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1
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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Han, Mee-Jung, and Sang Yup Lee. "The Escherichia coli Proteome: Past, Present, and Future Prospects." Microbiology and Molecular Biology Reviews 70, no. 2 (June 2006): 362–439. http://dx.doi.org/10.1128/mmbr.00036-05.
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SUMMARY Proteomics has emerged as an indispensable methodology for large-scale protein analysis in functional genomics. The Escherichia coli proteome has been extensively studied and is well defined in terms of biochemical, biological, and biotechnological data. Even before the entire E. coli proteome was fully elucidated, the largest available data set had been integrated to decipher regulatory circuits and metabolic pathways, providing valuable insights into global cellular physiology and the development of metabolic and cellular engineering strategies. With the recent advent of advanced proteomic technologies, the E. coli proteome has been used for the validation of new technologies and methodologies such as sample prefractionation, protein enrichment, two-dimensional gel electrophoresis, protein detection, mass spectrometry (MS), combinatorial assays with n-dimensional chromatographies and MS, and image analysis software. These important technologies will not only provide a great amount of additional information on the E. coli proteome but also synergistically contribute to other proteomic studies. Here, we review the past development and current status of E. coli proteome research in terms of its biological, biotechnological, and methodological significance and suggest future prospects.
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Bhawal, Ruchika, Ann L. Oberg, Sheng Zhang, and Manish Kohli. "Challenges and Opportunities in Clinical Applications of Blood-Based Proteomics in Cancer." Cancers 12, no. 9 (August 27, 2020): 2428. http://dx.doi.org/10.3390/cancers12092428.
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Blood is a readily accessible biofluid containing a plethora of important proteins, nucleic acids, and metabolites that can be used as clinical diagnostic tools in diseases, including cancer. Like the on-going efforts for cancer biomarker discovery using the liquid biopsy detection of circulating cell-free and cell-based tumor nucleic acids, the circulatory proteome has been underexplored for clinical cancer biomarker applications. A comprehensive proteome analysis of human serum/plasma with high-quality data and compelling interpretation can potentially provide opportunities for understanding disease mechanisms, although several challenges will have to be met. Serum/plasma proteome biomarkers are present in very low abundance, and there is high complexity involved due to the heterogeneity of cancers, for which there is a compelling need to develop sensitive and specific proteomic technologies and analytical platforms. To date, liquid chromatography mass spectrometry (LC-MS)-based quantitative proteomics has been a dominant analytical workflow to discover new potential cancer biomarkers in serum/plasma. This review will summarize the opportunities of serum proteomics for clinical applications; the challenges in the discovery of novel biomarkers in serum/plasma; and current proteomic strategies in cancer research for the application of serum/plasma proteomics for clinical prognostic, predictive, and diagnostic applications, as well as for monitoring minimal residual disease after treatments. We will highlight some of the recent advances in MS-based proteomics technologies with appropriate sample collection, processing uniformity, study design, and data analysis, focusing on how these integrated workflows can identify novel potential cancer biomarkers for clinical applications.
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Sobolev, Vladimir V., Anna G. Soboleva, Elena V. Denisova, Eva A. Pechatnikova, Eugenia Dvoryankova, Irina M. Korsunskaya, and Alexandre Mezentsev. "Proteomic Studies of Psoriasis." Biomedicines 10, no. 3 (March 7, 2022): 619. http://dx.doi.org/10.3390/biomedicines10030619.
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In this review paper, we discuss the contribution of proteomic studies to the discovery of disease-specific biomarkers to monitor the disease and evaluate available treatment options for psoriasis. Psoriasis is one of the most prevalent skin disorders driven by a Th17-specific immune response. Although potential patients have a genetic predisposition to psoriasis, the etiology of the disease remains unknown. During the last two decades, proteomics became deeply integrated with psoriatic research. The data obtained in proteomic studies facilitated the discovery of novel mechanisms and the verification of many experimental hypotheses of the disease pathogenesis. The detailed data analysis revealed multiple differentially expressed proteins and significant changes in proteome associated with the disease and drug efficacy. In this respect, there is a need for proteomic studies to characterize the role of the disease-specific biomarkers in the pathogenesis of psoriasis, develop clinical applications to choose the most efficient treatment options and monitor the therapeutic response.
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Petralia, Francesca, Nicole Tignor, Dmitri Rykunov, Boris Revas, Shrabanti Chowdhury, Azra Krek, Pichae Raman, et al. "TBIO-19. INTEGRATED GENOMIC, PROTEOMIC AND PHOSPHOPROTEOMIC ANALYSIS OF SEVEN TYPES OF PEDIATRIC BRAIN CANCER." Neuro-Oncology 22, Supplement_3 (December 1, 2020): iii470. http://dx.doi.org/10.1093/neuonc/noaa222.846.
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Abstract We performed a comprehensive proteogenomic analysis across seven childhood brain tumors for a deeper understanding of their functional biology. Whole genome sequencing, RNAseq, quantitative proteomic profiling and phosphoproteomics were performed on 219 fresh frozen tumor samples representing the histologic diagnoses of: low grade astrocytoma (93), ependymoma (32), high grade astrocytoma (26), medulloblastoma (22), ganglioglioma (18), craniopharyngioma (16) and atypical teratoid rhabdoid tumor (12). Unsupervised clustering analysis based on proteomics data reveals eight clusters with distinct protein profiles and pathway activities. While some clusters coincide with histologic diagnoses, a couple of clusters appear to be a mixture of different diagnoses, including one cluster consisting of “aggressive” tumors characterized by poor survival and high stemness scores. By integrating proteomic data with RNAseq and WGS data, we characterize the impact of mutations (H3K27M, BRAFV600E, BRAF fusion) and CNVs upon the proteome across various diagnoses. Multiomics based kinase-substrate association analysis and co-expression network analysis reveal targetable active kinase networks within these tumors. Proteomic data reveals unique biology associated with H3K27M mutation status in HGG and BRAF aberrations in LGG. Characterization of the tumor microenvironment through deconvolution analyses based on multi-omics data reveals 5 distinct tumor clusters associated with different populations of infiltrating immune cells and the relative activity of the immune system based upon the expression of pro-inflammation or immunosuppressive markers. This study reports the first large-scale deep comprehensive proteogenomic analysis crossing traditional histologic boundaries to uncover foundational pediatric brain tumor biology including functional insight that helps drive translational efforts.
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Wu, Jingyu, Zhifang Hao, Chen Ma, Pengfei Li, Liuyi Dang, and Shisheng Sun. "Comparative proteogenomics profiling of non-small and small lung carcinoma cell lines using mass spectrometry." PeerJ 8 (April 23, 2020): e8779. http://dx.doi.org/10.7717/peerj.8779.
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Background Evidences indicated that non-small-cell lung cancer (NSCLC) and small-cell lung cancer (SCLC) might originate from the same cell type, which however ended up to be two different subtypes of lung carcinoma, requiring different therapeutic regimens. We aimed to identify the differences between these two subtypes of lung cancer by using integrated proteome and genome approaches. Methods and Materials Two representative cell lines for each lung cancer subtype were comparatively analysed by quantitative proteomics, and their corresponding transcriptomics data were obtained from the Gene Expression Omnibus database. The integrated analyses of proteogenomic data were performed to determine key differentially expressed proteins that were positively correlated between proteomic and transcriptomic data. Result The proteomics analysis revealed 147 differentially expressed proteins between SCLC and NSCLC from a total of 3,970 identified proteins. Combined with available transcriptomics data, we further confirmed 14 differentially expressed proteins including six known and eight new lung cancer related proteins that were positively correlated with their transcriptomics data. These proteins are mainly involved in cell migration, proliferation, and invasion. Conclusion The proteogenomic data on both NSCLC and SCLC cell lines presented in this manuscript is complementary to existing genomic and proteomic data related to lung cancers and will be crucial for a systems biology-level understanding of the molecular mechanism of lung cancers. The raw mass spectrometry data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD015270.
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Wang, Xuchu. "Protein and Proteome Atlas for Plants under Stresses: New Highlights and Ways for Integrated Omics in Post-Genomics Era." International Journal of Molecular Sciences 20, no. 20 (October 21, 2019): 5222. http://dx.doi.org/10.3390/ijms20205222.
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In the post-genomics era, integrative omics studies for biochemical, physiological, and molecular changes of plants in response to stress conditions play more crucial roles. Among them, atlas analysis of plants under different abiotic stresses, including salinity, drought, and toxic conditions, has become more important for uncovering the potential key genes and proteins in different plant tissues. High-quality genomic data and integrated analyses of transcriptomic, proteomic, metabolomics, and phenomic patterns provide a deeper understanding of how plants grow and survive under environmental stresses. This editorial mini-review aims to synthesize the 27 papers including two timely reviews that have contributed to this Special Issue, which focuses on concluding the recent progress in the Protein and Proteome Atlas in plants under different stresses. It covers various aspects of plant proteins ranging from agricultural proteomics, structure and function of proteins, novel techniques and approaches for gene and protein identification, protein quantification, proteomics for post-translational modifications (PTMs), and new insights into proteomics. The proteomics-based results in this issue will help the readers to gain novel insights for the understanding of complicated physiological processes in crops and other important plants in response to stressed conditions. Furthermore, these target genes and proteins that are important candidates for further functional validation in economic plants and crops can be studied.
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Vowinckel, Jakob, Thomas Corwin, Jonathan Woodsmith, Tobias Treiber, Roland Bruderer, Lukas Reiter, Eike-Christin von Leitner, Karel Novy, Hartmut Juhl, and Oliver Rinner. "Proteome and phospho-proteome profiling for deeper phenotype characterization of colorectal cancer heterogeneity." Journal of Clinical Oncology 39, no. 15_suppl (May 20, 2021): e15536-e15536. http://dx.doi.org/10.1200/jco.2021.39.15_suppl.e15536.
