Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “proteomic profiling”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,135 records · Page 63Linked to original sources
PubMed2006

Functional proteomics approach to investigate the biological activities of cDNAs implicated in breast cancer.

Functional proteomics approaches that comprehensively evaluate the biological activities of human cDNAs may provide novel insights into disease pathogenesis. To systematically investigate the functional activity of cDNAs that have been implicated in breast carcinogenesis, we generated a collection of cDNAs relevant to breast cancer, the Breast Cancer 1000 (BC1000), and conducted screens to identify proteins that induce phenotypic changes that resemble events which occur during tumor initiation and progression. Genes were selected for this set using bioinformatics and data mining tools that identify genes associated with breast cancer. Greater than 1000 cDNAs were assembled and sequence verified with high-throughput recombination-based cloning. To our knowledge, the BC1000 represents the first publicly available sequence-validated human disease gene collection. The functional activity of a subset of the BC1000 collection was evaluated in cell-based assays that monitor changes in cell proliferation, migration, and morphogenesis in MCF-10A mammary epithelial cells expressing a variant of ErbB2 that can be inducibly activated through dimerization. Using this approach, we identified many cDNAs, encoding diverse classes of cellular proteins, that displayed activity in one or more of the assays, thus providing insights into a large set of cellular proteins capable of inducing functional alterations associated with breast cancer development.

Breast Neoplasms↗
PubMed2002

Proteomics: technologies and applications.

Enabling technologies for proteomics (studies examining the protein complement of the genome) have been in development for over 20 years. However, more recently, the field has become formalized by combining the techniques for large-scale protein separation (two-dimensional electrophoresis) with very precise, high fidelity approaches to the analysis and characterisation of the separated proteins (mass spectrometry). These approaches bring to reality the powerful scope of proteomics, enabling the interpretation of cell physiology through profiling the molecular biodescriptors of gene expression, the proteins. Applications of proteomics are wide ranging and can be found within many disciplines, although one of the more developed fields that is examined here covers the arena of toxicological and pharmacological profiling. In this paper, the current technologies that make proteomics possible will be looked at. Some areas where proteomics has delivered valuable biological insight will also be considered, and there will be a look towards future areas of development.

Electrophoresis, Gel, Two-Dimensional↗
PubMed2006

Unsupervised immunophenotypic profiling of chronic lymphocytic leukemia.

BACKGROUND: Proteomics and functional genomics have revolutionized approaches to disease classification. Like proteomics, flow cytometry (FCM) assesses concurrent expression of many proteins, with the advantage of using intact cells that may be differentially selected during analysis. However, FCM has generally been used for incremental marker validation or construction of predictive models based on known patterns, rather than as a tool for unsupervised class discovery. We undertook a retrospective analysis of clinical FCM data to assess the feasibility of a cell-based proteomic approach to FCM by unsupervised cluster analysis. METHODS: Multicolor FCM data on peripheral blood (PB) and bone marrow (BM) lymphocytes from 140 consecutive patients with B-cell chronic lymphoproliferative disorders (LPDs), including 81 chronic lymphocytic leukemia (CLLs), were studied. Expression was normalized for CD19 totals, and recorded for 10 additional B-cell markers. Data were subjected to hierarchical cluster analysis using complete linkage by Pearson's correlation. Analysis of CLL in PB samples (n = 63) discovered three major clusters. One cluster (14 patients) was skewed toward "atypical" CLL and was characterized by high CD20, CD22, FMC7, and light chain, and low CD23. The remaining two clusters consisted almost entirely (48/49) of cases recorded as typical BCLL. The smaller "typical" BCLL cluster differed from the larger cluster by high CD38 (P = 0.001), low CD20 (P = 0.001), and low CD23 (P = 0.016). These two typical BCLL clusters showed a trend toward a difference in survival (P = 0.1090). Statistically significant cluster stability was demonstrated by expanding the dataset to include BM samples, and by using a method of random sampling with replacement. CONCLUSIONS: This study supports the concept that unsupervised immunophenotypic profiling of FCM data can yield reproducible subtypes of lymphoma/chronic leukemia. Expanded studies are warranted in the use of FCM as an unsupervised class discovery tool, akin to other proteomic methods, rather than as a validation tool.

