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Proteomic investigation of the effect of salicylic acid on Arabidopsis seed germination and establishment of early defense mechanisms.

The influence of salicylic acid (SA) on elicitation of defense mechanisms in Arabidopsis (Arabidopsis thaliana) seeds and seedlings was assessed by physiological measurements combined with global expression profiling (proteomics). Parallel experiments were carried out using the NahG transgenic plants expressing the bacterial gene encoding SA hydroxylase, which cannot accumulate the active form of this plant defense elicitor. SA markedly improved germination under salt stress. Proteomic analyses disclosed a specific accumulation of protein spots regulated by SA as inferred by silver-nitrate staining of two-dimensional gels, detection of carbonylated (oxidized) proteins, and neosynthesized proteins with [35S]-methionine. The combined results revealed several processes potentially affected by SA. This molecule enhanced the reinduction of the late maturation program during early stages of germination, thereby allowing the germinating seeds to reinforce their capacity to mount adaptive responses in environmental water stress. Other processes affected by SA concerned the quality of protein translation, the priming of seed metabolism, the synthesis of antioxidant enzymes, and the mobilization of seed storage proteins. All the observed effects are likely to improve seed vigor. Another aspect revealed by this study concerned the oxidative stress entailed by SA in germinating seeds, as inferred from a characterization of the carbonylated (oxidized) proteome. Finally, the proteomic data revealed a close interplay between abscisic signaling and SA elicitation of seed vigor.

Adaptation, Physiological↗

Proteomic analysis of progressive factors in uterine cervical cancer.

Human papillomavirus (HPV) infections play a crucial role in the progress of cervical cancer. The high-risk HPV types are frequently associated with the development of malignant lesions. Some of the latest studies have demonstrated that the high-risk HPV 16 and 18 are predominantly detected in the more aggressive cancers. In the present study, we aimed to establish the proteomic profiles and characterization of the tumor related proteins by using two-dimensional gel electrophoresis (2-DE) and matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS). For proteomic analysis, patients infected by HPV 16 or 18 were included in this study. We compared nuclear protein and cytoplasmic protein, separately by using the subcellular fraction. Differential protein spots between cervical cancer with high-risk HPV, HPV 16 or HPV 18, and HaCaT cell lines were characterized by 2-DE. Those proteins analyzed by peptide mass fingerprinting based on MALDI-TOF MS and database searching were the products of oncogenes or proto-oncogenes, and the others were involved in the regulation of cell cycle, for general genomic stability, telomerase activation, and cell immortalization. However, there was no difference in protein characterization for cervical cancer between HPV 16 and HPV 18 infection. Nonetheless, these data are valuable for the mass identification of differentially expressed proteins involved in human uterine cervical cancer. Moreover, the data has enormous value for establishing the human uterine cervical cancer proteome database that can be used in screening a molecular marker for the further study of human uterine cervical cancer, and also for studying any correlation among the cancers induced by HPV.

Adult↗

Proteomic analysis of reporter genes for molecular imaging of transplanted embryonic stem cells.

Study of stem cells may reveal promising treatment for diseases. The fate and function of transplanted stem cells remain poorly defined. Recent studies demonstrate that reporter genes can monitor real-time survival of transplanted stem cells in living subjects. We examined the effects of a novel and versatile triple fusion (TF) reporter gene construction on embryonic stem (ES) cell function by proteomic analysis. Murine ES cells were stably transduced with a self-inactivating lentiviral vector containing fluorescence (firefly luciferase; Fluc), bioluminescence (monomeric red fluorescence protein; mRFP), and positron emission tomography (herpes simplex virus type 1 truncated thymidine kinase; tTK) reporter genes. Fluorescence-activated cell sorting (FACS) analysis isolated stably transduced populations. TF reporter gene effects on cellular function were evaluated by quantitative proteomic profiling of control ES cells versus ES cells stably expressing the TF construct (ES-TF). Overall, no significant changes in protein quantity were observed. TF reporter gene expression had no effect on ES cell viability, proliferation, and differentiation capability. Molecular imaging studies tracked ES-TF cell survival and proliferation in living animals. In summary, this is the first proteomic study, demonstrating the unique potential of reporter gene imaging for tracking ES cell transplantation non-invasively, repetitively, and quantitatively.

