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PubMed2025

A widespread protein misfolding mechanism is differentially rescued by chaperones based on gene essentiality.

Protein misfolding involving changes in non-covalent lasso entanglement (NCLE) status has been proposed based on simulations and biochemical assays of a small number of proteins. Here, we detect hallmarks of these misfolded states across hundreds of proteins by integrating E. coli proteome-wide limited-proteolysis mass spectrometry with structural datasets of protein native structures. Proteins containing native NCLEs are twice as likely to misfold, predominantly in regions where these NCLEs naturally occur. Surprisingly, the chaperones DnaK and GroEL do not typically correct this misfolding, except in the case of essential proteins. Statistical analysis links this differential rescue activity to weaker loop-closing contacts in the NCLEs of essential proteins, suggesting misfolding involving these loops is easier to rectify by chaperones. Molecular simulations indicate a mechanism where premature NCLE loop closure, prior to proper placement of the threading segment, leads to persistent misfolded states. This mechanism explains why, in the mass spectrometry data, proteins with NCLEs are more likely to misfold and misfold in NCLE regions. These results suggest widespread NCLE misfolding, that such misfolded states in non-essential proteins can bypass the refolding action of chaperones, and that some protein sequences may have evolved to allow chaperone rescue from this class of misfolding.

Journal Article↗

PubMed2005

Plant protein annotation in the UniProt Knowledgebase.

The Swiss-Prot, TrEMBL, Protein Information Resource (PIR), and DNA Data Bank of Japan (DDBJ) protein database activities have united to form the Universal Protein Resource (UniProt) Consortium. UniProt presents three database layers: the UniProt Archive, the UniProt Knowledgebase (UniProtKB), and the UniProt Reference Clusters. The UniProtKB consists of two sections: UniProtKB/Swiss-Prot (fully manually curated entries) and UniProtKB/TrEMBL (automated annotation, classification and extensive cross-references). New releases are published fortnightly. A specific Plant Proteome Annotation Program (http://www.expasy.org/sprot/ppap/) was initiated to cope with the increasing amount of data produced by the complete sequencing of plant genomes. Through UniProt, our aim is to provide the scientific community with a single, centralized, authoritative resource for protein sequences and functional information that will allow the plant community to fully explore and utilize the wealth of information available for both plant and non-plant model organisms.

Amino Acid Sequence↗

PubMed2025

Integrative analysis of gene expression and histone modifications for DES, DSP, GJA1 and SMOC2 in adipose tissue reveals potential relationship to cardiometabolic health.

BACKGROUND: Adipose tissue influences cardiometabolic health through its endocrine activity and its role in regulating inflammation, lipid metabolism, and cardiovascular function. The expression of cardiac-associated genes within adipose tissue may reflect or contribute to cardiometabolic risk, yet this relationship remains poorly understood. This study investigates the expression profiles of the cardiac function associated genes GJA1, DES, DSP and SMOC2 in human adipose tissue, and analyses their associations with cardiometabolic traits. Additionally, we explore epigenomic mechanisms that may underlie their differential gene expression. METHODS: Expression profiling and functional enrichment analyses were conducted to identify depot-specific cardiac gene expression patterns. Quantitative PCR validated gene expression in paired subcutaneous (SAT) and omental visceral adipose tissue (OVAT) samples from 78 individuals with obesity. Gene expression was further validated in three independent cohorts (N = 1,548 total). Associations with clinical traits were assessed using Spearman correlations and multivariate linear regression, adjusted for age, sex, and BMI. Integration with transcriptomic and proteomic datasets publicly available from the Adipose Tissue Knowledge Portal was performed to strengthen clinical relevance. Epigenomic profiling using genome-wide ChIP-seq for histone marks (H3K4me3, H3K4me1, H3K27ac, H3K27me3) was conducted in paired SAT and OVAT samples from five individuals. RESULTS: DES, DSP, GJA1, and SMOC2 were significantly upregulated in OVAT compared to SAT. DES, DSP, and SMOC2 showed consistent expression patterns across all cohorts, while GJA1 exhibited context-dependent regulation. Gene expression in SAT was negatively correlated with cardiometabolic traits, including blood pressure, insulin resistance, and liver function markers. These associations were confirmed by regression analysis and supported by publicly available multi-omics data. Epigenetic analyses revealed OVAT-specific enrichment of active histone marks and reduced repressive marks, supporting higher differential transcriptional activity in OVAT. CONCLUSIONS: Depot-specific gene expression of DES, DSP, and SMOC2 in adipose tissue is robustly linked to cardiometabolic traits and supported by distinct epigenetic landscapes in OVAT vs SAT, highlighting their potential as novel biomarkers for cardiometabolic health.

