Brigitte Schaeffer

Bacterial protein signals are associated with Crohn’s disease

Objective No Crohn’s disease (CD) molecular maker has advanced to clinical use, and independent lines of evidence support a central role of the gut microbial community in CD. Here we explore the feasibility of extracting bacterial protein signals relevant to CD, by interrogating myriads of intestinal bacterial proteomes from a small number of patients and healthy controls. Design We first developed and validated a workflow—including extraction of microbial communities, two-dimensional difference gel electrophoresis (2D-DIGE), and LC-MS/MS—to discover protein signals from CD-associated gut microbial communities. Then we used selected reaction monitoring (SRM) to confirm a set of candidates. In parallel, we used 16S rRNA gene sequencing for an integrated analysis of gut ecosystem structure and functions. Results Our 2D-DIGE-based discovery approach revealed an imbalance of intestinal bacterial functions in CD. Many proteins, largely derived from Bacteroides species, were over-represented, while under-represented proteins were mostly from Firmicutes and some Prevotella members. Most overabundant proteins could be confirmed using SRM. They correspond to functions allowing opportunistic pathogens to colonise the mucus layers, breach the host barriers and invade the mucosae, which could still be aggravated by decreased host-derived pancreatic zymogen granule membrane protein GP2 in CD patients. Moreover, although the abundance of most protein groups reflected that of related bacterial populations, we found a specific independent regulation of bacteria-derived cell envelope proteins. Conclusions This study provides the first evidence that quantifiable bacterial protein signals are associated with CD, which can have a profound impact on future molecular diagnosis.

Assessment of three mitochondrial genes (16S, Cytb, CO1) for identifying species in the Praomyini tribe (Rodentia: Muridae).

The Praomyini tribe is one of the most diverse and abundant groups of Old World rodents. Several species are known to be involved in crop damage and in the epidemiology of several human and cattle diseases. Due to the existence of sibling species their identification is often problematic. Thus an easy, fast and accurate species identification tool is needed for non-systematicians to correctly identify Praomyini species. In this study we compare the usefulness of three genes (16S, Cytb, CO1) for identifying species of this tribe. A total of 426 specimens representing 40 species (sampled across their geographical range) were sequenced for the three genes. Nearly all of the species included in our study are monophyletic in the neighbour joining trees. The degree of intra-specific variability tends to be lower than the divergence between species, but no barcoding gap is detected. The success rate of the statistical methods of species identification is excellent (up to 99% or 100% for statistical supervised classification methods as the k-Nearest Neighbour or Random Forest). The 16S gene is 2.5 less variable than the Cytb and CO1 genes. As a result its discriminatory power is smaller. To sum up, our results suggest that using DNA markers for identifying species in the Praomyini tribe is a largely valid approach, and that the CO1 and Cytb genes are better DNA markers than the 16S gene. Our results confirm the usefulness of statistical methods such as the Random Forest and the 1-NN methods to assign a sequence to a species, even when the number of species is relatively large. Based on our NJ trees and the distribution of all intraspecific and interspecific pairwise nucleotide distances, we highlight the presence of several potentially new species within the Praomyini tribe that should be subject to corroboration assessments.

Polyphasic characterization and genetic relatedness of low-virulence and virulent Listeria monocytogenes isolates

BackgroundCurrently, food regulatory authorities consider all Listeria monocytogenes isolates as equally virulent. However, an increasing number of studies demonstrate extensive variations in virulence and pathogenicity of L. monocytogenes strains. Up to now, there is no comprehensive overview of the population genetic structure of L. monocytogenes taking into account virulence level. We have previously demonstrated that different low-virulence strains exhibit the same mutations in virulence genes suggesting that they could have common evolutionary pathways. New low-virulence strains were identified and assigned to phenotypic and genotypic Groups using cluster analysis. Pulsed-field gel electrophoresis, virulence gene sequencing and multi-locus sequence typing analyses were performed to study the genetic relatedness and the population structure between the studied low-virulence isolates and virulent strains.ResultsThese methods showed that low-virulence strains are widely distributed in the two major lineages, but some are also clustered according to their genetic mutations. These analyses showed that low-virulence strains initially grouped according to their lineage, then to their serotypes and after which, they lost their virulence suggesting a relatively recent emergence.ConclusionsLoss of virulence in lineage II strains was related to point mutation in a few virulence genes (prfA, inlA, inlB, plcA). These strains thus form a tightly clustered, monophyletic group with limited diversity. In contrast, low-virulence strains of lineage I were more dispersed among the virulence strains and the origin of their loss of virulence has not been identified yet, even if some strains exhibited different mutations in prfA or inlA.

