Proteogenomic Features of the Highly Polymorphic Histidine-rich Glycoprotein (HRG) Arose Late in Evolution

Development of a novel histidine-rich glycoprotein measurement system as a biomarker for sepsis

The plasma histidine-rich glycoprotein concentration is a marker of sepsis severity. In this study, we generated selective and specific monoclonal antibodies against histidine-rich glycoprotein for use in a prototype enzyme-linked immunosorbent assay-based in vitro diagnostic system. First, we investigated the properties of monoclonal antibodies produced by 21 hybridomas that we developed using immunized mice, and we identified monoclonal antibodies 69-1A and 75-2D to be the most suitable combination for use in the sandwich enzyme-linked immunosorbent assay. Wild-type histidine-rich glycoprotein (Form-1, 75 kDa) with a proline residue at amino acid position 204 is the most common isoform of the protein in humans, followed by its variant (Form-2, 77 kDa), which has a serine residue at position 204. The epitope mapping was examined for the HRG amino acid sequence with 69-1A and 75-2D mAbs to achieve the identification of respective specific binding domains, though the other kinds of mAbs showed considerably complex domains. The identified epitopes recognized by 69-1A and 75-2D monoclonal antibodies did not span position 204. Furthermore, immunoprecipitation-immunoblotting analysis showed that the 69-1A and 75-2D monoclonal antibodies could bind to both Form-1 and Form-2 in human plasma samples. Thus, these two new antibodies can be used to clearly detect both forms of histidine-rich glycoproteins in human plasma samples. In our analysis of clinical samples by enzyme-linked immunosorbent assays using various combinations of our newly synthesized antibodies, we found that the histidine-rich glycoprotein concentration was significantly lower in plasma samples from septic patients than in those from healthy volunteers (p < 0.01). Thus, our novel analysis system using the new antibodies is expected to be a useful tool for sepsis research, and it may be adapted as an in vitro diagnostic tool for many other kinds of diseases in the future.

Integrative Bioinformatics Analysis for Targeting Hub Genes in Hepatocellular Carcinoma Treatment

Background

The damage in the liver and hepatocytes is where the primary liver cancer begins, and this is referred to as Hepatocellular Carcinoma (HCC). One of the best methods for detecting changes in gene expression of hepatocellular carcinoma is through bioinformatics approaches.

Objective

This study aimed to identify potential drug target(s) hubs mediating HCC progression using computational approaches through gene expression and protein-protein interaction datasets.

Methodology

Four datasets related to HCC were acquired from the GEO database, and Differentially Expressed Genes (DEGs) were identified. Using Evenn, the common genes were chosen. Using the Fun Rich tool, functional associations among the genes were identified. Further, protein-protein interaction networks were predicted using STRING, and hub genes were identified using Cytoscape. The selected hub genes were subjected to GEPIA and Shiny GO analysis for survival analysis and functional enrichment studies for the identified hub genes. The up-regulating genes were further studied for immunohistopathological studies using HPA to identify gene/protein expression in normal vs HCC conditions. Drug Bank and Drug Gene Interaction Database were employed to find the reported drug status and targets. Finally, STITCH was performed to identify the functional association between the drugs and the identified hub genes.

Results

The GEO2R analysis for the considered datasets identified 735 upregulating and 284 downregulating DEGs. Functional gene associations were identified through the Fun Rich tool. Further, PPIN network analysis was performed using STRING. A comparative study was carried out between the experimental evidence and the other seven data evidence in STRING, revealing that most proteins in the network were involved in protein-protein interactions. Further, through Cytoscape plugins, the ranking of the genes was analyzed, and densely connected regions were identified, resulting in the selection of the top 20 hub genes involved in HCC pathogenesis. The identified hub genes were: KIF2C, CDK1, TPX2, CEP55, MELK, TTK, BUB1, NCAPG, ASPM, KIF11, CCNA2, HMMR, BUB1B, TOP2A, CENPF, KIF20A, NUSAP1, DLGAP5, PBK, and CCNB2. Further, GEPIA and Shiny GO analyses provided insights into survival ratios and functional enrichment studied for the hub genes. The HPA database studies further found that upregulating genes were involved in changes in protein expression in Normal vs HCC tissues. These findings indicated that hub genes were certainly involved in the progression of HCC. STITCH database studies uncovered that existing drug molecules, including sorafenib, regorafenib, cabozantinib, and lenvatinib, could be used as leads to identify novel drugs, and identified hub genes could also be considered as potential and promising drug targets as they are involved in the gene-chemical interaction networks.

