Changes in microbial (Bacteria and Archaea) plankton community structure after artificial dispersal in grazer-free microcosms

Microbes are considered to have a global distribution due to their high dispersal capabilities. However, our knowledge of the way geographically distant microbial communities assemble after dispersal in a new environment is limited. In this study, we examined whether communities would converge because similar taxa would be selected under the same environmental conditions, or would diverge because of initial community composition, after artificial dispersal. To this aim, a microcosm experiment was performed, in which the temporal changes in the composition and diversity of different prokaryoplankton assemblages from three distant geographic coastal areas (Banyuls-sur-Mer in northwest Mediterranean Sea, Pagasitikos Gulf in northeast Mediterranean and Woods Hole, MA, USA in the northwest Atlantic), were studied. Diversity was investigated using amplicon pyrosequencing of the V1-V3 hypervariable regions of the 16S rRNA. The three assemblages were grown separately in particle free and autoclaved Banyuls-sur-mer seawater at 18 °C in the dark. We found that the variability of prokaryoplankton community diversity (expressed as richness, evenness and dominance) as well as the composition were driven by patterns observed in Bacteria. Regarding community composition, similarities were found between treatments at family level. However, at the OTU level microbial communities from the three different original locations diverge rather than converge during incubation. It is suggested that slight differences in the composition of the initial prokaryoplankton communities, resulted in separate clusters the following days even when growth took place under identical abiotic conditions

Fitness effects of spontaneous mutations in picoeukaryotic marine green algae

Estimates of the fitness effects of spontaneous mutations are important for understanding the adaptive potential of species. Here, we present the results of mutation accumulation experiments over 265– 512 sequential generations in four species of marine unicellular green algae, Ostreococcus tauri RCC4221, Ostreococcus mediterraneus RCC2590, Micromonas pusilla RCC299, and Bathycoccus prasinos RCC1105. Cell division rates, taken as a proxy for fitness, systematically decline over the course of the experiment in O. tauri, but not in the three other species where the MA experiments were carried out over a smaller number of generations. However, evidence of mutation accumulation in 24 MA lines arises when they are exposed to stressful conditions, such as changes in osmolarity or exposure to herbicides. The selection coefficients, estimated from the number of cell divisions/day, varies significantly between the different environmental conditions tested in MA lines, providing evidence for advantageous and deleterious effects of spontaneous mutations. This suggests a common environmental dependence of the fitness effects of mutations and allows the minimum mutation/genome/generation rates to be inferred at 0.0037 in these species

MicroRNAs as new player in rheumatoid arthritis

MicroRNAs (miRNAs) are small noncoding RNA molecules that negatively regulate gene expression at the post-transcriptional level. Currently, there are 939 mature human miRNA sequences listed in the Sanger updated miRNA registry. There are approximately 1500 predicted miRNAs in the human genome that may regulate the expression of one third of our genes. By controlling the accumulation of the target protein(s) in cells, these regulatory RNA molecules participate in key functions in many physiological networks and their deregulation has been implicated in the pathogenesis of serious human disorders, such as cancer and infection. The implication of miRNAs in immune-mediated disorders such as rheumatoid arthritis (RA) has recently emerged suggesting that miRNA-based therapeutic approaches may have a promising potential in these diseases. Here, we provide an overview of the state-of-the-art on miRNAs in RA, focusing on both systemic and local features of the pathology

Classification of patients with knee osteoarthritis in clinical phenotypes: data from the osteoarthritis initiative

<div><p>Objectives</p><p>The existence of phenotypes has been hypothesized to explain the large heterogeneity characterizing the knee osteoarthritis. In a previous systematic review of the literature, six main phenotypes were identified: Minimal Joint Disease (MJD), Malaligned Biomechanical (MB), Chronic Pain (CP), Inflammatory (I), Metabolic Syndrome (MS) and Bone and Cartilage Metabolism (BCM). The purpose of this study was to classify a sample of individuals with knee osteoarthritis (KOA) into pre-defined groups characterized by specific variables that can be linked to different disease mechanisms, and compare these phenotypes for demographic and health outcomes.</p><p>Methods</p><p>599 patients were selected from the OAI database FNIH at 24 months’ time to conduct the study. For each phenotype, cut offs of key variables were identified matching the results from previous studies in the field and the data available for the sample. The selection process consisted of 3 steps. At the end of each step, the subjects classified were excluded from the further classification stages. Patients meeting the criteria for more than one phenotype were classified separately into a ‘complex KOA’ group.</p><p>Results</p><p>Phenotype allocation (including complex KOA) was successful for 84% of cases with an overlap of 20%. Disease duration was shorter in the MJD while the CP phenotype included a larger number of Women (81%). A significant effect of phenotypes on WOMAC pain (F = 16.736 p <0.001) and WOMAC physical function (F = 14.676, p < 0.001) was identified after controlling for disease duration.</p><p>Conclusion</p><p>This study signifies the feasibility of a classification of KOA subjects in distinct phenotypes based on subgroup-specific characteristics.</p></div

Fluorescence microscopy tensor imaging representations for large-scale dataset analysis

Understanding complex biological systems requires the system-wide characterization of cellular and molecular features. Recent advances in optical imaging technologies and chemical tissue clearing have facilitated the acquisition of whole-organ imaging datasets, but automated tools for their quantitative analysis and visualization are still lacking. We have here developed a visualization technique capable of providing whole-organ tensor imaging representations of local regional descriptors based on fluorescence data acquisition. This method enables rapid, multiscale, analysis and virtualization of large-volume, high-resolution complex biological data while generating 3D tractographic representations. Using the murine heart as a model, our method allowed us to analyze and interrogate the cardiac microvasculature and the tissue resident macrophage distribution and better infer and delineate the underlying structural network in unprecedented detail

Inferring phytoplankton carbon and eco-physiological rates from diel cycles of spectral particulate beam-attenuation coefficient

The diurnal fluctuations in solar irradiance impose a fundamental frequency on ocean biogeochemistry. Observations of the ocean carbon cycle at these frequencies are rare, but could be considerably expanded by measuring and interpreting the inherent optical properties. A method is presented to analyze diel cycles in particulate beam-attenuation coefficient (&lt;i&gt;c&lt;/i&gt;&lt;sub&gt;p&lt;/sub&gt;) measured at multiple wavelengths. The method is based on fitting observations with a size-structured population model coupled to an optical model to infer the particle size distribution and physiologically relevant parameters of the cells responsible for the measured diel cycle in &lt;i&gt;c&lt;/i&gt;&lt;sub&gt;p&lt;/sub&gt;. Results show that the information related to size and contained in the spectral data can be exploited to independently estimate growth and loss rates during the day and night. In addition, the model can characterize the population of particles affecting the diel variability in &lt;i&gt;c&lt;/i&gt;&lt;sub&gt;p&lt;/sub&gt;. Application of this method to spectral &lt;i&gt;c&lt;/i&gt;&lt;sub&gt;p&lt;/sub&gt; measured at a station in the oligotrophic Mediterranean Sea suggests that most of the observed variations in &lt;i&gt;c&lt;/i&gt;&lt;sub&gt;p&lt;/sub&gt; can be ascribed to a synchronized population of cells with an equivalent spherical diameter around 4.6±1.5 μm. The inferred carbon biomass of these cells was about 5.2–6.0 mg m&lt;sup&gt;−3&lt;/sup&gt; and accounted for approximately 10% of the total particulate organic carbon. If successfully validated, this method may improve our in situ estimates of primary productivity