Effect of Temperature on the Formation of Microsclerotia of Verticillium dahliae

Microsclerotium formation by six isolates of Verticillium dahliae was studied at different temperatures both in vitro and in Arabidopsis thaliana . In vitro mycelial growth was optimal at 25°C, but microsclerotium formation was greatest at 20°C (two isolates) or 15–20°C (one isolate). Seedlings of A. thaliana were root-dipped in a conidial suspension, planted, and either placed at 5, 10, 15, or 25°C, or left at 20°C until the onset of senescence, after which some of the plants were placed at 5, 10, 15, or 25°C. The amount of microsclerotia per unit of shoot weight was assessed in relation to isolate and temperature. The optimal temperature for production of microsclerotia was 15–25°C. Two isolates each produced about 10 times more microsclerotia than each of the other four isolates. For these isolates, high R ²_adj.-values of 0.77 and 0.66 were obtained, with temperature and its square as highly significant (P   < 0.001) independent variables. R ²_adj.-values for the other isolates varied between 0.28 and 0.39. Moving plants to different temperatures at the onset of senescence led to microsclerotial densities that were intermediate between densities on plants that had grown at constantly 20°C and plants grown at other temperatures. This suggests that vascular colonization rate and rate of microsclerotium formation are similarly affected by temperature. The senescence rate of plants appeared unimportant except for plants grown at 25°C, which showed the highest amounts of microsclerotia per unit of plant weight in the most rapidly senescing plants.     

分别利用产甘油假丝酵母抗逆转录因子CgSTB5和CgSEF1对酿酒酵母菌株糠醛耐受改造

【背景】纤维素是生物转化解决能源问题的主要原料之一,其水解物中存在严重影响抑制菌株生长的糠醛,需脱毒才可应用于发酵,提高菌株耐受性是解决纤维素水解液实际生产应用的关键。【目的】酿酒酵母(Saccharomyces cerevisiae)是主要的纤维素水解液发酵工业菌株,但糠醛耐受性较低,通过分子改造获得具有高糠醛耐受性的菌株。【方法】利用新获得的产甘油假丝酵母(Candidaglycerinogenes)的相关抗逆转录因子CgSTB5、CgSEF1和CgCAS5,通过分子技术进行S.cerevisiae改造,考察其对酿酒酵母糠醛耐受性的影响,并尝试应用于未脱毒纤维素乙醇发酵。【结果】单个表达CgSTB5和CgSEF1的酿酒酵母,通过菌株点板实验表明菌株的糠醛耐受性提高25%以上,并且摇瓶发酵结果显示糠醛降解性能明显提高,生长延滞期明显缩短,S.cerevisiae W303/p414-CgSTB5的未脱毒纤维素乙醇发酵生产效率提高12.5%左右。【结论】转录因子CgSTB5和CgSEF1均能对提高酿酒酵母糠醛耐受性起到重要作用,并且有助于提高酿酒酵母菌株未脱毒纤维素乙醇发酵性能。     

Oral and Craniofacial Manifestations and Two Novel Missense Mutations of the NTRK1 Gene Identified in the Patient with Congenital Insensitivity to Pain with Anhidrosis

Congenital insensitivity to pain with anhidrosis (CIPA) is a rare inherited disorder of the peripheral nervous system resulting from mutations in neurotrophic tyrosine kinase receptor 1 gene (NTRK1), which encodes the high-affinity nerve growth factor receptor TRKA. Here, we investigated the oral and craniofacial manifestations of a Chinese patient affected by autosomal-recessive CIPA and identified compound heterozygosity in the NTRK1 gene. The affected boy has multisystemic disorder with lack of reaction to pain stimuli accompanied by self-mutilation behavior, the inability to sweat leading to defective thermoregulation, and mental retardation. Oral and craniofacial manifestations included a large number of missing teeth, nasal malformation, submucous cleft palate, severe soft tissue injuries, dental caries and malocclusion. Histopathological evaluation of the skin sample revealed severe peripheral nerve fiber loss as well as mild loss and absent innervation of sweat glands. Ultrastructural and morphometric studies of a shed tooth revealed dental abnormalities, including hypomineralization, dentin hypoplasia, cementogenesis defects and a dysplastic periodontal ligament. Genetic analysis revealed a compound heterozygosity- c.1561T>C and c.2057G>A in the NTRK1 gene. This report extends the spectrum of NTRK1 mutations observed in patients diagnosed with CIPA and provides additional insight for clinical and molecular diagnosis.     

Chronic hexosamine flux stimulates fatty acid oxidation by activating AMP-activated protein kinase in adipocytes

