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PubMed2004

TcruziDB: an integrated Trypanosoma cruzi genome resource.

TcruziDB (http://TcruziDB.org) is an integrated genome database for the parasitic organism Trypanosoma cruzi, the causative agent of Chagas' disease. The database currently incorporates all available sequence data (Genomic, BAC, EST) in a single user-friendly location. The database contains a variety of tools specifically designed for searching unannotated draft sequence via BLAST, keyword searches of pre-computed BLAST results, and protein motif searches. Release 1.0 of the database contains nearly 730 million bp of genome sequence from 1.1 million sequence reads generated by the TIGR-Karolinska-SBRI Trypanosoma cruzi Genome Consortium and 15 million bp of clustered EST and genomic sequence obtained from other sources. As annotation, microarray and proteomic data become available, the database will incorporate and integrate these data using the GUS (http://www.gusdb. org) relational framework.

Animals↗

PubMed2007

MicroSPE-nanoLC-ESI-MS/MS using 10-microm-i.d. silica-based monolithic columns for proteomics.

Silica-based monolithic capillary columns (25 cm x 10 microm i.d.) with integrated nanoESI emitters have been developed to provide high-quality and robust microSPE-nanoLC-ESI-MS analyses. The integrated nanoESI emitter adds no dead volume to the LC separation, allowing stable electrospray operation at flow rates of approximately 10 nL/min. In an initial application with a linear ion trap MS, we identified 5510 unique peptides that covered 1443 distinct Shewanella oneidensis proteins from a 300-ng tryptic digest sample in a single 4-h LC-MS/MS analysis. The use of an integrated monolithic ESI emitter provided enhanced resistance to clogging and provided good run-to-run reproducibility.

Chromatography, Liquid↗

PubMed2006

The Escherichia coli proteome: past, present, and future prospects.

Proteomics has emerged as an indispensable methodology for large-scale protein analysis in functional genomics. The Escherichia coli proteome has been extensively studied and is well defined in terms of biochemical, biological, and biotechnological data. Even before the entire E. coli proteome was fully elucidated, the largest available data set had been integrated to decipher regulatory circuits and metabolic pathways, providing valuable insights into global cellular physiology and the development of metabolic and cellular engineering strategies. With the recent advent of advanced proteomic technologies, the E. coli proteome has been used for the validation of new technologies and methodologies such as sample prefractionation, protein enrichment, two-dimensional gel electrophoresis, protein detection, mass spectrometry (MS), combinatorial assays with n-dimensional chromatographies and MS, and image analysis software. These important technologies will not only provide a great amount of additional information on the E. coli proteome but also synergistically contribute to other proteomic studies. Here, we review the past development and current status of E. coli proteome research in terms of its biological, biotechnological, and methodological significance and suggest future prospects.

Bacterial Proteins↗

PubMed2003

Programmed adsorption and release of proteins in a microfluidic device.

A microfluidic device has been developed that can adsorb proteins from solution, hold them with negligible denaturation, and release them on command. The active element in the device is a 4-nanometer-thick polymer film that can be thermally switched between an antifouling hydrophilic state and a protein-adsorbing state that is more hydrophobic. This active polymer has been integrated into a microfluidic hot plate that can be programmed to adsorb and desorb protein monolayers in less than 1 second. The rapid response characteristics of the device can be manipulated for proteomic functions, including preconcentration and separation of soluble proteins on an integrated fluidics chip.

Acrylic Resins↗

PubMed2006

Reverse genetics for proteomics: from proteomic discovery to scientific content.

Proteomic strategies generally result in rather descriptive outcomes. Although proteomic patterns may lead to understanding of cellular function or failure, they do not provide insights into cellular mechanisms of normal physiology or molecular mechanisms of disease. The integration of single events or patterns into complex biological processes and signaling networks must be functionally validated by studying these processes in a dynamic physiological context. Effective methodologies, applied at the level of intact cells and model organisms that monitor their function, topology and interaction of proteins are necessary and essential. Reverse genetic strategies can greatly advance the understanding of patterns identified through proteomic analysis. RNAi as an accessible method for routine functional analysis of cells and whole model organisms represents a quantum leap in revolutionizing reverse genetics. This paper aims at describing an integrated workflow linking proteome-based discovery to molecular analysis of single genes and proteins by RNAi, potentially contributing to the advanced understanding of molecular mechanisms in neurodegenerative disease, including Parkinson's disease.

Animals↗

PubMed2004

Human protein reference database as a discovery resource for proteomics.

