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PubMed2006

MeMo: a hybrid SQL/XML approach to metabolomic data management for functional genomics.

BACKGROUND: The genome sequencing projects have shown our limited knowledge regarding gene function, e.g. S. cerevisiae has 5-6,000 genes of which nearly 1,000 have an uncertain function. Their gross influence on the behaviour of the cell can be observed using large-scale metabolomic studies. The metabolomic data produced need to be structured and annotated in a machine-usable form to facilitate the exploration of the hidden links between the genes and their functions. DESCRIPTION: MeMo is a formal model for representing metabolomic data and the associated metadata. Two predominant platforms (SQL and XML) are used to encode the model. MeMo has been implemented as a relational database using a hybrid approach combining the advantages of the two technologies. It represents a practical solution for handling the sheer volume and complexity of the metabolomic data effectively and efficiently. The MeMo model and the associated software are available at http://dbkgroup.org/memo/. CONCLUSION: The maturity of relational database technology is used to support efficient data processing. The scalability and self-descriptiveness of XML are used to simplify the relational schema and facilitate the extensibility of the model necessitated by the creation of new experimental techniques. Special consideration is given to data integration issues as part of the systems biology agenda. MeMo has been physically integrated and cross-linked to related metabolomic and genomic databases. Semantic integration with other relevant databases has been supported through ontological annotation. Compatibility with other data formats is supported by automatic conversion.

Computer Simulation↗

PubMed2026

An overview of the use of proteomics and peptidomics to characterize alternative protein foods.

The global protein transition is accelerating the development of alternative protein foods, mainly derived from plants, insects, algae, fungi, and cellular agriculture. Ensuring the authenticity, safety, and nutritional adequacy of these emerging protein matrices requires molecular-level characterization beyond traditional compositional analyses. Proteomics and peptidomics have emerged as transformative analytical platforms capable of decoding the molecular signatures that define protein origin, structural integrity, digestibility, functionality, and health potential. The review comprehensively examines the application of proteomics, and peptidomics for profiling alternative protein foods. Further, the source authentication strategies based on species-specific protein and peptide biomarkers, detection of adulteration in complex matrices, and allergenicity assessment is discussed. Special attention is also given to nutritional proteomics with protein digestibility, gastrointestinal peptide release, and identification of bioactive sequences. SIGNIFICANCE: The importance of this review is that proteomics and peptidomics are becoming central in the management of the fast-growing environment of alternative protein foods, such as plant-based, insect, algal, fungal, and cultured meat products. It provides an explanation of the application of mass spectrometry-based processes to decode molecular signatures defining the origin of proteins, their structural integrity, digestibility, allergenicity, and bioactive properties, and thus directly contribute to safety, nutritional analysis, and authenticity of the product. Presentation of the article includes the integration of the knowledge of traditional muscle foods with alternative systems of proteins, where validated protein and peptide biomarkers are used in authentication, fraud detection, and allergy risk assessment in a wide variety of matrices. It also indicates the role of nutritional proteomics and peptidomics in informing the formulation strategy to promote digestibility and release of health-promoting peptides. In general, this review will guide scientists, the food industry, and regulatory bodies to use modern proteomic technologies in quality assurance, and decision-making, for the advancementof sustainable protein-based foods.

Proteomics↗

PubMed2026

Radiogenomics predicts immune microenvironment heterogeneity and response to combination immunotherapy in hepatocellular carcinoma.

