Knowledge Of Protein Expression Research Articles

Automated identification of protein expression intensity and classification of protein cellular locations in mouse brain regions from immunofluorescence images.

Knowledge of protein expression in mammalian brains at regional and cellular levels can facilitate understanding of protein functions and associated diseases. As the mouse brain is a typical mammalian brain considering cell type and structure, several studies have been conducted to analyze protein expression in mouse brains. However, labeling protein expression using biotechnology is costly and time-consuming. Therefore, automated models that can accurately recognize protein expression are needed. Here, we constructed machine learning models to automatically annotate the protein expression intensity and cellular location in different mouse brain regions from immunofluorescence images. The brain regions and sub-regions were segmented through learning image features using an autoencoder and then performing K-means clustering and registration to align with the anatomical references. The protein expression intensities for those segmented structures were computed on the basis of the statistics of the image pixels, and patch-based weakly supervised methods and multi-instance learning were used to classify the cellular locations. Results demonstrated that the models achieved high accuracy in the expression intensity estimation, and the F1 score of the cellular location prediction was 74.5%. This work established an automated pipeline for analyzing mouse brain images and provided a foundation for further study of protein expression and functions.

Proteomic characterization of eight-day-old equine blastocysts produced in vivo

Early embryo development is dependent on molecular signatures of the gametes, gene expression and maternal-embryo interactions. The proteome of the embryo reflects its developmental state and defines its fate. The present study aimed to describe the proteome of 8-day old equine blastocysts produced in vivo. Follicular dynamics of eight mares were monitored by ultrasonography. When a preovulatory follicle was confirmed, mares received GnRH and were inseminated. After eight days, four embryos were recovered. Proteins were extracted from the embryos, pooled and subjected to mass spectrometry. Four bioinformatic platforms were used to increase the accuracy of the protein identification as they have different capacities and coverages. Overall, there was a list of 4,014 proteins, but there were overlaps among the bioinformatic search engines. After elimination of redundancies (at 3 levels, based on identifiers, peptides and cross-bank mapping), a final count of 1,977 proteins were identified reliably in the samples of equine embryos. There were 1,413 proteins identified by the Peaks program, 316 by Proteome Discoverer, 165 by SearchGUI, and 83 proteins using the PepExplorer program. The low number of overlaps observed for PepExploreris due to the nature of that particular search, which used peptides sequenced but not identified by the Peaks program. Biological processes of the embryo proteins identified included cellular and metabolic processes. Cellular components involved the cell, organelle and protein-containing complex. Molecular functions of embryo proteins included binding, catalytic activities and structural molecule activity. Six pathways relevant to embryo development (FDR < 0.05, Panther platform) related to the TCA cycle, pyruvate metabolism, glycolysis, purine metabolism, and the pentose phosphate pathway. Proteins of the 8-day old equine embryos were associated with pathways of cell-cell communication and extracellular matrix remodeling and pathways related to intracellular remodeling, such as cytoskeleton regulation by Rho GTPase. In summary, the current approach based on mass spectrometry and a combination of search engines allowed the identification of 1,977 proteins in 8-day-old equine embryos. This is, to date, the largest atlas of proteins from embryos of the equine species. Knowledge of protein expression by the embryo helps to understand its physiology, metabolic events and future identification of biomarkers for health.

Deep Proteogenomics of a Photosynthetic Cyanobacterium.

Cyanobacteria, the evolutionary ancestors of plant chloroplasts, contribute substantially to the Earth's biogeochemical cycles and are of great interest for a sustainable economy. Knowledge of protein expression is the key to understanding cyanobacterial metabolism; however, proteome studies in cyanobacteria are limited and cover only a fraction of the theoretical proteome. Here, we performed a comprehensive proteogenomic analysis of the model cyanobacterium Synechocystis sp. PCC 6803 to characterize the expressed (phospho)proteome, re-annotate known and discover novel open reading frames (ORFs). By mapping extensive shotgun mass spectrometry proteomics data onto a six-frame translation of the Synechocystis genome, we refined the genomic annotation of 64 ORFs, including eight completely novel ORFs. Our study presents the largest reported (phospho)proteome dataset for a unicellular cyanobacterium, covering the expression of about 80% of the theoretical proteome under various cultivation conditions, such as nitrogen or carbon limitation. We report 568 phosphorylated S/T/Y sites that are present on numerous regulatory proteins, including the transcriptional regulators cyAbrB1 and cyAbrB2. We also catalogue the proteins that have never been detected under laboratory conditions and found that a large portion of them is plasmid-encoded. This dataset will serve as a resource, providing dedicated information on growth condition-dependent protein expression and phosphorylation.

