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Journal articles on the topic 'Proteomics'
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1
Krieg, Rene C., Cloud P. Paweletz, Lance A. Liotta, and Emanuel F. Petricoin. "Clinical Proteomics for Cancer Biomarker Discovery and Therapeutic Targeting." Technology in Cancer Research & Treatment 1, no. 4 (August 2002): 263–72. http://dx.doi.org/10.1177/153303460200100407.
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As we emerge into the post-genome era, proteomics finds itself as the driving force field as we translate the nucleic acid information archive into understanding how the cell actually works and how disease processes operate. Even so, the traditionally held view of proteomics as simply cataloging and developing lists of the cellular protein repertoire of a cell are now changing, especially in the sub-discipline of clinical proteomics. The most relevant information archive to clinical applications and drug development involves the elucidation of the information flow of the cell; the “software” of protein pathway networks and circuitry. The deranged circuitry of the cell as the drug target itself as well as the effect of the drug on not just the target, but also the entire network, is what we now are striving towards. Clinical proteomics, as a new and most exciting sub-discipline of proteomics, involves the bench-to-bedside clinical application of proteomic tools. Unlike the genome, there are potentially thousands of proteomes: each cell type has its own unique proteome. Moreover, each cell type can alter its proteome depending on the unique tissue microenvironment in which it resides, giving rise to multiple permutations of a single proteome. Since there is no polymerase chain reaction equivalent to proteomics- identifying and discovering the “wiring diagram” of a human diseased cell in a biopsy specimen remains a daunting challenge. New micro-proteomic technologies are being and still need to be developed to drill down into the proteomes of clinically relevant material. Cancer, as a model disease, provides a fertile environment to study the application of proteomics at the bedside. The promise of clinical proteomics and the new technologies that are developed is that we will detect cancer earlier through discovery of biomarkers, we will discover the next generation of targets and imaging biomarkers, and we can then apply this knowledge to patient-tailored therapy.
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Sukumaran, Pariveena, Ainun Aida Bahardin, Luqmanul Hakim Abdul Razak, and Mohd Harizal Senik. "Application of Proteomics in Alzheimer’s Disease: A Mini Review." SEPTEMBER 2023 19, no. 5 (September 11, 2023): 317–30. http://dx.doi.org/10.47836/mjmhs.19.5.38.
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Alzheimer’s disease (AD) is classified as one of neurodegenerative disease caused by neuronal death. It is characterized as memory impairment, including the inability to produce new memories. Since AD has low treatment effectiveness, proteomics research opens possibilities for advancement. Proteomics is the study of proteomes produced by the disease-bearing host to identify and understand diseases. In this case, to investigate the use of protein as a reliable molecular entity and their involvement in AD. Therefore, this review focused on three main applications of proteomics; the potential use of proteomics as a diagnostic tool for AD, the use of proteomics to assess the treatment progression of AD and the advancement in AD research. The review discussed three research areas utilizing the proteomics approach: ageing, behavioural, and demographic research of AD populations. Proteomic approaches have also been shown to be effective to discover the biomarkers for infectious diseases, cancers, heart diseases, and neurological disorders. Although much work remained to be done, the proteomics approach is an interesting method to be carried out in detecting AD at an earlier stage and will be very useful for AD treatment and management in the future.
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Mahajan, R., and P. Gupta. "Proteomics: taking over where genomics leaves off." Czech Journal of Genetics and Plant Breeding 46, No. 2 (June 29, 2010): 47–53. http://dx.doi.org/10.17221/34/2009-cjgpb.
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The proteomic studies are simultaneously developed in several directions and significantly influence our notions on the capabilities of biological sciences. The need for proteomics research is necessary as there are certain genes in a cell that encode proteins with specific functions. Using a variety of techniques, proteomics can be used to study how proteins interact within a system or how the protein expression changes in different parts of the body, in different stages of its life cycle and in different environmental conditions as every individual has one genome and many proteomes. Besides the qualitative and quantitative description of the expressed proteins, proteomics also deals with the analysis of mutual interactions of proteins. Thereby, candidate proteins can be identified which may be used as starting-points for diagnostic or even therapeutic approaches.
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Sokolowska, Izabela, Armand G. Ngounou Wetie, Alisa G. Woods, and Costel C. Darie. "Applications of Mass Spectrometry in Proteomics." Australian Journal of Chemistry 66, no. 7 (2013): 721. http://dx.doi.org/10.1071/ch13137.
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Characterisation of proteins and whole proteomes can provide a foundation to our understanding of physiological and pathological states and biological diseases or disorders. Constant development of more reliable and accurate mass spectrometry (MS) instruments and techniques has allowed for better identification and quantification of the thousands of proteins involved in basic physiological processes. Therefore, MS-based proteomics has been widely applied to the analysis of biological samples and has greatly contributed to our understanding of protein functions, interactions, and dynamics, advancing our knowledge of cellular processes as well as the physiology and pathology of the human body. This review will discuss current proteomic approaches for protein identification and characterisation, including post-translational modification (PTM) analysis and quantitative proteomics as well as investigation of protein–protein interactions (PPIs).
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Sadeesh, Nithin, Mauro Scaravilli, and Leena Latonen. "Proteomic Landscape of Prostate Cancer: The View Provided by Quantitative Proteomics, Integrative Analyses, and Protein Interactomes." Cancers 13, no. 19 (September 27, 2021): 4829. http://dx.doi.org/10.3390/cancers13194829.
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Prostate cancer is the second most frequent cancer of men worldwide. While the genetic landscapes and heterogeneity of prostate cancer are relatively well-known already, methodological developments now allow for studying basic and dynamic proteomes on a large scale and in a quantitative fashion. This aids in revealing the functional output of cancer genomes. It has become evident that not all aberrations at the genetic and transcriptional level are translated to the proteome. In addition, the proteomic level contains heterogeneity, which increases as the cancer progresses from primary prostate cancer (PCa) to metastatic and castration-resistant prostate cancer (CRPC). While multiple aspects of prostate adenocarcinoma proteomes have been studied, less is known about proteomes of neuroendocrine prostate cancer (NEPC). In this review, we summarize recent developments in prostate cancer proteomics, concentrating on the proteomic landscapes of clinical prostate cancer, cell line and mouse model proteomes interrogating prostate cancer-relevant signaling and alterations, and key prostate cancer regulator interactomes, such as those of the androgen receptor (AR). Compared to genomic and transcriptomic analyses, the view provided by proteomics brings forward changes in prostate cancer metabolism, post-transcriptional RNA regulation, and post-translational protein regulatory pathways, requiring the full attention of studies in the future.
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Walther, Tobias C., and Matthias Mann. "Mass spectrometry–based proteomics in cell biology." Journal of Cell Biology 190, no. 4 (August 23, 2010): 491–500. http://dx.doi.org/10.1083/jcb.201004052.
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The global analysis of protein composition, modifications, and dynamics are important goals in cell biology. Mass spectrometry (MS)–based proteomics has matured into an attractive technology for this purpose. Particularly, high resolution MS methods have been extremely successful for quantitative analysis of cellular and organellar proteomes. Rapid advances in all areas of the proteomic workflow, including sample preparation, MS, and computational analysis, should make the technology more easily available to a broad community and turn it into a staple methodology for cell biologists.
