| Chapter title |
Glycoprotein Enrichment Method Using a Selective Magnetic Nano-Probe Platform (MNP) Functionalized with Lectins.
|
|---|---|
| Chapter number | 5 |
| Book title |
Clinical Proteomics
|
| Published in |
Methods in molecular biology, October 2014
|
| DOI | 10.1007/978-1-4939-1872-0_5 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-1871-3, 978-1-4939-1872-0
|
| Authors |
Cova M, Oliveira-Silva R, Ferreira JA, Ferreira R, Amado F, Daniel-da-Silva AL, Vitorino R, Marta Cova, Rui Oliveira-Silva, José Alexandre Ferreira, Rita Ferreira, Francisco Amado, Ana Luísa Daniel-da-Silva, Rui Vitorino, Cova, Marta, Oliveira-Silva, Rui, Ferreira, José Alexandre, Ferreira, Rita, Amado, Francisco, Daniel-da-Silva, Ana Luísa, Vitorino, Rui |
| Editors |
Antonia Vlahou, Manousos Makridakis |
| Abstract |
Protein post-translational modifications (PTMs) have increasingly become a research field of incredible importance to fully understand the regulation of biological processes in health and disease. Among PTMs, glycosylation is one of the most studied for which contributed the development and improvement of enrichment techniques. Nowadays, glycoprotein enrichment methods are based on lectin affinity, covalent interactions, and hydrophilic interaction liquid chromatography (HILIC). Nonetheless, the nanotechnology era has fetched new methods to enrich glycoproteins from complex samples as human biological fluids. For instance, magnetic nanoparticles (MNPs) are being used as an interesting enrichment approach allowing a better characterization of glycoproteins and glycopeptides.In this chapter, we describe an enrichment method based on MNPs functionalized with lectins (Concavalin A, wheat germ agglutinin, and Maackia amurensis lectin) to enrich specific sets of glycoproteins from biological fluids. Moreover, it is proposed a bioinformatic strategy to deal with data retrieved from mass spectrometry analysis of enriched samples aiming the identification of relevant biological processes modulated by a given stimuli and, ultimately, of new biomarkers for disease screening/management. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 17 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Doctoral Student | 4 | 24% |
| Student > Ph. D. Student | 4 | 24% |
| Professor | 1 | 6% |
| Student > Bachelor | 1 | 6% |
| Student > Master | 1 | 6% |
| Other | 1 | 6% |
| Unknown | 5 | 29% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemistry | 4 | 24% |
| Medicine and Dentistry | 3 | 18% |
| Physics and Astronomy | 1 | 6% |
| Biochemistry, Genetics and Molecular Biology | 1 | 6% |
| Agricultural and Biological Sciences | 1 | 6% |
| Other | 1 | 6% |
| Unknown | 6 | 35% |