PKCα and PKCδ Regulate ADAM17-Mediated Ectodomain Shedding of Heparin Binding-EGF through Separate Pathways

Epidermal growth factor receptor (EGFR) signalling is initiated by the release of EGFR-ligands from membrane-anchored precursors, a process termed ectodomain shedding. This proteolytic event, mainly executed by A Disintegrin And Metalloproteases (ADAMs), is regulated by a number of signal transduction pathways, most notably those involving protein kinase C (PKC). However, the molecular mechanisms of PKC-dependent ectodomain shedding of EGFR-ligands, including the involvement of specific PKC isoforms and possible functional redundancy, are poorly understood. To address this issue, we employed a cell-based system of PMA-induced PKC activation coupled with shedding of heparin binding (HB)-EGF. In agreement with previous studies, we demonstrated that PMA triggers a rapid ADAM17-mediated release of HB-EGF. However, PMA-treatment also results in a protease-independent loss of cell surface HB-EGF. We identified PKCα as the key participant in the activation of ADAM17 and suggest that it acts in parallel with a pathway linking PKCδ and ERK activity. While PKCα specifically regulated PMA-induced shedding, PKCδ and ERK influenced both constitutive and inducible shedding by apparently affecting the level of HB-EGF on the cell surface. Together, these findings indicate the existence of multiple modes of regulation controlling EGFR-ligand availability and subsequent EGFR signal transduction

The Effects of Alignment Quality, Distance Calculation Method, Sequence Filtering, and Region on the Analysis of 16S rRNA Gene-Based Studies

Pyrosequencing of PCR-amplified fragments that target variable regions within the 16S rRNA gene has quickly become a powerful method for analyzing the membership and structure of microbial communities. This approach has revealed and introduced questions that were not fully appreciated by those carrying out traditional Sanger sequencing-based methods. These include the effects of alignment quality, the best method of calculating pairwise genetic distances for 16S rRNA genes, whether it is appropriate to filter variable regions, and how the choice of variable region relates to the genetic diversity observed in full-length sequences. I used a diverse collection of 13,501 high-quality full-length sequences to assess each of these questions. First, alignment quality had a significant impact on distance values and downstream analyses. Specifically, the greengenes alignment, which does a poor job of aligning variable regions, predicted higher genetic diversity, richness, and phylogenetic diversity than the SILVA and RDP-based alignments. Second, the effect of different gap treatments in determining pairwise genetic distances was strongly affected by the variation in sequence length for a region; however, the effect of different calculation methods was subtle when determining the sample's richness or phylogenetic diversity for a region. Third, applying a sequence mask to remove variable positions had a profound impact on genetic distances by muting the observed richness and phylogenetic diversity. Finally, the genetic distances calculated for each of the variable regions did a poor job of correlating with the full-length gene. Thus, while it is tempting to apply traditional cutoff levels derived for full-length sequences to these shorter sequences, it is not advisable. Analysis of β-diversity metrics showed that each of these factors can have a significant impact on the comparison of community membership and structure. Taken together, these results urge caution in the design and interpretation of analyses using pyrosequencing data

PPARγ Pro12Ala polymorphism and risk of acute coronary syndrome in a prospective study of Danes

<p>Abstract</p> <p>Background</p> <p>Acute coronary syndrome (ACS) is a major cause of morbidity and mortality in the western world. Peroxisome proliferator-activated receptor γ (PPARγ) plays a key role in the regulation of the energy balance, adipocyte differentiation and lipid biosynthesis. The aim was to investigate if the polymorphism <it>PPARγ2 </it>Pro¹²Ala, which encodes a less efficient transcription factor, was associated with risk of acute coronary disease and if there were interactions between this polymorphism and factors that modify PPARγ activity, such as alcohol intake, smoking, and use of non-steroidal anti-inflammatory medicine.</p> <p>Methods</p> <p>A case-cohort study including 1031 ACS cases and a sub-cohort of 1703 persons was nested within the population-based prospective study Diet, Cancer and Health of 57,053 individuals.</p> <p>Results</p> <p>Homozygous male variant allele carriers of <it>PPARγ2 </it>Pro¹²Ala were at higher risk of ACS (HR = 2.12, 95% CI: 1.00–4.48) than homozygous carriers of the Pro-allele. Among men, there was a statistically significant interaction between genotypes and alcohol intake such that homozygous variant allele carriers with a low alcohol intake were at higher risk of ACS (HR = 25.3, CI: 16.5–38.7) compared to homozygous common allele carriers (p for interaction < 0.0001). Overall, the association was only observed among homozygous variant allele carriers. Thus, all the observed associations were obtained in subgroups including small numbers of cases. It is therefore possible that the observed associations were due to chance.</p> <p>Conclusion</p> <p>In the present study, there were no consistent associations between PPARγ Pro¹²Ala and risk of ACS, and no consistent interaction with alcohol, BMI, NSAID or smoking in relation to ACS.</p

EMIRGE: reconstruction of full-length ribosomal genes from microbial community short read sequencing data

