A targeted glycoproteomics for breast cancer associated glycoproteins

The major objective of the work presented in this thesis was to develop a global targeted glycoproteomics method using lectin and glycan targeting antibodies for the identification of breast cancer associated glycoproteins in human plasma. A secondary focus was to recognize the concentration changes of glycosylation in a disease state. An analytical tool for glycoproteomics was developed for the selection of glycoproteins in normal and human breast cancer patient plasma. In order to identify more glycoproteins, Buttom-Up approaches (BUA) and Top-Down approaches (TDA) were examined. More proteins were identified by the Top-Down approach than Buttom-Up approach. Based on these results, TDA was applied in the targeted glycoproteomics studies. Lectin affinity chromatography using concanavalin A (Con A), Helix pomatia agglutinin (HPA), Lycopersicon esculentum lectin (LEL), Aleuria aurantia lectin (AAL) and Lens culinaris agglutinin (LCA) was used to investigate the utility of narrow selectivity lectins in the characterization of plasma glycoproteome diversity and to recognize cancer associated aberrations in glycosylation. Following affinity chromatographic selection, proteins were tryptically digested, the peptide fragments separated by reversed phase chromatography (RPC), and fractions from RPC identified by tandem mass spectrometry. The diversity of glycosylation found with narrow selectivity lectins was generally 2/3 that of Con A and not related to protein abundance. Small groups of proteins were found with each of the affinity columns, HPA, LEL, AAL, and LCA, that changed 3-fold or more in concentration between normal and breast cancer patient plasma. Although the number of cancer patients examined was too small to validate cancer marker candidates, they are clearly worth examining in a larger, more diverse patient population. Immunoaffinity chromatography (IAC) was also used to isolate and identify potential cancer biomarker glycoproteins by targeting disease-associated glycans. Glycoproteins were selected from plasma of disease-free and breast cancer patients with an anti-sialylated Lewis x (sLex) IAC column. After extensive washing of the IAC column to remove abundant proteins, the selected proteins were eluted with an acidic mobile phase and identified in two ways. The protocol used in route A involved the steps of tryptic digestion, reversed-phase chromatographic fractionation of the digest, and identification of peptides in collected RPC fractions by MALDI-MS/MS. Route B differed in that IAC selected proteins were further fractionated by reversed phase chromatography before proteolysis of individual chromatographic fractions and identification by MALDI MS/MS. Route A was the more efficacious of the two protocols in total number of proteins identified. Ig gamma-1 chain C region, Ig mu chain C region, vitronectin, histidine-rich glycoprotein, inter-alpha-trypsin inhibitor heavy chain H4, and proteoglycan-4 changed three fold or more in association with breast cancer. The potential of these candidates as cancer markers remains to be validated in much larger, more diverse populations of breast cancer patients

Map API-Based Evacuation Route Guidance System for Floods

Recently, human casualties and property damage caused by natural disasters have increased worldwide. Among these natural disasters, flood damage is affected by season. Depending on the concentration of precipitation in the summer, heavy rainfall can occur, thus resulting in typhoons, floods, and increased damage. To prevent such damages, the appropriate measures and research are being conducted in response to disasters. When a flash flood occurs, safe evacuation can be realized after detecting the situation and using announcements or laser indicators. However, these route guidance systems are typically used in fire or indoor environments, thus rendering them difficult to access outdoors. Therefore, we herein propose an evacuation route guidance system based on a map API that recognizes flood occurrences in forest areas, recreational forests, and parks. It calculates the route based on the map API and delivers the evacuation route to the nearest shelter to the user; and if there is a second problem on the moving evacuation route and it is difficult to proceed, the user’s current location is identified and the route to the next nearest shelter is provided. This will help you to evacuate safely

Water Level Prediction Model Applying a Long Short-Term Memory (LSTM)–Gated Recurrent Unit (GRU) Method for Flood Prediction

The damage caused by floods is increasing worldwide, and if floods can be predicted, the economic and human losses from floods can be reduced. A key parameter of flooding is water level data, and this paper proposes a water level prediction model using long short-term memory (LSTM) and a gated recurrent unit (GRU). As variables used as input data, meteorological data, including upstream and downstream water level, temperature, humidity, and precipitation, were used. The best results were obtained when the LSTM–GRU-based model and the Automated Synoptic Observing System (ASOS) meteorological data were included in the input data when experiments were performed with various model structures and different input data formats. As a result of the experiment, the mean squared error (MSE) value was 3.92, the Nash–Sutcliffe coefficient of efficiency (NSE) value was 0.942, and the mean absolute error (MAE) value was 2.22, the highest result in all cases. In addition, the test data included the historical maximum water level of 3552.38 cm in the study area, and the maximum water level error was also recorded as 55.49, the lowest result. Through this paper, it was possible to confirm the performance difference according to the composition of the input data and the time series prediction model. In a future study, we plan to implement a flood risk management system that can use the predicted water level to determine the risk of flooding, and evacuate in advance

Menin Enhances Androgen Receptor-Independent Proliferation and Migration of Prostate Cancer Cells

