Lieven Clement

Initial community evenness favours functionality under selective stress

Owing to the present global biodiversity crisis, the biodiversity–stability relationship and the effect of biodiversity on ecosystem functioning have become major topics in ecology. Biodiversity is a complex term that includes taxonomic, functional, spatial and temporal aspects of organismic diversity, with species richness (the number of species) and evenness (the relative abundance of species) considered among the most important measures. With few exceptions (see, for example, ref. 6), the majority of studies of biodiversity-functioning and biodiversity–stability theory have predominantly examined richness. Here we show, using microbial microcosms, that initial community evenness is a key factor in preserving the functional stability of an ecosystem. Using experimental manipulations of both richness and initial evenness in microcosms with denitrifying bacterial communities, we found that the stability of the net ecosystem denitrification in the face of salinity stress was strongly influenced by the initial evenness of the community. Therefore, when communities are highly uneven, or there is extreme dominance by one or a few species, their functioning is less resistant to environmental stress. Further unravelling how evenness influences ecosystem processes in natural and humanized environments constitutes a major future conceptual challenge.

Leaf Responses to Mild Drought Stress in Natural Variants of Arabidopsis

Arabidopsis accessions show different phenotypes in response to mild drought, yet a robust transcriptome response is conserved between the accessions. Although the response of plants exposed to severe drought stress has been studied extensively, little is known about how plants adapt their growth under mild drought stress conditions. Here, we analyzed the leaf and rosette growth response of six Arabidopsis (Arabidopsis thaliana) accessions originating from different geographic regions when exposed to mild drought stress. The automated phenotyping platform WIWAM was used to impose stress early during leaf development, when the third leaf emerges from the shoot apical meristem. Analysis of growth-related phenotypes showed differences in leaf development between the accessions. In all six accessions, mild drought stress reduced both leaf pavement cell area and number without affecting the stomatal index. Genome-wide transcriptome analysis (using RNA sequencing) of early developing leaf tissue identified 354 genes differentially expressed under mild drought stress in the six accessions. Our results indicate the existence of a robust response over different genetic backgrounds to mild drought stress in developing leaves. The processes involved in the overall mild drought stress response comprised abscisic acid signaling, proline metabolism, and cell wall adjustments. In addition to these known severe drought-related responses, 87 genes were found to be specific for the response of young developing leaves to mild drought stress.

Practical Tools to Implement Massive Parallel Pyrosequencing of PCR Products in Next Generation Molecular Diagnostics

Despite improvements in terms of sequence quality and price per basepair, Sanger sequencing remains restricted to screening of individual disease genes. The development of massively parallel sequencing (MPS) technologies heralded an era in which molecular diagnostics for multigenic disorders becomes reality. Here, we outline different PCR amplification based strategies for the screening of a multitude of genes in a patient cohort. We performed a thorough evaluation in terms of set-up, coverage and sequencing variants on the data of 10 GS-FLX experiments (over 200 patients). Crucially, we determined the actual coverage that is required for reliable diagnostic results using MPS, and provide a tool to calculate the number of patients that can be screened in a single run. Finally, we provide an overview of factors contributing to false negative or false positive mutation calls and suggest ways to maximize sensitivity and specificity, both important in a routine setting. By describing practical strategies for screening of multigenic disorders in a multitude of samples and providing answers to questions about minimum required coverage, the number of patients that can be screened in a single run and the factors that may affect sensitivity and specificity we hope to facilitate the implementation of MPS technology in molecular diagnostics.

Impact of variance components on reliability of absolute quantification using digital PCR

