講者：化學部　趙宇亮院士

講題：納米生物效應機制及其醫學應用
The Bio-chemical Mechanism of Nanomaterial Interacting with Living System

摘要：在納米尺度下，物質出現獨特的化學或生物活性，納米-生物體系表/介面相互作用所體現的特殊性質，已成為諸多前沿交叉學科（如納米醫學、藥物遞送技術、納米生物技術、納米生物醫學工程、腫瘤納米技術等）的科學基礎。到目前為止，納米材料（NMs）以及納米材料與生物體系相互作用的關鍵科學發現包括：（1）納米材料可能是開發下一代醫學（納米醫學）的最佳選擇；（2）納米材料容易穿透生物屏障；（3）納米材料很容易吸附血液中的蛋白質（蛋白冠）；（4）納米材料很容易穿過細胞膜進入細胞並誘導細胞內ROS的產生；（5）納米尺寸和納米表面化學在很大程度上決定了納米材料在體內或體外的功能和命運，等等。在講座中，我們將討論這些新現象背後的化學生物學機制，以及如何利用它們去高效調控腫瘤微環境，實現重大疾病智慧化治療的新原理與新方法。

Changes in the chemical and biological activities of materials at the nanoscale can lead to unique interactions with biological systems, which is directly relevant to many newly emerging frontier sciences in multidisciplinary fields such as nanomedicine, nanobiotechnology, nanotoxicology, nanobiomedical engineering, and cancer nanotechnology. So far, key discoveries related to nanomaterials (NMs) and their interactions with biosystems include that (i) NMs may be the best candidate for the development of the next-generation of medicine, known as nanomedicine; (ii) NMs could easily penetrate biological barriers and adsorb blood proteins, forming a protein corona; (iii) NMs hold the possibility of crossing cell membranes and inducing intracellular reactive oxygen species (ROS), and (iv) nanosizes and nanosurface chemistry largely determine functions and fates of NMs in vivo or in vitro. During the talk, we will discuss the latest achievements in nanomedicine, focusing on smart nanomaterials that target and regulate the tumor microenvironment to improve therapeutic outcomes for cancer treatment.
＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿

講者：生命科學和醫學學部　陳國強院士

講題：tRNA調控的FBXO22介導的mTOR 泛素化和氨基酸感應
tRNA-regulated FBXO22-mediated mTOR ubiquitination and amino acid sensing


摘要：F-box蛋白是SCF泛素連接?複合物的底物識別亞基.除可變F-box蛋白外，SCF由SKP1、CUL1和E3連接?RBX1組成。累積的證據表明，F-box蛋白是充滿希望的治療癌症包括造血惡性腫瘤的靶標。作為FBXO蛋白之一，FBXO22被認為在癌症發展和治療反應中起著關鍵作用。之前，我們已經報導FBXO22通過泛素化核PTEN發揮致瘤功能和靶向BACH1維持AML中的白血病幹細胞，促進白血病生成。在這裡，我們最近的發現：氨基酸饑餓可以通過uncharged tRNA-GCN2這一軸線使FBXO22磷酸化並導致其在漿內滯留，從而引起以漿定位為主的mTOR的泛素化。在功能上，mTOR泛素化能夠造成mTORC1底物招募能力的減弱，從而抑制mTORC1的活性。這些發現提示tRNA-GCN2-FBXO22-mTOR-Ub這一通路實現了mTORC1對氨基酸的全域感應。

F-box proteins are the substrate-recognition subunits of SCF ubiquitin ligase complex, which is composed of SKP1, CUL1 and the E3 ligase RBX1 besides variable F-box proteins. Accumulated lines of evidence showed that F-box proteins are promising targets for cancer therapy, including hematopoietic malignancies. As one of FBXO (F-box only) proteins, FBXO22 was believed to play a critical role in cancer development and therapy response. Previously, we showed that FBXO22 exerts tumorigenic function by ubiquitylating nuclear PTEN, and FBXO22 promotes leukemogenesis, especially is required for LSC maintenance in AML by targeting BACH1. Here we reported our recent finding that that upon amino acid depletion, rapid accumulation of uncharged tRNAs-activated control nonderepressible 2 (GCN2) phosphorylate FBXO22, which then accrues in the cytoplasm and ubiquitinates mammalian target of rapamycin (mTOR) ubiquitination, which inhibits its kinase activity by preventing substrate recruitment like rapamycin binding. Accordingly, mTOR ubiquitination rendered its insensitive to amino acid starvation both in vitro and in vivo.
＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿

