Caroline Truntzer

Interplays between nitric oxide and reactive oxygen species in cryptogein signalling

Nitric oxide (NO) has many functions in plants. Here, we investigated its interplays with reactive oxygen species (ROS) in the defence responses triggered by the elicitin cryptogein. The production of NO induced by cryptogein in tobacco cells was partly regulated through a ROS-dependent pathway involving the NADPH oxidase NtRBOHD. In turn, NO down-regulated the level of H2O2. Both NO and ROS synthesis appeared to be under the control of type-2 histone deacetylases acting as negative regulators of cell death. Occurrence of an interplay between NO and ROS was further supported by the finding that cryptogein triggered a production of peroxynitrite (ONOO(-)). Next, we showed that ROS, but not NO, negatively regulate the intensity of activity of the cryptogein-induced protein kinase NtOSAK. Furthermore, using a DNA microarray approach, we identified 15 genes early induced by cryptogein via NO. A part of these genes was also modulated by ROS and encoded proteins showing sequence identity to ubiquitin ligases. Their expression appeared to be negatively regulated by ONOO(-), suggesting that ONOO(-) mitigates the effects of NO and ROS. Finally, we provided evidence that NO required NtRBOHD activity for inducing cell death, thus confirming previous assumption that ROS channel NO through cell death pathways.

Alpha-defensins secreted by dysplastic granulocytes inhibit the differentiation of monocytes in chronic myelomonocytic leukemia.

Chronic myelomonocytic leukemia (CMML) is a clonal hematopoietic disorder that occurs in elderly patients. One of the main diagnostic criteria is the accumulation of heterogeneous monocytes in the peripheral blood. We further explored this cellular heterogeneity and observed that part of the leukemic clone in the peripheral blood was made of immature dysplastic granulocytes with a CD14(-)/CD24(+) phenotype. The proteome profile of these cells is dramatically distinct from that of CD14(+)/CD24(-) monocytes from CMML patients or healthy donors. More specifically, CD14(-)/CD24(+) CMML cells synthesize and secrete large amounts of alpha-defensin 1-3 (HNP1-3). Recombinant HNPs inhibit macrophage colony-stimulating factor (M-CSF)-driven differentiation of human peripheral blood monocytes into macrophages. Using transwell, antibody-mediated depletion, suramin inhibition of purinergic receptors, and competitive experiments with uridine diphosphate (UDP)/uridine triphosphate (UTP), we demonstrate that HNP1-3 secreted by CD14(-)/CD24(+) cells inhibit M-CSF-induced differentiation of CD14(+)/CD24(-) cells at least in part through P2Y6, a receptor involved in macrophage differentiation. Altogether, these observations suggest that a population of immature dysplastic granulocytes contributes to the CMML phenotype through production of alpha-defensins HNP1-3 that suppress the differentiation capabilities of monocytes.

Human infant saliva peptidome is modified with age and diet transition

In order to describe developmental changes in human salivary peptidome, whole saliva was obtained from 98 infants followed longitudinally at 3 and 6months of age. Data on teeth eruption and diet at the age of 6months were also recorded. Salivary peptide extracts were characterised by label-free MALDI-MS. Peptides differentially expressed between the two ages, and those significantly affected by teeth eruption or introduction of solid foods were identified by MALDI TOF-TOF and LC ESI MS-MS. Out of 81 peaks retained for statistical analysis, 26 were overexpressed at the age of 6months. Exposure to solid foods had a more pronounced effect on profiles (overexpression of nine peaks) than teeth eruption (overexpression of one peak). Differential peaks corresponded to fragments of acidic and basic PRPs, statherin and histatin. Comparison with existing knowledge on adult saliva peptidome revealed that proteolytic processing of salivary proteins is qualitatively quite comparable in infants and in adults. However, age and diet are modulators of salivary peptidome in human infants.

Genetic diversity and trait genomic prediction in a pea diversity panel.

