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Neuroscience Letters Sep 2023Spinal cord injury (SCI) is a devastating trauma of the central nervous system, with high levels of morbidity, disability, and mortality. To explore the underlying...
Spinal cord injury (SCI) is a devastating trauma of the central nervous system, with high levels of morbidity, disability, and mortality. To explore the underlying mechanism of SCI, we analyzed the proteome and phosphoproteome of rats at one week after SCI. We identified 465 up-regulated and 129 down-regulated differentially expressed proteins (DEPs), as well as 184 up-regulated and 40 down-regulated differentially expressed phosphoproteins (DEPPs). Using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis, we identified the biological characteristics of these proteins from the perspectives of cell component, biological process, and molecular function. We also found a lot of enriched functional pathways such as GABAergic synapse pathway, ErbB signaling pathway, tight junction, adherens junction. The integrated analysis of proteomics and phosphoproteomics yielded 22 differently expressed co-identified proteins of DEPs and DEPPs, which revealed strongly correlative patterns. These findings may help clarify the potential mechanisms of trauma and repair in SCI and may guide the development of novel treatments.
Topics: Rats; Animals; Gene Expression Profiling; Proteomics; Spinal Cord Injuries; Proteome; Spinal Cord
PubMed: 37597742
DOI: 10.1016/j.neulet.2023.137449 -
Developmental Cell Nov 2023Centrosomes are the major microtubule-organizing centers in animals and play fundamental roles in many cellular processes. Understanding how their composition varies...
Centrosomes are the major microtubule-organizing centers in animals and play fundamental roles in many cellular processes. Understanding how their composition varies across diverse cell types and how it is altered in disease are major unresolved questions, yet currently available centrosome isolation protocols are cumbersome and time-consuming, and they lack scalability. Here, we report the development of centrosome affinity capture (CAPture)-mass spectrometry (MS), a powerful one-step purification method to obtain high-resolution centrosome proteomes from mammalian cells. Utilizing a synthetic peptide derived from CCDC61 protein, CAPture specifically isolates intact centrosomes. Importantly, as a bead-based affinity method, it enables rapid sample processing and multiplexing unlike conventional approaches. Our study demonstrates the power of CAPture-MS to elucidate cell-type-dependent heterogeneity in centrosome composition, dissect hierarchical interactions, and identify previously unknown centrosome components. Overall, CAPture-MS represents a transformative tool to unveil temporal, regulatory, cell-type- and tissue-specific changes in centrosome proteomes in health and disease.
Topics: Animals; Proteomics; Proteome; Centrosome; Microtubule-Organizing Center; Microtubules; Mammals
PubMed: 37852252
DOI: 10.1016/j.devcel.2023.09.008 -
Cell Reports Oct 2023Bacterial ribonucleoprotein bodies (BR-bodies) are non-membrane-bound structures that facilitate mRNA decay by concentrating mRNA substrates with RNase E and the...
Bacterial ribonucleoprotein bodies (BR-bodies) are non-membrane-bound structures that facilitate mRNA decay by concentrating mRNA substrates with RNase E and the associated RNA degradosome machinery. However, the full complement of proteins enriched in BR-bodies has not been defined. Here, we define the protein components of BR-bodies through enrichment of the bodies followed by mass spectrometry-based proteomic analysis. We find 111 BR-body-enriched proteins showing that BR-bodies are more complex than previously assumed. We identify five BR-body-enriched proteins that undergo RNA-dependent phase separation in vitro with a complex network of condensate mixing. We observe that some RNP condensates co-assemble with preferred directionality, suggesting that RNA may be trafficked through RNP condensates in an ordered manner to facilitate mRNA processing/decay, and that some BR-body-associated proteins have the capacity to dissolve the condensate. Altogether, these results suggest that a complex network of protein-protein and protein-RNA interactions controls BR-body phase separation and RNA processing.
Topics: RNA; Proteome; Proteomics; Ribonucleoproteins; RNA, Messenger
PubMed: 37815915
DOI: 10.1016/j.celrep.2023.113229 -
Molecular & Cellular Proteomics : MCP Feb 2024Data-independent acquisition (DIA) mass spectrometry (MS) has emerged as a powerful technology for high-throughput, accurate, and reproducible quantitative proteomics.... (Review)
Review
Data-independent acquisition (DIA) mass spectrometry (MS) has emerged as a powerful technology for high-throughput, accurate, and reproducible quantitative proteomics. This review provides a comprehensive overview of recent advances in both the experimental and computational methods for DIA proteomics, from data acquisition schemes to analysis strategies and software tools. DIA acquisition schemes are categorized based on the design of precursor isolation windows, highlighting wide-window, overlapping-window, narrow-window, scanning quadrupole-based, and parallel accumulation-serial fragmentation-enhanced DIA methods. For DIA data analysis, major strategies are classified into spectrum reconstruction, sequence-based search, library-based search, de novo sequencing, and sequencing-independent approaches. A wide array of software tools implementing these strategies are reviewed, with details on their overall workflows and scoring approaches at different steps. The generation and optimization of spectral libraries, which are critical resources for DIA analysis, are also discussed. Publicly available benchmark datasets covering global proteomics and phosphoproteomics are summarized to facilitate performance evaluation of various software tools and analysis workflows. Continued advances and synergistic developments of versatile components in DIA workflows are expected to further enhance the power of DIA-based proteomics.
