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1. Accumulation of phytoene and astaxanthin and related genes expression in Haematococcus pluvialis under sodium acetate stress

Author

Cong, X, Zang, X, Dong, M, Wang, Z, He, B, Hou, L, Wei, X, Zhang, F, Shang, M, Yangzong, Z, Li, R, and Zhang, X

Subjects
Biology (General), QH301-705.5, Microbiology, QR1-502
Abstract

Phytoene and astaxanthin are 2 important carotenoids in the green alga Haematococcus pluvialis. Under environmental stress, the synthesis of phytoene in H. pluvialis increases significantly, and phytoene is converted to astaxanthin through enzymatic catalysis. This paper analyzes the relationship between astaxanthin and phytoene accumulation in carotenoid synthesis pathways under different concentrations of sodium acetate (NaAc) by high-performance liquid chromatography. The highest concentrations of phytoene and astaxanthin were observed at the NaAc concentration of 6 g l-1 on the 12th day of induction. The highest astaxanthin concentration achieved was 2.26 ± 0.28%. Therefore, we concluded that 6 g l-1 NaAc and induction for 12 d provided the optimal inducing conditions for astaxanthin accumulation in H. pluvialis. psy, pds, lcyB, β-carotene ketolase crtw, and crtz, which are genes related to phytoene and astaxanthin synthesis, were cloned and studied at the transcriptional level. crtw and crtz were continuously up-regulated since the first day of induction, while psy, pds, and lcyB were continuously up-regulated starting on the 3rd day of induction. These findings are important for enhancing our understanding of the mechanism of accumulation of phytoene and astaxanthin in H. pluvialis and provide a foundation for identifying the induction conditions necessary for optimizing astaxanthin production and increasing astaxanthin yields.

Published
2020
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2. Shock consolidation and the corresponding plasticity in nanopowdered Mg

Author

He, D. B., Wang, M. Y., Bi, W. B., Shang, M., Cai, Y., Deng, L., Zhang, X. M., Tang, J. F., and Wang, L.

Subjects
Condensed Matter - Materials Science
Abstract

Nanopowder consolidation under high strain rate shock compression is a potential method for synthesizing and processing bulk nanomaterials. A thorough investigation of the shock deformation of powder materials is of great engineering significance. Here we combine nonequilibrium molecular dynamics (NEMD) simulations and X-ray diffraction (XRD) simulation methods to investigate the deformation twinning and pore compaction in shock-compressed np-Mg. Significant anisotropy and strong dependence on crystallographic orientation are presented during shock-induced deformation twinning. During the shock stage, three typical types of twins were firstly induced, namely {11-21} twin (T1), {11-22} twin (T2) and {10-12} twin (T3). Most of them were generated in grains with a larger angle between the impact direction and the c-axis of the lattice. With the increase in strain rate, the types and quantities of twins continued to enrich, but they did not occur when the strain rate was too high. We also discussed the deformation mechanisms of the three types of twins and found that the coupling of slip and shuffle dominated twin deformation. In addition, void filling occurred due to the interaction of twinning and other plastic deformations, leading to the densification of np-Mg. During the release stage, an interesting reverse change was observed, where the twins produced by the impact receded, and twins were produced in grains that were previously difficult to produce., Comment: The present study investigates the impact consolidation and deformation damage of nanopowders under extreme shock conditions. The manuscript comprises 15 pages and includes 16 figures

Published
2024

6. Echogenic, Ultrasound-Sensitive Chitosan Nanodroplets for Spatiotemporally Controlled DKK-2 Gene Delivery to Prostate Cancer Cells

Author

Liu X, Shi D, Guo L, Zhou X, Shang M, Sun X, Meng D, Zhao Y, and Li J

Subjects
nanodroplets, chitosan, dkk-2, ultrasonic gene transfer, prostate cancer, Medicine (General), R5-920
Abstract

Xinxin Liu, Dandan Shi, Lu Guo, Xiaoying Zhou, Mengmeng Shang, Xiao Sun, Dong Meng, Yading Zhao, Jie Li Department of Ultrasound, Qilu Hospital of Shandong University, Jinan, People’s Republic of ChinaCorrespondence: Jie LiDepartment of Ultrasound, Qilu Hospital of Shandong University, Wenhua Western Road, Jinan 107#, People’s Republic of ChinaTel/Fax +86-531-82166101Email jieli301@163.comPurpose: To synthesize echogenic chitosan/perfluorohexane nanodroplets (CNDs) for DKK-2 gene delivering in a spatiotemporally controlled manner in vitro.Methods: The characteristics, contrast-enhanced ultrasound imaging, DNA binding and DNase protection capacity, DKK-2 gene transfection and effects on LNCaP cells of these CNDs were investigated.Results: The obtained CNDs showed positive surface charges and could attract the genetic cargo with negative surface charges to form nanocomplexes. Agarose gel electrophoresis confirmed binding of the CNDs and pDNA. DKK-2 pDNA-loaded CNDs, in combination with ultrasound, ruptured and released DKK-2 pDNA, entering LNCaP cells through nano-scale pores in the cell membrane, which further reduced the proliferation of LNCaP cells.Conclusion: These stable and safe CNDs may be a promising choice to achieve efficient ultrasound-mediated gene delivery to specific tissues in a spatiotemporally controlled manner.Keywords: nanodroplets, chitosan, DKK-2, ultrasonic gene transfer, prostate cancer

Published
2021

7. Ultrasound Mediated Destruction of LMW-HA-Loaded and Folate-Conjugated Nanobubble for TAM Targeting and Reeducation

Author

Sun X, Guo L, Shang M, Shi D, Liang P, Jing X, Meng D, Liu X, Zhou X, Zhao Y, and Li J

Subjects
ultrasound contrast agent, ultrasonic nanobubble, tumor associated macrophage, tumor microenvironment, contrast-enhanced ultrasound imaging., Medicine (General), R5-920
Abstract

Xiao Sun,1 Lu Guo,1 Mengmeng Shang,1 Dandan Shi,1 Ping Liang,2 Xuanxuan Jing,1 Dong Meng,1 Xinxin Liu,1 Xiaoying Zhou,1 Yading Zhao,1 Jie Li1 1Department of Ultrasound, Qilu Hospital of Shandong University, Jinan, People’s Republic of China; 2Department of Interventional Ultrasound, Chinese PLA General Hospital, Beijing, People’s Republic of ChinaCorrespondence: Jie LiDepartment of Ultrasound, Qilu Hospital of Shandong University, Wenhua Western Road, 107#, Jinan Tel/Fax +86-531-82166101Email jieli301@163.comPurpose: To synthesize and evaluate a novel folate-conjugated ultrasonic nanobubble (HA-FOL-NB) loading low-molecular-weight hyaluronic acid (LMW-HA) for specific tumor-associated macrophages (TAMs) targeting and reeducation.Methods: The characteristics, cytotoxicity, contrast-enhanced ultrasound imaging (CEUS), and targeting ability to TAMs of HA-FOL-NBs were investigated. The TAMs reprogramming function of HA-FOL-NBs combining ultrasound targeted nanobubble destruction was assessed as well.Results: HA-FOL-NBs (about 342 nm) showed remarkable contrast enhancement images, and higher targeting ability due to the folate to folate receptor interactions. Combined with ultrasound targeted nanobubble destruction, HA-FOL-NBs could specifically deliver LMW-HA into TAMs, thus exhibited stronger reeducation effect compared with free LMW-HA.Conclusion: These folate-conjugated and LMW-HA-loaded nanobubbles, with targeted CEUS imaging and TAMs reeducation, are expected to be a potential approach for tumor therapy based on TAMs, especially folate receptor-positive ones.Keywords: ultrasound contrast agent, ultrasonic nanobubble, tumor-associated macrophage, tumor microenvironment, contrast-enhanced ultrasound imaging

Published
2020

8. pH- and Ultrasound-Responsive Paclitaxel-Loaded Carboxymethyl Chitosan Nanodroplets for Combined Imaging and Synergistic Chemoradiotherapy

Author

Shang M, Sun X, Guo L, Shi D, Liang P, Meng D, Zhou X, Liu X, Zhao Y, and Li J

Subjects
nanodroplets, ultrasound contrast agents, chemoradiotherapy, radiosensitizer, paclitaxel, chitosan, Medicine (General), R5-920
Abstract

Mengmeng Shang,1 Xiao Sun,1 Lu Guo,1 Dandan Shi,1 Ping Liang,2 Dong Meng,1 Xiaoying Zhou,1 Xinxin Liu,1 Yading Zhao,1 Jie Li1 1Department of Ultrasound, Qilu Hospital of Shandong University, Jinan 250012, People’s Republic of China; 2Department of Interventional Ultrasound, Chinese PLA General Hospital, Beijing 100853, People’s Republic of ChinaCorrespondence: Jie LiDepartment of Ultrasound, Qilu Hospital of Shandong University, 107 Wenhua Xi Road, Jinan 250012, People’s Republic of ChinaTel +86-531-82166101Fax +86-531-86927544Email jieli301@163.comBackground: Synergistic chemoradiotherapy (CRT) has become a primary effective curative approach for many solid cancers. However, CRT is still associated with several obstacles, including the increases in side effects and systemic toxicity. Incorporating nanocarriers into CRT is a new and exciting approach to solve these obstacles. The purpose of the present study was to design a unique pH- and ultrasound-responsive perfluoropentane-encapsulated, paclitaxel (PTX)-loaded carboxymethyl chitosan nanodroplets (NDs) for combined imaging and synergistic CRT.Materials and Methods: The NDs were prepared by a homogenization/emulsion method. Their physicochemical properties, echogenicity and biocompatibility were evaluated. PTX-loaded NDs with a high loading efficiency and encapsulation efficiency were prepared and their pH-responsive drug release profile was determined by dialysis sack method. Then, PC3 cells were exposed to (1) PTX (4 μg/mL), (2) NDs (30 μg/mL), (3) PTX-loaded NDs (34 μg/mL), (4) RT (6 Gy), (5) RT (10 Gy), (6) combination of PTX (4 μg/mL), ultrasound (0.5 W/cm2, 30 s) and RT (6 Gy), (7) combination of NDs (30 μg/mL), ultrasound (0.5 W/cm2, 30 s) and RT (6Gy), (8) combination of PTX-loaded NDs (30 μg/mL), ultrasound (0.5 W/cm2, 30 s) and RT (6 Gy). 24 hrs later, CCK-8 assay, flow cytometry and migration assay were carried out to evaluate their therapeutic effects in CRT.Results: The desired NDs were successfully prepared, which were with round, spherical shapes, relatively smooth surfaces, core-shell structures and uniform in sizes (< 300 nm with PDI< 0.3 when at pH≧6.0). The NDs exhibited good abilities in pH-dependent charge conversion, biocompatibility and ultrasound contrast echogenicity. The in vitro drug release from PTX-loaded NDs (the highest loading efficiency and encapsulation efficiency were 20.35% and 91.58%) was pH dependent and exhibited an initial burst followed by a sustained drug release. The results of the CCK-8 assay, flow cytometry and migration assay all showed PTX-loaded NDs combined ultrasound and RT significantly enhanced cell responses in CRT.Conclusion: The pH- and ultrasound-responsive PTX-loaded NDs, which exhibited a high echogenicity, drug delivery ability and radiosensitization ability, could be a feasible option for combined imaging and novel enhancing approach in synergistic CRT.Keywords: nanodroplets, ultrasound contrast agents, chemoradiotherapy, radiosensitizer, paclitaxel, chitosan