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e15536 Background: The rise of precision oncology therapeutics requires deep understanding of the molecular mechanisms implicated in cancer biology. Colorectal cancer (CRC) is one of the first solid tumors to be molecularly characterized by defined genes and pathways. Advances in tumor profiling have revealed a profound molecular heterogeneity in CRC leading to the definition of several consensus molecular subtypes (CMS). However, this molecular heterogeneity is still largely defined on the genomic and transcriptomics level. To complement the understanding of genetically defined molecular subgroups, we performed large-scale deep proteomic and phospho-proteomic profiling of CRC patient biopsies and adjacent healthy control tissue, which has enabled to explore the phenotype and obtain more functional insights in cancer biology. Methods: Sample processing from 5-10 mg of tissue per sample was performed using a liquid handling robot. Phospho-peptide enrichment was carried out with a Kingfisher Flex device and MagReSyn Ti-IMAC magnetic beads. Data-Independent Acquisition (DIA) LC-MS/MS was performed on multiple platforms consisting of a Thermo Scientific Q Exactive HF-X mass spectrometer coupled to a Waters M-Class LC. Chromatography was operating at 5 µL/min, and separation was achieved using 45 min (whole proteome) and 60 min (phospho-proteome) gradients. Results: Indivumed has built IndivuType, the world’s first multi-omics database for individualized cancer therapy, analyzing the highest quality cancer biospecimens to generate the most comprehensive dataset, including genomics, transcriptomics, proteomics, and clinical outcome information. Enabled by the DIA technology, a mass spectrometric method developed by Biognosys that obtains peptide fragmentation data in a highly parallelized way with high sensitivity, more than 7,000 proteins in the whole proteome and 20,000 phospho-peptides in the phospho-proteome workflow were profiled across more than 900 resected tissue samples of various CMS of CRC. The resulting proteome and phospho-proteome data were integrated into the IndivuType database and cross-analyzed with genomic and transcriptomic markers. Through this combined analysis, novel insights in clinically relevant signaling pathways in CRC subtypes were revealed. Conclusions: The deep phenotypic profiling of cancer samples, using next generation proteomics and phospho-proteomics, has enabled us to go beyond the genomic level in the characterization of tumor molecular heterogeneity. This multi-omics approach provides a solid foundation to advance the understanding of cancer biology, unravel key molecular events, and support the identification of novel therapeutic targets for precision medicine in CRC.
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Carvalho, Paulo C., Diogo B. Lima, Felipe V. Leprevost, Marlon D. M. Santos, Juliana S. G. Fischer, Priscila F. Aquino, James J. Moresco, John R. Yates, and Valmir C. Barbosa. "Integrated analysis of shotgun proteomic data with PatternLab for proteomics 4.0." Nature Protocols 11, no. 1 (December 10, 2015): 102–17. http://dx.doi.org/10.1038/nprot.2015.133.
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Pruess, Manuela, Paul Kersey, and Rolf Apweiler. "Integrating Genomic and Proteomic Data: The Integr8 Project." Journal of Integrative Bioinformatics 1, no. 1 (December 1, 2004): 108–15. http://dx.doi.org/10.1515/jib-2004-9.
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Summary Integr8 (http://www.ebi.ac.uk/integr8/) has been developed to provide an integration layer for the exploitation of genomic and proteomic data. High-quality databases from major bioinformatics centres in Europe are included, and some core data and the relationships of biological entities to each other and to entries in other databases are stored. Thus, a framework exists that allows for new kinds of data to be integrated, and an entity-centric view of complete genomes and proteomes is offered. Integr8 is an automatically populated database, providing different entry points to the data, depending on the user’s entity of interest. The Proteome Analysis database for statistical analysis and the Genome Reviews for annotated genome information are the main developments within the Integr8 project. With the BioMart application, an interactive querying tool for performing customisable proteome analysis and data mining is offered. Future developments will especially focus on the Genome Reviews, including mapping not yet annotated protein sequences onto their corresponding genomes, generating new predictions for non-coding RNA genes, and generally extending the scope to lower metazoan organisms.
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Mok, Jeong-Hun, Minjoong Joo, Van-An Duong, Seonghyeon Cho, Jong-Moon Park, Young-Sic Eom, Tae-Hwa Song, Hee-Joung Lim, and Hookeun Lee. "Proteomic and Metabolomic Analyses of Maggots in Porcine Corpses for Post-Mortem Interval Estimation." Applied Sciences 11, no. 17 (August 26, 2021): 7885. http://dx.doi.org/10.3390/app11177885.
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Post-mortem interval (PMI) estimation is a critical task in forensic science. In this study, we used maggots collected from pig carcasses and applied an integrated proteomics and metabolomics approach to determine potential candidate substances for the estimation of PMI. After methanol precipitation, the supernatant containing metabolites and the protein pellet were separated and subjected to metabolomic and proteomic analyses using liquid chromatography-tandem mass spectrometry (LC-MS/MS). MS/MS data were analyzed for identification and quantification using Proteome Discoverer and Compound Discoverer software. A total of 573 metabolites and more than 800 porcine proteins were identified in maggots. This is the first dataset of proteins and metabolites in maggots collected from porcine carcasses. In this study, guanosine monophosphate, xanthine, inosine, adenosine, and guanine were detected with a similar tendency to increase during early days of maggot development and then decreased gradually. We broadly profiled various biomolecules through analysis in the spot of incident. Especially, we confirmed that proteome and metabolome profiling could be performed directly and indirectly.
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Cordwell, Stuart J. "Microbial proteomics: how far have we come?" Microbiology Australia 32, no. 4 (2011): 169. http://dx.doi.org/10.1071/ma11169.
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It is now over 15 years since the beginning of the microbial genome era. At that time, great interest was invested in the idea of understanding bacterial genome dynamics via the analysis of their protein components or 'proteomes'. As bacterial analysis had driven the early advances in genomic squencing, these organisms were amongst the first subjected to protein-based studies. it is fair to suggest in hindsight that the original hype did not match the outcomes. At that time the term proteome was coined, en masse protein analysis meant little more than two-dimensional electrophoresis gels and mainly Edman sequencing for protein identification. As such, only the most abundant cellular constituents could be analysed and interest soon turned for many researchers towards microarray-based transcriptomics where a much greater percentage of the genome could be surveyed. Despite this, substantial and continuing evidence has demonstrated that transcript and protein levels (and, of course, the associated substrates and products of the predicted protein functions) often do not correlate. Therefore, a truly ?systems biology? organism-wide approach is necessary, where genomics, transcriptomics, proteomics and metabolomics are integrated to understand how microbes respond to changes in their genetic or physiological environments, and thus generate new hypotheses for functional understanding. This article will examine how proteomics technology has evolved to the stage at which the total proteome can be elucidated, and provide a guide for ?best practice? for undertaking successful proteomics analyses.
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Avram, Oren, Aya Kigel, Anna Vaisman-Mentesh, Sharon Kligsberg, Shai Rosenstein, Yael Dror, Tal Pupko, and Yariv Wine. "PASA: Proteomic analysis of serum antibodies web server." PLOS Computational Biology 17, no. 1 (January 25, 2021): e1008607. http://dx.doi.org/10.1371/journal.pcbi.1008607.
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Motivation A comprehensive characterization of the humoral response towards a specific antigen requires quantification of the B-cell receptor repertoire by next-generation sequencing (BCR-Seq), as well as the analysis of serum antibodies against this antigen, using proteomics. The proteomic analysis is challenging since it necessitates the mapping of antigen-specific peptides to individual B-cell clones. Results The PASA web server provides a robust computational platform for the analysis and integration of data obtained from proteomics of serum antibodies. PASA maps peptides derived from antibodies raised against a specific antigen to corresponding antibody sequences. It then analyzes and integrates proteomics and BCR-Seq data, thus providing a comprehensive characterization of the humoral response. The PASA web server is freely available at https://pasa.tau.ac.il and open to all users without a login requirement.
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Shen, Shichen, Rebecca J. Kapphahn, Ming Zhang, Shuo Qian, Sandra R. Montezuma, Peng Shang, Deborah A. Ferrington, and Jun Qu. "Quantitative Proteomics of Human Retinal Pigment Epithelium Reveals Key Regulators for the Pathogenesis of Age-Related Macular Degeneration." International Journal of Molecular Sciences 24, no. 4 (February 7, 2023): 3252. http://dx.doi.org/10.3390/ijms24043252.
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Age-related macular degeneration (AMD) is the leading cause of blindness in elderly people, with limited treatment options available for most patients. AMD involves the death of retinal pigment epithelium (RPE) and photoreceptor cells, with mitochondria dysfunction being a critical early event. In the current study, we utilized our unique resource of human donor RPE graded for AMD presence and severity to investigate proteome-wide dysregulation involved in early AMD. Organelle-enriched fractions of RPE were isolated from donors with early AMD (n = 45) and healthy age-matched controls (n = 32) and were analyzed by UHR-IonStar, an integrated proteomics platform enabling reliable and in-depth proteomic quantification in large cohorts. A total of 5941 proteins were quantified with excellent analytical reproducibility, and with further informatics analysis, many biological functions and pathways were found to be significantly dysregulated in donor RPE samples with early AMD. Several of these directly pinpointed changes in mitochondrial functions, e.g., translation, ATP metabolic process, lipid homeostasis, and oxidative stress. These novel findings highlighted the value of our proteomics investigation by allowing a better understanding of the molecular mechanisms underlying early AMD onset and facilitating both treatment development and biomarker discovery.
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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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Almuhayawi, Mohammed S., Soad K. Al Jaouni, Samy Selim, Dalal Hussien M. Alkhalifah, Romina Alina Marc, Sidra Aslam, and Peter Poczai. "Integrated Pangenome Analysis and Pharmacophore Modeling Revealed Potential Novel Inhibitors against Enterobacter xiangfangensis." International Journal of Environmental Research and Public Health 19, no. 22 (November 10, 2022): 14812. http://dx.doi.org/10.3390/ijerph192214812.
