Drug design is crucial for the effective discovery of anti-cancer drugs. The success or failure of drug design often depends on the leading compounds screened in pre-clinical studies. Many efforts, such as in vivo animal experiments and in vitro drug screening, have improved this process, but these methods are usually expensive and laborious. In the post-genomics era, it is possible to seek leading compounds for large-scale candidate small-molecule screening with multiple OMICS datasets. In the present study, we developed a computational method of prioritizing small molecules as leading compounds by integrating transcriptomics and toxicogenomics data. This method provides priority lists for the selection of leading compounds, thereby reducing the time required for drug design. We found 11 known therapeutic small molecules for breast cancer in the top 100 candidates in our list, 2 of which were in the top 10. Furthermore, another 3 of the top 10 small molecules were recorded as closely related to cancer treatment in the DrugBank database. A comparison of the results of our approach with permutation tests and shared gene methods demonstrated that our OMICS data-based method is quite competitive. In addition, we applied our method to a prostate cancer dataset. The results of this analysis indicated that our method surpasses both the shared gene method and random selection. These analyses suggest that our method may be a valuable tool for directing experimental studies in cancer drug design, and we believe this time- and cost-effective computational strategy will be helpful in future studies in cancer therapy.
Assessing interactions of a glycan-binding protein (GBP) or lectin with glycans on a microarray generates large datasets, making it difficult to identify a glycan structural motif or determinant associated with the highest apparent binding strength of the GBP. We have developed a computational method, termed GlycanMotifMiner, that uses the relative binding of a GBP with glycans within a glycan microarray to automatically reveal the glycan structural motifs recognized by a GBP. We implemented the software with a web-based graphical interface for users to explore and visualize the discovered motifs. The utility of GlycanMotifMiner was determined using five plant lectins, SNA, HPA, PNA, Con A, and UEA-I. Data from the analyses of the lectins at different protein concentrations were processed to rank the glycans based on their relative binding strengths. The motifs, defined as glycan substructures that exist in a large number of the bound glycans and few non-bound glycans, were then discovered by our algorithm and displayed in a web-based graphical user interface (http://glycanmotifminer.emory.edu). The information is used in defining the glycan-binding specificity of GBPs. The results were compared to the known glycan specificities of these lectins generated by manual methods. A more complex analysis was also carried out using glycan microarray data obtained for a recombinant form of human galectin-8. Results for all of these lectins show that GlycanMotifMiner identified the major motifs known in the literature along with some unexpected novel binding motifs.
In this study we investigated the advantage of including network information in prioritizing disease genes of type 1 diabetes (T1D). First, a naïve Bayesian network (NBN) model was developed to integrate information from multiple data sources and to define a T1D-involvement probability score (PS) for each individual gene. The algorithm was validated using known functional candidate genes as a benchmark. Genes with higher PS were found to be more likely to appear in T1D-related publications. Next a new network activity metric was proposed to evaluate the T1D relevance of protein-protein interaction (PPI) subnetworks. The metric considered the contribution both from individual genes and from network topological characteristics. The predictions were confirmed by several independent datasets, including a genome wide association study (GWAS), and two large-scale human gene expression studies. We found that novel candidate genes in the T1D subnetworks showed more significant associations with T1D than genes predicted using PS alone. Interestingly, most novel candidates were not encoded within the human leukocyte antigen (HLA) region, and their expression levels showed correlation with disease only in cohorts with low-risk HLA genotypes. The results suggested the importance of mapping disease gene networks in dissecting the genetics of complex diseases, and offered a general approach to network-based disease gene prioritization from multiple data sources.
