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PubMed2026

High-throughput identification of endogenous biomolecular condensates and phase-separating proteins.

Biomolecular condensates formed through liquid-liquid phase separation regulate cellular processes, and their dysregulation causes disease. Current methods for identifying endogenous phase-separating proteins have low throughput and cannot capture dynamic responses to stimuli. Here we present a protocol combining osmotic compression or transforming growth factor-β (TGF-β) treatment to induce condensation with sucrose density gradient centrifugation and quantitative mass spectrometry to enable systematic, high-throughput identification of endogenous condensates and phase-separating proteins. The method exploits the density changes that occur when phase-separating proteins undergo oligomerization during condensate formation. In H1975 cells, we identified over 1,500 phase-separating proteins under osmotic compression or TGF-β treatment; 538 of these candidates were not present in PhaSepDB, a database that compiles in vivo, in vitro and omics-derived proteins. The approach detects constitutive condensates and proteins that dynamically phase-separate in response to osmotic stress or TGF-β signaling. This protocol provides proteome-wide analysis of fractions of proteins having different densities and enables temporal resolution of phase-separation events. The procedure takes ~9 d and requires expertise in cell culture, biochemistry and mass spectrometry. This method enables systematic study of biomolecular condensates and disease-associated phase-separation mechanisms.

Phase Separation↗
PubMedDate not supplied

Proteomic analysis of T cell activation in the presence of cyclosporin A: immunosuppressor and activator removal induces de novo protein synthesis.

Cyclosporin A (CsA), a fungal metabolite used in organ transplantation, blocks the immune responses by interfering with early activation signals preventing the induction of the IL2 gene. We have previously reported that the removal of the immunosuppressor provokes the transcription of the IL2 encoding gene. We have now investigated whether the transcription and translation of other genes accompanies this process. Withdrawal of CsA and Concanavalin A (ConA) from cultures of murine T cells activated by ConA in the presence of CsA leads to substantial changes in the pattern of radio-labelled proteins. A large number of polypeptides were synthesised de novo. In addition, a set of polypeptides detected prior to immunosuppressor elimination was not anymore synthesised. Finally, besides these qualitative changes, quantitative differences in terms of increased or decreased polypeptide abundance were also observed. The results demonstrate that activation in the presence of CsA has programmed the T cells to transcribe and translate a large number of genes, without further reactivation, once the immunosuppressor and the activator were removed.

Animals↗
PubMedDate not supplied

Targeted proteomics of low-level proteins in human plasma by LC/MSn: using human growth hormone as a model system.

This paper describes the profiling of human growth hormone (hGH) in human plasma in order to assess the dynamic range of the ion-trap mass spectrometer for proteomic studies of complex biological samples. Human growth hormone is an example of a low-level plasma protein in vivo, present at subfemtomole levels. This study was performed on a plasma sample in which hGH has been spiked at 10-fold above the natural level, that is approximately 16 pg/microL of plasma. Initially, the measurement was carried out without any sample enrichment and consisted of the following steps: the full set of plasma proteins were reduced, alkylated, and digested with trypsin, and the resulting peptides were separated on a capillary C-18 column and then detected by ion-trap mass spectrometry (1D LC/MS). In addition, this study provided a global view of the serum proteome with over 200 plasma proteins being preliminarily identified. In the MS/MS analysis, hGH was detected by characterization of the first tryptic peptide (T1). The initial identification was confirmed by alternative approaches, which also allowed the evaluation of different sample purification protocols. First, the plasma sample containing hGH was fractionated on a reversed-phase HPLC column and digested, and hGH could now be identified by MS/MS measurements of two tryptic peptides (T1 and T4) by the same 1D LC/MS protocol. In addition, the assignment of peptide identity was made with higher certainty (as measured by an algorithm score). The plasma sample was also fractionated by 1D and 2D gel electrophoresis, the selected bands were digested and analyzed again by the 1D LC/MS protocol. In both cases using the gel prepurifications, hGH was identified with additional peptides. Finally, the plasma sample was analyzed by 2D chromatography (ion exchange and reversed phase) on a new instrumental platform (ProteomeX), and hGH was identified by the observation of five tryptic peptides. In conclusion, these experiments were able to detect growth hormone in the low femtomole level with a dynamic range of 1 in 40 000 by several independent approaches. The amount of growth hormone, while 10-fold above normal in vivo levels, represents concentrations that may be present in disease states (such as acromegaly) and also in doping control measurements. These studies have demonstrated that shotgun sequencing approaches (LC/MS/MS) not only can profile high-abundance proteins in complex biological fluids but also have the potential to identify and quantitate low-level proteins present in such complex mixtures without extensive prepurification protocols. A key to such studies, however, is to use targeted approaches that reduce the complexity of the solute mixture that is presented to the mass spectrometer at a given time point. The various sample preparation protocols described here all improved the quality of the hGH measurement, although in this study the 2D chromatographic approach gave the greatest sequence coverage.

