Oral Presentation HUPO 2019 - 18th Human Proteome Organization World Congress

Visualization of a lectin microarray-based dataset for mouse tissue glycome mapping by a novel online tool, LM-GlycomeAtlas (64695)

Chiaki Nagai-Okatani 1 , Kiyoko F Aoki-Kinoshita 2 , Shuichi Kakuda 1 , Misugi Nagai 1 , Kozue Hagiwara 1 , Katsue Kiyohara 1 , Noriaki Fujita 1 , Yoshinori Suzuki 1 , Takashi Sato 1 , Kiyohiko Angata 1 , Atsushi Kuno 1
  1. National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba City, IBARAKI, Japan
  2. Soka University, Hachioji, Tokyo, Japan

Lectin microarray (LMA) is a successful glycan analysis tool based on the protein-chip technology [1]. Discovery of disease-related N-/O-glycosylation alterations on clinical tissue specimens is one of the good practical applications; accordingly, we have applied this glycomic profiling tool to the uppermost stream of glyco-biomarker development process. For effective discovery of biological roles and disease-specific alterations in protein glycosylation in tissue samples, it is important to know in advance the quantitative and qualitative variations of glycan structures expressed in various types of cells, sites, and tissues. To this end, using laser microdissection-assisted LMA, we have established a simple and reproducible method for high-throughput glycomic profiling of formalin-fixed paraffin-embedded tissue sections [2]. Using this “tissue glycome mapping” approach, we initiated a database construction project for glycomic profiles of mouse tissues in 2013, and have presented it in the previous HUPO congress. Here, we collected a total of 234 new glycomic profiling data obtained from nine tissue sections of two 8-week-old male C57BL/6J mice. We then provided this LMA-based dataset in the similar interface as GlycomeAtlas [3], a previously developed tool for mass spectrometry-based tissue glycomic profiling data, allowing users to easily compare the two types of data. This freely available online tool, called “LM-GlycomeAtlas”, allows users to visualize the LMA-based tissue glycomic profiling data, which are associated with the sample information, as an atlas. Since the present glycomic profiles can be compared with each other, the present dataset will facilitate to evaluate site- and tissue-specific glycosylation pattern. Taking advantage of its extensibility, this tool will be upgraded with the expansion of deposited data, including mouse tissues other than the present nine tissues as “Ver.1” [2,4]. The efforts in our lab may contribute to the efficient discovery of “glyco-seeds” for biomarkers and therapeutic targets with glycosylation alterations in disease model mice.

  1. Narimatsu H, et al. J Proteome Res 17:4097-4112 (2018).
  2. Zou X, et al. Sci Rep 7:43360 (2017).
  3. Konishi Y and Aoki-Kinoshita KF. Bioinformatics 28:2849-2850 (2012).
  4. Nagai-Okatani C et al. Lab Invest (in press).