Endogenous prion protein conversion is required for prion-induced neuritic alterations and neuronal death

Prions cause fatal neurodegenerative conditions and result from the conversion of host‐encoded cellular prion protein (PrPC) into abnormally folded scrapie PrP (PrPSc). Prions can propagate both in neurons and astrocytes, yet neurotoxicity mechanisms remain unclear. Recently, PrPC was proposed to mediate neurotoxic signaling of β‐sheet‐rich PrP and non‐PrP conformers independently of conversion. To investigate the role of astrocytes and neuronal PrPC in prion‐induced neurodegeneration, we set up neuron and astrocyte primary cocultures derived from PrP transgenic mice. In this system, prion‐infected astrocytes delivered ovine PrPSc to neurons lacking PrPC (prion‐resistant), or expressing a PrPC convertible (sheep) or not (mouse, human). We show that interaction between neuronal PrPC and exogenous PrPSc was not sufficient to induce neuronal death but that efficient PrPC conversion was required for prion‐associated neurotoxicity. Prion‐infected astrocytes markedly accelerated neurodegeneration in homologous cocultures compared to infected single neuronal cultures, despite no detectable neurotoxin release. Finally, PrPSc accumulation in neurons led to neuritic damages and cell death, both potentiated by glutamate and reactive oxygen species. Thus, conversion of neuronal PrPC rather than PrPC‐mediated neurotoxic signaling appears as the main culprit in prion‐induced neurodegeneration. We suggest that active prion replication in neurons sensitizes them to environmental stress regulated by neighboring cells, including astrocytes.— Cronier, S., Carimalo, J., Schaeffer, B., Jaumain, E., Béringue, V., Miquel, M.‐C., Laude, H., Peyrin, J.‐M. Endogenous prion protein conversion is required for prion‐induced neuritic alterations and neuronal death. FASEB J. 26, 3854–3861 (2012). www.fasebj.org

Assessing the Validity Domains of Graphical Gaussian Models in Order to Infer Relationships among Components of Complex Biological Systems

The study of the interactions of cellular components is an essential base step to understand the structure and dynamics of biological networks. Various methods were recently developed for this purpose. While most of them combine different types of data and a priori knowledge, methods based on graphical Gaussian models are capable of learning the network directly from raw data. They consider the full-order partial correlations which are partial correlations between two variables given the remaining ones, for modeling direct links between variables. Statistical methods were developed for estimating these links when the number of observations is larger than the number of variables. However, the rapid advance of new technologies that allow the simultaneous measure of genome expression, led to large-scale datasets where the number of variables is far larger than the number of observations. To get around this dimensionality problem, different strategies and new statistical methods were proposed. In this study we focused on statistical methods recently published. All are based on the fact that the number of direct relationships between two variables is very small in regards to the number of possible relationships, p(p-1)/2. In the biological context, this assumption is not always satisfied over the whole graph. It is essential to precisely know the behavior of the methods in regards to the characteristics of the studied object before applying them. For this purpose, we evaluated the validity domain of each method from wide-ranging simulated datasets. We then illustrated our results using recently published biological data.

Coalescent-Based DNA Barcoding: Multilocus Analysis and Robustness

DNA barcoding is the assignment of individuals to species using standardized mitochondrial sequences. Nuclear data are sometimes added to the mitochondrial data to increase power. A barcoding method for analysing mitochondrial and nuclear data is developed. It is a Bayesian method based on the coalescent model. Then this method is assessed using simulated and real data. It is found that adding nuclear data can reduce the number of ambiguous assignments. Finally, the robustness of coalescent-based barcoding to departures from model assumptions is studied using simulations. This method is found to be robust to past population size variations, to within-species population structures, and to designs that poorly sample populations within species. Supplementary Material is available online at www.liebertonline.com/cmb.

Nonlinear projection methods for visualizing Barcode data and application on two data sets

Developing tools for visualizing DNA sequences is an important issue in the Barcoding context. Visualizing Barcode data can be put in a purely statistical context, unsupervised learning. Clustering methods combined with projection methods have two closely linked objectives, visualizing and finding structure in the data. Multidimensional scaling (MDS) and Self‐organizing maps (SOM) are unsupervised statistical tools for data visualization. Both algorithms map data onto a lower dimensional manifold: MDS looks for a projection that best preserves pairwise distances while SOM preserves the topology of the data. Both algorithms were initially developed for Euclidean data and the conditions necessary to their good implementation were not satisfied for Barcode data. We developed a workflow consisting in four steps: collapse data into distinct sequences; compute a dissimilarity matrix; run a modified version of SOM for dissimilarity matrices to structure the data and reduce dimensionality; project the results using MDS. This methodology was applied to Astraptes fulgerator and Hylomyscus, an African rodent with debated taxonomy. We obtained very good results for both data sets. The results were robust against unbalanced species. All the species in Astraptes were well displayed in very distinct groups in the various visualizations, except for LOHAMP and FABOV that were mixed up. For Hylomyscus, our findings were consistent with known species, confirmed the existence of four unnamed taxa and suggested the existence of potentially new species.

Population networks from DNA sequences: methodological developments

Several classes of methods have been proposed for inferring the history of populations from genetic polymorphism data. As connectivity is a key factor to explain the structure of populations, several graph-based methods have been developed to this aim, using population genetics data. Here we propose an original method based on graphical models that uses DNA sequences to provide relationships between populations. We tested our method on various simulated data sets, describing typical demographic scenarios, for different parameters values. We found that our method behaved noticeably well for realistic demographic evolutionary processes and recovered suitably the migration processes. Our method provides thus a complementary tool for investigating population history based on genetic material.