ADP-ribosyl Cyclase 1↗
PubMed2005

Double standards in quantitative proteomics: direct comparative assessment of difference in gel electrophoresis and metabolic stable isotope labeling.

Quantitative protein expression profiling is a crucial part of proteomics and requires methods that are able to efficiently provide accurate and reproducible differential expression values for proteins in two or more biological samples. In this report we evaluate in a direct comparative assessment two state-of-the-art quantitative proteomic approaches, namely difference in gel electrophoresis (DiGE) and metabolic stable isotope labeling. Therefore, Saccharomyces cerevisiae was grown under well defined experimental conditions in chemostats under two single nutrient-limited growth conditions using (14)N- or (15)N-labeled ammonium sulfate as the single nitrogen source. Following lysis and protein extraction from the two yeast samples, the proteins were fluorescently labeled using different fluorescent CyDyes. Subsequently, the yeast samples were mixed, and the proteins were separated by two-dimensional gel electrophoresis. Following in-gel digestion, the resulting peptides were analyzed by mass spectrometry using a MALDI-TOF mass spectrometer. Relative ratios in protein expression between these two yeast samples were determined using both DiGE and metabolic stable isotope labeling. Focusing on a small, albeit representative, set of proteins covering the whole gel range, including some protein isoforms and ranging from low to high abundance, we observe that the correlation between these two methods of quantification is good with the differential ratios determined following the equation R(Met.Lab.) = 0.98R(DiGE) with r(2) = 0.89. Although the correlation between DiGE and metabolic stable isotope labeling is exceptionally good, we do observe and discuss (dis)advantages of both methods as well as in relation to other (quantitative) approaches.

Electrophoresis, Gel, Two-Dimensional↗
PubMed2003

Generation of bioreagents for protein chips.

Protein microarrays have the potential to dramatically increase the throughput of proteomic analysis. Protein expression profiling chips with distinct spots of immobilized protein capture agents will allow the simultaneous measurement of hundreds to thousands of proteins from one sample. In contrast to DNA chips, for which the capture probes are easily designed and synthesized, the development of content for protein biochips is a long and laborious process. Careful consideration must be given to the specificities desired, the format of the assay, and the requirements of the capture agents, as well as to process optimization to minimize development time and cost. Monoclonal antibodies have been the prime choice as protein capture agents for the majority of protein chips developed to date. New technologies for the production of protein capture agents are more amenable to automation than traditional monoclonal antibody production and therefore carry the promise for industrialization.

Affinity Labels↗
PubMed2005

Translational studies for target-based drugs.

The biological background for the clinical and prognostic heterogeneity among tumors within the same histological subgroup is due to individual variations in the biology of tumors. The number of investigations looking at the application of novel technologies within the setting of clinical trials is increasing. The most promising way to improve cancer treatment is to build clinical research strategies on intricate biological evidence. New genomic technologies have been developed over recent years. These techniques are able to analyze thousands of genes and their expression profiles simultaneously. The purpose of this approach is to discover new cancer biomarkers, to improve diagnosis, predict clinical outcomes of disease and response to treatment, and to select new targets for novel agents with innovative mechanisms of action. Gene expression profiles are also used to assist in selecting biomarkers of pharmacodynamic effects of drugs in the clinical setting. Biomarker monitoring in surrogate tissues may allow researchers to assess "proof of principle" of new treatments. Clinical studies of biomarkers monitoring toxicity profiles have also been done. Such pharmacodynamic markers usually respond to treatment earlier than clinical response, and as such may be useful predictors of efficacy. Epidermal growth factor receptor (EGFR) mutation in lung cancer tissues is a strong predictive biomarker for EGFR-targeted protein tyrosine kinase inhibitors. Monitoring of EGFR mutation has been broadly performed in retrospective and prospective clinical studies. However, global standardization for the assay system is essential for such molecular correlative studies. A more sensitive assay for EGFR mutation is now under evaluation for small biopsy samples. Microdissection for tumor samples is also useful for the sensitive detection of EGFR mutation. Novel approaches for the detection of EGFR mutation in other clinical samples such as cytology, pleural effusion and circulating tumor cells are ongoing.