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Oikonomou, Panos, Roberto Salatino, and Saeed Tavazoie. "In vivo mRNA display enables large-scale proteomics by next generation sequencing." Proceedings of the National Academy of Sciences 117, no. 43 (October 9, 2020): 26710–18. http://dx.doi.org/10.1073/pnas.2002650117.
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Large-scale proteomic methods are essential for the functional characterization of proteins in their native cellular context. However, proteomics has lagged far behind genomic approaches in scalability, standardization, and cost. Here, we introduce in vivo mRNA display, a technology that converts a variety of proteomics applications into a DNA sequencing problem. In vivo-expressed proteins are coupled with their encoding messenger RNAs (mRNAs) via a high-affinity stem-loop RNA binding domain interaction, enabling high-throughput identification of proteins with high sensitivity and specificity by next generation DNA sequencing. We have generated a high-coverage in vivo mRNA display library of the Saccharomyces cerevisiae proteome and demonstrated its potential for characterizing subcellular localization and interactions of proteins expressed in their native cellular context. In vivo mRNA display libraries promise to circumvent the limitations of mass spectrometry-based proteomics and leverage the exponentially improving cost and throughput of DNA sequencing to systematically characterize native functional proteomes.
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Duong, Van-An, and Hookeun Lee. "Bottom-Up Proteomics: Advancements in Sample Preparation." International Journal of Molecular Sciences 24, no. 6 (March 10, 2023): 5350. http://dx.doi.org/10.3390/ijms24065350.
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Liquid chromatography–tandem mass spectrometry (LC–MS/MS)-based proteomics is a powerful technique for profiling proteomes of cells, tissues, and body fluids. Typical bottom-up proteomic workflows consist of the following three major steps: sample preparation, LC–MS/MS analysis, and data analysis. LC–MS/MS and data analysis techniques have been intensively developed, whereas sample preparation, a laborious process, remains a difficult task and the main challenge in different applications. Sample preparation is a crucial stage that affects the overall efficiency of a proteomic study; however, it is prone to errors and has low reproducibility and throughput. In-solution digestion and filter-aided sample preparation are the typical and widely used methods. In the past decade, novel methods to improve and facilitate the entire sample preparation process or integrate sample preparation and fractionation have been reported to reduce time, increase throughput, and improve reproducibility. In this review, we have outlined the current methods used for sample preparation in proteomics, including on-membrane digestion, bead-based digestion, immobilized enzymatic digestion, and suspension trapping. Additionally, we have summarized and discussed current devices and methods for integrating different steps of sample preparation and peptide fractionation.
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Stubbs, Keith A., and David J. Vocadlo. "Affinity-Based Proteomics Probes; Tools for Studying Carbohydrate-Processing Enzymes." Australian Journal of Chemistry 62, no. 6 (2009): 521. http://dx.doi.org/10.1071/ch09140.
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As more information becomes available through the efforts of high-throughput screens, there is increasing pressure on the three main ‘omic’ fields, genomics, proteomics, and metabolomics, to organize this material into useful libraries that enable further understanding of biological systems. Proteomics especially is faced with two highly challenging tasks. The first is assigning the activity of thousands of putative proteins, the existence of which has been suggested by genomics studies. The second is to serve as a link between genomics and metabolomics by demonstrating which enzymes play roles in specific metabolic pathways. Underscoring these challenges in one area are the thousands of putative carbohydrate-processing enzymes that have been bioinformatically identified, mostly in prokaryotes, but that have unknown or unverified activities. Using two brief examples, we illustrate how biochemical pathways within bacteria that involve carbohydrate-processing enzymes present interesting potential antimicrobial targets, offering a clear motivation for gaining a functional understanding of biological proteomes. One method for studying proteomes that has been developed recently is to use synthetic compounds termed activity-based proteomics probes. Activity-based proteomic profiling using such probes facilitates rapid identification of enzyme activities within proteomes and assignment of function to putative enzymes. Here we discuss the general design principles for these probes with particular reference to carbohydrate-processing enzymes and give an example of using such a probe for the profiling of a bacterial proteome.
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Soleymani, Nooshinmehr, Soheil Sadr, Cinzia Santucciu, Shiva Dianaty, Narges Lotfalizadeh, Ashkan Hajjafari, Fatemeh Heshmati, and Hassan Borji. "Unveiling Novel Insights in Helminth Proteomics: Advancements, Applications, and Implications for Parasitology and Beyond." Biologics 4, no. 3 (September 19, 2024): 314–44. http://dx.doi.org/10.3390/biologics4030020.
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Helminths have developed intricate mechanisms to survive and evade the host’s immune responses. Hence, understanding the excretory-secretory products (ESPs) by helminths is crucial for developing control tools, including drug targets, vaccines, and potential therapies for inflammatory and metabolic disorders caused by them. Proteomics, the large-scale analysis of proteins, offers a powerful approach to unravel the complex proteomes of helminths and gain insights into their biology. Proteomics, as a science that delves into the functions of proteins, has the potential to revolutionize clinical therapies against parasitic infections that have developed anthelminthic resistance. Proteomic technologies lay a framework for accompanying genomic, reverse genetics, and pharmacokinetic approaches to provide more profound or broader coverage of the cellular mechanisms that underlie the response to anthelmintics. With the development of vaccines against helminth infections, proteomics has brought a major change to parasitology. The proteome of helminths can be analyzed comprehensively, revealing the complex network of proteins that enable parasite survival and pathogenicity. Furthermore, it reveals how parasites interact with hosts’ immune systems. The current article reviews the latest advancements in helminth proteomics and highlights their valuable contributions to the search for anthelminthic vaccines.
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Masood, Afshan, Hicham Benabdelkamel, and Assim Alfadda. "Obesity Proteomics: An Update on the Strategies and Tools Employed in the Study of Human Obesity." High-Throughput 7, no. 3 (September 12, 2018): 27. http://dx.doi.org/10.3390/ht7030027.
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Proteomics has become one of the most important disciplines for characterizing cellular protein composition, building functional linkages between protein molecules, and providing insight into the mechanisms of biological processes in a high-throughput manner. Mass spectrometry-based proteomic advances have made it possible to study human diseases, including obesity, through the identification and biochemical characterization of alterations in proteins that are associated with it and its comorbidities. A sizeable number of proteomic studies have used the combination of large-scale separation techniques, such as high-resolution two-dimensional gel electrophoresis or liquid chromatography in combination with mass spectrometry, for high-throughput protein identification. These studies have applied proteomics to comprehensive biochemical profiling and comparison studies while using different tissues and biological fluids from patients to demonstrate the physiological or pathological adaptations within their proteomes. Further investigations into these proteome-wide alterations will enable us to not only understand the disease pathophysiology, but also to determine signature proteins that can serve as biomarkers for obesity and related diseases. This review examines the different proteomic techniques used to study human obesity and discusses its successful applications along with its technical limitations.