Humans↗

PubMed2006

Application of ecotoxicogenomics for studying endocrine disruption in vertebrates and invertebrates.

Chemicals released into the environment potentially disrupt the endocrine system in wild animals and humans. Developing organisms are particularly sensitive to estrogenic chemicals. Exposure to estrogens or estrogenic chemicals during critical periods of development induces persistent changes in both reproductive and nonreproductive organs, including persistent molecular alterations. Estrogen-responsive genes and critical developmental windows of various animal species, therefore, need to be identified for investigators to understand the molecular basis of estrogenic activity during embryonic development. For investigators to understand molecular mechanisms of toxicity in various species, toxicogenomics/ecotoxicogenomics, defined as the integration of genomics (transcriptomics, proteomics, metabolomics) into toxicology and ecotoxicology, need to be established as powerful tools for research. As the initial step toward using genomics to examine endocrine-disrupting chemicals, estrogen receptors and other steroid hormone receptors have been cloned in various species, including reptiles, amphibians, and fish, and alterations in the expression of these genes in response to chemicals were investigated. We are identifying estrogen-responsive genes in mouse reproductive tracts using cDNA microarrays and trying to establish microarray systems in the American alligator, roach, medaka, and water fleas (Daphnia magna). It is too early to define common estrogen-responsive genes in various animal species; however, toxicogenomics and ectotoxicogenomics provide powerful tools to help us understand the molecular mechanism of chemical toxicities in various animal species.

Alligators and Crocodiles↗

PubMed2007

Extraction of nuclear proteins.

The integrity of a subcellular proteome such as the nucleus, is largely dependent on purification of the isolated compartment away from other cellular contaminants. The separation of high-purity nuclei from plants is a difficult task. However, successful purification has been achieved through a series of fractionation processes. Initially, centrifugation in a 2.0 M sucrose density gradient (1) or a percoll density gradient (2) was used to isolate nuclei from cultured rice suspension cells. A modified version of the sucrose gradient method described in Morre and Anderson (1) has proved to be more rapid and efficient for the isolation of nuclei from cultured rice suspension cells. The nuclei are uniform spheres with an average diameter of approx 20 microm. The nuclear proteins were prepared from the purified nuclei using lysis buffer (3) or SDS sample buffer (4). The purity of the isolated nuclear fraction was evaluated by Western blot analysis using antihistone H1 antibody, a specific antibody for nuclear proteins. Histone H1 was found in the nuclear fraction, but not in the supernatant fraction, suggesting that the preparation is enriched in nuclear proteins.

Blotting, Western↗

PubMed2005

Synergistic approaches to clinical oncology biomarker discovery.

Biomarkers in the clinical oncology field can have tremendous therapeutic impact especially if the marker is detected before clinical symptoms. This impact can be extended to the evaluation of clinical oncology treatments allowing evaluation of potential compounds to determine their efficacy in the disease treatment. The discovery of clinical biomarkers can consume time, resources and costs. Therefore, it is important that the most effective strategies are employed to discover these biomarkers. These strategies may include the integration of available genomic, proteomic and histopathological technologies, which could reduce the costs and aid in the validation of the biomarker. Certainly the type of biomarker needed to address a particularly defined problem will drive the type of technology. However, a single biomarker to diagnose a specific cancer can be as elusive as relying on a single technology. This review examines some of the technologies used to discover biomarkers and presents the use of combinatorial technical synergies to discover and validate potential clinical oncology biomarkers.

Biomarkers, Tumor↗

PubMed2005

Systems biology: new approaches to old environmental health problems.