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Enterobacter xiangfangensis is a novel, multidrug-resistant pathogen belonging to the Enterobacter genus and has the ability to acquire resistance to multiple antibiotic classes. However, there is currently no registered E. xiangfangensis drug on the market that has been shown to be effective. Hence, there is an urgent need to identify novel therapeutic targets and effective treatments for E. xiangfangensis. In the current study, a bacterial pan genome analysis and subtractive proteomics approach was employed to the core proteomes of six strains of E. xiangfangensis using several bioinformatic tools, software, and servers. However, 2611 nonredundant proteins were predicted from the 21,720 core proteins of core proteome. Out of 2611 nonredundant proteins, 372 were obtained from Geptop2.0 as essential proteins. After the subtractive proteomics and subcellular localization analysis, only 133 proteins were found in cytoplasm. All cytoplasmic proteins were examined using BLASTp against the virulence factor database, which classifies 20 therapeutic targets as virulent. Out of these 20, 3 cytoplasmic proteins: ferric iron uptake transcriptional regulator (FUR), UDP-2,3diacylglucosamine diphosphatase (UDP), and lipid-A-disaccharide synthase (lpxB) were chosen as potential drug targets. These drug targets are important for bacterial survival, virulence, and growth and could be used as therapeutic targets. More than 2500 plant chemicals were used to molecularly dock these proteins. Furthermore, the lowest-binding energetic docked compounds were found. The top five hit compounds, Adenine, Mollugin, Xanthohumol C, Sakuranetin, and Toosendanin demonstrated optimum binding against all three target proteins. Furthermore, molecular dynamics simulations and MM/GBSA analyses validated the stability of ligand–protein complexes and revealed that these compounds could serve as potential E. xiangfangensis replication inhibitors. Consequently, this study marks a significant step forward in the creation of new and powerful drugs against E. xiangfangensis. Future studies should validate these targets experimentally to prove their function in E. xiangfangensis survival and virulence.
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Song, Tao, Ying Zhu, Peng Zhang, Minzhu Zhao, Dezhang Zhao, Shijia Ding, Shisheng Zhu, and Jianbo Li. "Integrated Proteomics and Metabolomic Analyses of Plasma Injury Biomarkers in a Serious Brain Trauma Model in Rats." International Journal of Molecular Sciences 20, no. 4 (February 20, 2019): 922. http://dx.doi.org/10.3390/ijms20040922.
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Diffuse axonal injury (DAI) is a prevalent and serious brain injury with significant morbidity and disability. However, the underlying pathogenesis of DAI remains largely unclear, and there are still no objective laboratory-based tests available for clinicians to make an early diagnosis of DAI. An integrated analysis of metabolomic data and proteomic data may be useful to identify all of the molecular mechanisms of DAI and novel potential biomarkers. Therefore, we established a rat model of DAI, and applied an integrated UPLC-Q-TOF/MS-based metabolomics and isobaric tag for relative and absolute quantitation (iTRAQ)-based proteomic analysis to obtain unbiased profiling data. Differential analysis identified 34 metabolites and 43 proteins in rat plasma of the injury group. Two metabolites (acetone and 4-Hydroxybenzaldehyde) and two proteins (Alpha-1-antiproteinase and Alpha-1-acid glycoprotein) were identified as potential biomarkers for DAI, and all may play important roles in the pathogenesis of DAI. Our study demonstrated the feasibility of integrated metabolomics and proteomics method to uncover the underlying molecular mechanisms of DAI, and may help provide clinicians with some novel diagnostic biomarkers and therapeutic targets.
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Wei, Chen-Xuan, Michael Francis Burrow, Michael George Botelho, and W. Keung Leung. "Analysing Complex Oral Protein Samples: Complete Workflow and Case Analysis of Salivary Pellicles." Journal of Clinical Medicine 10, no. 13 (June 25, 2021): 2801. http://dx.doi.org/10.3390/jcm10132801.
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Studies on small quantity, highly complex protein samples, such as salivary pellicle, have been enabled by recent major technological and analytical breakthroughs. Advances in mass spectrometry-based computational proteomics such as Multidimensional Protein Identification Technology have allowed precise identification and quantification of complex protein samples on a proteome-wide scale, which has enabled the determination of corresponding genes and cellular functions at the protein level. The latter was achieved via protein-protein interaction mapping with Gene Ontology annotation. In recent years, the application of these technologies has broken various barriers in small-quantity-complex-protein research such as salivary pellicle. This review provides a concise summary of contemporary proteomic techniques contributing to (1) increased complex protein (up to hundreds) identification using minute sample sizes (µg level), (2) precise protein quantification by advanced stable isotope labelling or label-free approaches and (3) the emerging concepts and techniques regarding computational integration, such as the Gene Ontology Consortium and protein-protein interaction mapping. The latter integrates the structural, genomic, and biological context of proteins and genes to predict protein interactions and functional connections in a given biological context. The same technological breakthroughs and computational integration concepts can also be applied to other low-volume oral protein complexes such as gingival crevicular or peri-implant sulcular fluids.
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Li, Yize, Nadezhda V. Terekhanova, Daniel Cui Zhou, Kelly V. Ruggles, Samuel H. Payne, Michael Wendl, David Fenyő, and Li Ding. "Abstract 845: Pan-cancer proteogenomic signatures associated with HRD, MSI, APOBEC, and smoking." Cancer Research 82, no. 12_Supplement (June 15, 2022): 845. http://dx.doi.org/10.1158/1538-7445.am2022-845.
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Abstract The increased quality and data availability of large-scale transcriptomic, genomic, and proteomic studies require a pan-cancer integrated proteogenomic approach to define tumor molecular signatures accurately and identify new therapeutic targets. We comprehensively investigate more than 1000 samples across 12 cancer types from the Clinical Proteomics Tumor Analysis Consortium (CPTAC) and the International Cancer Proteogenome Consortium (ICPC). The types are comprised of breast (BR), colorectal (CO), and ovarian (OV) cancers, clear cell renal cell (ccRCC), head and neck squamous cell (HNSCC), lung squamous cell (LSCC), hepatitis B virus (HBV)-related hepatocellular (HCC), and endometrial (EC) carcinomas, lung adenocarcinoma (LUAD), pancreatic ductal adenocarcinoma (PDAC), and glioblastoma (GBM). In particular, we examine 8 Tumor Signature Associated Phenotypes (TSAPs), namely aristolochic acid (AA), aging, microsatellite instability (MSI), homologous recombination deficiency (HRD), POLE, APOBEC, smoking, and ultraviolet (UV) light exposure. This study is the first to report proteomic markers associated with these TSAPs on a pan-cancer level. In addition to genetic alterations and mutational signatures, we utilize multi-omics data of high-resolution proteome, phosphoproteome, acetylome, and gene expression to infer expression signatures of TSAPs by defining the most critical changes in the transcriptome and proteome accompanying the transitions to these TSAPs, especially markers that were uniquely found in proteomic data. We consolidated multi-omic data and calculated the novel quantitative Tumor Signature Associated Phenotypes (TSAPs) score to predict the TSAP status. For example, the use of proteomic markers for MSI-TSAP scoring can improve clinical testing of MSI status. We further study environmental exposure-related tumor proteogenomic signatures, immune proteogenomic signatures, and the association between the immune subtypes and TSAPs. Smoking strongly influences the tumor immune microenvironment and disease prognosis. We show that expression signatures can facilitate the prediction of TSAPs and help to uncover their underlying molecular mechanisms. By connecting these findings with druggable databases, we provide a link to actionable therapies, identify putative TSAP-related targets, and offer novel cues to optimize therapeutic options for patients, such as how additional targeting of genes up-regulated in PARP1 inhibitor-treated HRD tumors may overcome resistance. This will promote the identification not only of unique druggable targets, but also to determine putative novel therapeutic targets using integrated approaches. Citation Format: Yize Li, Nadezhda V. Terekhanova, Daniel Cui Zhou, Kelly V. Ruggles, Samuel H. Payne, Michael Wendl, David Fenyő, Li Ding. Pan-cancer proteogenomic signatures associated with HRD, MSI, APOBEC, and smoking [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 845.
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Cummings, Steven, Thomas Perls, and Evan Hadley. "Complementary and Integrated Studies of Longevity and Healthy Aging." Innovation in Aging 4, Supplement_1 (December 1, 2020): 851. http://dx.doi.org/10.1093/geroni/igaa057.3126.
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Abstract Five NIH-funded studies, the Long Life Family Study (LLFS, U19), the Longevity Consortium (LC, U19), Longevity Genomics (U24), and Protective Omics Profiles in Centenarians (UH2) work together to triangulate on mechanisms of extreme longevity and healthy aging with the ultimate goal of discovering predictors and targetable pathways. Linkage analyses by LLFS identified extremely strong genetic linkage peaks for cross-sectional as well as longitudinal trajectory rates-of-change phenotypes. Deep sequencing suggests these peaks are driven by rare, protective variants in selected pedigrees. In cross-species studies (UH2, LC), genomics, metabolomics and proteomics are used to exploit many-fold variances in natural life spans to discover protective mechanisms that explain some of these differences. Proteome analysis reveal several longevity-related proteins such as Cip1/p21, FOXO3, TOP2A, AKT1, RICTOR, INSR and SIRT6 harboring post translational modification sites that preferentially appear in short- versus long-lived species. The U24 effort developed a tool using genetically-mediated gene expression to prioritize genes for longevity translational efforts. We found that BLOC1S1 was associated with longevity and protection from atrial fibrillation and hearing loss without being associated with adverse events. This novel target is undergoing functional characterization. A proteomic assay (4,131 proteins, Somascan) annotated by genome-wide association study results in a total of 1,797 centenarians and 3,685 controls divided into independent discovery and replication sets, discovered significant and replicated over-expression (thus, pro-longevity) of BIRC2 and under-expression of APOB in carriers of the APOE ɛ-2 allele. A novel protein signature of rs2184061 (CDKN2a/CDKN2B in chromosome 9) was also associated with slower aging.
