K0036: Docking Small Molecules Belonging Laserpitium sp. into LCK Protein Upregulated in Rheumatoid Arthritis

It is well known that house dust mites (HDMs) are predominant sources of inhalant allergens associated with allergic disease. Therefore, sequenced house dust mite (HDM) genomes would certainly advance our understanding of HDM allergens, a common cause of human allergies. To produce annotated Dermatophagoides (D.) farinae and D. pteronyssinus genomes, we developed a combined genomic-transcriptomic-proteomic approach for the elucidation of HDM allergens. High quality D. farinae and D. pteronyssinus genomes and transcriptomes were assembled with high-throughput DNA sequencing platforms including PacBio, Illumina HiSeq and ion torrent. The mite‘s microbiome composition was at the same time determined and the predominant genus was validated immunohistochemically. Putative allergens were then evaluated with immunoblotting, immunosorbent assays, and skin prick tests. In this study, 79.79-Mb and 66.85-Mb genomes of D. farinae and D. pteronyssinus, respectively, was constructed. Moreover, the full gene structures of canonical allergens and non-canonical allergen homologues were produced. Using mass spectrometry analysis of D. farinae protein spots reactive to pooled sera from HDM-allergic patients, novel major ICBBB 2020 CONFERENCE ABSTRACT 18 allergens were found. In D. farinae, the predominant bacterial genus among 100 identified species was Enterobacter (63.4%), among them Enterobacter cloacae and Enterobacter hormaechei were most predominant. KEGG pathway analysis revealed a phototransduction pathway in D. farinae as well as thiamine and amino acid synthesis pathways suggestive of an endosymbiotic relationship between D. farinae and its microbiome. In summary, high quality HDM genomes produced from genomic, transcriptomic, and proteomic experiments revealed allergen genes and a diverse endosymbiotic microbiome, providing a tool for further identification and characterization of HDM allergens and development of diagnostics and immunotherapeutic vaccines. ICBBB 2020 CONFERENCE ABSTRACT 19 Keynote Speaker II Prof. Jean-Philippe Vert Google Brain, France and MINES ParisTech, France Jean-Philippe Vert is a research scientist at Google Brain, and adjunct researcher at MINES ParisTech. After a PhD in mathematics at ENS Paris in 2001 and a post-doc at Kyoto University, he held various academic positions at MINES ParisTech, Institut Curie, UC Berkeley and ENS Paris. His main research contributions are in machine learning and computational biology, in particular in cancer genomics and precisions medicine. Topic: ―Learning from Single-cell Genomics Data‖ Abstract—Single-cell genomics allows capture the diversity of individual cells at the molecular level, and has revolutionized our understanding of development processes or tumor heterogeneity. It also raises numerous modeling and computational challenges. In this talk I will present some approaches we developed for data normalization, gene network inference and integration of heterogeneous views from single-cell genomics data.Single-cell genomics allows capture the diversity of individual cells at the molecular level, and has revolutionized our understanding of development processes or tumor heterogeneity. It also raises numerous modeling and computational challenges. In this talk I will present some approaches we developed for data normalization, gene network inference and integration of heterogeneous views from single-cell genomics data. ICBBB 2020 CONFERENCE ABSTRACT 20 Keynote Speaker III Prof. Kuo-Sheng Cheng National Cheng Kung University, Taiwan Prof. Kuo-Sheng Cheng received his B.Sc, M.Sc, and Ph.D degrees from Department of Electrical Engineering, National Cheng Kung University, Tainan, TAIWAN. He also received his M.Sc degree from Department of Biomedical Engineering, Rensselaer Polytechnic Institute, USA. Currently, he is a professor with the Department of Biomedical Engineering, National Cheng Kung University. He also is the Director of Department of Maintenance and Engineering, National Cheng Kung University Hospital and the Director of Engineering and Technology Promotion Center, which is financial supported by Ministry of Science and Technology, TAIWAN. He was the past President of the Biomedical Engineering Society of TAIWAN. His research interests includes medical image processing, electrical impedance imaging and biomedical instrumentation. Topic: ―An Integrated Analysis System for Cephalometric Applications‖ Abstract—With the rapid advance of information and communication technologies, to develop the digital as well as smart dentistry becomes an important issue in oral medicine. In the procedures of conventional cephalometry, many steps rely on the manual processing such as the landmarking and superimposition. The automation of landmarking and superimposition are the first step in cephalometric analysis. Those points in cephalograms representing the anatomical structures of the skull are called