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Thanasupawat, Thatchawan, Aleksandra Glogowska, Christopher Pascoe, Sai Nivedita Krishnan, Maliha Munir, Farhana Begum, Jason Beiko, et al. "Slow Off-Rate Modified Aptamer (SOMAmer) Proteomic Analysis of Patient-Derived Malignant Glioma Identifies Distinct Cellular Proteomes." International Journal of Molecular Sciences 22, no. 17 (September 3, 2021): 9566. http://dx.doi.org/10.3390/ijms22179566.
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Malignant gliomas derive from brain glial cells and represent >75% of primary brain tumors. This includes anaplastic astrocytoma (grade III; AS), the most common and fatal glioblastoma multiforme (grade IV; GBM), and oligodendroglioma (ODG). We have generated patient-derived AS, GBM, and ODG cell models to study disease mechanisms and test patient-centered therapeutic strategies. We have used an aptamer-based high-throughput SOMAscan® 1.3K assay to determine the proteomic profiles of 1307 different analytes. SOMAscan® proteomes of AS and GBM self-organized into closely adjacent proteomes which were clearly distinct from ODG proteomes. GBM self-organized into four proteomic clusters of which SOMAscan® cluster 4 proteome predicted a highly inter-connected proteomic network. Several up- and down-regulated proteins relevant to glioma were successfully validated in GBM cell isolates across different SOMAscan® clusters and in corresponding GBM tissues. Slow off-rate modified aptamer proteomics is an attractive analytical tool for rapid proteomic stratification of different malignant gliomas and identified cluster-specific SOMAscan® signatures and functionalities in patient GBM cells.
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Schulze, W. "Environmental proteomics – what proteins from soil and surface water can tell us: a perspective." Biogeosciences Discussions 1, no. 1 (July 22, 2004): 195–218. http://dx.doi.org/10.5194/bgd-1-195-2004.
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Abstract. Mass spectrometry based proteomics is widely used to study cellular processes in model organisms. However, it has not much been applied in environmental research because it was thought that free proteins would not be sufficiently stable in the environments. Based on recent observations that protein can readily be detected as a component of dissolve organic carbon, this article gives an overview about the possible use of proteomic methods in ecology and environmental sciences. At this stage, there are two areas of interest: (1) the identification of phylogenetic groups contributing to the DOC pool, and (2) identification of the origin of specific enzymes that are important for ecosystem processes. In this paper methods of mass spectrometry based proteomics were applied to identify proteins from DOC and water samples from different environments. It is demonstrated, that environmental proteomics is capable to distinguish the active set of organisms of different horizons of soils, and from various sources of surface water. Currently the limitation is given by the present knowledge of the genome of soil organisms. In addition, environmental proteomics allows to relate protein presence to biogeochemical processes, and to identify the source organisms for specific enzymes. Taking laccases as an example, it is shown that this enzyme is excreted into soils by a whole range of organisms from different phylogenetic groups. Further applications, such as in pollution reseach are conceivable. In summary, environmental proteomcis opens a new area of research between the fields of microbiology and biogeochemistry.
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Ji, Qing, Fangshi Zhu, Xuan Liu, Qi Li, and Shi-bing Su. "Recent Advance in Applications of Proteomics Technologies on Traditional Chinese Medicine Research." Evidence-Based Complementary and Alternative Medicine 2015 (2015): 1–13. http://dx.doi.org/10.1155/2015/983139.
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Proteomics technology, a major component of system biology, has gained comprehensive attention in the area of medical diagnosis, drug development, and mechanism research. On the holistic and systemic theory, proteomics has a convergence with traditional Chinese medicine (TCM). In this review, we discussed the applications of proteomic technologies in diseases-TCM syndrome combination researches. We also introduced the proteomic studies on thein vivoandin vitroeffects and underlying mechanisms of TCM treatments using Chinese herbal medicine (CHM), Chinese herbal formula (CHF), and acupuncture. Furthermore, the combined studies of proteomics with other “-omics” technologies in TCM were also discussed. In summary, this report presents an overview of the recent advances in the application of proteomic technologies in TCM studies and sheds a light on the future global and further research on TCM.
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Komatsu, Setsuko, Myeong W. Oh, Hee Y. Jang, Soo J. Kwon, Hye R. Kim, Jung H. Ko, Sun H. Woo, and Yohei Nanjo. "Proteomic Analyses of Soybean Root Tips During Germination." Protein & Peptide Letters 21, no. 12 (November 5, 2014): 1308–19. http://dx.doi.org/10.2174/0929866521666140526152426.
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Plant root systems form complex networks with the surrounding soil environment and are controlled by both internal and external factors. To better understand the function of root tips of soybean during germination, three proteomic techniques were used to analyze the protein profiles of root tip cells. Proteins were extracted from the root tips of 4-dayold soybean seedlings and analyzed using two-dimensional (2D) gel electrophoresis-based proteomics, SDS-gel based proteomics, and gel-free proteomics techniques. A total of 121, 862, and 341 proteins were identified in root tips using the 2D gel-based, SDS gel-based, and gel-free proteomic techniques, respectively. The proteins identified by 2D gel-based proteomic analysis were predominantly localized in the cytoplasm, whereas nuclear-localized proteins were most commonly identified by the SDS gel-based and gel-free proteomics techniques. Of the 862 proteins identified in the SDS gelbased proteomic analysis, 190 were protein synthesis-related proteins. Furthermore, 24 proteins identified using the 2Dgel based proteomic technique shifted between acidic and basic isoelectric points, and 2 proteins, heat shock protein 70.2 and AAA-type ATPase, displayed two different molecular weights at the same isoelectric point. Taken together, these results suggest that a number of proteins related to protein synthesis and modification are activated in the root tips of soybean seedlings during germination.
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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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Yates, III, John R. "Recent technical advances in proteomics." F1000Research 8 (March 29, 2019): 351. http://dx.doi.org/10.12688/f1000research.16987.1.
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Mass spectrometry is one of the key technologies of proteomics, and over the last decade important technical advances in mass spectrometry have driven an increased capability for proteomic discovery. In addition, new methods to capture important biological information have been developed to take advantage of improving proteomic tools.
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Katamesh, Basant E., Pragyat Futela, Ann Vincent, Bright Thilagar, Mary Whipple, Abdul Rhman Hassan, Mohamed Abuelazm, Sanjeev Nanda, Christopher Anstine, and Abhinav Singla. "Navigating the Proteomic Landscape of Menopause: A Review." Medicina 60, no. 9 (September 9, 2024): 1473. http://dx.doi.org/10.3390/medicina60091473.