The global burden of dengue continues to worsen, specifically in tropical and subtropical countries, and has evolved as a major public health problem. We investigated the changes in serum proteome in dengue fever (DF) patients from a dengue-endemic area of India to obtain mechanistic insights about the disease pathogenesis, the host immune response, and identification of potential serum protein biomarkers of this infectious disease. In this study, serum samples from DF patients, healthy subjects, and patients with falciparum malaria (an infectious disease control) were investigated by 2D-DIGE in combination with MALDI-TOF/TOF MS. The findings were validated with Western blotting. Functional clustering of the identified proteins was performed using PANTHER and DAVID tools. Compared to the healthy controls, we found significant changes in the expression levels of 48 protein spots corresponding to 18 unique proteins (7 downregulated and 11 upregulated) in DF patients (p<0.05). Among these differentially-expressed proteins, 11 candidates exhibited different trends in dengue fever compared to falciparum malaria. Importantly, our results suggest that dengue virus infection leads to alterations in expression levels of multiple serum proteins involved in diverse and vital physiological pathways, including acute phase response signaling, complement cascades, hemostasis, and blood coagulation. For the first time we report here that the serum levels of hemopexin, haptoglobin, serum amyloid P, and kininogen precursor, are altered in DF. This study informs the pathogenesis and host immune response to dengue virus infection, as well as the current search for new diagnostic and molecular drug targets.
Imatinib mesylate (IM) is a potent tyrosine kinase inhibitor used as front-line therapy in chronic myeloid leukemia, a disease caused by the oncogenic kinase Bcr-Abl. Although the clinical success of IM set a new paradigm in molecular-targeted therapy, the emergence of IM resistance is a clinically significant problem. In an effort to obtain new insights into the mechanisms of adaptation and tolerance to IM, as well as the signaling pathways potentially affected by this drug, we performed a two-dimensional electrophoresis-based quantitative- and phospho-proteomic analysis in the eukaryotic model Saccharomyces cerevisiae. We singled out proteins that were either differentially expressed or differentially phosphorylated in response to IM, using the phosphoselective dye Pro-Q® Diamond, and identified 18 proteins in total. Ten were altered only at the content level (mostly decreased), while the remaining 8 possessed IM-repressed phosphorylation. These 18 proteins are mainly involved in cellular carbohydrate processes (glycolysis/gluconeogenesis), translation, protein folding, ion homeostasis, and nucleotide and amino acid metabolism. Remarkably, all 18 proteins have human functional homologs. A role for HSP70 proteins in the response to IM, as well as decreased glycolysis as a metabolic marker of IM action are suggested, consistent with findings from studies in human cell lines. The previously-proposed effect of IM as an inhibitor of vacuolar H+-ATPase function was supported by the identification of an underexpressed protein subunit of this complex. Taken together, these findings reinforce the role of yeast as a valuable eukaryotic model for pharmacological studies and identification of new drug targets, with potential clinical implications in drug reassignment or line extension under a personalized medicine perspective.
Selected reaction monitoring (SRM) is becoming the tool of choice for targeted quantitative proteomics. The fundamental principle of proteomic SRM is that, for a given protein of interest, there is a set of peptides that are unique to that protein. The characteristic retention time (RT), and intact peptide m/z of these so-called proteotypic peptides are then programmed into the mass spectrometer, along with the m/z of high-intensity product ions for targeted quantitation. The particular combination of RT, peptide m/z, and product m/z for a given peptide is referred to as a transition. Selection of the most appropriate set of transitions for a given set of proteins is crucial to any SRM experiment. We previously developed the web-based MRMaid tool, which suggested the optimal transitions for a given human protein by mining spectral evidence from a small in-house database. In this article we present a completely new implementation of MRMaid, which offers substantial improvements over the original. The new version, MRMaid 2.0, uses spectra from the EBI's PRIDE database, which massively increases the coverage and quality of transitions. Transition lists can now be generated for multiple proteins simultaneously, edited within the web browser, and exported for laboratory use.
