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Results for “Integrated proteomics”

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PubMed2005

Protein glycosylation analysis by liquid chromatography-mass spectrometry.

Liquid chromatography (LC)-mass spectrometry (MS) has developed into an invaluable technology for the analysis of protein glycosylation. This review focuses on the recent developments in LC and combinations thereof with MS for this field of research. Recently introduced methods for the structural analysis of released glycans (native or derivatised) as well as glycopeptides, on normal phase, reverse phase and graphitized carbon LC columns with online MS(/MS) will be reviewed. Performed on nano-scale or capillary-scale, these LC-MS methods operate at femtomole sensitivity and support the further integration of glycosylation analysis in proteomics methodology.

Chromatography, High Pressure Liquid↗

PubMed2006

Modeling the brain-pituitary-gonad axis in salmon.

To better understand the complexity of the brain-pituitary-gonad axis (BPG) in fish, we developed a biologically based pharmacodynamic model capable of accurately predicting the normal functioning of the BPG axis in salmon. This first-generation model consisted of a set of 13 equations whose formulation was guided by published values for plasma concentrations of pituitary- (FSH, LH) and ovary- (estradiol, 17alpha,20beta-dihydroxy-4-pregnene-3-one) derived hormones measured in Coho salmon over an annual spawning period. In addition, the model incorporated pertinent features of previously published mammalian models and indirect response pharmacodynamic models. Model-based equations include a description of gonadotropin releasing hormone (GnRH) synthesis and release from the hypothalamus, which is controlled by environmental variables such as photoperiod and water temperature. GnRH stimulated the biosynthesis of mRNA for FSH and LH, which were also influenced by estradiol concentration in plasma. The level of estradiol in the plasma was regulated by the oocytes, which moved along a maturation progression. Estradiol was synthesized at a basal rate and as oocytes matured, stimulation of its biosynthesis occurred. The BPG model can be integrated with toxico-genomic, -proteomic data, allowing linkage between molecular based biomarkers and reproduction in fish.

Animals↗

PubMed2024

Integrative Omics Reveals the Metabolic Patterns During Oocyte Growth.

Well-controlled metabolism is associated with high-quality oocytes and optimal development of a healthy embryo. However, the metabolic framework that controls mammalian oocyte growth remains unknown. In the present study, we comprehensively depict the temporal metabolic dynamics of mouse oocytes during in vivo growth through the integrated analysis of metabolomics and proteomics. Many novel metabolic features are discovered during this process. Of note, glycolysis is enhanced, and oxidative phosphorylation capacity is reduced in the growing oocytes, presenting a Warburg-like metabolic program. For nucleotide biosynthesis, the salvage pathway is markedly activated during oocyte growth, whereas the de novo pathway is evidently suppressed. Fatty acid synthesis and channeling into phosphoinositides are specifically elevated in oocytes accompanying primordial follicle activation; nevertheless, fatty acid oxidation is reduced in these oocytes simultaneously. Our data establish the metabolic landscape during in vivo oocyte growth and serve as a broad resource for probing mammalian oocyte metabolism.

Animals↗

PubMed2002

Staking out novelty on the genomic frontier.

Recent advances in genomics include global assessment and classification of genome content, high-throughput biological pathway construction, systematic identification of previously unpredicted genes and the in vitro creation of novel motifs with biological function not found in nature (extra-genomic gene discovery). The ability to make global surveys of transcriptomes has given rise to fields such as pharmacogenomics and toxicogenomics. These applications of genomics technologies, with conventional drug assessment methodologies, will lead to more tolerable drugs and a better understanding of clinical populations. Integration of pathway mapping, using proteomics married to expression, will also significantly affect how new therapeutics are discovered as cross-biological cross-pathway interactions lead to novel drug targets and better predictions of drug tolerance.

Alternative Splicing↗

PubMed2026

Hyperlactate-Associated Lysine Lactylome Remodeling in Laryngeal Squamous Cell Carcinoma.

