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PubMedDate not supplied

Defining the mandate of tuberculosis research in a postgenomic era.

The identification of Mycobacterium tuberculosis by Robert Koch in 1882 as the causative agent of tuberculosis, the release of the drug rifampicin in 1970 and the sequencing of the M. tuberculosis genome in 1998 are three major events that have revolutionized tuberculosis research. In spite of these breakthroughs, the continued status of tuberculosis as the largest killer amongst infectious diseases is an issue of major concern. Although directly observed short course chemotherapy exists to treat the disease, the emergence of drug-resistant strains has severely threatened the efficacy of the treatment. The recent sequencing of the M. tuberculosis genome holds promise for the development of new vaccines and the design of new drugs. This is all the more possible when the information from the genome sequence is combined with proteomics and structural and functional genomics. Such an integrated approach has led to the birth of a new field of research christened 'postgenomics' that holds substantial promise for the identification of novel drug targets and the potential to aid the development of new chemotherapeutic compounds to treat tuberculosis. The challenge before the scientific community therefore lies in elucidation of the wealth of information provided by the genome sequence and its translation into the design of novel therapies for the disease. All the major developments in the field of tuberculosis research after the sequencing of the M. tuberculosis genome will be discussed in this review.

Biomedical Research↗

PubMed2005

Systems biology in the cell nucleus.

The mammalian nucleus is arguably the most complex cellular organelle. It houses the vast majority of an organism's genetic material and is the site of all major genome regulatory processes. Reductionist approaches have been spectacularly successful at dissecting at the molecular level many of the key processes that occur within the nucleus, particularly gene expression. At the same time, the limitations of analyzing single nuclear processes in spatial and temporal isolation and the validity of generalizing observations of single gene loci are becoming evident. The next level of understanding of genome function is to integrate our knowledge of their sequences and the molecular mechanisms involved in nuclear processes with our insights into the spatial and temporal organization of the nucleus and to elucidate the interplay between protein and gene networks in regulatory circuits. To do so, catalogues of genomes and proteomes as well as a precise understanding of the behavior of molecules in living cells are required. Converging technological developments in genomics, proteomics, dynamics and computation are now leading towards such an integrated biological understanding of genome biology and nuclear function.

Animals↗

PubMed2006

Collection, processing, and storage of biological samples in epidemiologic studies: sex hormones, carotenoids, inflammatory markers, and proteomics as examples.

The measurement of biomarkers in blood specimens has become an integral component of many epidemiologic studies and introduces several decision points about specimen collection, processing, and storage for the investigator. We briefly discuss the current state of knowledge for four commonly assessed biomarkers: estrogens and other sex hormones, ascorbic acid and carotenoids, cytokines involved in the inflammatory response, and proteomics. Sex hormones are relatively robust to type of sample collected, delayed processing (if chilled), and long-term storage at <-70 degrees C. Ascorbic acid and carotenoids also are relatively robust to sample type and delayed processing (if chilled); however, the blood sample should not be exposed to sunlight and must be stored at <-70 degrees C to prevent substantial degradation. If ascorbic acid is of primary interest, an acid stabilizer should be added during processing. Less is known for cytokines and proteomics, although initial research suggests that these assays are sensitive to varying collection, processing, and storage methods. Overall, we recommend conducting pilot studies if any nonstandard collection, processing, or storage procedure is used. Finally, decisions about these issues depend primarily on the scientific questions of most interest, cost, flexibility, and resources.

Ascorbic Acid↗

PubMed2005

Analysis of gene ontology features in microarray data using the Proteome BioKnowledge Library.

