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PubMed2006

Proteomic analysis of acidic chaperones, and stress proteins in extreme halophile Halobacterium NRC-1: a comparative proteomic approach to study heat shock response.

BACKGROUND: Halobacterium sp. NRC-1 is an extremely halophilic archaeon and has adapted to optimal growth under conditions of extremely high salinity. Its proteome is highly acidic with a median pI of 4.9, a unique characteristic which helps the organism to adapt high saline environment. In the natural growth environment, Halobacterium NRC-1 encounters a number of stressful conditions including high temperature and intense solar radiation, oxidative and cold stress. Heat shock proteins and chaperones play indispensable roles in an organism's survival under many stress conditions. The aim of this study was to develop an improved method of 2-D gel electrophoresis with enhanced resolution of the acidic proteome, and to identify proteins with diverse cellular functions using in-gel digestion and LC-MS/MS and MALDI-TOF approach. RESULTS: A modified 2-D gel electrophoretic procedure, employing IPG strips in the range of pH 3-6, enabled improved separation of acidic proteins relative to previous techniques. Combining experimental data from 2-D gel electrophoresis with available genomic information, allowed the identification of at least 30 cellular proteins involved in many cellular functions: stress response and protein folding (CctB, PpiA, DpsA, and MsrA), DNA replication and repair (DNA polymerase A alpha subunit, Orc4/CDC6, and UvrC), transcriptional regulation (Trh5 and ElfA), translation (ribosomal proteins Rps27ae and Rphs6 of the 30 S ribosomal subunit; Rpl31eand Rpl18e of the 50 S ribosomal subunit), transport (YufN), chemotaxis (CheC2), and housekeeping (ThiC, ThiD, FumC, ImD2, GapB, TpiA, and PurE). In addition, four gene products with undetermined function were also identified: Vng1807H, Vng0683C, Vng1300H, and Vng6254. To study the heat shock response of Halobacterium NRC-1, growth conditions for heat shock were determined and the proteomic profiles under normal (42 degrees C), and heat shock (49 degrees C) conditions, were compared. Using a differential proteomic approach in combination with available genomic information, bioinformatic analysis revealed five putative heat shock proteins that were upregulated in cells subjected to heat stress at 49 degrees C, namely DnaJ, GrpE, sHsp-1, Hsp-5 and sHsp-2. CONCLUSION: The modified 2-D gel electrophoresis markedly enhanced the resolution of the extremely acidic proteome of Halobacterium NRC-1. Constitutive expression of stress proteins and chaperones help the organism to adapt and survive under extreme salinity and other stress conditions. The upregulated expression pattern of putative chaperones DnaJ, GrpE, sHsp-1, Hsp-5 and sHsp-2 under elevated temperature clearly suggests that Halobacterium NRC-1 has a sophisticated defense mechanism to survive in extreme environments.

Journal Article↗

PubMed2005

The potential of proteomics and peptidomics for allergy and asthma research.

Progress in the field of proteomics, the branch of biology that studies the full set of proteins derived from a given genome, is moving fast. Two-dimensional gel electrophoresis (2DG) separation of complex protein mixtures and the subsequent analysis of isolated protein spots by mass spectrometry allow fast and accurate identification of proteins. The comparison of spots from different samples separated on customized 2D gels allows the detection of punctual differences in their mobility and facilitates tracing back differences in protein expression, presence of isoforms, splice variants and posttranslational modifications by mass spectrometry. In spite of significant analytical challenges owing to the high complexity of the proteome and the challenge deriving from the necessity to process huge amounts of raw data generated by mass spectrometric profiling, proteomics has evolved to an indispensable tool in life sciences. A restricted window of the proteome that consists of peptides and small proteins not easily manageable by conventional gel electrophoresis prompted the development of separation methods based on liquid chromatography. This new research field termed peptidomics already contributed, together with proteomics to enlarge our knowledge about biological processes and supported by sophisticated bioinformatics tools, to the discovery of new diagnostic and therapeutic targets. The technological capabilities of biophysical separation, mass spectrometry and bioinformatics form the basis of discovery programs that aim at mining the proteome starting from microgram amounts of protein extracts derived from body fluids and tissues. Proteomics and peptidomics have a great potential to speed up allergy and asthma research, where disease- and tissue-specific samples are easy to obtain.