Biomarkers, Tumor↗
PubMed2026

Mining the sHSP20 (small heat-shock protein) gene family in finger millet (Eleusine coracana (L.) Gaertn.): structural, evolutionary and predicted abiotic-stress-responsive insights.

Small heat-shock proteins (sHSPs, the HSP20 family) are ATP-independent molecular chaperones that hold partially unfolded substrates and protect the proteome during heat and other abiotic stresses; every member is defined by a conserved &#x3b1;-crystallin domain (ACD). Finger millet (Eleusine coracana) is a climate-resilient, calcium-rich allotetraploid cereal of the semi-arid tropics whose HSP20 repertoire had not been catalogued. The present study is an entirely computational (in silico) analysis of the chromosome-scale reference genome of finger millet (NCBI GenBank assembly GCA_032690845.1, cultivar KNE 796-S). Mining the predicted proteome with the ACD profile (Pfam PF00011) and confirming every candidate by NCBI CD-search recovered 76 non-redundant ACD-bearing HSP20 genes (EcHSP20-1-EcHSP20-76). Based on phylogeny and TargetP-predicted localization, the members were classified into ten subfamilies: seven cytosolic/nuclear classes (C-I to C-VII, 60 members) together with chloroplastic (11), mitochondrial (3) and endoplasmic-reticulum (2) groups. The proteins ranged from 110 to 355 amino acids (12.1-39.2&#xa0;kDa) with theoretical pI of 4.85-9.69. The 76 loci were distributed over 14 of the 18 chromosomes and were conspicuously absent from chromosomes 8&#xa0;A, 8B, 9&#xa0;A and 9B, with pronounced clustering on chromosomes 1, 2, 3 and 6. Duplication analysis detected 149 paralogous pairs (49 homoeologous, 80 segmental/dispersed and 18 tandem); 147 of 148 pairs for which substitution rates could be calculated returned Ka/Ks&#x2009;<&#x2009;1 (mean 0.20), indicating strong purifying selection consistent with retention after whole-genome/allopolyploid duplication. Promoter analysis (PlantCARE) revealed enrichment of abscisic-acid-responsive (ABRE), MYB/MYC drought-related, STRE, DRE, low-temperature (LTR) and methyl-jasmonate/salicylic-acid elements, whereas canonical heat-shock elements (HSE) were not recovered. Expression profiling against a public drought transcriptome (SRP081350) showed that about half of the genes (39 of 76) are transcribed in leaf tissue, the expressed fraction being dominated by the cytosolic class C-I. This first finger-millet HSP20 catalogue provides a verified, reproducible framework and nominates computationally predicted candidate genes for future functional work on thermotolerance in cereals.

Allotetraploid↗
PubMed2006

Screening for genes preferentially expressed in the early phase of chondrogenesis.

This study reports a new system that is very effective in identifying genes closely related to the early phase of chondrogenic differentiation. While studying chondrogenesis in a progenitor cell line, ATDC5, we found that the amount of culture media overlying an ATDC5 monolayer affected the extent to which differentiation occurred. Therefore, to gain insight into the molecular mechanisms of chondrogenic differentiation, differential gene expression between differentiating and non-differentiating ATDC5 cultures was examined by suppression subtractive hybridization analysis. In this study, we focused on transcription factors that were identified in differentiating cultures, and found that activating transcription factor 5, ATF5, exhibited a conspicuous activation pattern using two methods to induce chondrogenesis of ATDC5 cells. Furthermore, ATF5 was found to be elevated in the developing limb bud by in situ hybridization in a pattern that was highly restricted to the cartilaginous anlagen, suggesting a positive association with ATF5 expression and chondrogenesis.

