Detecting Colorectal Cancer from Glycosylation Profiling Analysisi of Exosomes

A group from State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, China has reproted about Lectin microarray based glycan profiling of exosomes.

In this styudy, the differential analysis of glycosylation on the surface of exosomes between from three olorectal cancer cell lines (SW480, SW620, HCT116) and from one normal colon epithelial cell line (NCM460) has been done by using a lectin microarray. As a result, it has shown that UEA-I lectin could be used to detect aberant glycosylation of exosomes derived from SW480 cells.

And further, in this experiment, the limit of detection (LOD) of UEA-I lectin microarray was calculated to be as low as 2.7 × 105 extracellular vehicles/mL.

Surface Glycans of Microvesicles Derived from Endothelial Cells

A group from Shemyakin and Ovchinnikov Institute of Bioorganic Chemistry RAS, Moscow, Russia has reported about surface glycans of Microvesicles derived from endothelial Ccells.

As shown below, it is clearly shown that the surface glycans of MVs are dominated by α2-6-sialylated forms as N-glycans and the level of some Man-containing glycans are significantly decreased in MVs, comparing surface glycans of MVs and those of Cells.

Glycosylation of adeno-associated virus (AAV) used for gene therapy was investigated using Lectin microarrays

A group from Department of Biotechnology, Graduate School of Engineering, Osaka University, Osaka, Japan has reported about glycosylation of recombinant adeno-associated virus serotype 6 (rAAV6) which is used for Gene therapy.

Glycosylation of recombinant adeno-associated viruses (rAAVs) that are used for gene theraphy has been evaluated by Lectin microarrays.
It was found that rAAV6 was mainly O-glycosylated with the mucin-type glycans, O-GalNAc (Tn antigen), and mono- and di-sialylated Galβ1-3GalNAc (T antigen).

The purpose of this study was to see if glycosylation of rAAV6 can affect gene therapy safety in terms of immunogenecity and overall transduction efficacy of this method.
However, unfortunately, this study still could not evaluate the direct influence of mucin-type O-glycans on transduction efficiency.

Galectins bind to the N-glycan of FGFR1 and can directly activate the downstream signaling of FGFR1.

A group from Department of Protein Engineering, Faculty of Biotechnology, University of Wroclaw, Wroclaw, Poland has reported that galectin-1, -7, and -8 can activate FGFR1 signaling and control endocytosis.

N-glycans of FGFR1 are recognized by extracellular galectins (Gal-1, Gal-7, and Gal-8), which are not authentific ligand of FGFR1 (i.e., FGF1), and the binding of those galectins to FGFR1 trigers activation of the receptor and initiation of downstream signaling cascades. Subsequent endocytosis of activated FGFR1 serves as a major cellular mechanism for the downregulation of FGFR1 signaling.

Both FGF1 and Gal-1 directly activate FGFR1 and after short and intensive pulse of FGFR1 signaling, the receptor is shut down due to the induction of clathrin medited endocytosis, followed by lysosomal degradation of the receptor. Gal-7 and -8 also directly activate FGFR1 by the receptor clustering mechanism, but by inhibiting FGFR1 endocytosis and degradation, these galectins largely prolong FGFR1 signaling.

pFGFR means tyrosine-phosphorylated FGFR1

T-antigen could be a biomarker of progression-free survival in patients with glioblastoma ?

A group from Department of Neurosurgery, the First Affiliated Hospital of Anhui Medical University, Hefei, China has reported that T-antigen could be a biomarker of progression-free survival (PFS) in patients with glioblastoma (GMB).

They concluded from studies using Lectin microarrays that serum Jacalin-probed T-antigen levels, which were positively correlated with those in GBM tissues, may be used as a non-invasive biomarker of PFS, predicting GBM recurrences.

However, blog author thinks that their conclusion is problematic, because the glycan binding specificity of Jacalin is quite broad covering GlcNAcβ1-3GalNAc (Core3), Siaα2-3Galβ1-3GalNAc (sialyl T), Galβ1-3GalNAc (T-antigen), α-GalNAc (Tn-antigen), and also PNA could not discriminate glioblastoma so well, although it has high binding specificity to Galβ1-3GalNAc (T-antigen).

Combining lectin microarrays and machine learning will be a trend?

A group from Laboratory for Functional Glycomics, College of Life Sciences, Northwest University, China has reported about employing a combination of lectin microarrays and machine learning to identify alterations in serum glycopatterns with a special emphasis on its early detection.

In recent years, the number of Chinese papers has increased rapidly, and the probability of finding Japanese papers has decreased considerably when searching for papers.
This work from China proposes a method that uses the glycan profile of blood glycoproteins as a serum biomarker combining machine learning with lectin microarrays for the early detection of non-alcoholic fatty liver disease.

