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1
Jain, K. K. "Oncoproteomics: State-of-the-Art." Technology in Cancer Research & Treatment 1, no. 4 (August 2002): 219–20. http://dx.doi.org/10.1177/153303460200100401.
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Proteomics is a promising approach in the identification of proteins and biochemical pathways involved in carcinogenesis. Proteomic technologies are now being incorporated in oncology in the post-genomic era. Cancer involves alterations in protein expression and provides a good model not only for detection of biomarkers but also their use in drug discovery. Proteomics has an impact on diagnostics as well as drug discovery. Genomics still remains an important approach but the value of proteomics lies in the fact that most of the diagnostics and drugs target proteins. The importance of application of proteomics in oncology is recognized by the publication of this special issue of TRCT.
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Sharma, Vipin Kumar, and Ravi Kumar. "Current applications of proteomics: a key and novel approach." International Journal of Advances in Medicine 6, no. 6 (November 25, 2019): 1953. http://dx.doi.org/10.18203/2349-3933.ijam20195259.
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Proteomics represented vital applications of technologies in the identification and quantification of high to moderate proteins (cellular signalling networks) found in biological matrix such as tissues, cells and fluids. Proteomics based technical knowledge is applied and verified in several preclinical research settings such as invention of diagnostic markers for specific disease and have shown to be increased in clinical applications. Extensive studies on proteomics resulted in detection of biomarkers that have been highly advanced in using diseases for cancer, lungs, cardiovascular, renal and neuro-regenerative and Parkinson's disease by introducing human origins for biocompatibility such as urine and serum. Advancement in the proteomic methods is conferring candidate right direction for clinical usage. In this review, recent developments and widely used proteomics approaches such as Mass Spectrometry (MS), Microarray chips are elaborately addressed and also focused merits and demerits of commonly used advanced approaches such as Selected Reaction Monitoring (SRM), Parallel Reaction Monitoring (PRM) and Data Independent Acquisition (DIA) and other used proteomics and that roles, in order to aid clinicians, were also discussed in the light of biomedical applications.
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Kline, Rachel A., Lena Lößlein, Dominic Kurian, Judit Aguilar Martí, Samantha L. Eaton, Felipe A. Court, Thomas H. Gillingwater, and Thomas M. Wishart. "An Optimized Comparative Proteomic Approach as a Tool in Neurodegenerative Disease Research." Cells 11, no. 17 (August 26, 2022): 2653. http://dx.doi.org/10.3390/cells11172653.
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Recent advances in proteomic technologies now allow unparalleled assessment of the molecular composition of a wide range of sample types. However, the application of such technologies and techniques should not be undertaken lightly. Here, we describe why the design of a proteomics experiment itself is only the first step in yielding high-quality, translatable results. Indeed, the effectiveness and/or impact of the majority of contemporary proteomics screens are hindered not by commonly considered technical limitations such as low proteome coverage but rather by insufficient analyses. Proteomic experimentation requires a careful methodological selection to account for variables from sample collection, through to database searches for peptide identification to standardised post-mass spectrometry options directed analysis workflow, which should be adjusted for each study, from determining when and how to filter proteomic data to choosing holistic versus trend-wise analyses for biologically relevant patterns. Finally, we highlight and discuss the difficulties inherent in the modelling and study of the majority of progressive neurodegenerative conditions. We provide evidence (in the context of neurodegenerative research) for the benefit of undertaking a comparative approach through the application of the above considerations in the alignment of publicly available pre-existing data sets to identify potential novel regulators of neuronal stability.
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Gajahin Gamage, Nadeeka Thushari, Rina Miyashita, Kazutaka Takahashi, Shuichi Asakawa, and Jayan Duminda Mahesh Senevirathna. "Proteomic Applications in Aquatic Environment Studies." Proteomes 10, no. 3 (September 1, 2022): 32. http://dx.doi.org/10.3390/proteomes10030032.
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Genome determines the unique individualities of organisms; however, proteins play significant roles in the generation of the colorful life forms below water. Aquatic systems are usually complex and multifaceted and can take on unique modifications and adaptations to environmental changes by altering proteins at the cellular level. Proteomics is an essential strategy for exploring aquatic ecosystems due to the diverse involvement of proteins, proteoforms, and their complexity in basic and advanced cellular functions. Proteomics can expedite the analysis of molecular mechanisms underlying biological processes in an aquatic environment. Previous proteomic studies on aquatic environments have mainly focused on pollution assessments, ecotoxicology, their role in the food industry, and extraction and identification of natural products. Aquatic protein biomarkers have been comprehensively reported and are currently extensively applied in the pharmaceutical and medical industries. Cellular- and molecular-level responses of organisms can be used as indicators of environmental changes and stresses. Conversely, environmental changes are expedient in predicting aquatic health and productivity, which are crucial for ecosystem management and conservation. Recent advances in proteomics have contributed to the development of sustainable aquaculture, seafood safety, and high aquatic food production. Proteomic approaches have expanded to other aspects of the aquatic environment, such as protein fingerprinting for species identification. In this review, we encapsulated current proteomic applications and evaluated the potential strengths, weaknesses, opportunities, and threats of proteomics for future aquatic environmental studies. The review identifies both pros and cons of aquatic proteomics and projects potential challenges and recommendations. We postulate that proteomics is an emerging, powerful, and integrated omics approach for aquatic environmental studies.
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Glazyrin, Yury E., Dmitry V. Veprintsev, Irina A. Ler, Maria L. Rossovskaya, Svetlana A. Varygina, Sofia L. Glizer, Tatiana N. Zamay, et al. "Proteomics-Based Machine Learning Approach as an Alternative to Conventional Biomarkers for Differential Diagnosis of Chronic Kidney Diseases." International Journal of Molecular Sciences 21, no. 13 (July 7, 2020): 4802. http://dx.doi.org/10.3390/ijms21134802.
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Diabetic nephropathy, hypertension, and glomerulonephritis are the most common causes of chronic kidney diseases (CKD). Since CKD of various origins may not become apparent until kidney function is significantly impaired, a differential diagnosis and an appropriate treatment are needed at the very early stages. Conventional biomarkers may not have sufficient separation capabilities, while a full-proteomic approach may be used for these purposes. In the current study, several machine learning algorithms were examined for the differential diagnosis of CKD of three origins. The tested dataset was based on whole proteomic data obtained after the mass spectrometric analysis of plasma and urine samples of 34 CKD patients and the use of label-free quantification approach. The k-nearest-neighbors algorithm showed the possibility of separation of a healthy group from renal patients in general by proteomics data of plasma with high confidence (97.8%). This algorithm has also be proven to be the best of the three tested for distinguishing the groups of patients with diabetic nephropathy and glomerulonephritis according to proteomics data of plasma (96.3% of correct decisions). The group of hypertensive nephropathy could not be reliably separated according to plasma data, whereas analysis of entire proteomics data of urine did not allow differentiating the three diseases. Nevertheless, the group of hypertensive nephropathy was reliably separated from all other renal patients using the k-nearest-neighbors classifier “one against all” with 100% of accuracy by urine proteome data. The tested algorithms show good abilities to differentiate the various groups across proteomic data sets, which may help to avoid invasive intervention for the verification of the glomerulonephritis subtypes, as well as to differentiate hypertensive and diabetic nephropathy in the early stages based not on individual biomarkers, but on the whole proteomic composition of urine and blood.
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Bouamrani, Ali, Jessica Ternier, David Ratel, Alim-Louis Benabid, Jean-Paul Issartel, Elisabeth Brambilla, and François Berger. "Direct-Tissue SELDI-TOF Mass Spectrometry Analysis: A New Application for Clinical Proteomics." Clinical Chemistry 52, no. 11 (November 1, 2006): 2103–6. http://dx.doi.org/10.1373/clinchem.2006.070979.
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Abstract Background: New molecular profiling technologies can aid in analysis of small pathologic samples obtained by minimally invasive biopsy and may enable the discovery of key biomarkers synergistic with anatomopathologic analysis related to prognosis, therapeutic response, and innovative target validation. Thus proteomic analysis at the histologic level in healthy and pathologic settings is a major issue in the field of clinical proteomics. Methods: We used surface-enhanced laser desorption ionization-time-of-flight mass spectrometry (SELDI-TOF MS) technology with surface chromatographic subproteome enrichment and preservation of the spatial distribution of proteomic patterns to detect discrete modifications of protein expression. We performed in situ proteomic profiling of mouse tissue and samples of human cancer tissue, including brain and lung cancer. Results: This approach permitted the discrimination of glioblastomas from oligodendrogliomas and led to the identification of 3 potential markers. Conclusion: Direct tissue proteomic analysis is an original application of SELDI-TOF MS technology that can expand the use of clinical proteomics as a complement to the anatomopathological diagnosis.
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Laronga, Christine, and Richard R. Drake. "Proteomic Approach to Breast Cancer." Cancer Control 14, no. 4 (October 2007): 360–68. http://dx.doi.org/10.1177/107327480701400406.
