Anti-nociceptive effect of Faecalibacterium prausnitzii in non-inflammatory IBS-like models

International audienceVisceral pain and intestinal dysbiosis are associated with Irritable Bowel Syndrome (IBS), a common functional gastrointestinal disorder without available efficient therapies. In this study, a decrease of Faecalibacterium prausnitzii presence has been observed in an IBS-like rodent model induced by a neonatal maternal separation (NMS) stress. Moreover, it was investigated whether F. prausnitzii may have an impact on colonic sensitivity. The A2-165 reference strain, but not its supernatant, significantly decreased colonic hypersensitivity induced by either NMS in mice or partial restraint stress in rats. This effect was associated with a reinforcement of intestinal epithelial barrier. Thus, F. prausnitzii exhibits anti-nociceptive properties, indicating its potential to treat abdominal pain in IBS patients

Expression and trans-specific polymorphism of self-incompatibility RNases in Coffea (Rubiaceae)

Self-incompatibility (SI) is widespread in the angiosperms, but identifying the biochemical components of SI mechanisms has proven to be difficult in most lineages. Coffea (coffee; Rubiaceae) is a genus of old-world tropical understory trees in which the vast majority of diploid species utilize a mechanism of gametophytic self-incompatibility (GSI). The S-RNase GSI system was one of the first SI mechanisms to be biochemically characterized, and likely represents the ancestral Eudicot condition as evidenced by its functional characterization in both asterid (Solanaceae, Plantaginaceae) and rosid (Rosaceae) lineages. The S-RNase GSI mechanism employs the activity of class III RNase T2 proteins to terminate the growth of "self" pollen tubes. Here, we investigate the mechanism of Coffea GSI and specifically examine the potential for homology to S-RNase GSI by sequencing class III RNase T2 genes in populations of 14 African and Madagascan Coffea species and the closely related self-compatible species Psilanthus ebracteolatus. Phylogenetic analyses of these sequences aligned to a diverse sample of plant RNase T2 genes show that the Coffea genome contains at least three class III RNase T2 genes. Patterns of tissue-specific gene expression identify one of these RNase T2 genes as the putative Coffea S-RNase gene. We show that populations of SI Coffea are remarkably polymorphic for putative S-RNase alleles, and exhibit a persistent pattern of trans-specific polymorphism characteristic of all S-RNase genes previously isolated from GSI Eudicot lineages. We thus conclude that Coffea GSI is most likely homologous to the classic Eudicot S-RNase system, which was retained since the divergence of the Rubiaceae lineage from an ancient SI Eudicot ancestor, nearly 90 million years ago.United States National Science Foundation [0849186]; Society of Systematic Biologists; American Society of Plant Taxonomists; Duke University Graduate Schoolinfo:eu-repo/semantics/publishedVersio

Organization and molecular evolution of a disease-resistance gene cluster in coffee trees

<p>Abstract</p> <p>Background</p> <p>Most disease-resistance (R) genes in plants encode NBS-LRR proteins and belong to one of the largest and most variable gene families among plant genomes. However, the specific evolutionary routes of NBS-LRR encoding genes remain elusive. Recently in coffee tree (<it>Coffea arabica</it>), a region spanning the <it>S</it>_<it>H</it><it>3 </it>locus that confers resistance to coffee leaf rust, one of the most serious coffee diseases, was identified and characterized. Using comparative sequence analysis, the purpose of the present study was to gain insight into the genomic organization and evolution of the <it>S</it>_<it>H</it><it>3 </it>locus.</p> <p>Results</p> <p>Sequence analysis of the <it>S</it>_<it>H</it><it>3 </it>region in three coffee genomes, E^aand C^asubgenomes from the allotetraploid <it>C. arabica </it>and C^cgenome from the diploid <it>C. canephora</it>, revealed the presence of 5, 3 and 4 R genes in E^a, C^a, and C^cgenomes, respectively. All these R-gene sequences appeared to be members of a CC-NBS-LRR (CNL) gene family that was only found at the <it>S</it>_<it>H</it><it>3 </it>locus in <it>C. arabica</it>. Furthermore, while homologs were found in several dicot species, comparative genomic analysis failed to find any CNL R-gene in the orthologous regions of other eudicot species. The orthology relationship among the <it>S</it>_<it>H</it><it>3</it>-CNL copies in the three analyzed genomes was determined and the duplication/deletion events that shaped the <it>S</it>_<it>H</it><it>3 </it>locus were traced back. Gene conversion events were detected between paralogs in all three genomes and also between the two sub-genomes of <it>C. arabica</it>. Significant positive selection was detected in the solvent-exposed residues of the <it>S</it>_<it>H</it><it>3</it>-CNL copies.</p> <p>Conclusion</p> <p>The ancestral <it>S</it>_<it>H</it><it>3</it>-CNL copy was inserted in the <it>S</it>_<it>H</it><it>3 </it>locus after the divergence between Solanales and Rubiales lineages. Moreover, the origin of most of the <it>S</it>_<it>H</it><it>3</it>-CNL copies predates the divergence between <it>Coffea </it>species. The <it>S</it>_<it>H</it><it>3</it>-CNL family appeared to evolve following the birth-and-death model, since duplications and deletions were inferred in the evolution of the <it>S</it>_<it>H</it><it>3 </it>locus. Gene conversion between paralog members, inter-subgenome sequence exchanges and positive selection appear to be the major forces acting on the evolution of <it>S</it>_<it>H</it><it>3</it>-CNL in coffee trees.</p