BACKGROUND: The combination of immune checkpoint inhibitors (ICIs) with anti-angiogenic agents is the preferred first-line therapy option for patients with advanced hepatocellular carcinoma (HCC), yet only a subset of patients responds, urging the quest for prediction biomarkers. We aimed to integrate genomics with radiology to propose an immune-derived radiogenomics biomarker of response to such combination immunotherapy and evaluate its added value in clinical context. METHODS: We integrated bulk RNA sequencing (RNA-seq) and proteomics data of 994 HCC patients with single-cell RNA-seq data of 11 samples across multiple datasets to identify an immune-related signature (IRS) that may influence sensitivity or resistance to such combined immunotherapy strategy, followed by verification of selected marker genes using immunohistochemistry and cytological experiments. We then trained/validated a cross-modality radiogenomics biomarker using machine learning based on TCIA database that was further tested in multi-scale independent cohorts covering 754 HCC patients. RESULTS: Integrative multi-omics analysis identifed a parsimonious 2-gene prognostic signature including KPNA2 and SMG5 that was significantly associated with immune heterogeneity and response to combination immunotherapy. Machine-learning pipeline exported the optimal 4-feature radiogenomics biomarker using support vector machine that significantly discriminated prognosis (hazard ratio 1.415–1.890; p < 0.05 for all) and modestly predicted response to ICI plus anti-angiogenic therapy (area under the curve 0.720–0.829) in independent retrospective series across major imaging modalities (computed tomography/magnetic resonance imaging). In a prospective neoadjuvant cohort, this biomarker also showed favorable performance for predicting pathological response and tumor recurrence, accompanied by biological validation through single-cell RNA-seq analysis of pre-treatment biopsies. CONCLUSIONS: Our study provides a cross-device-cross-modal radiogenomics biomarker that can improve patient selection for emerging ICI plus anti-angiogenic therapy with novel potential therapeutic targets in HCC.

Humans↗

PubMed2006

C. elegans: an invaluable model organism for the proteomics studies of the cholesterol-mediated signaling pathway.

With the availability of its complete genome sequence and unique biological features relevant to human disease, Caenorhabditis elegans has become an invaluable model organism for the studies of proteomics, leading to the elucidation of nematode gene function. A journey from the genome to proteome of C. elegans may begin with preparation of expressed proteins, which enables a large-scale analysis of all possible proteins expressed under specific physiological conditions. Although various techniques have been used for proteomic analysis of C. elegans, systematic high-throughput analysis is still to come in order to accommodate studies of post-translational modification and quantitative analysis. Given that no integrated C. elegans protein expression database is available, it is about time that a global C. elegans proteome project is launched through which datasets of transcriptomes, protein-protein interaction and functional annotation can be integrated. As an initial target of a pilot project of the C. elegans proteome project, the cholesterol-mediated signaling pathway will be an excellent example since, like in other organisms, it is one of the key controlling pathways in cell growth and development in C. elegans. As this field tends to broaden to functional proteomics, there is a high demand to develop the versatile proteome informatics tools that can mange many different data in an integrative manner.

Animals↗

PubMed2002

Describing biological protein interactions in terms of protein states and state transitions: the LiveDIP database.

Biological protein-protein interactions differ from the more general class of physical interactions; in a biological interaction, both proteins must be in their proper states (e.g. covalently modified state, conformational state, cellular location state, etc.). Also in every biological interaction, one or both interacting molecules undergo a transition to a new state. This regulation of protein states through protein-protein interactions underlies many dynamic biological processes inside cells. Therefore, understanding biological interactions requires information on protein states. Toward this goal, DIP (the Database of Interacting Proteins) has been expanded to LiveDIP, which describes protein interactions by protein states and state transitions. This additional level of characterization permits a more complete picture of the protein-protein interaction networks and is crucial to an integrated understanding of genome-scale biology. The search tools provided by LiveDIP, Pathfinder, and Batch Search allow users to assemble biological pathways from all the protein-protein interactions collated from the scientific literature in LiveDIP. Tools have also been developed to integrate the protein-protein interaction networks of LiveDIP with large scale genomic data such as microarray data. An example of these tools applied to analyzing the pheromone response pathway in yeast suggests that the pathway functions in the context of a complex protein-protein interaction network. Seven of the eleven proteins involved in signal transduction are under negative or positive regulation of up to five other proteins through biological protein-protein interactions. During pheromone response, the mRNA expression levels of these signaling proteins exhibit different time course profiles. There is no simple correlation between changes in transcription levels and the signal intensity. This points to the importance of proteomic studies to understand how cells modulate and integrate signals. Integrating large scale, yeast two-hybrid data with mRNA expression data suggests biological interactions that may participate in pheromone response. These examples illustrate how LiveDIP provides data and tools for biological pathway discovery and pathway analysis.