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Background Breast cancer is the most common cancer affecting women worldwide. Despite tremendous advances in screening, diagnosis, and treatment, the causes of this disease remain elusive and complex. Proteomics is a rapidly developing field that can explore the heterogeneity of breast cancer and supplement the wealth of information gained from genomics. Methods This article serves as an overview of the application of matrix-assisted laser desorption/ionization source with a time-of-flight (MALDI-TOF) proteomic techniques as applied to breast cancer. Examples of the clinical applicability of MALDI-TOF mass spectrometry are provided but represent only a fraction of the potential uses yet to be discovered. In addition, a brief summary of the bioinformatics issues that surround proteomics is included. Results Mass spectrometry has provided new proteomic approaches to unravel the complexities of clinical specimens relevant to breast cancer diagnostics. In particular, MALDI-TOF mass spectrometry analysis has been used to differentiate cancer profiles from benign profiles in samples from sera, plasma, tissue, nipple fluid, and ductal lavage. Some discriminating proteins have subsequently been identified. Conclusions Mass spectrometry applications to breast cancer diagnostics continue to be developed but are evolving faster than bioinformatics/statistical analysis can adapt. The future of these techniques in terms of clinical investigation is limitless, but in terms of general applicability, these applications are currently cost-prohibitive.
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Giannopoulou, Eugenia G., Spiros D. Garbis, Antonia Vlahou, Sofia Kossida, George Lepouras, and Elias S. Manolakos. "Proteomic Feature Maps: A new visualization approach in proteomics analysis." Journal of Biomedical Informatics 42, no. 4 (August 2009): 644–53. http://dx.doi.org/10.1016/j.jbi.2009.01.007.
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Canetti, Diana, Francesca Brambilla, Nigel B. Rendell, Paola Nocerino, Janet A. Gilbertson, Dario Di Silvestre, Andrea Bergamaschi, et al. "Clinical Amyloid Typing by Proteomics: Performance Evaluation and Data Sharing between Two Centres." Molecules 26, no. 7 (March 29, 2021): 1913. http://dx.doi.org/10.3390/molecules26071913.
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Amyloidosis is a relatively rare human disease caused by the deposition of abnormal protein fibres in the extracellular space of various tissues, impairing their normal function. Proteomic analysis of patients’ biopsies, developed by Dogan and colleagues at the Mayo Clinic, has become crucial for clinical diagnosis and for identifying the amyloid type. Currently, the proteomic approach is routinely used at National Amyloidosis Centre (NAC, London, UK) and Istituto di Tecnologie Biomediche-Consiglio Nazionale delle Ricerche (ITB-CNR, Milan, Italy). Both centres are members of the European Proteomics Amyloid Network (EPAN), which was established with the aim of sharing and discussing best practice in the application of amyloid proteomics. One of the EPAN’s activities was to evaluate the quality and the confidence of the results achieved using different software and algorithms for protein identification. In this paper, we report the comparison of proteomics results obtained by sharing NAC proteomics data with the ITB-CNR centre. Mass spectrometric raw data were analysed using different software platforms including Mascot, Scaffold, Proteome Discoverer, Sequest and bespoke algorithms developed for an accurate and immediate amyloid protein identification. Our study showed a high concordance of the obtained results, suggesting a good accuracy of the different bioinformatics tools used in the respective centres. In conclusion, inter-centre data exchange is a worthwhile approach for testing and validating the performance of software platforms and the accuracy of results, and is particularly important where the proteomics data contribute to a clinical diagnosis.
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Chang, Chiz-Tzung, Chao-Yuh Yang, Fuu-Jen Tsai, Shih-Yi Lin, and Chao-Jung Chen. "Mass Spectrometry-Based Proteomic Study Makes High-Density Lipoprotein a Biomarker for Atherosclerotic Vascular Disease." BioMed Research International 2015 (2015): 1–13. http://dx.doi.org/10.1155/2015/164846.
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High-density lipoprotein (HDL) is a lipid and protein complex that consists of apolipoproteins and lower level HDL-associated enzymes. HDL dysfunction is a factor in atherosclerosis and decreases patient survival. Mass spectrometry- (MS-) based proteomics provides a high throughput approach for analyzing the composition and modifications of complex HDL proteins in diseases. HDL can be separated according to size, surface charge, electronegativity, or apoprotein composition. MS-based proteomics on subfractionated HDL then allows investigation of lipoprotein roles in diseases. Herein, we review recent developments in MS-based quantitative proteomic techniques, HDL proteomics and lipoprotein modifications in diseases, and HDL subfractionation studies. We also discuss future directions and perspectives in MS-based proteomics on HDL.
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Blanco-Colio, Luis Miguel, Juan Antonio López, Roxana Martínez-Pinna Albar, Jesús Egido, and José Luis Martín-Ventura. "Vascular proteomics, a translational approach: from traditional to novel proteomic techniques." Expert Review of Proteomics 6, no. 5 (October 2009): 461–64. http://dx.doi.org/10.1586/epr.09.66.
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Jørgensen Cehofski, Lasse, Anders Kruse, Benedict Kjærgaard, Allan Stensballe, Bent Honoré, and Henrik Vorum. "Dye-Free Porcine Model of Experimental Branch Retinal Vein Occlusion: A Suitable Approach for Retinal Proteomics." Journal of Ophthalmology 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/839137.
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Branch retinal vein occlusion induces complex biological processes in the retina that are generated by a multitude of interacting proteins. These proteins and their posttranslational modifications can effectively be studied using modern proteomic techniques. However, no method for studying large-scale protein changes following branch retinal vein occlusion has been available until now. Obtainment of retinal tissue exposed to branch retinal vein occlusion is only available through experimental animal models. Traditional models of experimental branch retinal vein occlusion require the use of Rose Bengal dye combined with argon laser photocoagulation. The use of Rose Bengal dye is problematic in proteomic studies as the dye can induce multiple protein modifications when irradiated. This paper presents a novel technique for proteomic analysis of porcine retinal tissue with branch retinal vein occlusion combining a dye-free experimental model with label-free liquid chromatography mass spectrometry based proteomics.
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Lee, Yong Yook, Sung-Won Kim, Soo-Hyun Youn, Sun Hee Hyun, Jong-Su Kyung, Gyo In, Chae-Kyu Park, et al. "Biological Effects of Korean Red Ginseng Polysaccharides in Aged Rat Using Global Proteomic Approach." Molecules 25, no. 13 (July 1, 2020): 3019. http://dx.doi.org/10.3390/molecules25133019.
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Much has been written on the physiological benefits of Korean Red Ginseng (KRG). Among its various components, ginsenosides have been widely investigated for their various pharmacological effects. However, polysaccharides are a major KRG component that has not received scrutiny similar to that of ginsenosides. The present study aims to fill that gap in the existing literature and to investigate the possible functions of polysaccharide in KRG. The researchers evaluated proteomic changes in non-saponin fractions with rich polysaccharides (NFP) in KRG. Based on the serum analysis, proteomics analysis of the liver and the spleen was additionally conducted to identify related functions. We validated the suggested functions of NFP with the galactosamine-induced liver injury model and the cyclophosphamide-induced immunosuppression model. Then, we evaluated the antimetastatic potential of NFP in the lungs. Further proteomics analysis of the spleen and liver after ingestion confirmed functions related to immunity, cancer, hepatoprotection, and others. Then, we validated the suggested corresponding functions of the NFP in vivo model. NFP showed immune-enhancing effects, inhibited melanoma cell metastasis in the lung, and decreased liver damage. The results show that using the proteomic approach uncovers the potential effects of polysaccharides in KRG, which include enhancing the immune system and protecting the liver.
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Saleem, Mahwish, Syed K. Raza, and Syed G Musharraf. "A comparative protein analysis of lung cancer, along with three controls using a multidimensional proteomic approach." Experimental Biology and Medicine 244, no. 1 (January 2019): 36–41. http://dx.doi.org/10.1177/1535370219826525.
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Lung cancer is an important respiratory disease accounting for millions of deaths worldwide. Developments in proteomics techniques and mass spectrometry offer comprehensive answers to unravel the complexities of lethal diseases such as lung cancer at the molecular level. The current study focuses on the proteomic profiling of lung cancer and its comparison with other controls including chronic smoker (high-risk individuals), obstructive pulmonary disease (COPD), and healthy control. A multistep proteomic strategy was used on the pooled plasma of each group including depletion of seven most abundant proteins, 2D-SDS-PAGE separation followed by matrix-assisted laser desorption/ionization time-of-flight/time-of-flight mass spectrometry (MALDI-TOF-TOF-MS) analysis. Total 23 proteins were identified, and out of them only 7 proteins were found to be expressed in increased amounts in disease and smoker groups as compared to healthy group including haptoglobin, retinol binding protein 4 (RBP 4), alpha-1 antitrypsin, Ig lambda 2 chain C region, Ig alpha-1-chain C region, clusterin, transthyretin (TTR). Haptoglobin and alpha-1-antitrypsin were found to be sequentially increased in healthy control along with smoker, COPD, and lung cancer. The differentially expressed proteins might have a prognostic potential to be used in the progression of COPD to ultimately lung cancer. Impact statement A multistep proteomics fractionation strategy was developed and validated for the discovery of proteomic biomarkers which could be used as potential diagnostic biomarkers for monitoring the progression of disease in smokers and COPD patients towards lung cancer.
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Vítámvás, P., K. Kosová, and I. T. Prášil. "Proteome analysis in plant stress research: a review." Czech Journal of Genetics and Plant Breeding 43, No. 1 (January 7, 2008): 1–6. http://dx.doi.org/10.17221/1903-cjgpb.
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Proteomic techniques that allow the identification and quantification of stress-related proteins, mapping of dynamics of their expression and posttranslational modifications represent an important approach in the research of plant stresses. In this review, we show an outline of proteomics methods and their applications in the research of plant resistance to various types of stresses.
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Tsuchida, Sachio, Mamoru Satoh, Masaki Takiwaki, and Fumio Nomura. "Current Status of Proteomic Technologies for Discovering and Identifying Gingival Crevicular Fluid Biomarkers for Periodontal Disease." International Journal of Molecular Sciences 20, no. 1 (December 26, 2018): 86. http://dx.doi.org/10.3390/ijms20010086.