The rapid pace at which genomic and proteomic data is being generated necessitates the development of tools and resources for managing data that allow integration of information from disparate sources. The Human Protein Reference Database (http://www.hprd.org) is a web-based resource based on open source technologies for protein information about several aspects of human proteins including protein-protein interactions, post-translational modifications, enzyme-substrate relationships and disease associations. This information was derived manually by a critical reading of the published literature by expert biologists and through bioinformatics analyses of the protein sequence. This database will assist in biomedical discoveries by serving as a resource of genomic and proteomic information and providing an integrated view of sequence, structure, function and protein networks in health and disease.

Computational Biology↗

PubMed2026

Discovery and validation of a multi-protein panel for predicting non-fatal major adverse cardiovascular events in diabetic kidney disease.

OBJECTIVE: To identify plasma protein biomarkers associated with incident non-fatal major adverse cardiovascular events (MACE) in diabetic kidney disease (DKD) patients. RESEARCH DESIGN AND METHODS: We analyzed 317 DKD patients from the UK Biobank. Plasma proteomics and clinical data (demographics, metabolism, renal function) were integrated. In an exploratory discovery phase, three sequential Cox regression models (crude, socio-demographic-adjusted, socio-demographic-metabolic adjusted) screened non-fatal MACE-associated proteins. To prevent information leakage, the cohort was then randomly split into training (70%) and testing (30%) sets; machine-learning feature selection, hyperparameter optimization, and final model development were performed exclusively within the training set. The associated proteins were input into the four-step machine-learning pipeline (LASSO-Cox, random survival forest, Boruta, XGBoost-Cox). Predictive performance was validated using Kaplan-Meier survival analyses, longitudinal trajectory modeling, and ROC benchmarking. An interactive web application was deployed for clinical implementation. RESULTS: Of 1,463 plasma proteins, 561 were associated with non-fatal MACE across Cox models, with 14 overlapping proteins. Nine core proteins (ANG, IL1R1, CXCL14, ESAM, PTGDS, HAVCR1, FGFR2, IGSF8, CCL3) were validated: ANG showed the strongest non-fatal MACE association (HR = 3.88, 95%CI 2.33-6.48, p<0.001), and all high-expression groups had elevated non-fatal MACE risk. GO/KEGG enrichment highlighted inflammatory-immune pathways like positive regulation of MAPK cascade, Cytokine-cytokine receptor interaction and PI3K-Akt signaling pathway as key mechanisms. The model integrating proteins, demographic factors, and clinical variables achieved the highest predictive performance across non-fatal MACE (AUC = 0.768), myocardial infarction (MI) (0.808), and stroke (0.816) outcomes, with superior stability in cross-validation. CoxBoost + Elastic Net framework was selected as the optimal framework via benchmarking of 101 algorithms. The model demonstrated favorable calibration in high-risk patients and yielded positive net clinical benefit across decision thresholds of 5% to 45%. The web tool (https://jiangli2941.github.io/MACE-prediction-v2/) enables input of 28 variables, outputs non-fatal MACE risk status, risk probability, and highlights abnormal indicators. CONCLUSION: Plasma proteomics combined with machine learning identifies robust non-fatal MACE predictors in DKD.

Humans↗

PubMed2004

Phenotype characterisation using integrated gene transcript, protein and metabolite profiling.

Multifactorial diseases present a significant challenge for functional genomics. Owing to their multiple compartmental effects and complex biomolecular activities, such diseases cannot be adequately characterised by changes in single components, nor can pathophysiological changes be understood by observing gene transcripts alone. Instead, a pattern of subtle changes is observed in multifactorial diseases across multiple tissues and organs with complex associations between corresponding gene, protein and metabolite levels. This article presents methods for exploratory and integrative analysis of pathophysiological changes at the biomolecular level. In particular, novel approaches are introduced for the following challenges: (i) data processing and analysis methods for proteomic and metabolomic data obtained by electrospray ionisation (ESI) liquid chromatography-tandem mass spectrometry (LC/MS); (ii) association analysis of integrated gene, protein and metabolite patterns that are most descriptive of pathophysiological changes; and (iii) interpretation of results obtained from association analyses in the context of known biological processes. These novel approaches are illustrated with the apolipoprotein E3-Leiden transgenic mouse model, a commonly used model of atherosclerosis. We seek to gain insight into the early responses of disease onset and progression by determining and identifying--well in advance of pathogenic manifestations of disease--the sets of gene transcripts, proteins and metabolites, along with their putative relationships in the transgenic model and associated wild-type cohort. Our results corroborate previous findings and extend predictions for three processes in atherosclerosis: aberrant lipid metabolism, inflammation, and tissue development and maintenance.