Published
2020

10. LincRNA-p21 leads to G1 arrest by p53 pathway in esophageal squamous cell carcinoma

Author

Zhang Y, Miao Y, Shang M, Liu M, Liu R, Pan E, Pu Y, and Yin L

Subjects
Esophageal squamous-cell carcinoma, LincRNA-p21, G1 arrest, p53 pathway, Fluorescence in Situ Hybridization, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Ying Zhang,1 Yan Miao,1 Muhe Shang,1 Mxin Liu,1 Ran Liu,1 Ench Pan,2 Yuepu Pu,1 Lihong Yin11Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing 210009, People’s Republic of China; 2Huaian Center for Disease Control and Prevention, Huaian 223001, People’s Republic of ChinaBackground: Esophageal squamous cell carcinoma (ESCC) is the fourth most common cause of cancer death in China. Long noncoding RNAs have emerged as critical regulators in cancer. Long intergenic noncoding RNA-p21, a kind of Long noncoding RNAs, LincRNA-p21 have been discussed dysregulated in several cancers, but its role in ESCC remains unknown. This study investigated the role of LincRNA-p21 in ESCC.Materials and methods: The LincRNA-p21 expression level and its association with esophageal cancer was determined in 64 tumor tissues of esophageal squamous cell carcinoma patients and cells using quantitative real-time reverse transcription PCR. Fluorescence in situ hybridization of single-RNA molecular probes was used to determine subcellular localization of LincRNA-p21. CCK8 and EdU assays were used for proliferation assay, flow cytometry was performed for apoptosis and cell-cycle distribution, and 24-well Mill cell chamber was made for measuring the abilities of migration and invasion after transfected with lentivirus-expressing LincRNA-p21 in EC109 cells. Then, quantitative real-time reverse transcription PCR and Western blot detected the expression of p21. Further, UC2288, an inhibitor of p21, was used to decrease the level of p21, and flow cytometry was used to detect cell cycle. Finally, screening for differential pathways from microarray analysis and expression of p53 and cyclin D were detected by Western blot.Results: LincRNA-p21 expression level was remarkably lower in tumor tissues versus nontumor tissues and lower in EC109 cells versus Het-1A cells. Statistical analysis found that LincRNA-p21 might enhance the risk of ESCC. We observed that LincRNA-p21 was expressed both in the nucleus and cytoplasm, and a larger proportion of LincRNA-p21 was observed in the cytoplasm. The results demonstrated that upregulating the expression of LincRNA-p21 could inhibit cell proliferation, migration, invasion, and the transition of cell cycle from G1 and promoted apoptosis of EC109. Then, we found that LincRNA-p21 promotes the expression of p21. Decreasing the level of p21 revealed that cell-cycle arrest was restored. Pathway analysis found p53 pathway was downregulated, and upregulation of LincRNA-p21 inhibited the expression of cyclin D.Conclusion: Our study suggests that LincRNA-p21 plays as a tumor inhibitor in ESCC development and LincRNA-p21 might induce G1 arrest through p53 signal pathway.Keywords: esophageal squamous-cell carcinoma, LincRNA-p21, G1 arrest, p53 pathway, Fluorescence in Situ Hybridization

Published
2019

13. Comparative Study of Perioperative and Oncological Outcomes Between Elderly Patients and Younger Patients Who Received Radical Cystectomy and Pelvic Lymph Node Dissection: A Single-Center Retrospective Study

Author

Wang H, Huang H, Shang M, Hao H, and Xi Z

Subjects
complications, survival outcomes, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, radical cystectomy, elderly, RC254-282
Abstract

Haixin Wang,1– 4,&ast; Haiwen Huang,1– 3,&ast; Meixia Shang,5 Han Hao,1– 3 Zhijun Xi1– 3 1Department of Urology, Peking University First Hospital, Beijing, People’s Republic of China; 2Institute of Urology, Peking University, National Urological Cancer Center, Beijing, People’s Republic of China; 3National Research Center for Genitourinary Oncology, Beijing, People’s Republic of China; 4Department of Urology, Yankuang New Journey General Hospital, Zoucheng, Shandong, People’s Republic of China; 5Department of Medical Statistics, Peking University First Hospital, Beijing, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Zhijun Xi; Han Hao, Department of Urology, Peking University First Hospital, 8 Xishiku Street, Xicheng District, Beijing, 100034, People’s Republic of China, Tel +86-10-83572481, Fax +86-10-66175710, Email xizhijun@hsc.pku.edu.cn; haohan1122@vip.sina.comPurpose: To compare the perioperative and survival outcomes of patients over 75 years and younger patients who received radical cystectomy.Patients and methods: A total of 119 patients aged ≥ 75 years and 488 patients aged < 75 years were enrolled. All patients underwent radical cystectomy with pelvic lymph node dissection. Clinical characteristics and perioperative outcomes were compared between the two groups. Overall survival and progression-free survival were analyzed by using the Kaplan–Meier method. Cox regression analysis and logistic regression analysis were used to identify the risk factors affecting the outcomes observed.Results: There was no significant difference in perioperative complications between the elderly patient group and the younger patient group (p = 0.349). The 5-year overall survival of elderly patients was lower than that of young patients (p < 0.001). Age ≥ 75 years was a risk factor for overall survival (HR = 1.69 [95% CI: 1.22– 2.35]; p = 0.002) and progression-free survival (HR = 1.69 [95% CI: 1.14– 2.50]; p = 0.008) for patients who received radical cystectomy but was not a poor risk factor for major complications (HR = 1.25 [95% CI: 0.47– 3.31]; p = 0.658) after radical cystectomy. In addition, preoperative renal insufficiency was associated with a higher risk of major complications.Conclusion: In our cohort, compared with younger patients, elderly patients aged ≥ 75 years had worse survival outcomes, but age ≥ 75 years was not a risk factor for major complications after radical cystectomy with pelvic lymph node dissection. Radical surgery should be encouraged for elderly patients who can tolerate aggressive treatments.Keywords: radical cystectomy, elderly, survival outcomes, complications