Microarray technology has resulted in an explosion of complex, valuable data. Integrating data analysis tools with a comprehensive underlying database would allow efficient identification of common properties among differentially regulated genes. In this study we sought to compare the utility of various databases in microarray analysis. The Proteome BioKnowledge Library (BKL), a manually curated, proteome-wide compilation of the scientific literature, was used to generate a list of Gene Ontology (GO) Biological Process (BP) terms enriched among proteins involved in cardiovascular disease. Analysis of DNA microarray data generated in a study of rat vascular smooth muscle cell responses revealed significant enrichment in a number of GO BPs that were also enriched among cardiovascular disease-related proteins. Using annotation from LocusLink and chip annotation from the Gene Expression Omnibus yielded fewer enriched cardiovascular disease-associated GO BP terms. Data sets of orthologous genes from mouse and human were generated using the BKL Retriever. Analysis of these sets focusing on BKL Disease annotation, revealed a significant association of these genes with cardiovascular disease. These results and the extensive presence of experimental evidence for BKL GO and Disease features, underscore the benefits of using this database for microarray analysis.

Animals↗

PubMed2006

PATRIC: the VBI PathoSystems Resource Integration Center.

The PathoSystems Resource Integration Center (PATRIC) is one of eight Bioinformatics Resource Centers (BRCs) funded by the National Institute of Allergy and Infection Diseases (NIAID) to create a data and analysis resource for selected NIAID priority pathogens, specifically proteobacteria of the genera Brucella, Rickettsia and Coxiella, and corona-, calici- and lyssaviruses and viruses associated with hepatitis A and E. The goal of the project is to provide a comprehensive bioinformatics resource for these pathogens, including consistently annotated genome, proteome and metabolic pathway data to facilitate research into counter-measures, including drugs, vaccines and diagnostics. The project's curation strategy has three prongs: 'breadth first' beginning with whole-genome and proteome curation using standardized protocols, a 'targeted' approach addressing the specific needs of researchers and an integrative strategy to leverage high-throughput experimental data (e.g. microarrays, proteomics) and literature. The PATRIC infrastructure consists of a relational database, analytical pipelines and a website which supports browsing, querying, data visualization and the ability to download raw and curated data in standard formats. At present, the site warehouses complete sequences for 17 bacterial and 332 viral genomes. The PATRIC website (https://patric.vbi.vt.edu) will continually grow with the addition of data, analysis and functionality over the course of the project.

Bioterrorism↗

PubMed2006

Constructing biological networks through combined literature mining and microarray analysis: a LMMA approach.

MOTIVATION: Network reconstruction of biological entities is very important for understanding biological processes and the organizational principles of biological systems. This work focuses on integrating both the literatures and microarray gene-expression data, and a combined literature mining and microarray analysis (LMMA) approach is developed to construct gene networks of a specific biological system. RESULTS: In the LMMA approach, a global network is first constructed using the literature-based co-occurrence method. It is then refined using microarray data through a multivariate selection procedure. An application of LMMA to the angiogenesis is presented. Our result shows that the LMMA-based network is more reliable than the co-occurrence-based network in dealing with multiple levels of KEGG gene, KEGG Orthology and pathway. AVAILABILITY: The LMMA program is available upon request.

Abstracting and Indexing↗

PubMed2003

Protein family classification and functional annotation.

With the accelerated accumulation of genomic sequence data, there is a pressing need to develop computational methods and advanced bioinformatics infrastructure for reliable and large-scale protein annotation and biological knowledge discovery. The Protein Information Resource (PIR) provides an integrated public resource of protein informatics to support genomic and proteomic research. PIR produces the Protein Sequence Database of functionally annotated protein sequences. The annotation problems are addressed by a classification-driven and rule-based method with evidence attribution, coupled with an integrated knowledge base system being developed. The approach allows sensitive identification, consistent and rich annotation, and systematic detection of annotation errors, as well as distinction of experimentally verified and computationally predicted features. The knowledge base consists of two new databases, sequence analysis tools, and graphical interfaces. PIR-NREF, a non-redundant reference database, provides a timely and comprehensive collection of all protein sequences, totaling more than 1,000,000 entries. iProClass, an integrated database of protein family, function, and structure information, provides extensive value-added features for about 830,000 proteins with rich links to over 50 molecular databases. This paper describes our approach to protein functional annotation with case studies and examines common identification errors. It also illustrates that data integration in PIR supports exploration of protein relationships and may reveal protein functional associations beyond sequence homology.

