Phototropism of rice (Oryza sativa L.) coleoptiles: fluence-response relationships, kinetics and photogravitropic equilibrium

Phototropism of rice (Oryza sativa L.) coleoptiles induced by unilateral blue light was characterized using red-light-grown seedlings. Phototropic fluence-response relationships, investigated mainly with submerged coleoptiles, revealed three response types previously identified in oat and maize coleoptiles: two pulse-induced positive phototropisms and a phototropism that depended on stimulation time. The effective ranges of fluences and fluence rates were comparable to those reported for maize. Compared with oats and maize, however, curvature responses in rice were much smaller and coleoptiles straightened faster after establishing the maximal curvature. When stimulated continuously, submerged coleoptiles developed curvature slowly over a period of 6 h, whereas air-grown coleoptiles, which showed smaller phototropic responsiveness, established a photogravitropic equilibrium from about 4 h of stimulation. The plot of the equilibrium angle against log fluence rates yielded a bell-shaped optimum curve that spanned over a relatively wide fluence-rate range; a maximal curvature of 25° occurred at a fluence rate of 1 μmol · m⁻² · s⁻¹. This optimum curve apparently reflects the light sensitivity of the steady-state phototropic response. Received: 28 June 1996 / Accepted: 30 July 1996     

The heat shock protein HSP70 promotes mouse NK cell activity against tumors that express inducible NKG2D ligands

The stress-inducible heat shock protein (HSP) 70 is known to function as an endogenous danger signal that can increase the immunogenicity of tumors and induce CTL responses. We show in this study that HSP70 also activates mouse NK cells that recognize stress-inducible NKG2D ligands on tumor cells. Tumor size and the rate of metastases derived from HSP70-overexpressing human melanoma cells were found to be reduced in T and B cell-deficient SCID mice, but not in SCID/beige mice that lack additionally functional NK cells. In the SCID mice with HSP70-overexpressing tumors, NK cells were activated so that they killed ex vivo tumor cells that expressed NKG2D ligands. In the tumors, the MHC class I chain-related (MIC) A and B molecules were found to be expressed. Interestingly, a counter selection was observed against the expression of MICA/B in HSP70-overexpressing tumors compared with control tumors in SCID, but not in SCID/beige mice, suggesting a functional relevance of MICA/B expression. The melanoma cells were found to release exosomes. HSP70-positive exosomes from the HSP70-overexpressing cells, in contrast to HSP70-negative exosomes from the control cells, were able to activate mouse NK cells in vitro to kill YAC-1 cells, which express NKG2D ligands constitutively, or the human melanoma cells, in which MICA/B expression was induced. Thus, HSP70 and inducible NKG2D ligands synergistically promote the activation of mouse NK cells resulting in a reduced tumor growth and suppression of metastatic disease.     

Methods to account for spatial autocorrelation in the analysis of species distributional data: a review

Species distributional or trait data based on range map (extent‐of‐occurrence) or atlas survey data often display spatial autocorrelation, i.e. locations close to each other exhibit more similar values than those further apart. If this pattern remains present in the residuals of a statistical model based on such data, one of the key assumptions of standard statistical analyses, that residuals are independent and identically distributed (i.i.d), is violated. The violation of the assumption of i.i.d. residuals may bias parameter estimates and can increase type I error rates (falsely rejecting the null hypothesis of no effect). While this is increasingly recognised by researchers analysing species distribution data, there is, to our knowledge, no comprehensive overview of the many available spatial statistical methods to take spatial autocorrelation into account in tests of statistical significance. Here, we describe six different statistical approaches to infer correlates of species’ distributions, for both presence/absence (binary response) and species abundance data (poisson or normally distributed response), while accounting for spatial autocorrelation in model residuals: autocovariate regression; spatial eigenvector mapping; generalised least squares; (conditional and simultaneous) autoregressive models and generalised estimating equations. A comprehensive comparison of the relative merits of these methods is beyond the scope of this paper. To demonstrate each method's implementation, however, we undertook preliminary tests based on simulated data. These preliminary tests verified that most of the spatial modeling techniques we examined showed good type I error control and precise parameter estimates, at least when confronted with simplistic simulated data containing spatial autocorrelation in the errors. However, we found that for presence/absence data the results and conclusions were very variable between the different methods. This is likely due to the low information content of binary maps. Also, in contrast with previous studies, we found that autocovariate methods consistently underestimated the effects of environmental controls of species distributions. Given their widespread use, in particular for the modelling of species presence/absence data (e.g. climate envelope models), we argue that this warrants further study and caution in their use. To aid other ecologists in making use of the methods described, code to implement them in freely available software is provided in an electronic appendix.     

