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PubMed2005

Lysosomal membrane proteomics and biogenesis of lysosomes.

This review focuses on events involved in the biogenesis of the lysosome. This organelle contains a diverse array of soluble, luminal proteins capable of digesting all the macromolecules in the cell. Altered function of lysosomes or its constituent enzymes has been implicated in a host of human pathologies, including storage diseases, cancer, and infectious and neurodegenerative diseases. Luminal enzymes are well-characterized, and aspects of how they are incorporated into lysosomes are known. However, little is known about the composition of the membrane surrounding the organelle or how the membrane is assembled. Our starting point to study lysosome biogenesis is to define the composition of the membrane by the use of proven methods for purification of lysosomes to near homogeneity and then to characterize membrane-associated and integral lysosomal membrane proteins. This has been achieved using advanced proteomics (electrophoretic or chromatographic separations of proteins followed by time-of-flight mass spectrometric identification of peptide sequences). To date, we have identified 55 proteins in the membrane-associated fraction and 215 proteins in the integral membrane. By applying these methods to mouse models of lysosome dysgenesis (such as BEIGE, Pale Ear, PEARL) that are related to human diseases such as Chediak-Higashi and Hermansky-Pudlak syndromes, it may be possible to define the membrane protein composition of lysosomes in each of these mutants and to determine how they differ from normal. Identifying proteins affected in the respective mutants may provide hints about how they are targeted to the lysosomal membrane and how failure to target them leads to disease; these features are pivotal to understanding lysosome biogenesis and have the potential to implicate lysosomes in a broad range of human pathologies.

Animals↗

PubMed2004

BOMP: a program to predict integral beta-barrel outer membrane proteins encoded within genomes of Gram-negative bacteria.

This work describes the development of a program that predicts whether or not a polypeptide sequence from a Gram-negative bacterium is an integral beta-barrel outer membrane protein. The program, called the beta-barrel Outer Membrane protein Predictor (BOMP), is based on two separate components to recognize integral beta-barrel proteins. The first component is a C-terminal pattern typical of many integral beta-barrel proteins. The second component calculates an integral beta-barrel score of the sequence based on the extent to which the sequence contains stretches of amino acids typical of transmembrane beta-strands. The precision of the predictions was found to be 80% with a recall of 88% when tested on the proteins with SwissProt annotated subcellular localization in Escherichia coli K 12 (788 sequences) and Salmonella typhimurium (366 sequences). When tested on the predicted proteome of E.coli, BOMP found 103 of a total of 4346 polypeptide sequences to be possible integral beta-barrel proteins. Of these, 36 were found by BLAST to lack similarity (E-value score < 1e-10) to proteins with annotated subcellular localization in SwissProt. BOMP predicted the content of integral beta-barrels per predicted proteome of 10 different bacteria to range from 1.8 to 3%. BOMP is available at http://www.bioinfo.no/tools/bomp.

Bacterial Outer Membrane Proteins↗

PubMed2003

ProteomeWeb: a web-based interface for the display and interrogation of proteomes.

The analysis of proteomes, i.e., the proteins expressed by biological organisms under a given set of conditions at a given time, requires separating complex protein mixtures into discrete protein components, measuring their relative abundances, and identifying the individual protein components. Many types of data are generated during the course of proteome analysis, including graphic images of the protein profiles, flat files containing numeric data, spreadsheets for assimilating numeric data, and relational database tables for integrating data from multiple experiments. As part of a project to describe the proteomes of microbes of interest to the U.S. Department of Energy, a World-Wide Web-based interface has been developed for the display of protein profiles generated by two-dimensional gel electrophoresis. The web interface is capable of obtaining protein identifications on the fly, interrogating the quantitative data in the context of available genome sequence information, and relating the proteome data to existing metabolic pathway databases. Analysis of protein expression profiles is expedited, providing the capability to efficiently determine the gene locations for proteins modulated in abundance in response to different growth conditions and to locate the positions of the proteins within specific metabolic pathways. The proteome of the archaeon Methanococcus jannaschii, a microbe for which the complete genome sequence is available, is used to demonstrate the capabilities of this evolving web interface (http://proteomeweb.anl.gov).

