A Multi-Omics Analysis of Recombinant Protein Production in Hek293 Cells

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座子标签法从在番茄中分离到一个新基因，该基因
突变导致生长缓慢、植株矮小柔弱、结实率低等，因
而命名为
。Sanz (1998)通过差异显示
技术从烟草中分离到参与细菌诱导的超敏反应过程
的新基因，并命名为 （pathogen­induced oxyge­
spectively. A fusion protein about 98 kD was detected by SDS­PAGE and Western blot in the induced recombinant BL21 (DE3)­RIPL
codon+ strain, and it accounts for 38% of the total bacterial proteins. The target protein was purified by the GST chromatography col­
第5 期
张美祥等: 拟南芥 Alpha­dioxygenase 2 原核表达、纯化及亚细胞定位预测
817
们蛋白序列的相似性为 71.4%。拟南芥 DOX1 在蛋
白序列、表达模式与最初的从烟草中分离到的
相似，其表达受病原及抗逆过程诱导(Koeduka
,2005)，拟南芥 DOX2 与最初从番茄中分离到
nase），到目前为止研究者已先后从番茄、烟草、拟南
芥、水稻、小麦等植 物中分离 到 18 个 同源基 因
（Hamberg
,2005）， 并 将 其 统 一 归 类 为 al­
pha­dioxygenase。
拟南芥中脂肪酸 alpha­dioxygenase 存在两个拷

氨基酸代谢重编程在肿瘤细胞及肿瘤相关巨噬细胞极化中的作用研究进展郑一帆，黄咏彤，张永成，杨永霞，黄文斌广东药科大学附属第一医院乳腺科，广州 510080摘要：氨基酸作为肿瘤细胞和肿瘤微环境（TME）中至关重要的代谢物质，其代谢平衡影响着肿瘤细胞的增长速度以及肿瘤相关巨噬细胞（TAMs）的免疫功能。

肿瘤细胞为满足自身的营养需求，与TAMs争夺氨基酸，使得TME 中的氨基酸平衡被打破。

多数肿瘤谷氨酰胺代谢需求增加，部分肿瘤TME中谷氨酰胺积累，抑制TAMs M1极化；肿瘤依赖于外源性蛋氨酸存活，TME中蛋氨酸缺乏则抑制TAMs M1极化；牛磺酸在肿瘤患者血清中水平下调，其本身可抑制巨噬细胞M1极化；此外大多数肿瘤依赖于外源性精氨酸，通过精氨酸耗竭可抑制肿瘤发展并限制巨噬细胞M2极化；色氨酸代谢途径增强促进肿瘤进展，产物犬尿氨酸积累促进TAMs M2极化；针对其余氨基酸，例如当外源性丝氨酸无法满足需求时，可产生内源性丝氨酸，而丝氨酸可支持TAMs产生促炎相关因子。

关键词：氨基酸代谢重编程；巨噬细胞；巨噬细胞极化；谷氨酰胺；精氨酸；犬尿氨酸；丝氨酸；蛋氨酸；牛磺酸doi：10.3969/j.issn.1002-266X.2023.12.027中图分类号：R730.2 文献标志码：A 文章编号：1002-266X（2023）12-0111-05肿瘤细胞具有异于正常组织细胞的增殖速度，这与其氨基酸代谢重编程密切相关。

氨基酸代谢重编程是指细胞改变某种或某些氨基酸的代谢模式以满足快速增殖带来的营养需要。

而这种改变可体现在肿瘤细胞内及肿瘤微环境（TME）中某种氨基酸的缺乏或积累。

一方面，肿瘤细胞通过调整氨基酸代［24］TAN B K，CHALOUNI M，CERON D S，et al.Atherosclerotic cardiovascular events in patients infected with human immunode⁃ficiency virus and hepatitis c virus［J］. Clin Infect Dis， 2021，72（9）：215-223.［25］ZHAO M， ZHUO C， LI Q， et al. Cytomegalovirus （CMV） infec⁃tion in HIV/AIDS patients and diagnostic values of CMV-DNAdetection across different sample types［J］.Ann Palliat Med，2020，9（5）：2710-2715.［26］KANNEL W B， MCGEE D， GORDON T. A general cardiovascu⁃lar risk profile： the framingham study［J］. Am J Cardiol， 1976，38（1）：46-51.［27］TRIANT V A， PEREZ J， REGAN S， et al. Cardiovascular risk prediction functions underestimate risk in hiv infection［J］. Circu⁃lation， 2018，137（21）：2203-2214.［28］FRIIS-MØLLER N， RYOM L， SMITH C， et al. An updated pre⁃diction model of the global risk of cardiovascular disease in HIV-positive persons：the data-collection on adverse effects of anti-HIV Drugs （D：A：D）study［J］. Eur J Prev Cardiol， 2016，23（2）：214-223.［29］DELABAYS B， CAVASSINI M， DAMAS J， et al. Cardiovascu⁃lar risk assessment in people living with HIV compared to the gen⁃eral population［J］. Eur J Prev Cardiol， 2022，29（4）：689-699.［30］SERRANO-VILLAR S，ESTRADA V，GÓMEZ-GARRE D，et al.Diagnosis of subclinical atherosclerosis in HIV-infected pa⁃tients： higher accuracy of the D：A：D risk equation over Framing⁃ham and SCORE algorithms［J］.Eur J Prev Cardiol，2014，21（6）：739-748.［31］荆凡辉，吕玮，李太生.HIV感染者免疫功能重建新视角：CD4/ CD8比值［J］.中国艾滋病性病，2018，24（6）：643-646.［32］CASTILHO J L， SHEPHERD B E， KOETHE J， et al. CD4 + / CD8 + ratio， age， and risk of serious noncommunicable diseasesin HIV-infected adults on antiretroviral therapy［J］.AIDS LondEngl， 2016，30（6）：899-908.［33］HOEL H， UELAND T， KNUDSEN A， et al. Soluble markers of interleukin 1 activation as predictors of first-time myocardial in⁃farction in HIV-infected individuals［J］. J Infect Dis， 2020，221（4）：506-509.［34］PEREIRA B，MAZZITELLI M，MILINKOVIC A，et e of coronary artery calcium scoring to improve cardiovascular riskstratification and guide decisions to start statin therapy in peopleliving with HIV［J］.J Acq Immun Def Synd，2020，85（1）：98-105.［35］STRINGHINI S， CARMELI C， JOKELA M， et al.Socioeconom⁃ic status and the 25×25risk factors as determinants of prema⁃ture mortality：a multicohort study and meta-analysis of 1.7 mil⁃lion men and women［J］.Lancet，2017，389（10075）：1229-1237.（收稿日期：2022-11-16）基金项目：广东省基础与应用基础研究基金项目（2022A1515012045）。