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Background and Objectives: Proteomics encompasses the exploration of protein composition, regulation, function, and pathways. Its influence spans diverse clinical fields and holds promise in addressing various women’s health conditions, including cancers, osteoporosis, and cardiovascular disorders. However, no comprehensive summary of proteomics and menopausal health exists. Our objective was to summarize proteomic profiles associated with diseases and disorders in peri- and postmenopausal women. Materials and Methods: We conducted a comprehensive search of databases including PubMed, Google Scholar, the Cochrane database, Elsevier, and ScienceDirect until 2022. A total of 253 studies were identified, and 41 studies met the inclusion criteria to identify data of interest. These included the study design, disease, and proteomics/proteins of significance, as described by the authors. Results: The 41 studies covered diverse areas, including bone disorders (10 studies), cardiovascular diseases (5 studies), oncological malignancies (10 studies), and various conditions, such as obesity, nonalcoholic liver disease, the effects of hormone replacement therapy, and neurological diseases (16 studies). The results of our study indicate that proteomic profiles correlate with heart disease in peri- and postmenopausal women, with distinct sex differences. Furthermore, proteomic profiles significantly differ between women with and without osteoporosis. Additionally, patients with breast, ovarian, and endometrial cancer exhibit notable variations in proteomic profiles compared to those without these conditions. Conclusions: Proteomics has the potential to enhance risk assessment and disease monitoring in peri- and postmenopausal women. By analyzing unique protein profiles, clinicians can identify individuals with heightened susceptibility to specific diseases or those already affected by established conditions. This review suggests that there is sufficient preliminary data related to proteomics in peri- and postmenopausal women for early identification of cardiovascular disease, osteoporosis, and cancers, disease monitoring, and tailoring individualized therapies. Rigorous validation studies involving large populations are essential before drawing definitive conclusions regarding the clinical applicability of proteomic findings.
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Rodríguez-Ulloa, Arielis, Jeovanis Gil, Yassel Ramos, Lilian Hernández-Álvarez, Lisandra Flores, Brizaida Oliva, Dayana García, et al. "Proteomic Study to Survey the CIGB-552 Antitumor Effect." BioMed Research International 2015 (2015): 1–18. http://dx.doi.org/10.1155/2015/124082.
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CIGB-552 is a cell-penetrating peptide that exertsin vitroandin vivoantitumor effect on cancer cells. In the present work, the mechanism involved in such anticancer activity was studied using chemical proteomics and expression-based proteomics in culture cancer cell lines. CIGB-552 interacts with at least 55 proteins, as determined by chemical proteomics. A temporal differential proteomics based on iTRAQ quantification method was performed to identify CIGB-552 modulated proteins. The proteomic profile includes 72 differentially expressed proteins in response to CIGB-552 treatment. Proteins related to cell proliferation and apoptosis were identified by both approaches. In line with previous findings, proteomic data revealed that CIGB-552 triggers the inhibition of NF-κB signaling pathway. Furthermore, proteins related to cell invasion were differentially modulated by CIGB-552 treatment suggesting new potentialities of CIGB-552 as anticancer agent. Overall, the current study contributes to a better understanding of the antitumor action mechanism of CIGB-552.
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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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Thelen, Jay J., and Ján A. Miernyk. "The proteomic future: where mass spectrometry should be taking us." Biochemical Journal 444, no. 2 (May 11, 2012): 169–81. http://dx.doi.org/10.1042/bj20110363.
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A newcomer to the -omics era, proteomics, is a broad instrument-intensive research area that has advanced rapidly since its inception less than 20 years ago. Although the ‘wet-bench’ aspects of proteomics have undergone a renaissance with the improvement in protein and peptide separation techniques, including various improvements in two-dimensional gel electrophoresis and gel-free or off-gel protein focusing, it has been the seminal advances in MS that have led to the ascension of this field. Recent improvements in sensitivity, mass accuracy and fragmentation have led to achievements previously only dreamed of, including whole-proteome identification, and quantification and extensive mapping of specific PTMs (post-translational modifications). With such capabilities at present, one might conclude that proteomics has already reached its zenith; however, ‘capability’ indicates that the envisioned goals have not yet been achieved. In the present review we focus on what we perceive as the areas requiring more attention to achieve the improvements in workflow and instrumentation that will bridge the gap between capability and achievement for at least most proteomes and PTMs. Additionally, it is essential that we extend our ability to understand protein structures, interactions and localizations. Towards these ends, we briefly focus on selected methods and research areas where we anticipate the next wave of proteomic advances.
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Tjalsma, Harold, Haike Antelmann, Jan D. H. Jongbloed, Peter G. Braun, Elise Darmon, Ronald Dorenbos, Jean-Yves F. Dubois, et al. "Proteomics of Protein Secretion by Bacillus subtilis: Separating the “Secrets” of the Secretome." Microbiology and Molecular Biology Reviews 68, no. 2 (June 2004): 207–33. http://dx.doi.org/10.1128/mmbr.68.2.207-233.2004.
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SUMMARY Secretory proteins perform a variety of important“ remote-control” functions for bacterial survival in the environment. The availability of complete genome sequences has allowed us to make predictions about the composition of bacterial machinery for protein secretion as well as the extracellular complement of bacterial proteomes. Recently, the power of proteomics was successfully employed to evaluate genome-based models of these so-called secretomes. Progress in this field is well illustrated by the proteomic analysis of protein secretion by the gram-positive bacterium Bacillus subtilis, for which ∼90 extracellular proteins were identified. Analysis of these proteins disclosed various“ secrets of the secretome,” such as the residence of cytoplasmic and predicted cell envelope proteins in the extracellular proteome. This showed that genome-based predictions reflect only∼ 50% of the actual composition of the extracellular proteome of B. subtilis. Importantly, proteomics allowed the first verification of the impact of individual secretion machinery components on the total flow of proteins from the cytoplasm to the extracellular environment. In conclusion, proteomics has yielded a variety of novel leads for the analysis of protein traffic in B. subtilis and other gram-positive bacteria. Ultimately, such leads will serve to increase our understanding of virulence factor biogenesis in gram-positive pathogens, which is likely to be of high medical relevance.
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Orsburn, Benjamin C. "Evaluation of the Sensitivity of Proteomics Methods Using the Absolute Copy Number of Proteins in a Single Cell as a Metric." Proteomes 9, no. 3 (July 20, 2021): 34. http://dx.doi.org/10.3390/proteomes9030034.
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Proteomic technology has improved at a staggering pace in recent years, with even practitioners challenged to keep up with new methods and hardware. The most common metric used for method performance is the number of peptides and proteins identified. While this metric may be helpful for proteomics researchers shopping for new hardware, this is often not the most biologically relevant metric. Biologists often utilize proteomics in the search for protein regulators that are of a lower relative copy number in the cell. In this review, I re-evaluate untargeted proteomics data using a simple graphical representation of the absolute copy number of proteins present in a single cancer cell as a metric. By comparing single-shot proteomics data to the coverage of the most in-depth proteomic analysis of that cell line acquired to date, we can obtain a rapid metric of method performance. Using a simple copy number metric allows visualization of how proteomics has developed in both sensitivity and overall dynamic range when using both relatively long and short acquisition times. To enable reanalysis beyond what is presented here, two available web applications have been developed for single- and multi-experiment comparisons with reference protein copy number data for multiple cell lines and organisms.
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Kalvodova, Lucie. "Understanding the proteomes using non-proteomics approaches: Expanding the scope of PROTEOMICS." PROTEOMICS 17, no. 1-2 (January 2017): 1770013. http://dx.doi.org/10.1002/pmic.201770013.
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Fu, Jianbo, Yongchao Luo, Minjie Mou, Hongning Zhang, Jing Tang, Yunxia Wang, and Feng Zhu. "Advances in Current Diabetes Proteomics: From the Perspectives of Label- free Quantification and Biomarker Selection." Current Drug Targets 21, no. 1 (December 20, 2019): 34–54. http://dx.doi.org/10.2174/1389450120666190821160207.