Multi-scale digital soil mapping with deep learning

We compared different methods of multi-scale terrain feature construction and their relative effectiveness for digital soil mapping with a Deep Learning algorithm. The most common approach for multi-scale feature construction in DSM is to filter terrain attributes based on different neighborhood sizes, however results can be difficult to interpret because the approach is affected by outliers. Alternatively, one can derive the terrain attributes on decomposed elevation data, but the resulting maps can have artefacts rendering the approach undesirable. Here, we introduce ‘mixed scaling’ a new method that overcomes these issues and preserves the landscape features that are identifiable at different scales. The new method also extends the Gaussian pyramid by introducing additional intermediate scales. This minimizes the risk that the scales that are important for soil formation are not available in the model. In our extended implementation of the Gaussian pyramid, we tested four intermediate scales between any two consecutive octaves of the Gaussian pyramid and modelled the data with Deep Learning and Random Forests. We performed the experiments using three different datasets and show that mixed scaling with the extended Gaussian pyramid produced the best performing set of covariates and that modelling with Deep Learning produced the most accurate predictions, which on average were 4–7% more accurate compared to modelling with Random Forests

Development of microsatellite markers for identifying Brazilian Coffea arabica varieties

Microsatellite markers, also known as SSRs (Simple Sequence Repeats), have proved to be excellent tools for identifying variety and determining genetic relationships. A set of 127 SSR markers was used to analyze genetic similarity in twenty five Coffea arabica varieties. These were composed of nineteen commercially important Brazilians and six interspecific hybrids of Coffea arabica, Coffea canephora and Coffealiberica. The set used comprised 52 newly developed SSR markers derived from microsatellite enriched libraries, 56 designed on the basis of coffee SSR sequences available from public databases, 6 already published, and 13 universal chloroplast microsatellite markers. Only 22 were polymorphic, these detecting 2-7 alleles per marker, an average of 2.5. Based on the banding patterns generated by polymorphic SSR loci, the set of twenty-five coffee varieties were clustered into two main groups, one composed of only Brazilian varieties, and the other of interspecific hybrids, with a few Brazilians. Color mutants could not be separated. Clustering was in accordance with material genealogy thereby revealing high similarity

The coffee genome provides insight into the convergent evolution of caffeine biosynthesis.

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Network-State Modulation of Power-Law Frequency-Scaling in Visual Cortical Neurons

Various types of neural-based signals, such as EEG, local field potentials and intracellular synaptic potentials, integrate multiple sources of activity distributed across large assemblies. They have in common a power-law frequency-scaling structure at high frequencies, but it is still unclear whether this scaling property is dominated by intrinsic neuronal properties or by network activity. The latter case is particularly interesting because if frequency-scaling reflects the network state it could be used to characterize the functional impact of the connectivity. In intracellularly recorded neurons of cat primary visual cortex in vivo, the power spectral density of Vm activity displays a power-law structure at high frequencies with a fractional scaling exponent. We show that this exponent is not constant, but depends on the visual statistics used to drive the network. To investigate the determinants of this frequency-scaling, we considered a generic recurrent model of cortex receiving a retinotopically organized external input. Similarly to the in vivo case, our in computo simulations show that the scaling exponent reflects the correlation level imposed in the input. This systematic dependence was also replicated at the single cell level, by controlling independently, in a parametric way, the strength and the temporal decay of the pairwise correlation between presynaptic inputs. This last model was implemented in vitro by imposing the correlation control in artificial presynaptic spike trains through dynamic-clamp techniques. These in vitro manipulations induced a modulation of the scaling exponent, similar to that observed in vivo and predicted in computo. We conclude that the frequency-scaling exponent of the Vm reflects stimulus-driven correlations in the cortical network activity. Therefore, we propose that the scaling exponent could be used to read-out the “effective” connectivity responsible for the dynamical signature of the population signals measured at different integration levels, from Vm to LFP, EEG and fMRI