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Heazlewood, Joshua L., and A. Harvey Millar. "Integrated plant proteomics — putting the green genomes to work." Functional Plant Biology 30, no. 5 (2003): 471. http://dx.doi.org/10.1071/fp03036.
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Protein analysis has been at the heart of plant science for many years, but with new questions emerging from an abundance of genomic information and further improvements in technology, there are now new opportunities to undertake large-scale analyses and to move to more complex systems than has been possible previously. This explosion of interest and data is often referred to simply as proteomics, which is the study of the complete set of proteins expressed at a given time and place, the proteome. As its name suggests proteomics is intricately linked to allied technologies such as genomics, transcriptomics and metabolomics. In this review of plant proteomics we outline a series of issues that face the practical user, particularly the largest problem that currently faces researchers, the myriad of options to choose from. The choices, problems and pitfalls of entering into gel-based and non-gel-based arraying techniques are discussed together with advances in pre-fractionation of samples, liquid chromatography separations and subcellular analyses. Issues relating to mass spectrometry analysis and the eventual protein identification are outlined, and the dilemmas of data storage and analysis are highlighted. During this tour we provide a series of references to the literature — experimental, theoretical and technical — to illustrate the breadth of current investigations using these techniques.
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Yu, Qing-Shan, Wan-Qing Feng, Lan-Lan Shi, Rui-Ze Niu, and Jia Liu. "Integrated Analysis of Cortex Single-Cell Transcriptome and Serum Proteome Reveals the Novel Biomarkers in Alzheimer’s Disease." Brain Sciences 12, no. 8 (August 1, 2022): 1022. http://dx.doi.org/10.3390/brainsci12081022.
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Blood-based proteomic analysis is a routine practice for detecting the biomarkers of human disease. The results obtained from blood alone cannot fully reflect the alterations of nerve cells, including neurons and glia cells, in Alzheimer’s disease (AD) brains. Therefore, the present study aimed to investigate novel potential AD biomarker candidates, through an integrated multi-omics approach in AD. We propose a comprehensive strategy to identify high-confidence candidate biomarkers by integrating multi-omics data from AD, including single-nuclei RNA sequencing (snRNA-seq) datasets of the prefrontal and entorhinal cortices, as wells as serum proteomic datasets. We first quantified a total of 124,658 nuclei, 8 cell types, and 3701 differentially expressed genes (DEGs) from snRNA-seq dataset of 30 human cortices, as well as 1291 differentially expressed proteins (DEPs) from serum proteomic dataset of 11 individuals. Then, ten DEGs/DEPs (NEBL, CHSY3, STMN2, MARCKS, VIM, FGD4, EPB41L2, PLEKHG1, PTPRZ1, and PPP1R14A) were identified by integration analysis of snRNA-seq and proteomics data. Finally, four novel candidate biomarkers (NEBL, EPB41L2, FGD4, and MARCKS) for AD further stood out, according to bioinformatics analysis, and they were verified by enzyme-linked immunosorbent assay (ELISA) verification. These candidate biomarkers are related to the regulation process of the actin cytoskeleton, which is involved in the regulation of synaptic loss in the AD brain tissue. Collectively, this study identified novel cell type-related biomarkers for AD by integrating multi-omics datasets from brains and serum. Our findings provided new targets for the clinical treatment and prognosis of AD.
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González-Fernández, Macarena, Tamara García-Barrera, Juan Jurado, María J. Prieto-Álamo, Carmen Pueyo, Juan López-Barea, and José Luis Gómez-Ariza. "Integrated application of transcriptomics, proteomics, and metallomics in environmental studies." Pure and Applied Chemistry 80, no. 12 (January 1, 2008): 2609–26. http://dx.doi.org/10.1351/pac200880122609.
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Here we report a preliminary working scheme for the integrative application of transcriptomic, proteomic, and metallomic methodologies in environmental monitoring, by using as sentinel the wildlife species Mus spretus and as reference the gene/protein sequence databases from the key model species Mus musculus. We have demonstrated that the absolute transcript expression signatures quantified by reverse transcription (RT) and real-time polymerase chain reaction (PCR) of selected key genes (e.g., those coding for biotransformation enzymes) in M. spretus is a useful and reliable novel biomonitoring end-point. The suitability of commercial M. musculus oligonucleotide arrays for genome-wide transcriptional profiling in M. spretus has been also shown. Transcriptomic studies indicate considerable gene sequence similarities between both mouse species. Based on these similarities, we have demonstrated the applicability in free-living M. spretus of high-throughput proteomic methods, based on matrix-assisted laser desorption/ionization with time-of-flight mass spectrometry (MALDI-TOFMS) analysis of tryptic 2D electrophoresis (2-DE) spot digest and peptide matching with M. musculus database. A metallomic approach based on size exclusion chromatography inductively coupled plasma-mass spectrometry (SEC-ICP-MS) was applied to trace metal-biomolecule profiles. A preliminary integration of these three -omics has been addressed to M. musculus/M. spretus couple, two rodent species that separated 3 million years ago. The integrated application of transcriptomic and proteomic data and the bidirectional use of metallomics and proteomics for selective isolation of metal-biomolecules are covered in the working scheme MEPROTRANS-triple-OMIC reported in this study.
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Xie, Biao, Wei Zhang, Qi Zhang, Qiuju Zhang, Yupeng Wang, Lin Sun, Meina Liu, and Ping Zhou. "An Integrated Transcriptomic and Proteomic Analysis Identifies Significant Novel Pathways for Henoch-Schönlein Purpura Nephritis Progression." BioMed Research International 2020 (June 20, 2020): 1–9. http://dx.doi.org/10.1155/2020/2489175.
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Background. Although Henoch-Schönlein purpura nephritis (HSPN) is characterized by glomerular deposition of aberrantly glycosylated immunoglobulin A1 (IgA1), the underlying mechanism of HSPN progression has not yet been completely elucidated. In this study, we integrated transcriptomic and proteomic analyses to explore the underlying mechanism of HSPN progression. Methods. RNA sequencing and tandem mass tag- (TMT-) based quantitative proteomics were used to gain serum transcriptomic and proteomic profiles of patients with different types of HSPN (3×type 1, 3×type 2, and 3×type 3). Student’s t-tests were performed to obtain the significance of the differential gene expression. The clusterProfiler package was used to conduct the functional annotation of the DEGs for both Gene Ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways. Results. A total of 2315 mRNAs and 30 proteins were differentially expressed between the different types of HSPN. 58 mRNAs and one protein changed continuously during HSPN development and are potential biomarkers for HSPN progression. The validation cohort (another 9 patients) confirmed the high-throughput results of the transcriptomic and proteomic analyses. A total of 385 significant pathways were related to HSPN progression, and four of them were closely related to clinical biochemical indicators and may play an important role in the progression of HSPN. Those pathways reveal that HSPN progression may be related to the inhibition of inflammation, promotion of apoptosis, and repair of renal injury. Conclusions. Four pathways were found to be closely related to HSPN progression, and it seems that HSPN progression is mainly due to the inhibition of inflammation, promotion of apoptosis, and repair of renal injury.
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Bludau, Isabell, Sander Willems, Wen-Feng Zeng, Maximilian T. Strauss, Fynn M. Hansen, Maria C. Tanzer, Ozge Karayel, Brenda A. Schulman, and Matthias Mann. "The structural context of posttranslational modifications at a proteome-wide scale." PLOS Biology 20, no. 5 (May 16, 2022): e3001636. http://dx.doi.org/10.1371/journal.pbio.3001636.
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The recent revolution in computational protein structure prediction provides folding models for entire proteomes, which can now be integrated with large-scale experimental data. Mass spectrometry (MS)-based proteomics has identified and quantified tens of thousands of posttranslational modifications (PTMs), most of them of uncertain functional relevance. In this study, we determine the structural context of these PTMs and investigate how this information can be leveraged to pinpoint potential regulatory sites. Our analysis uncovers global patterns of PTM occurrence across folded and intrinsically disordered regions. We found that this information can help to distinguish regulatory PTMs from those marking improperly folded proteins. Interestingly, the human proteome contains thousands of proteins that have large folded domains linked by short, disordered regions that are strongly enriched in regulatory phosphosites. These include well-known kinase activation loops that induce protein conformational changes upon phosphorylation. This regulatory mechanism appears to be widespread in kinases but also occurs in other protein families such as solute carriers. It is not limited to phosphorylation but includes ubiquitination and acetylation sites as well. Furthermore, we performed three-dimensional proximity analysis, which revealed examples of spatial coregulation of different PTM types and potential PTM crosstalk. To enable the community to build upon these first analyses, we provide tools for 3D visualization of proteomics data and PTMs as well as python libraries for data accession and processing.
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Leskoske, Kristin L., Sara A. Byron, Seema Plaisier, Apurva M. Hegde, Krystine Garcia-Mansfield, Ritin Sharma, Genevieve Bergendahl, et al. "Abstract 2013: Integrated proteomic analysis identifies four distinct subtypes of high-risk neuroblastoma." Cancer Research 82, no. 12_Supplement (June 15, 2022): 2013. http://dx.doi.org/10.1158/1538-7445.am2022-2013.