Asthma↗

PubMed2006

Proteome analysis of B-cell maturation.

Proteins affected by anti-mIgM stimulation during B-cell maturation were identified using 2-DE-based proteomics. We investigated the proteome profiles of stimulated and nonstimulated Ramos B-cells at eight time points during 5 d and compared the obtained proteomic data to the corresponding data from DNA-microarray studies. Anti-mIgM stimulation of the cells resulted in significant differences (> or =twofold) in the protein abundance close to 100 proteins and differences in post-translational protein modifications. Forty-eight up- or down-regulated proteins were identified by mass spectrometric methods and database searches. The identities of a further nine proteins were revealed by comparing their positions to the known proteins in other lymphocyte 2-DE databases. Several of the proteins are directly related to the functional and morphological characteristics of B-cells, such as cytoskeleton rearrangement and intracellular signalling triggered by the crosslinking of B-cell receptors. In addition to proteins known to be involved in human B-cell maturation, we identified several proteins that were not previously linked to lymphocyte differentiation. The results provide deeper insights into the process of B-cell maturation and may lead to novel therapeutic strategies for immunodeficiencies. An interactive 2-DE reference map is available at http://bioinf.uta.fi/BcellProteome.

B-Lymphocytes↗

PubMed2025

Integrative multi-omics profiling deciphers tumor microenvironment heterogeneity and immunotherapy vulnerabilities in lung neuroendocrine carcinomas.

INTRODUCTION: Lung neuroendocrine carcinomas (Lu-NECs) are rare, highly aggressive lung tumors with poor prognosis and limited therapeutic options. Understanding the tumor immune microenvironment (TIME) is crucial towards personalized therapeutic strategies. OBJECTIVES: This study aims to systematically characterize the heterogeneity and complexity of the TIME in Lu-NECs by integrating proteomic, transcriptomic, and genomic data. METHODS: We performed comprehensive immune-proteomic profiling of 76 Lu-NECs across diverse histopathological subtypes to elucidate intra-tumoral TIME heterogeneity at the proteomic level. Validation was conducted in multiple independent cohorts, including 112 Lu-NECs using immunohistochemistry, 147 Lu-NECs, and 17 small cell lung carcinoma samples using transcriptomics. We integrated proteomic, transcriptomic, genomic, and clinical data to assess molecular, immunological, and clinical features, as well as therapeutic vulnerabilities across different immune subtypes. RESULTS: We delineated the immuno-proteomic landscape of Lu-NECs and identified two major immuno-proteomic clusters with distinct immunological, molecular, and clinical characteristics. IPC1 was characterized by high immune cell infiltration, while IPC2 exhibited sparse immune cell presence. Genomic analysis revealed distinct mutational patterns, with IPC1 showing a higher incidence of APOBEC-associated mutation signatures and IPC2 being enriched for mutations associated with defective DNA mismatch repair and tobacco-related mutagens. Functional analyses indicated that IPC1 was related to immune and oncogenic signaling activity, whereas IPC2 was associated with cancer stemness and proliferation-related features. Furthermore, IPC1 and IPC2 demonstrated histological subtype-specific clinical benefits from postoperative chemotherapy. Finally, we developed a machine learning model (iPROM) to predict Lu-NECs immune classification and improve risk stratification, which was validated across multiple independent cohorts. CONCLUSIONS: This study advances the understanding of the tumor immune microenvironment in Lu-NECs through multi-omics characterization and highlights potential personalized therapeutic vulnerabilities tailored to the specific immune landscapes of Lu-NECs.

Humans↗

PubMed2026

Longitudinal clinical proteomics reveals pneumonia type-specific protein biomarkers and autoantibodies.

Community-acquired pneumonia is a major cause of morbidity and mortality globally. Specific molecular endotypes are currently not well defined, and different viral or bacterial pathogens may trigger specific host responses and pathogenic mechanisms. We performed longitudinal proteomic profiling of bronchoalveolar lavage fluid and plasma from bacterial, influenza, and SARS-CoV-2-driven pneumonia. Our analysis revealed highly pneumonia type-specific proteomic signatures, including COVID-19-specific antibodies locally produced in the lung. These antibodies showed biased immunoglobulin V-domain usage, linked to a CD69/CD83 plasma cell state associated with disease severity and degree of autoimmunity. Using mass spectrometry-driven autoantibody profiling in 2 independent COVID-19 cohorts, we identified 177 putative autoantibodies targeting extracellular matrix, nuclear, and immune-related proteins. Of note, temporal changes in autoantibody profiles correlated with clinical markers of inflammation, organ dysfunction, and duration of hospitalization. These findings highlight the autoimmune aspects of COVID-19 and provide potential biomarkers and therapeutic targets to help improve patient outcomes.