The environment plays a pivotal role as a human health determinant and presence of hazardous pollutants in the environment is often implicated in human disease. That pollutants cause human diseases however is often controversial because data connecting exposure to environmental hazards and human diseases are not well defined, except for some cancers and syndromes such as asthma. Understanding the complex nature of human-environment interactions and the role they play in determining the state of human health is one of the more compelling problems in public health. We are becoming more aware that the reductionist approach promulgated by current methods has not, and will not yield answers to the broad questions of population health risk analysis. If substantive applications of environment-gene interactions are to be made, it is important to move to a systems level approach, to take advantage of epidemiology and molecular genomic advances. Systems biology is the integration of genomics, transcriptomics, proteomics, and metabolomics together with computer technology approaches to elucidate environmentally caused disease in humans. We discuss the applications of environmental systems biology as a route to solution of environmental health problems.

Environmental Exposure↗

PubMed2002

Towards a bioinformatics network for Latin America and the Caribbean (LACBioNet).

Bioinformatics is increasingly recognised as a crucial field for research and development in the biological sciences, and forms an integral part of genomics, proteomics and modern biotechnology. Worldwide participation is important, and scientists in developing countries can contribute to this field. Regional networks for bioinformatics are highly beneficial for capacity strengthening and cooperation, and for establishing productive interactions between scientists in the fields of biological and informatics sciences. Such a network (LACBioNet) is being organised for Latin America and the Caribbean. Its immediate goals include the organisation and extension of nodes and services, information and communication, research and development in different specialty fields of bioinformatics, and training and human resource development.

Caribbean Region↗

PubMed2004

Splicing factors are differentially expressed in tumors.

Although alternative splicing of many genes has been found associated with different stages of tumorigenesis and splicing variants have been characterized as tumor markers, it is still not known whether these examples are sporadic or whether there is a broader association between the two phenomena. In this report we evaluated, through a bioinformatics approach, the expression of splicing factors in both normal and tumor tissues. This was possible by integrating data produced by proteomics, serial analysis of gene expression (SAGE) and microarray experiments. We observed a significant shift in the expression of splicing factors in tumors in both SAGE and microarray data, resulting from a large amount of experiments. We discuss that this supports the notion of a broader association between alternative splicing and cell transformation, and that splicing factors may be involved in oncogenic pathways.

Alternative Splicing↗

PubMed2006

Integration of two-dimensional LC-MS with multivariate statistics for comparative analysis of proteomic samples.

LC-MS-based proteomics requires methods with high peak capacity and a high degree of automation, integrated with data-handling tools able to cope with the massive data produced and able to quantitatively compare them. This paper describes an off-line two-dimensional (2D) LC-MS method and its integration with software tools for data preprocessing and multivariate statistical analysis. The 2D LC-MS method was optimized in order to minimize peptide loss prior to sample injection and during the collection step after the first LC dimension, thus minimizing errors from off-column sample handling. The second dimension was run in fully automated mode, injecting onto a nanoscale LC-MS system a series of more than 100 samples, representing fractions collected in the first dimension (8 fractions/sample). As a model study, the method was applied to finding biomarkers for the antiinflammatory properties of zilpaterol, which are coupled to the beta2-adrenergic receptor. Secreted proteomes from U937 macrophages exposed to lipopolysaccharide in the presence or absence of propanolol or zilpaterol were analysed. Multivariate statistical analysis of 2D LC-MS data, based on principal component analysis, and subsequent targeted LC-MS/MS identification of peptides of interest demonstrated the applicability of the approach.

Adrenergic beta-Antagonists↗

PubMed2004

Genomics and proteomics tools for compound mode-of-action studies in drug discovery.

A broad range of genomics and proteomics technologies are increasingly being integrated into emerging research fields such as pharmacogenomics, pharmacoproteomics, chemogenomics, chemical genetics, and chemical biology. Here we review applications of genomic and proteomic technologies to drug mechanism-of-action studies and how these are beginning to impact the drug discovery process.

Animals↗

PubMedDate not supplied

Proteolysis and mass spectrometric analysis of an integral membrane: aquaporin 0.

