Comprehensive Analysis Of Proteins Research Articles

Label-Free Multiplex Profiling of Exosomal Proteins with a Deep Learning-Driven 3D Surround-Enhancing SERS Platform for Early Cancer Diagnosis.

Identification of protein profiling on plasma exosomes by SERS can be a promising strategy for early cancer diagnosis. However, it is still challenging to detect multiple exosomal proteins simultaneously by SERS since the Raman signals of exosomes detected by conventional colloidal nanocrystals or two-dimensional SERS substrates are incomplete and complex. Herein, we develop a novel three-dimensional (3D) surround-enhancing SERS platform, named 3D se-SERS, for the multiplex detection of exosomal proteins. In this 3D se-SERS, proteins and exosomes are covered with "hotspots" generated by the gold nanoparticles, which surround the analytes densely and three-dimensionally, providing sensitive and comprehensive SERS signals. Combining this 3D se-SERS with a deep learning model, we successfully quantitatively profiled seven proteins including CD63, CD81, CD9, CD151, CD171, TSPAN8, and PD-L1 on the surface of plasma exosomes from patients, which can predict the occurrence and advancement of lung cancer. This 3D se-SERS integrating deep learning technique benefits from high sensitivity and significant multiplexing ability for comprehensive analysis of proteins and exosomes, demonstrating the potential of deep learning-driven 3D se-SERS technology for plasma exosome-based early cancer diagnosis.

Proteomic and phosphoproteomic reveal immune-related function of milk fat globule membrane in bovine milk of different lactation periods

Information regarding protein expression and phosphorylation modifications in the bovine milk fat globule membrane is scarce, particularly throughout various lactation periods. This study employed a complete proteome and phosphoproteome between bovine colostrum and mature milk. A total of 11 proteins were seen in both protein expression and phosphorylation levels. There were 400 proteins identified in only protein expression, and 104 phosphoproteins identified in only phosphorylation levels. A total of 232 significant protein characteristics were identified within the proteome and significant phosphorylation sites within 86 phosphoproteins of the phosphoproteome. Biological activities and pathways primarily exhibited associations with the immune system. Simultaneously, a comprehensive analysis of proteins and phosphorylation sites using a multi-omics approach. Hence, the data we have obtained has the potential to expand our understanding of how the bovine milk fat globule membrane might be utilized as a beneficial component in dairy products.

Abstract 3848: Comprehensive protein analysis to identify the proteins responsible for sarcopenia and their impact on antitumor immunotherapy in non-small cell lung cancer

Abstract Background: Previous retrospective studies have shown that sarcopenia (Sar) is associated with poor outcome in cancer patients (pts) receiving immune checkpoint inhibitors (ICIs). We conducted a prospective study to determine whether Sar-related proteins induce Sar and reduce the efficacy of ICI. Method: Non-small cell lung cancer (NSCLC) pts receiving ICI as first-line treatment at Niigata University Medical and Dental Hospital were eligible. Sar was diagnosed with physical performance and skeletal muscle mass. Comprehensive protein analysis of pre-treatment blood samples was performed to identify factors that correlate with muscle loss and ICI effects. Result: 41 patients were enrolled from October 2019 through August 2022. PFS for Sar was 4.6 months, 6.7 months for Pre-Sar, and 16.7 months for Non-Sar (P=0.0028). OS for Sar was not reached, 15.7 months for Pre-Sar, and not reached for Non-Sar (P=0.0014). Leptin, IL-1β, IL-12, and Pentraxin 3 were significantly correlated with loss of muscle mass. In contrast, leptin, insulin, IGF-1, GDF-8, Activin A, IL-2Ra, IL-8, MMP-3, osteopontin, and Pentraxin-3 were significantly associated with poor OS. Pentraxin 3 and leptin were associated with both the effectiveness of immunotherapy and sarcopenia. A multivariate analysis that considered factors commonly associated with the effectiveness of immunotherapy, including age, PD-L1 expression rate, and neutrophil-lymphocyte ratio, Pentraxin 3 was determined to be an independent prognostic factor. Conclusion: This study indicated that Sar-related proteins and inflammatory cytokines are involved in the development of Sar and ICI efficacy. Citation Format: Takaaki Masuda, Satoshi Watanabe, Tomoya Wakabayashi, Susumu Tanaka, Kunihiro Shono, Ryo Suzuki, Tomoki Sekiya, Naohiro Yanagimura, Masashi Arita, Aya Ohtsubo, Tomohiro Tanaka, Koichiro Nozaki, Yu Saida, Satoshi Hokari, Rie Kondo, Kenjiro Shima, Yosuke Kimura, Nobumasa Aoki, Yasuyoshi Ohshima, Toshiyuki Koya, Kazuko Sakai, Kazuto Nishio, Motohiko Yamazaki, Hiroyuki Ishikawa, Toshiaki Kikuchi. Comprehensive protein analysis to identify the proteins responsible for sarcopenia and their impact on antitumor immunotherapy in non-small cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3848.