Amino Acid Motifs↗

PubMed2004

Arabidopsis plasma membrane proteomics identifies components of transport, signal transduction and membrane trafficking.

In order to identify integral proteins and peripheral proteins associated with the plasma membrane, highly purified Arabidopsis plasma membranes from green tissue (leaves and petioles) were analyzed by mass spectrometry. Plasma membranes were isolated by aqueous two-phase partitioning, which yields plasma membrane vesicles with a cytoplasmic-side-in orientation and with a purity of 95%. These vesicles were turned inside-out by treatment with Brij 58 to remove soluble contaminating proteins enclosed in the vesicles and to remove loosely bound contaminating proteins. In total, 238 putative plasma membrane proteins were identified, of which 114 are predicted to have transmembrane domains or to be glycosyl phosphatidylinositol anchored. About two-thirds of the identified integral proteins have not previously been shown to be plasma membrane proteins. Of the 238 identified proteins, 76% could be classified according to function. Major classes are proteins involved in transport (17%), signal transduction (16%), membrane trafficking (9%) and stress responses (9%). Almost a quarter of the proteins identified in the present study are functionally unclassified and more than half of these are predicted to be integral.

Arabidopsis↗

PubMed2007

CYCLONET--an integrated database on cell cycle regulation and carcinogenesis.

Computational modelling of mammalian cell cycle regulation is a challenging task, which requires comprehensive knowledge on many interrelated processes in the cell. We have developed a web-based integrated database on cell cycle regulation in mammals in normal and pathological states (Cyclonet database). It integrates data obtained by 'omics' sciences and chemoinformatics on the basis of systems biology approach. Cyclonet is a specialized resource, which enables researchers working in the field of anticancer drug discovery to analyze the wealth of currently available information in a systematic way. Cyclonet contains information on relevant genes and molecules; diagrams and models of cell cycle regulation and results of their simulation; microarray data on cell cycle and on various types of cancer, information on drug targets and their ligands, as well as extensive bibliography on modelling of cell cycle and cancer-related gene expression data. The Cyclonet database is also accessible through the BioUML workbench, which allows flexible querying, analyzing and editing the data by means of visual modelling. Cyclonet aims to predict promising anticancer targets and their agents by application of Prediction of Activity Spectra for Substances. The Cyclonet database is available at http://cyclonet.biouml.org.

Animals↗

PubMed2006

A systems approach to model metastatic progression.

Proteomic profiling of human disease has seen much early activity with the accessibility of the newest generation of high-throughput platforms and technologies. Nevertheless, the nature of the dynamic physiologic milieu and high dimensionality of the data has complicated major diagnostic and prognostic breakthroughs. Our recent article in Cancer Cell delineates an integrative model for culling a molecular signature of metastatic progression in prostate cancer from proteomic and transcriptomic analyses and shows its facility as a predictor of prognosis. The study leveraged direct proteomic analysis of tumor tissue extracts, differential feature selection characterizing the proteomic alterations of prostate cancer subclasses, and integration with public and study-derived genomic data to construct a multiplex gene signature representing progression of indolent cancer to aggressive disease. This further predicted clinical outcome in a variety of solid tumors. This review describes the context of the work, the framework for the analysis itself, and a look forward to the promise of this systems approach to human disease.

Computational Biology↗

PubMed2026

A mechanism-guided framework for prioritizing membrane-interaction anti-Vibrio peptides from peptidomics data.