Humans↗

PubMed2005

Cardiovascular-related proteins identified in human plasma by the HUPO Plasma Proteome Project pilot phase.

Proteomic profiling of accessible bodily fluids, such as plasma, has the potential to accelerate biomarker/biosignature development for human diseases. The HUPO Plasma Proteome Project pilot phase examined human plasma with distinct proteomic approaches across multiple laboratories worldwide. Through this effort, we confidently identified 3020 proteins, each requiring a minimum of two high-scoring MS/MS spectra. A critical step subsequent to protein identification is functional annotation, in particular with regard to organ systems and disease. Performing exhaustive literature searches, we have manually annotated a subset of these 3020 proteins that have cardiovascular-related functions on the basis of an existing body of published information. These cardiovascular-related proteins can be organized into eight groups: markers of inflammation and/or cardiovascular disease, vascular and coagulation, signaling, growth and differentiation, cytoskeletal, transcription factors, channels/receptors and heart failure and remodeling. In addition, analysis of the peptide per protein ratio for MS/MS identification reveals group-specific trends. These findings serve as a resource to interrogate the functions of plasma proteins, and moreover, the list of cardiovascular-related proteins in plasma constitutes a baseline proteomic blueprint for the future development of biosignatures for diseases such as myocardial ischemia and atherosclerosis.

Arteriosclerosis↗

PubMed2006

A proteomic approach for unraveling the oncogenic H-Ras protein networks in NIH/3T3 mouse embryonic fibroblast cells.

To elucidate the oncogenic H-Ras network, we have established various stable and inducible oncogenic H-Ras-expressing NIH/3T3 mouse embryonic fibroblast cell clones, which express G12V H-Ras and G12R H-Ras proteins under the influence of a strong cytomegalovirus promoter and under the tight control of expression by an antibiotic, doxycycline, respectively. Here we provide a catalogue of proteome profiles in total cell lysates derived from oncogenic H-Ras-expressing NIH/3T3 cells. In this biological context, we compared total proteome changes by the combined methods of 2-DE, quantitative image analysis and MALDI-TOF MS analysis using both a stable expression system as well as an inducible expression system. There were a large number of common targets for oncogenic H-Ras, which were identified in both cell lines and consisted of 64 proteins (36 up-regulated and 28 down-regulated). Differentially regulated expression was further confirmed for some subsets of candidates by Western blot analysis using specific antibodies. Taken together, the results presented here show that comparative analysis of the proteome from the oncogenic H-Ras-expressing cells yielded interpretable data to elucidate protein networks directly and/or indirectly.

Animals↗

PubMed2004

Proteomics/genomics and signaling in lymphocytes.

Recent technological advances in genomics, proteomics and bioinformatics have offered new insights into the molecular mechanisms that underlie lymphocyte signaling and function, and the development of new tools in these areas has opened up new avenues for biological investigation. By adding a quantitative dimension to lymphocyte proteome profiling, molecular machines and spatiotemporal regulatory processes can now be analyzed using such discovery-driven approaches. Biologists employing genomic and proteomic tools are gathering data at increasing speed and their struggle to extract maximal biological information is helped by new software tools that enable the detailed comparison of multiple datasets.

Animals↗

PubMed2004

Proteomics techniques for cystic fibrosis research.

Numerous factors, other than mutations in the CFTR gene, affect the phenotypic variability of cystic fibrosis (CF). With a two-dimensional electrophoresis (2-DE) analysis of total protein expression profiles (proteomics) of CF versus non-CF cells it is possible to obtain an integrative picture of CF cellular alterations. Through this approach, proteins that interact differently with wild type- and mutant-CFTR can also be identified (interactomics). This can provide insight into CF pathophysiology as well as clues for novel therapeutic targets. Additionally, protein profiling can ultimately identify novel disease markers with the potential for a CF diagnosis not based on the analysis of CFTR gene.

Clinical Laboratory Techniques↗

PubMed2005

Proteomic analysis of differently expressed proteins in a mouse model for allergic asthma.