Algorithms↗

PubMed2026

Transcriptome changes in circulating immune cells of critical COVID-19 patients predict a specific metabolic and epigenetic imprint.

BACKGROUND: The progression to critical COVID-19 arises predominantly from a dysregulated host immune response although the underlying regulatory mechanisms still remain partially elusive. This limits a prompt prediction of the disease progression, reduces the therapeutic options and restrains our understanding of “long COVID”. METHODS: Here, we analyzed the transcriptome of peripheral blood mononuclear cells (PBMCs) collected from COVID-19 patients experiencing different degrees of the disease (mild and critical), and control patients enrolled in the clinical trial COntAGIouS as well as independent bulk RNA-seq, single-cell RNA-seq and proteomic datasets. RESULTS: In critical COVID-19 patients, the integrative analysis of transcriptomic data revealed an altered regulatory network involving microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and coding genes that control mRNA translation-related genes, epigenetics, and metabolism. In parallel, we observed an upregulation of tRNA aminoacylation genes in critical COVID-19 patients by the analysis of either bulk or single-cell RNA-seq data from publicly available independent cohorts. Additionally, we found increased expression of coding genes enriched for the cognate amino acids (glycine, alanine, isoleucine and tyrosine), all related to protein localization, post-translational modifications, and cell metabolism in our cohort. Similar alterations in amino acid frequency were found in an independent proteomic dataset. CONCLUSIONS: Collectively, our findings indicate a broad perturbation of the gene expression landscape that characterizes the aberrant host immune response in critical COVID-19 patients and is potentially coordinated by miRNA and tRNA metabolism alterations. TRIAL REGISTRATION: COntAGIouS, NCT04327570. Registered 26 March 2020, https://clinicaltrials.gov/ct2/show/NCT04327570 .

Female↗

PubMedDate not supplied

Toxicoproteomics: proteomics applied to toxicology and pathology.

Global measurement of proteins and their many attributes in tissues and biofluids defines the field of proteomics. Toxicoproteomics, as part of the larger field of toxicogenomics. seeks to identify critical proteins and pathways in biological systems that are affected by and respond to adverse chemical and environmental exposures using global protein expression technologies. Toxicoproteomics integrates 3 disciplinary areas: traditional toxicology and pathology, differential protein and gene expression analysis, and systems biology. Key topics to be reviewed are the evolution of proteomics, proteomic technology platforms and their capabilities with exemplary studies from biology and medicine, a review of over 50 recent studies applying proteomic analysis to toxicological research, and the recent development of databases designed to integrate -Omics technologies with toxicology and pathology. Proteomics is examined for its potential in discovery of new biomarkers and toxicity signatures, in mapping serum,plasma. and other biofluid proteomes, and in parallel proteomic and transcriptomic studies. The new field of toxicoproteomics is uniquely positioned toward an expanded understanding of protein expression during toxicity and environmental disease for the advancement of public health.

Animals↗

PubMedDate not supplied

Angiogenesis and microvascular remodeling: a brief history and future roadmap.

Angiogenesis and vessel remodeling determine the integrative control of the architectural structure and functional behaviors of the microcirculation over the lifetime of an organism. Vascular remodeling is the basis of promising therapeutic strategies, including vascularization of ischemic organs. The history of angiogenesis research is long-more than 250 years-and the Microcirculatory Society has been the birthplace of numerous techniques, assays, and scientific concepts that have stimulated massive research endeavors in the pharmaceutical and medical arena. At present, angiogenesis isa dynamic field in which the molecular genetic and proteomic components of the process are still being identified, while integrative systems approaches are once again being recognized as essential to understand microvascular assembly in vivo across multiple scales from cells to whole vessel networks. A short history of people and ideas in this field is presented, followed by discussion of emerging directions receiving intense attention today and major questions that remain unanswered. The primary conclusion is that the need for scientists trained in the integrative approaches nurtured by the Microcirculatory Society over the past 50 years has never been greater, as it is clear that a complete mechanistic understanding of vessel adaptation (based on genomic and proteomic supporting casts) will now require deeper studies of angiogenesis and microvascular remodeling in the exquisite complexity of the native microenvironment-the microcirculation.

Animals↗

PubMed2026

Unveiling the Molecular Secrets of Seaweeds: A Comprehensive Review of Bioinformatics Applications in Algal Research.