Laryngeal squamous cell carcinoma (LSCC) lacks reliable biomarkers, and the roles of lactate metabolism and lysine lactylation (Kla) remain largely unknown. We profiled the lysine lactylome of LSCC, paired it with adjacent normal tissues, and integrated the data with quantitative proteomic and transcriptomic analyses. LSCC exhibited a hyperlactate-associated phenotype characterized by dysregulated lactate-related genes (LRGs), altered protein abundance, increased tissue lactate, and globally increased Kla levels. Data-independent acquisition mass spectrometry (DIA-MS) identified 1616 Kla sites on 1468 peptides from 688 proteins, with most differential sites being upregulated in tumors. Differentially lactylated proteins were enriched in cell-matrix adhesion, cell migration, chromatin remodeling, and gene-regulatory processes and were clustered into cytoskeletal and nuclear regulatory modules. Multiple Kla sites were also detected on the core histones. Immunoblotting and tissue microarray analyses confirmed increased pan-Kla expression in the LSCC. Pan-Kla levels were independent of sex and age but positively correlated with the tumor stage and lymph-node metastasis. These findings provide a systematic resource for hyperlactate-associated lactylome remodeling in LSCCs and identify candidate Kla-related molecular features associated with clinicopathological progression for future functional and clinical evaluation.

Humans↗

PubMedDate not supplied

Role of biomarkers in monitoring exposures to chemicals: present position, future prospects.

Biomarkers are becoming increasingly important in toxicology and human health. Many research groups are carrying out studies to develop biomarkers of exposure to chemicals and apply these for human monitoring. There is considerable interest in the use and application of biomarkers to identify the nature and amounts of chemical exposures in occupational and environmental situations. Major research goals are to develop and validate biomarkers that reflect specific exposures and permit the prediction of the risk of disease in individuals and groups. One important objective is to prevent human cancer. This review presents a commentary and consensus views about the major developments on biomarkers for monitoring human exposure to chemicals. A particular emphasis is on monitoring exposures to carcinogens. Significant developments in the areas of new and existing biomarkers, analytical methodologies, validation studies and field trials together with auditing and quality assessment of data are discussed. New developments in the relatively young field of toxicogenomics possibly leading to the identification of individual susceptibility to both cancer and non-cancer endpoints are also considered. The construction and development of reliable databases that integrate information from genomic and proteomic research programmes should offer a promising future for the application of these technologies in the prediction of risks and prevention of diseases related to chemical exposures. Currently adducts of chemicals with macromolecules are important and useful biomarkers especially for certain individual chemicals where there are incidences of occupational exposure. For monitoring exposure to genotoxic compounds protein adducts, such as those formed with haemoglobin, are considered effective biomarkers for determining individual exposure doses of reactive chemicals. For other organic chemicals, the excreted urinary metabolites can also give a useful and complementary indication of exposure for acute exposures. These methods have revealed 'backgrounds' in people not knowingly exposed to chemicals and the sources and significance of these need to be determined, particularly in the context of their contribution to background health risks.

Biomarkers↗

PubMed2025

MiNEApy: enhancing enrichment network analysis in metabolic networks.

MOTIVATION: Modeling genome-scale metabolic networks (GEMs) helps understand metabolic fluxes in cells at a specific state under defined environmental conditions or perturbations. Elementary flux modes (EFMs) are powerful tools for simplifying complex metabolic networks into smaller, more manageable pathways. However, the enumeration of all EFMs, especially within GEMs, poses significant challenges due to computational complexity. Additionally, traditional EFM approaches often fail to capture essential aspects of metabolism, such as co-factor balancing and by-product generation. The previously developed Minimum Network Enrichment Analysis (MiNEA) method addresses these limitations by enumerating alternative minimal networks for given biomass building blocks and metabolic tasks. MiNEA facilitates a deeper understanding of metabolic task flexibility and context-specific metabolic routes by integrating condition-specific transcriptomics, proteomics, and metabolomics data. This approach offers significant improvements in the analysis of metabolic pathways, providing more comprehensive insights into cellular metabolism. RESULTS: Here, I present MiNEApy, a Python package reimplementation of MiNEA, which computes minimal networks and performs enrichment analysis. I demonstrate the application of MiNEApy on both a small-scale and a genome-scale model of the bacterium Escherichia coli, showcasing its ability to conduct minimal network enrichment analysis using minimal networks and context-specific data. AVAILABILITY AND IMPLEMENTATION: MiNEApy can be accessed at: https://github.com/vpandey-om/mineapy.