Published
2022

14. Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen

Author

Menden M, Wang D, Mason M, Szalai B, Bulusu K, Guan Y, Yu T, Kang J, Jeon M, Wolfinger R, Nguyen T, Zaslavskiy M, Jang I, Ghazoui Z, Ahsen M, Vogel R, Neto E, Norman T, Tang E, Garnett M, Di Veroli G, Fawell S, Stolovitzky G, Guinney J, Dry J, Saez-Rodriguez J, Abante J, Abecassis B, Aben N, Aghamirzaie D, Aittokallio T, Akhtari F, Al-lazikani B, Alam T, Allam A, Allen C, de Almeida M, Altarawy D, Alves V, Amadoz A, Anchang B, Antolin A, Ash J, Aznar V, Ba-alawi W, Bagheri M, Bajic V, Ball G, Ballester P, Baptista D, Bare C, Bateson M, Bender A, Bertrand D, Wijayawardena B, Boroevich K, Bosdriesz E, Bougouffa S, Bounova G, Brouwer T, Bryant B, Calaza M, Calderone A, Calza S, Capuzzi S, Carbonell-Caballero J, Carlin D, Carter H, Castagnoli L, Celebi R, Cesareni G, Chang H, Chen G, Chen H, Cheng L, Chernomoretz A, Chicco D, Cho K, Cho S, Choi D, Choi J, Choi K, Choi M, De Cock M, Coker E, Cortes-Ciriano I, Cserzo M, Cubuk C, Curtis C, Van Daele D, Dang C, Dijkstra T, Dopazo J, Draghici S, Drosou A, Dumontier M, Ehrhart F, Eid F, ElHefnawi M, Elmarakeby H, van Engelen B, Engin H, de Esch I, Evelo C, Falcao A, Farag S, Fernandez-Lozano C, Fisch K, Flobak A, Fornari C, Foroushani A, Fotso D, Fourches D, Friend S, Frigessi A, Gao F, Gao X, Gerold J, Gestraud P, Ghosh S, Gillberg J, Godoy-Lorite A, Godynyuk L, Godzik A, Goldenberg A, Gomez-Cabrero D, Gonen M, de Graaf C, Gray H, Grechkin M, Guimera R, Guney E, Haibe-Kains B, Han Y, Hase T, He D, He L, Heath L, Hellton K, Helmer-Citterich M, Hidalgo M, Hidru D, Hill S, Hochreiter S, Hong S, Hovig E, Hsueh Y, Hu Z, Huang J, Huang R, Hunyady L, Hwang J, Hwang T, Hwang W, Hwang Y, Isayev O, Walk O, Jack J, Jahandideh S, Ji J, Jo Y, Kamola P, Kanev G, Karacosta L, Karimi M, Kaski S, Kazanov M, Khamis A, Khan S, Kiani N, Kim A, Kim J, Kim K, Kim S, Kim Y, Kirk P, Kitano H, Klambauer G, Knowles D, Ko M, Kohn-Luque A, Kooistra A, Kuenemann M, Kuiper M, Kurz C, Kwon M, van Laarhoven T, Laegreid A, Lederer S, Lee H, Lee J, Lee Y, Leppaho E, Lewis R, Li J, Li L, Liley J, Lim W, Lin C, Liu Y, Lopez Y, Low J, Lysenko A, Machado D, Madhukar N, De Maeyer D, Malpartida A, Mamitsuka H, Marabita F, Marchal K, Marttinen P, Mason D, Mazaheri A, Mehmood A, Mehreen A, Michaut M, Miller R, Mitsopoulos C, Modos D, Van Moerbeke M, Moo K, Motsinger-Reif A, Movva R, Muraru S, Muratov E, Mushthofa M, Nagarajan N, Nakken S, Nath A, Neuvial P, Newton R, Ning Z, De Niz C, Oliva B, Olsen C, Palmeri A, Panesar B, Papadopoulos S, Park J, Park S, Pawitan Y, Peluso D, Pendyala S, Peng J, Perfetto L, Pirro S, Plevritis S, Politi R, Poon H, Porta E, Prellner I, Preuer K, Pujana M, Ramnarine R, Reid J, Reyal F, Richardson S, Ricketts C, Rieswijk L, Rocha M, Rodriguez-Gonzalvez C, Roell K, Rotroff D, de Ruiter J, Rukawa P, Sadacca B, Safikhani Z, Safitri F, Sales-Pardo M, Sauer S, Schlichting M, Seoane J, Serra J, Shang M, Sharma A, Sharma H, Shen Y, Shiga M, Shin M, Shkedy Z, Shopsowitz K, Sinai S, Skola D, Smirnov P, Soerensen I, Soerensen P, Song J, Song S, Soufan O, Spitzmueller A, Steipe B, Suphavilai C, Tamayo S, Tamborero D, Tang J, Tanoli Z, Tarres-Deulofeu M, Tegner J, Thommesen L, Tonekaboni S, Tran H, De Troyer E, Truong A, Tsunoda T, Turu G, Tzeng G, Verbeke L, Videla S, Vis D, Voronkov A, Votis K, Wang A, Wang H, Wang P, Wang S, Wang W, Wang X, Wennerberg K, Wernisch L, Wessels L, van Westen G, Westerman B, White S, Willighagen E, Wurdinger T, Xie L, Xie S, Xu H, Yadav B, Yau C, Yeerna H, Yin J, Yu M, Yun S, Zakharov A, Zamichos A, Zanin M, Zeng L, Zenil H, Zhang F, Zhang P, Zhang W, Zhao H, Zhao L, Zheng W, Zoufir A, Zucknick M, AstraZeneca-Sanger Drug Combinatio, Ege Üniversitesi, Gönen, Mehmet (ORCID 0000-0002-2483-075X & YÖK ID 237468), Menden, Michael P., Wang, Dennis, Mason, Mike J., Szalai, Bence, Bulusu, Krishna C., Guan, Yuanfang, Yu, Thomas, Kang, Jaewoo, Jeon, Minji, Wolfinger, Russ, Nguyen, Tin, Zaslavskiy, Mikhail, Jang, In Sock, Ghazoui, Zara, Ahsen, Mehmet Eren, Vogel, Robert, Neto, Elias Chaibub, Norman, Thea, Tang, Eric K. Y., Garnett, Mathew J., Di Veroli, Giovanni Y., Fawell, Stephen, Stolovitzky, Gustavo, Guinney, Justin, Dry, Jonathan R., Saez-Rodriguez, Julio, Abante, Jordi, Abecassis, Barbara Schmitz, Aben, Nanne, Aghamirzaie, Delasa, Aittokallio, Tero, Akhtari, Farida S., Al-lazikani, Bissan, Alam, Tanvir, Allam, Amin, Allen, Chad, de Almeida, Mariana Pelicano, Altarawy, Doaa, Alves, Vinicius, Amadoz, Alicia, Anchang, Benedict, Antolin, Albert A., Ash, Jeremy R., Romeo Aznar, Victoria, Ba-alawi, Wail, Bagheri, Moeen, Bajic, Vladimir, Ball, Gordon, Ballester, Pedro J., Baptista, Delora, Bare, Christopher, Bateson, Mathilde, Bender, Andreas, Bertrand, Denis, Wijayawardena, Bhagya, Boroevich, Keith A., Bosdriesz, Evert, Bougouffa, Salim, Bounova, Gergana, Brouwer, Thomas, Bryant, Barbara, Calaza, Manuel, Calderone, Alberto, Calza, Stefano, Capuzzi, Stephen, Carbonell-Caballero, Jose, Carlin, Daniel, Carter, Hannah, Castagnoli, Luisa, Celebi, Remzi, Cesareni, Gianni, Chang, Hyeokyoon, Chen, Guocai, Chen, Haoran, Chen, Huiyuan, Cheng, Lijun, Chernomoretz, Ariel, Chicco, Davide, Cho, Kwang-Hyun, Cho, Sunghwan, Choi, Daeseon, Choi, Jaejoon, Choi, Kwanghun, Choi, Minsoo, De Cock, Martine, Coker, Elizabeth, Cortes-Ciriano, Isidro, Cserzo, Miklos, Cubuk, Cankut, Curtis, Christina, Van Daele, Dries, Dang, Cuong C., Dijkstra, Tjeerd, Dopazo, Joaquin, Draghici, Sorin, Drosou, Anastasios, Dumontier, Michel, Ehrhart, Friederike, Eid, Fatma-Elzahraa, ElHefnawi, Mahmoud, Elmarakeby, Haitham, van Engelen, Bo, Engin, Hatice Billur, de Esch, Iwan, Evelo, Chris, Falcao, Andre O., Farag, Sherif, Fernandez-Lozano, Carlos, Fisch, Kathleen, Flobak, Asmund, Fornari, Chiara, Foroushani, Amir B. K., Fotso, Donatien Chedom, Fourches, Denis, Friend, Stephen, Frigessi, Arnoldo, Gao, Feng, Gao, Xiaoting, Gerold, Jeffrey M., Gestraud, Pierre, Ghosh, Samik, Gillberg, Jussi, Godoy-Lorite, Antonia, Godynyuk, Lizzy, Godzik, Adam, Goldenberg, Anna, Gomez-Cabrero, David, de Graaf, Chris, Gray, Harry, Grechkin, Maxim, Guimera, Roger, Guney, Emre, Haibe-Kains, Benjamin, Han, Younghyun, Hase, Takeshi, He, Di, He, Liye, Heath, Lenwood S., Hellton, Kristoffer H., Helmer-Citterich, Manuela, Hidalgo, Marta R., Hidru, Daniel, Hill, Steven M., Hochreiter, Sepp, Hong, Seungpyo, Hovig, Eivind, Hsueh, Ya-Chih, Hu, Zhiyuan, Huang, Justin K., Huang, R. Stephanie, Hunyady, Laszlo, Hwang, Jinseub, Hwang, Tae Hyun, Hwang, Woochang, Hwang, Yongdeuk, Isayev, Olexandr, Walk, Oliver Bear Don't, Jack, John, Jahandideh, Samad, Ji, Jiadong, Jo, Yousang, Kamola, Piotr J., Kanev, Georgi K., Karacosta, Loukia, Karimi, Mostafa, Kaski, Samuel, Kazanov, Marat, Khamis, Abdullah M., Khan, Suleiman Ali, Kiani, Narsis A., Kim, Allen, Kim, Jinhan, Kim, Juntae, Kim, Kiseong, Kim, Kyung, Kim, Sunkyu, Kim, Yongsoo, Kim, Yunseong, Kirk, Paul D. W., Kitano, Hiroaki, Klambauer, Gunter, Knowles, David, Ko, Melissa, Kohn-Luque, Alvaro, Kooistra, Albert J., Kuenemann, Melaine A., Kuiper, Martin, Kurz, Christoph, Kwon, Mijin, van Laarhoven, Twan, Laegreid, Astrid, Lederer, Simone, Lee, Heewon, Lee, Jeon, Lee, Yun Woo, Leppaho, Eemeli, Lewis, Richard, Li, Jing, Li, Lang, Liley, James, Lim, Weng Khong, Lin, Chieh, Liu, Yiyi, Lopez, Yosvany, Low, Joshua, Lysenko, Artem, Machado, Daniel, Madhukar, Neel, De Maeyer, Dries, Malpartida, Ana Belen, Mamitsuka, Hiroshi, Marabita, Francesco, Marchal, Kathleen, Marttinen, Pekka, Mason, Daniel, Mazaheri, Alireza, Mehmood, Arfa, Mehreen, Ali, Michaut, Magali, Miller, Ryan A., Mitsopoulos, Costas, Modos, Dezso, Van Moerbeke, Marijke, Moo, Keagan, Motsinger-Reif, Alison, Movva, Rajiv, Muraru, Sebastian, Muratov, Eugene, Mushthofa, Mushthofa, Nagarajan, Niranjan, Nakken, Sigve, Nath, Aritro, Neuvial, Pierre, Newton, Richard, Ning, Zheng, De Niz, Carlos, Oliva, Baldo, Olsen, Catharina, Palmeri, Antonio, Panesar, Bhawan, Papadopoulos, Stavros, Park, Jaesub, Park, Seonyeong, Park, Sungjoon, Pawitan, Yudi, Peluso, Daniele, Pendyala, Sriram, Peng, Jian, Perfetto, Livia, Pirro, Stefano, Plevritis, Sylvia, Politi, Regina, Poon, Hoifung, Porta, Eduard, Prellner, Isak, Preuer, Kristina, Angel Pujana, Miguel, Ramnarine, Ricardo, Reid, John E., Reyal, Fabien, Richardson, Sylvia, Ricketts, Camir, Rieswijk, Linda, Rocha, Miguel, Rodriguez-Gonzalvez, Carmen, Roell, Kyle, Rotroff, Daniel, de Ruiter, Julian R., Rukawa, Ploy, Sadacca, Benjamin, Safikhani, Zhaleh, Safitri, Fita, Sales-Pardo, Marta, Sauer, Sebastian, Schlichting, Moritz, Seoane, Jose A., Serra, Jordi, Shang, Ming-Mei, Sharma, Alok, Sharma, Hari, Shen, Yang, Shiga, Motoki, Shin, Moonshik, Shkedy, Ziv, Shopsowitz, Kevin, Sinai, Sam, Skola, Dylan, Smirnov, Petr, Soerensen, Izel Fourie, Soerensen, Peter, Song, Je-Hoon, Song, Sang Ok, Soufan, Othman, Spitzmueller, Andreas, Steipe, Boris, Suphavilai, Chayaporn, Tamayo, Sergio Pulido, Tamborero, David, Tang, Jing, Tanoli, Zia-ur-Rehman, Tarres-Deulofeu, Marc, Tegner, Jesper, Thommesen, Liv, Tonekaboni, Seyed Ali Madani, Tran, Hong, De Troyer, Ewoud, Truong, Amy, Tsunoda, Tatsuhiko, Turu, Gabor, Tzeng, Guang-Yo, Verbeke, Lieven, Videla, Santiago, Vis, Daniel, Voronkov, Andrey, Votis, Konstantinos, Wang, Ashley, Wang, Hong-Qiang Horace, Wang, Po-Wei, Wang, Sheng, Wang, Wei, Wang, Xiaochen, Wang, Xin, Wennerberg, Krister, Wernisch, Lorenz, Wessels, Lodewyk, van Westen, Gerard J. P., Westerman, Bart A., White, Simon Richard, Willighagen, Egon, Wurdinger, Tom, Xie, Lei, Xie, Shuilian, Xu, Hua, Yadav, Bhagwan, Yau, Christopher, Yeerna, Huwate, Yin, Jia Wei, Yu, Michael, Yu, MinHwan, Yun, So Jeong, Zakharov, Alexey, Zamichos, Alexandros, Zanin, Massimiliano, Zeng, Li, Zenil, Hector, Zhang, Frederick, Zhang, Pengyue, Zhang, Wei, Zhao, Hongyu, Zhao, Lan, Zheng, Wenjin, Zoufir, Azedine, Zucknick, Manuela, College of Engineering, Department of Industrial Engineering, Institute of Data Science, RS: FSE DACS IDS, Bioinformatica, RS: NUTRIM - R1 - Obesity, diabetes and cardiovascular health, RS: FHML MaCSBio, Promovendi NTM, Tero Aittokallio / Principal Investigator, Bioinformatics, Institute for Molecular Medicine Finland, Hu, Z, Fotso, DC, Menden, M, Wang, D, Mason, M, Szalai, B, Bulusu, K, Guan, Y, Yu, T, Kang, J, Jeon, M, Wolfinger, R, Nguyen, T, Zaslavskiy, M, Abante, J, Abecassis, B, Aben, N, Aghamirzaie, D, Aittokallio, T, Akhtari, F, Al-lazikani, B, Alam, T, Allam, A, Allen, C, de Almeida, M, Altarawy, D, Alves, V, Amadoz, A, Anchang, B, Antolin, A, Ash, J, Aznar, V, Ba-alawi, W, Bagheri, M, Bajic, V, Ball, G, Ballester, P, Baptista, D, Bare, C, Bateson, M, Bender, A, Bertrand, D, Wijayawardena, B, Boroevich, K, Bosdriesz, E, Bougouffa, S, Bounova, G, Brouwer, T, Bryant, B, Calaza, M, Calderone, A, Calza, S, Capuzzi, S, Carbonell-Caballero, J, Carlin, D, Carter, H, Castagnoli, L, Celebi, R, Cesareni, G, Chang, H, Chen, G, Chen, H, Cheng, L, Chernomoretz, A, Chicco, D, Cho, K, Cho, S, Choi, D, Choi, J, Choi, K, Choi, M, Cock, M, Coker, E, Cortes-Ciriano, I, Cserzo, M, Cubuk, C, Curtis, C, Daele, D, Dang, C, Dijkstra, T, Dopazo, J, Draghici, S, Drosou, A, Dumontier, M, Ehrhart, F, Eid, F, Elhefnawi, M, Elmarakeby, H, van Engelen, B, Engin, H, de Esch, I, Evelo, C, Falcao, A, Farag, S, Fernandez-Lozano, C, Fisch, K, Flobak, A, Fornari, C, Foroushani, A, Fotso, D, Fourches, D, Friend, S, Frigessi, A, Gao, F, Gao, X, Gerold, J, Gestraud, P, Ghosh, S, Gillberg, J, Godoy-Lorite, A, Godynyuk, L, Godzik, A, Goldenberg, A, Gomez-Cabrero, D, Gonen, M, de Graaf, C, Gray, H, Grechkin, M, Guimera, R, Guney, E, Haibe-Kains, B, Han, Y, Hase, T, He, D, He, L, Heath, L, Hellton, K, Helmer-Citterich, M, Hidalgo, M, Hidru, D, Hill, S, Hochreiter, S, Hong, S, Hovig, E, Hsueh, Y, Huang, J, Huang, R, Hunyady, L, Hwang, J, Hwang, T, Hwang, W, Hwang, Y, Isayev, O, Don't Walk, O, Jack, J, Jahandideh, S, Ji, J, Jo, Y, Kamola, P, Kanev, G, Karacosta, L, Karimi, M, Kaski, S, Kazanov, M, Khamis, A, Khan, S, Kiani, N, Kim, A, Kim, J, Kim, K, Kim, S, Kim, Y, Kirk, P, Kitano, H, Klambauer, G, Knowles, D, Ko, M, Kohn-Luque, A, Kooistra, A, Kuenemann, M, Kuiper, M, Kurz, C, Kwon, M, van Laarhoven, T, Laegreid, A, Lederer, S, Lee, H, Lee, J, Lee, Y, Lepp_aho, E, Lewis, R, Li, J, Li, L, Liley, J, Lim, W, Lin, C, Liu, Y, Lopez, Y, Low, J, Lysenko, A, Machado, D, Madhukar, N, Maeyer, D, Malpartida, A, Mamitsuka, H, Marabita, F, Marchal, K, Marttinen, P, Mason, D, Mazaheri, A, Mehmood, A, Mehreen, A, Michaut, M, Miller, R, Mitsopoulos, C, Modos, D, Moerbeke, M, Moo, K, Motsinger-Reif, A, Movva, R, Muraru, S, Muratov, E, Mushthofa, M, Nagarajan, N, Nakken, S, Nath, A, Neuvial, P, Newton, R, Ning, Z, Niz, C, Oliva, B, Olsen, C, Palmeri, A, Panesar, B, Papadopoulos, S, Park, J, Park, S, Pawitan, Y, Peluso, D, Pendyala, S, Peng, J, Perfetto, L, Pirro, S, Plevritis, S, Politi, R, Poon, H, Porta, E, Prellner, I, Preuer, K, Pujana, M, Ramnarine, R, Reid, J, Reyal, F, Richardson, S, Ricketts, C, Rieswijk, L, Rocha, M, Rodriguez-Gonzalvez, C, Roell, K, Rotroff, D, de Ruiter, J, Rukawa, P, Sadacca, B, Safikhani, Z, Safitri, F, Sales-Pardo, M, Sauer, S, Schlichting, M, Seoane, J, Serra, J, Shang, M, Sharma, A, Sharma, H, Shen, Y, Shiga, M, Shin, M, Shkedy, Z, Shopsowitz, K, Sinai, S, Skola, D, Smirnov, P, Soerensen, I, Soerensen, P, Song, J, Song, S, Soufan, O, Spitzmueller, A, Steipe, B, Suphavilai, C, Tamayo, S, Tamborero, D, Tang, J, Tanoli, Z, Tarres-Deulofeu, M, Tegner, J, Thommesen, L, Tonekaboni, S, Tran, H, Troyer, E, Truong, A, Tsunoda, T, Turu, G, Tzeng, G, Verbeke, L, Videla, S, Vis, D, Voronkov, A, Votis, K, Wang, A, Wang, H, Wang, P, Wang, S, Wang, W, Wang, X, Wennerberg, K, Wernisch, L, Wessels, L, van Westen, G, Westerman, B, White, S, Willighagen, E, Wurdinger, T, Xie, L, Xie, S, Xu, H, Yadav, B, Yau, C, Yeerna, H, Yin, J, Yu, M, Yun, S, Zakharov, A, Zamichos, A, Zanin, M, Zeng, L, Zenil, H, Zhang, F, Zhang, P, Zhang, W, Zhao, H, Zhao, L, Zheng, W, Zoufir, A, Zucknick, M, Jang, I, Ghazoui, Z, Ahsen, M, Vogel, R, Neto, E, Norman, T, Tang, E, Garnett, M, Veroli, G, Fawell, S, Stolovitzky, G, Guinney, J, Dry, J, Saez-Rodriguez, J, Menden, Michael P. [0000-0003-0267-5792], Mason, Mike J. [0000-0002-5652-7739], Yu, Thomas [0000-0002-5841-0198], Kang, Jaewoo [0000-0001-6798-9106], Nguyen, Tin [0000-0001-8001-9470], Ahsen, Mehmet Eren [0000-0002-4907-0427], Stolovitzky, Gustavo [0000-0002-9618-2819], Guinney, Justin [0000-0003-1477-1888], Saez-Rodriguez, Julio [0000-0002-8552-8976], Apollo - University of Cambridge Repository, Menden, Michael P [0000-0003-0267-5792], Mason, Mike J [0000-0002-5652-7739], Pathology, CCA - Cancer biology and immunology, Medical oncology laboratory, Neurosurgery, Chemistry and Pharmaceutical Sciences, AIMMS, Medicinal chemistry, Universidade do Minho, Department of Computer Science, Professorship Marttinen P., Aalto-yliopisto, and Aalto University