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Background: Due to its prevalence and negative impacts on both the economy and society, the diabetes mellitus (DM) has emerged as a worldwide concern. In light of this, the label-free quantification (LFQ) proteomics and diabetic marker selection methods have been applied to elucidate the underlying mechanisms associated with insulin resistance, explore novel protein biomarkers, and discover innovative therapeutic protein targets. Objective: The purpose of this manuscript is to review and analyze the recent computational advances and development of label-free quantification and diabetic marker selection in diabetes proteomics. Methods: Web of Science database, PubMed database and Google Scholar were utilized for searching label-free quantification, computational advances, feature selection and diabetes proteomics. Results: In this study, we systematically review the computational advances of label-free quantification and diabetic marker selection methods which were applied to get the understanding of DM pathological mechanisms. Firstly, different popular quantification measurements and proteomic quantification software tools which have been applied to the diabetes studies are comprehensively discussed. Secondly, a number of popular manipulation methods including transformation, pretreatment (centering, scaling, and normalization), missing value imputation methods and a variety of popular feature selection techniques applied to diabetes proteomic data are overviewed with objective evaluation on their advantages and disadvantages. Finally, the guidelines for the efficient use of the computationbased LFQ technology and feature selection methods in diabetes proteomics are proposed. Conclusion: In summary, this review provides guidelines for researchers who will engage in proteomics biomarker discovery and by properly applying these proteomic computational advances, more reliable therapeutic targets will be found in the field of diabetes mellitus.
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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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Hulahan, Taylor S., Laura Spruill, Elizabeth N. Wallace, Yeonhee Park, Robert B. West, Jeffrey R. Marks, E. Shelley Hwang, Richard R. Drake, and Peggi M. Angel. "Extracellular Microenvironment Alterations in Ductal Carcinoma In Situ and Invasive Breast Cancer Pathologies by Multiplexed Spatial Proteomics." International Journal of Molecular Sciences 25, no. 12 (June 19, 2024): 6748. http://dx.doi.org/10.3390/ijms25126748.
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Ductal carcinoma in situ (DCIS) is a heterogeneous breast disease that remains challenging to treat due to its unpredictable progression to invasive breast cancer (IBC). Contemporary literature has become increasingly focused on extracellular matrix (ECM) alterations with breast cancer progression. However, the spatial regulation of the ECM proteome in DCIS has yet to be investigated in relation to IBC. We hypothesized that DCIS and IBC present distinct ECM proteomes that could discriminate between these pathologies. Tissue sections of pure DCIS, mixed DCIS-IBC, or pure IBC (n = 22) with detailed pathological annotations were investigated by multiplexed spatial proteomics. Across tissues, 1,005 ECM peptides were detected in pathologically annotated regions and their surrounding extracellular microenvironments. A comparison of DCIS to IBC pathologies demonstrated 43 significantly altered ECM peptides. Notably, eight fibrillar collagen peptides could distinguish with high specificity and sensitivity between DCIS and IBC. Lesion-targeted proteomic imaging revealed heterogeneity of the ECM proteome surrounding individual DCIS lesions. Multiplexed spatial proteomics reported an invasive cancer field effect, in which DCIS lesions in closer proximity to IBC shared a more similar ECM profile to IBC than distal counterparts. Defining the ECM proteomic microenvironment provides novel molecular insights relating to DCIS and IBC.
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Pathade, Parag A., Vinod A. Bairagi, Yogesh S. Ahire, and Neela M. Bhatia. "Proteomics: Opportunities and Challenges." International Journal of Pharmaceutical Sciences and Nanotechnology 3, no. 4 (February 28, 2011): 1165–72. http://dx.doi.org/10.37285/ijpsn.2010.3.4.1.
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‘‘Proteomics’’, is the emerging technology leading to high-throughput identification and understanding of proteins. Proteomics is the protein equivalent of genomics and has captured the imagination of biomolecular scientists, worldwide. Because proteome reveals more accurately the dynamic state of a cell, tissue, or organism, much is expected from proteomics to indicate better disease markers for diagnosis and therapy monitoring.
Proteomics is expected to play a major role in biomedical research, and it will have a significant impact on the development of diagnostics and therapeutics for cancer, heart ailments and infectious diseases, in future. Proteomics research leads to the identification of new protein markers for diagnostic purposes and novel molecular targets for drug discovery.
Though the potential is great, many challenges and issues remain to be solved, such as gene expression, peptides, generation of low abundant proteins, analytical tools, drug target discovery and cost. A systematic and efficient analysis of vast genomic and proteomic data sets is a major challenge for researchers, today. Nevertheless, proteomics is the groundwork for constructing and extracting useful comprehension to biomedical research. This review article covers some opportunities and challenges offered by proteomics.
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Mayr, Manuel, Ursula Mayr, Yuen-Li Chung, Xiaoke Yin, John R. Griffiths, and Qingbo Xu. "Vascular proteomics: Linking proteomic and metabolomic changes." PROTEOMICS 4, no. 12 (December 2004): 3751–61. http://dx.doi.org/10.1002/pmic.200400947.
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Poetsch, Ansgar, and María Inés Marchesini. "Proteomics of Brucella." Proteomes 8, no. 2 (April 22, 2020): 8. http://dx.doi.org/10.3390/proteomes8020008.
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Brucella spp. are Gram negative intracellular bacteria responsible for brucellosis, a worldwide distributed zoonosis. A prominent aspect of the Brucella life cycle is its ability to invade, survive and multiply within host cells. Comprehensive approaches, such as proteomics, have aided in unravelling the molecular mechanisms underlying Brucella pathogenesis. Technological and methodological advancements such as increased instrument performance and multiplexed quantification have broadened the range of proteome studies, enabling new and improved analyses, providing deeper and more accurate proteome coverage. Indeed, proteomics has demonstrated its contribution to key research questions in Brucella biology, i.e., immunodominant proteins, host-cell interaction, stress response, antibiotic targets and resistance, protein secretion. Here, we review the proteomics of Brucella with a focus on more recent works and novel findings, ranging from reconfiguration of the intracellular bacterial proteome and studies on proteomic profiles of Brucella infected tissues, to the identification of Brucella extracellular proteins with putative roles in cell signaling and pathogenesis. In conclusion, proteomics has yielded copious new candidates and hypotheses that require future verification. It is expected that proteomics will continue to be an invaluable tool for Brucella and applications will further extend to the currently ill-explored aspects including, among others, protein processing and post-translational modification.
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Adán-Jiménez, Javier, Alejandro Sánchez-Salvador, Esperanza Morato, Jose Carlos Solana, Begoña Aguado, and Jose M. Requena. "A Proteogenomic Approach to Unravel New Proteins Encoded in the Leishmania donovani (HU3) Genome." Genes 15, no. 6 (June 13, 2024): 775. http://dx.doi.org/10.3390/genes15060775.