Animals↗

Individualised cancer therapeutics: dream or reality?

Traditional measures for treating metastatic cancer involve identification of the originating organ from which the neoplasm arose and empirical treatment with cytotoxic chemotherapy. Arguably, with the exception of haematological malignancies, demonstration of efficacy in solid tumours has been limited. Over the past half-decade, theoretical and technological advances have resulted in greater application of molecular science to drug design, which has enabled development of new 'targeted' therapeutics. However, generic chemotherapy paradigms have not changed. Establishment of the optimal population for 'targeted' therapeutics based on molecular diagnostics (i.e. genomic and proteomic characterisation) to identify sensitive tumour-host ecosystems in individual patients at the 'bedside', is not being done as part of routine oncology management. This review focuses on the concept of designing individualised therapeutics based on genomic and proteomic profile of malignant tissue. Genetic and epigenetic perturbations in signal pathways drive cancer growth, survival, invasion and metastatic spread. The burgeoning evidence which supports the concept that each patient's cancer has a unique complement of pathogenic genetic and molecular derangements is reviewed. Such evidence supports the strategy of individualised selection of a therapeutic complex from a menu of targeting options that best complements the specific oncomolecular profile of the 'tumour-host' system.

Antineoplastic Agents↗

DeepPlaque: a scalable multimodal platform for Aβ pathology and cell analysis in Alzheimer's disease.

Histological analysis is essential for understanding disease pathology and the microenvironment, particularly in Alzheimer's disease (AD), characterized by beta-amyloid (Aβ) plaques that exist as diffuse, fibrillar, and core species, with distinct toxicity levels. However, accurate classification of Aβ plaque types in postmortem brain tissues and profiling of surrounding cells present significant challenges. To address these challenges, we developed "DeepPlaque", an integrated system featuring "PlaqueNet", a deep learning model for automated classification of Aβ plaque species from diverse imaging platforms. DeepPlaque includes automated workflows for cellular phenotyping and proteomic profiling through targeted laser microdissection. PlaqueNet achieves expert-level accuracy (AUC > 90%) in classifying the 3 major Aβ plaque species, supporting consistent and large-scale annotation. By integrating spatial cellular phenotyping with laser microdissection, DeepPlaque enables high-throughput proteomic analysis of Aβ plaque niches, revealing that microglia are more abundant around core and fibrillar Aβ plaques, with increased expression of apolipoprotein E and amyloid precursor protein in core Aβ plaques. This customizable platform enhances the molecular and cellular characterization of Aβ plaque-associated environments, providing critical insights into AD pathology.

Alzheimer Disease↗

Profiling of abundant proteins associated with dichlorodiphenyltrichloroethane resistance in Drosophila melanogaster.

Dichlorodiphenyltrichloroethane (DDT) metabolism-based resistance in Drosophila melanogaster is a complex metabolic system associated with the transcription of detoxification related genes, ion transport, lipid and sugar metabolism pathways. However, little is known about the differences regarding the proteome of field- and laboratory-selected resistant Drosophila genotypes. We investigated the impact of DDT resistance in the abundant proteome of field- and laboratory- selected resistant Drosophila using a two-dimensional gel electrophoresis DDT reference map. Proteomic profiling was performed in two DDT susceptible genotypes (Canton-S and 91-C) and three DDT resistant lines (Rst(2)DDT(91-R), Rst(2)DDT(Wisconsin) and Rst(2)DDT(Hikone-R)). Protein spots were stained with Coomassie blue and compared using PDQuest software. Selected protein spots were cut out and analyzed using matrix assisted laser desorption-time of flight mass spectrometry. Querying the NCBInr. 10.21.2003 database with mass spectrometric data yielded the identity of 21 differentially translated proteins in Rst(2)DDT(91-R), Rst(2)DDT(Wisconsin) and Canton-S representing proteins putatively involved in biochemical pathways such as glycolysis and gluconeogenesis, the pentose phosphate pathway, the Krebs cycle and fatty acid oxidation. We hypothesize that both strategies are aimed to use of the pentose phosphate pathway to increase glucose utilization while Rst(2)DDT(91-R) relies primarily on glycolysis to produce reduced NADP and increase DDT detoxification. DDT exposure in Canton-S induced six proteins, while four proteins were repressed in Rst(2)DDT(Hikone-R). Our data suggest that insecticide resistance appears to impact different metabolic pathways in Drosophila genotypes selected with the same pesticide (DDT).