BackgroundDigital polymerase chain reaction (dPCR) is an increasingly popular technology for detecting and quantifying target nucleic acids. Its advertised strength is high precision absolute quantification without needing reference curves. The standard data analytic approach follows a seemingly straightforward theoretical framework but ignores sources of variation in the data generating process. These stem from both technical and biological factors, where we distinguish features that are 1) hard-wired in the equipment, 2) user-dependent and 3) provided by manufacturers but may be adapted by the user. The impact of the corresponding variance components on the accuracy and precision of target concentration estimators presented in the literature is studied through simulation.ResultsWe reveal how system-specific technical factors influence accuracy as well as precision of concentration estimates. We find that a well-chosen sample dilution level and modifiable settings such as the fluorescence cut-off for target copy detection have a substantial impact on reliability and can be adapted to the sample analysed in ways that matter. User-dependent technical variation, including pipette inaccuracy and specific sources of sample heterogeneity, leads to a steep increase in uncertainty of estimated concentrations. Users can discover this through replicate experiments and derived variance estimation. Finally, the detection performance can be improved by optimizing the fluorescence intensity cut point as suboptimal thresholds reduce the accuracy of concentration estimates considerably.ConclusionsLike any other technology, dPCR is subject to variation induced by natural perturbations, systematic settings as well as user-dependent protocols. Corresponding uncertainty may be controlled with an adapted experimental design. Our findings point to modifiable key sources of uncertainty that form an important starting point for the development of guidelines on dPCR design and data analysis with correct precision bounds. Besides clever choices of sample dilution levels, experiment-specific tuning of machine settings can greatly improve results. Well-chosen data-driven fluorescence intensity thresholds in particular result in major improvements in target presence detection. We call on manufacturers to provide sufficiently detailed output data that allows users to maximize the potential of the method in their setting and obtain high precision and accuracy for their experiments.

Faecal proteomics: A tool to investigate dysbiosis and inflammation in patients with cystic fibrosis

BACKGROUND Several microbial studies reported gut microbiota dysbiosis in patients with cystic fibrosis (CF). The functional consequences of this phenomenon are poorly understood. Faecal metaproteomics allows the quantitative analysis of host and microbial proteins to address functional changes resulting from this dysbiosis. METHODS We analysed faecal protein extracts from fifteen patients with CF that have pancreatic insufficiency and from their unaffected siblings by shotgun proteomics. Novel computational and statistical tools were introduced to evaluate changes in taxonomic composition and protein abundance. RESULTS Faecal protein extracts from patients with CF were dominated by host proteins involved in inflammation and mucus formation. Taxonomic analysis of the microbial proteins confirmed the strong reduction of butyrate reducers such as Faecalibacterium prausnitzii and increase of Enterobacteriaceae, Ruminococcus gnavus and Clostridia species. CONCLUSION Faecal metaproteomics provides insights in intestinal dysbiosis, inflammation in patients with CF and can be used to monitor different disease markers in parallel.

A sex-inducing pheromone triggers cell cycle arrest and mate attraction in the diatom Seminavis robusta

Although sexual reproduction is believed to play a major role in the high diversification rates and species richness of diatoms, a mechanistic understanding of diatom life cycle control is virtually lacking. Diatom sexual signalling is controlled by a complex, yet largely unknown, pheromone system. Here, a sex-inducing pheromone (SIP+) of the benthic pennate diatom Seminavis robusta was identified by comparative metabolomics, subsequently purified, and physicochemically characterized. Transcriptome analysis revealed that SIP+ triggers the switch from mitosis-to-meiosis in the opposing mating type, coupled with the transcriptional induction of proline biosynthesis genes, and the release of the proline-derived attraction pheromone. The induction of cell cycle arrest by a pheromone, chemically distinct from the one used to attract the opposite mating type, highlights the existence of a sophisticated mechanism to increase chances of mate finding, while keeping the metabolic losses associated with the release of an attraction pheromone to a minimum.

Estimating and Modeling Spatio-Temporal Correlation Structures for River Monitoring Networks

The European environmental legislation forces local authorities to improve the river water quality. In order to assess the presence of trends or the effect of certain actions on the river water quality, a statistical methodology is needed which can deal with data originating from river monitoring networks. Since both temporal and spatial components affect the output of such a monitoring network, their dependence structure has to be modeled. Current spatio-temporal models used for the analysis of data arising from environmental studies are not appropriate because they do not deal properly with the particular spatial dependence structure underlying river monitoring networks. In this article, a state-space model is developed in which the state variable is defined by a directed acyclic graph (DAG) derived from the river network topology. In reality the dependence structure based on the DAG may be obscured by environmental factors. This is taken into account by embedding the state variable in an observation model. Finally, the state-space model is extended with a linear model for the mean. An efficient ECM-like algorithm is proposed for parameter estimation, using the Kalman filter and smoother in both E- and CM-steps. In a case-study the method is applied to assess the effect of the activation of a waste water treatment plant on the dissolved oxygen concentration, in time as well as in space.

Peptide-level Robust Ridge Regression Improves Estimation, Sensitivity, and Specificity in Data-dependent Quantitative Label-free Shotgun Proteomics.