Databases, Protein↗

PubMed2006

Building the power house: recent advances in mitochondrial studies through proteomics and systems biology.

The emerging field of systems biology seeks to develop novel approaches to integrate heterogeneous data sources for effective analysis of complex living systems. Systemic studies of mitochondria have generated a large number of proteomic data sets in numerous species, including yeast, plant, mouse, rat, and human. Beyond component identification, mitochondrial proteomics is recognized as a powerful tool for diagnosing and characterizing complex diseases associated with these organelles. Various proteomic techniques for isolation and purification of proteins have been developed; each tailored to preserve protein properties relevant to study of a particular disease type. Examples of such techniques include immunocapture, which minimizes loss of posttranslational modification, 4-iodobutyltriphenylphosphonium labeling, which quantifies protein redox states, and surface-enhanced laser desorption ionization-time-of-flight mass spectrometry, which allows sequence-specific binding. With the rapidly increasing number of discovered molecular components, computational models are also being developed to facilitate the organization and analysis of such data. Computational models of mitochondria have been accomplished with top-down and bottom-up approaches and have been steadily improved in size and scope. Results from top-down methods tend to be more qualitative but are unbiased by prior knowledge about the system. Bottom-up methods often require the incorporation of a large amount of existing data but provide more rigorous and quantitative information, which can be used as hypotheses for subsequent experimental studies. Successes and limitations of the studies reviewed here provide opportunities and challenges that must be addressed to facilitate the application of systems biology to larger systems.

Animals↗

PubMed2003

Proteomics and systems biology approaches to signal transduction in sepsis.

Sepsis and resulting multiple system organ failure are the leading causes of mortality in intensive care units. Although it is generally appreciated that rampant, deregulated inflammatory pathways play a major role in sepsis, a comprehensive understanding based on the integrated response of multiple signal transduction pathways has remained elusive. Here we review the main signal transduction pathways involved in the progression from inflammation to sepsis and discuss emerging genomic, proteomic, and systems biology approaches to decipher how these signaling pathways converge to cause the septic state. We propose that an integrative approach involving functional proteomics will provide a quantitative and mechanistic description that unifies inflammatory signaling networks in sepsis and will identify critical regulatory nodes for therapeutic manipulation. These types of systems biology-based approaches may lead to more effective therapies than those currently available.

Humans↗

PubMed2004

Towards cooperative frameworks for modeling and integrating biological processes knowledge.

Data organization has become a strategic target for biologists due to the increasing volume of genomic data available for them. For this purpose, we need a complete knowledge model for representing biological system. In this paper, we deal with both processes for the creation and integration of shareable, reusable domain models within biology, which is a critical issue. In particular, this work introduces a new cooperative development approach for biology ontologies. This approach is based on the integration of the ontologies supplied by different human experts. Two experiments in biological domains are presented and their results discussed.

Artificial Intelligence↗

PubMed2003

Systems biology: integrating technology, biology, and computation.

The Human Genome Project has changed the worlds of biology and medicine-helping to catalyze two major paradigm changes: systems biology and predictive, preventive and personalized medicine. These two themes will dominate 21st century biology and medicine. I will discuss these changes and indicate how they may interface with with the process of aging.

Animals↗

PubMed2004

Applications and performance of a MALDI-ToF mass spectrometer with quadratic field reflectron technology.