© The Korean Society for Molecular and Cellular Biology.The androgen receptor (AR) is an important therapeutic target for treating prostate cancer (PCa). Moreover, there is an increasing need for understanding the AR-independent progression of tumor cells such as neuroendocrine prostate cancer (NEPC). Menin, which is encoded by multiple endocrine neoplasia type 1 (MEN1), serves as a direct link between AR and the mixed-lineage leukemia (MLL) complex in PCa development by activating AR target genes through histone H3 lysine 4 methylation. Although menin is a critical component of AR signaling, its tumorigenic role in AR-independent PCa cells remains unknown. Here, we compared the role of menin in AR-positive and AR-negative PCa cells via RNAi-mediated or pharmacological inhibition of menin. We demonstrated that menin was involved in tumor cell growth and metastasis in PCa cells with low or deficient levels of AR. The inhibition of menin significantly diminished the growth of PCa cells and induced apoptosis, regardless of the presence of AR. Additionally, transcriptome analysis showed that the expression of many metastasis-associated genes was perturbed by menin inhibition in AR-negative DU145 cells. Furthermore, wound-healing assay results showed that menin promoted cell migration in AR-independent cellular contexts. Overall, these findings suggest a critical function of menin in tumorigenesis and provide a rationale for drug development against menin toward targeting high-risk metastatic PCa, especially those independent of AR.N

Lectin Chromatography/Mass Spectrometry Discovery Workflow Identifies Putative Biomarkers of Aggressive Breast Cancers

We used a lectin chromatography/MS-based approach to screen conditioned medium from a panel of luminal (less aggressive) and triple negative (more aggressive) breast cancer cell lines (<i>n</i> = 5/subtype). The samples were fractionated using the lectins <i>Aleuria aurantia</i> (AAL) and <i>Sambucus nigra</i> agglutinin (SNA), which recognize fucose and sialic acid, respectively. The bound fractions were enzymatically <i>N</i>-deglycosylated and analyzed by LC–MS/MS. In total, we identified 533 glycoproteins, ∼90% of which were components of the cell surface or extracellular matrix. We observed 1011 glycosites, 100 of which were solely detected in ≥3 triple negative lines. Statistical analyses suggested that a number of these glycosites were triple negative-specific and thus potential biomarkers for this tumor subtype. An analysis of RNaseq data revealed that approximately half of the mRNAs encoding the protein scaffolds that carried potential biomarker glycosites were up-regulated in triple negative vs luminal cell lines, and that a number of genes encoding fucosyl- or sialyltransferases were differentially expressed between the two subtypes, suggesting that alterations in glycosylation may also drive candidate identification. Notably, the glycoproteins from which these putative biomarker candidates were derived are involved in cancer-related processes. Thus, they may represent novel therapeutic targets for this aggressive tumor subtype

Lectin Chromatography/Mass Spectrometry Discovery Workflow Identifies Putative Biomarkers of Aggressive Breast Cancers

We used a lectin chromatography/MS-based approach to screen conditioned medium from a panel of luminal (less aggressive) and triple negative (more aggressive) breast cancer cell lines (<i>n</i> = 5/subtype). The samples were fractionated using the lectins <i>Aleuria aurantia</i> (AAL) and <i>Sambucus nigra</i> agglutinin (SNA), which recognize fucose and sialic acid, respectively. The bound fractions were enzymatically <i>N</i>-deglycosylated and analyzed by LC–MS/MS. In total, we identified 533 glycoproteins, ∼90% of which were components of the cell surface or extracellular matrix. We observed 1011 glycosites, 100 of which were solely detected in ≥3 triple negative lines. Statistical analyses suggested that a number of these glycosites were triple negative-specific and thus potential biomarkers for this tumor subtype. An analysis of RNaseq data revealed that approximately half of the mRNAs encoding the protein scaffolds that carried potential biomarker glycosites were up-regulated in triple negative vs luminal cell lines, and that a number of genes encoding fucosyl- or sialyltransferases were differentially expressed between the two subtypes, suggesting that alterations in glycosylation may also drive candidate identification. Notably, the glycoproteins from which these putative biomarker candidates were derived are involved in cancer-related processes. Thus, they may represent novel therapeutic targets for this aggressive tumor subtype

Lectin Chromatography/Mass Spectrometry Discovery Workflow Identifies Putative Biomarkers of Aggressive Breast Cancers

We used a lectin chromatography/MS-based approach to screen conditioned medium from a panel of luminal (less aggressive) and triple negative (more aggressive) breast cancer cell lines (<i>n</i> = 5/subtype). The samples were fractionated using the lectins <i>Aleuria aurantia</i> (AAL) and <i>Sambucus nigra</i> agglutinin (SNA), which recognize fucose and sialic acid, respectively. The bound fractions were enzymatically <i>N</i>-deglycosylated and analyzed by LC–MS/MS. In total, we identified 533 glycoproteins, ∼90% of which were components of the cell surface or extracellular matrix. We observed 1011 glycosites, 100 of which were solely detected in ≥3 triple negative lines. Statistical analyses suggested that a number of these glycosites were triple negative-specific and thus potential biomarkers for this tumor subtype. An analysis of RNaseq data revealed that approximately half of the mRNAs encoding the protein scaffolds that carried potential biomarker glycosites were up-regulated in triple negative vs luminal cell lines, and that a number of genes encoding fucosyl- or sialyltransferases were differentially expressed between the two subtypes, suggesting that alterations in glycosylation may also drive candidate identification. Notably, the glycoproteins from which these putative biomarker candidates were derived are involved in cancer-related processes. Thus, they may represent novel therapeutic targets for this aggressive tumor subtype