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The high-throughput proteomics data generated by increasingly more sensible mass spectrometers greatly contribute to our better understanding of molecular and cellular mechanisms operating in live beings. Nevertheless, proteomics analyses are based on accurate genomic and protein annotations, and some information may be lost if these resources are incomplete. Here, we show that most proteomics data may be recovered by interconnecting genomics and proteomics approaches (i.e., following a proteogenomic strategy), resulting, in turn, in an improvement of gene/protein models. In this study, we generated proteomics data from Leishmania donovani (HU3 strain) promastigotes that allowed us to detect 1908 proteins in this developmental stage on the basis of the currently annotated proteins available in public databases. However, when the proteomics data were searched against all possible open reading frames existing in the L. donovani genome, twenty new protein-coding genes could be annotated. Additionally, 43 previously annotated proteins were extended at their N-terminal ends to accommodate peptides detected in the proteomics data. Also, different post-translational modifications (phosphorylation, acetylation, methylation, among others) were found to occur in a large number of Leishmania proteins. Finally, a detailed comparative analysis of the L. donovani and Leishmania major experimental proteomes served to illustrate how inaccurate conclusions can be raised if proteomes are compared solely on the basis of the listed proteins identified in each proteome. Finally, we have created data entries (based on freely available repositories) to provide and maintain updated gene/protein models. Raw data are available via ProteomeXchange with the identifier PXD051920.
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Fedorov, Ivan I., Sergey A. Protasov, Irina A. Tarasova, and Mikhail V. Gorshkov. "Ultrafast Proteomics." Biochemistry (Moscow) 89, no. 8 (August 2024): 1349–61. http://dx.doi.org/10.1134/s0006297924080017.
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Abstract Current stage of proteomic research in the field of biology, medicine, development of new drugs, population screening, or personalized approaches to therapy dictates the need to analyze large sets of samples within the reasonable experimental time. Until recently, mass spectrometry measurements in proteomics were characterized as unique in identifying and quantifying cellular protein composition, but low throughput, requiring many hours to analyze a single sample. This was in conflict with the dynamics of changes in biological systems at the whole cellular proteome level upon the influence of external and internal factors. Thus, low speed of the whole proteome analysis has become the main factor limiting developments in functional proteomics, where it is necessary to annotate intracellular processes not only in a wide range of conditions, but also over a long period of time. Enormous level of heterogeneity of tissue cells or tumors, even of the same type, dictates the need to analyze biological systems at the level of individual cells. These studies involve obtaining molecular characteristics for tens, if not hundreds of thousands of individual cells, including their whole proteome profiles. Development of mass spectrometry technologies providing high resolution and mass measurement accuracy, predictive chromatography, new methods for peptide separation by ion mobility and processing of proteomic data based on artificial intelligence algorithms have opened a way for significant, if not radical, increase in the throughput of whole proteome analysis and led to implementation of the novel concept of ultrafast proteomics. Work done just in the last few years has demonstrated the proteome-wide analysis throughput of several hundred samples per day at a depth of several thousand proteins, levels unimaginable three or four years ago. The review examines background of these developments, as well as modern methods and approaches that implement ultrafast analysis of the entire proteome.
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Vidal, Bernardo C., Joseph V. Bonventre, and Stephen I-Hong Hsu. "Towards the application of proteomics in renal disease diagnosis." Clinical Science 109, no. 5 (October 24, 2005): 421–30. http://dx.doi.org/10.1042/cs20050085.
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Proteomics is widely envisioned as playing a significant role in the translation of genomics to clinically useful applications, especially in the areas of diagnostics and prognostics. In the diagnosis and treatment of kidney disease, a major priority is the identification of disease-associated biomarkers. Proteomics, with its high-throughput and unbiased approach to the analysis of variations in protein expression patterns (actual phenotypic expression of genetic variation), promises to be the most suitable platform for biomarker discovery. Combining such classic analytical techniques as two-dimensional gel electrophoresis with more sophisticated techniques, such as MS, has enabled considerable progress to be made in cataloguing and quantifying proteins present in urine and various kidney tissue compartments in both normal and diseased physiological states. Despite these accomplishments, there remain a number of important challenges that will need to be addressed in order to pave the way for the universal acceptance of proteomics as a clinically relevant diagnostic tool. We discuss issues related to three such critical developmental tasks as follows: (i) completely defining the proteome in the various biological compartments (e.g. tissues, serum and urine) in both health and disease, which presents a major challenge given the dynamic range and complexity of such proteomes; (ii) achieving the routine ability to accurately and reproducibly quantify proteomic expression profiles; and (iii) developing diagnostic platforms that are readily applicable and technically feasible for use in the clinical setting that depend on the fruits of the preceding two tasks to profile multiple disease biomarkers.
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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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Aziz, Ahmad Fudail Eiyad, Norhamizah Roshidi, Nurulhasanah Othman, Khayriyyah Mohd Hanafiah, and Norsyahida Arifin. "Application of Proteomics to the Study of the Therapeutics and Pathogenicity of Giardia duodenalis." Diagnostics 12, no. 11 (November 9, 2022): 2744. http://dx.doi.org/10.3390/diagnostics12112744.
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Giardia duodenalis remains a neglected tropical disease. A key feature of the sustained transmission of Giardia is the ability to form environmentally resistant cysts. For the last 38 years, proteomics has been utilised to study various aspects of the parasite across different life cycle stages. Thirty-one articles have been published in PubMed from 2012 to 2022 related to the proteomics of G. duodenalis. Currently, mass spectrometry with LC-MS/MS and MALDI-TOF/TOF has been commonly utilised in proteomic analyses of Giardia, which enables researchers to determine potential candidates for diagnostic biomarkers as well as vaccine and drug targets, in addition to allowing them to investigate the virulence of giardiasis, the pathogenicity mechanisms of G. duodenalis, and the post-translational modifications of Giardia proteins throughout encystation. Over the last decade, valuable information from proteomics analyses of G. duodenalis has been discovered in terms of the pathogenesis and virulence of Giardia, which may provide guidance for the development of better means with which to prevent and reduce the impacts of giardiasis. Nonetheless, there is room for improving proteomics analyses of G. duodenalis, since genomic sequences for additional assemblages of Giardia have uncovered previously unknown proteins associated with the Giardia proteome. Therefore, this paper aims to review the applications of proteomics for the characterisation of G. duodenalis pathogenicity and the discovery of novel vaccine as well as drug targets, in addition to proposing some general directions for future Giardia proteomic research.
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Wu, Haifeng M., Ming Jin, and Clay B. Marsh. "Toward functional proteomics of alveolar macrophages." American Journal of Physiology-Lung Cellular and Molecular Physiology 288, no. 4 (April 2005): L585—L595. http://dx.doi.org/10.1152/ajplung.00305.2004.
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Alveolar macrophages (AM) belong to a phenotype of macrophages with distinct biological functions and important pathophysiological roles in lung health and disease. The molecular details determining AM differentiation from blood monocytes and AM roles in lung homeostasis are largely unknown. With the use of different technological platforms, advances in the field of proteomics have made it possible to search for differences in protein expression between AM and their precursor monocytes. Proteome features of each cell type provide new clues into understanding mononuclear phagocyte biology. In-depth analyses using subproteomics and subcellular proteomics offer additional information by providing greater protein resolution and detection sensitivity. With the use of proteomic techniques, large-scale mapping of phosphorylation differences between the cell types have become possible. Furthermore, two-dimensional gel proteomics can detect germline protein variants and evaluate the impact of protein polymorphisms on an individual's susceptibility to disease. Finally, surface-enhanced laser desorption and ionization (SELDI) time-of-flight mass spectrometry offers an alternative method to recognizing differences in protein patterns between AM and monocytes or between AM under different pathological conditions. This review details the current status of this field and outlines future directions in functional proteomic analyses of AM and monocytes. Furthermore, this review presents viewpoints of integrating proteomics with translational topics in lung diseases to define the mechanisms of disease and to uncover new diagnostic and therapeutic targets.