In-gel digestion coupled with mass spectrometry (GeLC-MS/MS)-based salivary proteomic profiling of canine oral tumors

BackgroundVarious types of oral tumors, either benign or malignant, are commonly found in dogs. Since saliva directly contacts the tumors and saliva collection is non-invasive, easily accessible and cost effective, salivary biomarkers are practical to be used for the diagnosis and/or prognosis of these diseases. However, there is limited knowledge of protein expression in saliva for canine oral tumors. The present study aimed to investigate novel biomarkers from the salivary proteome of dogs with early- and late-stage oral melanoma (EOM and LOM, respectively), oral squamous cell carcinoma (OSCC), benign oral tumors (BN), and periodontitis and healthy controls (CP), using an in-gel digestion coupled with mass spectrometry (GeLC-MS/MS). The relationships between protein candidates and chemotherapy drugs were explored and the expression of potential biomarkers in saliva and tissues was verified by western blot analysis.ResultsFor saliva samples, increased expression of protein tyrosine phosphatase non-receptor type 5 (PTPN5) was shown in all tumor groups compared with the CP group. Marked expression of PTPN5 was also observed in LOM and OSCC compared with that in BN and EOM. In addition, tumor protein p53 (p53), which appeared in the PTPN5–drug interactions, was exhibited to be expressed in all tumor groups compared with that in the CP group. For tissue samples, increased expression of p53 was shown in LOM compared with the control group.ConclusionPTPN5 and p53 were proposed to be potential salivary biomarkers of canine oral tumors.

Topographic contrast of ultrathin cryo-sections for correlative super-resolution light and electron microscopy.

Fluorescence microscopy reveals molecular expression at nanometer resolution but lacks ultrastructural context information. This deficit often hinders a clear interpretation of results. Electron microscopy provides this contextual subcellular detail, but protein identification can often be problematic. Correlative light and electron microscopy produces complimentary information that expands our knowledge of protein expression in cells and tissue. Inherent methodological difficulties are however encountered when combining these two very different microscopy technologies. We present a quick, simple and reproducible method for protein localization by conventional and super-resolution light microscopy combined with platinum shadowing and scanning electron microscopy to obtain topographic contrast from the surface of ultrathin cryo-sections. We demonstrate protein distribution at nuclear pores and at mitochondrial and plasma membranes in the extended topographical landscape of tissue.

A proteomic approach to the identification and characterisation of protein composition in wheat germ

Proteome analyses were carried out on commercial wheat germ of mature grain from the biscuit-making wheat cultivar, Rosella. Wheat germ protein extracts were fractionated by two-dimensional gel electrophoresis across two different immobilised pH gradients: pH 4.0-7.0 and 6.0-9.0. A total of 612 individual protein spots were excised from the gels and characterised by peptide mass fingerprinting. From these analyses, 347 individual proteins were identified from protein sequence database interrogation, and 301 different types of protein were catalogued according to protein function. The remaining 265 protein spots gave poor or no matches to proteins in the databases and were not identified in this study. Six different classes of enzymes were identified in the germ, many of them having roles in the mobilisation of energy reserves for germination. Abundantly expressed enzyme classes include the oxidoreductases, transferases and hydrolases. A comparison was also made between the major protein classes expressed in the germ and protein classes expressed in the endosperm from previous proteomic work. This study contributes significantly to our knowledge of protein expression and heterogeneity in the germ of wheat grain and forms the basis for future studies in regard to the characterisation of proteins during the initial stages of germination.

Protein profiling of complete mole and normal placenta using ProteinChip analysis on laser capture microdissected cells

Introduction Surface-enhanced laser desorption/ionization mass spectrometry (SELDI-MS) is a novel method for biomarker discovery that can provide a rapid protein expression profile from a variety of biological samples. Since SELDI-MS requires a small amount of biological material, this technique is ideal for analyzing proteins isolated from microdissected tissue samples. The current study was undertaken to investigate potential differences in protein expression between normal and molar trophoblast procured by laser capture microdissection (LCM) utilizing SELDI ProteinChip array technology. Further knowledge of protein expression in complete mole may advance our understanding of the pathogenesis of gestational trophoblastic diseases. Materials and methods Laser capture microdissected trophoblast cells from nine fresh complete moles were analyzed and compared to the trophoblast cells from 10 fresh normal placentas of comparable gestational age, using SELDI ProteinChip to identify potential differences in protein expression. Results Three metal binding polypeptides were identified with the estimated molecular weights of 11.3, 13.8, and 14.0 kDa, which appeared in significantly lower levels in complete mole as compared to normal trophoblast cells ( P < 0.001, P < 0.03, and P < 0.01). Discussion While further characterization of these protein peaks is important and necessary, our current work clearly demonstrates that the combined technology of SELDI and LCM is effective in distinguishing protein expression between normal placenta and complete mole. Further knowledge of protein expression in complete mole may advance our understanding of molecular mechanisms and improve management in gestational trophoblastic diseases.