BackgroundPea (Pisum sativum L.), a major pulse crop grown for its protein-rich seeds, is an important component of agroecological cropping systems in diverse regions of the world. New breeding challenges imposed by global climate change and new regulations urge pea breeders to undertake more efficient methods of selection and better take advantage of the large genetic diversity present in the Pisum sativum genepool. Diversity studies conducted so far in pea used Simple Sequence Repeat (SSR) and Retrotransposon Based Insertion Polymorphism (RBIP) markers. Recently, SNP marker panels have been developed that will be useful for genetic diversity assessment and marker-assisted selection.ResultsA collection of diverse pea accessions, including landraces and cultivars of garden, field or fodder peas as well as wild peas was characterised at the molecular level using newly developed SNP markers, as well as SSR markers and RBIP markers. The three types of markers were used to describe the structure of the collection and revealed different pictures of the genetic diversity among the collection. SSR showed the fastest rate of evolution and RBIP the slowest rate of evolution, pointing to their contrasted mode of evolution. SNP markers were then used to predict phenotypes -the date of flowering (BegFlo), the number of seeds per plant (Nseed) and thousand seed weight (TSW)- that were recorded for the collection. Different statistical methods were tested including the LASSO (Least Absolute Shrinkage ans Selection Operator), PLS (Partial Least Squares), SPLS (Sparse Partial Least Squares), Bayes A, Bayes B and GBLUP (Genomic Best Linear Unbiased Prediction) methods and the structure of the collection was taken into account in the prediction. Despite a limited number of 331 markers used for prediction, TSW was reliably predicted.ConclusionThe development of marker assisted selection has not reached its full potential in pea until now. This paper shows that the high-throughput SNP arrays that are being developed will most probably allow for a more efficient selection in this species.

Multivariate denoising methods combining wavelets and principal component analysis for mass spectrometry data

The identification of new diagnostic or prognostic biomarkers is one of the main aims of clinical cancer research. In recent years, there has been a growing interest in using mass spectrometry for the detection of such biomarkers. The MS signal resulting from MALDI‐TOF measurements is contaminated by different sources of technical variations that can be removed by a prior pre‐processing step. In particular, denoising makes it possible to remove the random noise contained in the signal. Wavelet methodology associated with thresholding is usually used for this purpose. In this study, we adapted two multivariate denoising methods that combine wavelets and PCA to MS data. The objective was to obtain better denoising of the data so as to extract the meaningful proteomic biological information from the raw spectra and reach meaningful clinical conclusions. The proposed methods were evaluated and compared with the classical soft thresholding denoising method using both real and simulated data sets. It was shown that taking into account common structures of the signals by adding a dimension reduction step on approximation coefficients through PCA provided more effective denoising when combined with soft thresholding on detail coefficients.

Mixed-model of ANOVA for measurement reproducibility in proteomics.

This work is a statistical analysis of reproducibility of a MALDI-TOF mass spectrometry experiment. Its aim is to evaluate measurement variability and compare peak intensities from two types of MALDI-TOF platforms. We compared and commented on the abilities of Principal Component Analysis and mixed-model analysis of variance to evaluate the biological variability and the technical variability of peak intensities in different patients. The properties and hypotheses of both methods are summarized and applied to spectra from plasma of patients with Hodgkin lymphoma. Principal Component Analysis checks rapidly the balance between the two variabilities; however, a mixed-model analysis of variance is necessary to quantify the biological and technical components of the experimental variance as well as their interactions and to split the total variance into between-subjects and within-subject components. The latter method helped to assess the reproducibility of measurements from two MALDI-TOF platforms and to decompose the technical variability according to the experimental design.

Protein mass spectra data analysis for clinical biomarker discovery: a global review

The identification of new diagnostic or prognostic biomarkers is one of the main aims of clinical cancer research. In recent years there has been a growing interest in using high throughput technologies for the detection of such biomarkers. In particular, mass spectrometry appears as an exciting tool with great potential. However, to extract any benefit from the massive potential of clinical proteomic studies, appropriate methods, improvement and validation are required. To better understand the key statistical points involved with such studies, this review presents the main data analysis steps of protein mass spectra data analysis, from the pre-processing of the data to the identification and validation of biomarkers.