Accurate quantification of proteins is one of the major tasks in current proteomics research. To address this issue, a wide range of stable isotope labeling techniques have been developed, allowing one to quantitatively study thousands of proteins by means of mass spectrometry. In this article, the FindPairs module of the PeakQuant software suite is detailed. It facilitates the automatic determination of protein abundance ratios based on the automated analysis of stable isotope-coded mass spectrometric data. Furthermore, it implements statistical methods to determine outliers due to biological as well as technical variance of proteome data obtained in replicate experiments. This provides an important means to evaluate the significance in obtained protein expression data. For demonstrating the high applicability of FindPairs, we focused on the quantitative analysis of proteome data acquired in 14N/15N labeling experiments. We further provide a comprehensive overview of the features of the FindPairs software, and compare these with existing quantification packages. The software presented here supports a wide range of proteomics applications, allowing one to quantitatively assess data derived from different stable isotope labeling approaches, such as 14N/15N labeling, SILAC, and iTRAQ. The software is publicly available at http://www.medizinisches-proteom-center.de/software and free for academic use.
Several approaches exist for the quantification of proteins in complex samples processed by liquid chromatography-mass spectrometry followed by fragmentation analysis (MS2). One of these approaches is label-free MS2-based quantification, which takes advantage of the information computed from MS2 spectrum observations to estimate the abundance of a protein in a sample. As a first step in this approach, fragmentation spectra are typically matched to the peptides that generated them by a search algorithm. Because different search algorithms identify overlapping but non-identical sets of peptides, here we investigate whether these differences in peptide identification have an impact on the quantification of the proteins in the sample. We therefore evaluated the effect of using different search algorithms by examining the reproducibility of protein quantification in technical repeat measurements of the same sample. From our results, it is clear that a search engine effect does exist for MS2-based label-free protein quantification methods. As a general conclusion, it is recommended to address the overall possibility of search engine-induced bias in the protein quantification results of label-free MS2-based methods by performing the analysis with two or more distinct search engines.
Traditional Chinese medicine (TCM) has been used for thousands of years to treat or prevent disease. The health care paradigm has shifted from a focus on disease to TCM therapy with a holistic approach. However, the actual value of TCM has not been fully recognized worldwide due to a lack of scientific approaches to its study. Today omics has become practically available, and resembles TCM in many aspects, and can serve as a key driving force for the translation of the traditional Chinese medical formulae (chinmediformulae) into practice, and will develop and advance the concept of the metabolomics of chinmediformulae (chinmedomics). Chinmedomics seeks to elucidate the therapeutic and synergistic properties and metabolism of chinmediformulae and the involved metabolic pathways using modern analytical techniques. It is an integral part of top-down systems biology, which aims to improve understanding of chinmediformulae. This approach of combining chinmedomics with chinmediformulae with modern health care systems may lead to a revolution in TCM therapy. Although the scientific study of chinmedomics is at an early stage and requires further scrutiny and validation, the approach has major implications to improve the efficacy of chinmediformulae. This article introduces and reviews the concept of chinmedomics, and highlights recent examples of the approach, which are presented for description and discussion.
Variation in drug response results from a combination of factors that include differences in gender, ethnicity, and environment, as well as genetic variation that may result in differences in mRNA and protein expression. This article presents two integrative analytic approaches that make use of both genome-wide SNP and mRNA expression data available on the same set of subjects: a step-wise integrative approach and a comprehensive analysis using sparse canonical correlation analysis (SCCA). In addition to applying standard SCCA, we present a novel modification of SCCA which allows different weighting for the various pair-wise relationships in the SCCA. These integrative approaches are illustrated with both simulated data and data from a pharmacogenomic study of the drug gemcitabine. Results from these analyses found little overlap in terms of genes detected, possibly detecting different biological mechanisms. In addition, we found the proposed weighted SCCA to outperform its unweighted counterpart in detecting associations between the genomic features and phenotype. Further research is needed to develop and assess new integrative methods for pharmacogenomic studies, as these types of analyses may uncover novel insights into the relationship between genomic variation and drug response.