A mechanism-guided framework for prioritizing membrane-interaction antimicrobial peptide candidates from proteomics-derived peptide mixtures is presented. The framework integrates conservative machine-learning-based antimicrobial peptide (AMP) screening with a literature-derived membrane-interaction plausibility (MAP) assessment and a data-driven membrane-interaction ranking function (AIPx), followed by structural visualization for interpretability. MAP encodes physicochemical characteristics commonly associated with peptide-membrane interaction and provides a graded plausibility assessment. Building upon this physicochemically interpretable framework, AIPx ranks peptides using feature weights calibrated from experimentally characterized anti-Vibrio peptides, where minimum inhibitory concentration (MIC) values are used as a coarse-grained ranking reference rather than a direct prediction target. In a peptidomics-based peptide fractionation study targeting Vibrio spp., AIPx exhibited a consistent relationship with experimentally observed antibacterial activity. Distributional analysis revealed that peptide fractions exhibiting high anti-Vibrio activity are characterized by enrichment of high-ranking peptides rather than by AMP abundance alone. By structuring AMP identification and prioritization as sequential stages, the MAP + AIPx framework enables interpretable and experimentally actionable candidate selection by reducing biologically implausible candidates. The framework facilitates species-oriented prioritization of AMP candidates, addressing a key challenge in antimicrobial peptide discovery where activity may depend on target-specific membrane characteristics. Moreover, the approach is extensible through species-specific calibration and supports interpretable, mechanism-informed prioritization in antimicrobial peptide discovery.

Proteomics↗

PubMed2003

BAD and glucokinase reside in a mitochondrial complex that integrates glycolysis and apoptosis.

Glycolysis and apoptosis are considered major but independent pathways that are critical for cell survival. The activity of BAD, a pro-apoptotic BCL-2 family member, is regulated by phosphorylation in response to growth/survival factors. Here we undertook a proteomic analysis to assess whether BAD might also participate in mitochondrial physiology. In liver mitochondria, BAD resides in a functional holoenzyme complex together with protein kinase A and protein phosphatase 1 (PP1) catalytic units, Wiskott-Aldrich family member WAVE-1 as an A kinase anchoring protein, and glucokinase (hexokinase IV). BAD is required to assemble the complex in that Bad-deficient hepatocytes lack this complex, resulting in diminished mitochondria-based glucokinase activity and blunted mitochondrial respiration in response to glucose. Glucose deprivation results in dephosphorylation of BAD, and BAD-dependent cell death. Moreover, the phosphorylation status of BAD helps regulate glucokinase activity. Mice deficient for BAD or bearing a non-phosphorylatable BAD(3SA) mutant display abnormal glucose homeostasis including profound defects in glucose tolerance. This combination of proteomics, genetics and physiology indicates an unanticipated role for BAD in integrating pathways of glucose metabolism and apoptosis.

Animals↗

PubMed2005

Combined proteome and metabolite-profiling analyses reveal surprising insights into yeast sulfur metabolism.

Metabolomics is considered as an emerging new tool for functional proteomics in the identification of new protein function or in projects aiming at modeling whole cell metabolism. When combined with proteome studies, metabolite-profiling analyses revealed unanticipated insights into the yeast sulfur pathway. In response to cadmium, the observed overproduction of glutathione, essential for the detoxification of the metal, can be entirely accounted for by a marked drop in sulfur-containing protein synthesis and a redirection of sulfur metabolite fluxes to the glutathione pathway. A kinetic analysis showed sequential and dramatic changes in intermediate sulfur metabolite pools within the first hours of the treatment. Strikingly, whereas proteome and metabolic data were positively correlated under cadmium conditions, proteome and metabolic data were negatively correlated during other growth conditions, i.e. methionine supplementation or sulfate starvation. These differences can be explained by alternative mechanisms in the regulation of Met4, the activator of the sulfur pathway. Whereas Met4 activity is controlled by the cellular cysteine content in response to sulfur source and availability, the present study suggests that Met4 activation under cadmium conditions is cysteine-independent. The results clearly indicate that the metabolic state of a cell cannot be safely predicted based solely on proteomic and/or gene expression data. Combined metabolome and proteome studies are necessary to draw a comprehensive and integrated view of cell metabolism.