Subjects
Drug Resistance, 02 engineering and technology, 13, PATHWAY, Antineoplastic Combined Chemotherapy Protocols, Molecular Targeted Therapy, Càncer, lcsh:Science, media_common, Cancer, Tumor, Settore BIO/18, Settore BIO/11, Drug combinations, High-throughput screening, Drug Synergism, purl.org/becyt/ford/1.2 [https], Genomics, Machine Learning, predictions, 3. Good health, ddc, Technologie de l'environnement, contrôle de la pollution, Benchmarking, 5.1 Pharmaceuticals, Cancer treatment, Farmacogenètica, Science & Technology - Other Topics, Development of treatments and therapeutic interventions, 0210 nano-technology, Human, Drug, media_common.quotation_subject, Science, 49/23, ADAM17 Protein, General Biochemistry, Genetics and Molecular Biology, 03 medical and health sciences, SDG 3 - Good Health and Well-being, RESOURCE, Machine learning, Genetics, Chimie, Humans, BREAST-CANCER, CELL, 49/98, Science & Technology, Antineoplastic Combined Chemotherapy Protocol, 45, MUTATIONS, Computational Biology, Androgen receptor, Breast-cancer, Gene, Cell, Inhibition, Resistance, Pathway, Mutations, Landscape, Resource, 631/114/1305, medicine.disease, Drug synergy, 49, 030104 developmental biology, Pharmacogenetics, Mutation, Ciências Médicas::Biotecnologia Médica, lcsh:Q, 631/154/1435/2163, Biomarkers, RESISTANCE, 0301 basic medicine, ING-INF/06 - BIOINGEGNERIA ELETTRONICA E INFORMATICA, Statistical methods, Computer science, General Physics and Astronomy, Datasets as Topic, Drug resistance, purl.org/becyt/ford/1 [https], Phosphatidylinositol 3-Kinases, Biotecnologia Médica [Ciências Médicas], Neoplasms, Science and technology, Phosphoinositide-3 Kinase Inhibitors, Multidisciplinary, Biomarkers, Tumor, Cell Line, Tumor, Drug Antagonism, Drug Resistance, Neoplasm, Treatment Outcome, Pharmacogenetic, article, ANDROGEN RECEPTOR, 49/39, 631/114/2415, 021001 nanoscience & nanotechnology, 692/4028/67, Multidisciplinary Sciences, 317 Pharmacy, Patient Safety, Systems biology, 3122 Cancers, INHIBITION, Computational biology, Cell Line, medicine, LANDSCAPE, Physique, Human Genome, Data Science, General Chemistry, AstraZeneca-Sanger Drug Combination DREAM Consortium, Astronomie, GENE, Good Health and Well Being, Pharmacogenomics, Genomic, Neoplasm, 631/553, Phosphatidylinositol 3-Kinase
Abstract

PubMed: 31209238, The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca’s large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells. © 2019, The Author(s)., National Institute for Health Research, NIHR Wellcome Trust, WT: 102696, 206194 Magyar Tudományos Akadémia, MTA Bayer 668858 PrECISE AstraZeneca, We thank the Genomics of Drug Sensitivity in Cancer and COSMIC teams at the Wellcome Trust Sanger Institute for help with the preparation of the molecular data, Denes Turei for help with Omnipath, and Katjusa Koler for help with matching drug names across combination screens. We thank AstraZeneca for funding and provision of data to the DREAM Consortium to run the challenge, and funding from the European Union Horizon 2020 research (under grant agreement No 668858 PrECISE to J.S.R.), the Joint Research Center for Computational Biomedicine (which is partially funded by Bayer AG) to J.S.R., National Institute for Health Research (NIHR) Sheffield Biomedical Research Center, Premium Postdoctoral Fellowship Program of the Hungarian Academy of Sciences. M.G lab is supported by Wellcome Trust (102696 and 206194)., Competing interests: K.C.B., Z.G., G.Y.D., E.K.Y.T., S.F., and J.R.D. are AstraZeneca employees. K.C.B., Z.G., E.K.Y.T., S.F., and J.R.D. are AstraZeneca shareholders. Y.G. receives personal compensation from Eli Lilly and Company, is a shareholder of Cleerly, Inc., and Ann Arbor Algorithms, Inc. M.G. receives research funding from AstraZeneca and has performed consultancy for Sanofi. The remaining authors declare no competing interests.