Animals↗

Analysis of membrane proteins from human chronic myelogenous leukemia cells: comparison of extraction methods for multidimensional LC-MS/MS.

An important strategy for "shotgun proteomics" profiling involves solution proteolysis of proteins, followed by peptide separation using multidimensional liquid chromatography and automated sequencing by mass spectrometry (LC-MS/MS). Several protocols for extracting and handling membrane proteins for shotgun proteomics experiments have been reported, but few direct comparisons of different protocols have been reported. We compare four methods for preparing membrane proteins from human cells, using acid labile surfactants (ALS), urea, and mixed organic-aqueous solvents. These methods were compared with respect to their efficiency of protein solubilization and proteolysis, peptide and protein recovery, membrane protein enrichment, and peptide coverage of transmembrane proteins. Overall, approximately 50-60% of proteins recovered were membrane-associated, identified from Gene Ontology annotations and transmembrane prediction software. Samples extracted with ALS, extracted with urea followed by dilution, or extracted with urea followed by desalting yielded comparable peptide recoveries and sequence coverage of transmembrane proteins. In contrast, suboptimal proteolysis was observed with organic solvent. Urea extraction followed by desalting may be a particularly useful approach, as it is less costly than ALS and yields satisfactory protein denaturation and proteolysis under conditions that minimize reactivity with urea-derived cyanate. Spectral counting was used to compare datasets of proteins from membrane samples with those of soluble proteins from K562 cells, and to estimate fold differences in protein abundances. Proteins most highly abundant in the membrane samples showed enrichment of integral membrane protein identifications, consistent with their isolation by differential centrifugation.

Cell Extracts↗

Proteomic changes associated with degeneration of myelin-forming cells in the central nervous system of c-myc transgenic mice.

Myelin is necessary for the conduction of high frequency and high velocity nerve impulses in the central nervous system of mammals, and severe neurological disturbances develop as a result of myelin loss. In this report, we have characterized changes in the brain proteomic profile of transgenic mice that develop a c-myc-induced degenerative disorder of myelin. Marked differences were seen in the accumulation of cytoskeletal proteins associated with the pathological condition fibrous gliosis in the optic nerves of affected animals, including upregulation of glial fibrillary acid protein and vimentin. In addition, the expression of several major myelin proteins, including five isoforms of myelin basic protein, four isoforms of cyclic nucleotide 3'-phosphodiesterase, and myelin-associated glycoprotein, was markedly reduced in the brains of c-myc transgenic mice as revealed by immunocytochemistry and by two-dimensional immunoblots. A number of novel proteomic disease marker candidates were revealed, which displayed pronounced changes in their expression profile. Sequence determination of these proteins and molecular cloning of their mRNAs will provide an opportunity to further evaluate their roles in the disease process.

3',5'-Cyclic-AMP Phosphodiesterases↗

Proteomics in clinical interventions: achievements and limitations in biomarker development.

Development of toxicological and clinical biomarkers for disease diagnosis, quantification of toxicant/drug responses and rapid patient care are major concerns in modern biology. Even after human genome sequencing, identification of specific molecular signatures for unambiguous correlation with toxicity and clinical interventions is a challenging task. Differential protein expression patterns and protein-protein interaction studies have started unraveling rigorous molecular explanation of multi-factorial and toxicant borne diseases. Proteome profiling is extensively used to investigate etiology of diseases, develop predictive biomarkers for toxicity and therapeutic interventions and potential strategies for treatment of complex and toxicant mediated diseases. In this review, achievements and limitations of proteomics in developing predictive biomarkers for toxicological and clinical interventions have been discussed.