Allergic asthma is associated with persistent functional and structural changes in the airways and involves many different cell types. Many proteins involved in allergic asthma have been identified individually, but complete protein profiles (proteome) have not yet been reported. Here we have used a differential proteome mapping strategy to identify tissue proteins that are differentially expressed in mice with allergic asthma and in normal mice. Mouse lung tissue proteins were separated using two-dimensional gel electrophoresis over a pH range between 4 and 7, digested, and then analyzed by matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MS). The proteins were identified using automated MS data acquisition. The resulting data were searched against a protein database using an internal Mascot search routine. This approach identified 15 proteins that were differentially expressed in the lungs of mice with allergic asthma and normal mice. All 15 proteins were identified by MS, and 9 could be linked to asthma-related symptoms, oxidation, or tissue remodeling. Our data suggest that these proteins may prove useful as surrogate biomarkers for quantitatively monitoring disease state progression or response to therapy.

Animals↗

PubMed2006

Selenium binding protein 1 in ovarian cancer.

Selenium binding protein 1 (SELENBP1) was identified to be the most significantly down-regulated protein in ovarian cancer cells by a membrane proteome profiling analysis. SELENBP1 expression levels in 4 normal ovaries, 8 benign ovarian tumors, 12 borderline ovarian tumors and 141 invasive ovarian cancers were analyzed with immunohistochemical assay. SELENBP1 expression was reduced in 87% cases of invasive ovarian cancer (122/141) and was significantly reduced in borderline tumors and invasive cancers (p<0.001). Cox multivariate analysis within the 141 invasive cancer tissues showed that SELENBP1 expression score was a potential prognostic indicator for unfavorable prognosis of ovarian cancer (hazard ratio [HR], 2.18; 95% CI=1.22-3.90; p=0.009). Selenium can disrupt the androgen pathway, which has been implicated in modulating SELENBP1 expression. We investigated the effects of selenium and androgen on normal human ovarian surface epithelial (HOSE) cells and cancer cells. Interestingly, SELENBP1 mRNA and protein levels were reduced by androgen and elevated by selenium treatment in the normal HOSE cells, whereas reversed responses were observed in the ovarian cancer cell lines. These results suggest that changes of SELENBP1 expression in malignant ovarian cancer are an indicator of aberration of selenium/androgen pathways and may reveal prognostic information of ovarian cancer.

Aged↗

PubMed2006

Proteomic analysis in cancer research: potential application in clinical use.

The ultimate goal of cancer proteomics is to adapt proteomic technologies for routine use in clinical laboratories for the purpose of diagnostic and prognostic classification of disease states, as well as in evaluating drug toxicity and efficacy. The novel technologies allows researchers to facilitate the comprehensive analyses of genomes, transcriptomes, and proteomes in health and disease. The information that is expected from such technologies may soon exert a dramatic change in cancer research and impact dramatically on the care of cancer patients. Analysis of tumor-specific proteomic profiles may also allow better understanding of tumor development and the identification of novel targets for cancer therapy. The localization of gene products, which is often difficult to deduce from the sequence, can be determined experimentally. Mechanisms, such as regulation of protein function by proteolysis, recycling, and isolation in cell compartments, affect gene products, not genes. Finally, protein-protein interactions and the molecular composition of cellular structures can be determined only at the protein level. The biological variability among patient samples as well as the great dynamic range of biomarker concentrations are currently the main challenges facing efforts to deduce diagnostic patterns that are unique to specific disease states. While several strategies exist to address this problem, we have tried to offer a wide perspective about the current possibilities.

Antibodies, Neoplasm↗

PubMed2003

Comparative analysis of proteome maps of Helicobacter pylori clinical isolates.

The gram-negative bacterium Helicobacter pylori is found in human gastric mucosa. A widely distributed H. pylori infection is associated with chronic gastritis, gastric and duodenal ulcers, and malignant neoplasms. In this study proteome maps of four H. pylori clinical isolates derived from patients of two Russian regions (Moscow/Moscow Region and Novosibirsk) were obtained using 2D-electrophoresis and MALDI-TOF-mass-spectrometry. Variability of some H. pylori proteins and the level of their expression have been evaluated. These four isolates could be easily subdivided into two equal groups characterized by the close proteome profiles and the isolate from Moscow Region and the isolate from Novosibirsk constituted one group. The present study demonstrates the potential of proteome technology, which can be employed together with genome and transcriptome studies for the multiparameter typing of clinical isolates of pathogenic microorganisms.