講者：化學部　嚴純華院士

講題：稀土發光納米材料：控制合成、發光調控與生物成像
Rare Earth Luminescent Nanomaterials: Controlled Synthesis, Luminescence Modulation and Bioimaging

摘要：稀土元素是發光材料的寶庫。稀土離子的4f電子組態具有豐富的階梯狀能級，可通過下遷移、上轉換、量子剪裁過程產生從紫外、可見到近紅外波段的發光。稀土發光材料具有（反）斯托克斯位移大、發光穩定性好、發光譜帶窄、發光壽命長、發光波長覆蓋範圍廣以及光子轉換效率高等特點，在照明與顯示、生物螢光標記、先進醫療、能源轉換等眾多終端應用領域展現出廣闊的應用前景。隨著納米科學的發展，稀土發光研究迎來了新機遇，納米尺度不僅為稀土發光提供了更多的調控維度，而且使其在生物醫學領域備受青睞。近二十年來，我們圍繞稀土發光納米材料開展了系統的研究工作，建立了單一前驅體熱分解合成方法，獲得了組成、尺寸、形貌、結構可控的單分散稀土納米晶，揭示了“鑭系收縮”效應對稀土納米材料結構的影響規律；研究了稀土納米材料上轉換與近紅外發光的調控規律，解析了稀土發光與納米材料的組成、局域結構及核殼結構的關聯，實現了高量子產率、多色可調的稀土發光；利用上轉換與近紅外發光的背景螢光低、穿透深度大等特點，實現了線蟲、斑馬魚、小鼠等多種模式生物的光學成像，研究了稀土納米材料的生物安全性，發展了稀土發光納米材料的高分辨生物成像及診療一體化。

Rare earth elements activate a wealth of luminescent materials. By virtue of the ladder-like energy levels in the 4f electron configuration, rare earth ions can generate tunable emissions ranging from ultraviolet, to visible and near-infrared through downshifting, upconversion and quantum-cutting processes. Rare earth luminescent materials are featured with large (anti-)Stokes shift, excellent photostability, sharp emission bandwidth, long emission lifetime, widely-spread emission wavelength, high photon conversion efficiency, etc. These attractive optical properties endow rare earth luminescent nanomaterials with great application potential in fields including lighting and displays, bioimaging, advanced health care, energy conversion and so on. With the development of nanoscience, rare earth luminescent nanomaterials are enabled with unprecedented excitation/emission tunability, meanwhile, a new avenue toward biomedicine is established. In the recent twenty years, we have carried out systematic investigations concerning rare earth luminescent nanomaterials. We established the single-source thermal decomposition method to synthesize monodisperse rare earth nanocrystals with controllable composition, size, morphology and phase structure, in which the correlation between “lanthanide contraction” effect and the phase structure of resulting rare earth nanocrystals was disclosed. By investigating the regulation rules of upconversion and near-infrared emissions of rare earth nanomaterials, we unraveled the influence from composition, local coordination structure and core/shell structure on the excitation/emission properties. A series of highly efficient and multicolor rare earth luminescent nanomaterials were obtained. Benefiting from the low auto-fluorescence background and remarkable penetration depth of upconversion and near-infrared emissions, we achieved bioimaging with C. elegans, zebra fish and mouse. The biotoxicity of rare earth nanomaterials was assessed with C. elegans, which showed that rare earth nanomaterials were highly biocompatible with no obvious biotoxicity. Recently, we developed a series of rare earth luminescent nanoprobes for high-resolution bioimaging and theranostics.
＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿

講者：生命科學和醫學學部　朱玉賢院士

講題：棉花基因組及功能基因組學研究最新進展與前景展望
Recent Advances and Future Perspectives in Cotton Genomics and Functional Genomics