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Periodontal disease is caused by bacteria in dental biofilms. To eliminate the bacteria, immune system cells release substances that inflame and damage the gums, periodontal ligament, or alveolar bone, leading to swollen bleeding gums, which is a sign of gingivitis. Damage from periodontal disease can cause teeth to loosen also. Studies have demonstrated the proteomic approach to be a promising tool for the discovery and identification of biochemical markers of periodontal diseases. Recently, many studies have applied expression proteomics to identify proteins whose expression levels are altered by disease. As a fluid lying in close proximity to the periodontal tissue, the gingival crevicular fluid (GCF) is the principal target in the search for periodontal disease biomarkers because its protein composition may reflect the disease pathophysiology. Biochemical marker analysis of GCF is effective for objective diagnosis in the early and advanced stages of periodontal disease. Periodontal diseases are also promising targets for proteomics, and several groups, including ours, have applied proteomics in the search for GCF biomarkers of periodontal diseases. This search is of continuing interest in the field of experimental and clinical periodontal disease research. In this article, we summarize the current situation of proteomic technologies to discover and identify GCF biomarkers for periodontal diseases.
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Tarbeeva, Svetlana, Anna Kozlova, Elizaveta Sarygina, Olga Kiseleva, Elena Ponomarenko, and Ekaterina Ilgisonis. "Food for Thought: Proteomics for Meat Safety." Life 13, no. 2 (January 17, 2023): 255. http://dx.doi.org/10.3390/life13020255.
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Foodborne bacteria interconnect food and human health. Despite significant progress in food safety regulation, bacterial contamination is still a serious public health concern and the reason for significant commercial losses. The screening of the microbiome in meals is one of the main aspects of food production safety influencing the health of the end-consumers. Our research provides an overview of proteomics findings in the field of food safety made over the last decade. It was believed that proteomics offered an accurate snapshot of the complex networks of the major biological machines called proteins. The proteomic methods for the detection of pathogens were armed with bioinformatics algorithms, allowing us to map the data onto the genome and transcriptome. The mechanisms of the interaction between bacteria and their environment were elucidated with unprecedented sensitivity, specificity, and depth. Using our web-based tool ScanBious for automated publication analysis, we analyzed over 48 000 scientific articles on antibiotic and disinfectant resistance and highlighted the benefits of proteomics for the food safety field. The most promising approach to studying safety in food production is the combination of classical genomic and metagenomic approaches and the advantages provided by proteomic methods with the use of panoramic and targeted mass spectrometry.
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Pitteri, Sharon, and Sam Hanash. "A Systems Approach to the Proteomic Identification of Novel Cancer Biomarkers." Disease Markers 28, no. 4 (2010): 233–39. http://dx.doi.org/10.1155/2010/270859.
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The proteomics field has experienced rapid growth with technologies achieving ever increasing accuracy, sensitivity, and throughput, and with availability of computational tools to address particular applications. Given that the proteome represents the most functional component encoded for in the genome, a systems approach to disease investigations and biomarker identification benefits substantially from integration of proteome level studies. Here we present proteomic approaches that have allowed systematic searches for potential cancer markers by integrating cancer cell profiling with additional sources of data, as illustrated with recent studies of ovarian cancer.
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Wang, Tao, and Robin B. Gasser. "Prospects of Using High-Throughput Proteomics to Underpin the Discovery of Animal Host–Nematode Interactions." Pathogens 10, no. 7 (June 30, 2021): 825. http://dx.doi.org/10.3390/pathogens10070825.
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Parasitic nematodes impose a significant public health burden, and cause major economic losses to agriculture worldwide. Due to the widespread of anthelmintic resistance and lack of effective vaccines for most nematode species, there is an urgent need to discover novel therapeutic and vaccine targets, informed through an understanding of host–parasite interactions. Proteomics, underpinned by genomics, enables the global characterisation proteins expressed in a particular cell type, tissue and organism, and provides a key to insights at the host–parasite interface using advanced high-throughput mass spectrometry-based proteomic technologies. Here, we (i) review current mass-spectrometry-based proteomic methods, with an emphasis on a high-throughput ‘bottom-up’ approach; (ii) summarise recent progress in the proteomics of parasitic nematodes of animals, with a focus on molecules inferred to be involved in host–parasite interactions; and (iii) discuss future research directions that could enhance our knowledge and understanding of the molecular interplay between nematodes and host animals, in order to work toward new, improved methods for the treatment, diagnosis and control of nematodiases.
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Navarrete-Perea, José, Steven P. Gygi, and Joao A. Paulo. "Temporal proteomic changes induced by nicotine in human cells: A quantitative proteomics approach." Journal of Proteomics 241 (June 2021): 104244. http://dx.doi.org/10.1016/j.jprot.2021.104244.
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Kondo, Tadashi, Daisuke Kubota, and Akira Kawai. "Application of Proteomics to Soft Tissue Sarcomas." International Journal of Proteomics 2012 (June 19, 2012): 1–15. http://dx.doi.org/10.1155/2012/876401.
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Soft tissue sarcomas are rare and account for less than 1% of all malignant cancers. Other than development of intensive therapies, the clinical outcome of patients with soft tissue sarcoma remains very poor, particularly when diagnosed at a late stage. Unique mutations have been associated with certain soft tissue sarcomas, but their etiologies remain unknown. The proteome is a functional translation of a genome, which directly regulates the malignant features of tumors. Thus, proteomics is a promising approach for investigating soft tissue sarcomas. Various proteomic approaches and clinical materials have been used to address clinical and biological issues, including biomarker development, molecular target identification, and study of disease mechanisms. Several cancer-associated proteins have been identified using conventional technologies such as 2D-PAGE, mass spectrometry, and array technology. The functional backgrounds of proteins identified were assessed extensively using in vitro experiments, thus supporting expression analysis. These observations demonstrate the applicability of proteomics to soft tissue sarcoma studies. However, the sample size in each study was insufficient to allow conclusive results. Given the low frequency of soft tissue sarcomas, multi-institutional collaborations are required to validate the results of proteomic approaches.
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Hohn, Andreas, Ivan Iovino, Fabrizio Cirillo, Hendrik Drinhaus, Kathrin Kleinbrahm, Lennert Boehm, Edoardo De Robertis, and Jochen Hinkelbein. "Bioinformatical Analysis of Organ-Related (Heart, Brain, Liver, and Kidney) and Serum Proteomic Data to Identify Protein Regulation Patterns and Potential Sepsis Biomarkers." BioMed Research International 2018 (March 21, 2018): 1–11. http://dx.doi.org/10.1155/2018/3576157.
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During the last years, proteomic studies have revealed several interesting findings in experimental sepsis models and septic patients. However, most studies investigated protein alterations only in single organs or in whole blood. To identify possible sepsis biomarkers and to evaluate the relationship between protein alteration in sepsis affected organs and blood, proteomics data from the heart, brain, liver, kidney, and serum were analysed. Using functional network analyses in combination with hierarchical cluster analysis, we found that protein regulation patterns in organ tissues as well as in serum are highly dynamic. In the tissue proteome, the main functions and pathways affected were the oxidoreductive activity, cell energy generation, or metabolism, whereas in the serum proteome, functions were associated with lipoproteins metabolism and, to a minor extent, with coagulation, inflammatory response, and organ regeneration. Proteins from network analyses of organ tissue did not correlate with statistically significantly regulated serum proteins or with predicted proteins of serum functions. In this study, the combination of proteomic network analyses with cluster analyses is introduced as an approach to deal with high-throughput proteomics data to evaluate the dynamics of protein regulation during sepsis.
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van Ginkel, Jetty, Mike Filius, Malwina Szczepaniak, Pawel Tulinski, Anne S. Meyer, and Chirlmin Joo. "Single-molecule peptide fingerprinting." Proceedings of the National Academy of Sciences 115, no. 13 (March 12, 2018): 3338–43. http://dx.doi.org/10.1073/pnas.1707207115.
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Proteomic analyses provide essential information on molecular pathways of cellular systems and the state of a living organism. Mass spectrometry is currently the first choice for proteomic analysis. However, the requirement for a large amount of sample renders a small-scale proteomics study challenging. Here, we demonstrate a proof of concept of single-molecule FRET-based protein fingerprinting. We harnessed the AAA+ protease ClpXP to scan peptides. By using donor fluorophore-labeled ClpP, we sequentially read out FRET signals from acceptor-labeled amino acids of peptides. The repurposed ClpXP exhibits unidirectional processing with high processivity and has the potential to detect low-abundance proteins. Our technique is a promising approach for sequencing protein substrates using a small amount of sample.
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Tsai, Chia-Feng, Rui Zhao, Sarah M. Williams, Ronald J. Moore, Kendall Schultz, William B. Chrisler, Ljiljana Pasa-Tolic, et al. "An Improved Boosting to Amplify Signal with Isobaric Labeling (iBASIL) Strategy for Precise Quantitative Single-cell Proteomics." Molecular & Cellular Proteomics 19, no. 5 (March 3, 2020): 828–38. http://dx.doi.org/10.1074/mcp.ra119.001857.