Cadmium↗

PubMed2025

Proteomic signatures and predictive modeling of cadmium-associated anxiety in middle-aged and elderly populations: an environmental exposure association study.

BACKGROUND: Emerging evidence implicates environmental contaminants such as cadmium (Cd) as modifiable risk factors for anxiety. Despite growing recognition of heavy metal toxicity in neuropsychiatric disorders, the molecular mechanisms linking environmental exposure to anxiety pathogenesis remain poorly understood. METHODS: Based on the established cohort of individuals with cognitive impairment in cadmium-contaminated areas, this cross-sectional association study enrolled 50 middle-aged and elderly hospitalized patients from these regions, adhering to the STROBE guidelines. Blood concentrations of cadmium (Cd), lead (Pb), and mercury (Hg) were analyzed in relation to anxiety severity assessed via the Hamilton Anxiety Rating Scale (HAMA). Plasma proteomic profiling was performed using data-independent acquisition (DIA) quantitative technology with an LC-MS/MS platform (timsTOF Pro, Bruker Daltonics), systematically characterizing 2,531 proteins across all samples. Machine learning techniques, specifically XGBoost and LASSO, were employed to identify biomarkers that were subsequently validated through mediation analysis and animal experiments, allowing for the screening of key protein signatures. Finally, clinical variables were integrated to construct a comprehensive model, which was then thoroughly evaluated. RESULTS: Anxious individuals exhibited significantly higher blood Cd levels than controls (β = 0.50, 95% CI: 0.07-0.93, p < 0.01), with anxiety positively correlating with depression (r = 0.62, p = 0.003) and inversely with ApoE3 genotype prevalence. Proteomics identified 120 differentially expressed proteins in anxious patients, enriched in oxidative phosphorylation and neurodegenerative pathways. CCDC126 emerged as a cadmium-associated biomarker, validated in rat models exposed to Cd. Combining CCDC126, blood Cd, Pb, and hypertension, a clinical prediction model achieved robust discrimination (AUC = 0.80, validation cohort). CONCLUSIONS: This first integrative environmental-proteomic study highlights cadmium's synergistic role in anxiety pathophysiology and psychiatric comorbidity. The predictive model offers translatable potential for early risk stratification, while CCDC126 provides mechanistic insights for targeted interventions in populations exposed to environmental pollutants.

Cadmium↗

PubMed2024

Identification of a Proteomic Signature for Predicting Immunotherapy Response in Patients With Metastatic Non-Small Cell Lung Cancer.

Immunotherapy has improved survival rates in patients with cancer, but identifying those who will respond to treatment remains a challenge. Advances in proteomic technologies have enabled the identification and quantification of nearly all expressed proteins in a single experiment. Integrating mass spectrometry with high-throughput technologies has facilitated comprehensive analysis of the plasma proteome in cancer, facilitating early diagnosis and personalized treatment. In this context, our study aimed to investigate the predictive and prognostic value of plasma proteome analysis using the SWATH-MS (Sequential Window Acquisition of All Theoretical Mass Spectra) strategy in newly diagnosed patients with non-small cell lung cancer (NSCLC) receiving pembrolizumab therapy. We enrolled 64 newly diagnosed patients with advanced NSCLC treated with pembrolizumab. Blood samples were collected from all patients before and during therapy. A total of 171 blood samples were analyzed using the SWATH-MS strategy. Plasma protein expression in metastatic NSCLC patients prior to receiving pembrolizumab was analyzed. A first cohort (discovery cohort) was employed to identify a proteomic signature predicting immunotherapy response. Thus, 324 differentially expressed proteins between responder and non-responder patients were identified. In addition, we developed a predictive model and found a combination of seven proteins, including ATG9A, DCDC2, HPS5, FIL1L, LZTL1, PGTA, and SPTN2, with stronger predictive value than PD-L1 expression alone. Additionally, survival analyses showed an association between the levels of ATG9A, DCDC2, SPTN2 and HPS5 with progression-free survival (PFS) and/or overall survival (OS). Our findings highlight the potential of proteomic technologies to detect predictive biomarkers in blood samples from NSCLC patients, emphasizing the correlation between immunotherapy response and the idenfied protein set.