Amino Acid Sequence↗

PubMed2005

YANA - a software tool for analyzing flux modes, gene-expression and enzyme activities.

BACKGROUND: A number of algorithms for steady state analysis of metabolic networks have been developed over the years. Of these, Elementary Mode Analysis (EMA) has proven especially useful. Despite its low user-friendliness, METATOOL as a reliable high-performance implementation of the algorithm has been the instrument of choice up to now. As reported here, the analysis of metabolic networks has been improved by an editor and analyzer of metabolic flux modes. Analysis routines for expression levels and the most central, well connected metabolites and their metabolic connections are of particular interest. RESULTS: YANA features a platform-independent, dedicated toolbox for metabolic networks with a graphical user interface to calculate (integrating METATOOL), edit (including support for the SBML format), visualize, centralize, and compare elementary flux modes. Further, YANA calculates expected flux distributions for a given Elementary Mode (EM) activity pattern and vice versa. Moreover, a dissection algorithm, a centralization algorithm, and an average diameter routine can be used to simplify and analyze complex networks. Proteomics or gene expression data give a rough indication of some individual enzyme activities, whereas the complete flux distribution in the network is often not known. As such data are noisy, YANA features a fast evolutionary algorithm (EA) for the prediction of EM activities with minimum error, including alerts for inconsistent experimental data. We offer the possibility to include further known constraints (e.g. growth constraints) in the EA calculation process. The redox metabolism around glutathione reductase serves as an illustration example. All software and documentation are available for download at http://yana.bioapps.biozentrum.uni-wuerzburg.de. CONCLUSION: A graphical toolbox and an editor for METATOOL as well as a series of additional routines for metabolic network analyses constitute a new user-friendly software for such efforts.

Algorithms↗

PubMed2003

Proteomics of the chloroplast envelope membranes from Arabidopsis thaliana.

The development of chloroplasts and the integration of their function within a plant cell rely on the presence of a complex biochemical machinery located within their limiting envelope membranes. To provide the most exhaustive view of the protein repertoire of chloroplast envelope membranes, we analyzed this membrane system using proteomics. To this purpose, we first developed a procedure to prepare highly purified envelope membranes from Arabidopsis chloroplasts. We then extracted envelope proteins using different methods, i.e. chloroform/methanol extraction and alkaline or saline treatments, in order to retrieve as many proteins as possible, from the most to least hydrophobic ones. Liquid chromatography tandem mass spectrometry analyses were then performed on each envelope membrane subfraction, leading to the identification of more than 100 proteins. About 80% of the identified proteins are known to be, or are very likely, located in the chloroplast envelope. The validation of localization in the envelope of two phosphate transporters exemplifies the need for a combination of strategies to perform the most exhaustive identification of genuine chloroplast envelope proteins. Interestingly, some of the identified proteins are found to be Nalpha-acetylated, which indicates the accurate location of the N terminus of the corresponding mature protein. With regard to function, more than 50% of the identified proteins have functions known or very likely to be associated with the chloroplast envelope. These proteins are a) involved in ion and metabolite transport, b) components of the protein import machinery, and c) involved in chloroplast lipid metabolism. Some soluble proteins, like proteases, proteins involved in carbon metabolism, or proteins involved in responses to oxidative stress, were associated with envelope membranes. Almost one-third of the proteins we identified have no known function. The present work helps understanding chloroplast envelope metabolism at the molecular level and provides a new overview of the biochemical machinery of the chloroplast envelope membranes.

Amino Acid Sequence↗

PubMed2025

Integrating multi-omics approaches in acute myeloid leukemia (AML): Advancements and clinical implications.