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Mass spectrometry (MS)-based proteomics has great potential for overcoming the limitations of antibody-based immunoassays for antibody-independent, comprehensive, and quantitative proteomic analysis of single cells. Indeed, recent advances in nanoscale sample preparation have enabled effective processing of single cells. In particular, the concept of using boosting/carrier channels in isobaric labeling to increase the sensitivity in MS detection has also been increasingly used for quantitative proteomic analysis of small-sized samples including single cells. However, the full potential of such boosting/carrier approaches has not been significantly explored, nor has the resulting quantitation quality been carefully evaluated. Herein, we have further evaluated and optimized our recent boosting to amplify signal with isobaric labeling (BASIL) approach, originally developed for quantifying phosphorylation in small number of cells, for highly effective analysis of proteins in single cells. This improved BASIL (iBASIL) approach enables reliable quantitative single-cell proteomics analysis with greater proteome coverage by carefully controlling the boosting-to-sample ratio (e.g. in general <100×) and optimizing MS automatic gain control (AGC) and ion injection time settings in MS/MS analysis (e.g. 5E5 and 300 ms, respectively, which is significantly higher than that used in typical bulk analysis). By coupling with a nanodroplet-based single cell preparation (nanoPOTS) platform, iBASIL enabled identification of ∼2500 proteins and precise quantification of ∼1500 proteins in the analysis of 104 FACS-isolated single cells, with the resulting protein profiles robustly clustering the cells from three different acute myeloid leukemia cell lines. This study highlights the importance of carefully evaluating and optimizing the boosting ratios and MS data acquisition conditions for achieving robust, comprehensive proteomic analysis of single cells.
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Annunziata, C. M., N. Azad, A. S. Dhamoon, G. Whiteley, and E. C. Kohn. "Ovarian cancer in the proteomics era." International Journal of Gynecologic Cancer 18, Suppl 1 (2008): 1–6. http://dx.doi.org/10.1111/j.1525-1438.2007.01096.x.
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Ovarian cancer presents a diagnostic challenge because of its subtle clinical presentation and elusive cell of origin. Two new technologies of proteomics have advanced the dissection of the underlying molecular signaling events and the proteomic characterization of ovarian cancer: mass spectrometry and protein array analysis. Mass spectrometry can provide a snapshot of a proteome in time and space, with sensitivity and resolution that may allow identification of the elusive “needle in the haystack” heralding ovarian cancer. Proteomic profiling of tumor tissue samples can survey molecular targets during treatment and quantify changes using reverse phase protein arrays generated from tumor samples captured by microdissection, lysed and spotted in serial dilutions for high-throughput analysis. This approach can be applied to identify the optimal biological dose of a targeted agent and to validate target to outcome link. The evolution of proteomic technologies has the capacity to advance rapidly our understanding of ovarian cancer at a molecular level and thus elucidate new directions for the treatment of this disease
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Kalaiselvi, B., and M. Thangamani. "Computational Approaches for Understanding High Quality Mass Spectrometry Proteomic Data." Journal of Computational and Theoretical Nanoscience 16, no. 2 (February 1, 2019): 516–20. http://dx.doi.org/10.1166/jctn.2019.7761.
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Computational approach of proteomic data science is used to identification and quantification of protein and provides the high-throughput data, concentration changes, interactions, posttranslational modifications and cellular localizations. The high-quality mass spectrometry recall to understanding the different sources of unsigned high-quality spectra features. The iterative computational method is interrogating the high efficiency of mass spectrometry protein data. The approach contains several databases searching with different search parameters, spectral library searching, modified peptides using blind search and genomic database searching. The mass spectrometry computational method is analysis the proteomics data focusing the key concepts with explanations, mass spectral feature detection, identifying the peptides, protein inference and control the false discovery rate. Then the method discusses the quantification of peptides and proteins, the downstream data analysis on machine learning, network analysis and multiomics integration of protein data and finally discuss the future of computational proteomics data.
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Hajjaji, Nawale, Mira Abbouchi, Lan Anh Nguyen, Samuel Charles, Sarah Leclercq, Delphine Bertin, Yves-Marie Robin, Isabelle Fournier, and Michel Salzet. "A novel proteomic mass spectrometry-based approach to reveal functionally heterogeneous tumor clones in breast cancer metastases and identify clone-specific drug targets." Journal of Clinical Oncology 38, no. 15_suppl (May 20, 2020): e13063-e13063. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.e13063.
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e13063 Background: Breast cancer mortality is expected to rise by almost 30% by 2030 worldwide, mainly due to the occurrence of distant metastases. The development of drugs specifically targeted at tumor drivers has not yet curbed resistance to treatment, which prevents metastases curability. There is a need for new molecular approaches to tackle metastases complex biology, particularly tumor heterogeneity, a main determinant of resistance. The aim of this study was to use a proteomic mass spectrometry-based approach to reveal functionally heterogeneous’ tumor subpopulations in breast cancer metastases, and identify clone specific drug targets. Methods: Metastasis biopsies (n = 21) were collected retrospectively from patients with advanced breast cancer treated at Oscar Lambret Cancer Center (Lille, France). Tumor heterogeneity was analyzed directly on FFPE tissue sections using MALDI mass spectrometry imaging (MSI) on a RapifleX Tissuetyper. Unsupervised spatial segmentation was performed to reveal tumor subpopulations with distinct proteomic profiles within each metastasis. The full proteomic characterization of these tumor clones was further performed with spatially resolved proteomic mass spectrometry. Results: MSI revealed that breast cancer metastases contained 2 to 5 functionally distinct tumor clones (proteomic clones). Although the clone profiles within a metastasis were correlated, unsupervised hierarchical clustering showed a clear distinction between them and specific proteomic signatures. Enrichment analysis showed that differentially expressed proteins were involved in a variety of biological processes or pathways including regulation of histone acetylation, extracellular matrix degradation, DNA repair, NOTCH pathway, estrogen-responsive target genes or exocytosis. The evolution of the proteomic clones profile during disease progression was also determined by comparison of paired biopsies. To identify the candidate treatments best fitted to metastasis heterogeneity, the specific proteomic signatures of the clones were matched against a druggable genome database. It was possible to unveil candidate drug targets personalized to each metastasis functional clone. Conclusions: MALDI mass spectrometry imaging combined with spatially resolved proteomics has the potential to tackle breast cancer metastases heterogeneity, and identify candidate drug targets specific to functional clones to personalize treatments.
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Montaño-Gutierrez, Luis F., Shinya Ohta, Georg Kustatscher, William C. Earnshaw, and Juri Rappsilber. "Nano Random Forests to mine protein complexes and their relationships in quantitative proteomics data." Molecular Biology of the Cell 28, no. 5 (March 2017): 673–80. http://dx.doi.org/10.1091/mbc.e16-06-0370.
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Ever-increasing numbers of quantitative proteomics data sets constitute an underexploited resource for investigating protein function. Multiprotein complexes often follow consistent trends in these experiments, which could provide insights about their biology. Yet, as more experiments are considered, a complex’s signature may become conditional and less identifiable. Previously we successfully distinguished the general proteomic signature of genuine chromosomal proteins from hitchhikers using the Random Forests (RF) machine learning algorithm. Here we test whether small protein complexes can define distinguishable signatures of their own, despite the assumption that machine learning needs large training sets. We show, with simulated and real proteomics data, that RF can detect small protein complexes and relationships between them. We identify several complexes in quantitative proteomics results of wild-type and knockout mitotic chromosomes. Other proteins covary strongly with these complexes, suggesting novel functional links for later study. Integrating the RF analysis for several complexes reveals known interdependences among kinetochore subunits and a novel dependence between the inner kinetochore and condensin. Ribosomal proteins, although identified, remained independent of kinetochore subcomplexes. Together these results show that this complex-oriented RF (NanoRF) approach can integrate proteomics data to uncover subtle protein relationships. Our NanoRF pipeline is available online.
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Wang, Jigang, Yew Mun Lee, Caixia Li, Ping Li, Zhen Li, Teck Kwang Lim, Zhiyuan Gong, and Qingsong Lin. "Dramatic Improvement of Proteomic Analysis of Zebrafish Liver Tumor by Effective Protein Extraction with Sodium Deoxycholate and Heat Denaturation." International Journal of Analytical Chemistry 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/763969.
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Majority of the proteomic studies on tissue samples involve the use of gel-based approach for profiling and digestion. The laborious gel-based approach is slowly being replaced by the advancing in-solution digestion approach. However, there are still several difficulties such as difficult-to-solubilize proteins, poor proteomic analysis in complex tissue samples, and the presence of sample impurities. Henceforth, there is a great demand to formulate a highly efficient protein extraction buffer with high protein extraction efficiency from tissue samples, high compatibility with in-solution digestion, reduced number of sample handling steps to reduce sample loss, low time consumption, low cost, and ease of usage. Here, we evaluated various existing protein extraction buffers with zebrafish liver tumor samples and found that sodium deoxycholate- (DOC-) based extraction buffer with heat denaturation was the most effective approach for highly efficient extraction of proteins from complex tissues such as the zebrafish liver tumor. A total of 4,790 proteins have been identified using shotgun proteomics approach with 2D LC, which to our knowledge is the most comprehensive study for zebrafish liver tumor proteome.
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Carrasco-Reinado, Rafael, Almudena Escobar-Niño, Carlos Fajardo, Ines M. Morano, Francisco Amil-Ruiz, Gonzalo Martinez-Rodríguez, Carlos Fuentes-Almagro, et al. "Development of New Antiproliferative Compound against Human Tumor Cells from the Marine Microalgae Nannochloropsis gaditana by Applied Proteomics." International Journal of Molecular Sciences 22, no. 1 (December 24, 2020): 96. http://dx.doi.org/10.3390/ijms22010096.
