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PubMed2005

Profiling the humoral immune response to infection by using proteome microarrays: high-throughput vaccine and diagnostic antigen discovery.

Despite the increasing availability of genome sequences from many human pathogens, the production of complete proteomes remains at a bottleneck. To address this need, a high-throughput PCR recombination cloning and expression platform has been developed that allows hundreds of genes to be batch-processed by using ordinary laboratory procedures without robotics. The method relies on high-throughput amplification of each predicted ORF by using gene specific primers, followed by in vivo homologous recombination into a T7 expression vector. The proteins are expressed in an Escherichia coli-based cell-free in vitro transcription/translation system, and the crude reactions containing expressed proteins are printed directly onto nitrocellulose microarrays without purification. The protein microarrays are useful for determining the complete antigen-specific humoral immune-response profile from vaccinated or infected humans and animals. The system was verified by cloning, expressing, and printing a vaccinia virus proteome consisting of 185 individual viral proteins. The chips were used to determine Ab profiles in serum from vaccinia virus-immunized humans, primates, and mice. Human serum has high titers of anti-E. coli Abs that require blocking to unmask vaccinia-specific responses. Naive humans exhibit reactivity against a subset of 13 antigens that were not associated with vaccinia immunization. Naive mice and primates lacked this background reactivity. The specific profiles between the three species differed, although a common subset of antigens was reactive after vaccinia immunization. These results verify this platform as a rapid way to comprehensively scan humoral immunity from vaccinated or infected humans and animals.

Animals↗
PubMed2006

Proteomic analysis reveals successive aberrations in protein expression from healthy mucosa to invasive head and neck cancer.

Development of head and neck squamous cell carcinoma (HNSCC) is a multistep process and in many cases involves a phenomenon coined 'field cancerization'. In order to identify changes in protein expression occurring at different stages of tumorigenesis and field cancerization, we analysed 113 HNSCCs and 73 healthy, 99 tumor-distant and 18 tumor-adjacent squamous mucosae by SELDI-TOF-MS on IMAC30 ProteinChip Arrays. Forty-eight protein peaks were differentially expressed between healthy mucosa and HNSCC. Calgizarrin (S100A11), the Cystein proteinase inhibitor Cystatin A, Acyl-CoA-binding protein, Stratifin (14-3-3 sigma), Histone H4, alpha- and beta-Hemoglobin, a C-terminal fragment of beta-hemoglobin and the alpha-defensins 1-3 were identified by mass spectrometry. The alpha-defensins showed various alterations in expression as validated by immunohistochemistry (IHC). Supervised prediction analysis revealed excellent classification of healthy mucosa (94.5% correctly classified) and tumor samples (92.9% correctly classified). Application of this classifier to the tumor-adjacent and tumor-distant mucosa samples disclosed dramatic changes: only 59.6% of the tumor-distant biopsies were classified as normal, 27.3% were predicted as aberrant or HNSCC. Strikingly, 72% of the tumor-adjacent mucosae were predicted as aberrant. These data provide evidence for the existence of genetically altered fields with inconspicuous histology. Comparison of the protein profiles in the tumor-distant-samples with clinical outcome of 32 patients revealed a significant association between aberrant profiles with tumor relapse events (P=0.018; Fisher's exact test, two-tailed). We conclude that proteomic profiling in conjunction with protein identification greatly outperforms histopathological diagnosis and may have significant predictive power for clinical outcome and personalized risk assessment.

Amino Acid Sequence↗
PubMed2003

Proteomic analysis of human bronchoalveolar lavage fluid: expression profiling of surfactant-associated protein A isomers derived from human pulmonary alveolar proteinosis using immunoaffinity detection.

Human bronchoalveolar lavage fluid (BALF) proteins from pulmonary alveolar proteinosis (PAP) obtained by washing the epithelial lining of the lung with phosphate-buffered saline, were separated using high resolution two-dimensional gel electrophoresis (2-DE) under denaturing and reducing conditions. By Western blotting, the proteins were transferred from polyacrylamide gel onto a chemical resilient membrane. The surfactant-associated protein A (SP-A) isomers were then identified with enhanced chemiluminescence detection (ECL) using antibody-antigen reaction. Some of the gels were treated with silver staining after 2-DE. The molecular masses of SP-A isomers in BALF from PAP ranged from 20.5 to 26, 26 to 32, and 32 to 42 kDa, respectively; and isoelectric points (pI) were in pH range of 4.5-5.4 under denaturing and reducing conditions. In the mass range of 20.5-26 kDa and pI of 4.5-5.4, there were five isomers, and in mass range of 26-32 kDa and pI of 4.5 to 5.4, there were at least eight isomers on the ECL detection film. However, in the mass range of 32-42 kDa and pI of 4.5-5.4, there were three isomers separated one from another but there was also a cluster of overlapping spots on the ECL detection film. Thus, this communication describes a characteristic 2-DE pattern of SP-A isomers in BALF from PAP as follows. (1) The five isomers of mass 20.5-26 kDa and pI of 4.5-5.4; (2) the eight isomers of mass 26-32 kDa and pI of 4.5-5.4; and (3) the three isomers of mass 32-42 kDa and pI of 4.5-5.4.