Published
2019
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20. Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen

Author

Menden, M, Wang, D, Mason, M, Szalai, B, Bulusu, K, Guan, Y, Yu, T, Kang, J, Jeon, M, Wolfinger, R, Nguyen, T, Zaslavskiy, M, Abante, J, Abecassis, B, Aben, N, Aghamirzaie, D, Aittokallio, T, Akhtari, F, Al-lazikani, B, Alam, T, Allam, A, Allen, C, de Almeida, M, Altarawy, D, Alves, V, Amadoz, A, Anchang, B, Antolin, A, Ash, J, Aznar, V, Ba-alawi, W, Bagheri, M, Bajic, V, Ball, G, Ballester, P, Baptista, D, Bare, C, Bateson, M, Bender, A, Bertrand, D, Wijayawardena, B, Boroevich, K, Bosdriesz, E, Bougouffa, S, Bounova, G, Brouwer, T, Bryant, B, Calaza, M, Calderone, A, Calza, S, Capuzzi, S, Carbonell-Caballero, J, Carlin, D, Carter, H, Castagnoli, L, Celebi, R, Cesareni, G, Chang, H, Chen, G, Chen, H, Cheng, L, Chernomoretz, A, Chicco, D, Cho, K, Cho, S, Choi, D, Choi, J, Choi, K, Choi, M, Cock, M, Coker, E, Cortes-Ciriano, I, Cserzo, M, Cubuk, C, Curtis, C, Daele, D, Dang, C, Dijkstra, T, Dopazo, J, Draghici, S, Drosou, A, Dumontier, M, Ehrhart, F, Eid, F, Elhefnawi, M, Elmarakeby, H, van Engelen, B, Engin, H, de Esch, I, Evelo, C, Falcao, A, Farag, S, Fernandez-Lozano, C, Fisch, K, Flobak, A, Fornari, C, Foroushani, A, Fotso, D, Fourches, D, Friend, S, Frigessi, A, Gao, F, Gao, X, Gerold, J, Gestraud, P, Ghosh, S, Gillberg, J, Godoy-Lorite, A, Godynyuk, L, Godzik, A, Goldenberg, A, Gomez-Cabrero, D, Gonen, M, de Graaf, C, Gray, H, Grechkin, M, Guimera, R, Guney, E, Haibe-Kains, B, Han, Y, Hase, T, He, D, He, L, Heath, L, Hellton, K, Helmer-Citterich, M, Hidalgo, M, Hidru, D, Hill, S, Hochreiter, S, Hong, S, Hovig, E, Hsueh, Y, Hu, Z, Huang, J, Huang, R, Hunyady, L, Hwang, J, Hwang, T, Hwang, W, Hwang, Y, Isayev, O, Don't Walk, O, Jack, J, Jahandideh, S, Ji, J, Jo, Y, Kamola, P, Kanev, G, Karacosta, L, Karimi, M, Kaski, S, Kazanov, M, Khamis, A, Khan, S, Kiani, N, Kim, A, Kim, J, Kim, K, Kim, S, Kim, Y, Kirk, P, Kitano, H, Klambauer, G, Knowles, D, Ko, M, Kohn-Luque, A, Kooistra, A, Kuenemann, M, Kuiper, M, Kurz, C, Kwon, M, van Laarhoven, T, Laegreid, A, Lederer, S, Lee, H, Lee, J, Lee, Y, Lepp_aho, E, Lewis, R, Li, J, Li, L, Liley, J, Lim, W, Lin, C, Liu, Y, Lopez, Y, Low, J, Lysenko, A, Machado, D, Madhukar, N, Maeyer, D, Malpartida, A, Mamitsuka, H, Marabita, F, Marchal, K, Marttinen, P, Mason, D, Mazaheri, A, Mehmood, A, Mehreen, A, Michaut, M, Miller, R, Mitsopoulos, C, Modos, D, Moerbeke, M, Moo, K, Motsinger-Reif, A, Movva, R, Muraru, S, Muratov, E, Mushthofa, M, Nagarajan, N, Nakken, S, Nath, A, Neuvial, P, Newton, R, Ning, Z, Niz, C, Oliva, B, Olsen, C, Palmeri, A, Panesar, B, Papadopoulos, S, Park, J, Park, S, Pawitan, Y, Peluso, D, Pendyala, S, Peng, J, Perfetto, L, Pirro, S, Plevritis, S, Politi, R, Poon, H, Porta, E, Prellner, I, Preuer, K, Pujana, M, Ramnarine, R, Reid, J, Reyal, F, Richardson, S, Ricketts, C, Rieswijk, L, Rocha, M, Rodriguez-Gonzalvez, C, Roell, K, Rotroff, D, de Ruiter, J, Rukawa, P, Sadacca, B, Safikhani, Z, Safitri, F, Sales-Pardo, M, Sauer, S, Schlichting, M, Seoane, J, Serra, J, Shang, M, Sharma, A, Sharma, H, Shen, Y, Shiga, M, Shin, M, Shkedy, Z, Shopsowitz, K, Sinai, S, Skola, D, Smirnov, P, Soerensen, I, Soerensen, P, Song, J, Song, S, Soufan, O, Spitzmueller, A, Steipe, B, Suphavilai, C, Tamayo, S, Tamborero, D, Tang, J, Tanoli, Z, Tarres-Deulofeu, M, Tegner, J, Thommesen, L, Tonekaboni, S, Tran, H, Troyer, E, Truong, A, Tsunoda, T, Turu, G, Tzeng, G, Verbeke, L, Videla, S, Vis, D, Voronkov, A, Votis, K, Wang, A, Wang, H, Wang, P, Wang, S, Wang, W, Wang, X, Wennerberg, K, Wernisch, L, Wessels, L, van Westen, G, Westerman, B, White, S, Willighagen, E, Wurdinger, T, Xie, L, Xie, S, Xu, H, Yadav, B, Yau, C, Yeerna, H, Yin, J, Yu, M, Yun, S, Zakharov, A, Zamichos, A, Zanin, M, Zeng, L, Zenil, H, Zhang, F, Zhang, P, Zhang, W, Zhao, H, Zhao, L, Zheng, W, Zoufir, A, Zucknick, M, Jang, I, Ghazoui, Z, Ahsen, M, Vogel, R, Neto, E, Norman, T, Tang, E, Garnett, M, Veroli, G, Fawell, S, Stolovitzky, G, Guinney, J, Dry, J, Saez-Rodriguez, J, Menden M. P., Wang D., Mason M. J., Szalai B., Bulusu K. C., Guan Y., Yu T., Kang J., Jeon M., Wolfinger R., Nguyen T., Zaslavskiy M., Abante J., Abecassis B. S., Aben N., Aghamirzaie D., Aittokallio T., Akhtari F. S., Al-lazikani B., Alam T., Allam A., Allen C., de Almeida M. P., Altarawy D., Alves V., Amadoz A., Anchang B., Antolin A. A., Ash J. R., Aznar V. R., Ba-alawi W., Bagheri M., Bajic V., Ball G., Ballester P. J., Baptista D., Bare C., Bateson M., Bender A., Bertrand D., Wijayawardena B., Boroevich K. A., Bosdriesz E., Bougouffa S., Bounova G., Brouwer T., Bryant B., Calaza M., Calderone A., Calza S., Capuzzi S., Carbonell-Caballero J., Carlin D., Carter H., Castagnoli L., Celebi R., Cesareni G., Chang H., Chen G., Chen H., Cheng L., Chernomoretz A., Chicco D., Cho K. -H., Cho S., Choi D., Choi J., Choi K., Choi M., Cock M. D., Coker E., Cortes-Ciriano I., Cserzo M., Cubuk C., Curtis C., Daele D. V., Dang C. C., Dijkstra T., Dopazo J., Draghici S., Drosou A., Dumontier M., Ehrhart F., Eid F. -E., ElHefnawi M., Elmarakeby H., van Engelen B., Engin H. B., de Esch I., Evelo C., Falcao A. O., Farag S., Fernandez-Lozano C., Fisch K., Flobak A., Fornari C., Foroushani A. B. K., Fotso D. C., Fourches D., Friend S., Frigessi A., Gao F., Gao X., Gerold J. M., Gestraud P., Ghosh S., Gillberg J., Godoy-Lorite A., Godynyuk L., Godzik A., Goldenberg A., Gomez-Cabrero D., Gonen M., de Graaf C., Gray H., Grechkin M., Guimera R., Guney E., Haibe-Kains B., Han Y., Hase T., He D., He L., Heath L. S., Hellton K. H., Helmer-Citterich M., Hidalgo M. R., Hidru D., Hill S. M., Hochreiter S., Hong S., Hovig E., Hsueh Y. -C., Hu Z., Huang J. K., Huang R. S., Hunyady L., Hwang J., Hwang T. H., Hwang W., Hwang Y., Isayev O., Don't Walk O. B., Jack J., Jahandideh S., Ji J., Jo Y., Kamola P. J., Kanev G. K., Karacosta L., Karimi M., Kaski S., Kazanov M., Khamis A. M., Khan S. A., Kiani N. A., Kim A., Kim J., Kim K., Kim S., Kim Y., Kirk P. D. W., Kitano H., Klambauer G., Knowles D., Ko M., Kohn-Luque A., Kooistra A. J., Kuenemann M. A., Kuiper M., Kurz C., Kwon M., van Laarhoven T., Laegreid A., Lederer S., Lee H., Lee J., Lee Y. W., Lepp_aho E., Lewis R., Li J., Li L., Liley J., Lim W. K., Lin C., Liu Y., Lopez Y., Low J., Lysenko A., Machado D., Madhukar N., Maeyer D. D., Malpartida A. B., Mamitsuka H., Marabita F., Marchal K., Marttinen P., Mason D., Mazaheri A., Mehmood A., Mehreen A., Michaut M., Miller R. A., Mitsopoulos C., Modos D., Moerbeke M. V., Moo K., Motsinger-Reif A., Movva R., Muraru S., Muratov E., Mushthofa M., Nagarajan N., Nakken S., Nath A., Neuvial P., Newton R., Ning Z., Niz C. D., Oliva B., Olsen C., Palmeri A., Panesar B., Papadopoulos S., Park J., Park S., Pawitan Y., Peluso D., Pendyala S., Peng J., Perfetto L., Pirro S., Plevritis S., Politi R., Poon H., Porta E., Prellner I., Preuer K., Pujana M. A., Ramnarine R., Reid J. E., Reyal F., Richardson S., Ricketts C., Rieswijk L., Rocha M., Rodriguez-Gonzalvez C., Roell K., Rotroff D., de Ruiter J. R., Rukawa P., Sadacca B., Safikhani Z., Safitri F., Sales-Pardo M., Sauer S., Schlichting M., Seoane J. A., Serra J., Shang M. -M., Sharma A., Sharma H., Shen Y., Shiga M., Shin M., Shkedy Z., Shopsowitz K., Sinai S., Skola D., Smirnov P., Soerensen I. F., Soerensen P., Song J. -H., Song S. O., Soufan O., Spitzmueller A., Steipe B., Suphavilai C., Tamayo S. P., Tamborero D., Tang J., Tanoli Z. -U. -R., Tarres-Deulofeu M., Tegner J., Thommesen L., Tonekaboni S. A. M., Tran H., Troyer E. D., Truong A., Tsunoda T., Turu G., Tzeng G. -Y., Verbeke L., Videla S., Vis D., Voronkov A., Votis K., Wang A., Wang H. -Q. H., Wang P. -W., Wang S., Wang W., Wang X., Wennerberg K., Wernisch L., Wessels L., van Westen G. J. P., Westerman B. A., White S. R., Willighagen E., Wurdinger T., Xie L., Xie S., Xu H., Yadav B., Yau C., Yeerna H., Yin J. W., Yu M., Yu M. H., Yun S. J., Zakharov A., Zamichos A., Zanin M., Zeng L., Zenil H., Zhang F., Zhang P., Zhang W., Zhao H., Zhao L., Zheng W., Zoufir A., Zucknick M., Jang I. S., Ghazoui Z., Ahsen M. E., Vogel R., Neto E. C., Norman T., Tang E. K. Y., Garnett M. J., Veroli G. Y. D., Fawell