Extracellular Vesicles From Mycoplasma gallisepticum: Modulators of Macrophage Activation and Virulence.

Extracellular vesicles (EVs) mediate intercellular communication by transporting proteins. To investigate the pathogenesis of Mycoplasma gallisepticum, a major threat to the poultry industry, we isolated and characterized M. gallisepticum-produced EVs. Our study highlights the significant impact of M. gallisepticum-derived EVs on immune function and macrophage apoptosis, setting them apart from other M. gallisepticum metabolites. These EVs dose-dependently enhance M. gallisepticum adhesion and proliferation, simultaneously modulating Toll-like receptor 2 and interferon γ pathways and thereby inhibiting macrophage activation. A comprehensive protein analysis revealed 117 proteins in M. gallisepticum-derived EVs, including established virulence factors, such as GapA, CrmA, VlhA, and CrmB. Crucially, these EV-associated proteins significantly contribute to M. gallisepticum infection. Our findings advance our comprehension of M. gallisepticum pathogenesis, offering insights for preventive strategies and emphasizing the pivotal role of M. gallisepticum-derived EVs and their associated proteins. This research sheds light on the composition and crucial role of M. gallisepticum-derived EVs in M. gallisepticum pathogenesis, aiding our fight against M. gallisepticum infections.

Identification of SAA1 as a novel metastasis marker in ovarian cancer and development of a graphene-based detection platform for early assessment.

Ovarian cancer (OC) is a prevalent gynecological malignancy with the highest mortality rate, which generally diagnosed at late stages due to the lack of effective early screening methods and the nonspecific symptoms. Hence, here we aim to identify new metastasis markers and develop a novel detection method with the characteristics of high sensitivity, rapid detection, high specificity, and low cost when compared with other conventional detection technologies. Blood from OC patients with or without metastasis were collected and analyzed by 4D Label free LC - MS/MS. Surgically resect samples from OC patients were collected for Single cell RNA sequencing (sc-RNA seq). Short hairpin RNA (shRNA) was used to silence SAA1 expression in SKOV3 and ID8 to verify the relationship between endogenous SAA1 and tumor invasion or metastasis. The functional graphene chips prepared by covalent binding were used for SAA1 detection. In our study, we identified Serum Amyloid A1 (SAA1) as a hematological marker of OC metastasis by comprehensive analysis of proteins in plasma from OC patients with or without metastasis using 4D Label free LC - MS/MS and gene expression patterns from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Further validation using tumor tissues and plasma from human OC and mouse OC model confirmed the correlation between SAA1 and tumor metastasis. Importantly, sc-RNA seq of human OC samples revealed that SAA1 was specifically expressed in tumor cells and upregulated in the metastasis group. The functional role of SAA1 in metastasis was demonstrated through experiments in vitro and in vivo. Based on these findings, we designed and investigated a graphene-based platform for SAA1 detection to predict the risk of metastasis of OC patients. Our study suggests that SAA1 is a biomarker of OC metastasis, and we have developed a rapid and highly sensitive platform using graphene chips to detection of plasma SAA1 for the early assessment of metastasis in OC patients.