Affinity Labels↗
PubMed2006

ProRata: A quantitative proteomics program for accurate protein abundance ratio estimation with confidence interval evaluation.

A profile likelihood algorithm is proposed for quantitative shotgun proteomics to infer the abundance ratios of proteins from the abundance ratios of isotopically labeled peptides derived from proteolysis. Previously, we have shown that the estimation variability and bias of peptide abundance ratios can be predicted from their profile signal-to-noise ratios. Given multiple quantified peptides for a protein, the profile likelihood algorithm probabilistically weighs the peptide abundance ratios by their inferred estimation variability, accounts for their expected estimation bias, and suppresses contribution from outliers. This algorithm yields maximum likelihood point estimation and profile likelihood confidence interval estimation of protein abundance ratios. This point estimator is more accurate than an estimator based on the average of peptide abundance ratios. The confidence interval estimation provides an "error bar" for each protein abundance ratio that reflects its estimation precision and statistical uncertainty. The accuracy of the point estimation and the precision and confidence level of the interval estimation were benchmarked with standard mixtures of isotopically labeled proteomes. The profile likelihood algorithm was integrated into a quantitative proteomics program, called ProRata, freely available at www.MSProRata.org.

Algorithms↗
PubMed2026

Profiler: an open web platform for multi-omics analysis.

MOTIVATION: High-throughput multi-omics technologies produce increasingly large and heterogeneous datasets that are difficult to analyze without advanced computational expertise. Existing bioinformatics tools are often fragmented or limited to specific omics types, hindering reproducibility and accessibility. There is a critical need for an integrated, user-friendly, and scalable platform capable of supporting multi-omics analyses across different data modalities. RESULTS: We present Profiler, an open-source, modular platform that unifies data import, quality control, preprocessing, statistical testing, machine and deep learning, biomarker discovery, pathway and drug-target enrichment, and survival modeling within a single reproducible environment. Built in Python with Streamlit, Profiler is available as both a web-based platform deployed on high-performance computing and a desktop version for local execution, enabling flexible usage across computational infrastructures. Profiler supports diverse omics modalities, including proteomics, transcriptomics, lipidomics, and electroencephalogram data. Through applications to glioblastoma proteomic, pancancer, and multi-omics datasets, Profiler reproduced known molecular subtypes, revealed potential therapeutic targets, and generated fully traceable analysis reports within minutes. By integrating advanced analytics behind an intuitive interface, Profiler democratizes multi-omics analysis and provides a robust, scalable foundation for systems biology and precision medicine research. AVAILABILITY AND IMPLEMENTATION: Profiler is open-source and freely available via its web platform (https://prism-profiler.univ-lille.fr) and GitHub (web version: https://github.com/yanisZirem/Profiler_v1_requests_datatests, desktop version: https://github.com/yanisZirem/prism-profiler), and archived on Zenodo (DOI: https://doi.org/10.5281/zenodo.17478158).

Software↗
PubMed2002

Complementary profiling of gene expression at the transcriptome and proteome levels in Saccharomyces cerevisiae.

Using an integrated genomic and proteomic approach, we have investigated the effects of carbon source perturbation on steady-state gene expression in the yeast Saccharomyces cerevisiae growing on either galactose or ethanol. For many genes, significant differences between the abundance ratio of the messenger RNA transcript and the corresponding protein product were observed. Insights into the perturbative effects on genes involved in respiration, energy generation, and protein synthesis were obtained that would not have been apparent from measurements made at either the messenger RNA or protein level alone, illustrating the power of integrating different types of data obtained from the same sample for the comprehensive characterization of biological systems and processes.

Databases as Topic↗
PubMed2000

Compositional bias and mimicry toward the nonself proteome in immunodominant T cell epitopes of self and nonself antigens.