The calcium (Ca2+) transporters, like Ca2+ channels, Ca2+ ATPases, and Ca2+ exchangers, are instrumental for signaling and transport. However, the mechanism by which they orchestrate the accumulation of Ca2+ in grain filling has not yet been investigated. Hence the present study was designed to identify the potential calcium transporter genes that may be responsible for the spatial accumulation of calcium during grain filling. In silico expression analyses were performed to identify Ca2+ transporters that predominantly express during the different developmental stages of Oryza sativa. A total of 13 unique calcium transporters (7 from massively parallel signature sequencing [MPSS] data analysis, and 9 from microarray analysis) were identified. Analysis of variance (ANOVA) revealed differential expression of the transporters across tissues, and principal component analysis (PCA) exhibited their seed-specific distinctive expression profile. Interestingly, Ca2+ exchanger genes are highly expressed in the initial stages, whereas some Ca2+ ATPase genes are highly expressed throughout seed development. Furthermore, analysis of the cis-elements located in the promoter region of the subset of 13 genes suggested that Dof proteins play essential roles in regulating the expression of Ca2+ transporter genes during rice seed development. Based on these results, we developed a hypothetical model explaining the transport and tissue specific distribution of calcium in developing cereal seeds. The model may be extrapolated to understand the mechanism behind the exceptionally high level of calcium accumulation seen in grains like finger millet.
Here we present a database, WikiCell, as a portal for a unified view of the human transcriptome. At present, WikiCell consists of Expressed Sequenced Tags (ESTs), and users can access, curate, and submit database data by interactive mode, and also can browse, query, upload, and download sequences. Researchers can utilize the transcriptome model based on a human taxonomy graph. The sequences in each model are sorted by attributes such as physiological and pathological samples. The Genbank EST data format are conserved. Gene information is provided, including housekeeping genes, taxonomy location, and gene ontology (GO) description. We believe that WikiCell provides a useful resource for defining expression pattern and tissue differentiation based on human taxonomy mode. It can be accessed at http://www.wikicell.org/.
The purpose of this study was to perform a comprehensive analysis of gene expression profiles in placentas from preeclamptic pregnancies versus normal placentas. Placental tissues were obtained immediately after delivery from women with normal pregnancies (n=6) and patients with preeclampsia (n=6). The gene expression profile was assessed by oligonucleotide-based DNA microarrays and validated by quantitative real-time RT-PCR. Functional relationships and canonical pathways/networks of differentially-expressed genes were evaluated by GeneSpring™ GX 11.0 software, and ingenuity pathways analysis (IPA). A total of 939 genes were identified that differed significantly in expression: 483 genes were upregulated and 456 genes were downregulated in preeclamptic placentas compared with normal placentas (fold change ≥2 and p<0.05 by unpaired t-test corrected with Bonferroni multiple testing). The IPA revealed that the primary molecular functions of these genes are involved in cellular function and maintenance, cellular development, cell signaling, and lipid metabolism. Pathway analysis provided evidence that a number of biological pathways, including Notch, Wnt, NF-κB, and transforming growth factor-β (TGF-β) signaling pathways, were aberrantly regulated in preeclampsia. In conclusion, our microarray analysis represents a comprehensive list of placental gene expression profiles and various dysregulated signaling pathways that are altered in preeclampsia. These observations may provide the basis for developing novel predictive, diagnostic, and prognostic biomarkers of preeclampsia to improve reproductive outcomes and reduce the risk for subsequent cardiovascular disease.
Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html
NDRG4 is a member of the N-myc downregulated gene family (NDRG) belonging to the alpha/beta hydrolase superfamily. We have previously documented discrepancy between our analysis of the expression and function of NDRG4 in glioblastoma multiforme (GBM) and a recent publication by Schilling et al., who reported that NDRG4 is upregulated in GBM compared to human cortex tissues and knock down of NDRG4 reduced the viability of GBM cells. In the present study, we found that NDRG4 is indeed downregulated, at both RNA and protein levels, by quantitative RT-PCR and Western blot analysis, in GBM compared to normal tissues, and that over expression of NDRG4 inhibited proliferation of GBM cells. These new observations can inform the selection of lead molecular compounds for drug discovery as well as novel diagnostics for GBM. They also lend evidence to NDRG4 a role of tumor suppressor.