Identification of proteins that contribute to flour strength among elite breeding lines in a breeding program

Gluten, which is a protein aggregate specific to wheat, is crucial in determining the physical properties of wheat flour dough. The gluten is primarily composed of glutenin and gliadin, which are multigenic and comprise diverse proteoforms. Consequently, it is difficult to analyze the quality of gluten by genetic analysis alone. Here, comprehensive protein analysis of wheat cultivars/strains was performed using two-dimensional fluorescence difference gel electrophoresis (2D-DIGE) that allows proteoforms to be resolved by spot position. AlveoConsistograph deformation energy (Fb value) was used as a metric for the strength of the dough. Major proteins resolved by 2D-DIGE were identified by LC/MS/MS. We verified that the Glu-B1al allele significantly influences the physical properties of the dough. Next, excluding the influence of the Glu-B1al allele, we identified proteins that contribute to the physical properties of the dough.Two γ-gliadins and five low molecular weight glutenins were identified. A positive correlation was observed between the spot volume ratio of γ-gliadin and its corresponding Fb value. Thus, γ-gliadin exists as multiple proteoforms, one of which may contribute to the physical properties of dough. Combining the proteins identified in this study will help breeders develop high quality flour.

Negative enrichment strategy combined with site-specific derivatization for the C-terminomics

Protein C-termini containing valuable biological information plays a vital role in various physiological processes, such as protein localization, protein recognition, and signal transduction in organisms. However, C-terminal peptide identification is still challenging due to their low abundance and similar physicochemical properties to other digested peptides. Herein, we developed a simple and mild strategy for the enrichment of C-terminal peptides that incorporates selectively 2-pyridinecarbaldehyde (2-PCA) derivatization of α-amine with negative enrichment by NHS resin. Two synthesized peptides were utilized to evaluate the efficiency of 2-PCA derivatization and optimize the coupling conditions of NHS resin. The feasibility of the method was further validated by enriching the C-terminus of the bovine serum albumin (BSA). Finally, this method was successfully applied to the C-terminus analysis of mouse brain tissue, identifying 404 protein C-termini with physicochemical properties unbiasedly. Additionally, the GO and KEGG analyses revealed that these identified proteins are crucial for proper brain function. In summary, our proposed method is effective and has the potential to facilitate comprehensive C-terminal analysis of proteins. SignificanceEffective enrichment methods are essential for the identification of the proteins C-terminus. In this study, a mild and simple method for negative C-terminal enrichment combined with site-specific derivatization was developed. The enrichment process was simplified and minimized sample loss simultaneously, using 2-PCA derivatization which has high α-amino specificity. Up to 346C-terminal proteins were identified in mouse brain tissue unbiasedly and reliably. This approach has the potential to facilitate comprehensive analysis of protein C-termini in a variety of biological contexts.

Structural and functional relationships of plant allergenic proteins: gastrointestinal metabolism and structural heterogeneity