We investigated whether and how molecular mimicry affects the shaping of the helper T cell repertoire. We implemented an algorithm that measures the probability of mimicry between epitopes of known immunogenicity and self or nonself proteomes. This algorithm yields 'similarity profiles', which represent the probability of matching between all contiguous overlapping peptides of the antigen under examination and those in the proteome(s) considered. Similarity profiles between helper T cell epitopes (of self or microbial antigens and allergens) and human or microbial SWISSPROT collections were produced. For each antigen, both collections yielded largely overlapping profiles, demonstrating that self-nonself discrimination does not rely on qualitative features that distinguish human from microbial peptides. However, epitopes whose probability of mimicry with self or nonself prevails are, respectively, tolerated or immunodominant and coexist within the same (auto-)antigen regardless of its self/nonself nature. Epitopes (on self and nonself antigens) can cross-stimulate T cells at increasing potency as their similarity with nonself augments. Mimicry, rather than complicating self-nonself discrimination, assists in the shaping of the immune repertoire and helps define the defensive or autoreactive potential of a T cell. Being a predictor of epitope immunogenicity, it bears relevance to vaccine design.

Algorithms↗
PubMed2006

Identification of parotid salivary biomarkers in Sjogren's syndrome by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry and two-dimensional difference gel electrophoresis.

OBJECTIVES: To identify the most significant salivary biomarkers in Sjögren's syndrome (SS) using proteomic methods. METHODS: Parotid saliva from 20 non-SS subjects and 41 primary SS patients was analysed. Protein expression profiles for each sample were generated by surface-enhanced laser desorption/ionization time-of-flight-mass spectrometry (SELDI-TOF-MS). Mean peak intensities of SS patients and non-SS subjects were compared by univariate analyses. Samples pooled by diagnosis (SS and non-SS) and labelled with different Cy dyes were compared by two-dimensional difference gel electrophoresis (2D-DIGE). Two protein levels that were most significantly different by SELDI-TOF-MS and 2D-DIGE were validated by enzyme-linked immunosorbent assay in individual samples. RESULTS: SELDI-TOF-MS of 10-200 kDa peaks revealed eight peaks with >2-fold changes in the SS group that differed from non-SS at P < 0.005. Peaks of 11.8, 12.0, 14.3, 80.6 and 83.7 kDa were increased, while 17.3, 25.4, and 35.4 kDa peaks were decreased in SS samples. 2D-DIGE identified significant increases of beta-2-microglobulin, lactoferrin, immunoglobulin (Ig) kappa light chain, polymeric Ig receptor, lysozyme C and cystatin C in all stages of SS. Two presumed proline-rich proteins, amylase and carbonic anhydrase VI, were reduced in the patient group. Three of these ten biomarkers have not been associated previously with SS. CONCLUSIONS: The salivary proteomic profile of SS is a mixture of increased inflammatory proteins and decreased acinar proteins when compared with non-SS. Future studies will test the ability of these biomarker levels, alone and in combination, to diagnose the salivary component of SS.

Amylases↗
PubMed2004

Biology of primate relaxin: a paracrine signal in early pregnancy?

Relaxin is a peptide hormone that exerts numerous effects in a variety of tissues across a broad range of species. Although first identified more than 75 years ago interest in relaxin biology has waxed and waned over the years consistent with peaks and troughs of new experimental data on its wide-ranging biological effects and advances in relaxin enabling technologies. Recent insights into species-dependent differences in relaxin biology during pregnancy have once again stimulated a relative surge of interest in the study of relaxin's reproductive biology. Identification and pharmacological characterization of orphaned relaxin receptors and exploration of its paracrine effects on pregnancy using genomic and proteomic technologies have succeeded in fueling current interest in relaxin research. Primates and non-primate vertebrates exhibit very disparate profiles of relaxin genomics, proteomics and functional biology. Non-human primates appear to exhibit a very close similarity to humans with respect to relaxin reproductive biology but the similarities and subtle differences are only just beginning to be understood. We, and others, have shown that relaxin produces significant changes to the non-human primate endometrium during the peri-implantation period that are consistent with relaxin's long perceived role as a paracrine modulator of pregnancy. The purpose of this review is to summarize the reproductive biology of relaxin in non-human primates with a specific emphasis on the paracrine role of ovarian and endometrial relaxin during embryo implantation and early pregnancy.

Animals↗
PubMed2006

New protein extraction/solubilization protocol for gel-based proteomics of rat (female) whole brain and brain regions.

