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.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11107246/

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.

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).
https://onlinelibrary.wiley.com/doi/10.1002/acn3.52082

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.
https://pubmed.ncbi.nlm.nih.gov/38698681/

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.
https://www.biorxiv.org/content/10.1101/2024.03.27.587044v1

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.
https://www.emukk.com/SADL-Easy_Eng/index.html

Detecting Triple-Negative Breast Cancer with using Glycan Profiling of Extracellular Vesicles

A group from Beijing Engineering Research Center for BioNanotechnology, CAS Key Laboratory of Standardization and Measurement for Nanotechnology, National Center for Nanoscience and Technology, Beijing, China, etc. has reported about glycan profiling of extracellular vesicles (EVs) for detecting triple-negative breast cancer (TNBC).
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10937950/

A panel of 3 lectins (ConA, WGA and RCA I) was used to detect the EV surface glycan profiles unique to TNBC.
As a result, they succeeded in getting an area under ROC curve (AUC) of 0.91 with using the weighted sum of 3 lectins (ConA, WGA and RCA I) for discriminatiing TNBC from other BCs and HDs.

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They say that prostate cancer can be detected by using its exosomes, but

A group from Institute of Chemistry, Slovak Academy of Sciences, Bratislava, etc. has reported about a measurement using a sandwich scheme with CD63, exosomes and SNA lectin for detecting prostate cancer.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10892626/

In this experment, exosomes produced by prostate cancer cells are examined as a new biomarker for detecting prostate cancer.

Comparing with exosomes produced by benign (control) cell line RWPE1 and carcinoma cell line 22Rv1, it was showen that
(1) the control exosomes mainly interacted with SNA and MAAII lectins; however, they exhibited a lower affinity than the carcinoma exosomes, and also
(2) PHA-L and PHA-E were only able to bind poorly to control-derived exosomes, while there were no interactions to carcinoma exosomes.
This result is quite reasonable because usually the signal intensity of PHA-L and PHA-E disapper with fully sialylated N-glycans suggesting that sialylation is stronger in carcinoma exsosomes than that of control exosomes.

However, blog author is skeptical about their conclusion that it is possible to perform measurements in a sandwich configuration, i.e., antibody/exosomes/lectin, because exsosmes are generally strongly sialylated and CD63 can not discriminate exsosomes produced by prostate cancer cells from other exosomes.

VVA Lectin characteristically binds to invasive urothelial carcinomas

A group from Department of Urology, Gifu University Graduate School of Medicine, Gifu, Japan, etc. has reported about characteristic glycan marker in invasive urothelial carcinomas.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10806140/

The study found that a specific lectin, VVL, was present in cases of invasive urothelial carcinoma and its variant components. More intense VVL staining was observed with invasive or muscle invasive urothelial carcinomas and urothelial carcinomas with variant components than that in non-invasive urothelial carcinomas

VVL recognizes the GalNAc residue linked to serine or threonine in a polypeptide Tn antigen. Other glycan structures, such as Galβ1,3GalNAc-α-Ser/Thr (T antigen) and GlcNAcα1,6-GalNAc-α-Ser/Thr, including terminal α1,4- and β1,4-linked GalNAc, were also recognized by VVL, but with a weaker affinity.

VVA will have the potential to serve as a promising target for drug delivery in future clinical studies.

The paper has been published: FDA announced High-throughput Glycan Profiling Analysis with a 9-Lectin Microarray for Therapeutic IgG1 mAbs

This paper regarding FDA’s dedicated lectin microarray (14-well lectin microarray using 9 kinds of lectins) for the evaluation of mAb drugs (IgG1) has been published.
This paper is related to the Mx blog post on Dec. 8th, 2023.
https://www.tandfonline.com/doi/epdf/10.1080/19420862.2024.2304268?needAccess=true

The 9 kinds of lectins used in this paper and those glycan binding specificities are summarized as follows.
rPhoSL -> core fucose
PHAE -> bisecting GlcNAc
PHAL -> tri/tetra antennary
MAL_I -> α2-3Sia
rPSL1a -> α2-6Sia
RCA120 -> β-Gal
rOTH3 -> terminal GlcNAc
rMan2 -> high mannose
rMOA -> α-Gal
Note that lectins with an “r” at the beginning of their name indicate that they are recombinant.
rOTH3, rMan2 are not official names.
Contact the author of this blog to learn what the official name is.

Pregnancy diagnosis targeting to changes in urinary glycopatterns

A group from Shaanxi Key Laboratory for Animal Conservation, College of Life Sciences, Northwest University, China, etc. has reported about a possibility of pregnancy diagnosis targeting changes in urinary glycopatterns.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10783609/

This study investigates the urinary glycopatterns of golden snub-nosed monkeys (GSM) with using lectin microarrays. It was shown that the types, amounts and structure of N-glycans and the proportion of sialylation and fucoslation of N-glycans are different between pregnant and non-pregnant females, and between (non-pregnant) females and males. This method will provide reference information for pregnancy diagnosis and sex identification, which will benefit the management of the animals.

where, pregnant (P) and non-pregnant (NP) females, and females (F) and males (M)

FDA announced High-throughput Glycan Profiling Analysis with a 9-Lectin Microarray for Therapeutic IgG1 mAbs

To evaluate glycan epitopes of therapeutic IgG1 mAbs, FDA has developed a new lectin microarray with 9 kinds of lectins, and has demonstrated its effectiveness for high-throughput glycan profiling analysis using GlycoStation Reader 2300 (GSR2300) .
https://www.fda.gov/media/169026/download
2023 FDA Science Forum

The new lectin microarray (IgG1-mAb-LecChip) developed by FDA immobilizes 9 kinds of lectins: rPhosL, rOTH3, RCA120, rMan2, MAL_I, rPSL1a, PHAE, rMOA, and PHEL, and uses a standard 14 wells LecChip format.
Glycan analysis of IgG1 mAbs can be performed using lectin microarrays without creaving glycans, making it possible to perform high-throughput glycan profiling analysis from intact IgG1.
FDA has recommended pharmaceutical companies to use IgG1-mAb-LecChip and GlycoStation to facilitate high-throughput glycan profiling analysis when developing IgG1 mAbs to assess batch-to-batch or biosimilar-to-innovator glycan epitopes.

The figure below shows how IgG1-mAb-LecChip, which was optimized for IgG1 glycan analysis, was developed using GlycoStation and LecChip (n=74 library).

As an example of showing the effectiveness of this technology, the figure below shows the result of evaluating the differences in glycosylation between Infliximab and its biosimilar using IgG1-mAB-LecChip and GSR2300. It can be clearly seen that there are significant differences in the abundance of High Mannose structure, sialic acid modification, and triantennary N-glycans.