Biology and interactions of two distinct monopartite begomoviruses and betasatellites associated with radish leaf curl disease in India

Background

Emerging whitefly transmitted begomoviruses are major pathogens of vegetable and fibre crops throughout the world, particularly in tropical and sub-tropical regions. Mutation, pseudorecombination and recombination are driving forces for the emergence and evolution of new crop-infecting begomoviruses. Leaf curl disease of field grown radish plants was noticed in Varanasi and Pataudi region of northern India. We have identified and characterized two distinct monopartite begomoviruses and associated beta satellite DNA causing leaf curl disease of radish (Raphanus sativus) in India.

Results

We demonstrate that RaLCD is caused by a complex of two Old World begomoviruses and their associated betasatellites. Radish leaf curl virus-Varanasi is identified as a new recombinant species, Radish leaf curl virus (RaLCV) sharing maximum nucleotide identity of 87.7% with Tomato leaf curl Bangladesh virus-[Bangladesh:2] (Accession number AF188481) while the virus causing radish leaf curl disease-Pataudi is an isolate of Croton yellow vein mosaic virus-[India] (CYVMV-IN) (Accession number AJ507777) sharing 95.8% nucleotide identity. Further, RDP analysis revealed that the RaLCV has a hybrid genome, a putative recombinant between Euphorbia leaf curl virus and Papaya leaf curl virus. Cloned DNA of either RaLCV or CYVMV induced mild leaf curl symptoms in radish plants. However, when these clones (RaLCV or CYVMV) were individually co-inoculated with their associated cloned DNA betasatellite, symptom severity and viral DNA levels were increased in radish plants and induced typical RaLCD symptoms. To further extend these studies, we carried out an investigation of the interaction of these radish-infecting begomoviruses and their associated satellite, with two tomato infecting begomoviruses (Tomato leaf curl Gujarat virus and Tomato leaf curl New Delhi virus). Both of the tomato-infecting begomoviruses showed a contrasting and differential interaction with DNA satellites, not only in the capacity to interact with these molecules but also in the modulation of symptom phenotypes by the satellites.

Conclusion

This is the first report and experimental demonstration of Koch's postulate for begomoviruses associated with radish leaf curl disease. Further observations also provide direct evidence of lateral movement of weed infecting begomovirus in the cultivated crops and the present study also suggests that the exchange of betasatellites with other begomoviruses would create a new disease complex posing a serious threat to crop production.     

UV radiation reduces epidermal cell expansion in Arabidopsis thaliana leaves without altering cellular microtubule organization