landmarks, which are routinely analyzed for diagnosis and treatment planning. In this integrated analysis system, the image processing module was developed for locating the landmarks of X-ray cephalogram automatically. The image was divided into eight rectangular subimages that containing all the useful landmarks. A genetic algorithm combined with perceptron was proposed for feature subimage extraction. All the sugimages were enhanced in the preprocessing stage. The pyramid method was applied to reduce the resolution of image, and the edges were detected by the appropriate edge detectors or the best orientation edge detector. The curve of each edge was adjusted elastically with the pre-stored models. Positions of landmarks could be then located immediately and the associated parameters could also be computed for diagnosis. Secondly, the analysis of the spatial changes of the craniofacial structures for orthodontic treatment or surgery always relies on the superimposition of preand post-treatment cephalometric tracings. A computerized superimposition module was also developed for solving this problem. The feature curves were detected and traced for the cranial base using the best oriental edge detector and Hough transform, and for the mandibular using the Laplacian of Gaussian andWith the rapid advance of information and communication technologies, to develop the digital as well as smart dentistry becomes an important issue in oral medicine. In the procedures of conventional cephalometry, many steps rely on the manual processing such as the landmarking and superimposition. The automation of landmarking and superimposition are the first step in cephalometric analysis. Those points in cephalograms representing the anatomical structures of the skull are called landmarks, which are routinely analyzed for diagnosis and treatment planning. In this integrated analysis system, the image processing module was developed for locating the landmarks of X-ray cephalogram automatically. The image was divided into eight rectangular subimages that containing all the useful landmarks. A genetic algorithm combined with perceptron was proposed for feature subimage extraction. All the sugimages were enhanced in the preprocessing stage. The pyramid method was applied to reduce the resolution of image, and the edges were detected by the appropriate edge detectors or the best orientation edge detector. The curve of each edge was adjusted elastically with the pre-stored models. Positions of landmarks could be then located immediately and the associated parameters could also be computed for diagnosis. Secondly, the analysis of the spatial changes of the craniofacial structures for orthodontic treatment or surgery always relies on the superimposition of preand post-treatment cephalometric tracings. A computerized superimposition module was also developed for solving this problem. The feature curves were detected and traced for the cranial base using the best oriental edge detector and Hough transform, and for the mandibular using the Laplacian of Gaussian and ICBBB 2020 CONFERENCE ABSTRACT 21 grouping methods. The superimposition was automated following the clinically available procedures. From the experimental results, it was shown that the cephalometric analysis may be improved with its accuracy and processing time using this proposed integrated analysis syste. ICBBB 2020 CONFERENCE ABSTRACT 22 Keynote Speaker IV Prof. Hans-Uwe Dahms Kaohsiung Medical University, Taiwan Dr. Hans-Uwe Dahms is a professor at Kaohsiung Medical University. He is interested in stress responses in general and within aquatic systems in particular. He, his colleagues and students integratively study pollution and the toxicology of stressors from physical, chemical, and biological sources. He is equally interested in climate change, the spread of diseases, antibiotic-resistance, food and drink safety from water sources, and integra-tive approaches in environmental and public health monitoring, risk assessment and management. He advised more than 25 Ph.D. students in their research and published more than 275 papers in scientific journals. He served as a reviewer for more than 70 SCI journals, as editorial board member of 12 reputed scientific journals, academic editor of PLosONE, and as editor in chief of FRONTIERS in Marine Pollution. Topic: ―Which Toxicity Evaluations Provide Better Risk Assessments–in situ, in vivo, in vitro, or in silico?‖ Abstract—Describing the toxicological profile of a substance is the first step required for risk assessments. Among a wide range of approaches are in vitro methods widely used to characterise toxicological properties including toxicokinetics with regulatory acceptance mainly confined to in vitro tests which investigate genotoxic endpoints. Chemoinformatics refers to in silico approaches that make use of computer applications or computer simulations. In silico predictive models generally provide fast and economic screening tools for compound properties. They allow a high throug