The rat is an accepted model for studying human psychiatric/neurological disorders. We provide a protocol for total soluble protein extraction using trichloroacetic acid/acetone (TCA/A) from rat (female) whole brain, 10 brain regions and the pituitary gland, and show that two-dimensional gel electrophoresis (2-DGE) using pre-cast immobilized pH (4-7) gradient (IPG) strip gels (13 cm) in the first dimension yields clean silver nitrate stained protein profiles. Though TCA/A precipitation may not be "ideal", the important choice here is the selection of an appropriate lysis buffer (LB) for solubilizing precipitated proteins. Our results reveal enrichment of protein spots by use of individual brain regions rather than whole brain, as well as the presence of differentially expressed spots in their proteomes. Thus individual brain regions provide improved protein coverage and are better suited for differential protein detection. Moreover, using a phosphoprotein-specific dye, in-gel detection of phosphoproteins was demonstrated. Representative high-resolution silver nitrate stained proteome profiles of rat whole brain total soluble protein are presented. Shortcomings apart (failure to separate membrane proteins), gel-based proteomics remains a viable option, and 2-DGE is the method of choice for generating high-resolution proteome maps of rat brain and brain regions.

Animals↗
PubMed2004

Recent advances in neuroproteomics and potential application to studies of drug addiction.

The rapidly growing field of proteomics seeks to track changes in protein expression function that underlie the growth and differentiation of individual cell types, both during normal development and during the onset and progression of disease. Recent years have seen great strides in mRNA expression analysis, and the development of new technologies for protein profiling. However, current methods are limited to analysis of the relative expression level of only a few hundred to perhaps 2000 proteins, well below the ability of DNA microarrays to potentially interrogate the mRNA expression of more than 25,000 genes. Proteomics faces a special challenge in studies of the nervous system, where cellular and sub-cellular architecture is among the most complex in the body. This article presents an overview of current proteomic profiling technologies, reviews the recent use of some of these approaches in studies of the nervous system, and discusses the potential application of neuroproteomics to studies of drug addiction.

Animals↗
PubMed2005

Proteomic-based prognosis of brain tumor patients using direct-tissue matrix-assisted laser desorption ionization mass spectrometry.

Clinical diagnosis and treatment decisions for a subset of primary human brain tumors, gliomas, are based almost exclusively on tissue histology. Approaches for glioma diagnosis can be highly subjective due to the heterogeneity and infiltrative nature of these tumors and depend on the skill of the neuropathologist. There is therefore a critical need to develop more precise, non-subjective, and systematic methods to classify human gliomas. To this end, mass spectrometric analysis has been applied to these tumors to determine glioma-specific protein patterns. Protein profiles have been obtained from human gliomas of various grades through direct analysis of tissue samples using matrix-assisted laser desorption ionization mass spectrometry (MS). Statistical algorithms applied to the MS profiles from tissue sections identified protein patterns that correlated with tumor histology and patient survival. Using a data set of 108 glioma patients, two patient populations, a short-term and a long-term survival group, were identified based on the tissue protein profiles. In addition, a subset of 57 patients diagnosed with high-grade, grade IV, malignant gliomas were analyzed and a novel classification scheme that segregated short-term and long-term survival patients based on the proteomic profiles was developed. The protein patterns described served as an independent indicator of patient survival. These results show that this new molecular approach to monitoring gliomas can provide clinically relevant information on tumor malignancy and is suitable for high-throughput clinical screening.

Biomarkers, Tumor↗
PubMed2004

Proteome reference maps of vegetative tissues in pea. An investigation of nitrogen mobilization from leaves during seed filling.

A proteomic approach was used to analyze protein changes during nitrogen mobilization (N mobilization) from leaves to filling seeds in pea (Pisum sativum). First, proteome reference maps were established for mature leaves and stems. They displayed around 190 Coomassie Blue-stained spots with pIs from 4 to 7. A total of 130 spots were identified by mass spectrometry as corresponding to 80 different proteins implicated in a variety of cellular functions. Although the leaf proteome map contained more abundant spots, corresponding to proteins involved in energy/carbon metabolism, than the stem map, their comparison revealed a highly similar protein profile. Second, the leaf proteome map was used to analyze quantitative variations in leaf proteins during N mobilization. Forty percent of the spots showed significant changes in their relative abundance in the total protein extract. The results confirmed the importance of Rubisco as a source of mobilizable nitrogen, and suggested that in pea leaves the rate of degradation of Rubisco may vary throughout N mobilization. Correlated with the loss of Rubisco was an increase in relative abundance of chloroplastic protease regulatory subunits. Concomitantly, the relative abundance of some proteins related to the photosynthetic apparatus (Rubisco activase, Rubisco-binding proteins) and of several chaperones increased. A role for these proteins in the maintenance of a Rubisco activation state and in the PSII repair during the intense proteolytic activity within the chloroplasts was proposed. Finally, two 14-3-3-like proteins, with a potential regulatory role, displayed differential expression patterns during the massive remobilization of nitrogen.