The prevalence of food allergies has increased in recent decades and affects up to 5% of the population, frequently resulting in severe symptoms. Specific structures of the corresponding proteins, the epitopes, are recognized by T‐cell receptors or IgE antibodies, resulting in an immune response. Many food allergens and immunogenic proteins for various plant‐based foods have been investigated thoroughly on different levels. However, research into the gastrointestinal metabolism of plant‐based food allergens on the molecular level is scarce, so far. The aim of this thesis was to characterize the gastrointestinal metabolism of food allergens and proteins involved in celiac disease, and to derive global properties that account for the immunogenic and allergenic potential of these proteins. Raw walnut, raw and cooked soybeans, and wheat‐based bread have been chosen as representatives for tree nuts, legumes and cereals, in order to adress the given research question. The project is divided into three sub‐steps, which are as follows: Adaption of the in vitro digestion model to different matrices and comprehensive analysis of proteins and peptides Development of a data‐processing strategy to enable visualisation of the resulting data Interpretation of the data regarding the gastrointestinal metabolism of relevant proteins in different matrices The in vitro digestion model by Minekus et al. was successfully adapted to simulate gastrointestinal digestion of a variety of food matrices. The analytical approach at protein level by SDS‐PAGE analysis and in‐gel digestion, combined with mass spectrometry at peptide level, enabled comprehensive identification of degradation products. As an example, up to 7279 peptides were assigned to soy proteins over the course of gastral digestion of raw soy, and a further 1027 peptides for samples of simulated intestinal digestion. In order to deal with these huge amounts of data, various data processing steps have been designed. Peptide identification by PEAKS® was extended using scripts in Python, to perform label‐free quantification of mass spectrometry data and to compare the identified degradation products with known epitopes. The multistage preprocessing approach developed in Python provided the basis for a set of data visualisations to further interpret the behaviour of proteins during gastrointestinal digestion. Analysis at protein and peptide levels revealed that quantities of most of the analysed proteins for all matrices exhibited stability against gastrointestinal digestion. Analysis of the resulting sequence coverage over the course of gastrointestinal digestion, combined with label‐free quantification in 3D surface plots, allowed conclusions on the behaviour of distinct protein regions over time. This approach provided detailed insights into the multistage formation and degradation mechanisms of differently sized peptides. ‐gliadin‐assigned peptides covering epitopes related to food allergy, as well as eight peptides covering celiac disease related epitopes, was characterized by label‐free quantification, outlining enhanced resistance against gastrointestinal digestion. Bioinformatic tools were applied to provide further characteristics of all proteins of interest related to the amino acid sequence. Sequence alignment with secondary structure features. The given results of this thesis provide insight into the stability of the proteins involved in adverse reactions to food, such as allergy or celiac disease, throughout the process of gastrointestinal digestion.

Quantitative Proteomic and Phosphoproteomic Profiling of Lung Tissues from Pulmonary Arterial Hypertension Rat Model.

Pulmonary arterial hypertension (PAH) is a rare but fatal disease characterized by elevated pulmonary vascular resistance and increased pressure in the distal pulmonary arteries. Systematic analysis of the proteins and pathways involved in the progression of PAH is crucial for understanding the underlying molecular mechanism. In this study, we performed tandem mass tags (TMT)-based relative quantitative proteomic profiling of lung tissues from rats treated with monocrotaline (MCT) for 1, 2, 3 and 4 weeks. A total of 6759 proteins were quantified, among which 2660 proteins exhibited significant changes (p-value < 0.05, fold change < 0.83 or >1.2). Notably, these changes included several known PAH-related proteins, such as Retnla (resistin-like alpha) and arginase-1. Furthermore, the expression of potential PAH-related proteins, including Aurora kinase B and Cyclin-A2, was verified via Western blot analysis. In addition, we performed quantitative phosphoproteomic analysis on the lungs from MCT-induced PAH rats and identified 1412 upregulated phosphopeptides and 390 downregulated phosphopeptides. Pathway enrichment analysis revealed significant involvement of pathways such as complement and coagulation cascades and the signaling pathway of vascular smooth muscle contraction. Overall, this comprehensive analysis of proteins and phosphoproteins involved in the development and progression of PAH in lung tissues provides valuable insights for the development of potential diagnostic and treatment targets for PAH.

Preliminary Exploration of Metabolic Mechanisms in Copper-Exposed Sepia esculenta Based on Transcriptome Analysis.

As a common and high-concentration heavy metal in the ocean, Cu can induce metal toxicity and significantly affect the metabolic function of marine organisms. Sepia esculenta is an important economic cephalopod found along the east coast of China, the growth, movement, and reproduction of which are all affected by heavy metals. Hitherto, the specific metabolic mechanism of heavy-metal exposure in S. esculenta is still unclear. In this study, we identified 1131 DEGs through transcriptome analysis of larval S. esculenta within 24 h of Cu exposure. GO and KEGG functional enrichment analysis results indicated that Cu exposure may affect purine metabolism, protein digestion and absorption, cholesterol metabolism, and other metabolic processes in S. esculenta larvae. It is worth noting that in this study we explore metabolic mechanism of Cu-exposed S. esculenta larvae through the comprehensive analysis of protein-protein interaction network and KEGG enrichment analysis for the first time and find 20 identified key and hub genes such as CYP7A1, CYP3A11, and ABCA1. Based on their expression, we preliminarily speculate that Cu exposure may inhibit multiple metabolic processes and induce metabolic disorders. Our results lay a foundation for further understanding the metabolic mechanism of S. esculenta against heavy metals and provide theoretical help for S. esculenta artificial breeding.

