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Recent advances in capillary separations for proteomics.

The sequencing of several organisms' genomes, including the human's one, has opened the way for the so-called postgenomic era, which is now routinely coined as "proteomics". The most basic task in proteomics remains the detection and identification of proteins from a biological sample, and the most traditional way to achieve this goal consists of protein separations performed by two-dimensional polyacrylamide gel electrophoresis (2-D PAGE). Still, the 2-D PAGE-mass spectrometry (MS) approach remains lacking in proteome coverage (for proteins having extreme isoelectric points or molecular masses as well as for membrane proteins), dynamic range, sensitivity, and throughput. Consequently, considerable efforts have been devoted to the development of non-gel-based proteome separation technologies in an effort to alleviate the shortcomings in 2-D PAGE while reserving the ability to resolve complex protein and peptide mixtures prior to MS analysis. This review focuses on the most recent advances in capillary-based separation techniques, including capillary liquid chromatography, capillary electrophoresis, and capillary electrokinetic chromatography, and combinations of multiples of these mechanisms, along with the coupling of these techniques to MS. Developments in capillary separations capable of providing extremely high resolving power and selective analyte enrichment are particularly highlighted for their roles within the broader context of a state-of-the-art integrated proteome effort. Miniaturized and integrated multidimensional peptide/protein separations using microfluidics are further summarized for their potential applications in high-throughput protein profiling toward biomarker discovery and clinical diagnosis.

Chromatography, Liquid↗

Proteomics in translational cancer research: toward an integrated approach.

Proteomics provides powerful tools for the study of clinically relevant samples in the context of translational cancer research. Here we briefly review applications of gel-based proteomics for the study of bladder and lung cancer using fresh tissue biopsies. In general, these studies have emphasized the potential of the technology for biomarker discovery, as well as for addressing the issue of cancer heterogeneity.

Biomarkers, Tumor↗

High-Throughput Proteomic and Glycoproteomic Analyses in Benign Prostatic Hyperplasia.

Benign prostatic hyperplasia (BPH) is a disease affecting the majority of aging men; 90% of men develop histological BPH by the time they reach their eighties. BPH can lead to bothersome lower urinary tract symptoms (LUTS), which may reduce quality of life. Many patients fail current treatment options and may progress to surgical intervention. Furthermore, diagnosis is reliant on symptom questionnaires and the cause of LUTS can be difficult to distinguish. Currently, BPH can only be definitively diagnosed through histological analysis of prostate tissue, which is not the standard of care. The resulting lack of clinical tissue samples is a major limitation in investigating disease pathology. Improved understanding of disease development and progression, along with objective biomarkers of disease, is needed for BPH. This investigation uses mass spectrometry (MS)-based proteomics and glycoproteomics to compare healthy prostate tissue with prostate tissue affected by BPH to address this gap in knowledge. By integrating proteomics and glycoproteomics, we identified 206 proteins and 44 glycopeptides that were significantly altered between BPH and control samples. These findings provide deeper insight into disease-associated pathways and may facilitate the identification of clinically relevant targets for further investigation.

Male↗

Phytolacca acinosa Roxb. induces intestinal toxicity through the histamine-MLCK-tight junction axis: Integrated evidence from proteomics, metabolomics, intestinal organoids and epithelial barrier validation.

Phytolacca acinosa Roxb. (PR) is a saponin-rich medicinal plant associated with gastrointestinal toxicity, but the mechanisms underlying PR-induced intestinal barrier injury remain unclear. In this study, raw PR extract was analytically characterized by UPLC-ZenoTOF-MS/MS, confirming triterpenoid saponins as the predominant constituents. C57BL/6 J mice were orally exposed to characterized PR extract (1.20 or 12.0 g/kg for 5 h), and Caco-2 cells and mouse intestinal organoids were used to assess epithelial toxicity and barrier disruption. Histopathology, ELISA, FITC-dextran permeability assays, immunofluorescence, CCK-8, LDH release, western blotting, DIA-based proteomics and untargeted metabolomics were integrated to define toxicological mechanisms. PR induced dose-dependent intestinal inflammation and barrier dysfunction, with the ileum as the most sensitive target. PR increased serum DAO and D-lactate and intestinal TNF-α and IL-1β, disrupted organoid morphology, enhanced epithelial permeability, and reduced ZO-1 expression. Proteomics revealed changes in inflammatory, lipid-metabolic, cytoskeletal and tight-junction pathways, including upregulation of MLCK3 and phospholipase-related proteins and downregulation of ZO-1 and ZO-2. Metabolomics identified histidine metabolism disturbance and histamine accumulation. Integrated multi-omics and pharmacological validation indicated that histamine activated the PLC/IP₃/Ca²⁺/CaM/MLCK cascade, promoting MLC phosphorylation, tight-junction disassembly and epithelial leakiness. MLCK inhibition partially restored ZO-1/ZO-2 expression and attenuated PR-induced epithelial injury. These findings identify the histamine-MLCK-tight junction axis as a key mechanism of PR-induced intestinal toxicity and support hazard identification of saponin-rich PR exposure.