Upon chronic UV treatment pavement cell expansion in Arabidopsis leaves is reduced, implying alterations in symplastic and apoplastic properties of the epidermal cells. In this study, the effect of UV radiation on microtubule patterning is analysed, as microtubules are thought to serve as guiding rails for the cellulose synthase complexes depositing cellulose microfibrils. Together with hemicelluloses, these microfibrils are regarded as the load-bearing components of the cell wall. Leaves of transgenic plants with fluorescently tagged microtubules (GFP-TUA6) were as responsive to UV as wild type plants. Despite the UV-induced reduction in cell elongation, confocal microscopy revealed that cellular microtubule arrangements were seemingly not affected by the UV treatments. This indicates an unaltered deposition of cellulose microfibrils in the presence of UV radiation. Therefore, we surmise that the reduction in cell expansion in UV-treated leaves is most probably due to changes in cell wall loosening and/or turgor pressure.Key words: arabidopsis, cell expansion, GFP-TUA6, leaf development, microtubule cytoskeleton, UV radiationPhotosynthetic functions such as solar light capture and carbon fixation are highly evolved features of plant leaves. To fulfil these functions in an optimal way, leaf development needs to be tuned to environmental conditions. Leaves are continuously exposed and subjected to environmental influences, which serve as co-regulators of leaf and plant development.¹ This ability of plants to adapt, secures the plant''s survival, even under non-optimal conditions. An example of a regulatory environmental parameter is solar light, indispensable for photosynthesis but potentially causing photoinhibition and/or UV-radiation stress. The highly energetic ultraviolet B (UV-B) rays of short wavelengths (280–315 nm) can both cause damage, as well as induce a range of specific metabolic and morphogenic plant responses. It was reported before that exposure to low dose UV radiation reduces Arabidopsis leaf size due to a decreased cell size.² Expansion of leaf epidermal cells of Arabidopsis thaliana is the combined action of promotion and restriction of growth, resulting in the typical irregular sinuous pavement cells. It has been postulated that cellulose microfibrils are responsible for generating a force opposing isotropic expansion by creating neck regions in between outgrowing lobes.³ As the microtubule cytoskeleton is believed to serve as guiding rails for the cellulose synthase complexes (CESAs),⁴ the deposition of the cellulose fibrils is intimately linked to the cortical microtubule arrangement. We have studied the UV-effect on microtubule organisation in leaf epidermal cells whose expansion had decreased upon this UV radiation. Microtubules in the adaxial pavement cells of the fourth leaf were monitored on several successive days in a transgenic line containing GFP fused to tubulin A6.⁵ The chronic UV treatment was started on day 0 when the plants were 2 weeks old, using UV exposure conditions as described in reference ². First the responsiveness of the GFP-TUA6 plants to UV radiation was evaluated. Similar to wild type (WT) plants,² the GFP-TUA6 plants had smaller leaves following 8 days of UV treatment (t-test, p < 0.01) (Fig. 1). This was caused by a significant reduction in the generalized cell area average of all measured cells, irrespective of the location within the leaf (Fig. 1; t-test, p < 0.01). In more detail, the average cell area within the base, middle and top zones of the GFP-TUA6 leaf was systematically lower in UV-treated leaves from 8 days after the treatment started onwards (data not shown).Open in a separate windowFigure 1Effect of UV radiation on leaf and cell area after different days of UV radiation. Open asterisks indicate a statistically significant difference in leaf area between UV-treated and control plants, black asterisks indicate statistically significant difference in cell area (t-test, *p < 0.05, **p < 0.01, ***p < 0.001). Error bars indicate the standard error for five different leaves at all measured time-points and 600, 170 and 180 cells at day 0, 8 and 12 respectively.As GFP-TUA6 leaves were as responsive to UV radiation as wild type leaves, confocal microscopy was used to visualize the organisation of the cortical microtubules facing the outer periclinal wall of the adaxial epidermis. No clear difference in microtubule (re)organization could be detected during the development of pavement cells, and throughout the UV treatment period. As shown in Figure 2 at day 2, pavement cells with comparable areas are similarly shaped in control and UV-irradiated plants and contain similar microtubule arrangements (Fig. 2 and marked cells). This means that microtubule organization is not directly affected by the UV exposure and that shape development proceeds in an analoguous manner as under control conditions. This lack of alteration in the microtubule arrangement can be observed for cells at the leaf tip, which were already in the process of lobe formation at the start of the exposure period, as well as for cells at the leaf base. Under our growth conditions, and in the monitored leaf number 4, cell proliferation still took place in this part of the leaf and lobes only started to appear on the cell surface. As microtubules are linked to the deposition of cellulose microfibrils, it can be assumed that no alterations in cellulose deposition occur upon UV treatment either. We can therefore conclude that the process of lobe formation and microtubule patterning is not impeded and that only the extent of cell expansion is restricted upon UV exposure.Open in a separate windowFigure 2Microtubule pattern in control and UV-exposed leaves visualized using GFP-TUA6 and confocal microscopy. Both images are from cells at the mid zone of the fourth leaf at day 2. Microtubules are similarly arranged in equally shaped and sized cells of control and UV-exposed leaves. The marked cells show a pattern whereby the tubules are centred in the neck regions between two outgrowing lobes.According to the Lockhart equation,⁶ cell (wall) growth is modulated by wall biomechanics and turgor pressure. Concerning turgor pressure, no clear differences in this factor between UV-exposed and control plants of Lactuca sativa L.⁷ and Pisum sativum⁸ could be observed, reinforcing the idea that especially the modulation of cell wall properties is the main factor causing the observed UV-induced reduction in cell expansion. Some reports indicate differential expression of wall loosening enzymes like expansins or xyloglucan endotransglycosylase/hydrolases (XTHs),^9,10 or cell wall strengthening enzymes as particular peroxidases⁷ after UV exposure. Another key event could involve UV-mediated changes in the phenylpropanoid pathway, which may cause changes in the lignin biosynthesis. As shown by the literature^11–14 lignin may well be an important modulator of cell wall architecture in Arabidopsis and therefore alterations in lignin synthesis could form the basis for morphological modifications. Further research on the cell wall properties of UV-treated plants may resolve this uncertainty.As a general conclusion we can state that the patterning of microtubules is not altered, but that alterations in cell wall composition or arrangements are the most plausible candidates for the observed reduction in pavement cell expansion upon chronic UV treatment.     