Amino Acid Sequence↗
PubMed2005

Towards a human proteome atlas: high-throughput generation of mono-specific antibodies for tissue profiling.

A great need exists for the systematic generation of specific antibodies to explore the human proteome. Here, we show that antibodies specific to human proteins can be generated in a high-throughput manner involving stringent affinity purification using recombinant protein epitope signature tags (PrESTs) as immunogens and affinity-ligands. The specificity of the generated affinity reagents, here called mono-specific antibodies (msAb), were validated with a novel protein microarray assay. The success rate for 464 antibodies generated towards human proteins was more than 90% as judged by the protein array assay. The antibodies were used for parallel profiling of patient biopsies using tissue microarrays generated from 48 human tissues. Comparative analysis with well-characterized monoclonal antibodies showed identical or similar specificity and expression patterns. The results suggest that a comprehensive atlas containing extensive protein expression and subcellular localization data of the human proteome can be generated in an efficient manner with mono-specific antibodies.

Animals↗
PubMed2025

Multi-Omics and Integrative Analytics in Natural Products Discovery.

Natural products (NPs) have long been an essential source of new bioactive compounds for drug discovery; however, traditional methods for screening and isolating these compounds can be slow and often yield diminishing returns. Fortunately, advanced multi-omics and computational approaches present powerful solutions to these challenges. This review highlights innovative methodologies that integrate metabolomics, genomics, transcriptomics, and proteomics with bioinformatics and analytical chemistry to accelerate NP discovery. For instance, untargeted metabolomics platforms like high-resolution liquid chromatography-tandem mass spectrometry (LC-MS/MS) and Global Natural Products Social (GNPS) molecular networking allow for comprehensive profiling of new compounds, while targeted isotope-labeling strategies enhance this process. Additionally, genome and metagenome mining tools such as antibiotics and secondary metabolite analysis shell (antiSMASH), Deep Biosynthetic Gene Cluster (DeepBGC), and Pipeline for Reconstructing Integrated Syntheses of Metabolites (PRISM) quickly identify biosynthetic gene clusters (BGCs) in both cultured and uncultured organisms, often using heterologous expression to validate products. Transcriptomic analyses, including RNA sequencing (RNA-seq), co-expression networks, and fluxomics, help clarify how pathways are regulated, while quantitative proteomics techniques like tandem mass tags/isobaric tags for relative and absolute quantitation (TMT/iTRAQ) and label-free methods, along with chemoproteomics approaches such as cellular thermal shift assay and thermal proteome profiling (TPP), uncover molecular targets and their mechanisms of action. This review also places significant emphasis on the role of artificial intelligence (AI) and machine learning (ML) in integrating multi-omics data, spanning activities from constructing gene-metabolite correlation networks to leveraging knowledge graphs and graph neural networks for data fusion and functional prediction. Finally, this review concludes by discussing the synergistic benefits of multi-omics for natural-product discovery, addressing current technical challenges, and exploring future directions toward high-throughput, intelligent data integration for next-generation NP research.

Biological Products↗
PubMed2003

Quantitative proteome analysis: methods and applications.

With the completion of the genomic sequencing of a number of species, including that of humans, much attention is currently focused on how the information in these sequences might be interpreted in terms of the structure, function, and control of biologic systems and processes. Quantitative proteome analysis, the global analysis of protein expression, is increasingly being used as a method to study steady-state and perturbation-induced changes in protein profiles. The rationale for quantitative proteome analysis is described, along with a new technology for high throughput quantitative profiling of proteins in complex mixtures and its current status with selected applications.

Affinity Labels↗
PubMedDate not supplied

A pharmacoproteomics study of the cancer cell line EKVX using capillary-LC/MS/MS.

The chemotherapeutic agent camptothecin, 10-OH (CPT,10-OH), was shown to act synergistically with the epithelial growth factor receptor (EGFR) inhibitor (AG1478) against several transformed cell lines. To study the cellular response to these drugs, the non-small-cell lung carcinoma cell line, EKVX, was treated with these compounds either alone or in combination. We performed a proteomic analysis using capillary-HPLC coupled with electrospray ion trap mass spectrometry (capillary-LC-ESI/MS) of a tryptic digest to obtain a global protein profile of the EKVX cell line and identify changes in protein expression. The combination of AG1478 and CPT,10-OH showed synergistic cytotoxicity and also changed the expression of multiple proteins, while individual treatments showed a lesser effect on protein expression. Thus, the synergistic action of AG1478 and CPT,10-OH was reflected in altered protein profiles, showing that a proteomic analysis can serve to evaluate chemotherapeutic agents and their combinations.

Amino Acid Sequence↗