Animals↗

Proteomic and metabolomic profiling reveals dysregulation of immune states, mucin-type glycosylation and steroid metabolism in extramammary Paget's disease.

BACKGROUND: Extramammary Paget's disease is a rare cutaneous adenocarcinoma characterized by mucin-rich Paget cells and chronic inflammation, yet its molecular basis remains unclear. OBJECTIVE: To systematically characterize the proteomic and metabolomic landscape of EMPD, uncover immune heterogeneity, and identify molecular pathways underlying tumor progression and microenvironment remodeling. METHODS: We performed integrated proteomic and metabolomic analyses on 92 male tumor patients and 30 healthy controls, identifying 10,217 proteins and 1466 metabolites. RESULTS: Extramammary Paget's disease lesions exhibited broad activation of inflammatory pathways. Immune profiling further uncovered substantial inflammatory heterogeneity, delineating immune-cold and immune-hot subtypes, with the latter associated with stronger invasive potential. Aberrant mucin-type glycosylation was also prominent, featuring Tn-modified MUC1 and MUC5AC accompanied by elevated GALNT7, GALNT6, GALNT4, and ST6GAL1, which correlated with inflammatory intensity. Metabolomic data demonstrated elevated levels of testosterone, dehydroepiandrosterone, and related intermediates in tumor tissues, indicating an androgen-enriched metabolic profile in extramammary Paget's disease. CONCLUSION: These findings reveal immune, glycoproteomic, and metabolomic pathways in extramammary Paget's disease pathogenesis and provide novel insights for molecular classification and therapeutic targeting.

Humans↗

Longevity of cardiac and skeletal muscle proteins is dependent on tissue and subcellular compartmentation patterns.

Myocytes are exceptionally long-lived cells that must maintain proteome integrity over decades while adjusting for changes in functional output and metabolic demand. We used in vivo stable isotope labeling combined with mass spectrometry proteomics and correlated multi-isotope imaging mass spectrometry to quantify and visualize protein turnover across cardiac, fast-twitch, and slow-twitch skeletal muscles, creating a resource of hundreds of individual protein turnover rates from each tissue. We found that cardiac muscle has the highest rate of protein turnover, followed by slow-twitch skeletal muscle and then fast-twitch skeletal muscle, and that these different rates of protein turnover are driven by different levels of muscle use, rather than myosin isoform composition. We also identified protein age heterogeneity at the myofiber and sarcomere levels. These findings uncover fundamental principles of muscle protein maintenance and have broad implications for understanding cellular aging, muscle disease, and the design of therapeutic strategies targeting muscle protein turnover.

Animals↗

Systemic Proteome Profiling to Differentiate Primary Glomerular Diseases.