Femtoliter-Droplet Mass Spectrometry Interface Utilizing Nanofluidics for Ultrasmall and High-Sensitivity Analysis.

In the fields of biology and medicine, comprehensive protein analysis at the single-cell level utilizing mass spectrometry (MS) with pL sample volumes and zmol to amol sensitivity is required. Our group has developed nanofluidic analytical pretreatment methods that exploit nanochannels for downsizing chemical unit operations to fL-pL volumes. In the field of analytical instruments, mass spectrometers have advanced to achieve ultrahigh sensitivity. However, a method to interface between fL-pL pretreatments and mass spectrometers without sample loss and dispersion is still challenging. In this study, we developed an MS interface utilizing nanofluidics to achieve high-sensitivity detection. After charging analyte molecules by an applied voltage through an electrode, the liquid sample was converted to fL droplets by a nanofluidic device. Considering the inertial force that acts on the droplets, the droplets were carried with a controlled trajectory, even in turbulent air flow, and injected into a mass spectrometer with 100% efficiency. A module for heat transfer was designed and constructed, by which all of the injected droplets were vaporized to produce gas-phase ions. The detection of caffeine ions was achieved at a limit of detection of 1.52 amol, which was 290 times higher than a conventional MS interface by electrospray ionization with sample dispersion combined with a similar mass spectrometer. Therefore, sensitivity that was 2 orders of magnitude higher could be realized due to the 100% sample injection rate. The present study provides a new methodology for the analysis of ultrasmall samples with high-sensitivity, such as protein molecules produced from a single cell.

Using PPI-GA for identifying protein complexes in protein-protein interaction networks

The major focus of an interdisciplinary field of study termed proteomics is a comprehensive analysis of proteins to evaluate their genetic variety, examine their distinctions, and respond to stresses. Proteomics' major goal is to use protein chemistry, bioinformatics, and biology to detect and measure proteins as well as analyze their post-translational modifications and interactions. Any disruption in the interacting network of proteins can lead to biological abnormalities and diseases like Alzheimer's and cancer. The majority of existing computational approaches for detecting protein complexes are based on certain topological properties of protein-protein networks (PPI). In this paper, it initially provides a novel encoding technique for representing the clarification and solution, and next we recommend the concept of PPI-GA, an innovative clustering based algorithm which is based on genetic approach algorithm which employs a innovative multi based objective excellence function to find out the protein complexes. A different two gold based standard and the real-world based datasets are been used to evaluate proposed algorithm. The obtained result shows that the suggested method can discover key protein complexes and that it offers more accurate results than existing protein complex identification techniques.

Immune Response Is Key to Genetic Mechanisms of SARS-CoV-2 Infection With Psychiatric Disorders Based on Differential Gene Expression Pattern Analysis.