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Eurich, Chris, Peter A. Fields, and Elizabeth Rice. "Proteomics: Protein Identification Using Online Databases." American Biology Teacher 74, no. 4 (April 1, 2012): 250–55. http://dx.doi.org/10.1525/abt.2012.74.4.8.
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Proteomics is an emerging area of systems biology that allows simultaneous study of thousands of proteins expressed in cells, tissues, or whole organisms. We have developed this activity to enable high school or college students to explore proteomic databases using mass spectrometry data files generated from yeast proteins in a college laboratory course. Students upload files of “unknown” proteins from our public website, enter them into a proteomics search engine (Mascot), identify the proteins, and use additional proteomics websites to learn about their functions and three-dimensional structures. This activity is suitable for use in units exploring protein structure and function, metabolism, or bioinformatics.
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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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Ouni, Emna, Didier Vertommen, and Christiani A. Amorim. "The Human Ovary and Future of Fertility Assessment in the Post-Genome Era." International Journal of Molecular Sciences 20, no. 17 (August 28, 2019): 4209. http://dx.doi.org/10.3390/ijms20174209.
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Proteomics has opened up new avenues in the field of gynecology in the post-genome era, making it possible to meet patient needs more effectively and improve their care. This mini-review aims to reveal the scope of proteomic applications through an overview of the technique and its applications in assisted procreation. Some of the latest technologies in this field are described in order to better understand the perspectives of its clinical applications. Proteomics seems destined for a promising future in gynecology, more particularly in relation to the ovary. Nevertheless, we know that reproductive biology proteomics is still in its infancy and major technical and ethical challenges must first be overcome.
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Kalantari, Shiva, Ameneh Jafari, Raheleh Moradpoor, Elmira Ghasemi, and Ensieh Khalkhal. "Human Urine Proteomics: Analytical Techniques and Clinical Applications in Renal Diseases." International Journal of Proteomics 2015 (November 29, 2015): 1–17. http://dx.doi.org/10.1155/2015/782798.
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Urine has been in the center of attention among scientists of clinical proteomics in the past decade, because it is valuable source of proteins and peptides with a relative stable composition and easy to collect in large and repeated quantities with a noninvasive procedure. In this review, we discuss technical aspects of urinary proteomics in detail, including sample preparation, proteomic technologies, and their advantage and disadvantages. Several recent experiments are presented which applied urinary proteome for biomarker discovery in renal diseases including diabetic nephropathy, immunoglobulin A (IgA) nephropathy, focal segmental glomerulosclerosis, lupus nephritis, membranous nephropathy, and acute kidney injury. In addition, several available databases in urinary proteomics are also briefly introduced.
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Dunkley, T. P. J., P. Dupree, R. B. Watson, and K. S. Lilley. "The use of isotope-coded affinity tags (ICAT) to study organelle proteomes in Arabidopsis thaliana." Biochemical Society Transactions 32, no. 3 (June 1, 2004): 520–23. http://dx.doi.org/10.1042/bst0320520.
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Organelle proteomics is the analysis of the protein contents of a subcellular compartment. Proteins identified in subcellular proteomic studies can only be assigned to an organelle if there are no contaminants present in the sample preparation. As a result, the majority of plant organelle proteomic studies have focused on the chloroplast and mitochondria, which can be isolated relatively easily. However, the isolation of components of the endomembrane system is far more difficult due to their similar sizes and densities. For this reason, quantitative proteomics methods are being developed to enable the assignment of proteins to a specific component of the endomembrane system without the need to obtain pure organelles.
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Nichols, Heather L., Ning Zhang, and Xuejun Wen. "Proteomics and genomics of microgravity." Physiological Genomics 26, no. 3 (August 2006): 163–71. http://dx.doi.org/10.1152/physiolgenomics.00323.2005.
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Many serious adverse physiological changes occur during spaceflight. In the search for underlying mechanisms and possible new countermeasures, many experimental tools and methods have been developed to study microgravity caused physiological changes, ranging from in vitro bioreactor studies to spaceflight investigations. Recently, genomic and proteomic approaches have gained a lot of attention; after major scientific breakthroughs in the fields of genomics and proteomics, they are now widely accepted and used to understand biological processes. Understanding gene and/or protein expression is the key to unfolding the mechanisms behind microgravity-induced problems and, ultimately, finding effective countermeasures to spaceflight-induced alterations. Significant progress has been made in identifying the genes/proteins responsible for these changes. Although many of these genes and/or proteins were observed to be either upregulated or downregulated, however, no large-scale genomics and proteomics studies have been published so far. This review aims at summarizing the current status of microgravity-related genomics and proteomics studies and stimulating large-scale proteomics and genomics research activities.
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Geng, Ruihui, Zhaoshen Li, Shude Li, and Jun Gao. "Proteomics in Pancreatic Cancer Research." International Journal of Proteomics 2011 (August 14, 2011): 1–5. http://dx.doi.org/10.1155/2011/365350.
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Pancreatic cancer is a highly aggressive malignancy with a poor prognosis and deeply affects the life of people. Therefore, the earlier diagnosis and better treatments are urgently needed. In recent years, the proteomic technologies are well established and growing rapidly and have been widely applied in clinical applications, especially in pancreatic cancer research. In this paper, we attempt to discuss the development of current proteomic technologies and the application of proteomics to the field of pancreatic cancer research. This will explore the potential perspective in revealing pathogenesis, making the diagnosis earlier and treatment.
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Saviola, Anthony J., Fernanda Negrão, and John R. Yates. "Proteomics of Select Neglected Tropical Diseases." Annual Review of Analytical Chemistry 13, no. 1 (June 12, 2020): 315–36. http://dx.doi.org/10.1146/annurev-anchem-091619-093003.
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Technological advances in mass spectrometry have enabled the extensive identification, characterization, and quantification of proteins in any biological system. In disease processes proteins are often altered in response to external stimuli; therefore, proteomics, the large-scale study of proteins and their functions, represents an invaluable tool for understanding the molecular basis of disease. This review highlights the use of mass spectrometry–based proteomics to study the pathogenesis, etiology, and pathology of several neglected tropical diseases (NTDs), a diverse group of disabling diseases primarily associated with poverty in tropical and subtropical regions of the world. While numerous NTDs have been the subject of proteomic studies, this review focuses on Buruli ulcer, dengue, leishmaniasis, and snakebite envenoming. The proteomic studies highlighted provide substantial information on the pathogenic mechanisms driving these diseases; they also identify molecular targets for drug discovery and development and uncover promising biomarkers that can assist in early diagnosis.