Animals↗

Biomarker profiling of plasma from acute coronary syndrome patients. Application of ProteinChip Array analysis.

AIM: Acute coronary syndrome (ACS) is one of the leading causes of death in the world and remains a complex pathophysiologic process involving inflammatory, hemostatic and vascular processes. The purpose of this study was to identify unique proteomic biomarkers present in patients with ACS using a newly developed proteomic profiling technique, surface enhanced laser desorption/ionization (SELDI). METHODS: Citrated plasma samples obtained from clinically confirmed cases of ACS (n=100) and age matched controls (n=25) were profiled using SELDI-time of flight (TOF)-mass spectrometry (Ciphergen Biosystems, Freemont, CA, USA). A strong anion exchange (SAX) ProteinChip Array was used to profile these samples. In addition to spectra profiles, protein density plots were be obtained from the generated molecular profile. RESULTS: The SELDI profile in the molecular weight (MW) range of 0-150 kDa revealed a prominent 66.3 kDa albumin peak along with several distinct components at 28 kDa, 13.7 kDa and 6.5 kDa. Additional minor molecular components were also noted in the lower MW range (<6 kDa). There was a cluster of peaks between 10 and 12 kDa that were unique to the patients with ACS; about 1/3 of the ACS patients exhibited these peaks as evident in the ProteinChip Array spectrum. None of the age-matched controls exhibited the peaks in this MW range, nor did the normal human plasma pool that was used as an additional control. The relative intensity of these novel molecular components in the range of 10-12 kDa represent unique proteins/peptides which are generated in specific pathologic states associated with ACS. CONCLUSIONS: These observations suggest that patients with ACS have a unique cluster of molecular components that are present in their SELDI profile. It might be possible to use these patterns to identify high-risk patients who may be more susceptible to the development of unstable plaque, which may eventually lead to myocardial infarction. Identification and characterization of these molecular components will also help in the understanding of the pathogenesis of ACS. These unique peaks may represent pathologic proteins, novel inflammatory mediators or protease cleavage products. Further studies need to be done to better characterize and identify these molecular components and their pathologic role in ACS.

Acute Disease↗

Integrated analysis of amide proton transfer weighted MRI and proteomics uncovers altered protein dynamics in glioblastoma.

PURPOSE: Elevated amide proton transfer-weighted (APTw) MRI signals in glioblastoma (GBM) are often linked to increased intracellular mobile proteins, but the associated molecular patterns in human tissue remain unclear. We examined the relationship between regional APTw features and cellular protein composition and profiled proteomic differences between tumor and peritumoral tissue. METHODS: In this single-center prospective study, preoperative MRI data were integrated with intraoperative neuronavigation for 12 image-guided tissue samples (8 tumor and 4 peritumoral). Total, cytoplasmic, and nuclear proteins were quantified using bicinchoninic acid (BCA) assay. Data-independent acquisition (DIA) proteomics identified exploratory differentially expressed proteins (DEPs), followed by functional enrichment and protein-protein interaction (PPI) network analyses. Transcript-level expression patterns and survival associations were queried in The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) datasets to provide indirect external clinical context. RESULTS: Tumor regions showed higher APTw signals than peritumoral regions (p&#x2009;<&#x2009;0.001) and increased cytoplasmic protein concentration (p&#x2009;<&#x2009;0.05), without a corresponding increase in total or nuclear protein levels. DIA identified 654 DEPs. Further analysis highlighted 36 higher-significance DEPs, and prioritized 12 hub proteins in the PPI network. In public transcriptomic datasets, ERBB2, RUNX1, and SHC1 showed higher expression in GBM and were associated with poorer overall survival. CONCLUSION: These findings suggest that elevated APTw signal in GBM may be associated with increased cytoplasmic protein content and distinct proteomic alterations. This imaging-proteomic framework provides exploratory regional context for future mechanistic and follow-up studies, but larger, spatially matched and independently validated cohorts are required to confirm the molecular contributors to APTw contrast.

Humans↗

Proteomic and transcriptional characterization of aromatic degradation pathways in Rhodoccocus sp. strain TFB.