Existing evidence demonstrates that coronavirus disease 2019 (COVID-19) leads to psychiatric illness, despite its main clinical manifestations affecting the respiratory system. People with mental disorders are more susceptible to COVID-19 than individuals without coexisting mental health disorders, with significantly higher rates of severe illness and mortality in this population. The incidence of new psychiatric diagnoses after infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is also remarkably high. SARS-CoV-2 has been reported to use angiotensin-converting enzyme-2 (ACE2) as a receptor for infecting susceptible cells and is expressed in various tissues, including brain tissue. Thus, there is an urgent need to investigate the mechanism linking psychiatric disorders to COVID-19. Using a data set of peripheral blood cells from patients with COVID-19, we compared this to data sets of whole blood collected from patients with psychiatric disorders and used bioinformatics and systems biology approaches to identify genetic links. We found a large number of overlapping immune-related genes between patients infected with SARS-CoV-2 and differentially expressed genes of bipolar disorder (BD), schizophrenia (SZ), and late-onset major depressive disorder (LOD). Many pathways closely related to inflammatory responses, such as MAPK, PPAR, and TGF-β signaling pathways, were observed by enrichment analysis of common differentially expressed genes (DEGs). We also performed a comprehensive analysis of protein–protein interaction network and gene regulation networks. Chemical–protein interaction networks and drug prediction were used to screen potential pharmacologic therapies. We hope that by elucidating the relationship between the pathogenetic processes and genetic mechanisms of infection with SARS-CoV-2 with psychiatric disorders, it will lead to innovative strategies for future research and treatment of psychiatric disorders linked to COVID-19.

Expression atlas of avian neural crest proteins: Neurulation to migration

Neural crest (NC) cells are a dynamic population of embryonic stem cells that create various adult tissues in vertebrate species including craniofacial bone and cartilage and the peripheral and enteric nervous systems. NC development is thought to be a conserved and complex process that is controlled by a tightly-regulated gene regulatory network (GRN) of morphogens, transcription factors, and cell adhesion proteins. While multiple studies have characterized the expression of several GRN factors in single species, a comprehensive protein analysis that directly compares expression across development is lacking. To address this lack in information, we used three closely related avian models, Gallus gallus (chicken), Coturnix japonica (Japanese quail), and Pavo cristatus (Indian peafowl), to compare the localization and timing of four GRN transcription factors, PAX7, SNAI2, SOX9, and SOX10, from the onset of neurulation to migration. While the spatial expression of these factors is largely conserved, we find that quail NC cells express SNAI2, SOX9, and SOX10 proteins at the equivalent of earlier developmental stages than chick and peafowl. In addition, quail NC cells migrate farther and more rapidly than the larger organisms. These data suggest that despite a conservation of NC GRN players, differences in the timing of NC development between species remain a significant frontier to be explored with functional studies.

Recent advances in glycopeptide enrichment and mass spectrometry data interpretation approaches for glycoproteomics analyses

蛋白质糖基化是生物体内最重要的翻译后修饰之一,在蛋白质稳定性、细胞内和细胞间信号转导、激素活化或失活和免疫调节等生理过程和病理进程中发挥重要作用。而异常的蛋白质糖基化往往和多种疾病的发生发展密切相关,目前应用于临床检测的多种肿瘤生物标志物大多属于糖蛋白或者糖抗原。因此在组学层次系统分析蛋白质糖基化的变化对阐明生物体内糖基化修饰的调控机理和发现新型疾病标志物都非常重要。基于质谱的蛋白质组学技术为全面分析蛋白质及其修饰提供了有效的分析手段。在自下而上的蛋白质组学研究中,由于完整糖基化肽段同时存在性质各异的肽段骨架和糖链结构、糖肽的相对丰度和离子化效率较低以及糖基化修饰有高度异质性等特点,完整糖肽的分析比其他翻译后修饰更加困难。近年来,为了更全面、系统地分析蛋白质糖基化,研究人员发展了一些新技术,包括完整糖肽的富集技术、质谱的碎裂模式和数据采集模式、质谱数据的解析方法和定量策略等等,大力推进了该领域的研究水平,也为研究蛋白质糖基化相关的生物标志物提供了技术支持。该篇综述主要关注近年来基于质谱的糖蛋白质组学研究中的新进展,重点介绍针对完整N-和O-糖基化肽段的富集新技术和谱图解析新方法,并讨论其在肿瘤早期诊断方面的应用潜力。

Characterization of Five Transmembrane Proteins: With Focus on the Tweety, Sideroflexin, and YIP1 Domain Families.