Twenty-first century life sciences have transformed into data-enabled (also called data-intensive, data-driven, or big data) sciences. They principally depend on data-, computation-, and instrumentation-intensive approaches to seek comprehensive understanding of complex biological processes and systems (e.g., ecosystems, complex diseases, environmental, and health challenges). Federal agencies including the National Science Foundation (NSF) have played and continue to play an exceptional leadership role by innovatively addressing the challenges of data-enabled life sciences. Yet even more is required not only to keep up with the current developments, but also to pro-actively enable future research needs. Straightforward access to data, computing, and analysis resources will enable true democratization of research competitions; thus investigators will compete based on the merits and broader impact of their ideas and approaches rather than on the scale of their institutional resources. This is the Final Report for Data-Intensive Science Workshops DISW1 and DISW2. The first NSF-funded Data Intensive Science Workshop (DISW1, Seattle, WA, September 19–20, 2010) overviewed the status of the data-enabled life sciences and identified their challenges and opportunities. This served as a baseline for the second NSF-funded DIS workshop (DISW2, Washington, DC, May 16–17, 2011). Based on the findings of DISW2 the following overarching recommendation to the NSF was proposed: establish a community alliance to be the voice and framework of the data-enabled life sciences. After this Final Report was finished, Data-Enabled Life Sciences Alliance (DELSA, www.delsall.org) was formed to become a Digital Commons for the life sciences community.
Cancer is one of the leading noncommunicable diseases that vastly impacts both developed and developing countries. Truly innovative diagnostics that inform disease susceptibility, prognosis, and/or response to treatment (theragnostics) are seriously needed for global public health and personalized medicine for patients with cancer. This study examined the structure and content of glycosaminoglycans (GAGs) in lethal and nonlethal breast cancer tissues from six patients. The glycosaminoglycan content isolated from tissue containing lethal cancer tumors was approximately twice that of other tissues. Molecular weight analysis showed that glycosaminoglycans from cancerous tissue had a longer weight average chain length by an average of five disaccharide units, an increase of approximately 15%. Dissacharide analysis found differences in sulfation patterns between cancerous and normal tissues, as well as sulfation differences in GAG chains isolated from patients with lethal and nonlethal cancer. Specifically, cancerous tissue showed an increase in sulfation at the “6S” position of CS chains and an increase in the levels of the HS disaccharide NSCS. Patients with lethal cancer showed a decrease in HS sulfation, with lower levels of “6S” and higher levels of the unsulfated “0S” disaccharide. Although these findings come from a limited sample size, they indicate that structural changes in GAGs exist between cancerous and noncancerous tissues and between tissues from patients with highly metastatic cancer and cancer that was successfully treated by chemotherapy. Based on these findings, we hypothesize that (1) there are putative changes in the body's construction of GAGs as tissue becomes cancerous; (2) there may be innate structural person-to-person variations in GAG composition that facilitate the metastasis of tumors in some patients when they develop cancer.
Malignant gliomas are the most frequent type of primary brain tumors. Patients' outcome has not improved despite new therapeutics, thus underscoring the need for a better understanding of their genetics and a fresh approach to treatment. The lack of reproducibility in the classification of many gliomas presents an opportunity where genomics may be paramount for accurate diagnosis and therefore best for therapeutic decisions. The aim of this work is to identify large and focal copy number abnormalities (CNA) and loss of heterozygosity (LOH) events in a malignant glioma population. We hypothesized that these explorations will allow discovery of genetic markers that may improve diagnosis and predict outcome. DNA from glioma specimens were subjected to CNA and LOH analyses. Our studies revealed more than 4000 CNA and several LOH loci. Losses of chromosomes 1p and/or 19q, 10, 13, 14, and 22 and gains of 7, 19, and 20 were found. Several of these alterations correlated significantly with histology and grade. Further, LOH was detected at numerous chromosomes. Interestingly, several of these loci harbor genes with potential or reported tumor suppressor properties. These novel genetic signatures may lead to critical insights into diagnosis, classification, prognosis, and design of individualized therapies.