Importance of data structure in comparing two dimension reduction methods for classification of microarray gene expression data

BackgroundWith the advance of microarray technology, several methods for gene classification and prognosis have been already designed. However, under various denominations, some of these methods have similar approaches. This study evaluates the influence of gene expression variance structure on the performance of methods that describe the relationship between gene expression levels and a given phenotype through projection of data onto discriminant axes.ResultsWe compared Between-Group Analysis and Discriminant Analysis (with prior dimension reduction through Partial Least Squares or Principal Components Analysis). A geometric approach showed that these two methods are strongly related, but differ in the way they handle data structure. Yet, data structure helps understanding the predictive efficiency of these methods. Three main structure situations may be identified. When the clusters of points are clearly split, both methods perform equally well. When the clusters superpose, both methods fail to give interesting predictions. In intermediate situations, the configuration of the clusters of points has to be handled by the projection to improve prediction. For this, we recommend Discriminant Analysis. Besides, an innovative way of simulation generated the three main structures by modelling different partitions of the whole variance into within-group and between-group variances. These simulated datasets were used in complement to some well-known public datasets to investigate the methods behaviour in a large diversity of structure situations. To examine the structure of a dataset before analysis and preselect an a priori appropriate method for its analysis, we proposed a two-graph preliminary visualization tool: plotting patients on the Between-Group Analysis discriminant axis (x-axis) and on the first and the second within-group Principal Components Analysis component (y-axis), respectively.ConclusionDiscriminant Analysis outperformed Between-Group Analysis because it allows for the dataset structure. An a priori knowledge of that structure may guide the choice of the analysis method. Simulated datasets with known properties are valuable to assess and compare the performance of analysis methods, then implementation on real datasets checks and validates the results. Thus, we warn against the use of unchallenging datasets for method comparison, such as the Golub dataset, because their structure is such that any method would be efficient.

Multi-omics profiling reveals that eating difficulties developed consecutively to artificial nutrition in the neonatal period are associated to specific saliva composition

Prolonged enteral or parenteral nutrition in neonatal periods sometimes results in eating difficulties persisting for years, with reduced food intake through the oral route and thereby reduced stimulation of the oral cavity. Aiming at describing the consequences on oral physiology, saliva of 21 children with eating difficulties (ED) was compared to that of 23 healthy controls, using various omics and targeted methods. Overall, despite heterogeneity within the groups (age, medication etc.), the three spectral methods (MALDI-TOF, SELDI-TOF, (1)H NMR) allowed discriminating ED and controls, confirming that oral stimulation by food intake plays a role in shaping the composition of saliva. Saliva of ED patients exhibited a lower antioxidant status and lower levels of the salivary protease inhibitors cystatins. Other discriminant features (IgA1, dimethylamine) may relate to modified oral and/or intestinal microbial ecology. Finally, salivary profiles of ED patients were partly comparable to those of subjects with exacerbated gustatory sensitivities, in particular with reduced abundance of cystatin SN and higher abundance of zinc-alpha-2-glycoprotein. Whether this translates taste hypersensitivity and contributes to the eating difficulties deserves further attention.

A measure of the impact of CV incompleteness on prediction error estimation with application to PCA and normalization.

BackgroundIn applications of supervised statistical learning in the biomedical field it is necessary to assess the prediction error of the respective prediction rules. Often, data preparation steps are performed on the dataset—in its entirety—before training/test set based prediction error estimation by cross-validation (CV)—an approach referred to as “incomplete CV”. Whether incomplete CV can result in an optimistically biased error estimate depends on the data preparation step under consideration. Several empirical studies have investigated the extent of bias induced by performing preliminary supervised variable selection before CV. To our knowledge, however, the potential bias induced by other data preparation steps has not yet been examined in the literature. In this paper we investigate this bias for two common data preparation steps: normalization and principal component analysis for dimension reduction of the covariate space (PCA). Furthermore we obtain preliminary results for the following steps: optimization of tuning parameters, variable filtering by variance and imputation of missing values.MethodsWe devise the easily interpretable and general measure CVIIM (“CV Incompleteness Impact Measure”) to quantify the extent of bias induced by incomplete CV with respect to a data preparation step of interest. This measure can be used to determine whether a specific data preparation step should, as a general rule, be performed in each CV iteration or whether an incomplete CV procedure would be acceptable in practice. We apply CVIIM to large collections of microarray datasets to answer this question for normalization and PCA.ResultsPerforming normalization on the entire dataset before CV did not result in a noteworthy optimistic bias in any of the investigated cases. In contrast, when performing PCA before CV, medium to strong underestimates of the prediction error were observed in multiple settings.ConclusionsWhile the investigated forms of normalization can be safely performed before CV, PCA has to be performed anew in each CV split to protect against optimistic bias.