Animals↗
PubMed2006

Effect of 3D scaffold and dynamic culture condition on the global gene expression profile of mouse embryonic stem cells.

We have previously demonstrated that mouse embryonic stem (ES) cells differentiated on three-dimensional (3D), highly porous, tantalum-based scaffolds (Cytomatrixtrade mark) have significantly higher hematopoietic differentiation efficiency than those cultured under conventional two-dimensional (2D) tissue culture conditions. In addition, ES cell-seeded scaffolds cultured inside spinner bioreactors showed further enhancement in hematopoiesis compared to static conditions. In the present study, we evaluated how these various biomaterial-based culture conditions, e.g. 2D vs. 3D scaffolds and static vs. dynamic, influence the global gene expression profile of differentiated ES cells. We report that compared to 2D tissue culture plates, cells differentiated on porous, Cytomatrixtrade mark scaffolds possess significantly higher expression levels of extracellular matrix (ECM)-related genes, as well as genes that regulate cell growth, proliferation and differentiation. In addition, these differences in gene expression were more pronounced in 3D dynamic culture compared to 3D static culture. We report specific genes that are either uniquely expressed under each condition or are quantitatively regulated, i.e. over expressed or inhibited by a specific culture environment. We conclude that that biomaterial-based 3D cultures, especially under dynamic conditions, might favor efficient hematopoietic differentiation of ES cells by stimulating increased expression of specific ECM proteins, growth factors and cell adhesion related genes while significantly down-regulating genes that act to inhibit expression of these molecules.

Animals↗
PubMed2006

Protein expression profiling of the shrimp cellular response to white spot syndrome virus infection.

To better understand the pathogenesis of white spot syndrome virus (WSSV) and to determine which cell pathways might be affected after WSSV infection, two-dimensional gel electrophoresis (2-DE) was used to produce protein expression profiles from samples taken at 48 h post-infection (hpi) from the stomachs of Litopenaeus vannamei (also called Penaeus vannamei) that were either specific pathogen free or else infected with WSSV. Seventy-five protein spots that consistently showed either a marked change (>50%) in accumulated levels or else were highly expressed throughout the course of WSSV infection were selected for further study. After in-gel trypsin digestion followed by LC-nanoESI-MS/MS, bioinformatics databases were searched for matches. A total of 53 proteins were identified, with functions that included energy production, calcium homeostasis, nucleic acid synthesis, signaling/communication, oxygen carrier/transportation, and SUMO-related modification. 2-DE results were shown to be consistent with relative EST database data from a previously developed EST database of two Penaeus monodon cDNA libraries. For seven selected genes, 2-DE and EST data were also compared with transcriptional time-course RT-PCR data. This study is the first global analysis of differentially expressed proteins in WSSV-infected shrimp, and in addition to increasing our understanding of the molecular pathogenesis of this virus-associated shrimp disease, the results presented here should be useful both for identifying potential biomarkers and for developing antiviral measures.

Animals↗
PubMed2025

Glutamate molecular structure and protein affect the inhibition of breast cancer cell metastasis: Cell-derived exosomes inhibitory effects through the MAPK signaling pathway.

The aim of this study was to investigate the inhibitory effect of glutamate molecular structure and protein on breast cancer cell metastasis and the potential inhibitory mechanism of cell-derived exosomes via MAPK signaling pathway. Breast cancer cell lines with high metastatic potential were selected by in vitro cell culture technique. The effects of specific inhibitors of glutamic acid on the proliferation and metastasis of breast cancer cells were studied. Changes in protein expression profiles were analyzed by proteomics techniques to identify key proteins associated with breast cancer metastasis. Breast cancer cells were treated with inhibitors of the MAPK signaling pathway to evaluate their effect on cell metastasis and compare with exosome treatment. The results showed that the specific inhibitors of glutamate molecular structure could significantly inhibit the proliferation and metastasis of breast cancer cells. Proteomic analysis revealed several down-regulated proteins that are closely related to breast cancer metastasis.