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Proteomics is a crucial tool for unravelling the molecular dynamics of essential biological processes, becoming a pivotal technique for basic and applied research. Diverse bioinformatic tools are required to manage and explore the huge amount of information obtained from a single proteomics experiment. Thus, functional annotation and protein–protein interactions are evaluated in depth leading to the biological conclusions that best fit the proteomic response in the system under study. To gain insight into potential applications of the identified proteins, a novel approach named “Applied Proteomics” has been developed by comparing the obtained protein information with the existing patents database. The development of massive sequencing technology and mass spectrometry (MS/MS) improvements has allowed the application of proteomics nonmodel microorganisms, which have been deeply described as a novel source of metabolites. Between them, Nannochloropsis gaditana has been pointed out as an alternative source of biomolecules. Recently, our research group has reported the first complete proteome analysis of this microalga, which was analysed using the applied proteomics concept with the identification of 488 proteins with potential industrial applications. To validate our approach, we selected the UCA01 protein from the prohibitin family. The recombinant version of this protein showed antiproliferative activity against two tumor cell lines, Caco2 (colon adenocarcinoma) and HepG-2 (hepatocellular carcinoma), proving that proteome data have been transformed into relevant biotechnological information. From Nannochloropsis gaditana has been developed a new tool against cancer—the protein named UCA01. This protein has selective effects inhibiting the growth of tumor cells, but does not show any effect on control cells. This approach describes the first practical approach to transform proteome information in a potential industrial application, named “applied proteomics”. It is based on a novel bioalgorithm, which is able to identify proteins with potential industrial applications. From hundreds of proteins described in the proteome of N. gaditana, the bioalgorithm identified over 400 proteins with potential uses; one of them was selected as UCA01, “in vitro” and its potential was demonstrated against cancer. This approach has great potential, but the applications are potentially numerous and undefined.
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Banerjee, Upamanyu, and Ulisses M. Braga-Neto. "Bayesian ABC-MCMC Classification of Liquid Chromatography–Mass Spectrometry Data." Cancer Informatics 14s5 (January 2015): CIN.S30798. http://dx.doi.org/10.4137/cin.s30798.
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Proteomics promises to revolutionize cancer treatment and prevention by facilitating the discovery of molecular biomarkers. Progress has been impeded, however, by the small-sample, high-dimensional nature of proteomic data. We propose the application of a Bayesian approach to address this issue in classification of proteomic profiles generated by liquid chromatography-mass spectrometry (LC-MS). Our approach relies on a previously proposed model of the LC-MS experiment, as well as on the theory of the optimal Bayesian classifier (OBC). Computation of the OBC requires the combination of a likelihood-free methodology called approximate Bayesian computation (ABC) as well as Markov chain Monte Carlo (MCMC) sampling. Numerical experiments using synthetic LC-MS data based on an actual human proteome indicate that the proposed ABC-MCMC classification rule outperforms classical methods such as support vector machines, linear discriminant analysis, and 3-nearest neighbor classification rules in the case when sample size is small or the number of selected proteins used to classify is large.
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Azuaje, Francisco, Sang-Yoon Kim, Daniel Perez Hernandez, and Gunnar Dittmar. "Connecting Histopathology Imaging and Proteomics in Kidney Cancer through Machine Learning." Journal of Clinical Medicine 8, no. 10 (September 25, 2019): 1535. http://dx.doi.org/10.3390/jcm8101535.
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Proteomics data encode molecular features of diagnostic value and accurately reflect key underlying biological mechanisms in cancers. Histopathology imaging is a well-established clinical approach to cancer diagnosis. The predictive relationship between large-scale proteomics and H&E-stained histopathology images remains largely uncharacterized. Here we investigate such associations through the application of machine learning, including deep neural networks, to proteomics and histology imaging datasets generated by the Clinical Proteomic Tumor Analysis Consortium (CPTAC) from clear cell renal cell carcinoma patients. We report robust correlations between a set of diagnostic proteins and predictions generated by an imaging-based classification model. Proteins significantly correlated with the histology-based predictions are significantly implicated in immune responses, extracellular matrix reorganization, and metabolism. Moreover, we showed that the genes encoding these proteins also reliably recapitulate the biological associations with imaging-derived predictions based on strong gene–protein expression correlations. Our findings offer novel insights into the integrative modeling of histology and omics data through machine learning, as well as the methodological basis for new research opportunities in this and other cancer types.
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McCague, Cathal, and Lucian Beer. "Radioproteomics in patients with ovarian cancer." British Journal of Radiology 94, no. 1125 (September 1, 2021): 20201331. http://dx.doi.org/10.1259/bjr.20201331.
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Radioproteomics is the integration of proteomics, the systematic study of the protein expression of an organism, with radiomics, the extraction and analysis of large numbers of quantitative features from medical images. This article examines this developing field, and it’s application in high grade serous ovarian carcinoma. Seminal proteomic studies in the area of ovarian cancer, such as the PROVAR and CPTA studies are discussed, along side recent research, such as that highlighting the central role of methyltransferase nicotinamide N-methyltransferase as the metabolic regulation of cancer progression in the tumour stroma. Finally, this article considers a novel, hypothesis generating approach to integrate CT-based qualitative and radiomic features with proteomic analysis, and the future direction of the field. Combined advances in radiomic, proteomic and genomic analysis has the potential to signal the age of true precision medicine, where treatment is centered specifically on the molecular profile of the tumour, rather than based on empirical knowledge, thus altering the course of a disease that has the highest mortality of all cancers of the female reproductive system.
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Johnson, Leigh Ann, Fan Zhang, Stephanie Large, James Hall, and Sidney E. O’Bryant. "The impact of comorbid depression–diabetes on proteomic outcomes among community-dwelling Mexican Americans with mild cognitive impairment." International Psychogeriatrics 32, no. 1 (October 29, 2019): 17–23. http://dx.doi.org/10.1017/s1041610219001625.
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ABSTRACTBackground:Mexican Americans suffer from a disproportionate burden of modifiable risk factors, which may contribute to the health disparities in mild cognitive impairment (MCI) and Alzheimer’s disease (AD).Objective:The purpose of this study was to elucidate the impact of comorbid depression and diabetes on proteomic outcomes among community-dwelling Mexican American adults and elders.Methods:Data from participants enrolled in the Health and Aging Brain among Latino Elders study was utilized. Participants were 50 or older and identified as Mexican American (N = 514). Cognition was assessed via neuropsychological test battery and diagnoses of MCI and AD adjudicated by consensus review. The sample was stratified into four groups: Depression only, Neither depression nor diabetes, Diabetes only, and Comorbid depression and diabetes. Proteomic profiles were created via support vector machine analyses.Results:In Mexican Americans, the proteomic profile of MCI may change based upon the presence of diabetes. The profile has a strong inflammatory component and diabetes increases metabolic markers in the profile.Conclusion:Medical comorbidities may impact the proteomics of MCI and AD, which lend support for a precision medicine approach to treating this disease.
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Dytfeld, Dominik, Malathi Kandarpa, John R. Strahler, Dattatreya Mellacheruvu, Suchitra Subramani, Stephanie J. Kraftson, Lambert Ngoka, Alexey Nesvizhskii, Arun Sreekumar, and Andrzej J. Jakubowiak. "Proteomic Signature Predicting Achievement of Very Good Partial Response In Patients with Multiple Myeloma Based On Complementary Label-Free and iTRAQ Quantitative Proteome Analysis." Blood 116, no. 21 (November 19, 2010): 1902. http://dx.doi.org/10.1182/blood.v116.21.1902.1902.
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Abstract Abstract 1902 Introduction: Multiple myeloma (MM) remains mostly incurable. Novel therapies have improved response rates, which are now reaching 100%. More importantly, number of recent studies showed that the depth of response, e.g. achievement of at least 90% reduction of the disease (≥VGPR) is associated with longer disease control. Therefore, improving VGPR rates and establishing predictors of VGPR to a given regimen may be an important clinical goal. High throughput quantitative proteomics may offer greater insight into the actual biology of the malignant cell than genome analysis and therefore, may be more useful in the development of personalized therapy. The objective of this study is to establish a proteomic signature predicting achievement of at least VGPR to initial treatment with bortezomib (Velcade®), pegylated liposomal doxorubicin, and dexamethasone (VDD). We previously reported preliminary proteomic profile of malignant plasma cells (PCs) obtained from a set of naïve MM pts enrolled in the VDD trial (Dytfeld et al., ASH 2009). Here we present the results of differential proteomic analysis of MM PCs of all available samples from the frontline VDD study (≥VGPR vs. <VGPR) using two independent and complementary quantitative proteomic platforms. We also compared the proteomic profile with gene expression data. Preliminary validation of the biomarkers of response prediction is presented. Methods: PCs were acquired from pre-treatment bone marrow specimens after obtaining informed consent from patients (pts), and were thereafter enriched with a RosetteSep® negative selection kit. Quantitative proteomic analysis of PCs from 17 naïve pts with MM from the VDD study was performed using iTRAQ approach in 8-plex variant. To increase confidence of analysis, label-free quantitative proteomics (LF) based on spectra counting was conducted on PCs from 12 pts. In iTRAQ experiments, proteins were processed with reagents according to the manufacturer's protocol followed by SCX fractionation and LC-MS/MS analysis (4800 Plus MALDI TOF/TOF). Peptides from the MM1S cell line were used as a reference. The data were analyzed using ProteinPilot™. For LF analysis, proteins were fractionated before trypsin digestion on Bis-Tris-Gel and subsequently run on LC-ESI-MS/MS on a linear trap mass spectrometer (LTQ Orbitrap). A database search was carried out using X!Tandem followed by Trans-proteomic Pipeline. At least 1.5-fold difference in expression in both platforms was used as a cut-off value. To correlate proteomics with gene expression of dysregulated proteins of interest, mRNA levels were analyzed by quantitative real time PCR (RT-PCR). Validation of proteomic findings on proteins of interest was performed using Western Blot. Results: We identified a total of 894 proteins in 3 iTRAQ experiments with high confidence (FDR<1%) and 1058 proteins by LF approach. Based on iTRAQ analysis, 20 proteins were found up-regulated in samples from pts with ≥VGPR (8 out of 17 pts) while 14 were down- regulated. Using LF approach, 284 proteins were elevated in the ≥VGPR group (6 out of 12 pts) while 315 proteins were down-regulated. Both iTRAQ and LF methods showed 15 differentially expressed proteins in common and 14 of them showed identical up or down trends. Interestingly, among differentially expressed proteins, there were proteins involved in proteasome activation (PSME1 and TXNL1), protection against oxidative stress (TXN and TXNDC5), glucose and cholesterol metabolism (TP1, APOA1 and ACAT1) and apoptosis (MX1). RT-PCR performed on a subset of genes confirmed the trend in differential expression between pts with ≥VGPR and <VGPR for TXNDC5 and PSME1. No change in mRNA expression levels was observed in TXN, APOA1, TPI1 and MX1while the trend in expression was reversed for ACAT1. Western blot analysis performed to date validated differential expression of PSME1. Conclusions: We present patient-derived proteomic characteristics of MM cells using two independent proteomic platforms. As a proof of concept, analysis of PCs obtained from pts enrolled in the frontline VDD study shows differential expression of 34 proteins in pts who achieved ≥VGPR vs. pts with <VGPR. Correlation with gene expression and further validation and functional analysis are in progress. This study was supported by a grant from the Multiple Myeloma Research Foundation. Disclosures: Jakubowiak: Millennium, Celgene, Bristol-Myers Squibb, Johnson & Johnson Ortho-Centocor: Honoraria; Millennium, Bristol-Myers Squibb: Membership on an entity's Board of Directors or advisory committees; Millennium, Celgene, Centocor-Ortho Biotech: Speakers Bureau.