Rhodococcus sp. strain TFB is a versatile gram-positive bacterium able to grow on a wide variety of aromatic compounds as carbon and energy sources. Since the strain is refractory to genetic analysis, a proteomic approach was used to study the metabolic pathways involved in the catabolism of such compounds by analyzing differentially induced proteins. The most marked difference was observed when the proteome profiles of phthalate-grown cells were compared with those cultured in the presence of tetralin- or naphthalene, suggesting that different metabolic pathways are involved in the degradation of mono- and polyaromatic compounds. Comparison with the proteome of glucose-grown cells indicated that each pathway was specifically induced by the corresponding aromatic compound. A combination of proteomics and molecular biology led to the identification of 14 proteins (65-80% identical to known Pht proteins) that describe a complete pathway for the catabolism of phthalate to central metabolites via intradiol cleavage of protochatechuic acid. Chaperonins were also induced in phthalate-grown cells, indicating that growth on this compound induces a stress response. Absence of catabolite repression by glucose was observed by both transcriptional and proteome analysis, suggesting that Rhodococcus sp. strain TFB may have advantages over other tightly regulated strains in bioremediation.

Amino Acid Sequence↗

Machine learning-assisted plasma PEA proteomics enables differential diagnosis of melancholic depression and bipolar disorder.

Differentiating bipolar disorder (BD) from major depressive disorder (MDD) remains a critical unmet need in psychiatry due to overlapping clinical presentations and the absence of reliable biological markers. In this study, we assessed the capacity of multivariate machine learning models to accurately differentiate BD from MDD with melancholic features using plasma proteomic profiles obtained via Proximity Extension Assay (PEA) technology. A total of 67 participants were included (23 BD, 20 MDD, and 24 HC), and plasma protein expression was assessed using the Olink Target 96 Neurology panel. Differential proteomic analysis revealed distinct disorder-specific expression patterns, identifying 21 differentially expressed proteins in BD versus MDD, 18 in BD versus healthy controls, and 7 in MDD versus healthy controls. Using a stepwise feature reduction strategy, machine learning models were trained on three feature sets comprising all proteins, the top 20 most informative proteins, and the top 5 most beneficial proteins, and evaluated across BD-MDD, BD-HC, and MDD-HC classification tasks using five algorithms. For BD-MDD discrimination, the Random Forest model achieved the highest performance when trained on the top 5 protein set (LXN, HAGH, MATN3, PLXNB1, and CTSC), yielding an AUC of 0.905, with similarly strong performance observed using the top 20 protein set. Feature importance analysis highlighted proteins involved in neurodevelopmental processes, immune regulation, and extracellular matrix organization. Overall, these findings demonstrate that integrating plasma proteomics with machine learning enables robust differentiation between BD and MDD with melancholic features, supporting the development of scalable and biologically informed diagnostic tools for precision psychiatry.

Bipolar disorder↗

Application of the random forest classification algorithm to a SELDI-TOF proteomics study in the setting of a cancer prevention trial.

A thorough discussion of the random forest (RF) algorithm as it relates to a SELDI-TOF proteomics study is presented, with special emphasis on its application for cancer prevention: specifically, what makes it an efficient, yet reliable classifier, and what makes it optimal among the many available approaches. The main body of the paper treats the particulars of how to successfully apply the RF algorithm in a proteomics profiling study to construct a classifier and discover peak intensities most likely responsible for the separation between the classes.

Algorithms↗

A comparison study in the proteomic signatures of multipotent germline stem cells, embryonic stem cells, and germline stem cells.

Germline stem (GS) cells can only differentiate into germline cells, while multipotent germ stem (mGS) cells, like embryonic stem (ES) cells, can differentiate into various somatic cells and tissues. The proteomic profiles in GS and mGS cells were compared by two-dimensional gel electrophoresis. Ten down-regulated and 16 up-regulated proteins were differentially expressed in mGS cells in comparison to GS cells, and these proteomic characteristics were very much similar to those in ES cells indicating that multipotency of mGS and ES cells is based on a common molecular event(s). Protein identification by mass spectrometry revealed that these proteins were functionally involved in cell signaling, transcription factors, metabolism, and protein folding. The identified proteins in the present study may thus reveal its biological characteristics and functional property in self-renewal and multipotency.