Humans↗
PubMed2002

Predictive non-linear modeling of complex data by artificial neural networks.

An artificial neural network (ANN) is an artificial intelligence tool that identifies arbitrary nonlinear multiparametric discriminant functions directly from experimental data. The use of ANNs has gained increasing popularity for applications where a mechanistic description of the dependency between dependent and independent variables is either unknown or very complex. This machine learning technique can be roughly described as a universal algebraic function that will distinguish signal from noise directly from experimental data. The application of ANNs to complex relationships makes them highly attractive for the study of biological systems. Recent applications include the analysis of expression profiles and genomic and proteomic sequences.

Biochemical Phenomena↗
PubMed2002

Metabolic modeling: a tool of drug discovery in the post-genomic era.

The Human Genome Project, DNA microarrays, proteome chips and metabolic profiles, among others, are generating unprecedented amounts of valuable information about the genetic and metabolic responses of organisms to stimuli. This wealth of information poses a great scientific challenge, namely the development of novel, effective methods for functional analysis and interpretation. It is proposed here that biomathematical systems models must accompany data generation and management. A particularly effective framework for this purpose is canonical modeling based on Biochemical Systems Theory. The key concept of this theory is the formulation of biological phenomena as systems of differential equations, in which all processes are represented as products of power-law functions.

Biochemical Phenomena↗
PubMed2005

Gene identification signature (GIS) analysis for transcriptome characterization and genome annotation.

We have developed a DNA tag sequencing and mapping strategy called gene identification signature (GIS) analysis, in which 5' and 3' signatures of full-length cDNAs are accurately extracted into paired-end ditags (PETs) that are concatenated for efficient sequencing and mapped to genome sequences to demarcate the transcription boundaries of every gene. GIS analysis is potentially 30-fold more efficient than standard cDNA sequencing approaches for transcriptome characterization. We demonstrated this approach with 116,252 PET sequences derived from mouse embryonic stem cells. Initial analysis of this dataset identified hundreds of previously uncharacterized transcripts, including alternative transcripts of known genes. We also uncovered several intergenically spliced and unusual fusion transcripts, one of which was confirmed as a trans-splicing event and was differentially expressed. The concept of paired-end ditagging described here for transcriptome analysis can also be applied to whole-genome analysis of cis-regulatory and other DNA elements and represents an important technological advance for genome annotation.

5' Flanking Region↗
PubMedDate not supplied

A role for oligonucleotide-based RNA-knock down technologies in functional genomics.

Functional genomics is inundating the pharmaceutical industry with large numbers of potential gene targets from several sources such as gene expression profiling experiments (DNA microchips, proteomics) or database mining. Oligonucleotide-based RNA-knock down technologies such as antisense or RNA interference can aid in the filtering and prioritization of target candidates in the drug discovery process.

Drug Industry↗
PubMed2006

TSSub: eukaryotic protein subcellular localization by extracting features from profiles.

UNLABELLED: This paper introduces a new subcellular localization system (TSSub) for eukaryotic proteins. This system extracts features from both profiles and amino acid sequences. Four different features are extracted from profiles by four probabilistic neural network (PNN) classifiers, respectively (the amino acid composition from whole profiles; the amino acid composition from the N-terminus of profiles; the dipeptide composition from whole profiles and the amino acid composition from fragments of profiles). In addition, a support vector machine (SVM) classifier is added to implement the residue-couple feature extracted from amino acid sequences. The results from the five classifiers are fused by an additional SVM classifier. The overall accuracies of this TSSub reach 93.0 and 77.4% on Reinhardt and Hubbard's eukaryotic protein dataset and Huang and Li's eukaryotic protein dataset, respectively. The comparison with existing methods results shows TSSub provides better prediction performance than existing methods. AVAILABILITY: The web server is available from http://166.111.24.5/webtools/TSSub/index.html.

Algorithms↗