S., Stolovitzky G., Guinney J., Dry J. R., Saez-Rodriguez J., Menden, M, Wang, D, Mason, M, Szalai, B, Bulusu, K, Guan, Y, Yu, T, Kang, J, Jeon, M, Wolfinger, R, Nguyen, T, Zaslavskiy, M, Abante, J, Abecassis, B, Aben, N, Aghamirzaie, D, Aittokallio, T, Akhtari, F, Al-lazikani, B, Alam, T, Allam, A, Allen, C, de Almeida, M, Altarawy, D, Alves, V, Amadoz, A, Anchang, B, Antolin, A, Ash, J, Aznar, V, Ba-alawi, W, Bagheri, M, Bajic, V, Ball, G, Ballester, P, Baptista, D, Bare, C, Bateson, M, Bender, A, Bertrand, D, Wijayawardena, B, Boroevich, K, Bosdriesz, E, Bougouffa, S, Bounova, G, Brouwer, T, Bryant, B, Calaza, M, Calderone, A, Calza, S, Capuzzi, S, Carbonell-Caballero, J, Carlin, D, Carter, H, Castagnoli, L, Celebi, R, Cesareni, G, Chang, H, Chen, G, Chen, H, Cheng, L, Chernomoretz, A, Chicco, D, Cho, K, Cho, S, Choi, D, Choi, J, Choi, K, Choi, M, Cock, M, Coker, E, Cortes-Ciriano, I, Cserzo, M, Cubuk, C, Curtis, C, Daele, D, Dang, C, Dijkstra, T, Dopazo, J, Draghici, S, Drosou, A, Dumontier, M, Ehrhart, F, Eid, F, Elhefnawi, M, Elmarakeby, H, van Engelen, B, Engin, H, de Esch, I, Evelo, C, Falcao, A, Farag, S, Fernandez-Lozano, C, Fisch, K, Flobak, A, Fornari, C, Foroushani, A, Fotso, D, Fourches, D, Friend, S, Frigessi, A, Gao, F, Gao, X, Gerold, J, Gestraud, P, Ghosh, S, Gillberg, J, Godoy-Lorite, A, Godynyuk, L, Godzik, A, Goldenberg, A, Gomez-Cabrero, D, Gonen, M, de Graaf, C, Gray, H, Grechkin, M, Guimera, R, Guney, E, Haibe-Kains, B, Han, Y, Hase, T, He, D, He, L, Heath, L, Hellton, K, Helmer-Citterich, M, Hidalgo, M, Hidru, D, Hill, S, Hochreiter, S, Hong, S, Hovig, E, Hsueh, Y, Hu, Z, Huang, J, Huang, R, Hunyady, L, Hwang, J, Hwang, T, Hwang, W, Hwang, Y, Isayev, O, Don't Walk, O, Jack, J, Jahandideh, S, Ji, J, Jo, Y, Kamola, P, Kanev, G, Karacosta, L, Karimi, M, Kaski, S, Kazanov, M, Khamis, A, Khan, S, Kiani, N, Kim, A, Kim, J, Kim, K, Kim, S, Kim, Y, Kirk, P, Kitano, H, Klambauer, G, Knowles, D, Ko, M, Kohn-Luque, A, Kooistra, A, Kuenemann, M, Kuiper, M, Kurz, C, Kwon, M, van Laarhoven, T, Laegreid, A, Lederer, S, Lee, H, Lee, J, Lee, Y, Lepp_aho, E, Lewis, R, Li, J, Li, L, Liley, J, Lim, W, Lin, C, Liu, Y, Lopez, Y, Low, J, Lysenko, A, Machado, D, Madhukar, N, Maeyer, D, Malpartida, A, Mamitsuka, H, Marabita, F, Marchal, K, Marttinen, P, Mason, D, Mazaheri, A, Mehmood, A, Mehreen, A, Michaut, M, Miller, R, Mitsopoulos, C, Modos, D, Moerbeke, M, Moo, K, Motsinger-Reif, A, Movva, R, Muraru, S, Muratov, E, Mushthofa, M, Nagarajan, N, Nakken, S, Nath, A, Neuvial, P, Newton, R, Ning, Z, Niz, C, Oliva, B, Olsen, C, Palmeri, A, Panesar, B, Papadopoulos, S, Park, J, Park, S, Pawitan, Y, Peluso, D, Pendyala, S, Peng, J, Perfetto, L, Pirro, S, Plevritis, S, Politi, R, Poon, H, Porta, E, Prellner, I, Preuer, K, Pujana, M, Ramnarine, R, Reid, J, Reyal, F, Richardson, S, Ricketts, C, Rieswijk, L, Rocha, M, Rodriguez-Gonzalvez, C, Roell, K, Rotroff, D, de Ruiter, J, Rukawa, P, Sadacca, B, Safikhani, Z, Safitri, F, Sales-Pardo, M, Sauer, S, Schlichting, M, Seoane, J, Serra, J, Shang, M, Sharma, A, Sharma, H, Shen, Y, Shiga, M, Shin, M, Shkedy, Z, Shopsowitz, K, Sinai, S, Skola, D, Smirnov, P, Soerensen, I, Soerensen, P, Song, J, Song, S, Soufan, O, Spitzmueller, A, Steipe, B, Suphavilai, C, Tamayo, S, Tamborero, D, Tang, J, Tanoli, Z, Tarres-Deulofeu, M, Tegner, J, Thommesen, L, Tonekaboni, S, Tran, H, Troyer, E, Truong, A, Tsunoda, T, Turu, G, Tzeng, G, Verbeke, L, Videla, S, Vis, D, Voronkov, A, Votis, K, Wang, A, Wang, H, Wang, P, Wang, S, Wang, W, Wang, X, Wennerberg, K, Wernisch, L, Wessels, L, van Westen, G, Westerman, B, White, S, Willighagen, E, Wurdinger, T, Xie, L, Xie, S, Xu, H, Yadav, B, Yau, C, Yeerna, H, Yin, J, Yu, M, Yun, S, Zakharov, A, Zamichos, A, Zanin, M, Zeng, L, Zenil, H, Zhang, F, Zhang, P, Zhang, W, Zhao, H, Zhao, L, Zheng, W, Zoufir, A, Zucknick, M, Jang, I, Ghazoui, Z, Ahsen, M, Vogel, R, Neto, E, Norman, T, Tang, E, Garnett, M, Veroli, G, Fawell, S, Stolovitzky, G, Guinney, J, Dry, J, Saez-Rodriguez, J, Menden M. P., Wang D., Mason M. J., Szalai B., Bulusu K. C., Guan Y., Yu T., Kang J., Jeon M., Wolfinger R., Nguyen T., Zaslavskiy M., Abante J., Abecassis B. S., Aben N., Aghamirzaie D., Aittokallio T., Akhtari F. S., Al-lazikani B., Alam T., Allam A., Allen C., de Almeida M. P., Altarawy D., Alves V., Amadoz A., Anchang B., Antolin A. A., Ash J. R., Aznar V. R., Ba-alawi W., Bagheri M., Bajic V., Ball G., Ballester P. J., Baptista D., Bare C., Bateson M., Bender A., Bertrand D., Wijayawardena B., Boroevich K. A., Bosdriesz E., Bougouffa S., Bounova G., Brouwer T., Bryant B., Calaza M., Calderone A., Calza S., Capuzzi S., Carbonell-Caballero J., Carlin D., Carter H., Castagnoli L., Celebi R., Cesareni G., Chang H., Chen G., Chen H., Cheng L., Chernomoretz A., Chicco D., Cho K. -H., Cho S., Choi D., Choi J., Choi K., Choi M., Cock M. D., Coker E., Cortes-Ciriano I., Cserzo M., Cubuk C., Curtis C., Daele D. V., Dang C. C., Dijkstra T., Dopazo J., Draghici S., Drosou A., Dumontier M., Ehrhart F., Eid F. -E., ElHefnawi M., Elmarakeby H., van Engelen B., Engin H. B., de Esch I., Evelo C., Falcao A. O., Farag S., Fernandez-Lozano C., Fisch K., Flobak A., Fornari C., Foroushani A. B. K., Fotso D. C., Fourches D., Friend S., Frigessi A., Gao F., Gao X., Gerold J. M., Gestraud P., Ghosh S., Gillberg J., Godoy-Lorite A., Godynyuk L., Godzik A., Goldenberg A., Gomez-Cabrero D., Gonen M., de Graaf C., Gray H., Grechkin M., Guimera R., Guney E., Haibe-Kains B., Han Y., Hase T., He D., He L., Heath L. S., Hellton K. H., Helmer-Citterich M., Hidalgo M. R., Hidru D., Hill S. M., Hochreiter S., Hong S., Hovig E., Hsueh Y. -C., Hu Z., Huang J. K., Huang R. S., Hunyady L., Hwang J., Hwang T. H., Hwang W., Hwang Y., Isayev O., Don't Walk O. B., Jack J., Jahandideh S., Ji J., Jo Y., Kamola P. J., Kanev G. K., Karacosta L., Karimi M., Kaski S., Kazanov M., Khamis A. M., Khan S. A., Kiani N. A., Kim A., Kim J., Kim K., Kim S., Kim Y., Kirk P. D. W., Kitano H., Klambauer G., Knowles D., Ko M., Kohn-Luque A., Kooistra A. J., Kuenemann M. A., Kuiper M., Kurz C., Kwon M., van Laarhoven T., Laegreid A., Lederer S., Lee H., Lee J., Lee Y. W., Lepp_aho E., Lewis R., Li J., Li L., Liley J., Lim W. K., Lin C., Liu Y., Lopez Y., Low J., Lysenko A., Machado D., Madhukar N., Maeyer D. D., Malpartida A. B., Mamitsuka H., Marabita F., Marchal K., Marttinen P., Mason D., Mazaheri A., Mehmood A., Mehreen A., Michaut M., Miller R. A., Mitsopoulos C., Modos D., Moerbeke M. V., Moo K., Motsinger-Reif A., Movva R., Muraru S., Muratov E., Mushthofa M., Nagarajan N., Nakken S., Nath A., Neuvial P., Newton R., Ning Z., Niz C. D., Oliva B., Olsen C., Palmeri A., Panesar B., Papadopoulos S., Park J., Park S., Pawitan Y., Peluso D., Pendyala S., Peng J., Perfetto L., Pirro S., Plevritis S., Politi R., Poon H., Porta E., Prellner I., Preuer K., Pujana M. A., Ramnarine R., Reid J. E., Reyal F., Richardson S., Ricketts C., Rieswijk L., Rocha M., Rodriguez-Gonzalvez C., Roell K., Rotroff D., de Ruiter J. R., Rukawa P., Sadacca B., Safikhani Z., Safitri F., Sales-Pardo M., Sauer S., Schlichting M., Seoane J. A., Serra J., Shang M. -M., Sharma A., Sharma H., Shen Y., Shiga M., Shin M., Shkedy Z., Shopsowitz K., Sinai S., Skola D., Smirnov P., Soerensen I. F., Soerensen P., Song J. -H., Song S. O., Soufan O., Spitzmueller A., Steipe B., Suphavilai C., Tamayo S. P., Tamborero D., Tang J., Tanoli Z. -U. -R., Tarres-Deulofeu M., Tegner J., Thommesen L., Tonekaboni S. A. M., Tran H., Troyer E. D., Truong A., Tsunoda T., Turu G., Tzeng G. -Y., Verbeke L., Videla S., Vis D., Voronkov A., Votis K., Wang A., Wang H. -Q. H., Wang P. -W., Wang S., Wang W., Wang X., Wennerberg K., Wernisch L., Wessels L., van Westen G. J. P., Westerman B. A., White S. R., Willighagen E., Wurdinger T., Xie L., Xie S., Xu H., Yadav B., Yau C., Yeerna H., Yin J. W., Yu M., Yu M. H., Yun S. J., Zakharov A., Zamichos A., Zanin M., Zeng L., Zenil H., Zhang F., Zhang P., Zhang W., Zhao H., Zhao L., Zheng W., Zoufir A., Zucknick M., Jang I. S., Ghazoui Z., Ahsen M. E., Vogel R., Neto E. C., Norman T., Tang E. K. Y., Garnett M. J., Veroli G. Y. D., Fawell S., Stolovitzky G., Guinney J., Dry J. R., and Saez-Rodriguez J.