Acute myeloid leukemia (AML) is a highly heterogeneous and aggressive hematologic malignancy characterized by clonal proliferation of myeloid precursors. Despite significant advancements in genomic profiling and targeted therapies, patient outcomes remain suboptimal due to disease complexity, resistance mechanisms, and high relapse rates. The integration of multi-omics approaches-spanning genomics, epigenomics, transcriptomics, proteomics, and metabolomics-has revolutionized AML research, offering a comprehensive understanding of leukemogenesis, tumor heterogeneity, and therapeutic vulnerabilities. Recent studies leveraging high-throughput sequencing, mass spectrometry, and advanced computational tools have uncovered novel biomarkers, clonal evolution dynamics, and microenvironmental interactions that drive AML progression and resistance. For instance, single-cell multi-omics has revealed chemotherapy-resistant leukemic stem cell populations, while proteogenomic analyses have identified actionable targets such as MCL1 and metabolic dependencies like OXPHOS. Clinically, integrated omics platforms are refining risk stratification, minimal residual disease (MRD) monitoring, and personalized therapy selection. However, challenges such as data integration complexity, cost barriers, and ethical considerations remain. This review highlights the transformative potential of multi-omics in AML, emphasizing recent advancements in technology, biomarker discovery, and therapeutic innovation. By bridging the gap between molecular insights and clinical practice, multi-omics integration promises to redefine AML management, paving the way for precision oncology and improved patient outcomes.

Humans↗

PubMed2026

Serum Proteomic Profiling Implicates a Dysregulated Neurohormonal-Inflammatory Axis in Post-Fontan Sinus Tachycardia.

BACKGROUND: Postoperative sinus tachycardia is a poorly understood complication following the Fontan procedure. The molecular signaling cascades triggering acute tachycardia remain uncharacterized, limiting therapeutic innovation. Here, we present a retrospective study leveraging serum proteomics and machine learning to identify the molecular drivers of postoperative Fontan sinus tachycardia. METHODS: We integrated a clinically relevant ovine Fontan model with continuous telemetric heart rate monitoring and human patient data. Serum proteomics coupled with least absolute shrinkage and selection operator and Boruta machine learning algorithms were used to identify protein panels predictive of postoperative sinus tachycardia. Cross-species validation was performed by comparing proteomic signatures from sheep and pediatric patients undergoing Glenn or Fontan surgery. RESULTS: Ovine Fontan animals demonstrated significant heart rate elevation beginning on postoperative day 1, peaking at postoperative day 3 (159.4±11.7 bpm versus preoperative, 105.3±10.5 bpm; P=0.0002), before trending toward baseline by postoperative day 10. This pattern was mirrored in human patients with a more modest magnitude. Surgical controls did not exhibit tachycardia. The principal component most correlated with heart rate (principal component 1: r=0.78, P=2.2×10-4) was enriched for inflammatory and neural pathways. The Boruta algorithm identified an 11-protein panel with strong predictive power (area under the receiver operating characteristic curve, 0.963). Cross-species comparison demonstrated that angiotensinogen, angiotensin-converting enzyme, and pentraxin 3 were similarly dysregulated in both species postoperatively. CONCLUSIONS: This study provides molecular evidence implicating a dysregulated neurohormonal-inflammatory axis in acute postoperative Fontan sinus tachycardia and establishes a foundation for developing targeted diagnostics and therapeutics for this complication.

Animals↗

PubMed2026

Integrated multi-omics profiling identifies aging-related molecular signatures and convergent interferon signaling in systemic lupus erythematosus.