Cell Extracts↗

PubMed2006

Genetics and molecular profiling of multiple myeloma: novel tools for clinical management?

The understanding of molecular events involved in multiple myeloma (MM) development as well as of mechanisms underlying sensitivity/resistance to anticancer drugs has been dramatically increased by the wide-spread use of modern technologies for genetic analysis, global gene expression and proteomic profiling. Such analytical approaches, which are presently supported by reliable bioinformatic tools, have depicted a new scenario for the development of molecular-based anti-MM agents and for predicting clinical outcome. IgH translocations or a hyperdiploid state are emerging as early genetic signatures of MM which lead to deregulated expression of cyclin D. At present however, the major challenge remains the definition of the potential role of cytogenetic techniques and molecular profiling technologies in individual patient management. Here we will describe the prospective potential and current achievements of such technologies which might produce major advancements in the treatment of this still incurable disease.

Chromosome Aberrations↗

PubMed2026

The investigation of human cerebrospinal fluid exosome in spinal cord injury.

Spinal cord injury (SCI) leads to severe neurological and functional impairments, yet reliable biomarkers for assessing injury severity and predicting recovery remain limited. Cerebrospinal fluid (CSF) is in direct contact with the central nervous system and provides a valuable source for detecting molecular changes after SCI. Although exosomal microRNAs (miRNAs) and proteins are increasingly recognized as mediators of intercellular communication, the role of human CSF exosomes in SCI has not been systematically investigated. To identify exosome-based biomarkers and potential therapeutic targets, we analyzed CSF and serum exosomes from patients with acute SCI using RNA sequencing and proteomic profiling. Weighted gene co-expression network analysis identified 6 gene modules significantly associated with injury severity and neurological recovery at 3 months. Proteomic analysis revealed a 5-protein panel that distinguished complete from incomplete SCI and a 4-protein panel that predicted neurological improvement. Additionally, 15 CSF-specific and 9 serum-specific exosomal miRNAs were identified independent of injury severity. Among 10 tested miRNAs associated with neurological recovery, 7 regulated astrocyte proliferation, and 6 promoted neurite extension and synapse formation. Overall, this study provides a comprehensive characterization of CSF exosomal miRNAs and proteins in human SCI and identifies molecular signatures associated with injury severity and recovery.

Humans↗

PubMed2025

ITIH4 alleviates OVA-induced asthma by regulating lung-gut microbiota.

BACKGROUND: Inter-alpha-trypsin inhibitor heavy chain 4 (ITIH4), a Type 2 acute phase protein, is critical for resolving inflammation and promoting tissue repair. While its role in chronic respiratory diseases is recognized, its effects on asthma remain unclear. This study investigated the effects of ITIH4 on the modulation of lung and gut microbiota, the attenuation of allergic inflammation, and the improvement of respiratory outcomes in an asthma mouse model. METHODS: Six-week-old male Balb/c mice were divided into five groups: control, ITIH4, ovalbumin (OVA), and two OVA + ITIH4 treatment groups at different doses. Lung function and oxygen saturation were measured, and bronchoalveolar lavage fluid (BALF) was analyzed for white blood cell counts and cytokines. Lung and gut microbiota were profiled using 16 S rRNA gene sequencing, and short-chain fatty acids (SCFAs) were measured using gas chromatography-mass spectrometry (GC-MS). Proteomic profiling of intestinal tissues was conducted to identify ITIH4-associated signaling pathways. RESULTS: ITIH4 administration significantly mitigated OVA-induced asthma symptoms by reducing weight loss, airway resistance, and tissue damping (p < 0.05). Histological analysis showed decreased airway wall thickening and lung injury scores (p < 0.05). ITIH4 also lowered BALF eosinophils and lymphocytes, IgE, and Th2 cytokines (IL-4, IL-5, and IL-13) (p < 0.05). ITIH4 treatment modulated microbiome composition, enriching Gram-positive taxa (Nocardioidaceae and Acholeplasmataceae) and depleting Gram-negative Helicobacteraceae (p < 0.05). SCFAs correlated with microbiome alterations, notably reduced 4-methylpentanoic acid levels (p < 0.05). Proteomic analysis revealed a dose-dependent activation of granzyme A signaling and suppression of metabolic and solute transport pathways. CONCLUSIONS: ITIH4 ameliorates asthma symptoms by modulating lung and gut microbiota, dampening Th2-driven inflammation, and restoring mucosal immune balance. These findings support ITIH4 as a potential candidate for microbiome-targeted asthma therapy.