A new matrix-assisted laser desorption/ionization time of flight mass spectrometer (MALDI-ToF MS), developed specifically for the identification and characterization of proteins and peptides in proteomic investigations, is described. The mass spectrometer which can be integrated with the 2-D gel electrophoresis workflow is a bench-top instrument, enabling rapid, reliable and unattended protein identification in low-, as well as high-throughput proteomics applications. To obtain precise information on peptide sequences, the instrument utilizes a timed ion gate and a unique quadratic field reflectron (Z2 technology), allowing single-run, post-source decay (PSD) of selected peptides. In this study, the performance of the instrument in reflectron, PSD and linear mode, respectively, was investigated. The results showed that the limit of detection for a single peptide in reflectron mode was 125 amol with a signal to noise ratio exceeding 20. Average mass resolution for peptides larger than 2000 u was around 13,000 full width, half maximum (FWHM). The limit for protein identification during peptide mass fingerprinting (PMF) was 500 amol with a sequence coverage of 18%. Mass error during PMF analysis was less than 15 ppm for 17 out of 25 (68%) identified peptides. In PSD mode, a complete series of y-ions of a CAF-derivatized peptide could be obtained from 3.75 fmol of material. The average mass error of PSD-generated fragments was less than 0.14 u. Finally, in linear mode, intact proteins with molecular masses greater than 300,000 u were detected with mass errors below 0.2%.

Electrophoresis, Gel, Two-Dimensional↗

PubMed2005

Functional ingredient production: application of global metabolic models.

The biotechnology industry continuously explores new ways to improve the performance of microbial strains in fermentation processes. Recent focus has been on new genome-wide modelling approaches in functional genomics, which aim to take full advantage of genome sequence data, transcription profiling, proteomics and metabolite profiling for strain improvement. The integration of global metabolic models with genetic and regulatory models will be essential for the practice of metabolic engineering for strain improvement to move forward, simply because we cannot rely on our intuition to grasp the complexity of the biological systems involved.

Bacterial Physiological Phenomena↗

PubMed2004

In Silico Metabolic Model and Protein Expression of Haemophilus influenzae Strain Rd KW20 in Rich Medium.

The intermediary metabolism of Haemophilus influenzae strain Rd KW20 was studied by a combination of protein expression analysis using a recently developed direct proteomics approach, mutational analysis, and mathematical modeling. Special emphasis was placed on carbon utilization, sugar fermentation, TCA cycle, and electron transport of H. influenzae cells grown microaerobically and anaerobically in a rich medium. The data indicate that several H. influenzae metabolic proteins similar to Escherichia coli proteins, known to be regulated by low concentrations of oxygen, were well expressed in both growth conditions in H. influenzae. An in silico model of the H. influenzae metabolic network was used to study the effects of selective deletion of certain enzymatic steps. This allowed us to define proteins predicted to be essential or non-essential for cell growth and to address numerous unresolved questions about intermediary metabolism of H. influenzae. Comparison of data from in vivo protein expression with the protein list associated with a genome-scale metabolic model showed significant coverage of the known metabolic proteome. This study demonstrates the significance of an integrated approach to the characterization of H. influenzae metabolism.

Biochemistry↗

PubMed2026

Proteomic and phosphoproteomic profiles of time-dependent dynamic changes in LPS-induced macrophage polarization.

The temporal proteomic and phosphoproteomic reprogramming during early M1 macrophage polarization (0-6 h) remains poorly understood. We performed time-resolved proteomic and phosphoproteomic analyses of LPS-stimulated RAW264.7 macrophages at seven time points within 6 h. Time-clustering of differentially expressed molecules revealed two patterns: initial change with partial recovery, and sustained dysregulation. Upregulated proteins and phosphorylation sites were enriched in the Rho GTPase signaling pathway, T-cell receptor signaling pathway, NF-κB cascade, osteoclast differentiation pathway, and antiviral immune pathway. Downregulated pathways were associated with cell cycle regulation, chromatin remodeling, RNA metabolism, and mRNA processing, indicating resource reallocation to prioritize acute inflammatory responses. Kinase-substrate network analysis confirmed the mitogen-activated protein kinase (MAPK), cyclin-dependent kinase (CDK), protein kinase B (AKT), and ribosomal S6 kinase (RSK) families as core upstream phosphorylation regulators. Integrated analysis revealed synergistic and antagonistic relationships between proteomic and phosphoproteomic changes. This study provides a temporal molecular atlas of M1 polarization, delineating inflammatory signaling dynamics and offering a basis for therapeutic target discovery in inflammatory diseases. SIGNIFICANCE: Macrophage M1 polarization is a central event in innate immune defense against pathogenic invasion, yet its dysregulation is a pivotal driver of the onset and progression of a broad spectrum of inflammation-associated disorders, spanning autoimmune diseases, infectious conditions and inflammatory bone diseases, making the dissection of its molecular regulatory mechanisms an urgent research priority in immunology and translational medicine. Dynamic molecular events within 0-6 h after LPS stimulation are critical for initiating and shaping M1 inflammatory activation, yet systematic time-resolved proteomic and phosphoproteomic profiling remains insufficient.In this study, we comprehensively characterized temporal proteome and phosphoproteome changes at seven consecutive time points during macrophage polarization, clarified two distinct dynamic molecular patterns, identified core signaling pathways and key kinase regulators involved in inflammatory reprogramming, and uncovered the leading role of post-translational phosphorylation modifications in initiating polarization. This work delineates the time-series molecular atlas of early macrophage activation, provides novel insights into the temporal regulatory mechanism of inflammatory signaling networks, and lays a solid experimental foundation for exploring new intervention targets and regulatory nodes in clinical translational research.