Recent advances in high-throughput sequencing, bioinformatics, and multi-omics technologies have transformed seaweed research by overcoming long-standing challenges associated with complex genomes, diverse life cycles, and limited genomic resources. This review provides a comprehensive overview of bioinformatics approaches used to investigate seaweed genomics, transcriptomics, proteomics, metabolomics, microbiomes, and functional genomics, with emphasis on the computational tools and databases that support these analyses. Applications of bioinformatics in phylogenetics, drug discovery, microbiome characterization, and the development of biofuels, nutraceuticals, pharmaceuticals, and sustainable agriculture are also discussed. Particular attention is given to emerging strategies involving multi-omics integration, genome editing, artificial intelligence, machine learning, and synthetic biology that are reshaping seaweed research. The review further examines current challenges, including incomplete genomic resources, data standardization, and the need for experimental validation of computational predictions. Collectively, these advances highlight the growing role of bioinformatics in enabling systems-level understanding of seaweed biology and accelerating their translation into sustainable biotechnological and marine bioeconomy applications.

macroalgal genomics↗

PubMed2004

The hydrophobic proteome of mitochondrial membranes from Arabidopsis cell suspensions.

The development of mitochondria and the integration of their function within a plant cell rely on the presence of a complex biochemical machinery located within their limiting membranes. The aim of the present work was: (1) to enhance our understanding of the biochemical machinery of mitochondrial membranes and (2) to test the versatility of the procedure developed for the identification of the hydrophobic proteome of the chloroplast envelope [Molecular and Cellular Proteomics 2 (2003) 325-345]. A proteomic analysis was performed, to provide the most exhaustive view of the protein repertoire of these membranes. For this purpose, highly purified mitochondria were prepared from Arabidopsis cultured cells and membrane proteins were extracted. To get a more exhaustive array of membrane proteins from Arabidopsis mitochondria, from the most to the less hydrophobic ones, various extraction procedures (chloroform/methanol extraction, alkaline or saline treatments) were applied. LC-MS/MS analyses were then performed on each membrane subfraction, leading to the identification of more than 110 proteins. The identification of these proteins is discussed with respect to their mitochondrial localization, their physicochemical properties and their implications in the metabolism of mitochondria. In order to provide a new overview of the biochemical machinery of the plant mitochondria, proteins identified during this work were compared to the lists of proteins identified during previous proteomic analyses performed on plant and algae mitochondria (Arabidopsis, pea, Chlamydomonas, rice, etc.). A total of 502 proteins are listed. About 40% of the 114 proteins identified during this work were not identified during previous proteomic studies performed on mitochondria.

Arabidopsis↗

PubMed2006

High-throughput screening of cellulase F mutants from multiplexed plasmid sets using an automated plate assay on a functional proteomic robotic workcell.

BACKGROUND: The field of plasmid-based functional proteomics requires the rapid assay of proteins expressed from plasmid libraries. Automation is essential since large sets of mutant open reading frames are being cloned for evaluation. To date no integrated automated platform is available to carry out the entire process including production of plasmid libraries, expression of cloned genes, and functional testing of expressed proteins. RESULTS: We used a functional proteomic assay in a multiplexed setting on an integrated plasmid-based robotic workcell for high-throughput screening of mutants of cellulase F, an endoglucanase from the anaerobic fungus Orpinomyces PC-2. This allowed us to identify plasmids containing optimized clones expressing mutants with improved activity at lower pH. A plasmid library of mutagenized clones of the celF gene with targeted variations in the last four codons was constructed by site-directed PCR mutagenesis and transformed into Escherichia coli. A robotic picker integrated into the workcell was used to inoculate medium in a 96-well deep well plate, combining the transformants into a multiplexed set in each well, and the plate was incubated on the workcell. Plasmids were prepared from the multiplexed culture on the liquid handler component of the workcell and used for in vitro transcription/translation. The multiplexed expressed recombinant proteins were screened for improved activity and stability in an azo-carboxymethylcellulose plate assay. The multiplexed wells containing mutants with improved activity were identified and linked back to the corresponding multiplexed cultures stored in glycerol. Spread plates were prepared from the glycerol stocks and the workcell was used to pick single colonies from the spread plates, prepare plasmid, produce recombinant protein, and assay for activity. The screening assay and subsequent deconvolution of the multiplexed wells resulted in identification of improved CelF mutants and corresponding optimized clones in expression-ready plasmids. CONCLUSION: The multiplex method using an integrated automated platform for high-throughput screening in a functional proteomic assay allows rapid identification of plasmids containing optimized clones ready for use in subsequent applications including transformations to produce improved strains or cell lines.

Journal Article↗

PubMed2004

Proteomics-on-a-chip: the challenge to couple lab-on-a-chip unit operations.