A Critical Assessment of Feature Selection Methods for Biomarker Discovery in Clinical Proteomics

In this paper, we compare the performance of six different feature selection methods for LC-MS-based proteomics and metabolomics biomarker discovery—t test, the Mann–Whitney–Wilcoxon test (mww test), nearest shrunken centroid (NSC), linear support vector machine–recursive features elimination (SVM-RFE), principal component discriminant analysis (PCDA), and partial least squares discriminant analysis (PLSDA)—using human urine and porcine cerebrospinal fluid samples that were spiked with a range of peptides at different concentration levels. The ideal feature selection method should select the complete list of discriminating features that are related to the spiked peptides without selecting unrelated features. Whereas many studies have to rely on classification error to judge the reliability of the selected biomarker candidates, we assessed the accuracy of selection directly from the list of spiked peptides. The feature selection methods were applied to data sets with different sample sizes and extents of sample class separation determined by the concentration level of spiked compounds. For each feature selection method and data set, the performance for selecting a set of features related to spiked compounds was assessed using the harmonic mean of the recall and the precision (f-score) and the geometric mean of the recall and the true negative rate (g-score). We conclude that the univariate t test and the mww test with multiple testing corrections are not applicable to data sets with small sample sizes (n = 6), but their performance improves markedly with increasing sample size up to a point (n > 12) at which they outperform the other methods. PCDA and PLSDA select small feature sets with high precision but miss many true positive features related to the spiked peptides. NSC strikes a reasonable compromise between recall and precision for all data sets independent of spiking level and number of samples. Linear SVM-RFE performs poorly for selecting features related to the spiked compounds, even though the classification error is relatively low.Biomarkers play an important role in advancing medical research through the early diagnosis of disease and prognosis of treatment interventions (1, 2). Biomarkers may be proteins, peptides, or metabolites, as well as mRNAs or other kinds of nucleic acids (e.g. microRNAs) whose levels change in relation to the stage of a given disease and which may be used to accurately assign the disease stage of a patient. The accurate selection of biomarker candidates is crucial, because it determines the outcome of further validation studies and the ultimate success of efforts to develop diagnostic and prognostic assays with high specificity and sensitivity. The success of biomarker discovery depends on several factors: consistent and reproducible phenotyping of the individuals from whom biological samples are obtained; the quality of the analytical methodology, which in turn determines the quality of the collected data; the accuracy of the computational methods used to extract quantitative and molecular identity information to define the biomarker candidates from raw analytical data; and finally the performance of the applied statistical methods in the selection of a limited list of compounds with the potential to discriminate between predefined classes of samples. De novo biomarker research consists of a biomarker discovery part and a biomarker validation part (3). Biomarker discovery uses analytical techniques that try to measure as many compounds as possible in a relatively low number of samples. The goal of subsequent data preprocessing and statistical analysis is to select a limited number of candidates, which are subsequently subjected to targeted analyses in large number of samples for validation.Advanced technology, such as high-performance liquid chromatography–mass spectrometry (LC-MS),¹ is increasingly applied in biomarker discovery research. Such analyses detect tens of thousands of compounds, as well as background-related signals, in a single biological sample, generating enormous amounts of multivariate data. Data preprocessing workflows reduce data complexity considerably by trying to extract only the information related to compounds resulting in a quantitative feature matrix, in which rows and columns correspond to samples and extracted features, respectively, or vice versa. Features may also be related to data preprocessing artifacts, and the ratio of such erroneous features to compound-related features depends on the performance of the data preprocessing workflow (4). Preprocessed LC-MS data sets contain a large number of features relative to the sample size. These features are characterized by their m/z value and retention time, and in the ideal case they can be combined and linked to compound identities such as metabolites, peptides, and proteins. In LC-MS-based proteomics and metabolomics studies, sample analysis is so time consuming that it is practically impossible to increase the number of samples to a level that balances the number of features in a data set. Therefore, the success of biomarker discovery depends on powerful feature selection methods that can deal with a low sample size and a high number of features. Because of the unfavorable statistical situation and the risk of overfitting the data, it is ultimately pivotal to validate the selected biomarker candidates in a larger set of independent samples, preferably in a double-blinded fashion, using targeted analytical methods (1).Biomarker selection is often based