Abstract

The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca’s large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.

Published
2019

21. Miniature auto-perfusion bioreactor system with spiral microfluidic cell retention device

Author

Yin, L, Au, WY, Yu, CC, Kwon, T, Lai, Z, Shang, M, Warkiani, ME, Rosche, R, Lim, CT, and Han, J

Subjects
Bioreactors, Cricetulus, Cricetinae, Lab-On-A-Chip Devices, Cell Culture Techniques, Animals, CHO Cells, Biotechnology
Abstract

Medium perfusion is critical in maintaining high cell concentration in cultures. The conventional membrane filtration method for medium exchange has been challenged by the fouling and clogging of the membrane filters in long-term cultures. In this study, we present a miniature auto-perfusion system that can be operated inside a common-size laboratory incubator. The system is equipped with a spiral microfluidic chip for cell retention to replace conventional membrane filters, which fundamentally overcomes the clogging and fouling problem. We showed that the system supported continuous perfusion culture of Chinese hamster ovary (CHO) cells in suspension up to 14 days without cell retention chip replacement. Compared to daily manual medium change, 25% higher CHO cell concentration can be maintained at an average auto-perfusion rate of 196 ml/day in spinner flask at 70 ml working volume (2.8 VVD). The auto-perfusion system also resulted in better cell quality at high concentrations, in terms of higher viability, more uniform and regular morphology, and fewer aggregates. We also demonstrated the potential application of the system for culturing mesenchymal stem cells on microcarriers. This miniature auto-perfusion system provides an excellent solution to maintain cell-favorable conditions and high cell concentration in small-scale cultures for research and clinical uses.

Published
2021

22. An L₁-and-L₂-Norm-Oriented Latent Factor Model for Recommender Systems

Author

Wu, D, Shang, M, Luo, X, and Wang, Z

Subjects
Recommender system (RS), Latent factor (LF) analysis, High-dimensional and sparse (HiDS) matrix, L1 norm, L2 norm
Abstract

A recommender system (RS) is highly efficient in filtering people's desired information from high-dimensional and sparse (HiDS) data. To date, a latent factor (LF)-based approach becomes highly popular when implementing a RS. However, current LF models mostly adopt single distance-oriented Loss like an L₂ norm-oriented one, which ignores target data's characteristics described by other metrics like an L₁ norm-oriented one. To investigate this issue, this article proposes an L₁-and-L₂-norm-oriented LF (L³F) model. It adopts twofold ideas: 1) aggregating L₁ norm's robustness and L₂ norm's stability to form its Loss and 2) adaptively adjusting weights of L₁ and L₂ norms in its Loss. By doing so, it achieves fine aggregation effects with L₁ norm-oriented Loss's robustness and L₂ norm-oriented Loss's stability to precisely describe HiDS data with outliers. Experimental results on nine HiDS datasets generated by real systems show that an L³F model significantly outperforms state-of-the-art models in prediction accuracy for missing data of an HiDS dataset. Its computational efficiency is also comparable with the most efficient LF models. Hence, it has good potential for addressing HiDS data from real applications.

Published
2021

30. LincRNA-ROR promotes metastasis and invasion of esophageal squamous cell carcinoma by regulating miR-145/FSCN1

Author

Shang M, Wang X, Zhang Y, Gao Z, Wang T, and Liu R

Subjects
lncRNA, ESCC, ROR, ceRNA, miR-145, lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens, lcsh:RC254-282, FSCN1
Abstract

Muhe Shang, Xianghu Wang, Ying Zhang, Zhikui Gao, Tian Wang, Ran Liu Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, Jiangsu, China Background and objective: In an attempt to discover a new biomarker for early diagnosis and prognosis of esophageal squamous cell carcinoma (ESCC), the regulation mechanism of large intergenic non-coding RNA–regulator of reprogramming (lincRNA-ROR) as a microRNA (miRNA) sponge was studied.Patients and methods: ROR expression in 91 pairs of ESCC tissue samples and matched adjacent tissues was quantified with real-time fluorescent quantitative polymerase chain reaction (qRT-PCR). The ROR–miRNA–mRNA regulatory network was built with 161 esophageal cancer (EC) tissues and 11 adjacent tumor tissues from The Cancer Genome Atlas (TCGA) database. A total of 96 cases of ESCC from TCGA database were collected for analysis on survival rates. The regulatory relationship between ROR, miR-145 and FSCN1 was verified in ESCC cells via qRT-PCR, dual luciferase reporter (DLR) assay, RNA immunoprecipitation (RIP) and Western blotting. The transwell method was used to detect cell migration and invasion.Results: ROR expression in ESCC tumor tissues was significantly higher than in the adjacent tissues, p

Published
2018

31. Ultraviolet photochemistry of Co(super III)L(H2O)SO3(super +) (L = Me6(14)dieneN4, (14)aneN4) complexes. Quandaries about the linkage isomerization to O-bonded sulfite and the photogeneration of cobalt(I) in sequential biphotonic photolysis

Author

Gibney, S.C., Ferraudi, G., and Shang, M.

Subjects
Complex compounds -- Spectra, Inorganic compounds -- Research, Laser photochemistry -- Research, Isomerization -- Research, Chemistry
Abstract

The photoinduced reduction and linkage isomerization of CoL(H2O)(SO3)(super +) (L = Me6(14)dieneN4, (14)aneN4) has been studied. Results indicate that SO3(super ._) radicals and Co(II) complexes are formed in the charge-transfer bands of the macrocycles when they are irradiated. There is also linkage isomerization to the O-bonded sulfite product as well as photoaquation. Time-resolved spectra reveal that Co(III) photoproducts are formed during excited-state processes.

Published
1999

32. Comparison of an adaptive signal system to time based coordination plan along a signalized arterial.

Author

Jeon, H., Benekohal, R. F., Garshasebi, B., and Shang, M.