Protein Phosphatase 1 (PP1) is a major serine/threonine-phosphatase whose activity is dependent on its binding to regulatory subunits known as PP1 interacting proteins (PIPs), responsible for targeting PP1 to a specific cellular location, specifying its substrate or regulating its action. Today, more than 200 PIPs have been described involving PP1 in panoply of cellular mechanisms. Moreover, several PIPs have been identified that are tissue and event specific. In addition, the diversity of PP1/PIP complexes can further be achieved by the existence of several PP1 isoforms that can bind preferentially to a certain PIP. Thus, PP1/PIP complexes are highly specific for a particular function in the cell, and as such, they are excellent pharmacological targets. Hence, an in-depth survey was taken to identify specific PP1α PIPs in human brain by a high-throughput Yeast Two-Hybrid approach. Sixty-six proteins were recognized to bind PP1α, 39 being novel PIPs. A large protein interaction databases search was also performed to integrate with the results of the PP1α Human Brain Yeast Two-Hybrid and a total of 246 interactions were retrieved.
Surplus accumulation of regulatory T cells (Tregs) is known to be at the bottom of many morbid conditions, among them being neuropsychiatric diseases. In particular, Tregs may inhibit Th1 cells, including brain autoimmune lymphocytes, controlling the local microglial response and brain tissue homeostasis. The present study was undertaken in an attempt to suggest a novel approach for the treatment of maladaptation to mental stress associated with excessive Treg accumulation. Recently it was shown that alkylating drugs (ADs), such as melphalan and cyclophosphamide (Cy) in the dose 100-fold lower than cytostatic one are capable to disturb signal transduction by IL-2R. In this study we demonstrated that IL-2R is not a unique receptor, which may be blocked with ADs. Similar effect has been shown for two other surface receptors: TNFR and Fas. Molecular mechanisms of the receptor blockage were investigated on the model of TNF signaling. Study of NF-κB activity in nuclear extracts showed that alkylating agents act at the level of surface receptor or of the receptor platform. It was also shown that ADs administration in ultralow doses results in selective elimination of Tregs. In this study we used a new laboratory model of Treg accumulation in mice. Such Treg accumulation was associated with cognitive and behavioral abnormalities, which may be prevented by Cy administration.
We have developed a cellular system constituted of human telomerase immortalized fibroblasts that gradually underwent neoplastic transformation during propagation in culture. We exploited this cellular system to investigate gene and miRNA transcriptional programs in cells at different stages of propagation, representing five different phases along the road to transformation, from non-transformed cells up to tumorigenic and metastatic ones. Here we show that gene and miRNA expression profiles were both able to divide cells according to their transformation phase. We identified more than 1,700 genes whose expression was highly modulated in cells at at least one propagation stage and we found that the number of modulated genes progressively increased at successive stages of transformation. These genes identified processes significantly deregulated in tumorigenic cells, such as cell differentiation, cell movement and extracellular matrix remodeling, cell cycle and apoptosis, together with upregulation of several cancer testis antigens. Alterations in cell cycle, apoptosis, and cancer testis antigen expression were particular hallmarks of metastatic cells. A parallel deregulation of a panel of 43 miRNAs strictly connected to the p53 and c-Myc pathways and with oncogenic/oncosuppressive functions was also found. Our results indicate that cen3tel cells can be a useful model for human fibroblast neoplastic transformation, which appears characterized by complex and peculiar alterations involving both genetic and epigenetic reprogramming, whose elucidation could provide useful insights into regulatory networks underlying cancerogenesis.