Inferring topology from clustering coefficients in protein-protein interaction networks

Background  

Although protein-protein interaction networks determined with high-throughput methods are incomplete, they are commonly used to infer the topology of the complete interactome. These partial networks often show a scale-free behavior with only a few proteins having many and the majority having only a few connections. Recently, the possibility was suggested that this scale-free nature may not actually reflect the topology of the complete interactome but could also be due to the error proneness and incompleteness of large-scale experiments.     

Observations below multiple lower limits of quantification: How to estimate the mean and variance

Multiple lower limits of quantification (MLOQs) result if various laboratories are involved in the analysis of concentration data and some observations are too low to be quantified. For normally distributed data under MLOQs there exists only the multiple regression method of Helsel to estimate the mean and variance. We propose a simple imputation method and two new maximum likelihood estimation methods: the multiple truncated sample method and the multiple censored sample method. A simulation study is conducted to compare the performances of the newly introduced methods to Helsel's via the criteria root mean squared error (RMSE) and bias of the parameter estimates. Two and four lower limits of quantification (LLOQs), various amounts of unquantifiable observations and two sample sizes are studied. Furthermore, the robustness is investigated under model misspecification. The methods perform with decreasing accuracy for increasing rates of unquantified observations. Increasing sample sizes lead to smaller bias. There is almost no change in the performance between two and four LLOQs. The magnitude of the variance impairs the performance of all methods. For a smaller variance, the multiple censored sample method leads to superior estimates regarding the RMSE and bias, whereas Helsel's method is superior regarding the bias for a larger variance. Under model misspecification, Helsel's method was inferior to the other methods. Estimating the mean, the multiple censored sample method performed better, whereas the multiple truncated sample method performs best in estimating the variance. Summarizing, for a large sample size and normally distributed data we recommend to use Helsel's method. Otherwise, the multiple censored sample method should be used to obtain estimates of the mean and variance of data including MLOQs.     

Modification of tyrosine hydroxylase activity by chloral derived beta-carbolines in vitro

Beta-carbolines have been suggested to be involved in the pathogenesis of Parkinson's disease as a result of their structural similarity to the neurotoxin N -methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP). The chloral-derived beta-carboline derivative 1-trichloromethyl-1,2,3,4-tetrahydro-beta-carboline (TaClo) causes cell loss in neuronal and glial cell cultures and induces a slowly developing neurodegenerative process in rats. In our experiments, effects of TaClo and its derivatives 2-methyl-TaClo (2-Me-TaClo), and 1-dichloromethylene-1,2,3,4-tetrahydro-beta-carboline (1-CCl(2) -THbetaC) on tyrosine hydroxylase (TH) activity were investigated in TH assays using homogenate preparations of the rat nucleus accumbens and recombinant human TH (hTH1). TH activity was determined in vitro by measuring l-DOPA production with HPLC-ECD. Using homogenate preparations, TaClo, 2-Me-TaClo, and 1-CCl(2) -THbetaC inhibited TH in concentrations of 0.1 mm, while 1-CCl(2) -THbetaC in low concentrations enhanced TH activity. When TH was activated by PACAP-27, TaClo, 2-Me-TaClo, or 1-CCl(2) -THbetaC also inhibited activated enzyme activity in high concentrations. However, in the case of 2-Me-TaClo and 1-CCl(2) -THbetaC a biphasic effect was observed with a marked increase of TH activity in the nanomolar range. In our experiments using recombinant hTH1, TaClo, 2-Me-TaClo, or 1-CCl(2) -THbetaC did not modify enzyme activity. After activation of hTH1 by PKA all the tetrahydro-beta-carbolines investigated in this study decreased l-DOPA formation. We suggest that these beta-carbolines modulate dopamine synthesis by interacting with a protein kinase TH-activating system.     

Kinetic screening of antibody-Im7 conjugates by capture on a colicin E7 DNase domain using optical biosensors

Antibody generation by phage display and related in vitro display technologies routinely yields large panels of clones detected in primary end-point screenings such as enzyme-linked immunosorbent assay (ELISA). However, for the development of clinical lead candidates, rapid determination of secondary characteristics such as kinetics and thermodynamics is of nearly equal importance. Surface plasmon resonance-based biosensors are ideal tools for carrying out such high-throughput secondary screenings, allowing preliminary but confident ranking and identification of lead clones. A key feature of these assays is the stable and reversible capture of antibody fragments from crude samples leading to high-resolution kinetic analysis of library outputs. Here we exploit the high-affinity interaction between the naturally occurring nuclease domain of E. coli colicin E7 (DNaseE7) and its cognate partner, the immunity protein 7 (Im7), to develop a ligand capture system suitable for accurate kinetic ranking of library clones. We demonstrate generic applicability for a range of antibody formats: scFv antibodies, diabodies, antigen binding fragments (Fabs), and shark V_NAR single domain antibodies. The system is adaptable and reproducible, with comparable results achieved for both the Biacore T100 and ProteOn XPR36 array biosensors.