Calibration↗
PubMed2005

The heart is already working.

Understanding the logic of living systems requires knowledge of the mechanisms involved at the levels at which functionality is expressed. This information resides neither in the genome, nor even in the individual proteins that genes code for. No functionality is expressed at these levels. It emerges as the result of interactions between many proteins relating to each other in multiple cascades and in interaction with the cellular environment. There is therefore no alternative to copying nature and computing these interactions to determine the logic of healthy and diseased states. The rapid growth in biological databases, models of cells, tissues and organs and the development of powerful computing hardware and algorithms have made it possible to explore functionality in a quantitative manner all the way from the level of genes to the physiological function of whole organs and regulatory systems. I use models of the heart to demonstrate that we can now go all the way from individual genetic information (on mutations, for example) to exploring the consequences at a whole-organ level.

Coronary Circulation↗
PubMedDate not supplied

Prediction of peptide retention at different HPLC conditions from multiple linear regression models.

To quantitatively characterize the structure of a peptide and to predict its gradient retention time at given HPLC conditions three structural descriptors are used: (i) logarithm of the sum of retention times of the amino acids composing the peptide, log SumAA, (ii) logarithm of the van der Waals volume of the peptide, log VDW(Vol), (iii) and the logarithm of the peptide's calculated n-octanol-water partition coefficient, clog P. The log SumAA descriptor is obtained from empirical data for 20 natural amino acids, determined in a given HPLC system. The two other descriptors are calculated from the peptides' structural formulas using molecular modeling methods. The quantitative structure-retention relationships (QSRR), build by multiple linear regression, describe HPLC retention of peptide on a given chromatographic system on which the retention of the 20 amino acids was predetermined. A structurally diversified series of 98 peptides was employed. The predicted gradient retention times on several chromatographic systems were in good agreement with the experimental data. The QSRR equations, derived for a given system operated at variable gradient times and temperatures allowed for the prediction of peptide retention in that system. Matching the experimental HPLC retention to the theoretically predicted for a presumed peptide could facilitate original protein identification in proteomics. In conjunction with MS data, prediction of the retention time for a given peptide might be used to improve the confidence of peptide identifications and to increase the number of correctly identified peptides.

Amino Acid Sequence↗
PubMed2004

Multidimensional separations in the pharmaceutical arena.

The introduction of novel, powerful and rapid multidimensional separation and characterization methods has produced revolutionary global changes at the genome, proteome and metabolome level, bringing about a radical transition in our views of living systems, at the molecular level. The age of proteomics and metabolomics demands high-resolution multidimensional separation techniques. Multidimensional gas and liquid chromatography techniques, in addition to capillary and microchip electrophoresis methods, offer increased resolution and sensitivity, while also affording adequate throughput and reproducibility to meet the demands of the modern pharmaceutical industry. Coupled with MS, these techniques provide not only separation but also reliable identification of the sample components. The resolving power of these methods has proved to be superior over individual one-dimensional approaches, enabling the comprehensive separation of complex biological mixtures, with excellent resolution and reproducibility. High capacity computer systems that are capable of rigorous qualitative and quantitative analysis of the separation profiles allow the establishment and mining of large databases. Examples of various modern multidimensional separation techniques, and their integration with MS, are reviewed, here, with respect to pharmaceutical analysis.

