Insights into Bioinformatic Applications for Glycosylation: Instigating an Awakening towards Applying Glycoinformatic Resources for Cancer Diagnosis and Therapy

Insights into the role of glycosyltransferase in the targeted treatment of gastric cancer

Gastric cancer is a remarkably heterogeneous tumor. Despite some advances in the diagnosis and treatment of gastric cancer in recent years, the precise treatment and curative outcomes remain unsatisfactory. Poor prognosis continues to pose a major challenge in gastric cancer. Therefore, it is imperative to identify effective targets to improve the treatment and prognosis of gastric cancer patients. It should be noted that glycosylation, a novel form of posttranslational modification, is a process capable of regulating protein function and influencing cellular activities. Currently, numerous studies have shown that glycosylation plays vital roles in the occurrence and progression of gastric cancer. As crucial enzymes that regulate glycan synthesis in glycosylation processes, glycosyltransferases are potential targets for treating GC. Hence, investigating the regulation of glycosyltransferases and the expression of associated proteins in gastric cancer cells is highly important. In this review, the related glycosyltransferases and their related signaling pathways in gastric cancer, as well as the existing inhibitors of glycosyltransferases, provide more possibilities for targeted therapies for gastric cancer.

Exposing the molecular heterogeneity of glycosylated biotherapeutics

The heterogeneity inherent in today's biotherapeutics, especially as a result of heavy glycosylation, can affect a molecule's safety and efficacy. Characterizing this heterogeneity is crucial for drug development and quality assessment, but existing methods are limited in their ability to analyze intact glycoproteins or other heterogeneous biotherapeutics. Here, we present an approach to the molecular assessment of biotherapeutics that uses proton-transfer charge-reduction with gas-phase fractionation to analyze intact heterogeneous and/or glycosylated proteins by mass spectrometry. The method provides a detailed landscape of the intact molecular weights present in biotherapeutic protein preparations in a single experiment. For glycoproteins in particular, the method may offer insights into glycan composition when coupled with a suitable bioinformatic strategy. We tested the approach on various biotherapeutic molecules, including Fc-fusion, VHH-fusion, and peptide-bound MHC class II complexes to demonstrate efficacy in measuring the proteoform-level diversity of biotherapeutics. Notably, we inferred the glycoform distribution for hundreds of molecular weights for the eight-times glycosylated fusion drug IL22-Fc, enabling correlations between glycoform sub-populations and the drug's pharmacological properties. Our method is broadly applicable and provides a powerful tool to assess the molecular heterogeneity of emerging biotherapeutics.

Glycoproteomic contributions to hepatocellular carcinoma research: a 2023 update

INTRODUCTION

Hepatocellular carcinoma (HCC) represents a significant burden globally, which ranks sixth among the most frequently diagnosed cancers and stands as the third leading cause of cancer-related mortality. Glycoproteomics, as an important branch of proteomics, has already made significant achievements in the field of HCC research. Aberrant protein glycosylation has shown to promote the malignant transformation of hepatocytes by modulating a wide range of tumor-promoting signaling pathways. The glycoproteome provides valuable information for understanding cancer progression, tumor immunity, and clinical outcome, which could serve as potential diagnostic, prognostic, and therapeutic tools in HCC.

AREAS COVERED

In this review, recent advances of glycoproteomics contribute to clinical applications (diagnosis and prognosis) and molecular mechanisms (hepatocarcinogenesis, progression, stemness and recurrence, and drug resistance) of HCC are summarized.

EXPERT OPINION

Glycoproteomics shows promise in HCC, enhancing early detection, risk stratification, and personalized treatments. Challenges include sample heterogeneity, diverse glycans structures, sensitivity issues, complex workflows, limited databases, and incomplete understanding of immune cell glycosylation. Addressing these limitations requires collaborative efforts, technological advancements, standardization, and validation studies. Future research should focus on targeting abnormal protein glycosylation therapeutically. Advancements in glycobiomarkers and glycosylation-targeted therapies will greatly impact HCC diagnosis, prognosis, and treatment.

Artificial intelligence in the analysis of glycosylation data

Glycans are complex, yet ubiquitous across biological systems. They are involved in diverse essential organismal functions. Aberrant glycosylation may lead to disease development, such as cancer, autoimmune diseases, and inflammatory diseases. Glycans, both normal and aberrant, are synthesized using extensive glycosylation machinery, and understanding this machinery can provide invaluable insights for diagnosis, prognosis, and treatment of various diseases. Increasing amounts of glycomics data are being generated thanks to advances in glycoanalytics technologies, but to maximize the value of such data, innovations are needed for analyzing and interpreting large-scale glycomics data. Artificial intelligence (AI) provides a powerful analysis toolbox in many scientific fields, and here we review state-of-the-art AI approaches on glycosylation analysis. We further discuss how models can be analyzed to gain mechanistic insights into glycosylation machinery and how the machinery shapes glycans under different scenarios. Finally, we propose how to leverage the gained knowledge for developing predictive AI-based models of glycosylation. Thus, guiding future research of AI-based glycosylation model development will provide valuable insights into glycosylation and glycan machinery.

Role of Glycoproteins during Fruit Ripening and Seed Development

Approximately thirty percent of the proteins synthesized in animal or plant cells travel through the secretory pathway. Seventy to eighty percent of those proteins are glycosylated. Thus, glycosylation is an important protein modification that is related to many cellular processes, such as differentiation, recognition, development, signal transduction, and immune response. Additionally, glycosylation affects protein folding, solubility, stability, biogenesis, and activity. Specifically, in plants, glycosylation has recently been related to the fruit ripening process. This review aims to provide valuable information and discuss the available literature focused on three principal topics: (I) glycosylations as a key posttranslational modification in development in plants, (II) experimental and bioinformatics tools to analyze glycosylations, and (III) a literature review related to glycosylations in fruit ripening. Based on these three topics, we propose that it is necessary to increase the number of studies related to posttranslational modifications, specifically protein glycosylation because the specific role of glycosylation in the posttranslational process and how this process affects normal fruit development and ripening remain unclear to date.

Rational design of hyper-glycosylated human follicle-stimulating hormone analogs (a bioinformatics approach)

N-glycosylation is a complex mechanism in which the carbohydrate molecules bind to the Asn amino acid in the N-glycan consensus sequence (AsnXxxThr/Ser sequon, where Xxx is any residue, excluding Pro). Introduction of additional N-linked glycosylation site into proposed location in the protein causes to its hyper-glycosylation and can enhance the protein characteristics to provide promising prospects in treatment. Glycoengineering is a favorably used strategy to design and generate hyper-glycosylated variants. In this research, human follicle-stimulating hormone (HuFSH) was considered to identify appropriate positions for adding novel N-glycan sites. A rational computational strategy was applied to predict functional/structural variations induced through changes in polypeptide chain. We analyzed the amino acid chain of FSH to find out the proper locations to introduce asparagine and/or threonine for creating novel N-glycan positions. This analysis resulted in the recognition of 40 possible N-glycosylation positions, and then the eight adequate ones were chosen for additional investigation. The model validation techniques were used to examine 3-dimensional structures of the chosen mutant proteins. Finally, 2 mutants with a further glycan site were recommended as eligible FSH hyper-glycosylated analogs, which may be regarded for subsequent experimental studies. Our in silico approach may decrease tedious and time-wasting laboratory researches of the mutants.Communicated by Ramaswamy H. Sharma.