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Abstract Neuroblastoma is the most common solid extracranial tumor in children. Despite intensive multi-modal therapy, 5-year survival rates for patients with high-risk disease remain at approximately 50%. Neuroblastomas typically have low mutational burden and harbor few recurrent mutations making the identification of new therapeutic targets challenging. To characterize the proteomic landscape of high-risk neuroblastoma, we performed liquid chromatography-mass spectrometry-based deep expression proteomics and phosphoproteomics on 30 neuroblastoma tumors from 26 patients with high-risk disease. Our integrated analysis identified four distinct proteomic subgroups of high-risk neuroblastoma that were not otherwise apparent based on clinical or genomic features. The four subgroups were named based on their defining proteomic signature: C1-Mixed, C2-Neuronal, C3-Functional MYCN and C4-Stromal. Only one third of C3-Functional MYCN tumors had amplification of MYCN. Segmental chromosomal losses and gains were enriched in, but not exclusive to, C1-Mixed and C3-Functional MYCN tumors. The activities of multiple kinases including CDK2, CDK7 and MEK2 differed significantly between subgroups. C3-Functional MYCN and C4-Stromal tumors were enriched for immune and stromal cells, respectively. Focal adhesion signaling was specifically upregulated in C4-Stromal tumors suggesting increased extracellular matrix interactions in this tumor subgroup. C2-Neuronal tumors were enriched for axon guidance and neurotrophin signaling pathways. Rho family GTPase signaling was also evident in multiple tumor subgroups. C1-Mixed and C3-Functional MYCN tumors had elevated expression of RNA processing proteins which was associated with increased alternative splicing. Splicing analysis also identified multiple novel protein coding splice events that were shared amongst multiple neuroblastoma tumors and outliers compared to both the Genotype-Tissue Expression (GTEx) dataset and a panel of commercial reference tissues. Protein domain analysis of these novel splice variants suggested that these novel protein isoforms may have aberrant functions that contribute to tumorigenesis. In conclusion, phosphoproteomic analysis can identify candidate pathways for the development of new therapies for patients with high-risk neuroblastoma. Citation Format: Kristin L. Leskoske, Sara A. Byron, Seema Plaisier, Apurva M. Hegde, Krystine Garcia-Mansfield, Ritin Sharma, Genevieve Bergendahl, Abhinav Nagulapally, William Ferguson, Jaqueline Kraveka, Javier Oesterheld, William P. Hendricks, Giselle L. Saulnier Sholler, Jeffrey M. Trent, Patrick Pirrotte. Integrated proteomic analysis identifies four distinct subtypes of high-risk neuroblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2013.
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Vitorino, Rui, Sofia Guedes, João Pinto da Costa, and Václav Kašička. "Microfluidics for Peptidomics, Proteomics, and Cell Analysis." Nanomaterials 11, no. 5 (April 26, 2021): 1118. http://dx.doi.org/10.3390/nano11051118.
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Microfluidics is the advanced microtechnology of fluid manipulation in channels with at least one dimension in the range of 1–100 microns. Microfluidic technology offers a growing number of tools for manipulating small volumes of fluid to control chemical, biological, and physical processes relevant to separation, analysis, and detection. Currently, microfluidic devices play an important role in many biological, chemical, physical, biotechnological and engineering applications. There are numerous ways to fabricate the necessary microchannels and integrate them into microfluidic platforms. In peptidomics and proteomics, microfluidics is often used in combination with mass spectrometric (MS) analysis. This review provides an overview of using microfluidic systems for peptidomics, proteomics and cell analysis. The application of microfluidics in combination with MS detection and other novel techniques to answer clinical questions is also discussed in the context of disease diagnosis and therapy. Recent developments and applications of capillary and microchip (electro)separation methods in proteomic and peptidomic analysis are summarized. The state of the art of microchip platforms for cell sorting and single-cell analysis is also discussed. Advances in detection methods are reported, and new applications in proteomics and peptidomics, quality control of peptide and protein pharmaceuticals, analysis of proteins and peptides in biomatrices and determination of their physicochemical parameters are highlighted.
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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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Hancock, William, Alex Apffel, John Chakel, Karen Hahnenberger, Gargi Choudhary, Joseph A. Traina, and Erno Pungor. "Peer Reviewed: Integrated Genomic/Proteomic Analysis." Analytical Chemistry 71, no. 21 (November 1999): 742A—748A. http://dx.doi.org/10.1021/ac9907641.
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Byrnes, Robert W., Eoin Fahy, and Shankar Subramaniam. "A Laboratory Information Management System for High-Throughput Experimental Lipidomics: Minimal Information Required for the Analysis of Lipidomics Experiments (MIALE)." JALA: Journal of the Association for Laboratory Automation 12, no. 4 (August 2007): 230–38. http://dx.doi.org/10.1016/j.jala.2007.04.002.
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Sequencing of the human genome has opened the way and provided the impetus for building a comprehensive picture of a mammalian cell. Significant efforts are underway in the fields of genomics and proteomics to identify all genes and proteins in a given organism. The goal is a complete map of the genes, gene products, and their interaction networks in a functioning cell. The next step in establishing a comprehensive picture of a cell will be to integrate the cell's metabolome with the rapidly developing genomic and proteomic maps. A cell's metabolome, however, is such an enormous and complex entity that characterizing it can only be approached in sections. Our group of laboratories, the LIPID MAPS consortium, has focused on the lipid section of the metabolome. We have implemented a Lipid Metabolites and Pathways Strategy, termed LIPID MAPS, that applies a global integrated approach to the study of lipidomics in cells and tissues. This paper describes key aspects of the design, implementation, and accessibility features of a Laboratory Information Management System (LIMS) which serves the LIPID MAPS consortium. This software serves as a model system for integrating experimental information obtained by laboratories participating in metabolomics studies. (JALA 2007;12:230–8)
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Carrera, Mónica, Josafat Marina Ezquerra-Brauer, and Santiago P. Aubourg. "Characterization of the Jumbo Squid (Dosidicus gigas) Skin By-Product by Shotgun Proteomics and Protein-Based Bioinformatics." Marine Drugs 18, no. 1 (December 29, 2019): 31. http://dx.doi.org/10.3390/md18010031.
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Jumbo squid (Dosidicus gigas) is one of the largest cephalopods, and represents an important economic fishery in several regions of the Pacific Ocean, from southern California in the United States to southern Chile. Large and considerable discards of this species, such as skin, have been reported to constitute an important source of potential by-products. In this paper, a shotgun proteomics approach was applied for the first time to the characterization of the jumbo squid (Dosidicus gigas) skin proteome. A total of 1004 different peptides belonging to 219 different proteins were identified. The final proteome compilation was investigated by integrated in-silico studies, including gene ontology (GO) term enrichment, pathways, and networks studies. Potential new valuable bioactive peptides such as antimicrobial, bioactive collagen peptides, antihypertensive and antitumoral peptides were predicted to be present in the jumbo squid skin proteome. The integration of the global proteomics results and the bioinformatics analysis of the jumbo squid skin proteome show a comprehensive knowledge of this fishery discard and provide potential bioactive peptides of this marine by-product.
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FORST, CHRISTIAN V., LAWRENCE CABUSORA, KWASI G. MAWUENYEGA, and XIAN CHEN. "BIOLOGICAL SYSTEMS ANALYSIS BY A NETWORK PROTEOMICS APPROACH AND SUBCELLULAR PROTEIN PROFILING." Advances in Complex Systems 09, no. 04 (December 2006): 299–314. http://dx.doi.org/10.1142/s0219525906000835.
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We provide a systematic analysis of a biological system, the microbial pathogen Mycobacterium tuberculosis (Mtb) by directly profiling its gene products. This analysis combines high-throughput proteomics and biocomputational approaches to elucidate the globally expressed complements of the three subcellular compartments (the cell wall, membrane and cytosol) of Mtb. We report the compartmentalization of 1,044 identified proteins using proteomics methods. Genome-based biological network analyses were performed and integrated with proteomics data to reconstruct response networks. From the reconstructed response networks for fatty acid degradation and lipid biosynthesis pathways in Mtb, we identified proteins whose involvements in these pathways were not previously suspected. Furthermore, the subcellular localizations of these expressed proteins provide interesting insights into the compartmentalization of these pathways, which appear to traverse from cell wall to cytoplasm. Results of this large-scale subcellular proteome profile of Mtb have confirmed and validated the computational network hypothesis that functionally related proteins work together in larger organizational structures.
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Kasimir-Bauer, Sabine, Joanna Roder, Eva Obermayr, Sven Mahner, Ignace Vergote, Liselore Loverix, Elena Braicu, et al. "Definition and Independent Validation of a Proteomic-Classifier in Ovarian Cancer." Cancers 12, no. 9 (September 4, 2020): 2519. http://dx.doi.org/10.3390/cancers12092519.
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Mass-spectrometry-based analyses have identified a variety of candidate protein biomarkers that might be crucial for epithelial ovarian cancer (EOC) development and therapy response. Comprehensive validation studies of the biological and clinical implications of proteomics are needed to advance them toward clinical use. Using the Deep MALDI method of mass spectrometry, we developed and independently validated (development cohort: n = 199, validation cohort: n = 135) a blood-based proteomic classifier, stratifying EOC patients into good and poor survival groups. We also determined an age dependency of the prognostic performance of this classifier, and our protein set enrichment analysis showed that the good and poor proteomic phenotypes were associated with, respectively, lower and higher levels of complement activation, inflammatory response, and acute phase reactants. This work highlights that, just like molecular markers of the tumor itself, the systemic condition of a patient (partly reflected in proteomic patterns) also influences survival and therapy response in a subset of ovarian cancer patients and could therefore be integrated into future processes of therapy planning.
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Hurt, Elaine M., Suneetha B. Thomas, Benjamin Peng, and William L. Farrar. "Integrated molecular profiling of SOD2 expression in multiple myeloma." Blood 109, no. 9 (December 27, 2006): 3953–62. http://dx.doi.org/10.1182/blood-2006-07-035162.
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Abstract Reactive oxygen species are known to be involved in several cellular processes, including cell signaling. SOD2 is a key enzyme in the conversion of reactive oxygen species and has been implicated in a host of disease states, including cancer. Using an integrated, whole-cell approach encompassing epigenetics, genomics, and proteomics, we have defined the role of SOD2 in multiple myeloma. We show that the SOD2 promoter is methylated in several cell lines and there is a correlative decrease in expression. Furthermore, myeloma patient samples have decreased SOD2 expression compared with healthy donors. Overexpression of SOD2 results in decreased proliferation and altered sensitivity to 2-methoxyestradiol–induced DNA damage and apoptosis. Genomic profiling revealed regulation of 65 genes, including genes involved in tumorigenesis, and proteomic analysis identified activation of the JAK/STAT pathway. Analysis of nearly 400 activated transcription factors identified 31 transcription factors with altered DNA binding activity, including XBP1, NFAT, forkhead, and GAS binding sites. Integration of data from our gestalt molecular analysis has defined a role for SOD2 in cellular proliferation, JAK/STAT signaling, and regulation of several transcription factors.
