向日葵对苯胺废水的光合生理响应及净化效果

为探讨向日葵对苯胺废水的耐受性及其应用于苯胺污染废水植物修复的可行性,采用水培试验方法,以美国油葵1号为材料,研究了不同苯胺浓度胁迫对向日葵光合和叶绿素荧光参数的影响,以及向日葵对苯胺的吸收、积累和净化效果。研究发现:100 mg/L的苯胺对向日葵的净光合速率(P_n)、气孔导度(L_s)和蒸腾速率(T_r)有显著的促进作用,而对生长与叶绿素荧光相关参数无显著的影响。当苯胺浓度升高到200—400 mg/L时,向日葵的鲜重、净光合速率(P_n)、实际光化学效率(Φ_(PSⅡ)),最大光化学效率(F_v/F_m)以及光化学淬灭系数(qP)比对照组显著降低,而胞间CO_2浓度(C_i)显著升高,NPQ呈现出先升高后降低的趋势,并在苯胺浓度为200 mg/L时达到最大值。综合分析表明,当苯胺浓度升高到200 mg/L到400 mg/L时,苯胺对向日葵的净光合速率的抑制以非气孔因素为主。当苯胺浓度为500 mg/L时,导致向日葵死亡。另外,向日葵对100 mg/L苯胺废水中苯胺的去除率最高,达到80.97%。苯胺主要在向日葵的地上部分积累,随着苯胺浓度的升高,向日葵中叶片苯胺的浓度逐渐升高,茎中的苯胺含量变化不显著,而根中的苯胺含量较低。     

Gene expression changes in plants and microorganisms exposed to nanomaterials

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Autophagic flux analysis of Arabidopsis seedlings exposed to salt stress

In plant cells, autophagy is required for efficient recycling of cytoplasmic macromolecules in vacuoles. It was previously shown that autophagy-deficient mutants also exhibited hypersensitivity to various abiotic stresses, such as salt, osmotic changes, heat, drought, and oxidative damage. However, it has not been clearly determined whether autophagy is induced or inhibited by these environmental stressors. Using the GFP-ATG8 (green fluorescent protein fused to AUTOPHAGY-RELATED PROTEIN 8) processing assay and confocal microscopy, we assessed autophagic flux of Arabidopsis seedlings exposed to salt stress. Treatment with 150 mM NaCl resulted in an increase in the processing of GFP-ATG8. Notably, the effects of concanamycin A, an inhibitor of vacuolar proton pumps, on GFP-ATG8 processing indicated that the apparent increase in GFP-ATG8 processing by salt-induced stress was due to inefficient vacuolar degradation of the GFP moiety processed from GFP-ATG8. Salt and osmotic stresses did not increase the abundance of autophagic vesicles in the root cells. Although NaCl, KCl, and mannitol did not greatly inhibit the vacuolar trafficking of GFP-ATG8, LiCl partially inhibited autophagy. These data indicated that NaCl stress neither increases nor substantially inhibits autophagic flux. Our work illustrates the importance of autophagic flux analysis to assess the effect of abiotic stresses on plant autophagy.     

Gene expression analysis of largemouth bass exposed to estradiol,nonylphenol, and p,p'-DDE

The purpose of this study was to determine the specific expression profile of 132 genes, some of which are estrogen responsive, in largemouth bass (LMB) following exposure to estradiol (E₂), or to two hormonally active agents, 4-nonylphenol (4-NP) and 1,1-dichloro-2, 2-bis (p-chlorophenyl) ethylene (p,p′-DDE), using gene array technology. The results of these experiments show that LMB exposed to E₂ and 4-NP had similar, but not identical genetic signatures for the genes examined, some of which are known to be estrogen-responsive genes. The differences suggest that 4-NP may have additional modes of action that are independent of the estrogen receptor (ER). We have also shown that exposure of male LMB to p,p′-DDE results in an increase in some estrogen-responsive genes. But in female LMB, the observed changes were a down-regulation of the normally up-regulated estrogen responsive genes. Other genes were also down-regulated. These results suggest that p,p′-DDE may affect regulation of genes differently in male and female LMB. This study further suggests that gene arrays have the potential to map out the gene activation pathways of hormonally active compounds.     