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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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Yihunie, Fanuel Bizuayehu, Mequanint Addisu Belete, Gizachew Fentahun, Solomon Getachew, and Teshager Dubie. "Diagnostic and Therapeutic Application of Proteomics in Infectious Disease." Advances in Cell and Gene Therapy 2023 (August 24, 2023): 1–6. http://dx.doi.org/10.1155/2023/5510791.
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The study of an organism’s genome, often known as “genomics,” has advanced quickly, producing a wealth of publicly accessible genetic data. Despite how valuable the genome is; proteins essentially control most aspects of cell function. Proteomics, or the comprehensive study of proteins, has emerged as an important technology for disease characterization, diagnosis, prognosis, drug development, and therapy. Proteomics technologies are now used to support the diagnosis and treatment of both infectious and noninfectious diseases. Nevertheless, it is more difficult to describe a proteomic profile since a single gene product may result in a number of unique proteins, and proteins have a wider range of chemical configurations. The proteome profiles of a particular organism, tissue, or cell are impacted by a variety of environmental factors, including those triggered by infectious agents. This review intends to highlight the applications of proteomics in the study of disease diagnosis and treatment. In this review, the different technologies used in proteomics studies, like two-dimensional gel electrophoresis, mass spectrometry, and protein microarray as well as biomarker discovery and drug target identification using proteomics, have also been focused on.
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Jiang, Will, Jennifer C. Jones, Uma Shankavaram, Mary Sproull, Kevin Camphausen, and Andra V. Krauze. "Analytical Considerations of Large-Scale Aptamer-Based Datasets for Translational Applications." Cancers 14, no. 9 (April 29, 2022): 2227. http://dx.doi.org/10.3390/cancers14092227.
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The development and advancement of aptamer technology has opened a new realm of possibilities for unlocking the biocomplexity available within proteomics. With ultra-high-throughput and multiplexing, alongside remarkable specificity and sensitivity, aptamers could represent a powerful tool in disease-specific research, such as supporting the discovery and validation of clinically relevant biomarkers. One of the fundamental challenges underlying past and current proteomic technology has been the difficulty of translating proteomic datasets into standards of practice. Aptamers provide the capacity to generate single panels that span over 7000 different proteins from a singular sample. However, as a recent technology, they also present unique challenges, as the field of translational aptamer-based proteomics still lacks a standardizing methodology for analyzing these large datasets and the novel considerations that must be made in response to the differentiation amongst current proteomic platforms and aptamers. We address these analytical considerations with respect to surveying initial data, deploying proper statistical methodologies to identify differential protein expressions, and applying datasets to discover multimarker and pathway-level findings. Additionally, we present aptamer datasets within the multi-omics landscape by exploring the intersectionality of aptamer-based proteomics amongst genomics, transcriptomics, and metabolomics, alongside pre-existing proteomic platforms. Understanding the broader applications of aptamer datasets will substantially enhance current efforts to generate translatable findings for the clinic.
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Woodland, Breyer, Aleksandar Necakov, and Jens R. Coorssen. "Optimized Proteome Reduction for Integrative Top–Down Proteomics." Proteomes 11, no. 1 (March 6, 2023): 10. http://dx.doi.org/10.3390/proteomes11010010.
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Integrative top–down proteomics is an analytical approach that fully addresses the breadth and complexity needed for effective and routine assessment of proteomes. Nonetheless, any such assessments also require a rigorous review of methodology to ensure the deepest possible quantitative proteome analyses. Here, we establish an optimized general protocol for proteome extracts to improve the reduction of proteoforms and, thus, resolution in 2DE. Dithiothreitol (DTT), tributylphosphine (TBP), and 2-hydroxyethyldisulfide (HED), combined and alone, were tested in one-dimensional SDS-PAGE (1DE), prior to implementation into a full 2DE protocol. Prior to sample rehydration, reduction with 100 mM DTT + 5 mM TBP yielded increased spot counts, total signal, and spot circularity (i.e., decreased streaking) compared to other conditions and reduction protocols reported in the literature. The data indicate that many widely implemented reduction protocols are significantly ‘under-powered’ in terms of proteoform reduction and thus, limit the quality and depth of routine top–down proteomic analyses.
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Bespyatykh, Ju A., E. A. Shitikov, and E. N. Ilina. "Proteomics for the Investigation of Mycobacteria." Acta Naturae 9, no. 1 (March 15, 2017): 15–25. http://dx.doi.org/10.32607/20758251-2017-9-1-15-25.
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The physiology of Mycobacterium tuberculosis, the causative agent of tuberculosis, is being studied with intensity. However, despite the genomic and transcriptomic data available today, the pathogenic potential of these bacteria remains poorly understood. Therefore, proteomic approaches seem relevant in studying mycobacteria. This review covers the main stages in the proteomic analysis methods used to study mycobacteria. The main achievements in the area of M. tuberculosis proteomics are described in general. Special attention is paid to the proteomic features of the Beijing family, which is widespread in Russia. Considering that the proteome is a set of all the proteins in the cell, post-translational modifications of mycobacterium proteins are also described.
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Listopad, Stanislav, Christophe Magnan, Le Z. Day, Aliya Asghar, Andrew Stolz, John A. Tayek, Zhang-Xu Liu, Jon M. Jacobs, Timothy R. Morgan, and Trina M. Norden-Krichmar. "Identification of integrated proteomics and transcriptomics signature of alcohol-associated liver disease using machine learning." PLOS Digital Health 3, no. 2 (February 9, 2024): e0000447. http://dx.doi.org/10.1371/journal.pdig.0000447.
Full textAbstract:
Distinguishing between alcohol-associated hepatitis (AH) and alcohol-associated cirrhosis (AC) remains a diagnostic challenge. In this study, we used machine learning with transcriptomics and proteomics data from liver tissue and peripheral mononuclear blood cells (PBMCs) to classify patients with alcohol-associated liver disease. The conditions in the study were AH, AC, and healthy controls. We processed 98 PBMC RNAseq samples, 55 PBMC proteomic samples, 48 liver RNAseq samples, and 53 liver proteomic samples. First, we built separate classification and feature selection pipelines for transcriptomics and proteomics data. The liver tissue models were validated in independent liver tissue datasets. Next, we built integrated gene and protein expression models that allowed us to identify combined gene-protein biomarker panels. For liver tissue, we attained 90% nested-cross validation accuracy in our dataset and 82% accuracy in the independent validation dataset using transcriptomic data. We attained 100% nested-cross validation accuracy in our dataset and 61% accuracy in the independent validation dataset using proteomic data. For PBMCs, we attained 83% and 89% accuracy with transcriptomic and proteomic data, respectively. The integration of the two data types resulted in improved classification accuracy for PBMCs, but not liver tissue. We also identified the following gene-protein matches within the gene-protein biomarker panels: CLEC4M-CLC4M, GSTA1-GSTA2 for liver tissue and SELENBP1-SBP1 for PBMCs. In this study, machine learning models had high classification accuracy for both transcriptomics and proteomics data, across liver tissue and PBMCs. The integration of transcriptomics and proteomics into a multi-omics model yielded improvement in classification accuracy for the PBMC data. The set of integrated gene-protein biomarkers for PBMCs show promise toward developing a liquid biopsy for alcohol-associated liver disease.