This review describes a vision of a proteomics-on-a-chip device to separate, detect and identify the proteome. It guides the reader towards a development strategy, avoiding some of the pitfalls. It also describes the current state-of-the-art developments in proteomic analysis including available technologies, current market issues, the elements of an envisaged proteomics-on-a-chip device, the required microfabrication processes and the integration of the elements into one device. Address-flow microfluidics is a tool for connecting separation and detection platforms. The final section contains an expert opinion on the recommended development strategies, benefits of proteomics-on-a-chip in the life sciences and the anticipated market.

Drug Design↗

PubMed2005

The role of proteomics in the assessment of premature rupture of fetal membranes.

The presence and integrity of amniotic fluid is fundamental for the normal development of the human fetus during pregnancy. Its production rate changes throughout pregnancy and is mainly related to the functions of the different fetal, placental and amniotic compartments. Premature rupture of the membranes (PROM) occurs in about 5% of deliveries, with complications such as infection and preterm birth. The management of patients with PROM, regardless of gestational age, remains controversial, and it is therefore important to develop new biological tests in order to achieve accurate diagnoses by identifying the presence of specific amniotic fluid markers in vaginal environment. We recently showed the usefulness of amniotic fluid proteomics in identifying a series of peptides that were absent from the corresponding maternal plasma. Several peptides corresponded to fragments of plasma proteins. Two peptides, absent from plasma samples of pregnant women, were identified in amniotic fluid. They corresponded to the COOH-terminal parts of perlecan (SwissProt: P98160) and of agrin (SwissProt: O00468) protein cores, two major heparan sulfate proteoglycans of basement membranes. In this review we will discuss modern proteomic strategies that may improve the laboratory assessment of PROM, and will focus on some of the biochemical characteristics of agrin and perlecan fragments identified in amniotic fluid.

Amino Acid Sequence↗

PubMed2026

Free polyphenols and multi-omics traits underlying antioxidant variation across Paeonia lactiflora leaf cultivars.

Leaves of Paeonia lactiflora are underutilized by-products with potential as natural antioxidant sources. In this study, 18 cultivars were evaluated for phytochemical composition and in vitro antioxidant capacity. Total phenolic content correlated strongly with DPPH and ABTS activities, and the comprehensive antioxidant index identified 'Coral Charm' and 'Hangshao' as representative high- and low-antioxidant cultivars, respectively. Untargeted metabolomics detected 2677 metabolites and identified 908 differential metabolites between the two cultivars. Targeted phenolic profiling quantified 27 compounds, among which 11 differed significantly between the two cultivars. Catechin and epicatechin were enriched in 'Coral Charm', with contents of 6.62 and 0.397 ng/mg, respectively, compared with 0.012 and 0.002 ng/mg in 'Hangshao'. (+)-Dihydroquercetin was also more abundant in 'Coral Charm', while caffeic acid showed an upward trend. Proteomic analysis identified 423 differentially expressed proteins, mainly associated with secondary metabolite biosynthesis, redox homeostasis, and central carbon metabolism. Integrated analysis identified pyruvate metabolism as the only pathway significantly enriched in both metabolomic and proteomic datasets. Molecular docking predicted favorable binding between representative phenolics and selected proteins. These findings link cultivar-dependent antioxidant variation in peony leaves with free-phenolic accumulation and pathway-level metabolic differences, supporting the selection and utilization of antioxidant-rich peony leaf resources.

Antioxidants↗

PubMed2006

Chimeric plastid proteome in the Florida "red tide" dinoflagellate Karenia brevis.

Current understanding of the plastid proteome comes almost exclusively from studies of plants and red algae. The proteome in these taxa has a relatively simple origin via integration of proteins from a single cyanobacterial primary endosymbiont and the host. However, the most successful algae in marine environments are the chlorophyll c-containing chromalveolates such as diatoms and dinoflagellates that contain a plastid of red algal origin derived via secondary or tertiary endosymbiosis. Virtually nothing is known about the plastid proteome in these taxa. We analyzed expressed sequence tag data from the toxic "Florida red tide" dinoflagellate Karenia brevis that has undergone a tertiary plastid endosymbiosis. Comparative analyses identified 30 nuclear-encoded plastid-targeted proteins in this chromalveolate that originated via endosymbiotic or horizontal gene transfer (HGT) from multiple different sources. We identify a fundamental divide between plant/red algal and chromalveolate plastid proteomes that reflects a history of mixotrophy in the latter group resulting in a highly chimeric proteome. Loss of phagocytosis in the "red" and "green" clades effectively froze their proteomes, whereas chromalveolate lineages retain the ability to engulf prey allowing them to continually recruit new, potentially adaptive genes through subsequent endosymbioses and HGT. One of these genes is an electron transfer protein (plastocyanin) of green algal origin in K. brevis that likely allows this species to thrive under conditions of iron depletion.

Algal Proteins↗