Network models combined with gene expression studies have become useful tools for studying complex diseases like Alzheimer's disease. We constructed a “Core” Alzheimer's disease protein interaction network by human curation of the primary literature. The Core network consisted of 775 nodes and 2,204 interactions. To our knowledge, this is the most comprehensive and accurate protein interaction network yet constructed for Alzheimer's disease. An “Expanded” network was computationally constructed by adding additional proteins that interacted with Core network proteins, and consisted of 4,945 nodes and 26,064 interactions. We then mapped existing gene expression studies to the Core network. This combined data model identified the MAPK/ERK pathway and clathrin-mediated receptor endocytosis as key pathways in Alzheimer's disease. Important proteins in the MAPK/ERK pathway that interacted in the Core network formed a downregulated cluster of nodes, whereas clathrin and several clathrin accessory proteins that interacted in the Core network formed an upregulated cluster of nodes. The MAPK/ERK pathway is a key component in synaptic plasticity and learning, processes disrupted in Alzheimer's. Clathrin and clathrin adaptor proteins are involved in the endocytosis of the APP protein that can lead to increased intracellular levels of amyloid beta peptide, contributing to the progression of Alzheimer's.
Apoptosis is an important morphogenetic event in embryogenesis as well as during postnatal life. In the last 2 decades, apoptosis in tooth development (odontogenesis) has been investigated with gradually increasing focus on the mechanisms and signaling pathways involved. The molecular machinery responsible for apoptosis exhibits a high degree of conservation but also organ and tissue specific patterns. This review aims to discuss recent knowledge about apoptotic signaling networks during odontogenesis, concentrating on the mouse, which is often used as a model organism for human dentistry. Apoptosis accompanies the entire development of the tooth and corresponding remodeling of the surrounding bony tissue. It is most evident in its role in the elimination of signaling centers within developing teeth, removal of vestigal tooth germs, and in odontoblast and ameloblast organization during tooth mineralization. Dental apoptosis is caspase dependent and proceeds via mitochondrial mediated cell death with possible amplification by Fas-FasL signaling modulated by Bcl-2 family members.
Cadmium is a toxic heavy metal causing iron deficiency in the shoot and light sensitivity of photosynthetic tissues that leads to decreased photosynthetic performance and biomass production. Light intensity had strong impact on both photosynthetic activity and metal accumulation of cadmium-treated plants. At elevated irradiation, cadmium accumulation increased due to the higher dry mass of plants, but its allocation hardly changed. A considerable amount of iron accumulated in the roots, and iron concentration was higher in leaves developed at moderate rather than low irradiation. At the same time, the higher the irradiation the lower the maximal photochemical quantum efficiency. The decreased photochemical efficiency, however, started to recover after a week of Cd treatment at moderate light without substantial change in metal concentrations but following the accumulation of green fluorescent compounds. Both cadmium treatment and higher light caused the accumulation of flavonoids in leaf mesophyll vacuoles/chloroplasts, but accumulation of flavonols, fluorescing at 510 nm, was characteristic to cadmium stress. Therefore, flavonoids, which may act by scavenging reactive radicals, chelating Cd, and shielding against excess irradiation, play an important part in Cd stress tolerance of Populus, and may have special impact on its phytoremediation capacity.
Plant development and productivity are negatively regulated by adverse environmental conditions. The identification of stress-regulatory genes, networks, and signaling molecules should allow the development of novel strategies to obtain tolerant plants. Polyamines (PAs) are polycationic compounds with a recognized role in plant growth and development, as well as in abiotic and biotic stress responses. During the last years, knowledge on PA functions has been achieved using genetically modified plants with altered PA levels. In this review, we combine the information obtained from global transcriptome analyses in transgenic Arabidopsis plants with altered putrescine or spermine levels. Comparison of common and specific gene networks affected by elevation of endogenous PAs, support the view that these compounds actively participate in stress signaling through intricate crosstalks with abscisic acid (ABA), Ca2+ signaling and other hormonal pathways in plant defense and development.