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Huang, Benjamin J., Jenny L. Smith, Timothy I. Shaw, Scott N. Furlan, Rhonda E. Ries, Amanda R. Leonti, Erin Lynn Crowgey, et al. "Integrated Transcriptomics and Proteomics Identifies Therapeutic Targets in Pediatric Acute Myeloid Leukemia." Blood 138, Supplement 1 (November 5, 2021): 1296. http://dx.doi.org/10.1182/blood-2021-153970.
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Abstract Acute myeloid leukemia (AML) remains a therapeutic challenge with high mortality rates despite intensive and myeloablative therapies. While immunotherapies targeting CD19 have yielded remarkable outcomes in acute lymphoblastic leukemia, identifying similar antigen therapeutic targets in AML remains a challenge due to inherent heterogeneity associated with AML and overlapping immunophenotypes with normal hematopoietic stem and myeloid cell populations. Transcriptional heterogeneity within pediatric AML has primarily been linked to underlying fusion. Therefore, we integrated large transcriptomics and proteomics datasets from AML and normal tissues to identify potential targets expressed in leukemias, but not in normal bone marrow or other normal tissue types. To identify candidate therapeutic targets in pediatric AML, we leveraged transcriptome sequencing data from bone marrow aspirates or peripheral blood collected from 1,481 children, adolescents, and young adults with AML at the time of diagnosis. Patients were enrolled on one of four Children's Oncology Group trials spanning the past three decades: CCG-2961, AAML03P1, AAML0531, and AAML1031. We also leveraged transcriptome sequencing from normal bone marrow (NBM) and normal CD34+ hematopoietic stem and progenitor cells (HSPCs) in order to exclude targets that are highly expressed during normal hematopoiesis. Finally, we performed additional filtering based on proteomic databases to exclude targets that lack membrane localization (Human Protein Atlas, UniProt, and Ensembl) or that are highly expressed on normal tissue types (Human Proteome Map, Human Protein Atlas, and Proteomics DB databases) (Figure 1A). First, we computed the log expression ratio between AMLs and NBM/HSCPs for all protein coding genes. We next selected genes expressed greater than a threshold of two standard deviations above the mean in 50% or more of AMLs (Figure 1B). Additionally, we further selected genes on the basis of differential gene expression and absolute expression thresholds. This analysis was repeated for our entire pediatric AML cohort and the following AML subtypes: RUNX1-RUNX1T1, CBFB-MYH11, KMT2A-MLLT3, KMT2A-MLLT10, KMT2A-MLLT4, KMT2A-ELL, KMT2A-MLLT1, KMT2A-MLLT11, NUP98-NSD1, NUP98-KDM5A, and CBFA2T3-GLIS2. Candidate therapeutic targets were filtered based on membrane localization and normal tissue expression using the aforementioned proteomics databases (Figure 1C and 1D). Based on this algorithm, we identified a nonzero number of candidate therapeutic targets for each of our pediatric AML subtypes (Figure 1D). Intriguingly, we found no overlap between targets identified in our pediatric, adolescent, and young adult cohort and a previous similar analysis performed in AMLs diagnosed in older patients (PMID 29017060). This study demonstrates that by combining our large transcriptomics dataset with pre-existing proteomics datasets, we are able to identify a collection of candidate therapeutic targets in pediatric AML. Importantly, zero targets were identified that were inclusive to all pediatric AMLs within our cohort, which underscores the transcriptional heterogeneity that our group and others have previously identified in pediatric AML. Future preclinical and clinical studies will need to account for this heterogeneity by prioritizing targets on the basis of underlying molecular alteration. Our study comprises a platform and dataset of candidate targets for further functional validation and/or immunotherapy targeting studies in pediatric AML. Figure 1 Figure 1. Disclosures Shaw: T-Cell and/or Gene Therapy for Cancer: Patents & Royalties.
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Deng, Ning, Zhenye Li, Chao Pan, and Huilong Duan. "freeQuant: A Mass Spectrometry Label-Free Quantification Software Tool for Complex Proteome Analysis." Scientific World Journal 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/137076.
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Study of complex proteome brings forward higher request for the quantification method using mass spectrometry technology. In this paper, we present a mass spectrometry label-free quantification tool for complex proteomes, called freeQuant, which integrated quantification with functional analysis effectively. freeQuant consists of two well-integrated modules: label-free quantification and functional analysis with biomedical knowledge. freeQuant supports label-free quantitative analysis which makes full use of tandem mass spectrometry (MS/MS) spectral count, protein sequence length, shared peptides, and ion intensity. It adopts spectral count for quantitative analysis and builds a new method for shared peptides to accurately evaluate abundance of isoforms. For proteins with low abundance, MS/MS total ion count coupled with spectral count is included to ensure accurate protein quantification. Furthermore, freeQuant supports the large-scale functional annotations for complex proteomes. Mitochondrial proteomes from the mouse heart, the mouse liver, and the human heart were used to evaluate the usability and performance of freeQuant. The evaluation showed that the quantitative algorithms implemented in freeQuant can improve accuracy of quantification with better dynamic range.
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Wang, Zhiquan, Jianfeng Hua, Yunlong Yin, Chunsun Gu, Chaoguang Yu, Qin Shi, Jinbo Guo, Lei Xuan, and Fangyuan Yu. "An Integrated Transcriptome and Proteome Analysis Reveals Putative Regulators of Adventitious Root Formation in Taxodium ‘Zhongshanshan’." International Journal of Molecular Sciences 20, no. 5 (March 11, 2019): 1225. http://dx.doi.org/10.3390/ijms20051225.
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Adventitious root (AR) formation from cuttings is the primary manner for the commercial vegetative propagation of trees. Cuttings is also the main method for the vegetative reproduction of Taxodium ‘Zhongshanshan’, while knowledge of the molecular mechanisms regulating the processes is limited. Here, we used mRNA sequencing and an isobaric tag for relative and absolute quantitation-based quantitative proteomic (iTRAQ) analysis to measure changes in gene and protein expression levels during AR formation in Taxodium ‘Zhongshanshan’. Three comparison groups were established to represent the three developmental stages in the AR formation process. At the transcript level, 4743 genes showed an expression difference in the comparison groups as detected by RNA sequencing. At the protein level, 4005 proteins differed in their relative abundance levels, as indicated by the quantitative proteomic analysis. A comparison of the transcriptome and proteome data revealed regulatory aspects of metabolism during AR formation and development. In summary, hormonal signal transduction is different at different developmental stages during AR formation. Other factors related to carbohydrate and energy metabolism and protein degradation and some transcription factor activity levels, were also correlated with AR formation. Studying the identified genes and proteins will provide further insights into the molecular mechanisms controlling AR formation.
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Goltsman, Daniela S. Aliaga, Vincent J. Denef, Steven W. Singer, Nathan C. VerBerkmoes, Mark Lefsrud, Ryan S. Mueller, Gregory J. Dick, et al. "Community Genomic and Proteomic Analyses of Chemoautotrophic Iron-Oxidizing “Leptospirillum rubarum” (Group II) and “Leptospirillum ferrodiazotrophum” (Group III) Bacteria in Acid Mine Drainage Biofilms." Applied and Environmental Microbiology 75, no. 13 (May 8, 2009): 4599–615. http://dx.doi.org/10.1128/aem.02943-08.
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ABSTRACT We analyzed near-complete population (composite) genomic sequences for coexisting acidophilic iron-oxidizing Leptospirillum group II and III bacteria (phylum Nitrospirae) and an extrachromosomal plasmid from a Richmond Mine, Iron Mountain, CA, acid mine drainage biofilm. Community proteomic analysis of the genomically characterized sample and two other biofilms identified 64.6% and 44.9% of the predicted proteins of Leptospirillum groups II and III, respectively, and 20% of the predicted plasmid proteins. The bacteria share 92% 16S rRNA gene sequence identity and >60% of their genes, including integrated plasmid-like regions. The extrachromosomal plasmid carries conjugation genes with detectable sequence similarity to genes in the integrated conjugative plasmid, but only those on the extrachromosomal element were identified by proteomics. Both bacterial groups have genes for community-essential functions, including carbon fixation and biosynthesis of vitamins, fatty acids, and biopolymers (including cellulose); proteomic analyses reveal these activities. Both Leptospirillum types have multiple pathways for osmotic protection. Although both are motile, signal transduction and methyl-accepting chemotaxis proteins are more abundant in Leptospirillum group III, consistent with its distribution in gradients within biofilms. Interestingly, Leptospirillum group II uses a methyl-dependent and Leptospirillum group III a methyl-independent response pathway. Although only Leptospirillum group III can fix nitrogen, these proteins were not identified by proteomics. The abundances of core proteins are similar in all communities, but the abundance levels of unique and shared proteins of unknown function vary. Some proteins unique to one organism were highly expressed and may be key to the functional and ecological differentiation of Leptospirillum groups II and III.
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Huang, Kuo-Yang, Po-Jung Huang, Fu-Man Ku, Rose Lin, John F. Alderete, and Petrus Tang. "Comparative Transcriptomic and Proteomic Analyses of Trichomonas vaginalis following Adherence to Fibronectin." Infection and Immunity 80, no. 11 (August 27, 2012): 3900–3911. http://dx.doi.org/10.1128/iai.00611-12.