KEY POINTS: Plasma proteome profiling identified distinct signatures across biopsy-proven primary glomerular disease subtypes. An elastic net model using 93 proteins classified primary glomerular disease subtypes and controls, with external validation. Integrating proteomics with machine learning yields biologically interpretable insights in primary glomerular diseases. BACKGROUND: Primary GN is a heterogeneous group of kidney disorders where understanding of their pathophysiology remains incomplete. Despite the diagnostic potential of high-throughput proteomics, constrained proteomic depth and a reliance on binary comparisons have left the feasibility of using systemic signatures to differentiate multiple GN subtypes largely unexplored. METHODS: To identify protein signatures that noninvasively differentiate major primary glomerular disease subtypes and provide mechanistic insights, we performed large-scale systemic proteome profiling of 5416 plasma proteins via Olink Explore HT in a discovery cohort ( n =147) and an external validation cohort ( n =85) of Korean participants (mean age, 41±13 years; 46% female). The study population included patients with four GN subtypes-focal segmental glomerulosclerosis, IgA nephropathy, minimal change disease, and membranous nephropathy-alongside healthy controls. We developed a machine learning (ML) model using logistic regression with elastic net regularization to classify disease groups based on proteomic profiles and evaluated its performance in the independent validation cohort. RESULTS: Plasma proteome profiles were distinct among disease subtypes, emerging as a significant source of data variation independent of conventional markers such as eGFR or proteinuria levels. The ML model performed robustly in both the discovery and validation cohorts, achieving an area under the receiver operating characteristic curve >0.8 for differentiating minimal change disease, membranous nephropathy, and IgA nephropathy. The model, even without clinical information, correctly identified 93% of minimal change disease cases (14 of 15) and 63% of IgA nephropathy cases (20 of 32), but its performance was limited for focal segmental glomerulosclerosis, with only 21% of cases (three of 14) correctly classified. Functional analysis of key proteins highlighted distinct biologic pathways, such as hemostasis in minimal change disease. CONCLUSIONS: We identified distinct systemic proteome signatures for primary glomerular diseases, where disease subtype served as a major determinant of proteomic variance alongside conventional clinical markers. ML models demonstrated robust discriminatory performance for minimal change disease, membranous nephropathy, and IgA nephropathy, underscoring the potential for proteome-based classification.

Humans↗

Proteomics based on high-efficiency capillary separations.

Identifying and quantifying in a high throughput manner the proteins expressed by cells, tissues or an organism provides the basis for understanding the functions of its constituents at a "systems" level. As a result, proteome analysis has increasingly become the focus of significant interest and research over the past decade. This is especially true following the recent stunning achievements in genomics analyses. However, unlike the static genome, the complexities and dynamism of the proteome present significant analytical challenges and demand highly efficient separations and detection technologies. A number of recent technological advancements have been in direct response to these challenges. Currently, strategically mated combinations of sophisticated separations techniques and advanced mass spectrometric detection represent the best approach to addressing the intricacies of the proteome. Liquid-phase separations, often within capillaries, are increasingly recognized as the best separations technique for this approach. In combination on-line with mass spectrometry, liquid-phase separations provide the improved analytical sensitivity, sample throughput, and quantitation capabilities necessitated by the multifaceted problems within proteomics analyses. This review focuses primarily on current high-efficiency capillary separations techniques, including both capillary liquid chromatography and capillary electrophoresis, applied to the analysis of complex proteomic samples. We emphasize developments at our laboratory and illustrate technical advances that attempt to review the role of separations within the broader context of a state-of-the-art integrated proteomics effort.

Chromatography, Liquid↗

Opposing kinase signaling may underlie the inverse relationship between cancer and Alzheimer's disease.

Cancer and Alzheimer's disease (AD) are leading causes of mortality and exhibit an inverse relationship, where AD patients have reduced cancer risk and vice versa. However, the molecular basis of this relationship remains poorly understood. We reanalyzed published proteomic and phosphoproteomic datasets to investigate this relationship. Differentially abundant proteins were identified in lung adenocarcinoma and glioblastoma samples relative to controls and compared with proteins altered in AD brains, revealing 37 proteins with opposing abundance patterns. Protein-protein interaction and pathway analyses revealed enrichment in kinase signaling and phosphorylation pathways. Phosphoproteomic analysis identified 52 differentially phosphorylated sites with opposing patterns, while kinase-substrate enrichment analysis identified 44 kinases with opposing inferred activity profiles. Integration of kinase activity and phosphosite data identified 29 kinase-phosphosite pairs, including 4 prioritized pairs with opposing patterns relevant to both diseases. Across seven independent cancer cohorts, 17 of 20 statistically significant phosphosite-cohort comparisons (85%) were concordant with the discovery findings, supporting reproducibility of the prioritized phosphosites. Together, these findings highlight opposing kinase signaling as a prominent feature of the inverse relationship and suggest potential biomarkers and therapeutic targets. This study provides a novel systems-level framework for investigating inverse relationships, supported by an R Shiny application for data exploration (https://advscancer.shinyapps.io/advscancer/). SIGNIFICANCE: This study presents an integrated proteomic and phosphoproteomic framework for investigating the inverse relationship between cancer and Alzheimer's disease (AD). By integrating differential protein abundance, phosphosite phosphorylation, inferred kinase activity, and curated kinase-substrate relationships, we identified opposing signaling patterns and prioritized four kinase-phosphosite pairs. Independent evaluation across seven CPTAC cancer cohorts supported the reproducibility of the prioritized phosphosite patterns. These findings provide insight into molecular processes potentially associated with the inverse relationship between cancer and AD, identify candidate biomarkers and therapeutic targets, and demonstrate the value of systems-level, data-driven approaches for investigating shared and opposing disease processes.