Metabolic Networks and Pathways↗

PubMed2004

Sensitivity, principal component and flux analysis applied to signal transduction: the case of epidermal growth factor mediated signaling.

MOTIVATION: Novel high-throughput genomic and proteomic tools are allowing the integration of information from a range of biological assays into a single conceptual framework. This framework is often described as a network of biochemical reactions. We present strategies for the analysis of such networks. RESULTS: The direct differential method is described for the systematic evaluation of scaled sensitivity coefficients in reaction networks. Principal component analysis, based on an eigenvalue-eigenvector analysis of the scaled sensitivity coefficient matrix, is applied to rank individual reactions in the network based on their effect on system output. When combined with flux analysis, sensitivity analysis allows model reduction or simplification. Using epidermal growth factor (EGF) mediated signaling and trafficking as an example of signal transduction, we demonstrate that sensitivity analysis quantitatively reveals the dependence of dual-phosphorylated extracellular signal-regulated kinase (ERK) concentration on individual reaction rate constants. It predicts that EGF mediated reactions proceed primarily via an Shc-dependent pathway. Further, it suggests that receptor internalization and endosomal signaling are important features regulating signal output only at low EGF dosages and at later times.

Algorithms↗

PubMed2025

Stepping out of the dark: how metabolomics shed light on fungal biology.

Metabolomics, a critical tool for analyzing small-molecule metabolites, integrates with genomics, transcriptomics, and proteomics to provide a systems-level understanding of fungal biology. By mapping metabolic networks, it elucidates regulatory mechanisms driving physiological and ecological adaptations. In fungal pathogenesis, metabolomics reveals host-pathogen dynamics, identifying virulence factors like gliotoxin in Aspergillus fumigatus and metabolic shifts, such as glyoxylate cycle upregulation in Candida albicans. Ecologically, it highlights fungal responses to abiotic stressors, including osmolyte production like trehalose, enhancing survival in extreme environments. These insights highlight metabolomics' role in decoding fungal persistence and niche colonization. In drug discovery, it aids target identification by profiling biosynthetic pathways, supporting novel antifungal and nanostructured therapy development. Combined with multi-omics, metabolomics advances insights into fungal pathogenesis, ecological interactions, and therapeutic innovation, offering translational potential for addressing antifungal resistance and improving treatment outcomes for fungal infections. Its progress shed light on complex fungal molecular profiles, advancing discovery and innovation in fungal biology.

Metabolomics↗

PubMed2004

Protein interaction mapping on a functional shotgun sequence of Rickettsia sibirica.

Protein interaction maps can reveal novel pathways and functional complexes, allowing 'guilt by association' annotation of uncharacterized proteins. To address the need for large-scale protein interaction analyses, a bacterial two-hybrid system was coupled with a whole genome shotgun sequencing approach for microbial genome analysis. We report the first large-scale proteomics study using this system, integrating de novo genome sequencing with functional interaction mapping and annotation in a high-throughput format. We apply the approach by shotgun sequencing and annotating the genome of Rickettsia sibirica strain 246, an obligate intracellular human pathogen among the Spotted Fever Group rickettsiae. The bacteria invade endothelial cells and cause lysis after large amounts of progeny have accumulated. Little is known about specific Rickettsial virulence factors and their mode of pathogenicity. Analysis of the combined genomic sequence and protein-protein interaction data for a set of virulence related Type IV secretion system (T4SS) proteins revealed over 250 interactions and will provide insight into the mechanism of Rickettsial pathogenicity.

Bacterial Proteins↗

PubMed2026

Progressive salinity drives flavonoid branch reprogramming in Anoectochilus roxburghii.