The hexosamine biosynthesis pathway (HBP) serves as a nutrient sensor and has been implicated in the development of type 2 diabetes. We previously demonstrated that fatty acid oxidation was enhanced in transgenic mouse adipocytes, wherein the rate-limiting enzyme of the HBP, glutamine:fructose-6-phosphate amidotransferase (GFA), was overexpressed. To explore the molecular mechanism of the HBP-induced fatty acid oxidation in adipocytes, we studied AMP-activated protein kinase (AMPK), an energy sensor that stimulates fatty acid oxidation by regulating acetyl-CoA carboxylase (ACC) activity. Phosphorylation and activity of AMPK were increased in transgenic fat pads and in 3T3L1 adipocytes treated with glucosamine to stimulate hexosamine flux. Glucosamine also stimulated phosphorylation of ACC and fatty acid oxidation in 3T3L1 adipocytes, and these stimulatory effects were diminished by adenovirus-mediated expression of a dominant negative AMPK in 3T3L1 adipocytes. Conversely, blocking the HBP with a GFA inhibitor reduced AMPK activity, ACC phosphorylation, and fatty acid oxidation. These changes are not explained by alterations in the cellular AMP/ATP ratio. Further demonstrating that AMPK is regulated by the HBP, we found that AMPK was recognized by succinylated wheat germ agglutinin, which specifically binds O-GlcNAc. The levels of AMPK in succinylated wheat germ agglutinin precipitates correlated with hexosamine flux in mouse fat pads and 3T3L1 adipocytes. Moreover, removal of O-GlcNAc by hexosaminidase reduced AMPK activity. We conclude that chronically high hexosamine flux stimulates fatty acid oxidation by activating AMPK in adipocytes, in part through O-linked glycosylation.     

Robust smooth segmentation approach for array CGH data analysis

MOTIVATION: Array comparative genomic hybridization (aCGH) provides a genome-wide technique to screen for copy number alteration. The existing segmentation approaches for analyzing aCGH data are based on modeling data as a series of discrete segments with unknown boundaries and unknown heights. Although the biological process of copy number alteration is discrete, in reality a variety of biological and experimental factors can cause the signal to deviate from a stepwise function. To take this into account, we propose a smooth segmentation (smoothseg) approach. METHODS: To achieve a robust segmentation, we use a doubly heavy-tailed random-effect model. The first heavy-tailed structure on the errors deals with outliers in the observations, and the second deals with possible jumps in the underlying pattern associated with different segments. We develop a fast and reliable computational procedure based on the iterative weighted least-squares algorithm with band-limited matrix inversion. RESULTS: Using simulated and real data sets, we demonstrate how smoothseg can aid in identification of regions with genomic alteration and in classification of samples. For the real data sets, smoothseg leads to smaller false discovery rate and classification error rate than the circular binary segmentation (CBS) algorithm. In a realistic simulation setting, smoothseg is better than wavelet smoothing and CBS in identification of regions with genomic alterations and better than CBS in classification of samples. For comparative analyses, we demonstrate that segmenting the t-statistics performs better than segmenting the data. AVAILABILITY: The R package smoothseg to perform smooth segmentation is available from http://www.meb.ki.se/~yudpaw.     

Metabolic network properties help assign weights to elementary modes to understand physiological flux distributions

MOTIVATION: Elementary modes (EMs) analysis has been well established. The existing methodologies for assigning weights to EMs cannot be directly applied for large-scale metabolic networks, since the tremendous number of modes would make the computation a time-consuming or even an impossible mission. Therefore, developing more efficient methods to deal with large set of EMs is urgent. RESULT: We develop a method to evaluate the performance of employing a subset of the elementary modes to reconstruct a real flux distribution by using the relative error between the real flux vector and the reconstructed one as an indicator. We have found a power function relationship between the decrease of relative error and the increase of the number of the selecting EMs, and a logarithmic relationship between the increases of the number of non-zero weighted EMs and that of the number of the selecting EMs. Our discoveries show that it is possible to reconstruct a given flux distribution by a selected subset of EMs from a large metabolic network and furthermore, they help us identify the 'governing modes' to represent the cellular metabolism for such a condition.     

Multidimensional local false discovery rate for microarray studies

MOTIVATION: The false discovery rate (fdr) is a key tool for statistical assessment of differential expression (DE) in microarray studies. Overall control of the fdr alone, however, is not sufficient to address the problem of genes with small variance, which generally suffer from a disproportionally high rate of false positives. It is desirable to have an fdr-controlling procedure that automatically accounts for gene variability. METHODS: We generalize the local fdr as a function of multiple statistics, combining a common test statistic for assessing DE with its standard error information. We use a non-parametric mixture model for DE and non-DE genes to describe the observed multi-dimensional statistics, and estimate the distribution for non-DE genes via the permutation method. We demonstrate this fdr2d approach for simulated and real microarray data. RESULTS: The fdr2d allows objective assessment of DE as a function of gene variability. We also show that the fdr2d performs better than commonly used modified test statistics. AVAILABILITY: An R-package OCplus containing functions for computing fdr2d() and other operating characteristics of microarray data is available at http://www.meb.ki.se/~yudpaw.     

Finding regions of significance in SELDI measurements for identifying protein biomarkers

MOTIVATION: There is a well-recognized potential of protein expression profiling using the surface-enhanced laser desorption and ionization technology for discovering biomarkers that can be applied in clinical diagnosis, prognosis and therapy prediction. The pre-processing of the raw data, however, is still problematic. METHODS: We focus on the peak detection step, where the standard method is marked by poor specificity. Currently, scientists need to inspect individual spectra visually and laboriously in order to verify that spectral peaks identified by the standard method are real. Motivated by this multi-spectral process, we investigate an analytical approach-called RS for 'regions of significance'-that reduces the data to a single spectrum of F-statistics capturing significant variability between spectra. To account for multiple testing, we use a false discovery rate criterion for identifying potentially interesting proteins. RESULTS: We show that RS has better operating characteristics than several existing methods and demonstrate routine applications on a number of large datasets.