Humans↗

Protective effects of liver-derived apolipoprotein A1 against heat stress-induced hypothalamic lipid metabolism and blood-brain barrier integrity.

Heat stress (HS), a prevalent occupational and environmental hazard, has increasingly been recognized as a major contributor to multiple physiological disorders. The hypothalamus, a key regulator of thermoregulation and endocrine signaling, is especially susceptible to metabolic and inflammatory disturbances induced by HS. This study investigates the interplay among lipid metabolism, blood-brain barrier (BBB) integrity, and neuroinflammation in the hypothalamus under HS conditions, with a specific focus on apolipoprotein A1 (APOA1) as a potential protective factor. To achieve this, we integrated proteomic and lipidomic analyses with experimental validation in porcine and murine models. Proteomic analysis identified 266 differentially expressed proteins (DEPs) in the hypothalamus following HS, with significant enrichment in lipid metabolism pathways-especially glycerophospholipid (GP) metabolism-in which APOA1 displayed a marked increase. Lipidomic profiling further revealed HS-induced disruptions in phosphatidylcholine (PC), phosphatidylethanolamine (PE), and cardiolipin (CL) metabolism. Additionally, blood-brain barrier integrity was compromised, as evidenced by increased perivascular IgG extravasation, reduced pericyte coverage, and decreased expression of tight junction proteins ZO-1 and Occludin. HS also triggered pronounced neuroinflammation, characterized by elevated levels of iNOS, GFAP, and pro-inflammatory cytokines (TNF-α, IL-1β, and IL-6). Notably, administration of D-4F, an APOA1 mimetic peptide, alleviated blood-brain barrier damage, reduced neuroinflammation, and preserved synaptic integrity, thereby suggesting a neuroprotective role for APOA1 in HS-induced hypothalamic dysfunction. These findings underscore the critical role of lipid metabolism in maintaining hypothalamic homeostasis under HS conditions and position APOA1 as a key regulator with potential therapeutic implications for mitigating HS-related neuroinflammatory and metabolic disturbances.

Blood-Brain Barrier↗

AI proteomics: from protein identification to virtual cells.

Artificial intelligence (AI) is transforming scientific research, including proteomics. In this Perspective, we highlight key mass spectrometry (MS)-based proteomics areas where AI is driving innovation, ranging from protein identification to building AI virtual cells. These include improving peptide and protein identification and quantification; characterizing protein-protein interactions and protein complexes; advancing spatial and perturbation proteomics; integrating multi-omics data; and, ultimately, enabling AI virtual cells. Finally, we call for global collaboration among data producers, data consumers and other stakeholders to establish an AI-friendly ecosystem for MS-based proteomics, laying the foundation for transformative advancements in proteomics driven by AI.

Proteomics↗

Proteomics: recent applications and new technologies.

Interest in proteomics as a tool for drug development and a myriad of other applications continues to expand at a rapid rate. Proteomic analyses have recently been conducted on tissues, biofluids, subcellular components and enzymatic pathways as well as various disease and toxicological states, in both animal models and man. In addition, several recent studies have attempted to integrate proteomics data with genomics and/or metabonomics data in a systems biology approach. The translation of proteomic technology and bioinformatics tools to clinical samples, such as in the areas of disease and toxicity biomarkers, represents one of the major opportunities and challenges facing this field. An ongoing challenge in proteomics continues to be the analysis of the serum proteome due to the vast number and complexity of proteins estimated to be present in this biofluid. Aside from the removal of the most abundant proteins, a number of interesting approaches have recently been suggested that may help reduce the overall complexity of serum analysis. In keeping with the increasing interest in applications of proteomics, the tools available for proteomic analyses continue to improve and expand. For example, enhanced tools (such as software and labeling procedures) continue to be developed for the analysis of 2D gels and protein quantification. In addition, activity-based probes are now being used to tag, enrich and isolate distinct sets of proteins based on enzymatic activity. One of the most active areas of development involves microarrays. Antibody-based microarrays have recently been released as commercial products while numerous additional capture agents (e.g. aptamers) and many additional types of microarrays are being explored.