BACKGROUND: Systemic lupus erythematosus (SLE) is characterized by chronic immune activation and molecular alterations that overlap with aging-related biological processes. However, how these alterations are organized across molecular layers and whether they converge on shared regulatory networks remain incompletely understood. METHODS: We performed an integrative multi-omics analysis combining in-house proteomic and phosphoproteomic data from 130 patients with SLE and 90 healthy controls (HCs) and publicly available transcriptomic datasets comprising 1,461 SLE patients. Proteins and phosphorylation sites were annotated using established aging-related gene resources. Differential protein abundance and phosphorylation changes were analyzed across disease-status and disease-activity comparisons. Nominal P-value thresholds were used for exploratory feature selection, whereas FDR-adjusted P values were used to assess robustness after multiple-testing correction. Kinase-substrate enrichment, transcription factor annotation, and cell-type-resolved transcriptomic comparison were used to explore potential regulatory programs. RESULTS: We identified 128 nominally altered proteins annotated to aging-related biological processes, including genomic instability, mitochondrial dysfunction, and epigenetic alterations. Phosphoproteomic analysis revealed 36 nominally altered phosphorylation sites, including previously unreported sites in IFI16 (S153, S780) and PKCδ (S507, S664). Clustering analysis demonstrated heterogeneous protein co-regulation patterns across disease states. Kinase activity inference suggested altered activity of TBK1 and IKKβ. TF analysis further highlighted STAT1, RELA, and PML as potential central nodes within the inferred regulatory network. Notably, these multi-omic alterations were not randomly distributed but showed convergence toward shared signaling pathways, particularly those related to interferon responses. CONCLUSIONS: This integrative multi-omics study identifies inflammatory and interferon-dominated molecular alterations in SLE PBMCs that overlap with aging-related biological processes and converge on shared regulatory networks. These findings provide a hypothesis-generating framework for investigating the intersection between chronic immune activation and aging-related molecular remodeling in SLE.

Humans↗

PubMed2026

On-filter fractionation by empFASP improves identification of membrane peptides in proteomic experiments.

Membrane proteins remain among the most analytically challenging targets in bottom-up proteomics due to their limited solubility and low abundance of protease-accessible sites within transmembrane domains. In addition, hydrophobic peptides are frequently lost during detergent removal and the on-filter processing steps. Here, we present empFASP, a straightforward on-filter-fractionation-based modification of the enhanced filter-aided sample preparation (eFASP) workflow that enhances recovery of membrane-embedded peptides otherwise lost during digestion and cleanup. The method combines controlled on-filter inversion with sequential ethyl acetate extraction at defined pH values, enabling recovery of peptide material retained on the filter and redistributed into detergent micelles. Compared with SP3 and SP4 in HEK293T lysates, empFASP increased unique hydrophobic peptide identifications by up to 48% and increased the proportion of detected transmembrane peptides. Application to mouse mitochondrial membranes and phosphatidylethanolamine-deficient and PE-containing Escherichia coli membranes showed that the additional fractions of empFASP contribute complementary recovery of hydrophobic and membrane-associated peptides, with the strongest gains observed at the peptide level. Because empFASP requires no specialized reagents or instrumentation, it can be readily implemented in standard proteomics workflows to improve coverage of membrane-embedded regions. SIGNIFICANCE: The empFASP (enhanced membrane peptide) workflow offers a practical solution to one of the persistent limitations in membrane proteomics-the underrepresentation of hydrophobic and transmembrane peptides in standard digests. By integrating simple pH-controlled extractions into an on-filter format, empFASP recovers peptides otherwise lost through adsorption or detergent micelle retention, substantially improving coverage of the membrane proteome. This method expands the analytical reach of bottom-up proteomics without requiring specialized instrumentation, making it immediately applicable for studies of membrane topology, protein-lipid interactions, and the structural consequences of altered membrane composition.

Proteomics↗

PubMed2003

New directions of miniaturization within the proteomics research area.

An overview of the current trends within protein expression profiling is given where multidimensional separation of both gel- and liquid-phase techniques linked to mass spectrometry is viewed as a major route in the global proteome mapping. A clear trend in these biochemical developments is the effort to sequence and identify low-abundant protein expressions where assay miniaturization and integrated sample processing plays a central role. Two areas of miniaturization within the proteomics field are addressed: (i) sample cleanup and enrichment, and (ii) silicon microstructure developments for protein chip microarrays.

Miniaturization↗

PubMed2004

New directions of miniaturization within the biomarker research area.

An overview of the current trends within protein expression profiling is given where multidimensional separation of both gel and liquid phase techniques linked to mass spectrometry is viewed as a major route in the global proteome mapping. A clear trend in these biochemical developments is the effort to sequence and identify low-abundant protein expressions where assay miniaturization and integrated sample processing play a central role. Two areas of miniaturization within the proteomics field are addressed: (i) sample cleanup and enrichment, and (ii) silicon microstructure developments for protein chip microarrays.