on classification methods that are preceded by feature selection methods (filters) or which have built-in feature selection modules (wrappers and embedded methods) that can be used to select a list of compounds/peaks/features that provide the best classification performance for predefined sample groups (e.g. healthy versus diseased) (5). Classification methods are able to classify an unknown sample into a predefined sample class. Univariate feature selection methods such as filters (t test or Wilcoxon–Mann–Whitney tests) cannot be used for sample classification. Other classification methods such as the nearest shrunken centroid method have intrinsic feature selection ability, whereas other classification methods such as principal component discriminant analysis (PCDA) and partial least squares regression coupled with discriminant analysis (PLSDA) should be augmented with a feature selection method. There are classifiers having no feature selection option that perform the classification using all variables, such as support vector machines that use non-linear kernels (6). Classification methods without the ability to select features cannot be used for biomarker discovery, because these methods aim to classify samples into predefined classes but cannot identify the limited number of variables (features or compounds) that form the basis of the classification (6, 7). Different statistical methods with feature selection have been developed according to the complexity of the analyzed data, and these have been extensively reviewed (5, 6, 8, 9). Ways of optimizing such methods to improve sensitivity and specificity are a major topic in current biomarker discovery research and in the many “omics-related” research areas (6, 10, 11). Comparisons of classification methods with respect to their classification and learning performance have been initiated. Van der Walt et al. (12) focused on finding the most accurate classifiers for simulated data sets with sample sizes ranging from 20 to 100. Rubingh et al. (13) compared the influence of sample size in an LC-MS metabolomics data set on the performance of three different statistical validation tools: cross validation, jack-knifing model parameters, and a permutation test. That study concluded that for small sample sets, the outcome of these validation methods is influenced strongly by individual samples and therefore cannot be trusted, and the validation tool cannot be used to indicate problems due to sample size or the representativeness of sampling. This implies that reducing the dimensionality of the feature space is critical when approaching a classification problem in which the number of features exceeds the number of samples by a large margin. Dimensionality reduction retains a smaller set of features to bring the feature space in line with the sample size and thus allow the application of classification methods that perform with acceptable accuracy only when the sample size and the feature size are similar.In this study we compared different classification methods focusing on feature selection in two types of spiked LC-MS data sets that mimic the situation of a biomarker discovery study. Our results provide guidelines for researchers who will engage in biomarker discovery or other differential profiling “omics” studies with respect to sample size and selecting the most appropriate feature selection method for a given data set. We evaluated the following approaches: univariate t test and Mann–Whitney–Wilcoxon test (mww test) with multiple testing correction (14), nearest shrunken centroid (NSC) (15, 16), support vector machine–recursive features elimination (SVM-RFE) (17), PLSDA (18), and PCDA (19). PCDA and PLSDA were combined with the rank-product as a feature selection criterion (20). These methods were evaluated with data sets having three characteristics: different biological background, varying sample size, and varying within- and between-class variability of the added compounds. Data were acquired via LC-MS from human urine and porcine cerebrospinal fluid (CSF) samples that were spiked with a set of known peptides (true positives) at different concentration levels. These samples were then combined in two classes containing peptides spiked at low and high concentration levels. The performance of the classification methods with feature selection was measured based on their ability to select features that were related to the spiked peptides. Because true positives were known in our data set, we compared performance based on the f-score (the harmonic mean of precision and recall) and the g-score (the geometric mean of accuracy).     

Auditory pre-experience modulates classification of affect intensity: evidence for the evaluation of call salience by a non-human mammal,the bat <Emphasis Type="Italic">Megaderma lyra</Emphasis>

Introduction

Immediate responses towards emotional utterances in humans are determined by the acoustic structure and perceived relevance, i.e. salience, of the stimuli, and are controlled via a central feedback taking into account acoustic pre-experience. The present study explores whether the evaluation of stimulus salience in the acoustic communication of emotions is specifically human or has precursors in mammals. We created different pre-experiences by habituating bats (Megaderma lyra) to stimuli based on aggression, and response, calls from high or low intensity level agonistic interactions, respectively. Then we presented a test stimulus of opposite affect intensity of the same call type. We compared the modulation of response behaviour by affect intensity between the reciprocal experiments.