Flavonoids play critical roles in plant adaptation to abiotic stress; however, how salt stress modulates metabolic flux distribution within flavonoid branches remains poorly understood, particularly in non-model medicinal plants. Here, we integrated targeted metabolomics, transcriptomics, and proteomics to examine flavonoid regulation in Anoectochilus roxburghii under 0, 50, 100, and 200 mmol·L- 1 NaCl. Metabolite profiling showed that salinity reshaped flavonoid composition rather than uniformly increasing flavonoid abundance. A metabolite-derived branch bias index (MI), representing the balance between reductive branch metabolites and flavonol products, increased under salt treatment, peaked at 100 mmol·L- 1 NaCl, and declined at 200 mmol·L- 1, indicating maximal branch bias under moderate stress followed by partial rebalancing under severe stress. Transcriptomic analysis showed induction of upstream phenylpropanoid and flavonoid entry genes, including PAL, 4CL, and CHS, whereas F3H was suppressed and FLS showed no induction. Furthermore, several short-chain dehydrogenase/reductase homologs (IFR-like SDR homologs) were upregulated, and the transcript-derived reductive branch index (EI) increased progressively across the salt gradient. EI was positively associated with MI, although the relationship was not strictly proportional under severe stress (200 mmol·L- 1 NaCl). Proteomic profiling further provided supportive evidence for sustained activation of upstream flavonoid biosynthesis, such as salt-induced accumulation of chalcone synthase (CHS) protein, complementing the transcriptomic and metabolomic datasets. Together, these results indicate that salt stress reorganizes flavonoid metabolism in A. roxburghii through persistent upstream activation and branch-specific regulation, favoring the reductive branch under moderate salinity.

Orchidaceae↗

PubMed2005

Individualised cancer therapeutics: dream or reality? Therapeutics construction.

The analysis of DNA microarray and proteomic data, and the subsequent integration into functional expression sets, provides a circuit map of the hierarchical cellular networks responsible for sustaining the viability and environmental competitiveness of cancer cells, that is, their robust systematics. These technologies can be used to 'snapshot' the unique patterns of molecular derangements and modified interactions in cancer, and allow for strategic selection of therapeutics that best match the individual profile of the tumour. This review highlights technology that can be used to selectively disrupt critical molecular targets and describes possible vehicles to deliver the synthesised molecular therapeutics to the relevant cellular compartments of the malignant cells. RNA interference (RNAi) involves a group of evolutionarily conserved gene silencing mechanisms in which small sequences of double-stranded RNA or intrinsic antisense RNA trigger mRNA cleavage or translational repression, respectively. Although RNAi molecules can be synthesised to 'silence' virtually any gene, even if upregulated, a mechanism for selective delivery of RNAi effectors to sites of malignant disease remains challenging. The authors will discuss gene-modified conditionally replicating viruses as candidate vehicles for the delivery of RNAi.

Animals↗

PubMed2025

Rehabilomics Strategies Enabled by Cloud-Based Rehabilitation: Scoping Review.

BACKGROUND: Rehabilomics, or the integration of rehabilitation with genomics, proteomics, metabolomics, and other "-omics" fields, aims to promote personalized approaches to rehabilitation care. Cloud-based rehabilitation offers streamlined patient data management and sharing and could potentially play a significant role in advancing rehabilomics research. This study explored the current status and potential benefits of implementing rehabilomics strategies through cloud-based rehabilitation. OBJECTIVE: This scoping review aimed to investigate the implementation of rehabilomics strategies through cloud-based rehabilitation and summarize the current state of knowledge within the research domain. This analysis aims to understand the impact of cloud platforms on the field of rehabilomics and provide insights into future research directions. METHODS: In this scoping review, we systematically searched major academic databases, including CINAHL, Embase, Google Scholar, PubMed, MEDLINE, ScienceDirect, Scopus, and Web of Science to identify relevant studies and apply predefined inclusion criteria to select appropriate studies. Subsequently, we analyzed 28 selected papers to identify trends and insights regarding cloud-based rehabilitation and rehabilomics within this study's landscape. RESULTS: This study reports the various applications and outcomes of implementing rehabilomics strategies through cloud-based rehabilitation. In particular, a comprehensive analysis was conducted on 28 studies, including 16 (57%) focused on personalized rehabilitation and 12 (43%) on data security and privacy. The distribution of articles among the 28 studies based on specific keywords included 3 (11%) on the cloud, 4 (14%) on platforms, 4 (14%) on hospitals and rehabilitation centers, 5 (18%) on telehealth, 5 (18%) on home and community, and 7 (25%) on disease and disability. Cloud platforms offer new possibilities for data sharing and collaboration in rehabilomics research, underpinning a patient-centered approach and enhancing the development of personalized therapeutic strategies. CONCLUSIONS: This scoping review highlights the potential significance of cloud-based rehabilomics strategies in the field of rehabilitation. The use of cloud platforms is expected to strengthen patient-centered data management and collaboration, contributing to the advancement of innovative strategies and therapeutic developments in rehabilomics.