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ABSTRACTThe morphological transformation ofTrichomonas vaginalisfrom an ellipsoid form in batch culture to an adherent amoeboid form results from the contact of parasites with vaginal epithelial cells and with immobilized fibronectin (FN), a basement membrane component. This suggests host signaling of the parasite. We applied integrated transcriptomic and proteomic approaches to investigate the molecular responses ofT. vaginalisupon binding to FN. A transcriptome analysis was performed by using large-scale expressed-sequence-tag (EST) sequencing. A total of 20,704 ESTs generated from batch culture (trophozoite-EST) versus FN-amoeboid trichomonad (FN-EST) cDNA libraries were analyzed. The FN-EST library revealed decreased amounts of transcripts that were of lower abundance in the trophozoite-EST library. There was a shift by FN-bound organisms to the expression of transcripts encoding essential proteins, possibly indicating the expression of genes for adaptation to the morphological changes needed for the FN-adhesive processes. In addition, we identified 43 differentially expressed proteins in the proteomes of FN-bound and unbound trichomonads. Among these proteins, cysteine peptidase, glyceraldehyde-3-phosphate dehydrogenase (an FN-binding protein), and stress-related proteins were upregulated in the FN-adherent cells. Stress-related genes and proteins were highly expressed in both the transcriptome and proteome of FN-bound organisms, implying that these genes and proteins may play critical roles in the response to adherence. This is the first report of a comparative proteomic and transcriptomic analysis after the binding ofT. vaginalisto FN. This approach may lead to the discovery of novel virulence genes and affirm the role of genes involved in disease pathogenesis. This knowledge will permit a greater understanding of the complex host-parasite interplay.
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Kim, Seong-Jae, Ohgew Kweon, Richard C. Jones, James P. Freeman, Ricky D. Edmondson, and Carl E. Cerniglia. "Complete and Integrated Pyrene Degradation Pathway in Mycobacterium vanbaalenii PYR-1 Based on Systems Biology." Journal of Bacteriology 189, no. 2 (November 3, 2006): 464–72. http://dx.doi.org/10.1128/jb.01310-06.
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ABSTRACT Mycobacterium vanbaalenii PYR-1 was the first bacterium isolated by virtue of its ability to metabolize the high-molecular-weight polycyclic aromatic hydrocarbon (PAH) pyrene. We used metabolic, genomic, and proteomic approaches in this investigation to construct a complete and integrated pyrene degradation pathway for M. vanbaalenii PYR-1. Genome sequence analyses identified genes involved in the pyrene degradation pathway that we have proposed for this bacterium. To identify proteins involved in the degradation, we conducted a proteome analysis of cells exposed to pyrene using one-dimensional gel electrophoresis in combination with liquid chromatography-tandem mass spectrometry. Database searching performed with the M. vanbaalenii PYR-1 genome resulted in identification of 1,028 proteins with a protein false discovery rate of <1%. Based on both genomic and proteomic data, we identified 27 enzymes necessary for constructing a complete pathway for pyrene degradation. Our analyses indicate that this bacterium degrades pyrene to central intermediates through o-phthalate and the β-ketoadipate pathway. Proteomic analysis also revealed that 18 enzymes in the pathway were upregulated more than twofold, as indicated by peptide counting when the organism was grown with pyrene; three copies of the terminal subunits of ring-hydroxylating oxygenase (NidAB2, MvanDraft_0817/0818, and PhtAaAb), dihydrodiol dehydrogenase (MvanDraft_0815), and ring cleavage dioxygenase (MvanDraft_3242) were detected only in pyrene-grown cells. The results presented here provide a comprehensive picture of pyrene metabolism in M. vanbaalenii PYR-1 and a useful framework for understanding cellular processes involved in PAH degradation.
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Low, Teck Yew, Sebastiaan van Heesch, Henk van den Toorn, Piero Giansanti, Alba Cristobal, Pim Toonen, Sebastian Schafer, et al. "Quantitative and Qualitative Proteome Characteristics Extracted from In-Depth Integrated Genomics and Proteomics Analysis." Cell Reports 5, no. 5 (December 2013): 1469–78. http://dx.doi.org/10.1016/j.celrep.2013.10.041.
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Liu, You-Pi, Weng Man Chong, Harry Huang, Yi-De Chen, Chia-Wen Chung, Hsiao-Jen Chang, Chih-Wei Chang, and Jung-Chi Liao. "Abstract 3875: De novo spatial proteomic profiling of immune synapses using machine learning-guided microscoop." Cancer Research 82, no. 12_Supplement (June 15, 2022): 3875. http://dx.doi.org/10.1158/1538-7445.am2022-3875.
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Abstract The spatial proteome of the immune synapse (IS) between a target cell and a lymphocyte is fundamentally important to understand the mechanism of cell-mediated immunity for both immuno-oncology and therapeutic applications. In this research, we used Microscoop, a fully-automatic microscope system integrated with a machine learning-based algorithm, to best determine ISs for proteomic mapping. We used Raji B cells as antigen-presenting cells (APCs) and induced the formation of ISs by incubating with Jurkat T cells. Multiple IS images were applied to train our algorithm using convolution neural network-based deep learning. A sequential process including fluorescence imaging, deep learning-enabled pattern generation, and photochemical labeling was implemented to achieve spatial-specific biotinylation of the IS proteins. Moreover, Microscoop is capable to repeat the process automatically on thousands of fields of view to label sufficient immune-synaptic proteins with biotin-tag for further enrichment allowing protein identification with mass spectrometry. We have successfully labeled and isolated proteins from spatially reorganized interfaces between T cells and APCs. Following MS-based proteomic analysis, several hundreds of proteins were identified, including the proteins known to be specifically associated with T-cell receptor (TCR) activation such as LCK, one of the major factors involved in TCR signaling at ISs. More interestingly, we identified several proteins novel for ISs, including proteins involved in phosphatidylinositol signaling. Our data showcases the capability of subcellular de novo spatial proteomics of the Microscoop technology, revealing novel factors responsible for initiating the immune response of a lymphocyte and shedding light on immune checkpoint signaling and tumor immunotherapy. Citation Format: You-Pi Liu, Weng Man Chong, Harry Huang, Yi-De Chen, Chia-Wen Chung, Hsiao-Jen Chang, Chih-Wei Chang, Jung-Chi Liao. De novo spatial proteomic profiling of immune synapses using machine learning-guided microscoop [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3875.
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Gajadhar, Aaron S., Margaret K. Donovan, Harsharn Auluck, Yan Berk, Yuandan Lou, Theo Platt, and Serafim Batzoglou. "Abstract 6348: A cloud-scalable software suite for large-cohort proteogenomics data analysis and visualization." Cancer Research 82, no. 12_Supplement (June 15, 2022): 6348. http://dx.doi.org/10.1158/1538-7445.am2022-6348.
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Abstract Comprehensive assessment of the flow of genetic information through multi-omic data integration can reveal the molecular consequences of genetic variation underlying human disease. Next generation sequencing (NGS) is used to identify genetic variants and characterize gene function (e.g. transcriptome and epigenome), while mass spectrometry is used to assess the proteome through characterization of protein abundances, modifications, and interactions. A new plasma profiling platform, the Proteograph࣪ Product Suite, leverages multiple nanoparticles with distinct physiochemical properties to enable deep plasma proteome analyses at scale. Here, we present a cloud-based, data analysis software platform called Proteograph Analysis Suite (PAS) for proteogenomic data analyses through the integration of proteomics data derived from the Proteograph with genomic variant information derived from NGS experiments. PAS features include an experiment data management system, analysis protocols, an analysis setup wizard, and tools for reviewing and visualizing results. PAS can support both Data Independent Analysis (DIA) and Data Dependent Analysis (DDA) proteomics workflows and is compatible with variant call format (vcf) files from NGS workflows to enable personalized database searches. To assess quality of the resulting data PAS includes various metrics like peptide/protein group intensity, protein sequence coverage, relative protein abundance distribution, peptide and protein group counts. Visualizations including principal component analysis, hierarchical clustering, and heatmaps allow intuitive identification of experimental trends. To enable biological insights, differential expression analyses results are reported with interactive visualizations such as volcano plots, protein interaction maps, and protein-set enrichment. From data to insight, PAS provides an easy-to-use and efficient suite of functionality to enable proteogenomic data analysis. Integration of proteomics and genomics data require a variety of tools, many of which require command-line interfaces and operating system-specific requirements that can act as a barrier for researchers to adapt new data analysis tools. Here, we demonstrate the utility of PAS by analyzing samples from the Proteograph NSCLC plasma dataset1. PAS can analyze VCF files generated from NGS pipelines in combination with mass spec data to identify peptide variants using personalized libraries. Using the cloud-based architecture computational tasks are distributed for rapid analysis. The integrated proteogenomics viewers allow variant IDs to be interpreted in the context of genomic coordinates, protein sequence, functional domains and features. Together, these results show the utility of PAS for seamless and fast proteomic data analysis. Reference: 1 Blume, J. E. et al. Nature Communications, 2020 Citation Format: Aaron S. Gajadhar, Margaret K. Donovan, Harsharn Auluck, Yan Berk, Yuandan Lou, Theo Platt, Serafim Batzoglou. A cloud-scalable software suite for large-cohort proteogenomics data analysis and visualization [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6348.
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Haider, Saad, and Ranadip Pal. "Integrated Analysis of Transcriptomic and Proteomic Data." Current Genomics 14, no. 2 (February 1, 2013): 91–110. http://dx.doi.org/10.2174/1389202911314020003.
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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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Liu, Fuxiao, Bo Ni, and Rong Wei. "Comparative Proteomic Profiling: Cellular Metabolisms Are Mainly Affected in Senecavirus A-Inoculated Cells at an Early Stage of Infection." Viruses 13, no. 6 (May 31, 2021): 1036. http://dx.doi.org/10.3390/v13061036.