Results

For aggression call stimuli, the bats responded to the dishabituation stimuli independent of affect intensity, emphasising the attention-grabbing function of this call type. For response call stimuli, the bats responded to a high affect intensity test stimulus after experiencing stimuli of low affect intensity, but transferred habituation to a low affect intensity test stimulus after experiencing stimuli of high affect intensity. This transfer of habituation was not due to over-habituation as the bats responded to a frequency-shifted control stimulus. A direct comparison confirmed the asymmetric response behaviour in the reciprocal experiments.

Conclusions

Thus, the present study provides not only evidence for a discrimination of affect intensity, but also for an evaluation of stimulus salience, suggesting that basic assessment mechanisms involved in the perception of emotion are an ancestral trait in mammals.

     

Reflections on univariate and multivariate analysis of metabolomics data

Metabolomics experiments usually result in a large quantity of data. Univariate and multivariate analysis techniques are routinely used to extract relevant information from the data with the aim of providing biological knowledge on the problem studied. Despite the fact that statistical tools like the t test, analysis of variance, principal component analysis, and partial least squares discriminant analysis constitute the backbone of the statistical part of the vast majority of metabolomics papers, it seems that many basic but rather fundamental questions are still often asked, like: Why do the results of univariate and multivariate analyses differ? Why apply univariate methods if you have already applied a multivariate method? Why if I do not see something univariately I see something multivariately? In the present paper we address some aspects of univariate and multivariate analysis, with the scope of clarifying in simple terms the main differences between the two approaches. Applications of the t test, analysis of variance, principal component analysis and partial least squares discriminant analysis will be shown on both real and simulated metabolomics data examples to provide an overview on fundamental aspects of univariate and multivariate methods.     

Population-based nutrikinetic modeling of polyphenol exposure

The beneficial health effects of fruits and vegetables have been attributed to their polyphenol content. These compounds undergo many bioconversions in the body. Modeling polyphenol exposure of humans upon intake is a prerequisite for understanding the modulating effect of the food matrix and the colonic microbiome. This modeling is not a trivial task and requires a careful integration of measuring techniques, modeling methods and experimental design. Moreover, both at the population level as well as the individual level polyphenol exposure has to be quantified and assessed. We developed a strategy to quantify polyphenol exposure based on the concept of nutrikinetics in combination with population-based modeling. The key idea of the strategy is to derive nutrikinetic model parameters that summarize all information of the polyphenol exposure at both individual and population level. This is illustrated by a placebo-controlled crossover study in which an extract of wine/grapes and black tea solids was administered to twenty subjects. We show that urinary and plasma nutrikinetic time-response curves can be used for phenotyping the gut microbial bioconversion capacity of individuals. Each individual harbours an intrinsic microbiota composition converting similar polyphenols from both test products in the same manner and stable over time. We demonstrate that this is a novel approach for associating the production of two gut-mediated γ-valerolactones to specific gut phylotypes. The large inter-individual variation in nutrikinetics and γ-valerolactones production indicated that gut microbial metabolism is an essential factor in polyphenol exposure and related potential health benefits.     

Towards a Hierarchical Strategy to Explore Multi-Scale IP/MS Data for Protein Complexes