Transmembrane proteins are involved in many essential cell processes such as signal transduction, transport, and protein trafficking, and hence many are implicated in different disease pathways. Further, as the structure and function of proteins are correlated, investigating a group of proteins with the same tertiary structure, i.e., the same number of transmembrane regions, may give understanding about their functional roles and potential as therapeutic targets. This analysis investigates the previously unstudied group of proteins with five transmembrane-spanning regions (5TM). More than half of the 58 proteins identified with the 5TM architecture belong to 12 families with two or more members. Interestingly, more than half the proteins in the dataset function in localization activities through movement or tethering of cell components and more than one-third are involved in transport activities, particularly in the mitochondria. Surprisingly, no receptor activity was identified within this dataset in large contrast with other TM groups. The three major 5TM families, which comprise nearly 30% of the dataset, include the tweety family, the sideroflexin family and the Yip1 domain (YIPF) family. We also analyzed the evolutionary origin of these three families. The YIPF family appears to be the most ancient with presence in bacteria and archaea, while the tweety and sideroflexin families are first found in eukaryotes. We found no evidence of common decent for these three families. About 30% of the 5TM proteins have prominent expression in the brain, liver, or testis. Importantly, 60% of these proteins are identified as cancer prognostic markers, where they are associated with clinical outcomes of various tumor types. Nearly 10% of the 5TMs are still not fully characterized and further investigation of their functional activities and expression is warranted. This study provides the first comprehensive analysis of proteins with the 5TM architecture, providing details of their unique characteristics.

A Bayesian semi-parametric model for thermal proteome profiling

The thermal stability of proteins can be altered when they interact with small molecules, other biomolecules or are subject to post-translation modifications. Thus monitoring the thermal stability of proteins under various cellular perturbations can provide insights into protein function, as well as potentially determine drug targets and off-targets. Thermal proteome profiling is a highly multiplexed mass-spectrommetry method for monitoring the melting behaviour of thousands of proteins in a single experiment. In essence, thermal proteome profiling assumes that proteins denature upon heating and hence become insoluble. Thus, by tracking the relative solubility of proteins at sequentially increasing temperatures, one can report on the thermal stability of a protein. Standard thermodynamics predicts a sigmoidal relationship between temperature and relative solubility and this is the basis of current robust statistical procedures. However, current methods do not model deviations from this behaviour and they do not quantify uncertainty in the melting profiles. To overcome these challenges, we propose the application of Bayesian functional data analysis tools which allow complex temperature-solubility behaviours. Our methods have improved sensitivity over the state-of-the art, identify new drug-protein associations and have less restrictive assumptions than current approaches. Our methods allows for comprehensive analysis of proteins that deviate from the predicted sigmoid behaviour and we uncover potentially biphasic phenomena with a series of published datasets.

279-OR: Hydroxylation Regulation of Hepatic Gluconeogenesis via Prolyl-Hydroxylase 3 in Mice

Hepatic gluconeogenesis is critical for metabolic adaption in response to fasting, and its abnormal elevation contributes to hyperglycemia and type 2 diabetes. However, whether and how hydroxylation regulates hepatic gluconeogenesis remains largely unknown. Here we identified prolyl hydroxylase 3 (PHD3) of the hydroxylase family as a key regulator for hepatic gluconeogenesis. Liver-specific knockout of PHD3 (PHD3 LKO) mice show attenuated fasting gluconeogenic genes, glycaemia and hepatic capacity to produce glucose as evidenced by a pyruvate tolerance test. The efficacies of glucagon or adenylyl cyclase activator forskolin on stimulating the expression of gluconeogenic enzymes, such as PEPCK and G6Pase, and on glucose output are abolished in PHD3-/- mouse primary hepatocytes. To determine whether the prolyl hydroxylase activity is required for PHD3’s effects on promoting gluconeogenesis, liver-specific prolyl hydroxylase-deficient knockin mice with alanine mutation in PHD3 at Pro196 (PHD3 KI) were generated. Similar to PHD3 LKO mice, PHD3 KI mice exhibit attenuated fasting gluconeogenic genes, glycaemia and hepatic capacity to produce glucose, suggesting that PHD3 regulates hepatic gluconeogenesis in a proline hydroxylase-dependent manner. Moreover, several cellular proteins that associate with PHD3 were identified by comprehensive protein analysis using LC-MS/MS followed by bioinformatic analysis including GSK3β, IRS2 and OGT, which may act as PHD3 substrates and play roles in mediating PHD3’s effects on regulating glucose metabolism. Intriguingly, expression levels of PHD3 are increased in the liver diet-induced insulin resistant mice. Together, these results demonstrate that hydroxylation plays a critical role in the regulation of hepatic gluconeogenesis under physiological and insulin resistant conditions. Hyperactivation of PHD3 may underscore the potential role of PHD3 in the development of sustained gluconeogenesis, hyperglycemia and type 2 diabetes. Disclosure Y. Xue: None. J. Gao: None. X. Dai: None. A. Cui: None. Y. Li: None. G. Cai: None. F. Ma: None. Z. Liu: None. Y. Liu: None. W. Su: None. Z. Zheng: None. D. Ding: None. Y. Jiang: None. Funding National Key Research and Development Project of China (2019YFA0802502); National Natural Science Foundation of China (81925008); Shanghai Science and Technology Commission (19140903300)