Animals↗

PubMed2006

Functional genomics and proteomics in the clinical neurosciences: data mining and bioinformatics.

The goal of this chapter is to introduce some of the available computational methods for expression analysis. Genomic and proteomic experimental techniques are briefly discussed to help the reader understand these methods and results better in context with the biological significance. Furthermore, a case study is presented that will illustrate the use of these analytical methods to extract significant biomarkers from high-throughput microarray data. Genomic and proteomic data analysis is essential for understanding the underlying factors that are involved in human disease. Currently, such experimental data are generally obtained by high-throughput microarray or mass spectrometry technologies among others. The sheer amount of raw data obtained using these methods warrants specialized computational methods for data analysis. Biomarker discovery for neurological diagnosis and prognosis is one such example. By extracting significant genomic and proteomic biomarkers in controlled experiments, we come closer to understanding how biological mechanisms contribute to neural degenerative diseases such as Alzheimers' and how drug treatments interact with the nervous system. In the biomarker discovery process, there are several computational methods that must be carefully considered to accurately analyze genomic or proteomic data. These methods include quality control, clustering, classification, feature ranking, and validation. Data quality control and normalization methods reduce technical variability and ensure that discovered biomarkers are statistically significant. Preprocessing steps must be carefully selected since they may adversely affect the results of the following expression analysis steps, which generally fall into two categories: unsupervised and supervised. Unsupervised or clustering methods can be used to group similar genomic or proteomic profiles and therefore can elucidate relationships within sample groups. These methods can also assign biomarkers to sub-groups based on their expression profiles across patient samples. Although clustering is useful for exploratory analysis, it is limited due to its inability to incorporate expert knowledge. On the other hand, classification and feature ranking are supervised, knowledge-based machine learning methods that estimate the distribution of biological expression data and, in doing so, can extract important information about these experiments. Classification is closely coupled with feature ranking, which is essentially a data reduction method that uses classification error estimation or other statistical tests to score features. Biomarkers can subsequently be extracted by eliminating insignificantly ranked features. These analytical methods may be equally applied to genetic and proteomic data. However, because of both biological differences between the data sources and technical differences between the experimental methods used to obtain these data, it is important to have a firm understanding of the data sources and experimental methods. At the same time, regardless of the data quality, it is inevitable that some discovered biomarkers are false positives. Thus, it is important to validate discovered biomarkers. The validation process may be slow; yet, the overall biomarker discovery process is significantly accelerated due to initial feature ranking and data reduction steps. Information obtained from the validation process may also be used to refine data analysis procedures for future iteration. Biomarker validation may be performed in a number of ways - bench-side in traditional labs, web-based electronic resources such as gene ontology and literature databases, and clinical trials.

Animals↗

PubMed2006

Multivariate analysis of protein profiles of metal hyperaccumulator Thlaspi caerulescens accessions.

Thlaspi caerulescens is increasingly acknowledged as one of the best models for studying metal hyperaccumulation in plants. In order to study the mechanisms underlying metal hyperaccumulation, we used proteomic profiling to identify differences in protein intensities among three T. caerulescens accessions with pronounced differences in tolerance, uptake and root to shoot translocation of Zn and Cd. Proteins were separated using two-dimensional electrophoresis and stained with SYPRO Orange. Intensity values and quality scores were obtained for each spot by using PDQuest software. Principal component analysis was used to test the separation of the protein profiles of the three plant accessions at various metal exposures, and to detect groups of proteins responsible for the differences. Spot sets representing individual proteins were analysed with the analysis of variance and non-parametric Kruskal-Wallis test. Clearest differences were seen among the Thlaspi accessions, while the effects of metal exposures were less pronounced. The 48 tentatively identified spots represent core metabolic functions (e.g. photosynthesis, nitrogen assimilation, carbohydrate metabolism) as well as putative signalling and regulatory functions. The possible roles of some of the proteins in heavy metal accumulation and tolerance are discussed.

Cadmium↗