Protein interaction in cells can be described at different levels. At a low interaction level, proteins function together in small, stable complexes and at a higher level, in sets of interacting complexes. All interaction levels are crucial for the living organism, and one of the challenges in proteomics is to measure the proteins at their different interaction levels. One common method for such measurements is immunoprecipitation followed by mass spectrometry (IP/MS), which has the potential to probe the different protein interaction forms. However, IP/MS data are complex because proteins, in their diverse interaction forms, manifest themselves in different ways in the data. Numerous bioinformatic tools for finding protein complexes in IP/MS data are currently available, but most tools do not provide information about the interaction level of the discovered complexes, and no tool is geared specifically to unraveling and visualizing these different levels. We present a new bioinformatic tool to explore IP/MS datasets for protein complexes at different interaction levels and show its performance on several real–life datasets. Our tool creates clusters that represent protein complexes, but unlike previous methods, it arranges them in a tree–shaped structure, reporting why specific proteins are predicted to build a complex and where it can be divided into smaller complexes. In every data analysis method, parameters have to be chosen. Our method can suggest values for its parameters and comes with adapted visualization tools that display the effect of the parameters on the result. The tools provide fast graphical feedback and allow the user to interact with the data by changing the parameters and examining the result. The tools also allow for exploring the different organizational levels of the protein complexes in a given dataset. Our method is available as GNU-R source code and includes examples at www.bdagroup.nl.     

Oseltamivir-resistant pandemic (H1N1)2009 in Yemen - case report

To investigate West Nile virus (WNV) circulation in rural populations in Gabon, we undertook a large serological survey focusing on human rural populations, using two different ELISA assays. A sample was considered positive when it reacted in both tests. A total of 2320 villagers from 115 villages were interviewed and sampled. Surprisingly, the WNV-specific IgG prevalence was high overall (27.2%) and varied according to the ecosystem: 23.7% in forested regions, 21.8% in savanna, and 64.9% in the lakes region. The WNV-specific IgG prevalence rate was 30% in males and 24.6% in females, and increased with age. Although serological cross-reactions between flaviviruses are likely and may be frequent, these findings strongly suggest that WNV is widespread in Gabon. The difference in WNV prevalence among ecosystems suggests preferential circulation in the lakes region. The linear increase with age suggests continuous exposure of Gabonese populations to WNV. Further investigations are needed to determine the WNV cycle and transmission patterns in Gabon.     

Metabolic Profiling of the Response to an Oral Glucose Tolerance Test Detects Subtle Metabolic Changes

Background

The prevalence of overweight is increasing globally and has become a serious health problem. Low-grade chronic inflammation in overweight subjects is thought to play an important role in disease development. Novel tools to understand these processes are needed. Metabolic profiling is one such tool that can provide novel insights into the impact of treatments on metabolism.

Methodology

To study the metabolic changes induced by a mild anti-inflammatory drug intervention, plasma metabolic profiling was applied in overweight human volunteers with elevated levels of the inflammatory plasma marker C-reactive protein. Liquid and gas chromatography mass spectrometric methods were used to detect high and low abundant plasma metabolites both in fasted conditions and during an oral glucose tolerance test. This is based on the concept that the resilience of the system can be assessed after perturbing a homeostatic situation.

Conclusions

Metabolic changes were subtle and were only detected using metabolic profiling in combination with an oral glucose tolerance test. The repeated measurements during the oral glucose tolerance test increased statistical power, but the metabolic perturbation also revealed metabolites that respond differentially to the oral glucose tolerance test. Specifically, multiple metabolic intermediates of the glutathione synthesis pathway showed time-dependent suppression in response to the glucose challenge test. The fact that this is an insulin sensitive pathway suggests that inflammatory modulation may alter insulin signaling in overweight men.     

Integrating functional genomics data using maximum likelihood based simultaneous component analysis

Background  

In contemporary biology, complex biological processes are increasingly studied by collecting and analyzing measurements of the same entities that are collected with different analytical platforms. Such data comprise a number of data blocks that are coupled via a common mode. The goal of collecting this type of data is to discover biological mechanisms that underlie the behavior of the variables in the different data blocks. The simultaneous component analysis (SCA) family of data analysis methods is suited for this task. However, a SCA may be hampered by the data blocks being subjected to different amounts of measurement error, or noise. To unveil the true mechanisms underlying the data, it could be fruitful to take noise heterogeneity into consideration in the data analysis. Maximum likelihood based SCA (MxLSCA-P) was developed for this purpose. In a previous simulation study it outperformed normal SCA-P. This previous study, however, did not mimic in many respects typical functional genomics data sets, such as, data blocks coupled via the experimental mode, more variables than experimental units, and medium to high correlations between variables. Here, we present a new simulation study in which the usefulness of MxLSCA-P compared to ordinary SCA-P is evaluated within a typical functional genomics setting. Subsequently, the performance of the two methods is evaluated by analysis of a real life Escherichia coli metabolomics data set.