Identification of Dysregulated Complement Activation Pathways Driven by N-Glycosylation Alterations in T2D Patients

Investigation of the synergistic effect mechanism underlying sequential use of palbociclib and cisplatin through integral proteomic and glycoproteomic analysis

Chemoresistance largely hampers the clinical use of chemodrugs for cancer patients, combination or sequential drug treatment regimens have been designed to minimize chemotoxicity and resensitize chemoresistance. In this work, the cytotoxic effect of cisplatin was found to be enhanced by palbociclib pretreatment in HeLa cells. With the integration of liquid chromatography-mass spectrometry-based proteomic and N-glycoproteomic workflow, we found that palbociclib alone mainly enhanced the N-glycosylation alterations in HeLa cells, while cisplatin majorly increased the different expression proteins related to apoptosis pathways. As a result, the sequential use of two drugs induced a higher expression level of apoptosis proteins BAX and BAK. Those altered N-glycoproteins induced by palbociclib were implicated in pathways that were closely associated with cell membrane modification and drug sensitivity. Specifically, the top four frequently glycosylated proteins FOLR1, L1CAM, CD63, and LAMP1 were all associated with drug resistance or drug sensitivity. It is suspected that palbociclib-induced N-glycosylation on the membrane protein allowed the HeLa cell to become more vulnerable to cisplatin treatment. Our study provides new insights into the mechanisms underlying the sequential use of target drugs and chemotherapy drugs, meanwhile suggesting a high-efficiency approach that involves proteomic and N-glycoproteomic to facilitate drug discovery.

Integrated plasma proteomics and N-glycoproteomics reveals alterations in the N-glycosylation in Chinese hepatocellular carcinoma patients

Hepatocellular carcinoma (HCC) is a life-threatening disease that presents diagnostic challenges due to the absence of reliable biomarkers. Recently, plasma proteomics and glycoproteomics have emerged as powerful tools for identifying potential diagnostic biomarkers for various diseases. In this study, we conducted a comprehensive proteomic and glycoproteomic analysis of plasma samples from 11 HCC patients and 11 healthy control (HC) individuals. We identified 20 differentially expressed (DE) proteins and 32 DE intact glycosylated peptides (IGPs) that can effectively differentiate between HCC patients and HC samples. Our findings demonstrate that IGP profiles had better predictive power than protein profiles for screening HCC. Pathways associated with DE proteins and IGPs were identified. It was reported that the protein expression level of galectin 3 binding protein (LGALS3BP) and its N-linked glycosylation at the N398 and N551 sites might serve as valuable candidates for HCC diagnosis. These results highlight the importance of N-glycoproteomics in advancing our understanding of HCC and suggest possible candidates for the future diagnosis of this disease.

SEAOP: a statistical ensemble approach for outlier detection in quantitative proteomics data

Quality control in quantitative proteomics is a persistent challenge, particularly in identifying and managing outliers. Unsupervised learning models, which rely on data structure rather than predefined labels, offer potential solutions. However, without clear labels, their effectiveness might be compromised. Single models are susceptible to the randomness of parameters and initialization, which can result in a high rate of false positives. Ensemble models, on the other hand, have shown capabilities in effectively mitigating the impacts of such randomness and assisting in accurately detecting true outliers. Therefore, we introduced SEAOP, a Python toolbox that utilizes an ensemble mechanism by integrating multi-round data management and a statistics-based decision pipeline with multiple models. Specifically, SEAOP uses multi-round resampling to create diverse sub-data spaces and employs outlier detection methods to identify candidate outliers in each space. Candidates are then aggregated as confirmed outliers via a chi-square test, adhering to a 95% confidence level, to ensure the precision of the unsupervised approaches. Additionally, SEAOP introduces a visualization strategy, specifically designed to intuitively and effectively display the distribution of both outlier and non-outlier samples. Optimal hyperparameter models of SEAOP for outlier detection were identified by using a gradient-simulated standard dataset and Mann-Kendall trend test. The performance of the SEAOP toolbox was evaluated using three experimental datasets, confirming its reliability and accuracy in handling quantitative proteomics.