Cloud Computing↗

PubMed2006

Challenges for molecular profiling of chronic fatigue syndrome.

Chronic fatigue syndrome (CFS) is prevalent, disabling and costly. Despite extensive literature describing the epidemiology and clinical aspects of CFS, it has been recalcitrant to diagnostic biomarker discovery and therapeutic intervention. This is due to the fact that CFS is a complex illness defined by self-reported symptoms and diagnosed by the exclusion of medical and psychiatric diseases that may explain the symptoms. Studies attempting to dissect the pathophysiology are challenging to design as CFS affects multiple body systems, making the choice of which system to study dependent on an investigators area of expertise. However, the peripheral blood appears to be facilitating the molecular profiling of several diseases, such as CFS, that involve bodywide perturbations that are mediated by the CNS. Successful molecular profiling of CFS will require the integration of genetic, genomic and proteomic data with environmental and behavioral data to define the heterogeneity in order to optimize intervention.

Animals↗

PubMedDate not supplied

[New effect biomarkers].

The major research goals for researchers developing biomarkers of effect are the development and validation of biomarkers that permit the prediction of the risk of disease in individuals and groups. One important objective is to prevent human cancer. This article reviews the most recent analytical methodologies, validation studies and field trials together with auditing and quality assessment of the necessary data based on scientific grounds. Consideration is given to new developments in the relatively young field of toxicogenomics, possibly leading to the identification of early changes that may lead to both cancer and non-cancer end points. Although the creation and development of reliable databases integrating information from genomic and proteomic research programmes should offer a contribution to the prediction of risks and prevention of diseases related to chemical exposure, the most promising future application of these technologies lies in the molecular diagnosis of diseases whose nosography will probably be redefined.

Biomarkers↗

PubMed2026

Comprehensive multi-post-translational modifications profiling reveals age-associated remodeling in skeletal muscle.

Sarcopenia, characterized by the progressive loss of skeletal muscle mass and function, is a major hallmark of aging. Post-translational modifications (PTMs) play essential roles in regulating protein activity and cellular homeostasis; however, how multiple PTMs are remodeled during skeletal muscle aging remains incompletely characterized. Here, we performed comprehensive multi-layered proteomic profiling of skeletal muscle from young (3-month-old) and aged (24-month-old) mice, systematically quantifying the global proteome together with five major PTMs: acetylation, phosphorylation, N-glycosylation, O-glycosylation, and ubiquitination. In total, we identified 5 337 proteins and mapped thousands of PTM sites, generating an integrated atlas of age-associated proteomic and PTM remodeling in skeletal muscle. Pathway enrichment analyses revealed distinct modification-specific patterns: acetylation and phosphorylation were predominantly associated with metabolic and mitochondrial-related pathways; N-glycosylation was enriched in immune- and secretory pathway-related processes; O-glycosylation was associated with muscle contraction-related pathways; and ubiquitination was preferentially linked to cytoskeletal organization in muscle cells. Correlation analyses further uncovered diverse association patterns among different PTMs across protein- and modification-level datasets. Phosphorylation and ubiquitination exhibited consistent positive associations, whereas acetylation and ubiquitination showed both inverse and concordant co-variation patterns across subsets of proteins. Phosphorylation and O-glycosylation displayed heterogeneous association patterns across different proteins, and acetylation and phosphorylation demonstrated positive correlations with distinct age-associated directional changes across protein subsets. Together, these results provide a comprehensive, multi-dimensional view of age-associated remodeling of the skeletal muscle proteome and multiple PTM layers, offering a valuable resource for understanding molecular alterations accompanying muscle aging and sarcopenia.