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Guruceaga, Elizabeth, Alba Garin-Muga, and Victor Segura. "MiTPeptideDB: a proteogenomic resource for the discovery of novel peptides." Bioinformatics 36, no. 1 (June 27, 2019): 205–11. http://dx.doi.org/10.1093/bioinformatics/btz530.
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Abstract Motivation The principal lines of research in MS/MS based Proteomics have been directed toward the molecular characterization of the proteins including their biological functions and their implications in human diseases. Recent advances in this field have also allowed the first attempts to apply these techniques to the clinical practice. Nowadays, the main progress in Computational Proteomics is based on the integration of genomic, transcriptomic and proteomic experimental data, what is known as Proteogenomics. This methodology is being especially useful for the discovery of new clinical biomarkers, small open reading frames and microproteins, although their validation is still challenging. Results We detected novel peptides following a proteogenomic workflow based on the MiTranscriptome human assembly and shotgun experiments. The annotation approach generated three custom databases with the corresponding peptides of known and novel transcripts of both protein coding genes and non-coding genes. In addition, we used a peptide detectability filter to improve the computational performance of the proteomic searches, the statistical analysis and the robustness of the results. These innovative additional filters are specially relevant when noisy next generation sequencing experiments are used to generate the databases. This resource, MiTPeptideDB, was validated using 43 cell lines for which RNA-Seq experiments and shotgun experiments were available. Availability and implementation MiTPeptideDB is available at http://bit.ly/MiTPeptideDB. Supplementary information Supplementary data are available at Bioinformatics online.
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Lapinel, Nicole, Jessie Guidry, Mary Varkey, Manish Rijal, Arnold Zea, and Juzar Ali. "76215 Implementation of Proteomics as a Diagnostic tool for Nontuberculous mycobacteria (NTM) Infection." Journal of Clinical and Translational Science 5, s1 (March 2021): 140–41. http://dx.doi.org/10.1017/cts.2021.759.
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ABSTRACT IMPACT: Implementation of proteomics as a diagnostic tool for Nontuberculous mycobacteria (NTM) infection can provide a more accurate, efficient and cost-effective means for effectively diagnosing disease and enacting timely management decisions which can revolutionize patient care. OBJECTIVES/GOALS: Proteomic analysis is a proven diagnostic modality enabling rapid identification of microorganisms. We sought to apply proteomics to detect proteins unique to the most clinically relevant NTM. We then determined whether these unique proteomes could be used to successfully identify NTM species from in vitro cocktail preparations. METHODS/STUDY POPULATION: NTM reference strains for M. avium, m. intracellulare, m.chimaera, m. abscessus abscessus, m. abscessus massiliense and m. abscessus boletti were cultured in vitro and subjected to proteomic analysis using Liquid Chromatography tandem-Mass Spectrometry (LCMS). Tandem Mass Tag (TMT) data acquisition utilized an MS3 approach for data collection using Proteome Discoverer 2.4.A comparative analysis of the proteome of each of these six species was performed quantitatively using LCMS. The process was repeated for three technical replicates and analyzed using the SEQUEST algorithm. Only high scoring peptides were considered utilizing a false discovery rate (FDR) of 1%. Once species-specific proteins were identified, we validated detection in individual and mixed samples of the six reference strains. RESULTS/ANTICIPATED RESULTS: The proteomic profiling of the six NTM reference strains successfully demonstrated proteins unique to each of the MAC species and MABC subspecies. Proteomic MAC species analysis produced between 327 to 2,540 unique peptides for each of the 3 species. MABC proteomic analysis identified between 17-74 unique peptides for each of the 3 subspecies. Fifteen different mixed preparations of MAC and MABC were then subjected to LCMS analysis and compared against the proteome profiles already curated for the six strains. We accurately identified at least one NTM in the majority of the samples (10/15). In three samples (3/15), the NTM was not correctly identified; in two of the samples (2/15) we were unable to determine the identity of NTM within the preparation. Further database curation will be performed to hone these results. DISCUSSION/SIGNIFICANCE OF FINDINGS: Proteomic analysis of in vitro reference strains successfully demonstrated protein fingerprints specific to six common disease-causing strains of NTM. Such findings can be used to evaluate clinical samples enabling more efficient diagnostic specificity. Further research will focus on identification of NTM in sputum samples of infected patients.
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Ramirez, Juanma, Gorka Prieto, Anne Olazabal-Herrero, Eva Borràs, Elvira Fernandez-Vigo, Unai Alduntzin, Nerea Osinalde, et al. "A Proteomic Approach for Systematic Mapping of Substrates of Human Deubiquitinating Enzymes." International Journal of Molecular Sciences 22, no. 9 (May 3, 2021): 4851. http://dx.doi.org/10.3390/ijms22094851.
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The human genome contains nearly 100 deubiquitinating enzymes (DUBs) responsible for removing ubiquitin moieties from a large variety of substrates. Which DUBs are responsible for targeting which substrates remain mostly unknown. Here we implement the bioUb approach to identify DUB substrates in a systematic manner, combining gene silencing and proteomics analyses. Silencing of individual DUB enzymes is used to reduce their ubiquitin deconjugating activity, leading to an increase of the ubiquitination of their substrates, which can then be isolated and identified. We report here quantitative proteomic data of the putative substrates of 5 human DUBs. Furthermore, we have built a novel interactive database of DUB substrates to provide easy access to our data and collect DUB proteome data from other groups as a reference resource in the DUB substrates research field.
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Lahiri, Shibojyoti, Wasim Aftab, Lena Walenta, Leena Strauss, Matti Poutanen, Artur Mayerhofer, and Axel Imhof. "MALDI-IMS combined with shotgun proteomics identify and localize new factors in male infertility." Life Science Alliance 4, no. 3 (January 6, 2021): e202000672. http://dx.doi.org/10.26508/lsa.202000672.
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Spermatogenesis is a complex multi-step process involving intricate interactions between different cell types in the male testis. Disruption of these interactions results in infertility. Combination of shotgun tissue proteomics with MALDI imaging mass spectrometry is markedly potent in revealing topological maps of molecular processes within tissues. Here, we use a combinatorial approach on a characterized mouse model of hormone induced male infertility to uncover misregulated pathways. Comparative testicular proteome of wild-type and mice overexpressing human P450 aromatase (AROM+) with pathologically increased estrogen levels unravels gross dysregulation of spermatogenesis and emergence of pro-inflammatory pathways in AROM+ testis. In situ MS allowed us to localize misregulated proteins/peptides to defined regions within the testis. Results suggest that infertility is associated with substantial loss of proteomic heterogeneity, which define distinct stages of seminiferous tubuli in healthy animals. Importantly, considerable loss of mitochondrial factors, proteins associated with late stages of spermatogenesis and steroidogenic factors characterize AROM+ mice. Thus, the novel proteomic approach pinpoints in unprecedented ways the disruption of normal processes in testis and provides a signature for male infertility.
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Tan, Chee Fan, Hui San Teo, Jung Eun Park, Bamaprasad Dutta, Shun Wilford Tse, Melvin Khee-Shing Leow, Walter Wahli, and Siu Kwan Sze. "Exploring Extracellular Vesicles Biogenesis in Hypothalamic Cells through a Heavy Isotope Pulse/Trace Proteomic Approach." Cells 9, no. 5 (May 25, 2020): 1320. http://dx.doi.org/10.3390/cells9051320.