Peptide intensities from mass spectra are increasingly used for relative quantitation of proteins in complex samples. However, numerous issues inherent to the mass spectrometry workflow turn quantitative proteomic data analysis into a crucial challenge. We and others have shown that modeling at the peptide level outperforms classical summarization-based approaches, which typically also discard a lot of proteins at the data preprocessing step. Peptide-based linear regression models, however, still suffer from unbalanced datasets due to missing peptide intensities, outlying peptide intensities and overfitting. Here, we further improve upon peptide-based models by three modular extensions: ridge regression, improved variance estimation by borrowing information across proteins with empirical Bayes and M-estimation with Huber weights. We illustrate our method on the CPTAC spike-in study and on a study comparing wild-type and ArgP knock-out Francisella tularensis proteomes. We show that the fold change estimates of our robust approach are more precise and more accurate than those from state-of-the-art summarization-based methods and peptide-based regression models, which leads to an improved sensitivity and specificity. We also demonstrate that ionization competition effects come already into play at very low spike-in concentrations and confirm that analyses with peptide-based regression methods on peptide intensity values aggregated by charge state and modification status (e.g. MaxQuants peptides.txt file) are slightly superior to analyses on raw peptide intensity values (e.g. MaxQuants evidence.txt file).

ViVaMBC: estimating viral sequence variation in complex populations from illumina deep-sequencing data using model-based clustering

BackgroundDeep-sequencing allows for an in-depth characterization of sequence variation in complex populations. However, technology associated errors may impede a powerful assessment of low-frequency mutations. Fortunately, base calls are complemented with quality scores which are derived from a quadruplet of intensities, one channel for each nucleotide type for Illumina sequencing. The highest intensity of the four channels determines the base that is called. Mismatch bases can often be corrected by the second best base, i.e. the base with the second highest intensity in the quadruplet. A virus variant model-based clustering method, ViVaMBC, is presented that explores quality scores and second best base calls for identifying and quantifying viral variants. ViVaMBC is optimized to call variants at the codon level (nucleotide triplets) which enables immediate biological interpretation of the variants with respect to their antiviral drug responses.ResultsUsing mixtures of HCV plasmids we show that our method accurately estimates frequencies down to 0.5%. The estimates are unbiased when average coverages of 25,000 are reached. A comparison with the SNP-callers V-Phaser2, ShoRAH, and LoFreq shows that ViVaMBC has a superb sensitivity and specificity for variants with frequencies above 0.4%. Unlike the competitors, ViVaMBC reports a higher number of false-positive findings with frequencies below 0.4% which might partially originate from picking up artificial variants introduced by errors in the sample and library preparation step.ConclusionsViVaMBC is the first method to call viral variants directly at the codon level. The strength of the approach lies in modeling the error probabilities based on the quality scores. Although the use of second best base calls appeared very promising in our data exploration phase, their utility was limited. They provided a slight increase in sensitivity, which however does not warrant the additional computational cost of running the offline base caller. Apparently a lot of information is already contained in the quality scores enabling the model based clustering procedure to adjust the majority of the sequencing errors. Overall the sensitivity of ViVaMBC is such that technical constraints like PCR errors start to form the bottleneck for low frequency variant detection.

An extension of the Wilcoxon-Mann-Whitney test for analyzing RT-qPCR data.

Abstract Classical approaches for analyzing reverse transcription quantitative polymerase chain reaction (RT-qPCR) data commonly require normalization before assessing differential expression (DE). Normalization often has a substantial effect on the interpretation and validity of the subsequent analysis steps, but at the same time it causes a reduction in variance and introduces dependence among the normalized outcomes. These effects can be substantial, however, they are typically ignored. Most normalization techniques and methods for DE focus on mean expression and are sensitive to outliers. Moreover, in cancer studies, for example, oncogenes are often only expressed in a subsample of the populations during sampling. This primarily affects the skewness and the tails of the distribution and the mean is therefore not necessarily the best effect size measure within these experimental setups. In our contribution, we propose an extension of the Wilcoxon-Mann-Whitney test which incorporates a robust normalization, and the uncertainty associated with normalization is propagated into the final statistical summaries for DE. Our method relies on semiparametric regression models that focus on the probability P{Y≤Y′}, where Y and Y′ denote independent responses for different subject groups. This effect size is robust to outliers, while remaining informative and intuitive when DE affects the shape of the distribution instead of only the mean. We also extend our approach for assessing DE for multiple features simultaneously. Simulation studies show that the test has a good performance, and that it is very competitive with standard methods for this platform. The method is illustrated on two neuroblastoma studies.