Animals↗

Target identification and validation in drug discovery: the role of proteomics.

Proteomics, the study of cellular protein expression, is an evolving technology platform that has the potential to identify novel proteins involved in key biological processes in the cell that may serve as potential drug targets. While proteomics has considerable theoretical promise, individual cells/tissues have the potential to generate many millions of proteins while the current analytical technologies that involve the use of time-consuming two dimensional gel electrophoresis (2DIGE) and various mass spectrometry (MS) techniques are unable to handle complex biological samples without multiple high-resolution purification steps to reduce their complexity. This can significantly limit the speed of data generation and replication and requires the use of bioinformatic algorithms to reconstitute the parent proteome, a process that does not always result in a reproducible outcome. In addition, membrane bound proteins, e.g., receptors and ion channels, that are the targets of many existing drugs, are not amenable to study due, in part, to limitations in current proteomic techniques and also to these being present in low abundance and thus disproportionally represented in proteome profiles. Subproteomes with reduced complexity have been used to generate data related to specific, hypothesis-driven questions regarding target identification, protein-interaction networks and signaling pathways. However progress to date, with the exception of diagnostic proteomics in the field of cancer, has been exceedingly slow with an inability to put such studies in the context of a larger proteome, limiting the value of the information. Additionally the pathway for target validation (which can be more accurately described at the preclinical level as target confidence building) remains unclear. It is important that the ability to measure and interrogate proteomes matches expectations, avoiding a repetition of the disappointment and subsequent skepticism that accompanied what proved to be unrealistic expectations for the rapid contribution of data based on the genome maps, to biomedical research.

Computational Biology↗

Two-dimensional microcolumn separation platform for proteomics consisting of on-line coupled capillary isoelectric focusing and capillary electrochromatography. 1. Evaluation of the capillary-based two-dimensional platform with proteins, peptides, and human serum.

In this report, an on-line coupling of capillary isoelectric focusing (CIEF) to capillary electrochromatography (CEC) is developed via a nanoinjector valve for performing two-dimensional (2D) proteomics separation. CIEF constitutes the first separation dimension, while CEC operates as the second separation dimension. Besides the orthogonal migration mechanisms of the two capillary-based separation modes, which lead to a 2D system whose overall peak capacity is the product of the peak capacity of the individual modes, the solvent of the CIEF mode is a weak eluent for the reversed-phase CEC (RP-CEC) mode, thus, allowing the transferring of focused fractions from CIEF to CEC without inducing band broadening, and instead zone sharpening would result. In fact, the transferred focused protein fraction from the CIEF column to the CEC column will stay tightly adsorbed to the inlet top of the CEC column until it will be eluted and separated into its protein components with a hydro-organic mobile phase. The theoretical peak capacity of the CIEF-CEC 2D platform is estimated at n(CIEF) (= 560) x n(CEC) (= 97) = 54 320. This peak capacity is more than needed for proteomics profiling. Also, only a fraction of this peak capacity is needed when looking at heart cuts for performing subproteomics. The 2D platform described here offers the convenience to generate the needed peak capacity to solve a given proteomic separation problem. This is facilitated by the RP-CEC dimension, which ensures rapid isocratic separation of proteins and peptides and rapid solvent change and column equilibration and avoids lengthy gradient elution. The RP-CEC column is based on neutral C17 monolith, which offers high separation efficiency and relatively high column permeability. To the best of our knowledge, the proposed 2D platform combining CIEF and CEC is reported for the first time for proteins and proteomics.

Amino Acid Sequence↗

Proteomic applications for the early detection of cancer.

The ability of physicians to effectively treat and cure cancer is directly dependent on their ability to detect cancers at their earliest stages. Proteomic analyses of early-stage cancers have provided new insights into the changes that occur in the early phases of tumorigenesis and represent a new resource of candidate biomarkers for early-stage disease. Studies that profile proteomic patterns in body fluids also present new opportunities for the development of novel, highly sensitive diagnostic tools for the early detection of cancer.

Biomarkers, Tumor↗