Chromatography, Gas↗
PubMed2005

The search for the genetic basis of hypertension.

PURPOSE OF REVIEW: This review surveys the literature on the search for the genetic basis of hypertension during the 10 months since November 2003. The goals set forth by this search are defined and the highlights of the work accomplished are provided. RECENT FINDINGS: The search for the genetic basis of hypertension is ongoing, generating an abundance of new data. These data consist of a large number of candidate genes, association of previously known and novel candidate genes with various facets of hypertension, detection of new quantitative trait loci and identification of genes that mediate susceptibility to hypertension. The renin-zangiotensin-aldosterone system continues to dominate the interest of investigators. Other gene systems are also emerging but a single-gene system cannot be singled out beyond the renin-angiotensin-aldosterone system and the data are mostly sporadic and do not reflect a guided or coordinated effort to resolve unanswered issues. The notion that hypertension is polygenic is reinforced, yet few data are provided as to the actual number of genes involved, gene-gene interaction or gene-environment interaction. Advanced biotechnological tools involving transcriptomics and proteomics are underused. SUMMARY: Research on the genetic basis of hypertension has generated over the past year a large number of candidate genes and tied them to various aspects of hypertension. How these genes fit into the complex pathophysiological network that induces hypertension remains unclear. The task of putting together these genes into a cohesive framework still lies ahead, but promises to enlighten us as to the true nature of hypertension, the pathogenic mechanisms involved and improved therapeutic and preventive measures.

Biomedical Research↗
PubMed2002

Generation of kidney transcriptomes using serial analysis of gene expression.

Chronic renal disease initiation and progression remain incompletely understood. Genomewide expression monitoring should clarify the mechanisms which cause progressive renal disease by determining how clusters of genes coordinately change their activity. Serial analysis of gene expression (SAGE) is a technique of expression profiling which permits simultaneous and quantitative analysis of 9- to 13-bp sequence tags that correspond to unique mRNAs. Key principles of the technique are use of PCR in a manner to minimize distortion and serial concatenation of tags which facilitates sequencing and permits identification of many expressed genes in a single cDNA molecule. Tags are extracted from many concatenated sequences, counted using software, and identified by comparison with existing gene databases. In aggregate, gene expression profiles generated from a tag library comprise a transcriptome which represents a comprehensive and quantitative profile of genes expressed at the time of analysis. These global snapshots of gene expression patterns can better define basic cell biology and provide insights into disease pathogenesis by simultaneously determining the net consequences of gene-gene and gene-environment interactions on expression of thousands of genes. Rather than applying a priori assumptions (i.e., hypothesis testing), transcriptome analysis is hypothesis generating and requires no prior knowledge of gene expression. SAGE kidney transcriptomes, from normal animals and animals with progressive kidney disease, are being produced and can be analyzed for novel pathogenetic mechanisms. The use of SAGE and other genomic and proteomic tools should result in a better understanding of kidney disease pathogenesis and in identification of new therapeutic targets.

Animals↗
PubMed2025

Association of Lung Quantitative CT Scan Textures With Systemic Inflammation and Mortality in COPD.