Humans↗

PubMed2003

Multi-dimensional liquid phase based separations in proteomics.

This review covers recent developments towards the implementation of multi-dimensional (MuD) liquid phase based systems for proteome investigations. Although two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) has been used as a standard approach in proteomics, its drawbacks including the limited dynamic range and molecular mass range, together with lack of on-line integration with biological mass spectrometery (Bio-MS) have limited its widespread use and applications in proteomics. In the meantime, various liquid-phase based multi-dimensional separation techniques have been explored. Especially, with the emergence of the combination of nanoflow capillary high-performance liquid chromatography (cHPLC) and Bio-MS, attention is again refocused on utilizing multi-dimensional liquid-phase based separation of proteins. Some remarkable applications of on-line analysis of intact proteins and on-column digested proteins, and the emergence of approaches such as multiple HPLC-electrospray ionization tandem MS and capillary array electrophoresis-matrix assisted laser desorption ionization MS, have stimulated thinking towards developing a automated multi-dimensional system (MuDSy) that integrates liquid phase based separation, digestion and identification of proteins in complex biological mixtures.

Chromatography, Liquid↗

PubMedDate not supplied

Analysis of nucleic acids by FTICR MS.

Fourier transform ion cyclotron resonance (FTICR) mass spectrometry represents a unique platform with which to study nucleic acids and non-covalent complexes containing nucleic acids moieties. In particular, systems in which very high mass measurement accuracy is required, very complex mixtures are to be analyzed, or very limited amounts of sample are available may be uniquely suited to interrogation by FTICR mass spectrometry. Although the FTICR platform is now broadly deployed as an integral component of many high-end proteomics-based research efforts, momentum is still building for the application of the platform towards nucleic acid-based analyses. In this work, we review fundamental aspects of nucleic acid analysis by FTICR, focusing primarily on the analysis of DNA oligonucleotides but also describing applications related to the characterization of RNA constructs. The goal of this review article is to give the reader a sense of the breadth and scope of the status quo of FTICR analysis of nucleic acids and to summarize a few recently published reports in which researchers have exploited the performance attributes of FTICR to characterize nucleic acids in support of basic and applied research disciplines including genotyping, drug discovery, and forensic analyses.

Cyclotrons↗

PubMed2006

Protease pathways in peptide neurotransmission and neurodegenerative diseases.

1. Recent research demonstrates the critical importance of neuroproteases for the production of peptide neurotransmitters, and for the production of toxic peptides in major neurodegenerative diseases that include Alzheimer's (AD) and Huntington's diseases. This review describes the strategies utilized to identify the appropriate proteases responsible for producing active peptides for neurotransmission, with application of such approaches for defining protease mechanisms in neurodegenerative diseases. 2. Integration of multidisciplinary approaches in neurobiology, biochemistry, chemistry, proteomics, molecular biology, and genetics has been utilized for neuroprotease studies. These investigations have identified secretory vesicle cathepsin L for the production of the enkephalin opioid peptide neurotransmitter and other neuropeptides. Furthermore, new results using these strategies have identified secretory vesicle cathepsin B for the production of beta-amyloid (Abeta) in the major regulated secretory pathway that provides activity-dependent secretion of Abeta peptides, which accumulate in AD. 3. CNS neuroproteases that participate in peptide neurotransmission and in neurodegenerative diseases represent new candidate drug targets that may be explored in future research for the development of novel therapeutic agents for neurological conditions.

Animals↗