Animals↗

Cilia.Pro database of ciliary proteins from vertebrates, Chlamydomonas, and Caenorhabditis.

Cilia and flagella are microtubule-based organelles that generate force and sense the extracellular environment. In humans, these structures are essential for development, homeostasis, and reproduction, with defects contributing to a wide array of congenital and degenerative disorders. As cilia were present on the last common ancestor of all eukaryotes, research on cilia across model organisms holds significant relevance for understanding human disease. The green alga Chlamydomonas, which diverged from the human lineage with the animal-plant split, shares striking similarities in ciliary structure and function with humans. Two decades ago, our group published the proteome of the Chlamydomonas cilium, identifying hundreds of new ciliary proteins that were organized in an online database. Since then, advances have brought us a more comprehensive understanding of both Chlamydomonas and mammalian cilia. Our database, www.Cilia.Pro, has been continually updated to integrate proteomic, transcriptomic, and genomic data from Chlamydomonas and Caenorhabditis along with humans, and other vertebrates providing a valuable tool for the ciliary research community.

Cilia↗

Bacterial proteomics and vaccine development.

Until recently, the development of vaccines for use in humans relied on the response to attenuated or whole-cell preparations, or empirically selected antigens. The post-genomic era holds the possibility of rational design of novel vaccines for important human pathogens. The discovery and development of these new vaccines is likely to be accomplished through integrated proteomic strategies. Although most proteomic studies are based on two-dimensional gel electrophoresis (2D-PAGE) as a separation technique, new methods have been developed within the past two years that provide complementary information concerning microbial protein expression. The 2D-PAGE technique in combination with Western blotting has been successfully applied in the discovery of antigens from Helicobacter pylori, Chlamydia trachomatis and Borrelia garinii. Two-dimensional semi-preparative electrophoresis has provided complementary information regarding membrane protein expression in a strain of H. pylori. Through two-dimensional liquid chromatography-tandem mass spectrometry, the most comprehensive information to date regarding protein expression in yeast was obtained. This technique may shortly become an important tool in vaccinology. This review of the current state of bacterial proteomics as applied in vaccinology presents analytical techniques for protein separation, proteomics without gels, reverse vaccinology, and functional approaches to the identification of virulence proteins in microbes.

Bacterial Proteins↗

Occupationally relevant vibrations and the brain: frequency-dependent proteomics signatures in a rat model.

INTRODUCTION: Occupational exposure to whole-body vibration (WBV), particularly in agricultural environments, has been associated with adverse cognitive and physiological effects. This study examined the neurophysiological impact of WBV in a rat model at 4 Hz and 30 Hz, frequencies representative of off-road and on-road vehicle operation. METHODOLOGY: Forty-four Sprague-Dawley rats were assigned to control (0 Hz), low-frequency (4 Hz), or high-frequency (30 Hz) vibration conditions. After three days of exposure, brain tissues were collected and analyzed using mass spectrometry-based proteomics to identify differentially expressed proteins. RESULTS: Proteomic profiling revealed distinct, frequency-dependent alterations in brain protein expression. Compared with controls, 32 cognition-related proteins were differentially regulated at 4 Hz and 29 at 30 Hz, with 13 differing between the two vibration conditions. Principal component analysis showed clear separation among groups, indicating unique proteomic signatures for each exposure frequency. Functional enrichment and protein-protein interaction analyses demonstrated involvement of synaptic plasticity, cytoskeletal organization, calcium regulation, and neurotransmitter release. Exposure to 4 Hz was associated with the upregulation of proteins involved in calcium homeostasis and synaptic integrity, suggesting potential disruption of cognitive processes. In contrast, 30 Hz increased the expression of proteins related to axonal guidance and neuroprotection, indicating a less clearly adverse response that may reflect adaptive or potentially beneficial effects. DISCUSSION: These findings provide new insight into biological mechanisms underlying WBV-induced cognitive changes and underscore the importance of vibration frequency in shaping neurophysiological outcomes. They also establish a foundation for future studies integrating proteomics with behavioural assessments in animals and humans.

Animals↗

Integrated multi-omics profiling of amniotic fluid identifies predictive biomarkers for fetal growth restriction trajectories.

