3</em> in β-ocimene biosynthesis, as well as <em>JsAOC1</em> and <em>JsAOS</em> in jasmonic acid biosynthesis, were functionally validated. The genome assembled in this study for <em>J. sambac</em> offers a basic genetic resource for studying floral scent and jasmonate biosynthesis, and provides a foundation for functional genomic research and variety improvements in <em>Jasminum</em>.</p></div>","PeriodicalId":12528,"journal":{"name":"Genomics, Proteomics & Bioinformatics","volume":null,"pages":null},"PeriodicalIF":9.5,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10372924/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9882382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computational Approaches and Challenges in Spatial Transcriptomics","authors":"Shuangsang Fang ,&nbsp;Bichao Chen ,&nbsp;Yong Zhang ,&nbsp;Haixi Sun ,&nbsp;Longqi Liu ,&nbsp;Shiping Liu ,&nbsp;Yuxiang Li ,&nbsp;Xun Xu","doi":"10.1016/j.gpb.2022.10.001","DOIUrl":"10.1016/j.gpb.2022.10.001","url":null,"abstract":"<div><p>The development of <strong>spatial transcriptomics</strong> (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 <strong>data interpretation</strong>. 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 <strong>data quality</strong>, conducting efficient interpretation and in-depth knowledge mining both at the single-cell and tissue-wide levels, and conducting <strong>multi-omics integration</strong> 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 <strong>computational approaches</strong> to these problems in light of corresponding issues and challenges, and present forward-looking insights into algorithm development.</p></div>","PeriodicalId":12528,"journal":{"name":"Genomics, Proteomics & Bioinformatics","volume":null,"pages":null},"PeriodicalIF":9.5,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10372921/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9884387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"KinasePhos 3.0: Redesign and Expansion of the Prediction on Kinase-specific Phosphorylation Sites","authors":"Renfei Ma ,&nbsp;Shangfu Li ,&nbsp;Wenshuo Li ,&nbsp;Lantian Yao ,&nbsp;Hsien-Da Huang ,&nbsp;Tzong-Yi Lee","doi":"10.1016/j.gpb.2022.06.004","DOIUrl":"10.1016/j.gpb.2022.06.004","url":null,"abstract":"<div><p>The purpose of this work is to enhance KinasePhos, a machine learning-based <strong>kinase-specific phosphorylation site prediction</strong> 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 <span>https://awi.cuhk.edu.cn/KinasePhos/download.html</span><svg><path></path></svg> or <span>https://github.com/tom-209/KinasePhos-3.0-executable-file</span><svg><path></path></svg>.</p></div>","PeriodicalId":12528,"journal":{"name":"Genomics, Proteomics & Bioinformatics","volume":null,"pages":null},"PeriodicalIF":9.5,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10373160/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9890344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"What Has Genomics Taught An Evolutionary Biologist?","authors":"Jianzhi Zhang","doi":"10.1016/j.gpb.2023.01.005","DOIUrl":"10.1016/j.gpb.2023.01.005","url":null,"abstract":"<div><p>Genomics, an interdisciplinary field of biology on the structure, function, and <strong>evolution</strong> 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—<strong>variation</strong>, <strong>interaction</strong>, and <strong>selection</strong>—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.</p></div>","PeriodicalId":12528,"journal":{"name":"Genomics, Proteomics & Bioinformatics","volume":null,"pages":null},"PeriodicalIF":9.5,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10373158/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10261052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LDHA Desuccinylase Sirtuin 5 as A Novel Cancer Metastatic Stimulator in Aggressive Prostate Cancer","authors":"Oh Kwang Kwon ,&nbsp;In Hyuk Bang ,&nbsp;So Young Choi ,&nbsp;Ju Mi Jeon ,&nbsp;Ann-Yae Na ,&nbsp;Yan Gao ,&nbsp;Sam Seok Cho ,&nbsp;Sung Hwan Ki ,&nbsp;Youngshik Choe ,&nbsp;Jun Nyung Lee ,&nbsp;Yun-Sok Ha ,&nbsp;Eun Ju Bae ,&nbsp;Tae Gyun Kwon ,&nbsp;Byung-Hyun Park ,&nbsp;Sangkyu Lee","doi":"10.1016/j.gpb.2022.02.004","DOIUrl":"10.1016/j.gpb.2022.02.004","url":null,"abstract":"<div><p>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 (<strong>SIRT5</strong>), 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 <strong>lactate dehydrogenase A</strong> (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.</p></div>","PeriodicalId":12528,"journal":{"name":"Genomics, Proteomics & Bioinformatics","volume":null,"pages":null},"PeriodicalIF":9.5,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10372916/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9884369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}