Animals↗

PubMed2026

Transcriptomic and proteomic signatures underlying nymphal adaptation and foam production in the forage pest Mahanarva spectabilis.

The spittlebug Mahanarva spectabilis (Distant, 1909) (Hemiptera: Cercopidae) is an important pest of forage grasses in South America, where its nymphs cause pasture damage by feeding on xylem sap and producing a characteristic foam that protects them against environmental stressors. To investigate the molecular basis of this adaptation, we integrated RNA-seq analysis of nymphs with LC-MS/MS proteomics of the Batelli gland, the primary source of foam secretion. De novo assembly of 100,666 unigenes revealed broad functional diversity, with strong representation of detoxification enzymes (CYP450s, GSTs, UGTs, carboxylesterases), transporters and ion pumps, cuticle proteins, and stress- and immunity-related genes. Nearly 16% of loci exhibited alternative splicing, particularly within detoxification, chemosensory and osmoregulatory gene families, highlighting evidence of transcriptomic variability. Signal peptide and secreted protein predictions identified 168 high-confidence candidate secreted proteins, including detoxification enzymes, proteases, structural proteins and immune-related factors, several of which are consistent with antimicrobial and surfactant-related functions. Proteomic profiling of the Batelli gland confirmed 500 proteins, enriched in chaperones, metabolic enzymes, detoxification pathways and osmoregulatory components, with the most abundant proteins corresponding to Hsp70 chaperones, ATP synthases, cuticle proteins and carbonic anhydrases. Together, these results provide an integrative transcriptomic and proteomic overview for M. spectabilis nymphs, highlighting genes and proteins associated with xylem feeding, foam production and responses potentially related to environmental stress tolerance. This comprehensive dataset not only advances the understanding of spittlebug biology but also identifies candidate molecular targets that may inform innovative strategies for controlling nymphal stages and mitigating spittlebug damage in forage systems.

Animals↗

PubMed2006

NERVE: new enhanced reverse vaccinology environment.

BACKGROUND: Since a milestone work on Neisseria meningitidis B, Reverse Vaccinology has strongly enhanced the identification of vaccine candidates by replacing several experimental tasks using in silico prediction steps. These steps have allowed scientists to face the selection of antigens from the predicted proteome of pathogens, for which cell culture is difficult or impossible, saving time and money. However, this good example of bioinformatics-driven immunology can be further developed by improving in silico steps and implementing biologist-friendly tools. RESULTS: We introduce NERVE (New Enhanced Reverse Vaccinology Environment), an user-friendly software environment for the in silico identification of the best vaccine candidates from whole proteomes of bacterial pathogens. The software integrates multiple robust and well-known algorithms for protein analysis and comparison. Vaccine candidates are ranked and presented in a html table showing relevant information and links to corresponding primary data. Information concerning all proteins of the analyzed proteome is not deleted along selection steps but rather flows into an SQL database for further mining and analyses. CONCLUSION: After learning from recent years' works in this field and analysing a large dataset, NERVE has been implemented and tuned as the first available tool able to rank a restricted pool (approximately 8-9% of the whole proteome) of vaccine candidates and to show high recall (approximately 75-80%) of known protective antigens. These vaccine candidates are required to be "safe" (taking into account autoimmunity risk) and "easy" for further experimental, high-throughput screening (avoiding possibly not soluble antigens). NERVE is expected to help save time and money in vaccine design and is available as an additional file with this manuscript; updated versions will be available at http://www.bio.unipd.it/molbinfo.

Algorithms↗