Due to hydrophobicity, structural analysis of integral membrane proteins poses a formidable challenge for current mass spectrometry-based proteomics approaches. Herein, we demonstrate results from optimized sample preparation and enzymatic proteolysis procedures for the complete primary structure determination of a targeted integral membrane protein, lens aquaporin 0 (AQP0). Plasma membrane from bovine lens tissue was alkali treated and tryptic digestion was performed in optimized acetonitrile-ammonium bicarbonate solution. Full sequence coverage of AQP0 was observed as tryptic peptides using both matrix-assisted laser desorption/ionization mass spectrometry (MALDI MS) and capillary liquid chromatography tandem mass spectrometry (cLC/MS/MS). An amino acid mutation of Thr to Ile/Leu at residue 199 was deduced based on MS/MS results. In a complementary effort to fully sequence the protein, peptic digestion was developed to take advantage of hydrophobic protein solubility in organic acid as well as the decreased activity of pepsin at low pH. Peptic digestion in 10% formic acid (pH 1.2) generated peptides of 500 to 3000 Da and gave 100% sequence coverage by cLC/MS/MS. In addition to post-translational modifications reported previously, a new phosphorylation site at serine 229 and two oxidation sites at tryptophan 202 and 205 were detected on the protein. These methodologies provide complementary detergent- and CNBr-free procedures for detailed analysis of this important membrane channel protein and offer promise for analysis of the integral membrane proteome.

Amino Acid Sequence↗

PubMed2006

Proteomic analysis of the mouse mammary gland is a powerful tool to identify novel proteins that are differentially expressed during mammary development.

After lactation, the mouse mammary gland undergoes apoptosis and tissue remodelling as the gland reverts to its prepregnant state. This complex change was investigated using 2-DE. An integrated database was produced from lactation and involution proteomes. Forty-four molecular cluster indexes (MCIs) that showed altered expression from lactation to involution were selected for MS analysis. Of these, 32 gave protein annotations, 18 of which were unequivocal proteins. Selected proteins were then studied across all of development, including pregnancy, using data integrated from another proteome database. Two proteins, the RNA polymerase B transcription factor 3 (BTF3) and the minichromosome maintenance protein 3 (MCM3), although initially selected on the basis of the lactation/involution criteria, had expression profiles that indicated an additional role in mammary development and were further analysed. BTF3, a transcription factor previously not described in the mammary gland, was up-regulated strongly in pregnancy, indicating an involvement in alveolar growth. MCM3's expression was greatest in pregnancy and late involution, decreasing through lactation. Immunohistochemistry localised MCM3 to the mammary epithelium, where a greater proportion of cells stained than for the proliferation marker Ki67. MCM3 expression during lactation may identify cells that are licensed to repopulate the gland during cell loss in lactation and following involution.

Animals↗

PubMed2026

Multi-omics dynamic profiling reveals predictive biomarkers for first-line immunochemotherapy in extensive-stage small-cell lung cancer.