PPI-GA: A Novel Clustering Algorithm to Identify Protein Complexes within Protein-Protein Interaction Networks Using Genetic Algorithm

Comprehensive analysis of proteins to evaluate their genetic diversity, study their differences, and respond to the tensions is the main subject of an interdisciplinary field of study called proteomics. The main objective of the proteomics is to detect and quantify proteins and study their post-translational modifications and interactions using protein chemistry, bioinformatics, and biology. Any disturbance in proteins interactive network can act as a source for biological disorders and various diseases such as Alzheimer and cancer. Most current computational methods for discovering protein complexes are usually based on specific topological characteristics of protein-protein networks (PPI). To identify the protein complexes, in this paper, we, first, present a new encoding method to represent solutions; we then propose a new clustering algorithm based on the genetic algorithm, named PPI-GA, employing a new multiobjective quality function. The proposed algorithm is evaluated on two gold standard and real-world datasets. The result achieved demonstrates that the proposed algorithm can detect important protein complexes, and it provides more accurate results compared with state-of-the-art protein complex identification algorithms.

Detailed Postmortem Profiling of Inflammatory Mediators Expression Revealed Post-inflammatory Alternation in the Superior Temporal Gyrus of Schizophrenia.

Recent studies have lent support to the possibility that inflammation is associated with the pathology of schizophrenia. In the study of measurement of inflammatory mediators, which are markers of inflammation, elevated inflammatory cytokine levels in the brain and blood have been reported in patients with schizophrenia. Several postmortem brain studies have also reported changes in the expression of inflammatory cytokines. However, it is not clear how these elevated inflammatory cytokines interact with other inflammatory mediators, and their association with the pathology of schizophrenia. We comprehensively investigated the expression of 30 inflammatory mediators in the superior temporal gyrus (STG) of 24 patients with schizophrenia and 26 controls using a multiplex method. Overall, inflammatory mediator expression in the STG was mostly unchanged. However, the expression of interleukin (IL)1-α and interferon-gamma-inducible protein (IP)-10 was decreased [IL-1α, median (IQR), 0.51 (0.37–0.70) vs. 0.87 (0.47–1.23), p = 0.01; IP-10, 13.99 (8.00–36.64) vs. 30.29 (10.23–134.73), p = 0.05], whereas that of IFN-α was increased [2.34 (1.84–4.48) vs. 1.94 (1.39–2.36), p = 0.04] in schizophrenia, although these alterations did not remain significant after multiple testing. Clustering based on inflammatory mediator expression pattern and analysis of upstream transcription factors using pathway analysis revealed that the suppression of IL-1α and IP-10 protein expression may be induced by regulation of a common upstream pathway. Neuroinflammation is important in understanding the biology of schizophrenia. While neuroimaging has been previously used, direct observation to determine the expression of inflammatory mediators is necessary. In this study, we identified protein changes, previously unreported, using comprehensive protein analysis in STG. These results provide insight into post-inflammatory alternation in chronic schizophrenia.