Gene expression of metalloproteinase and its inhibitor in mesangial cells exposed to high glucose.

To clarify the roles of metalloproteinases and their inhibitor (TIMP) in diabetic glomerulopathy, we studied the effect of a high glucose concentration on the gene expression of metalloproteinase transin and TIMP as well as collagen type IV and laminin in cultured rat mesangial cells (MCs). In the high glucose group, collagen type IV, laminin, and TIMP mRNA levels were all elevated in a concentration-dependent manner, whereas transin expression was suppressed. Osmotic control of high glucose with mannitol selectively stimulated TIMP expression. We hypothesize that high glucose decreases matrix-degrading activity as well as increases matrix productivity in MCs.     

Ground based studies of gene expression in Arabidopsis exposed to gravity stresses

As a link in the preparation of the MULTIGEN experiment, which will take place on the International Space Station, ground based studies of the gene expression in Arabidopsis thaliana were performed. Microarray technology was used to screen Arabidopsis seedlings exposed to simulated hypogravity on a Random Positioning Machine and a 1 x g control sample. This screening showed differential expression in 177 out of approximately 8000 genes. Some of these genes can be grouped into functional categories, e.g. general metabolism, biogenesis of cellular components, cellular transport and transport facilitation, and cell rescue and defense response. However, about 50% of the genes encode proteins with unknown function. Based on the above results a new "in-house" cDNA microarray was constructed. Some of the selected genes on this microarray (e.g. Xyloglucan endotransglycosylase, At2g18800) showed differential expression both in Arabidopsis exposed to hypergravity and simulated hypogravity by use of a centrifuge and a Random Positioning Machine.     

Insect cell expression of recombinant imidazoleglycerolphosphate dehydratase of Arabidopsis and wheat and inhibition by triazole herbicides.

Imidazoleglycerolphosphate dehydratase (IGPD; EC 4.2.1.19), which is involved in the histidine biosynthetic pathway of Arabidopsis thaliana and wheat (Triticum aestivum), has been expressed in insect cells using the baculovirus expression vector system. N-terminal amino acid sequencing indicated that recombinant IGPDs (rIGPDs) were produced as mature forms via nonspecific proteolytic cleavages in the putative transit peptide region. The wheat rIGPD contained one Mn atom per subunit, and the Mn was involved in the assembly of the subunits to form active IGPDs. Protein-blotting analysis, using antibodies raised against the wheat rIGPD, indicated that IGPD was located in the chloroplasts of wheat. The rIGPDs of Arabidopsis and wheat, which were 86% identical in their primary structures deduced from the cDNAs, exhibited similar properties in terms of the molecular mass, pH optimum, and the Km for the substrate, imidazoleglycerolphosphate. However, the nonselective herbicides 3-amino-1,2,4-triazole and a newly synthesized triazole [(1R*, 3R*)-[3-hydroxy-3-(2H-[1,2,4]triazole-3-yl)-cyclohexyl]- phosphonic acid], inhibited Arabidopsis and wheat IGPDs in a mixed-type and a competitive manner, respectively.     

Selection of aquatic microbiota exposed to the herbicides flufenacet and metazachlor

Herbicides are important, ubiquitous environmental contaminants, but little is known about their interaction with bacterial aquatic communities. Here, we sampled a protected natural freshwater habitat and characterised its microbiome in interaction with herbicides. We evolved the freshwater microbiomes in a microcosm assay of exposure (28 days) to flufenacet and metazachlor at environmental concentrations of 0.5, 5 and 50 μg L⁻¹. Inhibitory effects of herbicides were exemplarily assessed in cultured bacteria from the same pond (Pseudomonas alcaligenes, Paenibacillus amylolyticus and Microbacterium hominis). Findings were compared to long-term concentrations as provided by local authorities. Here, environmental concentrations reached up to 11 μg L⁻¹ (flufenacet) and 76 μg L⁻¹ (metazachlor). Bacteria were inhibited at minimum inhibitory concentrations far above these values; however, concentrations of 50 μg L⁻¹ of flufenacet resulted in measurable growth impairment. While most herbicide-exposed microcosm assays did not differ from controls, Acidobacteria were selected at high environmental concentrations of herbicides. Alpha-diversity (e.g., taxonomic richness on phylum level) was reduced when aquatic microbiomes were exposed to 50 μg metazachlor or flufenacet. One environmental strain of P. alcaligenes showed resistance to high concentrations of flufenacet (50 g L⁻¹). In total, this study reveals that ecologic imbalance due to herbicide use significantly impacts aquatic microbiomes.     