Conclusion

The present study involved various integrated bioinformatics approaches, analyzing gene expression and protein-protein interaction datasets, resulting in the identification of 20 top-ranked hubs involved in the progression of HCC. They are KIF2C, CDK1, TPX2, CEP55, MELK, TTK, BUB1, NCAPG, ASPM, KIF11, CCNA2, HMMR, BUB1B, TOP2A, CENPF, KIF20A, NUSAP1, DLGAP5, PBK, and CCNB2. Gene-chemical interaction network studies uncovered that existing drug molecules, including sorafenib, regorafenib, cabozantinib, and lenvatinib, can be used as leads to identify novel drugs, and the identified hub genes can be promising drug targets. The current study underscores the significance of targeting these hub genes and utilizing existing molecules to generate new molecules to combat liver cancer effectively and can be further explored in terms of drug discovery research to develop treatments for HCC.

Genotype-dependent N-glycosylation and newly exposed O-glycosylation affect plasmin-induced cleavage of histidine-rich glycoprotein (HRG)

Histidine-rich glycoprotein (HRG) is an abundant plasma protein harboring at least three N-glycosylation sites. HRG integrates many biological processes, such as coagulation, antiangiogenic activity, and pathogen clearance. Importantly, HRG is known to exhibit five genetic variants with minor allele frequencies of more than 10%. Among them, Pro204Ser can induce a fourth N-glycosylation site (Asn202). Considerable efforts have been made to reveal the biological function of HRG, whereas data on HRG glycosylation are scarcer. To close this knowledge gap, we used C18-based LC-MS/MS to study the glycosylation characteristics of six HRG samples from different sources. We used endogenous HRG purified from human plasma and compared its glycosylation to that of the recombinant HRG produced in Chinese hamster ovary cells or human embryonic kidney 293 cells, targeting distinct genotypic isoforms. In endogenous plasma HRG, every N-glycosylation site was occupied predominantly with a sialylated diantennary complex-type glycan. In contrast, in the recombinant HRGs, all glycans showed different antennarities, sialylation, and core fucosylation, as well as the presence of oligomannose glycans, LacdiNAcs, and antennary fucosylation. Furthermore, we observed two previously unreported O-glycosylation sites in HRG on residues Thr273 and Thr274. These sites together showed more than 90% glycan occupancy in all HRG samples studied. To investigate the potential relevance of HRG glycosylation, we assessed the plasmin-induced cleavage of HRG under various conditions. These analyses revealed that the sialylation of the N- and O-glycans as well as the genotype-dependent N-glycosylation significantly influenced the kinetics and specificity of plasmin-induced cleavage of HRG.

Proteomics and genomics insights on malignant osteosarcoma

Osteosarcoma is a malignant osseous neoplasm. Osteosarcoma is a primary bone malignancy capable of producing osteoid tissue or immature bones. A subsequent malignant degeneration of the primary bone pathology occurs less frequently in adults. The over-expression of several proteins, including Heat shock proteins, Cofilin, Annexins, Insulin-like growth factor, transforming growth factor-β, Receptor tyrosine kinase, Ezrin, Runx2, SATB2, ATF4, Annexins, cofilin, EGFR, VEGF, retinoblastoma 1 (Rb1) and secreted protein, has been associated to the development and progression of osteosarcoma. These proteins are involved in cell adhesion, migration, invasion, and the control of cell cycle and apoptosis. In genomic studies, osteosarcoma has been associated with several genetic abnormalities, including chromosomal rearrangements, gene mutations, and gene amplifications. These differentially expressed proteins could be used as early identification biomarkers or treatment targets. Proteomics and genomics play significant parts in enhancing our molecular understanding of osteosarcoma, and their integration provides essential insights into this aggressive bone cancer. This review will discuss the tumour biology that has assisted in helping us better understand the causes of osteosarcoma and how they could potentially be used to find new treatment targets and enhance the survival rate for osteosarcoma patients.