Topics: Proteomics; Mass Spectrometry; Software; Gene Library; Proteome
PubMed: 38182042
DOI: 10.1016/j.mcpro.2024.100712 -
Expert Review of Proteomics Apr 2024Male infertility is a major public health concern globally. Proteomics has revolutionized our comprehension of male fertility by identifying potential infertility... (Review)
Review
INTRODUCTION
Male infertility is a major public health concern globally. Proteomics has revolutionized our comprehension of male fertility by identifying potential infertility biomarkers and reproductive defects. Studies comparing sperm proteome with other male reproductive tissues have the potential to refine fertility diagnostics and guide infertility treatment development.
AREAS COVERED
This review encapsulates literature using proteomic approaches to progress male reproductive biology. Our search methodology included systematic searches of databases such as PubMed, Scopus, and Web of Science for articles up to 2023. Keywords used included 'male fertility proteomics,' 'spermatozoa proteome,' 'testis proteomics,' 'epididymal proteomics,' and 'non-hormonal male contraception.' Inclusion criteria were robust experimental design, significant contributions to male fertility, and novel use of proteomic technologies.
EXPERT OPINION
Expert analysis shows a shift from traditional research to an integrative approach that clarifies male reproductive health's molecular intricacies. A gap exists between proteomic discoveries and clinical application. The expert opinions consolidated here not only navigate the current findings but also chart the future proteomic applications for scientific and clinical breakthroughs. We underscore the need for continued investment in proteomic research - both in the technological and collaborative arenas - to further unravel the secrets of male fertility, which will be central to resolving fertility issues in the coming era.
Topics: Male; Proteomics; Humans; Infertility, Male; Spermatozoa; Fertility; Proteome; Animals; Biomarkers
PubMed: 38536015
DOI: 10.1080/14789450.2024.2327553 -
Proteomics Jun 2024How cells regulate protein levels is a central question of biology. Over the past decades, molecular biology research has provided profound insights into the mechanisms... (Review)
Review
How cells regulate protein levels is a central question of biology. Over the past decades, molecular biology research has provided profound insights into the mechanisms and the molecular machinery governing each step of the gene expression process, from transcription to protein degradation. Recent advances in transcriptomics and proteomics have complemented our understanding of these fundamental cellular processes with a quantitative, systems-level perspective. Multi-omic studies revealed significant quantitative, kinetic and functional differences between the genome, transcriptome and proteome. While protein levels often correlate with mRNA levels, quantitative investigations have demonstrated a substantial impact of translation and protein degradation on protein expression control. In addition, protein-level regulation appears to play a crucial role in buffering protein abundances against undesirable mRNA expression variation. These findings have practical implications for many fields, including gene function prediction and precision medicine.
Topics: Systems Biology; Humans; Proteomics; Proteome; Transcriptome; Animals; Gene Expression Regulation; RNA, Messenger; Proteolysis; Protein Biosynthesis; Proteins
PubMed: 38012370
DOI: 10.1002/pmic.202200220 -
Cancer Research Nov 2023Proteomics is a powerful approach that can rapidly enhance our understanding of cancer development. Detailed characterization of the genetic, pharmacogenomic, and immune...
UNLABELLED
Proteomics is a powerful approach that can rapidly enhance our understanding of cancer development. Detailed characterization of the genetic, pharmacogenomic, and immune landscape in relation to protein expression in patients with cancer could provide new insights into the functional roles of proteins in cancer. By taking advantage of the genotype data from The Cancer Genome Atlas and protein expression data from The Cancer Proteome Atlas, we characterized the effects of genetic variants on protein expression across 31 cancer types and identified approximately 100,000 protein quantitative trait loci (pQTL). Among these, over 8000 pQTLs were associated with patient overall survival. Furthermore, characterization of the impact of protein expression on more than 350 imputed anticancer drug responses in patients revealed nearly 230,000 significant associations. In addition, approximately 21,000 significant associations were identified between protein expression and immune cell abundance. Finally, a user-friendly data portal, GPIP (https://hanlaboratory.com/GPIP), was developed featuring multiple modules that enable researchers to explore, visualize, and browse multidimensional data. This detailed analysis reveals the associations between the proteomic landscape and genetic variation, patient outcome, the immune microenvironment, and drug response across cancer types, providing a resource that may offer valuable clinical insights and encourage further functional investigations of proteins in cancer.