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Studies have shown that the process of extracellular vesicles (EVs) secretion and lysosome status are linked. When the lysosome is under stress, the cells would secrete more EVs to maintain cellular homeostasis. However, the process that governs lysosomal activity and EVs secretion remains poorly defined and we postulated that certain proteins essential for EVs biogenesis are constantly synthesized and preferentially sorted to the EVs rather than the lysosome. A pulsed stable isotope labelling of amino acids in cell culture (pSILAC) based quantitative proteomics methodology was employed to study the preferential localization of the newly synthesized proteins into the EVs over lysosome in mHypoA 2/28 hypothalamic cell line. Through proteomic analysis, we found numerous newly synthesized lysosomal enzymes—such as the cathepsin proteins—that preferentially localize into the EVs over the lysosome. Chemical inhibition against cathepsin D promoted EVs secretion and a change in the EVs protein composition and therefore indicates its involvement in EVs biogenesis. In conclusion, we applied a heavy isotope pulse/trace proteomic approach to study EVs biogenesis in hypothalamic cells. The results demonstrated the regulation of EVs secretion by the cathepsin proteins that may serve as a potential therapeutic target for a range of neurological disorder associated with energy homeostasis.
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Staunton, Lisa, Kathleen O'Connell, and Kay Ohlendieck. "Proteomic Profiling of Mitochondrial Enzymes during Skeletal Muscle Aging." Journal of Aging Research 2011 (2011): 1–9. http://dx.doi.org/10.4061/2011/908035.
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Mitochondria are of central importance for energy generation in skeletal muscles. Expression changes or functional alterations in mitochondrial enzymes play a key role during myogenesis, fibre maturation, and various neuromuscular pathologies, as well as natural fibre aging. Mass spectrometry-based proteomics suggests itself as a convenient large-scale and high-throughput approach to catalogue the mitochondrial protein complement and determine global changes during health and disease. This paper gives a brief overview of the relatively new field of mitochondrial proteomics and discusses the findings from recent proteomic surveys of mitochondrial elements in aged skeletal muscles. Changes in the abundance, biochemical activity, subcellular localization, and/or posttranslational modifications in key mitochondrial enzymes might be useful as novel biomarkers of aging. In the long term, this may advance diagnostic procedures, improve the monitoring of disease progression, help in the testing of side effects due to new drug regimes, and enhance our molecular understanding of age-related muscle degeneration.
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Daniel-Fischer, Lisa, Isabel J. Sobieszek, Anja Wagner, Juan Manuel Sacnun, Bruno Watschinger, Christoph Aufricht, Klaus Kratochwill, and Rebecca Herzog. "In-Depth Analysis of the Extracorporeal Proteome Adsorbed to Dialysis Membranes during Hemodialysis." Membranes 12, no. 11 (November 9, 2022): 1120. http://dx.doi.org/10.3390/membranes12111120.
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Used hemodialysis membranes (HD-M) are a valuable reservoir of biological information. Proteins bind to HD-M, but whether this process depends on the type of membrane or patient factors or selectively affects specific protein classes has not been adequately elucidated. State-of-the-art proteomics techniques are capable of identifying and quantifying this therapy-specific subproteome to enable the analysis of disease- or membrane-induced pathophysiologies. We demonstrate the feasibility of the deep proteomic characterization of the extracorporeal proteome adsorbed to HD-M. A shotgun proteomics approach using nano-flow liquid chromatography coupled to mass-spectrometry identified 1648 unique proteins eluted by a chaotropic buffer from the HD-M of eight patients. In total, 995 proteins were present in all eluates; a more stringent approach showed that a core proteome of 310 proteins could be identified independently in all samples. Stability of the dialyzer proteome was demonstrated by a >90% re-identification rate on longitudinal samples of a single patient. The core proteome showed an overrepresentation of pathways of hemostasis and the immune system, and showed differences in membrane materials (polysulfone vs. helixone). This study demonstrates that optimized conditions combined with high-performance proteomics enable the in-depth exploration of the subproteome bound to HD-M, yielding a stable core proteome that can be exploited to study patient-specific factors and improve hemodialysis therapy.
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Vinik, Yaron, Francisco Gabriel Ortega, Gordon B. Mills, Yilling Lu, Menucha Jurkowicz, Sharon Halperin, Mor Aharoni, Mordechai Gutman, and Sima Lev. "Proteomic analysis of circulating extracellular vesicles identifies potential markers of breast cancer progression, recurrence, and response." Science Advances 6, no. 40 (October 2020): eaba5714. http://dx.doi.org/10.1126/sciadv.aba5714.
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Proteomic profiling of circulating small extracellular vesicles (sEVs) represents a promising, noninvasive approach for early detection and therapeutic monitoring of breast cancer (BC). We describe a relatively low-cost, fast, and reliable method to isolate sEVs from plasma of BC patients and analyze their protein content by semiquantitative proteomics. sEV-enriched fractions were isolated from plasma of healthy controls and BC patients at different disease stages before and after surgery. Proteomic analysis of sEV-enriched fractions using reverse phase protein array revealed a signature of seven proteins that differentiated BC patients from healthy individuals, of which FAK and fibronectin displayed high diagnostic accuracy. The size of sEVs was significantly reduced in advanced disease stage, concomitant with a stage-specific protein signature. Furthermore, we observed protein-based distinct clusters of healthy controls, chemotherapy-treated and untreated postsurgery samples, as well as a predictor of high risk of cancer relapse, suggesting that the applied methods warrant development for advanced diagnostics.
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Smith, Helen, Sharon Grant, Paula Meleady, Michael Henry, Donal O’Gorman, Martin Clynes, and Richard Murphy. "Yeast Mannan-Rich Fraction Modulates Endogenous Reactive Oxygen Species Generation and Antibiotic Sensitivity in Resistant E. coli." International Journal of Molecular Sciences 24, no. 1 (December 22, 2022): 218. http://dx.doi.org/10.3390/ijms24010218.
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Mannan-rich fraction (MRF) isolated from Saccharomyces cerevisiae has been studied for its beneficial impact on animal intestinal health. Herein, we examined how MRF affected the formation of reactive oxygen species (ROS), impacting antibiotic susceptibility in resistant Escherichia coli through the modulation of bacterial metabolism. The role of MRF in effecting proteomic change was examined using a proteomics-based approach. The results showed that MRF, when combined with bactericidal antibiotic treatment, increased ROS production in resistant E. coli by 59.29 ± 4.03% compared to the control (p ≤ 0.05). We further examined the effect of MRF alone and in combination with antibiotic treatment on E. coli growth and explored how MRF potentiates bacterial susceptibility to antibiotics via proteomic changes in key metabolic pathways. Herein we demonstrated that MRF supplementation in the growth media of ampicillin-resistant E. coli had a significant impact on the normal translational control of the central metabolic pathways, including those involved in the glycolysis–TCA cycle (p ≤ 0.05).
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Arderiu, Gemma, Guiomar Mendieta, Alex Gallinat, Carmen Lambert, Alberto Díez-Caballero, Carlos Ballesta, and Lina Badimon. "Type 2 Diabetes in Obesity: A Systems Biology Study on Serum and Adipose Tissue Proteomic Profiles." International Journal of Molecular Sciences 24, no. 1 (January 3, 2023): 827. http://dx.doi.org/10.3390/ijms24010827.
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Obesity is associated with metabolic disorders such as insulin resistance and type 2 diabetes mellitus (T2DM), further increasing an already heightened cardiovascular risk. Here, amongst obese class III bariatric surgery patients, we have investigated the effect of T2DM in serum and in two, same patient, adipose tissue (AT) depots through proteomic profile expression analyses. Serum and AT samples from subcutaneous (SAT) and visceral (VAT) fat were collected during bariatric surgery. Bead-based targeted multiplex assay systems were used to simultaneously detect and quantify multiple targets in serum samples (targeted proteomics) and analyze changes in adipokine serum composition. AT samples were assessed through an untargeted proteomics approach. Through a systems biology analysis of the proteomic data, information on the affected biological pathways was acquired. In obese class III individuals, the presence of T2DM induced a significantly higher systemic release of ghrelin, GLP-1, glucagon, MMP3, BAFF, chitinase 3-like 1, TNF-R1 and TNF-R2, and a lower systemic release of IL-8. SAT and VAT proteomes belonging to the same patient showed significant differences in local protein content. While the proteins upregulated in VAT were indicative of metabolic dysregulation, SAT protein upregulation suggested adequate endocrine regulation. The presence of T2DM significantly affected VAT protein composition through the upregulation of dysregulating metabolic pathways, but SAT protein composition was not significantly modified. Our results show that T2DM induces metabolic dysregulation in obese individuals with changes in systemic marker levels and impairment of proteostasis in VAT but not in SAT.
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Senavirathna, Lakmini, Cheng Ma, Ru Chen, and Sheng Pan. "Spectral Library-Based Single-Cell Proteomics Resolves Cellular Heterogeneity." Cells 11, no. 15 (August 7, 2022): 2450. http://dx.doi.org/10.3390/cells11152450.