Animals↗

PubMed2003

Use of artificial neural networks for the accurate prediction of peptide liquid chromatography elution times in proteome analyses.

The use of artificial neural networks (ANNs) is described for predicting the reversed-phase liquid chromatography retention times of peptides enzymatically digested from proteome-wide proteins. To enable the accurate comparison of the numerous LC/MS data sets, a genetic algorithm was developed to normalize the peptide retention data into a range (from 0 to 1), improving the peptide elution time reproducibility to approximately 1%. The network developed in this study was based on amino acid residue composition and consists of 20 input nodes, 2 hidden nodes, and 1 output node. A data set of approximately 7000 confidently identified peptides from the microorganism Deinococcus radiodurans was used for the training of the ANN. The ANN was then used to predict the elution times for another set of 5200 peptides tentatively identified by MS/MS from a different microorganism (Shewanella oneidensis). The model was found to predict the elution times of peptides with up to 54 amino acid residues (the longest peptide identified after tryptic digestion of S. oneidensis) with an average accuracy of approximately 3%. This predictive capability was then used to distinguish with high confidence isobar peptides otherwise indistinguishable by accurate mass measurements as well as to uncover peptide misidentifications. Thus, integration of ANN peptide elution time prediction in the proteomic research will increase both the number of protein identifications and their confidence.

Amino Acid Sequence↗

PubMed2005

Central nervous system drug development: an integrative biomarker approach toward individualized medicine.

Drug development for CNS disorders faces the same formidable hurdles as other therapeutic areas: escalating development costs; novel drug targets with unproven therapeutic potential; and health care systems and regulatory agencies demanding more compelling demonstrations of the value of new drug products. Extensive clinical testing remains the core of registration of new compounds; however, traditional clinical trial methods are falling short in overcoming these development hurdles. The most common CNS disorders targeted for drug treatment are chronic, slowly vitiating processes manifested by highly subjective and context dependent signs and symptoms. With the exception of a few rare familial degenerative disorders, they have ill-defined or undefined pathophysiology. Samples selected for treatment trials using clinical criteria are inevitably heterogeneous, and dependence on traditional endpoints results in early proof-of-concept trials being long and large, with very poor signal to noise. It is no wonder that pharmaceutical and biotechnology companies are looking to biomarkers as an integral part of decision-making process supported by new technologies such as genetics, genomics, proteomics, and imaging as a mean of rationalizing CNS drug development. The present review represent an effort to illustrate the integration of such technologies in drug development supporting the path of individualized medicine.

Animals↗

PubMed2004

Identifying biomarkers and surrogates of tumors (cancer biometrics): correlation with immunotherapies and immune cells.

The presence of inflammatory cells within cancer has been described for quite some time by pathologists, with generally improved outcome associated with their presence in various epithelial neoplasms. Most remarkably, this has included dendritic cells and T cells but more recently NK cells as well. Coupled with the rapid evolution of molecular technology, microarray analyses of primary tumors, serum and tumor proteomics, tumor capture analyses in the peripheral blood (together with quantitative RT-PCR), and novel histochemical markers and tissue microarrays, this provides the opportunity to establish a more effective means to study and classify into subsets various forms of cancer. Much of the current controversy in cancer diagnosis and pathologic assessment of prognosis lies in the application of these techniques in concert with other molecular tools including DNA microarrays, expression of histochemically defined cytokines, proangiogenic factors, and oncogene products, and correlating this with clinical relevance. Molecular detection technologies such as reverse transcriptase polymerase chain reaction, proteomics, and microarray analyses will be validated based on their integration with conventional cancer pathology and cancer diagnostics. Further work is needed to establish which cancer biomarkers and surrogates should be routinely measured and in which settings, and determining the appropriate sample size for such assays that can be validated in retrospective and prospective clinical studies. The ability to integrate these rapidly evolving strategies will consume much of our coordinate effort in cancer and cancer therapeutics for the near future.

Biomarkers, Tumor↗

PubMed2006

Proteomic analysis of bacterial-blight defense-responsive proteins in rice leaf blades.