BACKGROUND: COPD is characterized by persistent inflammation that is responsible for remodeling the bronchovascular bundles (BVBs), which may lead to poor quality of life. Quantitative CT (QCT) scan textures of the lung can capture local disease patterns of inflammation and related respiratory morbidity. RESEARCH QUESTION: Are BVB textures, obtained from the adaptive multiple feature method, associated with systemic inflammation, morbidity, and mortality in COPD? STUDY DESIGN AND METHODS: We analyzed data from the Subpopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS; n = 2,981) and the Genetic Epidemiology of COPD (COPDGene) study (n = 10,305). The predictors included 2 QCT scan biomarkers, the BVB and CT density gradient (CTDG) textures, age, sex, BMI, race, smoking status, pack-years of smoking, CT scan-detected emphysema, and square root of the wall area of a hypothetical airway with a 10-mm lumen perimeter (Pi10). Outcomes included plasma biomarker concentrations from Meso Scale Discovery proteomics assays and CBC counts, both as markers of inflammation, along with FEV1, FEV1 to FVC ratio, St. George's Respiratory Questionnaire score, 6-minute walk distance, and modified Medical Research Council dyspnea scale score. Associations of these QCT scan textures with FEV1 decline and all-cause mortality also were investigated. RESULTS: Increased BVB texture was associated significantly with elevated neutrophil and monocyte counts and the neutrophil to lymphocyte ratio, independent of clinical covariates, CT scan-detected emphysema, and Pi10. Elevated CTDG was associated with increased neutrophil count, NLR, and tumor necrosis factor &#x3b1;. Increased CTDG and BVB textures also were associated with a lower FEV1 and 6-minute walk distance. CTDG at baseline was also associated with decline in FEV1 at the 5-year follow-up in the COPDGene study. We observed a significant association of both BVB texture (SPIROMICS: hazard ratio [HR], 1.084 [95% CI, 1.035-1.135; P < .001]; COPDGene: HR, 1.106 [95% CI, 1.080-1.131; P < .001]) and CTDG texture (SPIROMICS: HR, 1.033 [95% CI, 1.003-1.064; P = .03]; COPDGene: HR, 1.079 [95% CI, 1.061-1.096; P < .001]) with all-cause mortality independent of CT scan-detected emphysema and Pi10. INTERPRETATION: QCT scan textures may provide imaging evidence of the spatial heterogeneity of lung inflammation and overall disease burden in COPD. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov; Nos.: NCT01969344 (SPIROMICS) and NCT00608764 (COPDGene); URL: www. CLINICALTRIALS: gov.

Humans↗
PubMed2005

Systems biology and the heart.

Understanding the logic of living systems requires knowledge of the mechanisms involved at the levels at which functionality is expressed. This information does not reside in the genome, nor even in the individual proteins that genes code for. No functionality is expressed at these levels. It emerges as the result of interactions between many proteins relating to each other in multiple cascades and in interaction with the cellular environment. There is, therefore, no alternative to copying nature and computing these interactions to determine the logic of healthy and diseased states. The rapid growth in biological databases, models of cells, tissues and organs and the development of powerful computing hardware and algorithms have made it possible to explore functionality in a quantitative manner all the way from the level of genes to the physiological function of whole organs and regulatory systems. I use models of the heart to demonstrate that we can now go all the way from individual genetic information (on mutations, for example) to exploring the consequences at a whole organ level.

Animals↗
PubMed2004

High-throughput phenomics: experimental methods for mapping fluxomes.

Many technologies have been developed to help explain the phenotypic consequences of genetic and/or environmental modifications in areas like functional genomics, pharmaceutical research and metabolic engineering. The missing link in contemporary functional analyses that focus on the analysis of cellular components is the capacity to directly observe functional units. By linking genes and proteins to higher level biological functions, the molecular fluxes through metabolic networks (the fluxome) determine the cellular phenotype. Quantitative monitoring of such whole network operations by methods of metabolic flux analysis, thus bridges the gap by providing a global perspective of the integrated regulation at the transcriptional, translational and metabolic level. This review highlights recent developments towards high-throughput flux analysis.

Animals↗
PubMed2003

The connectivity structure, giant strong component and centrality of metabolic networks.