BACKGROUND: Extensive-stage small-cell lung cancer (ES-SCLC) is associated with a poor prognosis. Although first-line immunochemotherapy improves clinical outcomes, robust prognostic biomarkers for this treatment modality remain unavailable. The aim of this study was to identify non-invasive, easily accessible, and dynamically monitored biomarkers of ES-SCLC by machine learning integrating serum metabolomics, lipidomics, and proteomics at multiple time points. METHODS: A total of 816 serum samples were collected from ES-SCLC patients receiving first-line immunotherapy combined with chemotherapy or first-line chemotherapy for metabolomics, lipidomics, and proteomics analysis. The immunochemotherapy cohort was randomly divided into training and validation subsets at a 6:4 ratio. Biomarkers were identified using machine learning algorithms, and their prognostic significance was evaluated through receiver operating characteristic (ROC) analysis, Kaplan–Meier survival analysis, and multivariate Cox regression. Potential metabolic pathways and mechanisms were further explored via integrated multi-omic analysis. RESULTS: The immunochemotherapy exhibited a prolonged median progression-free survival (PFS) and higher objective response rate (ORR) compared to the chemotherapy group. A total of 5 serum metabolites (uric acid, L-aspartate-semialdehyde, dimethisterone, xanthine, L-cysteine), 6 lipids (Cer d18:1/26:0, Cer d18:2/25:0, SM d18:1/20:1, SM d17:1/25:1, DG O-18:1_16:0, PS 18:0_24:0), and 3 proteins (ACIN1, ACSL4, PHGDH) were identified and constructed into independent prognostic models. Among patients receiving immunochemotherapy, those categorized as low-risk based on the model demonstrated significantly longer PFS compared with those in the high-risk group. These prognostic signatures also retained predictive value in patients who underwent second-line treatment with anlotinib plus immunochemotherapy. Integrated analysis revealed that glycine, serine, and threonine metabolism was the commonly enriched pathway across all three omics layers. Notably, PHGDH (protein), L-aspartate-semialdehyde and L-cysteine (metabolites), and PS (18:0_24:0) (lipid), key elements in this pathway, were all incorporated in the predictive model. In addition, models of the composition of these substances after one cycle of treatment can still predict the prognosis of patients. CONCLUSION: In this study, we constructed and validated a set of non-invasive, dynamically monitorable prognostic models (containing 5 metabolites, 6 lipids, and 3 proteins) using machine learning by integrating multiple time point data from the serum metabolome, lipid panel, and proteome to accurately distinguish the prognostic risk of patients with ES-SCLC receiving immunochemotherapy. PFS was significantly prolonged in patients in the low-risk group, and this model remains predictive in the subsequent second-line treatment with anlotinib in combination with immunochemotherapy. Glycine-serine-threonine metabolic pathway may be the key mechanism, of which PHGDH, L-aspartate semialdehyde, L-cysteine and PS (18:0_24:0) are the core predictors. This study provides the first multi-omics dynamic prognostic tool for ES-SCLC immunochemotherapy and reveals potential therapeutic targets.

Humans↗

PubMed2003

Detection of regulatory circuits by integrating the cellular networks of protein-protein interactions and transcription regulation.

The post-genomic era is marked by huge amounts of data generated by large-scale functional genomic and proteomic experiments. A major challenge is to integrate the various types of genome-scale information in order to reveal the intra- and inter- relationships between genes and proteins that constitute a living cell. Here we present a novel application of classical graph algorithms to integrate the cellular networks of protein-protein interactions and transcription regulation. We demonstrate how integration of these two networks enables the discovery of simple as well as complex regulatory circuits that involve both protein-protein and protein-DNA interactions. These circuits may serve for positive or negative feedback mechanisms. By applying our approach to data from the yeast Saccharomyces cerevisiae, we were able to identify known simple and complex regulatory circuits and to discover many putative circuits, whose biological relevance has been assessed using various types of experimental data. The newly identified relations provide new insight into the processes that take place in the cell, insight that could not be gained by analyzing each type of data independently. The computational scheme that we propose may be used to integrate additional functional genomic and proteomic data and to reveal other types of relations, in yeast as well as in higher organisms.

DNA, Fungal↗

PubMed2004

High throughput gene expression profiling: a molecular approach to integrative physiology.

Integrative physiology emphasizes the importance of understanding multiple pathways with overlapping, complementary, or opposing effects and their interactions in the context of intact organisms. The DNA microarray technology, the most commonly used method for high-throughput gene expression profiling, has been touted as an integrative tool that provides insights into regulatory pathways. However, the physiology community has been slow in acceptance of these techniques because of early failure in generating useful data and the lack of a cohesive theoretical framework in which experiments can be analysed. With recent advances in both technology and analysis, we propose a concept of multidimensional integration of physiology that incorporates data generated by DNA microarray and other functional, genomic, and proteomic approaches to achieve a truly integrative understanding of physiology. Analysis of several studies performed in simpler organisms or in mammalian model animals supports the feasibility of such multidimensional integration and demonstrates the power of DNA microarray as an indispensable molecular tool for such integration. Evaluation of DNA microarray techniques indicates that these techniques, despite limitations, have advanced to a point where the question-driven profiling research has become a feasible complement to the conventional, hypothesis-driven research. With a keen sense of homeostasis, global regulation, and quantitative analysis, integrative physiologists are uniquely positioned to apply these techniques to enhance the understanding of complex physiological functions.

Animals↗

PubMed2026

Identification and genetic validation of potential therapeutic targets for pulmonary hypertension through multi-omics causal inference.