Plants exhibit resistance against incompatible pathogens, via localized and systemic responses as part of an integrated defense mechanism. To study the compatible and incompatible interactions between rice and bacteria, a proteomic approach was applied. Rice cv. Java 14 seedlings were inoculated with compatible (Xo7435) and incompatible (T7174) races of Xanthomonas oryzae pv. oryzae (Xoo). Cytosolic and membrane proteins were fractionated from the leaf blades and separated by 2-D PAGE. From 366 proteins analyzed, 20 were differentially expressed in response to bacterial inoculation. These proteins were categorized into classes related to energy (30%), metabolism (20%), and defense (20%). Among the 20 proteins, ribulose-1,5-bisphosphate carboxylase/oxygenase large subunit (RuBisCO LSU) was fragmented into two smaller proteins by T7174 and Xo7435 inoculation. Treatment with jasmonic acid (JA), a signaling molecule in plant defense responses, changed the level of protein accumulation for 5 of the 20 proteins. Thaumatin-like protein and probenazole-inducible protein (PBZ) were commonly up-regulated by T7174 and Xo7435 inoculation and JA treatment. These results suggest that synthesis of the defense-related thaumatin-like protein and PBZ are stimulated by JA in the defense response pathway of rice against bacterial blight.

Cluster Analysis↗

PubMed2004

Proteomic signature of human cancer cells.

We assessed proteomic profiles as biomarkers for monitoring cell phenotypes. Protein expression profiles were obtained by fluorescence two-dimensional difference gel electrophoresis (2-D-DIGE), in which quantitative ability is improved by labeling proteins with fluorescent dyes prior to electrophoresis. Integrated protein spot intensities were analyzed by a statistical approach. The proteomic data of two groups of cell lines: (1) adenocarcinoma (AC) cell lines derived from lung, pancreas and colon tissues and (2) lung cancer cell lines with different histological backgrounds, including AC, squamous cell carcinoma and small cell carcinoma, were assessed on the basis of prior biological information. Hierarchical clustering analysis and principal component analysis were used to divide the cell lines into subgroups on the basis of similarities between their protein expression profiles. The majority of cell lines were grouped according to their organ of origin or histological background. A machine-learning algorithm selected 32 protein spots that were responsible for the classification. The results indicate that proteomic data generated by 2-D-DIGE can provide a signature of essential cell phenotypes, suggesting that it might be possible to apply this technique to developing tumor markers that could identify the organ of origin of metastatic tumors and contribute to the differential diagnosis of lung cancer.

Cell Line, Tumor↗

PubMed2026

PASTA: versatile tyramide-oligonucleotide amplification for multimodal spatial biology.

Spatial proteomics is limited by detection sensitivity, multiplexing and multimodal integration, leaving a gap between discovery and clinical assays. Here we present protein and nucleic acid serial tyramide amplification (PASTA), using horseradish peroxidase-mediated oligonucleotide deposition and cyclical imaging for high-plex, multimodal spatial profiling. Compatible with conjugated antibodies and in situ hybridization, PASTA enables simultaneous protein and RNA codetection from formalin-fixed, paraffin-embedded samples, providing a cost-effective bridge from discovery to clinical validation.

Tyramine↗

PubMed2006

EBP, a program for protein identification using multiple tandem mass spectrometry datasets.

MS/MS combined with database search methods can identify the proteins present in complex mixtures. High throughput methods that infer probable peptide sequences from enzymatically digested protein samples create a challenge in how best to aggregate the evidence for candidate proteins. Typically the results of multiple technical and/or biological replicate experiments must be combined to maximize sensitivity. We present a statistical method for estimating probabilities of protein expression that integrates peptide sequence identifications from multiple search algorithms and replicate experimental runs. The method was applied to create a repository of 797 non-homologous zebrafish (Danio rerio) proteins, at an empirically validated false identification rate under 1%, as a resource for the development of targeted quantitative proteomics assays. We have implemented this statistical method as an analytic module that can be integrated with an existing suite of open-source proteomics software.

Algorithms↗