Coding

Part:BBa_K5151005

Designed by: KUO, YUN-HSIN   Group: iGEM24_NYCU-Formosa   (2024-08-22)

CD97 Introduction

Early detection of diseases is crucial. To achieve this, we can focus on detecting disease biomarkers to aid in early diagnosis. Currently, clinical tests for disease biomarkers, such as ELISA, RIA, and mass spectrometry, are widely used but often require lengthy detection times. Many researchers have developed biosensors and rapid diagnostic tools (RDTs). However, there are still numerous challenges in detecting disease biomarkers, mainly due to the complexity of samples and the limitations in sensitivity and specificity of the detection technologies.
To address these issues, our team has proposed a new strategy this year by utilizing a pre-trained Natural Language Processing (NLP) model to identify potential biomarkers for diseases[1]. Through this approach, we aim to overcome the current bottlenecks in detection technology, providing a more efficient and cost-effective method for early diagnosis and timely treatment of diseases. This not only helps shorten diagnosis time but also improves diagnostic accuracy and patient outcomes.
Ultimately, our model identified the top ten highly relevant diseases, and we selected leukemia as the target for rapid detection to demonstrate the feasibility of our strategy. Based on the model’s results, we chose CD97 from all proteins associated with leukemia as the biomarker[2]. Subsequently, we aim to use an electrochemical detector to measure the expression level of CD97 to determine whether a patient has the disease.
CD97，Cluster of differentiation 97 is encoded by the ADGRE5 gene[3] [4] [5]. CD97 is a member of the adhesion G protein-coupled receptor (GPCR) family[6] [7]. CD97 is widely expressed on, among others, hematopoietic stem and progenitor cells, immune cells, epithelial cells, muscle cells as well as their malignant counterparts[8] [9] [10] [11] [12] [13].

Reference

[1] Available at: https://2024.igem.wiki/nycu-formosa/model
[2] Baloun, H., Kaltenhäuser, S., Schick, J., Oestreich, M., Deichmann, M., Sohlbach, C., … & Yildirim, Ö. (2019). CD97 is a critical regulator of acute myeloid leukemia cell growth. The Journal of Experimental Medicine, 216(10), 2362-2379. Available at: https://doi.org/10.1084/jem.20181264
[3] Hamann J, Eichler W, Hamann D, Kerstens HM, Poddighe PJ, Hoovers JM, et al. (Aug 1995). "Expression cloning and chromosomal mapping of the leukocyte activation antigen CD97, a new seven-span transmembrane molecule of the secretion receptor superfamily with an unusual extracellular domain". Journal of Immunology. 155 (4): 1942–50. Available at: https://journals.aai.org/jimmunol/article/155/4/1942/28884/Expression-cloning-and-chromosomal-mapping-of-the
[4] Hamann J, Hartmann E, van Lier RA (Feb 1996). "Structure of the human CD97 gene: exon shuffling has generated a new type of seven-span transmembrane molecule related to the secretin receptor superfamily". Genomics. 32 (1): 144–7. Available at: https://doi.org/10.1006%2Fgeno.1996.0092
[5] Hamann J, Aust G, Araç D, Engel FB, Formstone C, Fredriksson R, et al. (April 2015). "International Union of Basic and Clinical Pharmacology. XCIV. Adhesion G protein-coupled receptors". Pharmacological Reviews. 67 (2): 338–367. Available at: https://doi.org/10.1124%2Fpr.114.009647
[6] Stacey M, Yona S (2011). Adhesion-GPCRs: Structure to Function (Advances in Experimental Medicine and Biology). Berlin: Springer. Available at: https://en.wikipedia.org/wiki/Special:BookSources/978-1-4419-7912-4
[7] Langenhan T, Aust G, Hamann J (May 2013). "Sticky signaling--adhesion class G protein-coupled receptors take the stage". Science Signaling. 6 (276): re3. Available at: https://doi.org/10.1126%2Fscisignal.2003825
[8] van Pel M, Hagoort H, Hamann J, Fibbe WE (Aug 2008). "CD97 is differentially expressed on murine hematopoietic stem-and progenitor-cells". Haematologica. 93 (8): 1137–44. Available at: https://doi.org/10.3324%2Fhaematol.12838
[9] Eichler W, Hamann J, Aust G (Nov 1997). "Expression characteristics of the human CD97 antigen". Tissue Antigens. 50 (5): 429–38. Available at: https://doi.org/10.1111%2Fj.1399-0039.1997.tb02897.x
[10] Jaspars LH, Vos W, Aust G, Van Lier RA, Hamann J (Apr 2001). "Tissue distribution of the human CD97 EGF-TM7 receptor". Tissue Antigens. 57 (4): 325–31. Available at: https://doi.org/10.1034%2Fj.1399-0039.2001.057004325.x
[11] Aust G, Wandel E, Boltze C, Sittig D, Schütz A, Horn LC, et al. (Apr 2006). "Diversity of CD97 in smooth muscle cells". Cell and Tissue Research. 324 (1): 139–47. Available at: https://doi.org/10.1007%2Fs00441-005-0103-2
[12] Veninga H, Becker S, Hoek RM, Wobus M, Wandel E, van der Kaa J, et al. (Nov 2008). "Analysis of CD97 expression and manipulation: antibody treatment but not gene targeting curtails granulocyte migration". Journal of Immunology. 181 (9): 6574–83. Available at: https://doi.org/10.4049%2Fjimmunol.181.9.6574
[13] Zyryanova T, Schneider R, Adams V, Sittig D, Kerner C, Gebhardt C, et al. (2014). "Skeletal muscle expression of the adhesion-GPCR CD97: CD97 deletion induces an abnormal structure of the sarcoplasmatic reticulum but does not impair skeletal muscle function". PLOS ONE. 9 (6): e100513. Available at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4065095

Sequence and Features


Assembly Compatibility:
  • 10
    COMPATIBLE WITH RFC[10]
  • 12
    COMPATIBLE WITH RFC[12]
  • 21
    COMPATIBLE WITH RFC[21]
  • 23
    COMPATIBLE WITH RFC[23]
  • 25
    COMPATIBLE WITH RFC[25]
  • 1000
    COMPATIBLE WITH RFC[1000]


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Categories
Parameters
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