In silico identification and functional prediction of differentially expressed genes in South Asian populations associated with type 2 diabetes

Type 2 diabetes (T2D) is one of the major metabolic disorders in humans caused by hyperglycemia and insulin resistance syndrome. Although significant genetic effects on T2D pathogenesis are experimentally proved, the molecular mechanism of T2D in South Asian Populations (SAPs) is still limited. Hence, the current research analyzed two Gene Expression Omnibus (GEO) and 17 Genome-Wide Association Studies (GWAS) datasets associated with T2D in SAP to identify DEGs (differentially expressed genes). The identified DEGs were further analyzed to explore the molecular mechanism of T2D pathogenesis following a series of bioinformatics approaches. Following PPI (Protein-Protein Interaction), 867 potential DEGs and nine hub genes were identified that might play significant roles in T2D pathogenesis. Interestingly, CTNNB1 and RUNX2 hub genes were found to be unique for T2D pathogenesis in SAPs. Then, the GO (Gene Ontology) showed the potential biological, molecular, and cellular functions of the DEGs. The target genes also interacted with different pathways of T2D pathogenesis. In fact, 118 genes (including HNF1A and TCF7L2 hub genes) were directly associated with T2D pathogenesis. Indeed, eight key miRNAs among 2582 significantly interacted with the target genes. Even 64 genes were downregulated by 367 FDA-approved drugs. Interestingly, 11 genes showed a wide range (9-43) of drug specificity. Hence, the identified DEGs may guide to elucidate the molecular mechanism of T2D pathogenesis in SAPs. Therefore, integrating the research findings of the potential roles of DEGs and candidate drug-mediated downregulation of marker genes, future drugs or treatments could be developed to treat T2D in SAPs.

Endothelium dysfunction and thrombosis in COVID-19 with type 2 diabetes

SARS-CoV-2 can directly or indirectly damage endothelial cells. Endothelial injury, especially phosphatidylserine (PS) exposure on the outer membrane of cells, can more easily promote thrombosis. Type 2 diabetes(T2D) patients were more susceptible to COVID-19, they had more severe symptoms, higher risk of thrombotic complications, and longer duration of post-COVID-19 sequelae. This review provided a detailed overview of the mechanisms underlying endothelial dysfunction in T2D patients with COVID-19 (including long COVID), which may be influenced by hyperglycemia, hypoxia, and pro-inflammatory environments. The mechanisms of thrombosis in T2D patients with COVID-19 are also explored, particularly the effects of increased numbers of PS-exposing particles, blood cells, and endothelial cells on hypercoagulability. Given the high risk of thrombosis in T2D patients with COVID-19, early antithrombotic therapy can both minimize the impact of the disease on patients and maximize the chances of improvement, thereby alleviating patient suffering. We provided detailed guidance on antithrombotic drugs and dosages for mild, moderate, and severe patients, emphasizing that the optimal timing of thromboprophylaxis is a critical factor in influencing prognosis. Considering the potential interactions between antidiabetic, anticoagulant, and antiviral drugs, we proposed practical and comprehensive management recommendations to supplement the incomplete efficacy of vaccines in the diabetic population, reduce the incidence of post-COVID-19 sequelae, and improve patient quality of life.

Investigation on acquired palbociclib resistance by LC-MS based multi-omics analysis