Lipopolysaccharides↗

PubMed2006

NovelFam3000--uncharacterized human protein domains conserved across model organisms.

BACKGROUND: Despite significant efforts from the research community, an extensive portion of the proteins encoded by human genes lack an assigned cellular function. Most metazoan proteins are composed of structural and/or functional domains, of which many appear in multiple proteins. Once a domain is characterized in one protein, the presence of a similar sequence in an uncharacterized protein serves as a basis for inference of function. Thus knowledge of a domain's function, or the protein within which it arises, can facilitate the analysis of an entire set of proteins. DESCRIPTION: From the Pfam domain database, we extracted uncharacterized protein domains represented in proteins from humans, worms, and flies. A data centre was created to facilitate the analysis of the uncharacterized domain-containing proteins. The centre both provides researchers with links to dispersed internet resources containing gene-specific experimental data and enables them to post relevant experimental results or comments. For each human gene in the system, a characterization score is posted, allowing users to track the progress of characterization over time or to identify for study uncharacterized domains in well-characterized genes. As a test of the system, a subset of 39 domains was selected for analysis and the experimental results posted to the NovelFam3000 system. For 25 human protein members of these 39 domain families, detailed sub-cellular localizations were determined. Specific observations are presented based on the analysis of the integrated information provided through the online NovelFam3000 system. CONCLUSION: Consistent experimental results between multiple members of a domain family allow for inferences of the domain's functional role. We unite bioinformatics resources and experimental data in order to accelerate the functional characterization of scarcely annotated domain families.

Animals↗

PubMed2026

Scalable, generalizable and uncertainty-aware integration of spatial multiomics across diverse modalities and platforms with SCIGMA.

Recent advances in spatial omics technologies have enabled simultaneous profiling of transcriptomic, proteomic, epigenomic, metabolomic and imaging data at high spatial resolution, offering unprecedented opportunities to dissect tissue complexity. However, integrating these diverse and large-scale spatial multimodal datasets remains a major computational challenge. We present SCIGMA, a scalable and generalizable deep learning framework for spatial multiomics integration. SCIGMA introduces an uncertainty-aware contrastive learning objective and multiview graph neural networks to preserve modality-specific signals while learning biologically meaningful joint representations. Unlike previous methods, SCIGMA provides spatially resolved uncertainty estimates, interpretably identifying regions of biological or technical heterogeneity. SCIGMA supports integration of up to five modalities, and its modular framework is extensible to future technologies with even more modalities. It also scales to more than 1 million spatial locations, enabling analysis of high-resolution datasets such as Visium HD and Xenium Prime. We evaluated SCIGMA across 19 datasets spanning 8 modalities, 10 tissues and 9 platforms. On benchmarkable datasets, SCIGMA outperformed other methods in spatial domain detection, modality preservation, feature reconstruction and reproducibility. SCIGMA identifies biologically meaningful structures, refined spatial domains and modality-specific regulatory programs, providing a robust, flexible and future-ready solution for scalable spatial multimodal integration.