BACKGROUND: Fetal growth restriction (FGR) is a complex condition with highly heterogeneous clinical outcomes, making prenatal distinction between transient and persistent growth failure challenging. This study aims to identify amniotic fluid (AF) biomarkers capable of differentiating distinct FGR trajectories and characterizing persistent growth failure mechanisms. METHODS: Integrated proteomic and metabolomic profiling was performed on AF samples from transient FGR (n&#x2009;=&#x2009;11), persistent FGR (n&#x2009;=&#x2009;9), and healthy controls (n&#x2009;=&#x2009;13). Diagnostic and prognostic models were developed using multivariate analysis. Selected protein candidates were validated via ELISA in an independent cohort (n&#x2009;=&#x2009;69). RESULTS: Multi-omics analysis revealed distinct molecular signatures for FGR stratification. A two-protein diagnostic panel (PDGFA and phospho-STAT5A) achieved an AUC of 1.000 in the discovery stage and 0.780 in the external validation cohort. For prognostic assessment, a molecular signature including IREB2, HLA-C, and PLXNB2 accurately predicted persistent growth failure from transient recovery (AUC = 0.966). Cross-platform integration highlighted the mass spectrometry-derived WASHC2C as a central hub protein with a significant progressive increase across the control, transient, and persistent groups (p&#x2009;<&#x2009;0.001). CONCLUSIONS: This study establishes a multi-omics framework for prenatal FGR stratification. Our findings identify distinct molecular&#xa0;signatures reflecting&#xa0;the intrauterine environment and provide high-performance molecular tools for predicting divergent fetal growth trajectories to guide personalized clinical decision-making.

Humans↗

Proteomics: a technology-driven and technology-limited discovery science.

An emerging field for the analysis of biological systems is the study of the complete protein complement of the genome, the 'proteome'. There are several complementary tools available for proteome analysis including 2D protein electrophoresis and mass spectrometry. Emerging technologies for proteome analysis include spotted-array-based methods and microfluidic devices. Taken together, these technologies provide a wealth of information that is useful in discovery-based science. However, there are some key limitations of these approaches and new technology is required to be able to fully integrate proteomic information with information obtained about DNA sequence, mRNA profiles and metabolite concentrations into effective models of biological systems.

Electrophoresis, Gel, Two-Dimensional↗

Proteomic study for the cellular responses to Cd2+ in Schizosaccharomyces pombe through amino acid-coded mass tagging and liquid chromatography tandem mass spectrometry.

Cadmium (Cd(2+)) is one of well-known toxic heavy metal ions. To gain a global understanding how Cd(2+) affects cells at the molecular level, we systematically studied the cellular response of the fission yeast Schizosaccharomyces pombe to Cd(2+) using our integrated proteomic strategy of amino acid-coded mass tagging (AACT) and liquid chromatography-tandem mass spectrometry. Our proteome-wide investigation unequivocally identified 1133 S. pombe proteins. Of which, the AACT-based quantitative analysis revealed 106 up-regulated and 55 down-regulated proteins on the Cd(2+) exposure. The most prevalent functional class in the up-regulated proteins, approximately 28% of our profile, was the proteins involved in protein biosynthesis, showing a time-dependent biphasic expression pattern characteristic with rapid initial induction and later repression. Most significantly, 27 proteins functionally classified as cell rescue and defense were up-regulated for oxygen and radical detoxification, heat shock response, and other stress response. Furthermore, the large precursor sequence coverage of our AACT approach allowed us to unequivocally identify and quantitate different isozymes for glutathione S-transferase, which have close similarity in their amino acid sequence. Our quantitative dataset also showed that 80% of the up-regulated proteins found in the S. pombe response were different from those in the Saccharomyces cerevisiae response. The function of some of the key identifications was validated through biochemical assays. It is very interesting that the induction of cysteine synthase expression was not observed in our study, although it has been proven as a critical enzyme to supply free cysteines for the enhancing synthesis of Cd(2+)-sequestering molecules such as glutathione and phytochelatins in plants and some yeasts. Our quantitative proteomic result instead suggested that, as an alternative mechanism for the detoxification of Cd(2+), S. pombe produced significantly higher level of inorganic sulfide to immobilize cellular Cd(2+) as a form of CdS nanocrystallites capped with glutathione and/or phytochelatins.

Amino Acid Sequence↗