【Abstract】 Objective To steadily express HBsAg linked with glycoprotein E (g E ) of varicellazoster virus(VZV) in Picha pastoris and evaluate whether the recombinant protein forms into virus-like particles(VLPs) after preliminary purification. Methods Four-copy recombinant plasmid for HBsAg-gE was constructed， followed by linearization with endonucleases before transformed to Picha pastoris KM71 by electroporation, and the expression was induced by methanol. The expressed recombinant protein was identified by both Western Blot and ELISA. Sucrose density gradient centrifugation and gel exclusion chromatography were performed to purify the recombinant protein before observed by transmission electron microscopy. Results The four-copy recombinant plasmid for HBsAg-gE was identified by double enzymatic digestion. The expressed recombinant protein bound to mouse monoclonal antibody against gE and polyclonal antibody against HBsAg specifically and its relative molecular mass was about 90 000. The protein also showed a positive ELISA result of HBsAg and gathered between zones 40% and 55% after sucrose density gradient centrifugation. VLPs with a diameter of 20 nm were observed by transmission electron microscopy. Conclusion VLPs of HBsAg-gE are successfully expressed in Picha pastoris, laying a foundation for developing the VZV recombinant vaccine in yeast system.

Microbial Bioinformatics Analysis Microbial bioinformatics analysis plays a crucial role in understanding the complex interactions between microorganisms and their environment. This field encompasses various computational techniques and tools to analyze microbial data, including genomics, metagenomics, transcriptomics, and proteomics. The insights gained from these analyses have profound implications for diverse areas such as human health, agriculture, environmental sustainability, and biotechnology. Inthis discussion, we will delve into the significance of microbial bioinformatics analysis, its applications, challenges, and future prospects. One of the primary applications of microbial bioinformatics analysis is in understanding the role of microorganisms in human health. The human microbiome, which consists of trillions of microorganisms residing in and on the human body, has been the subject of extensive research. Through metagenomic analysis, researchers can characterize the composition and function of the microbiome, leading to insights into its impact on human physiology, immune function, and susceptibility to diseases. This has paved the way for the development of microbiome-based therapies and personalized medicine, offering new avenues for treating various health conditions. Inaddition to human health, microbial bioinformatics analysis is instrumental in addressing agricultural challenges. By studying the microbiota present in soil and plants, researchers can identify beneficial microorganisms that promote plant growth, suppress pathogens, and enhance nutrient uptake. This knowledge is harnessed to develop sustainable agricultural practices, including the use of biofertilizers and biopesticides, reducing the reliance on chemical inputs and mitigating environmental impact. Furthermore, understanding the microbial ecology of livestock has implications for animal health, food safety, and the development of probiotics for livestock management. The field of environmental microbiology also benefits significantly from bioinformatics analysis. Metagenomic studies of microbial communities in diverse habitats, such as oceans, soil, and extreme environments, provide insights into the roles of microorganisms in biogeochemical cycles, pollutant degradation, and ecosystem resilience. This knowledge is invaluable for environmental monitoring, remediation efforts, and the sustainable management of natural resources. Moreover, the discovery of novel enzymes andmetabolic pathways from environmental microorganisms holds promise for biotechnological applications, including biofuel production, bioremediation, and the synthesis of bioactive compounds. Despite the immense potential of microbial bioinformatics analysis, several challenges need to be addressed. One significant hurdle is the vast amount of data generated from high-throughput sequencing technologies, which necessitates robust computational tools and infrastructure for data storage, processing, and analysis. Moreover, the integration of multi-omics data and the interpretation of complex microbial interactions require advanced bioinformatic algorithms and interdisciplinary collaboration. Standardization of methodologies and data sharing practices is another area that warrants attention to ensure the reproducibility and comparability of microbial studies. Looking ahead, the future of microbial bioinformatics analysis holds promise for further advancements. The ongoing development of machine learning and artificial intelligence techniques presents opportunities for predictive modeling of microbial behavior, biomarker discovery, and drug development. Additionally, the emergence of single-cell sequencing technologies offers unprecedented resolution in studying individual microbial cells, unraveling microbial heterogeneity, and uncovering rare taxa with functional significance. Furthermore, the continued exploration of uncharted microbial diversity in various ecosystems is expected to yield novel bioactive compounds and biotechnological innovations. In conclusion, microbial bioinformatics analysis is a dynamic and multidisciplinary field withfar-reaching implications for human health, agriculture, environmental sustainability, and biotechnology. The integration of computational approaches with microbiology has revolutionized our understanding of microbial systems, paving the way for innovative applications and solutions to global challenges. As we navigate the complexities of microbial communities and their interactions, continued research, technological advancements, and collaborative efforts will be pivotal in unlocking the full potential of microbial bioinformatics analysis.。