Multiomics↗

PubMed2006

Analysis of the synaptic vesicle proteome using three gel-based protein separation techniques.

Synaptic vesicles are key organelles in neurotransmission. Their functions are governed by a unique set of integral and peripherally associated proteins. To obtain a complete protein inventory, we immunoisolated synaptic vesicles from rat brain to high purity and performed a gel-based analysis of the synaptic vesicle proteome. Since the high hydrophobicity of integral membrane proteins hampers their resolution by gel electrophoretic techniques, we applied in parallel three different gel electrophoretic methods for protein separation prior to MS. Synaptic vesicle proteins were subjected to either 1-D SDS-PAGE along with nano-LC ESI-MS/MS or to the 2-D gel electrophoretic techniques benzyldimethyl-n-hexadecylammonium chloride (BAC)/SDS-PAGE, and double SDS (dSDS)-PAGE in combination with MALDI-TOF-MS. We demonstrate that the combination of all three methods provides a comprehensive survey of the proteinaceous inventory of the synaptic vesicle membrane compartment. The identified synaptic vesicle proteins include transporters, soluble N-ethylmaleimide-sensitive factor attachment protein receptors (SNAREs), synapsins, rab and rab-interacting proteins, additional guanine nucleotide triphosphate (GTP) binding proteins, cytoskeletal proteins, and proteins modulating synaptic vesicle exo- and endocytosis. In addition, we identified novel proteins of unknown function. Our results demonstrate that the parallel application of three different gel-based approaches in combination with mass spectrometry permits a comprehensive analysis of the synaptic vesicle proteome that is considerably more complex than previously anticipated.

Animals↗

PubMed2006

Genetics, transcriptomics, and proteomics of Alzheimer's disease.

OBJECTIVE: To provide an updated overview of the methods used in genetic, transcriptomic, and proteomic studies in Alzheimer's disease and to demonstrate the importance of those methods for the improvement of the current diagnostic and therapeutic possibilities. DATA SOURCES: MEDLINE-based search of 233 peer-reviewed articles published between 1975 and 2006. DATA SYNTHESIS: Alzheimer's disease is a genetically heterogeneous disorder. Rare mutations in the amyloid precursor protein, presenilin 1, and presenilin 2 genes have shown the importance of the amyloid metabolism for its development. In addition, converging evidence from population-based genetic studies, gene expression studies, and protein profile studies in the brain and in the cerebrospinal fluid suggest the existence of several pathogenetic pathways such as amyloid precursor protein processing, beta-amyloid degradation, tau phosphorylation, proteolysis, protein misfolding, neuroinflammation, oxidative stress, and lipid metabolism. CONCLUSIONS: The development of high-throughput genotyping methods and of elaborated statistical analyses will contribute to the identification of genetic risk profiles related to the development and course of this devastating disease. The integration of knowledge derived from genetic, transcriptomic, and proteomic studies will greatly advance our understanding of the causes of Alzheimer's disease, improve our capability of establishing an early diagnosis, help define disease subgroups, and ultimately help to pave the road toward improved and tailored treatments.

Alzheimer Disease↗

PubMed2004

Multidimensional signatures in antimicrobial peptides.

Conventional analyses distinguish between antimicrobial peptides by differences in amino acid sequence. Yet structural paradigms common to broader classes of these molecules have not been established. The current analyses examined the potential conservation of structural themes in antimicrobial peptides from evolutionarily diverse organisms. Using proteomics, an antimicrobial peptide signature was discovered to integrate stereospecific sequence patterns and a hallmark three-dimensional motif. This striking multidimensional signature is conserved among disulfide-containing antimicrobial peptides spanning biological kingdoms, and it transcends motifs previously limited to defined peptide subclasses. Experimental data validating this model enabled the identification of previously unrecognized antimicrobial activity in peptides of known identity. The multidimensional signature model provides a unifying structural theme in broad classes of antimicrobial peptides, will facilitate discovery of antimicrobial peptides as yet unknown, and offers insights into the evolution of molecular determinants in these and related host defense effector molecules.

Amino Acid Sequence↗