However, this type of method was already developed by us about six years ago, and although the target was different from the above paper, its excellent effectiveness has been demonstrated.
The following website explains that it is possible to characterize target cells quite accurately by using deep learning and lectin microarrays for glycoproteins secreted by cells into the culture media.
Combining Deep Learning and Lectin microarrays

Detailed N-glycan analysis combining Glycan profiles taken by Lectin Microarrays and AI

A group from Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA, etc. has reported a lectin and AI-based approach to predict N-glycan structures and determine their relative abundance in purified proteins based on lectin-binding patterns.

This method can be used when the number of glycanss to be evaluated is limited, but there are a lot of problems when applying it generally.

A similar software named “SA/DL easy” had been created by Mx using Deep Learning as a core technology 5 years ago. By using this software, you can quickly do the same thing.
The problem lies in the tedious work of creating training data, or preparing a large number of expressed glycan structures whose structures have been properly identified, and obtaining glycan profiles.

From the roles of galectins in epithelial-to-mesenchymal transition particulary in cancer

I have read a review article on the epithelial-to-mesenchymal transition (EMT) of galectins written by the groups of CEBICEM, Facultad de Medicina y Ciencia, Universidad San Sebastián, Santiago, Chile, and others. The following is the typical phrases extracted from this review.

In gastric cancer, increased levels of Gal-1 have been associated with lower overall and disease-free survival, as well as with an increased incidence of lymph node metastasis in patients. Gastric cancer cell lines produce Gal-1, which promotes EMT and increases proliferation, invasion and metastatic potential of these cells. In ovarian cancer, serum samples show that Gal-1 levels are increased and correlate with a higher histological grade and lymph node metastasis. In ovarian cancer cell lines, Gal-1 overexpression promotes EMT and increases cell migration and invasion through the activation of the MAPK-JNK/p38 signaling pathway, while silencing of Gal-1 has opposite effects. High levels of Gal-1 are detected in stromal cells from gastric cancer and pancreatic ductal adenocarcinoma tumors in correlation with an EMT phenotype of carcinoma cells. Gal-1-overexpression in pancreatic stellate cells (PSC) induces EMT in co-cultured pancreatic carcinoma cells, enhancing their proliferation and invasion through the NF-κB pathway. Downregulation of Gal-3 expression reduces tumor growth in xenograft colon cancer models whereas its overexpression enhances the metastatic potential of cancer cells. In breast, colon, and prostate cancer cell lines exogenously added Gal-3 promotes EMT by its interaction with Trop-2, a highly-glycosylated membrane protein involved in cancer progression. Gal-4 has been reported in human prostate cancer tissues with expression levels correlating with metastasis and poor patient survival. Gal-8 is a widely expressed galectin in human tissues and carcinomas and has been associated with an unfavorable prognosis in various types of cancer. Gal-8 contributes to cancer progression and metastasis by regulating the production of immunoregulatory cytokines, thereby facilitating the recruitment of cancer cells to metastatic sites.

In other words, different types of galectins are involved in cancer in various places, but I think the issue is the degree of the contribution of galectin involvement. Glycans and lectins basically play regulatory roles except for innate immunity and congenital disorder of glycosylation (CDG).

Therefore, when trying to cure disease from a view point of glycans and lectins, I think it is necessary to narrow down the disease to those in which these are involved with higher contributions.
What do you think? ?

α2,3-sialylation is essential for melanoma growth and progression

A group from Department of Pathology, NYU Grossman School of Medicine, New York, USA etc. has reported about cganges in glycosylation of melanoma.

It has shown using lectin microarrays that α1,2 fucose decreased in primary melanoma compared to nevi.
Interestingly, core fucose was high in nevi and lower in primary melanoma but then regained in metastatic melanoma.
It was also observed that 2,3 syalylation increased significantly in both primary and metastatic melanoma compared to nevi.

Changes in glycosylation in Pancreatic Ductal Adenocarcinoma mediated by KRAS mutations

A group from Department of Gastrointestinal and Hepato-Biliary-Pancreatic Surgery, Faculty of Medicine, University of Tsukuba, Japan, etc. has reported about changes in glycosylation in Pancreatic Ductal Adenocarcinoma mediated by KRAS gene mutations.

It was shown that Fucosilation and mannosylation were upregurated in pancreatin ductal adenocarcinoma with KRAS gene mutations.
The lectins enriched in KRAS mutants included fucose-binding lectins (AAL, rAAL, AOL, rAOL, rRSIIL, and UEAI) and mannose-binding lectins (rRSL, rBC2LCA, rPAIIL, and NPA).