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Senecavirus A (SVA), also known as Seneca Valley virus, belongs to the genus Senecavirus in the family Picornaviridae. SVA can cause vesicular disease and epidemic transient neonatal losses in pigs. This virus efficiently propagates in some non-pig-derived cells, like the baby hamster kidney (BHK) cell line and its derivate (BSR-T7/5). Conventionally, a few proteins or only one protein is selected for exploiting a given mechanism concerning cellular regulation after SVA infection in vitro. Proteomics plays a vital role in the analysis of protein profiling, protein-protein interactions, and protein-directed metabolisms, among others. Tandem mass tag-labeled liquid chromatography-tandem mass spectrometry combined with the parallel reaction monitoring technique is increasingly used for proteomic research. In this study, this combined method was used to uncover separately proteomic profiles of SVA- and non-infected BSR-T7/5 cells. Furthermore, both proteomic profiles were compared with each other. The proteomic profiling showed that a total of 361 differentially expressed proteins were identified, out of which, 305 and 56 were upregulated and downregulated in SVA-infected cells at 12 h post-inoculation, respectively. GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) enrichment analyses showed that cellular metabolisms were affected mainly in SVA-inoculated cells at an early stage of infection. Therefore, an integrated metabolic atlas remains to be explored via metabolomic methods.
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Collins, Lisamarie A., Shama P. Mirza, Ahmed H. Kissebah, and Michael Olivier. "Integrated approach for the comprehensive characterization of lipoproteins from human plasma using FPLC and nano-HPLC-tandem mass spectrometry." Physiological Genomics 40, no. 3 (February 2010): 208–15. http://dx.doi.org/10.1152/physiolgenomics.00136.2009.
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The implication of the various lipoprotein classes in the development of atherosclerotic cardiovascular disease has served to focus a great deal of attention on these particles over the past half-century. Using knowledge gained by the sequencing of the human genome, recent research efforts have been directed toward the elucidation of the proteomes of several lipoprotein subclasses. One of the challenges of such proteomic experimentation is the ability to initially isolate plasma lipoproteins subsequent to their analysis by mass spectrometry. Although several methods for the isolation of plasma lipoproteins are available, the most commonly utilized techniques require large sample volumes and may cause destruction and dissociation of lipoprotein particle-associated proteins. Fast protein liquid chromatography (FPLC) is a nondenaturing technique that has been validated for the isolation of plasma lipoproteins from relatively small sample volumes. In this study, we present the use of FPLC in conjunction with nano-HPLC-ESI-tandem mass spectrometry as a new integrated methodology suitable for the proteomic analysis of human lipoprotein fractions. Results from our analysis show that only 200 μl of human plasma suffices for the isolation of whole high density lipoprotein (HDL) and the identification of the majority of all known HDL-associated proteins using mass spectrometry of the resulting fractions.
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Garrido-Gomez, Tamara, Francisco Dominguez, Juan Antonio Lopez, Emilio Camafeita, Alicia Quiñonero, Jose Antonio Martinez-Conejero, Antonio Pellicer, Ana Conesa, and Carlos Simón. "Modeling Human Endometrial Decidualization from the Interaction between Proteome and Secretome." Journal of Clinical Endocrinology & Metabolism 96, no. 3 (March 1, 2011): 706–16. http://dx.doi.org/10.1210/jc.2010-1825.
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Context: Decidualization of the human endometrium, which involves morphological and biochemical modifications of the endometrial stromal cells (ESCs), is a prerequisite for adequate trophoblast invasion and placenta formation. Objective: This study aims to investigate the proteome and secretome of in vitro decidualized ESCs. These data were combined with published genomic information and integrated to model the human decidualization interactome. Design: Prospective experimental case–control study. Setting: A private research foundation. Patients: Sixteen healthy volunteer ovum donors. Intervention: Endometrial samples were obtained, and ESCs were isolated and decidualized in vitro. Main Outcome Measures: Two-dimensional difference in-gel electrophoresis, matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry, Western blot, human protein cytokine array, ELISA, and bioinformatics analysis were performed. Results: The proteomic analysis revealed 60 differentially expressed proteins (36 over- and 24 underexpressed) in decidualized versus control ESCs, including known decidualization markers (cathepsin B) and new biomarkers (transglutaminase 2, peroxiredoxin 4, and the ACTB protein). In the secretomic analysis, a total of 13 secreted proteins (11 up- and 2 down-regulated) were identified, including well-recognized markers (IGF binding protein-1 and prolactin) and novel ones (myeloid progenitor inhibitory factor-1 and platelet endothelial cell adhesion molecule-1). These proteome/secretome profiles have been integrated into a decidualization interactome model. Conclusions: Proteomic and secretomic have been used as hypothesis-free approaches together with complex bioinformatics to model the human decidual interactome for the first time. We confirm previous knowledge, describe new molecules, and we have built up a model for human in vitro decidualization as invaluable tool for the diagnosis, therapy, and interpretation of biological phenomena.
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Hu, Chenyue W., Steven M. Kornblau, Alex Bisberg, and Amina A. Qutub. "Standard Proteomic Analysis (SPA) in AML: An Integrated Procedure for Discovering Hypoxia and Angiogenesis Patterns." Blood 124, no. 21 (December 6, 2014): 1056. http://dx.doi.org/10.1182/blood.v124.21.1056.1056.
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Abstract Introduction The heterogeneity of acute myeloid leukemia (AML) remains a great barrier to finding a cure for the disease. Despite our best efforts, the current classification system based on phenotypes and genetic mutations are insufficient to capture and characterize each AML subpopulation. This could result in a mismatch of drugs for a particular patient, an impediment to drug discovery, and an inadequate understanding of AML biology. A promising solution to this challenge is profiling patient samples using proteomics. However, researchers are restricted in their power to fully interpret this massive proteomic data due to a lack of standard AML-tailored computational procedures. In this study, we developed a cocktail of computational methods to analyze the AML proteomic data in conjunction with clinical data. This procedure, Standard Proteomic Analysis (SPA), is designed to help researchers identify unique patient groups, discover prognostic biomarkers, find drug targets and understand transitions between pathway activation states. We applied SPA to a set of AML proteomic data with a focus on hypoxia and angiogenesis to illustrate its utility. Methods The procedure of SPA is shown in Figure 1. We used Prototype Clustering to estimate the optimal number of patient clusters, and used k-means to obtain the cluster assignment for each patient. Standard Kaplan-Meier curve and log-rank tests were performed to examine how patient clustering impacts patient survival, whereas chi-square test was performed to evaluate the association between clinical correlates and the clustering. Principal Component Analysis was used to map the normal samples on top of the patient samples, in order to distinguish normal states from diseased states. To expand searches for drug targets beyond the key proteins, we built a protein network by combining the computationally derived connections from the data using glasso with the experimentally validated connections from public databases (e.g. String and KEGG). All of the results were visualized using an interactive platform Easel, where each patient could be tracked simultaneously across graphs. The example AML proteomic dataset was obtained by assaying 511 new AML patient samples using reverse phase protein array (RPPA). The RPPA was probed with 231 strictly validated antibodies, including antibodies against three hypoxia regulators (HIF1A, VHL, EGLN1) and two angiogenesis regulators (KDR, VASP). The normal bone marrow derived CD34+ cells were used for comparison. Results Using SPA, we first identified four patient clusters with distinct protein expression patterns (Figure 1A). Most patients displayed canonical hypoxic (C3) and non-hypoxic (C2) patterns, featuring high and low HIF1A with opposite expression of the others. The two non-canonical patterns (C1 & C4) indicate a decoupling between HIF1A and its known regulators (e.g., EGLN, VHL) and targets (e.g., KDR). C1 features high HIF1A, EGLN and VHL but low KDR and VASP. C4 is the opposite. The mapping of normal samples to patient samples (Figure 1B) suggested that non-canonical patterns might be disease specific. From the clinical correlates table (Figure 1D), we observed an association between canonical patterns and cell lineage differentiation, with C3 governing undifferentiated FAB M0/M1 cases and C2 dominant in monocytic M4/M5 subtypes. Furthermore, C1 was associated with favorable cytogenetics, but hypoxic patterns (C1 & C3) were adverse factors for overall survival among patients with intermediate cytogenetics (Figure 1C). The expanded protein networks (Figure 1E) revealed an umbrella of proteins in other pathways associated with each of the five proteins, including, e.g. a negative correlation between VASP and apoptosis proteins (BAD, BCL2, AIFM1), which has not been reported before. Conclusions We developed and applied an AML-tailored procedure, SPA, to analyze hypoxia and angiogenesis clinical proteomic data. Using SPA, we were able to identify four AML subpopulations with two disease specific patterns, discover the dependency between cell lineage development and canonical patterns, and explore potential drug targets beyond hypoxia and angiogenesis that are associated with each pattern. We believe SPA could be applied broadly and greatly expedite the drug discovery process in leukemia. Figure 1 Figure 1. Disclosures No relevant conflicts of interest to declare.
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Mawuenyega, Kwasi G., Christian V. Forst, Karen M. Dobos, John T. Belisle, Jin Chen, E. Morton Bradbury, Andrew R. M. Bradbury, and Xian Chen. "Mycobacterium tuberculosisFunctional Network Analysis by Global Subcellular Protein Profiling." Molecular Biology of the Cell 16, no. 1 (January 2005): 396–404. http://dx.doi.org/10.1091/mbc.e04-04-0329.
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Trends in increased tuberculosis infection and a fatality rate of ∼23% have necessitated the search for alternative biomarkers using newly developed postgenomic approaches. Here we provide a systematic analysis of Mycobacterium tuberculosis (Mtb) by directly profiling its gene products. This analysis combines high-throughput proteomics and computational approaches to elucidate the globally expressed complements of the three subcellular compartments (the cell wall, membrane, and cytosol) of Mtb. We report the identifications of 1044 proteins and their corresponding localizations in these compartments. Genome-based computational and metabolic pathways analyses were performed and integrated with proteomics data to reconstruct response networks. From the reconstructed response networks for fatty acid degradation and lipid biosynthesis pathways in Mtb, we identified proteins whose involvements in these pathways were not previously suspected. Furthermore, the subcellular localizations of these expressed proteins provide interesting insights into the compartmentalization of these pathways, which appear to traverse from cell wall to cytoplasm. Results of this large-scale subcellular proteome profile of Mtb have confirmed and validated the computational network hypothesis that functionally related proteins work together in larger organizational structures.