Palbociclib is a specific CDK4/6 inhibitor that has been widely applied in multiple types of tumors. Different from cytotoxic drugs, the anticancer mechanism of palbociclib mainly depends on cell cycle inhibition. Therefore, the resistance mechanism is different. For clinical cancer patients, drug resistance is inevitable for almost all cancer therapies including palbociclib. We have trained palbociclib resistant cells in vitro to simulate the clinical situation and applied LC-MS multi-omics analysis methods including proteomic, metabolomic, and glycoproteomic techniques, to deeply understand the underly mechanism behind the resistance. As a result of proteomic analysis, the resistant cells were found to rely on altered metabolic pathways to keep proliferation. Metabolic processes related to carbohydrates, lipids, DNA, cellular proteins, glucose, and amino acids were observed to be upregulated. Most dramatically, the protein expressions of COX-1 and NDUFB8 have been detected to be significantly overexpressed by proteomic analysis. When a COX-1 inhibitor was hired to combine with palbociclib, a synergistic effect could be obtained, suggesting the altered COX-1 involved metabolic pathway is an important reason for the acquired palbociclib resistance. The KEGG pathway of N-glycan biosynthesis was identified through metabolomics analysis. N-glycoproteomic analysis was therefore included and the global glycosylation was found to be elevated in the palbociclib-resistant cells. Moreover, integration analysis of glycoproteomic data allowed us to detect a lot more proteins that have been glycosylated with low abundances, these proteins were considered to be overwhelmed by those highly abundant proteins during regular proteomic LC-MS detection. These low-abundant proteins are mainly involved in the cellular biology processes of cell migration, the regulation of chemotaxis, as well as the glycoprotein metabolic process which offered us great more details on the roles played by N-glycosylation in drug resistance. Our result also verified that N-glycosylation inhibitors could enhance the cell growth inhibition of palbociclib in resistant cells. The high efficiency of the integrated multi-omics analysis workflow in discovering drug resistance mechanisms paves a new way for drug development. With a clear understanding of the resistance mechanism, new drug targets and drug combinations could be designed to resensitize the resistant tumors.

Organ-specific metabolic pathways distinguish prediabetes, type 2 diabetes, and normal tissues

Environmental and genetic factors cause defects in pancreatic islets driving type 2 diabetes (T2D) together with the progression of multi-tissue insulin resistance. Mass spectrometry proteomics on samples from five key metabolic tissues of a cross-sectional cohort of 43 multi-organ donors provides deep coverage of their proteomes. Enrichment analysis of Gene Ontology terms provides a tissue-specific map of altered biological processes across healthy, prediabetes (PD), and T2D subjects. We find widespread alterations in several relevant biological pathways, including increase in hemostasis in pancreatic islets of PD, increase in the complement cascade in liver and pancreatic islets of PD, and elevation in cholesterol biosynthesis in liver of T2D. Our findings point to inflammatory, immune, and vascular alterations in pancreatic islets in PD that are hypotheses to be tested for potential contributions to hormonal perturbations such as impaired insulin and increased glucagon production. This multi-tissue proteomic map suggests tissue-specific metabolic dysregulations in T2D.

Functional Identification of Complement Factor D and Analysis of Its Expression during GCRV Infection in Grass Carp (Ctenopharyngodon idella)

Complement factor D (Df) is a serine protease well known for activating the alternative pathway (AP) in mammals by promoting the cleavage of complement component 3 (C3), thus becoming involved in innate defense. In teleost fish, however, the functional mechanisms of Df in the AP and against pathogen infection are far from clear. In the present study, we cloned and characterized the Df gene, CiDf, from grass carp (Ctenopharyngodon idella) and analyzed its function in promoting C3 cleavage and expression changes after grass carp reovirus (GCRV) infection. The open reading frame of CiDf was found to be 753 bp, encoding 250 amino acids with a molecular mass of 27.06 kDa. CiDf harbors a conserved Tryp_SPc domain, with three conserved residues representing the catalytic triad and three conserved binding sites in the substrate specificity pocket. Pairwise alignment showed that CiDf shares the highest identity (96%) and similarity (98%) with Df from Anabarilius grahami. Phylogenetic analysis indicated that CiDf and other fish Dfs formed a distinct evolutionary branch. Similar to most Dfs from other vertebrates, the CiDf gene structure is characterized by four introns and five exons. The incubation of recombinant CiDf protein with grass carp serum significantly increased the C3b content, demonstrating the conserved function of CiDf in the AP in promoting C3 cleavage, similar to Dfs in mammals. CiDf mRNA expression was widely detected in various tissues and levels were relatively higher in the liver, spleen, and intestine of grass carp. During GCRV infection over a 168-hour period, a high level of CiDf mRNA expression in the liver, spleen, and intestine was maintained at 144 and 168 h, suggesting AP activity at the late stage of GCRV infection. Collectively, the above results reveal the conserved structure and function of CiDf and its distinct expression patterns after GCRV infection, which provide a key basis for studying the roles of Df and AP during GCRV infection in the grass carp C. idella.