摘要：陸地棉是在1~2百萬年前由亞洲棉（A基因組）和雷蒙德氏棉（D基因組）雜交和染色體加倍而形成的。本項目測序並組裝了二倍體棉花A、D基因組及四倍體棉花AtDt基因組，發現亞洲棉基因組為1,724 Mb，雷蒙德氏棉775 Mb，陸地棉2,173 Mb。進一步研究表明, LTR (長末端重複的反轉座子) 插入導致了棉花A基因組與D基因組約兩倍的差異。A0，而不是前人推測的A1或A2，演化產生了主導世界棉纖維產業的四倍體陸地棉中的At亞基因組，解決了困擾棉花研究相當長時期的理論問題。利用BN-page從次生壁合成期棉纖維細胞膜成分中分離到一個巨大的CES複合體，初步鑒定為36聚體。通過上海光源16B1線站研究發現，次生壁合成期棉纖維細胞維纖絲束包含了72根葡聚糖鏈，而其它所有材料中沒有發現超過36根葡聚糖鏈，充分說明了棉纖維結構的特殊性。在TE、特別是LTR含量較高的大基因組中，蛋白質及功能RNA編碼基因總量隨著轉錄組測序量的加深而顯著增加。雷蒙德氏棉4到400 G六個轉錄組分別組裝出10,000多個新的蛋白質編碼基因。深入分析發現，基因區基因 (genic genes) 總量完全不變但基因間區基因數量 (Intergenic genes, ITG genes) 從4G中的2,157個增加到100G中的10,284個，表明若要獲得某個基因組內蛋白質編碼基因總量，測序深度必須達到100倍基因組或更高。該工作顯著提高了人們對A基因組棉花特別是中國亞洲棉群體進化的認知，也為棉花遺傳研究和分子育種提供了豐富的新基因資源。

Cotton is one of the most important crops for textile fiber in the world, accounting for about 35% of the world's annual fiber demand. It is often used as a model system to study plant polyploidization, cell elongation and cell wall biogenesis. Based on in-depth comparative genomics, phylogenetics as well as population genetic analyses, we obtained compelling evidence to suggest that all existent A-genomes, including A1-, A2-genome and At-subgenome in G. hirsutum, may have originated from a common ancestor (referred to as A0). The finding of A0 as a common ancestor for A1-, A2-genome and the At-subgenome solves the puzzle regarding all previous unfit phylogenetic investigations and thus puts a last nail in the coffin of the argument regarding At-subgenome origin. Cotton fiber is known to produce large quantities of celluloses, which is synthesized and further assembled into cell wall microfibrils by cellulose synthase complex (CSC). During its secondary cell wall (SCW) synthesis period, GhCesA 4, 7 and 8 were assembled into a previously uncharacterized 36-mer-like cellulose synthase supercomplex (CSS). Further studies showed that the microfibrils from SCW of wild-type cotton fibers contain 72 glucan chains in a bundle, different from all other plant materials studied. By extremely-deep sequencing analysis, we find that the numbers of annotated protein-coding or functional RNA genes in cotton and in most assembled other plant genomes were significantly underestimated. This large set of previously unknown genes were located mainly in LTR-rich intergenic regions and were evolved mainly via recent LTR-burst events. They escaped previous annotations due mainly to their low transcription rate and were have to be included in future genome assemblies since they may be important for species-specificity formation. Comparing to the commonly annotated genic genes, most of these new ITG genes were single exon genes expressed at extremely low levels.

＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿

講者：地學部　龔健雅院士

講題：遙感智慧解譯的研究進展與挑戰
Advances and challenges in intelligent interpretation of remote sensing

摘要：人工智慧已經在許多領域得到快速發展和廣泛應用，在遙感影像智慧解譯方面也取得重要研究進展，並有部分場景得到應用。但是，遙感影像智慧解譯的規模化業務化應用還不夠成熟。報告分析了目前遙感智慧解譯存在的問題，包括樣本數量偏少、種類不全、缺乏標準規範，現有的深度學習網路框架難以適應多源遙感影像智慧解譯的需要。報告人介紹了所在團隊最新的遙感智慧解譯研究成果，包括多樣性和規範化的樣本庫LuojiaSET和遙感智慧解譯的專用深度學習網路框架LuojiaNET的設計和研究進展及深度學習在遙感自動解譯中的典型應用。

Artificial intelligence has been rapidly developed and widely applied in many fields. Important research progress has been made in intelligent interpretation of remote sensing images, and some scenarios have been applied. However, the large-scale application of intelligent interpretation of remote sensing images is not mature enough. The report analyzes the problems existing in intelligent interpretation of remote sensing, including the small number of samples, incomplete categories, lack of standards and specifications, and the existing deep learning network framework is difficult to meet the needs of intelligent interpretation of multi-source remote sensing images. The speaker introduced his team's latest research achievements in intelligent remote sensing interpretation, including the design and research progress of LuojiaSET, a diversified and standardized sample database, and LuojiaNET, a deep learning network framework for intelligent remote sensing interpretation, as well as the typical application of deep learning in intelligent remote sensing interpretation.