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Dissecting the proteome of cell types and states at single-cell resolution, while being highly challenging, has significant implications in basic science and biomedicine. Mass spectrometry (MS)-based single-cell proteomics represents an emerging technology for system-wide, unbiased profiling of proteins in single cells. However, significant challenges remain in analyzing an extremely small amount of proteins collected from a single cell, as a proteome-wide amplification of proteins is not currently feasible. Here, we report an integrated spectral library-based single-cell proteomics (SLB-SCP) platform that is ultrasensitive and well suited for a large-scale analysis. To overcome the low MS/MS signal intensity intrinsically associated with a single-cell analysis, this approach takes an alternative approach by extracting a breadth of information that specifically defines the physicochemical characteristics of a peptide from MS1 spectra, including monoisotopic mass, isotopic distribution, and retention time (hydrophobicity), and uses a spectral library for proteomic identification. This conceptually unique MS platform, coupled with the DIRECT sample preparation method, enabled identification of more than 2000 proteins in a single cell to distinguish different proteome landscapes associated with cellular types and heterogeneity. We characterized individual normal and cancerous pancreatic ductal cells (HPDE and PANC-1, respectively) and demonstrated the substantial difference in the proteomes between HPDE and PANC-1 at the single-cell level. A significant upregulation of multiple protein networks in cancer hallmarks was identified in the PANC-1 cells, functionally discriminating the PANC-1 cells from the HPDE cells. This integrated platform can be built on high-resolution MS and widely accepted proteomic software, making it possible for community-wide applications.
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Harder, B., E. Casavant, J. McBride, K. T. Park, W. R. Mathews, V. G. Anania, and A. Lekkerkerker. "P052 Comprehensive proteomic profiling of stool from UC and CD patients reveals expected and novel mucosal pathobiology, enabling identification of candidate non-invasive biomarkers to monitor mucosal disease activity." Journal of Crohn's and Colitis 15, Supplement_1 (May 1, 2021): S160. http://dx.doi.org/10.1093/ecco-jcc/jjab076.181.
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Abstract Background Inflammatory bowel disease (IBD) is a multifactorial disorder characterized by chronic gastrointestinal (GI) inflammation. In clinical practice, physicians urgently need biomarkers to monitor changes in mucosal disease to manage treatment. Non-invasive tools such as fecal biomarkers could allow for frequent monitoring of mucosal disease activity and may reflect inflammation of the entire GI tract. Towards identification of novel non-invasive fecal biomarkers, we performed a comprehensive, unbiased, analysis of the human fecal proteome from ulcerative colitis (UC), Crohn’s disease (CD) patients, and healthy controls (HC), focusing on human secreted proteins using proteomic approaches. Methods Fecal extraction processes optimized for recovery of secreted proteins were applied to stool samples of mild to severe UC and CD patients and HCs, 20 samples per group. An unbiased survey of the human fecal proteome of the IBD and HC stool extracts was performed using both data-dependent acquisition and data-independent acquisition methods on QExactive HF and Fusion Lumos mass spectrometers. Data were analyzed in Spectronaut for peptide identification, followed by analysis in MS Stats for protein quantification. Fecal calprotectin (fCAL) levels were measured in these stool samples using the Buhlmann EK-CAL immunoassay kit to correlate fCAL levels to proteomic calprotectin (S100A8/S100A9 heterodimer). Results We identified and quantified 594 differentially expressed proteins in stool using proteomics, including known proteins perturbed in IBD pathobiology. Elevated levels of neutrophilic proteins, e.g. fCal, neutrophil elastase, lactoferrin and leucine-rich Α-2 glycoprotein 1 were detected as well as proteins linked to rectal bleeding and ulceration e.g. hemoglobin subunit alpha 1 and beta. Pancreatic enzymes, suggested to be associated with active disease, were also detected. Overall, UC and CD contained many proteins not detected in HC. Differential abundances were observed between UC and CD patients, possibly reflecting differences in mucosal pathophysiology. The abundance of proteomic calprotectin correlated highly with the fCAL levels as determined by immunoassay (Spearman rank order > 0.9 for S100A8 and S100A9 to fCAL), validating the fecal proteomic approach to uncover known and novel proteins of IBD biology. Conclusion We developed and qualified methods to perform fecal proteomics on IBD stool samples, enabling identification of known and novel proteins of mucosal UC and CD disease biology. In depth analysis of these proteomic datasets may reveal novel fecal biomarker candidates, targeted to reflect mucosal disease activity and potentially serve as non-invasive surrogates for endoscopy and help guide treatment in IBD patients.
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Ceolin Mariano, Douglas Oscar, Úrsula Castro de Oliveira, André Junqueira Zaharenko, Daniel Carvalho Pimenta, Gandhi Rádis-Baptista, and Álvaro Rossan de Brandão Prieto-da-Silva. "Bottom-Up Proteomic Analysis of Polypeptide Venom Components of the Giant Ant Dinoponera Quadriceps." Toxins 11, no. 8 (July 29, 2019): 448. http://dx.doi.org/10.3390/toxins11080448.
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Ant species have specialized venom systems developed to sting and inoculate a biological cocktail of organic compounds, including peptide and polypeptide toxins, for the purpose of predation and defense. The genus Dinoponera comprises predatory giant ants that inoculate venom capable of causing long-lasting local pain, involuntary shaking, lymphadenopathy, and cardiac arrhythmias, among other symptoms. To deepen our knowledge about venom composition with regard to protein toxins and their roles in the chemical–ecological relationship and human health, we performed a bottom-up proteomics analysis of the crude venom of the giant ant D. quadriceps, popularly known as the “false” tocandiras. For this purpose, we used two different analytical approaches: (i) gel-based proteomics approach, wherein the crude venom was resolved by denaturing sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and all protein bands were excised for analysis; (ii) solution-based proteomics approach, wherein the crude venom protein components were directly fragmented into tryptic peptides in solution for analysis. The proteomic data that resulted from these two methodologies were compared against a previously annotated transcriptomic database of D. quadriceps, and subsequently, a homology search was performed for all identified transcript products. The gel-based proteomics approach unequivocally identified nine toxins of high molecular mass in the venom, as for example, enzymes [hyaluronidase, phospholipase A1, dipeptidyl peptidase and glucose dehydrogenase/flavin adenine dinucleotide (FAD) quinone] and diverse venom allergens (homologous of the red fire ant Selenopsis invicta) and venom-related proteins (major royal jelly-like). Moreover, the solution-based proteomics revealed and confirmed the presence of several hydrolases, oxidoreductases, proteases, Kunitz-like polypeptides, and the less abundant inhibitor cysteine knot (ICK)-like (knottin) neurotoxins and insect defensin. Our results showed that the major components of the D. quadriceps venom are toxins that are highly likely to damage cell membranes and tissue, to cause neurotoxicity, and to induce allergic reactions, thus, expanding the knowledge about D. quadriceps venom composition and its potential biological effects on prey and victims.
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Chen, Yanyu, Wenyun Hou, Miner Zhong, and Bin Wu. "Comprehensive Proteomic Analysis of Colon Cancer Tissue Revealed the Reason for the Worse Prognosis of Right-Sided Colon Cancer and Mucinous Colon Cancer at the Protein Level." Current Oncology 28, no. 5 (September 15, 2021): 3554–72. http://dx.doi.org/10.3390/curroncol28050305.
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To clarify the molecular mechanisms underlying the poor prognosis of right-sided and mucinous colon cancer at the proteomic level. A tandem mass tag-proteomics approach was used to identify differentially expressed proteins (DEPs) in colon carcinoma tissues from different locations and with different histological types to reveal the underlying mechanisms of these differences at the protein level. In additional, the DEPs were analyzed using bioinformatics methods. The proteomics profiles among colon cancers with different tumor locations and histological types were dramatically distinguished. In terms of tumor locations, the right-sided carcinoma specific DEPs may promote the tumor progression via activating inflammation, metastasis associated pathways. When referring to histological types, the mucinous colon cancers perhaps increased the invasion and metastasis through distinct mechanisms in different tumor locations. For mucinous cancer located in right-sided colon, the mucinous specific DEPs were mainly associated with ECM-related remodeling and the IL-17 signal pathway. For mucinous cancer located in left-sided colon, the mucinous specific DEPs showed a strong relationship with ACE2/Ang-(1–7)/MasR axis. The proteomics profiles of colon cancers showed distinct differences related to locations and histological types. These results suggested a distinct mechanism underlying the diverse subtypes of colon cancers.
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Zhang, Zheyu, Wenbo Wang, Ling Jin, Xin Cao, Gonghui Jian, Ning Wu, Xia Xu, Ye Yao, and Dongsheng Wang. "iTRAQ-Based Quantitative Proteomics Analysis of the Protective Effect of Yinchenwuling Powder on Hyperlipidemic Rats." Evidence-Based Complementary and Alternative Medicine 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/3275096.
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Yinchenwuling powder (YCL) is an effective traditional Chinese medicine formula to modulate lipid levels. In this study, we established hyperlipidemic rat models and treated them with YCL. The serum concentrations of lipid, malondialdehyde (MDA), endothelin-1 (ET-1), and calcitonin gene-related peptide (CGRP) were measured. Adventitia-free vascular proteins between hyperlipidemic rats and YCL-treated rats were identified using iTRAQ-based quantitative proteomics research approach. Proteins with 1.3-fold difference were analyzed through bioinformatics, and proteomic results were verified by Western blot. The results showed that the serum levels of TC, TG, LDL-C, ET-1, and MDA were significantly decreased, whereas the HDL-C and CGRP levels were significantly increased in the YCL-treated group. Proteomics technology identified 4,382 proteins, and 15 proteins were selected on the basis of their expression levels and bioinformatics. Of these proteins, 2 (Adipoq and Gsta1) were upregulated and 13 (C3, C4, C6, Cfh, Cfp, C8g, C8b, Lgals1, Fndc1, Fgb, Fgg, Kng1, and ApoH) were downregulated in the YCL-treated rats. Their functions were related to immunity, inflammation, coagulation and hemostasis, oxidation and antioxidation, and lipid metabolism and transport. The validated results of ApoH were consistent with the proteomics results. This study enhanced our understanding on the therapeutic effects and mechanism of YCL on hyperlipidemia.