MOTIVATION: Structural and functional analysis of genome-based large-scale metabolic networks is important for understanding the design principles and regulation of the metabolism at a system level. The metabolic network is conventionally considered to be highly integrated and very complex. A rational reduction of the metabolic network to its core structure and a deeper understanding of its functional modules are important. RESULTS: In this work, we show that the metabolites in a metabolic network are far from fully connected. A connectivity structure consisting of four major subsets of metabolites and reactions, i.e. a fully connected sub-network, a substrate subset, a product subset and an isolated subset is found to exist in metabolic networks of 65 fully sequenced organisms. The largest fully connected part of a metabolic network, called 'the giant strong component (GSC)', represents the most complicated part and the core of the network and has the feature of scale-free networks. The average path length of the whole network is primarily determined by that of the GSC. For most of the organisms, GSC normally contains less than one-third of the nodes of the network. This connectivity structure is very similar to the 'bow-tie' structure of World Wide Web. Our results indicate that the bow-tie structure may be common for large-scale directed networks. More importantly, the uncovered structure feature makes a structural and functional analysis of large-scale metabolic network more amenable. As shown in this work, comparing the closeness centrality of the nodes in the GSC can identify the most central metabolites of a metabolic network. To quantitatively characterize the overall connection structure of the GSC we introduced the term 'overall closeness centralization index (OCCI)'. OCCI correlates well with the average path length of the GSC and is a useful parameter for a system-level comparison of metabolic networks of different organisms. SUPPLEMENTARY INFORMATION: http://genome.gbf.de/bioinformatics/

Bacteria↗
PubMed2005

Gene expression profiles of normal human fibroblasts after exposure to ionizing radiation: a comparative study of low and high doses.

Several types of cellular responses to ionizing radiation, such as the adaptive response or the bystander effect, suggest that low-dose radiation may possess characteristics that distinguish it from its high-dose counterpart. Accumulated evidence also implies that the biological effects of low-dose and high-dose ionizing radiation are not linearly distributed. We have investigated, for the first time, global gene expression changes induced by ionizing radiation at doses as low as 2 cGy and have compared this to expression changes at 4 Gy. We applied cDNA microarray analyses to G1-arrested normal human skin fibroblasts subjected to X irradiation. Our data suggest that both qualitative and quantitative differences exist between gene expression profiles induced by 2 cGy and 4 Gy. The predominant functional groups responding to low-dose radiation are those involved in cell-cell signaling, signal transduction, development and DNA damage responses. At high dose, the responding genes are involved in apoptosis and cell proliferation. Interestingly, several genes, such as cytoskeleton components ANLN and KRT15 and cell-cell signaling genes GRAP2 and GPR51, were found to respond to low-dose radiation but not to high-dose radiation. Pathways that are specifically activated by low-dose radiation were also evident. These quantitative and qualitative differences in gene expression changes may help explain the non-linear correlation of biological effects of ionizing radiation from low dose to high dose.

Cells, Cultured↗
PubMed2004

Flow cytogenetics and plant genome mapping.

The application of flow cytometry and sorting (flow cytogenetics) to plant chromosomes did not begin until the mid-1980s, having been delayed by difficulties in preparation of suspensions of intact chromosomes and discrimination of individual chromosome types. These problems have been overcome during the last ten years. So far, chromosome analysis and sorting has been reported in 17 species, including major legume and cereal crops. While chromosome classification by flow cytometry (flow karyotyping) may be used for quantitative detection of structural and numerical chromosome changes, chromosomes purified by flow sorting were found to be invaluable in a broad range of applications. These included physical mapping using PCR, high-resolution cytogenetic mapping using FISH and PRINS, production of recombinant DNA libraries, targeted isolation of markers, and protein analysis. A great potential is foreseen for the use of sorted chromosomes for construction of chromosome and chromosome-arm-specific BAC libraries, targeted isolation of low-copy (genic) sequences, high-throughput physical mapping of ESTs and other DNA sequences by hybridization to DNA arrays, and global characterization of chromosomal proteins using approaches of proteomics. This paper provides a comprehensive review of the methodology and application of flow cytogenetics, and assesses its potential for plant genome analysis.

Chromosomes, Plant↗
PubMed2004

Post-transcriptional expression regulation in the yeast Saccharomyces cerevisiae on a genomic scale.