SIGNIFICANCE
Comprehensive characterization of the relationship between protein expression and the genetic, pharmacogenomic, and immune landscape of tumors across cancer types provides a foundation for investigating the role of protein expression in cancer development and treatment.
Topics: Humans; Proteomics; Pharmacogenetics; Proteome; Genotype; Neoplasms; Tumor Microenvironment
PubMed: 37548539
DOI: 10.1158/0008-5472.CAN-23-0758 -
Nature Methods Oct 2023Protein complexes are responsible for the enactment of most cellular functions. For the protein complex to form and function, its subunits often need to be present at...
Protein complexes are responsible for the enactment of most cellular functions. For the protein complex to form and function, its subunits often need to be present at defined quantitative ratios. Typically, global changes in protein complex composition are assessed with experimental approaches that tend to be time consuming. Here, we have developed a computational algorithm for the detection of altered protein complexes based on the systematic assessment of subunit ratios from quantitative proteomic measurements. We applied it to measurements from breast cancer cell lines and patient biopsies and were able to identify strong remodeling of HDAC2 epigenetic complexes in more aggressive forms of cancer. The presented algorithm is available as an R package and enables the inference of changes in protein complex states by extracting functionally relevant information from bottom-up proteomic datasets.
Topics: Humans; Proteome; Proteomics; Algorithms; MCF-7 Cells; Computational Biology
PubMed: 37749212
DOI: 10.1038/s41592-023-02011-w -
GeroScience Apr 2024Using mouse models and high-throughput proteomics, we conducted an in-depth analysis of the proteome changes induced in response to seven interventions known to increase...
Using mouse models and high-throughput proteomics, we conducted an in-depth analysis of the proteome changes induced in response to seven interventions known to increase mouse lifespan. This included two genetic mutations, a growth hormone receptor knockout (GHRKO mice) and a mutation in the Pit-1 locus (Snell dwarf mice), four drug treatments (rapamycin, acarbose, canagliflozin, and 17α-estradiol), and caloric restriction. Each of the interventions studied induced variable changes in the concentrations of proteins across liver, kidney, and gastrocnemius muscle tissue samples, with the strongest responses in the liver and limited concordance in protein responses across tissues. To the extent that these interventions promote longevity through common biological mechanisms, we anticipated that proteins associated with longevity could be identified by characterizing shared responses across all or multiple interventions. Many of the proteome alterations induced by each intervention were distinct, potentially implicating a variety of biological pathways as being related to lifespan extension. While we found no protein that was affected similarly by every intervention, we identified a set of proteins that responded to multiple interventions. These proteins were functionally diverse but tended to be involved in peroxisomal oxidation and metabolism of fatty acids. These results provide candidate proteins and biological mechanisms related to enhancing longevity that can inform research on therapeutic approaches to promote healthy aging.
Topics: Mice; Animals; Longevity; Proteome; Proteomics; Transcription Factors; Receptors, Somatotropin
PubMed: 37653270
DOI: 10.1007/s11357-023-00917-z -
Bioinformatics (Oxford, England) Aug 2023Mass spectrometry (MS)-based proteomics has become the most powerful approach to study the proteome of given biological and clinical samples. Advancements in sample...
SUMMARY
Mass spectrometry (MS)-based proteomics has become the most powerful approach to study the proteome of given biological and clinical samples. Advancements in sample preparation and MS detection have extended the application of proteomics but have also brought new demands on data analysis. Appropriate proteomics data analysis workflow mainly requires quality control, hypothesis testing, functional mining, and visualization. Although there are numerous tools for each process, an efficient and universal tandem analysis toolkit to obtain a quick overall view of various proteomics data is still urgently needed. Here, we present DEP2, an updated version of DEP we previously established, for proteomics data analysis. We amended the analysis workflow by incorporating alternative approaches to accommodate diverse proteomics data, introducing peptide-protein summarization and coupling biological function exploration. In summary, DEP2 is a well-rounded toolkit designed for protein- and peptide-level quantitative proteomics data. It features a more flexible differential analysis workflow and includes a user-friendly Shiny application to facilitate data analysis.
AVAILABILITY AND IMPLEMENTATION
DEP2 is available at https://github.com/mildpiggy/DEP2, released under the MIT license. For further information and usage details, please refer to the package website at https://mildpiggy.github.io/DEP2/.
Topics: Proteomics; Data Analysis; Mass Spectrometry; Proteome; Quality Control
PubMed: 37624922
DOI: 10.1093/bioinformatics/btad526