Subjects
ADAPTIVE control systems, TRAFFIC flow, KEY performance indicators (Management)
Abstract

Field evaluation of adaptive signal control technologies (ASCT) is important in assessing its contribution to operational efficiency of intersections. ASCT systems can respond to traffic volume changes, thus it could potentially outperform a time-based signal coordination (TBC) plan. This paper compares the operational efficiency of SynchroGreen, an ASCT system, to a TBC plan (called base case) on an urban arterial. The ASCT performance was evaluated based on combined analyses of the changes in volume, delay, and queue length. For the combined analyses, a performance indicator (PI) was determined whether traffic operation was significantly Improved, Unchanged, or Deteriorated (with 91% confidence level). Among the 56 lane groups (approaches), the PI improved in 5% of lane groups (3 cases), unchanged in 32% of lane groups (18 cases) and deteriorated in 63% of lane groups (35 cases). Out of the 35 deteriorated cases, volume significantly increased in 3, did not change in 21, and decreased in 11. The 3 deteriorated cases may be attributed to the system's inability to respond to volume increase adequately; however, for the 32 lane groups in which volume did not increase significantly, the deterioration in PI was not expected. The TBC plan on this corridor was running very efficiently and SynchroGreen adaptive system did not perform as good or better than the TBC. [ABSTRACT FROM AUTHOR]

Published
2022
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33. Effect of recombinant phycocyanin on photosynthetic system in Chlamydomonas reinhardtii

Author

Cong X, Hou L, Xiaofeng Xu, Zang X, Yangzong Z, Guo Y, Wei X, Zhang F, Rui Li, Shang M, and Zhigang Wang

Subjects
Cyanobacteria, biology, Biochemistry, Chemistry, Phycobiliprotein, Thylakoid, Phycocyanin, Chlamydomonas reinhardtii, Phycobilisome, macromolecular substances, biology.organism_classification, Photosynthesis, Chlorophyll fluorescence
Abstract

The phycobilisome is an important photosynthetic antenna in the photosynthetic cyanobacteria, and phycocyanin is one of the main components of phycobilisomes. It helps cells absorb green light that green-lineage photo-synthetic organisms cannot. In this work, phycocyanin, heme oxidase and ferredoxin oxidoreductase from Arthrospira platensis FACHB 314 were successfully expressed in the green algae Chlamydomonas reinhardtii. Then the effects of this expression on the photosynthesis and growth of C. reinhardtii were detected. Transcriptional level analysis showed that the phycocyanin gene was successfully expressed stably in the transgenic strains. The results of low-temperature fluorescence emission spectra and chlorophyll fluorescence showed that recombinant phycocyanin has considerable optical activity. The expression of phycocyanin, heme oxidase and ferredoxin oxidoreductase in low-light conditions is particularly evident in the promotion of photosynthesis in C. reinhardtii. The growth of transgenic strains was significantly promoted in the early growth phase under low-light conditions. However, the final growth and biomass accumulation of transgenic C. reinhardti were inhibited by this expression. In this paper, the possibility of photoenergy transfer between phycocyanin and heterologous host thylakoid membrane was researched, which provided a useful attempt for the construction of a new photosynthetic system using phycobiliprotein from cyanobacteria.One-sentence summaryPhycocyanin from Arthrospira platensis FACHB 314 expressed in Chlamydomonas reinhardtii can effect the photosynthetic system of C. reinhardtii.

Published
2019
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37. WR-3 Waveguide Bandpass Filters Fabricated Using High Precision CNC Machining and SU-8 Photoresist Technology

Author

Liu, Bo, Yang, Hao, Dhayalan, Yuvaraj, Shang, M, Lancaster, Michael, Wang, H, and Huggard, P.G

Subjects
T1, TA
Abstract

This paper presents two WR-3 band (220-325 GHz) filters, one fabricated in metal using high precision computer numerically controlled milling and the other made with metallized SU-8 photoresist technology. Both are based on three coupled resonators, and are designed for a 287.3-295.9-GHz passband, and a 30-dB rejection between 317.7 and 325.9 GHz. The first filter is an extracted pole filter coupled by irises, and is precision milled using the split-block approach. The second filter is composed of three silver-coated SU-8 layers, each 432 μm thick. The filter structures are specially chosen to take advantage of the fabrication processes. When fabrication tolerances are accounted for, very good agreement between measurements and simulations are obtained, with median passband insertion losses of 0.41 and 0.45 dB for the metal and SU-8 devices, respectively. These two filters are potential replacements of frequency selective surface filters used in heterodyne radiometers for unwanted sideband rejection.

Published
2017

41. Popularity and Novelty Dynamics in Evolving Networks

Author

Abbas, K, Shang, M, Abbasi, A ; https://orcid.org/0000-0001-9136-1837, Luo, X, Xu, JJ, Zhang, YX, Abbas, K, Shang, M, Abbasi, A ; https://orcid.org/0000-0001-9136-1837, Luo, X, Xu, JJ, and Zhang, YX

Abstract

Network science plays a big role in the representation of real-world phenomena such as user-item bipartite networks presented in e-commerce or social media platforms. It provides researchers with tools and techniques to solve complex real-world problems. Identifying and predicting future popularity and importance of items in e-commerce or social media platform is a challenging task. Some items gain popularity repeatedly over time while some become popular and novel only once. This work aims to identify the key-factors: popularity and novelty. To do so, we consider two types of novelty predictions: items appearing in the popular ranking list for the first time; and items which were not in the popular list in the past time window, but might have been popular before the recent past time window. In order to identify the popular items, a careful consideration of macro-level analysis is needed. In this work we propose a model, which exploits item level information over a span of time to rank the importance of the item. We considered ageing or decay effect along with the recent link-gain of the items. We test our proposed model on four various real-world datasets using four information retrieval based metrics.

Published
2018

43. Human cytomegalovirus may promote tumour progression by upregulating arginase-2

Author

Costa, H., Xu, X., Overbeek, G., Vasaikar, S., Pawan K. Patro, C., Kostopoulou, O. N., Jung, M., Shafi, G., Ananthaseshan, S., Tsipras, G., Davoudi, B., Mohammad, A. -A, Lam, H., Strååt, Klas, Wilhelmi, V., Shang, M., Tegner, J., Tong, J. C., Wong, K. T., Söderberg-Naucler, C., Yaiw, K. -C, Costa, H., Xu, X., Overbeek, G., Vasaikar, S., Pawan K. Patro, C., Kostopoulou, O. N., Jung, M., Shafi, G., Ananthaseshan, S., Tsipras, G., Davoudi, B., Mohammad, A. -A, Lam, H., Strååt, Klas, Wilhelmi, V., Shang, M., Tegner, J., Tong, J. C., Wong, K. T., Söderberg-Naucler, C., and Yaiw, K. -C

Abstract

Background: Both arginase (ARG2) and human cytomegalovirus (HCMV) have been implicated in tumorigenesis. However, the role of ARG2 in the pathogenesis of glioblastoma (GBM) and the HCMV effects on ARG2 are unknown. We hypothesize that HCMV may contribute to tumorigenesis by increasing ARG2 expression. Results: ARG2 promotes tumorigenesis by increasing cellular proliferation, migration, invasion and vasculogenic mimicry in GBM cells, at least in part due to overexpression of MMP2/9. The nor-NOHA significantly reduced migration and tube formation of ARG2-overexpressing cells. HCMV immediate-early proteins (IE1/2) or its downstream pathways upregulated the expression of ARG2 in U-251 MG cells. Immunostaining of GBM tissue sections confirmed the overexpression of ARG2, consistent with data from subsets of Gene Expression Omnibus. Moreover, higher levels of ARG2 expression tended to be associated with poorer survival in GBM patient by analyzing data from TCGA. Methods: The role of ARG2 in tumorigenesis was examined by proliferation-, migration-, invasion-, wound healing- and tube formation assays using an ARG2- overexpressing cell line and ARG inhibitor, N (omega)-hydroxy-nor-L-arginine (nor-NOHA) and siRNA against ARG2 coupled with functional assays measuring MMP2/9 activity, VEGF levels and nitric oxide synthase activity. Association between HCMV and ARG2 were examined in vitro with 3 different GBM cell lines, and ex vivo with immunostaining on GBM tissue sections. The viral mechanism mediating ARG2 induction was examined by siRNA approach. Correlation between ARG2 expression and patient survival was extrapolated from bioinformatics analysis on data from The Cancer Genome Atlas (TCGA). Conclusions: ARG2 promotes tumorigenesis, and HCMV may contribute to GBM pathogenesis by upregulating ARG2., QC 20161115

Published
2016
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44. Liquid phase oxidation chemistry in continuous-flow microreactors

Author

Gemoets, H.P.L., Su, Y., Shang, M., Hessel, V., Luque, R., Noel, T., Gemoets, H.P.L., Su, Y., Shang, M., Hessel, V., Luque, R., and Noel, T.

Abstract

Continuous-flow liquid phase oxidation chemistry in microreactors receives a lot of attention as the reactor provides enhanced heat and mass transfer characteristics, safe use of hazardous oxidants, high interfacial areas, and scale-up potential. In this review, an up-to-date overview of both technological and chemical aspects of liquid phase oxidation chemistry in continuous-flow microreactors is given. A description of mass and heat transfer phenomena is provided and fundamental principles are deduced which can be used to make a judicious choice for a suitable reactor. In addition, the safety aspects of continuous-flow technology are discussed. Next, oxidation chemistry in flow is discussed, including the use of oxygen, hydrogen peroxide, ozone and other oxidants in flow. Finally, the scale-up potential for continuous-flow reactors is described

Published
2016

45. Characterization of CKβ8 and CKβ8-1: Two Alternatively Spliced Forms of Human β-Chemokine, Chemoattractants for Neutrophils, Monocytes, and Lymphocytes, and Potent Agonists at CC Chemokine Receptor 1

Author

Byung S. Youn, Byoung S. Kwon, Kathleen Antol, Scott Cooper, Hal E. Broxmeyer, Malcolm J. Fraser, and Shang M. Zhang

Subjects
chemistry.chemical_classification, CCR1, Chemokine, biology, Monocyte, Immunology, Alternative splicing, Chemotaxis, Cell Biology, Hematology, Biochemistry, Molecular biology, Amino acid, medicine.anatomical_structure, chemistry, RNA splicing, medicine, biology.protein, CC chemokine receptors
Abstract

Two new members of human β-chemokine cDNA were isolated based on structural and functional similarities to human leukotactin-1. One of these clones was identical to the previously isolated human β-chemokine, CKβ8, whereas the other is a splicing variant of CKβ8, therefore named CKβ8-1. CKβ8 was short in 51 nucleotides (17 amino acids) compared with CKβ8-1. The mature proteins of CKβ8-1 and CKβ8 consisted of 116 and 99 amino acids with calculated molecular weights of 12,500 and 10,950, respectively. Both CKβ8-1 and CKβ8 were potent agonists at CCR1. These chemokines chemoattracted neutrophils, monocytes, and lymphocytes. They also significantly suppressed colony formation by human bone marrow, granulocyte-macrophage, erythroid, and multipotential progenitor cells stimulated by combinations of growth factors. To our knowledge, this is the first example that an alternative splicing produces two active β-chemokines from a single gene.

Published
1998
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