Based on large-scale data for the yeast Saccharomyces cerevisiae (protein and mRNA abundance, translational status, transcript length), we investigate the relation of transcription, translation, and protein turnover on a genome-wide scale. We elucidate variations between different spatial cell compartments and functional modules by comparing protein-to-mRNA ratios, translational activity, and a novel descriptor for protein-specific degradation (protein half-life descriptor). This analysis helps to understand the cell's strategy to use transcriptional and post-transcriptional regulation mechanisms for managing protein levels. For instance, it is possible to identify modules that are subject to suppressed translation under normal conditions ("translation on demand"). In order to reduce inconsistencies between the datasets, we compiled a new reference mRNA abundance dataset and we present a novel approach to correct large microarray signals for a saturation bias. Accounting for ribosome density based on transcript length rather than ORF length improves the correlation of observed protein levels to translational activity. We discuss potential causes for the deviations of these correlations. Finally, we introduce a quantitative descriptor for protein degradation (protein half-life descriptor) and compare it to measured half-lives. The study demonstrates significant post-transcriptional control of protein levels for a number of different compartments and functional modules, which is missed when exclusively focusing on transcript levels.

Cell Compartmentation↗
PubMedDate not supplied

e-molecular shapes and properties.

Due to recent computer technology advances, shape analysis has gained importance in all domains. In drug design and proteomics, molecular surfaces (van der Waals surface, solvent accessible surface, solvent excluded surface, polar surface area, electron density surface, separating surface, etc.), buried surfaces (gap, cleft, cavity, etc.) as well as shape properties of these surfaces, can be easily computed and visualized via the Internet. Freely available resources from the Internet for academic use, are reviewed.

Computer Simulation↗
PubMed2004

Identification of multiple proteins whose synthetic rates are enhanced by high amino acid levels in rat hepatocytes.

Amino acids are key regulators of protein synthesis in liver. However, it remains to be determined whether amino acids stimulate synthesis of all or certain specific liver proteins. No techniques are currently available to simultaneously measure synthetic rates of several individual proteins. Here we report studies performed on rat hepatocyte primary cultures in which we used metabolic labeling with [(14)C]leucine, two-dimensional gel electrophoresis (2DGE), and tandem mass spectrometry to identify proteins that showed increased leucine incorporation when high amino acid levels were present in the media. Rat hepatocytes were isolated by in situ collagenase perfusion, cultured in serum-free medium containing insulin, and incubated for 2, 4, and 8 h in media of standard and high amino acid concentrations. SDS-PAGE and 2DGE were performed to separate proteins from cell lysates. Proteins that consistently showed increased synthesis on triplicate cultures, as detected by phosphorimaging of gels, were identified by tandem mass spectrometry. The combination of these approaches enabled the detection of 16 specific liver proteins whose synthetic rates were enhanced by increased amino acid concentration. These proteins are involved in specific functions such as translation initiation, protein folding and modification, oxidative phosphorylation, antioxidant defense, signal transduction, and transport, as well as cell motility and tissue integrity. No quantitative changes for any of these proteins were detected by gel staining, indicating that no detectable changes in protein concentration occurred. In contrast, measurable changes in synthetic rates occurred in 16 proteins. In conclusion, amino acids stimulate the synthesis of several liver proteins with important cellular functions.

Amino Acids↗
PubMed2005

Sample preparation for peptides and proteins in biological matrices prior to liquid chromatography and capillary zone electrophoresis.

The determination of peptides and proteins in a biological matrix normally includes a sample-preparation step to obtain a sample that can be injected into a separation system in such a way that peptides and proteins of interest can be determined qualitatively and/or quantitatively. This can be a rather challenging, labourious and/or time-consuming process. The extract obtained after sample preparation is further separated using a compatible separation system. Liquid chromatography (LC) is the generally applied technique for this purpose, but capillary zone electrophoresis (CZE) is an alternative, providing fast, versatile and efficient separations. In this review, the recent developments in the combination of sample-preparation procedures with LC and CZE, for the determination of peptides and proteins, will be discussed. Emphasis will be on purification from and determination in complex biological matrices (plasma, cell lysates, etc.) of these compounds and little attention will be paid to the proteomics area. Additional focus will be put on sample-preparation conditions, which can be 'hard' or 'soft', and on selectivity issues. Selectivity issues will be addressed in combination with the used separation technique and a comparison between LC and CZE will be made.

Chromatography, Liquid↗