Pulmonary hypertension (PH) underscores the urgent need for novel therapeutic targets. This study aimed to employ a proteome-wide Mendelian randomization (MR) approach to systematically identify circulating proteins causally associated with PH, thereby providing genetically validated candidate targets for drug development. We adopted a 2-sample MR design, integrating large-scale plasma proteomic quantitative trait loci (pQTL) data (encompassing 4148 proteins) and summary statistics from a large-scale PH genome-wide association study (2047 cases, 8301 controls). Candidate targets were screened through a multilayered analytical pipeline comprising proteomic MR, transcriptomic MR, and summary-data-based Mendelian randomization. The ultimately identified MR-Identified Causal Candidate Targets (MR-ICTs) underwent rigorous Bayesian colocalization analysis, followed by biological characterization through functional enrichment analysis, single-cell transcriptomics, and phenome-wide association studies. Through robust genetic causal inference, this study provides that circulating proteins such as LYZ, GREM2, NID1, and PF4V1 play causal roles in PH pathogenesis. These findings offer a set of rigorously genetically validated, high-priority therapeutic targets for developing novel PH treatments, specifically addressing key pathological mechanisms such as innate immunity, BMP signaling pathway dysregulation, and platelet activation. Our multi-dimensional analysis ultimately identified 6 MR-ICTs causally associated with PH. Notably, the causal associations for lysozyme C (LYZ), gremlin-2 (GREM2), nidogen-1 (NID1), and platelet factor 4 variant 1 (PF4V1) were stringently validated by Bayesian colocalization analysis (posterior probability for hypothesis 4 [PPH4], indicating a shared causal variant, > 0.99). Functional enrichment analysis revealed significant involvement of these targets in immune response and TGF-β signaling pathways. Single-cell analysis further elucidated their cell-type-specific expression, with LYZ predominantly expressed in monocytes and PF4V1 almost exclusively in platelets.

Hypertension, Pulmonary↗

PubMed2026

Machine Learning-Driven Prediction of Coronary Artery Disease Risk Based on UK Biobank Plasma Proteomics.

BACKGROUND: Coronary artery disease (CAD) is a leading global cause of mortality, yet the predictive accuracy of conventional risk models is limited. Here, we integrate conventional risk factors, polygenic risk scores, and large-scale proteomics to develop a unified model for enhanced CAD risk prediction. METHODS: Using data from UK Biobank, participants with plasma proteomics and genetic risk data were included after excluding prevalent CAD. Participants from England were split into training (n=32 330) and internal validation (n=13 857) sets, and Scotland/Wales participants formed an external validation set (n=5775). Incident CAD was ascertained from linked health records. A 202-protein proteomic risk score was derived by least absolute shrinkage and selection operator Cox regression, and CatBoost models were trained using conventional risk factors alone and with incremental addition of polygenic risk scores and protein proteomic risk scores; Shapley Additive Explanations-guided forward selection identified a compact protein panel. RESULTS: Across cohorts, the median age was 58 years and ∼45% were men. Protein proteomic risk score was dose-dependently associated with CAD risk. Compared with conventional risk factors alone, integrating polygenic risk scores and protein proteomic risk scores improved discrimination, with the area under the curve increasing from 0.750 (95% CI, 0.732-0.767) to 0.789 (95% CI, 0.772-0.805) in internal validation and from 0.717 (95% CI, 0.683-0.750) to 0.762 (95% CI, 0.732-0.791) in external validation. A 9-protein panel (GDF15 [growth differentiation factor 15], MMP12 [matrix metalloproteinase 12], NPPB [natriuretic peptide B], PGF [placental growth factor], REN [renin], ADGRG2 [adhesion G-protein coupled receptor], ACE2 [angiotensin-converting enzyme 2], CDCP1 [CUB domain-containing protein 1], CXCL17 [C-X-C motif chemokine ligand 17)]) captured most proteomic predictive information. CONCLUSIONS: Our findings demonstrate that integrating conventional risk factors, polygenic risk scores, and proteomic data improves CAD risk prediction. This study highlights the utility of proteomics in precision cardiovascular medicine and simplified risk stratification tools.

Humans↗