Sensitive and Quantitative MS-bases Glycomic Mapping Platform

NIH Pandemic-Era Grants

Pandemic Era Grants

2022

Document text

Principal Investigator: Yehia  Mechref
Organization: TEXAS TECH UNIVERSITY
Fiscal Year: 2022
Award: $302,855
Funding agency: National Institute of General Medical Sciences

Glycosylation is one of the most complex protein modifications; more than 50% of mammalian
proteins are glycosylated. The fact that there are 100,000 proteoforms coded by only ~20,300
genes identified in the human genome emphasizes the importance of posttranslational
modifications (PTMs) like glycosylation. Aberrant protein glycosylation has been implicated in
many diseases, such as Alzheimer’s disease, congenital/metabolic disorders, diabetes,
inflammation, Parkinson’s disease, bacterial/viral infectious diseases, and various cancers. More
recently, glycans have been associated with coronavirus spike glycoproteins, including the SARS-
CoV-2 virion. The diverse biological roles of glycans and their implications in diseases have
created a demand for reliable qualitative and quantitative glycomic approaches, which facilitates
sensitive investigation of glycan changes in different biological and biomedical samples. Mass
spectrometry (MS) is the most efficient technique in glycomics due to its high sensitivity and
capacity for acquiring structural information. However, glycomic research remains a challenge
because of the microheterogeneity of glycan compositions in complex biological samples; the
relatively low abundance in nature and low ionization efficiency in MS analysis; and the existence
of variant positional and linkage isomers caused by the biosynthesis process. To overcome these
challenges, several separation methods have been coupled to MS. Despite the development of
these separation techniques, isomeric separation of glycans remains insufficient. There is an
increasing demand for more efficient isomeric separation approaches since glycan isomers have
been related to different diseases.
 The main aim of this proposal is to provide easily accessible, adaptable, and affordable
strategies for better separation and characterization of glycans and glycan isomers derived from
different glycoconjugates. Aim 1 is focused on finding a replacement for porous craphitic carbon
columns that sufferes from low reprodcubility and loss of resolution and efficiency with time. The
in-house mesoporous graphitic carbon (MGC)-LC-MS will be investigated for both permethylated
(Aim 1a) and native (Aim 2b) isomeric separation of N- and O-glycans, glycolipid glycans, and
free oligosaccharides. Other alternatives (also part of Aim 1a), such as 50 cm and 200 cm micro
pillar array columns (μPAC)-C18-LC-MS, and a 50 cm long capillary nanoC18-LC-MS, will also
be evaluated to achieve an improved isomeric separation of glycan isomers. Subsequently, GUI
will be utilized to improve the identification of glycan isomers. A GUI libraries for the separation
strategies developed in Aim 1 will be established to normalize the possible retention time shift
among different runs (Aim 2). LC-M based glycomics quantitative strategies will be developed
and assessed in Aim 3. The combination of permethylation and TMT will capitalize on the
advantages of both techniques, providing enhanced sensitivity and accuracy in glycan
quantitation (Aim 3a). Additionally, the combination of 8-plex isotopic permethylation and 6-plex
TMT will facilitate a reliable 48-plex, thus significantly increasing the throughput of analysis (Aim
3b). Furthermore, with the improved separation and identification (Aim 1 and 2), PRM will be
employed to assist the better quantitation of glycans and the differentiation of glycan isomers. A
library containing the fragments fingerprint of each glycan isomer will be created to aid the glycan
isomeric identification (Aim 3c). The development of automated software for easy, fast, and
accurate glycomic data processing is the main focus of Aim 4. Finally, the aforementioned
strategies and tools will be applied to clinical samples to address a variety of biological and
biomedical issues such as COVID-19, breast cancer brain metastasis, liver cancer vs. cirrhosis,
kidney diseases, algae development, and others (Aim 5). The development of the proposed
methods and algorithms will help us and our collaborators to better understand the attributes and
biomedical significance of glycan isomers in the development and progression of esophageal,
breast, and liver cancer. Furthermore, we expect the analytical tools and algorithms proposed
here to benefit researchers and scientists who are interested in understanding the biological
attributes of glycan isomers in other systems.

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