Gene expression data analysis using a novel approach to biclustering combining discrete and continuous data

Many different methods exist for pattern detection in gene expression data. In contrast to classical methods, biclustering has the ability to cluster a group of genes together with a group of conditions (replicates, set of patients or drug compounds). However, since the problem is NP-complex, most algorithms use heuristic search functions and therefore might converge towards local maxima. By using the results of biclustering on discrete data as a starting point for a local search function on continuous data, our algorithm avoids the problem of heuristic initialization. Similar to OPSM, our algorithm aims to detect biclusters whose rows and columns can be ordered such that row values are growing across the bicluster's columns and vice-versa. Results have been generated on the yeast genome (Saccharomyces cerevisiae), a human cancer dataset and random data. Results on the yeast genome showed that 89% of the one hundred biggest non-overlapping biclusters were enriched with Gene Ontology annotations. A comparison with OPSM and ISA demonstrated a better efficiency when using gene and condition orders. We present results on random and real datasets that show the ability of our algorithm to capture statistically significant and biologically relevant biclusters.     

Urine analysis of patients exposed to phenylarsenic compounds via accidental pollution

New methods involving high-performance liquid chromatography/inductively coupled plasma mass spectrometry were examined for the determination of phenylarsenic compounds derived from chemical warfare agents. Several methods were examined for the separation of diphenylarsinic acid (DPAA), phenylarsonic acid, phenylmethylarsinic acid (PMAA), phenyldimethylarsine oxide, and diphenylmethylarsine oxide. Analysis of the urine samples of the patients exposed to phenylarsenic compounds indicated that the main phenylarsenic components were DPAA and PMAA; moreover, some unknown arsenicals, which were also found in contaminated groundwater and rice samples, were also detected.     

A multi-step approach to time series analysis and gene expression clustering

MOTIVATION: The huge growth in gene expression data calls for the implementation of automatic tools for data processing and interpretation. RESULTS: We present a new and comprehensive machine learning data mining framework consisting in a non-linear PCA neural network for feature extraction, and probabilistic principal surfaces combined with an agglomerative approach based on Negentropy aimed at clustering gene microarray data. The method, which provides a user-friendly visualization interface, can work on noisy data with missing points and represents an automatic procedure to get, with no a priori assumptions, the number of clusters present in the data. Cell-cycle dataset and a detailed analysis confirm the biological nature of the most significant clusters. AVAILABILITY: The software described here is a subpackage part of the ASTRONEURAL package and is available upon request from the corresponding author. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

Gene expression network analysis and applications to immunology

We address the problem of using expression data and prior biological knowledge to identify differentially expressed pathways or groups of genes. Following an idea of Ideker et al. (2002), we construct a gene interaction network and search for high-scoring subnetworks. We make several improvements in terms of scoring functions and algorithms, resulting in higher speed and accuracy and easier biological interpretation. We also assign significance levels to our results, adjusted for multiple testing. Our methods are successfully applied to three human microarray data sets, related to cancer and the immune system, retrieving several known and potential pathways. The method, denoted by the acronym GXNA (Gene eXpression Network Analysis) is implemented in software that is publicly available and can be used on virtually any microarray data set. SUPPLEMENTARY INFORMATION: The source code and executable for the software, as well as certain supplemental materials, can be downloaded from http://stat.stanford.edu/~serban/gxna.