Recovery of ribosomal small subunit genes by assembly of short read community DNA sequence data generally fails, making taxonomic characterization difficult. Here, we solve this problem with a novel iterative method, based on the expectation maximization algorithm, that reconstructs full-length small subunit gene sequences and provides estimates of relative taxon abundances. We apply the method to natural and simulated microbial communities, and correctly recover community structure from known and previously unreported rRNA gene sequences. An implementation of the method is freely available at https://github.com/csmiller/EMIRGE

Deep Sequencing Analyses of Low Density Microbial Communities: Working at the Boundary of Accurate Microbiota Detection

Introduction: Accurate analyses of microbiota composition of low-density communities (10 3 –10 4 bacteria/sample) can be challenging. Background DNA from chemicals and consumables, extraction biases as well as differences in PCR efficiency can significantly interfere with microbiota assessment. This study was aiming to establish protocols for accurate microbiota analysis at low microbial density. Methods: To examine possible effects of bacterial density on microbiota analyses we compared microbiota profiles of seria

Pyrosequencing as a tool for better understanding of human microbiomes

Next-generation sequencing technologies have revolutionized the analysis of microbial communities in diverse environments, including the human body. This article reviews several aspects of one of these technologies, the pyrosequencing technique, including its principles, applications, and significant contribution to the study of the human microbiome, with especial emphasis on the oral microbiome. The results brought about by pyrosequencing studies have significantly contributed to refining and augmenting the knowledge of the community membership and structure in and on the human body in healthy and diseased conditions. Because most oral infectious diseases are currently regarded as biofilm-related polymicrobial infections, high-throughput sequencing technologies have the potential to disclose specific patterns related to health or disease. Further advances in technology hold the perspective to have important implications in terms of accurate diagnosis and more effective preventive and therapeutic measures for common oral diseases

Comparing the Bacterial Diversity of Acute and Chronic Dental Root Canal Infections

This study performed barcoded multiplex pyrosequencing with a 454 FLX instrument to compare the microbiota of dental root canal infections associated with acute (symptomatic) or chronic (asymptomatic) apical periodontitis. Analysis of samples from 9 acute abscesses and 8 chronic infections yielded partial 16S rRNA gene sequences that were taxonomically classified into 916 bacterial species-level operational taxonomic units (OTUs) (at 3% divergence) belonging to 67 genera and 13 phyla. The most abundant phyla in acute infections were Firmicutes (52%), Fusobacteria (17%) and Bacteroidetes (13%), while in chronic infections the dominant were Firmicutes (59%), Bacteroidetes (14%) and Actinobacteria (10%). Members of Fusobacteria were much more prevalent in acute (89%) than in chronic cases (50%). The most abundant/prevalent genera in acute infections were Fusobacterium and Parvimonas. Twenty genera were exclusively detected in acute infections and 18 in chronic infections. Only 18% (n = 165) of the OTUs at 3% divergence were shared by acute and chronic infections. Diversity and richness estimators revealed that acute infections were significantly more diverse than chronic infections. Although a high interindividual variation in bacterial communities was observed, many samples tended to group together according to the type of infection (acute or chronic). This study is one of the most comprehensive in-deep comparisons of the microbiota associated with acute and chronic dental root canal infections and highlights the role of diverse polymicrobial communities as the unit of pathogenicity in acute infections. The overall diversity of endodontic infections as revealed by the pyrosequencing technique was much higher than previously reported for endodontic infections

Impaired Carbohydrate Digestion and Transport and Mucosal Dysbiosis in the Intestines of Children with Autism and Gastrointestinal Disturbances

Gastrointestinal disturbances are commonly reported in children with autism, complicate clinical management, and may contribute to behavioral impairment. Reports of deficiencies in disaccharidase enzymatic activity and of beneficial responses to probiotic and dietary therapies led us to survey gene expression and the mucoepithelial microbiota in intestinal biopsies from children with autism and gastrointestinal disease and children with gastrointestinal disease alone. Ileal transcripts encoding disaccharidases and hexose transporters were deficient in children with autism, indicating impairment of the primary pathway for carbohydrate digestion and transport in enterocytes. Deficient expression of these enzymes and transporters was associated with expression of the intestinal transcription factor, CDX2. Metagenomic analysis of intestinal bacteria revealed compositional dysbiosis manifest as decreases in Bacteroidetes, increases in the ratio of Firmicutes to Bacteroidetes, and increases in Betaproteobacteria. Expression levels of disaccharidases and transporters were associated with the abundance of affected bacterial phylotypes. These results indicate a relationship between human intestinal gene expression and bacterial community structure and may provide insights into the pathophysiology of gastrointestinal disturbances in children with autism

Comparative Analysis of Pyrosequencing and a Phylogenetic Microarray for Exploring Microbial Community Structures in the Human Distal Intestine

Background Variations in the composition of the human intestinal microbiota are linked to diverse health conditions. High-throughput molecular technologies have recently elucidated microbial community structure at much higher resolution than was previously possible. Here we compare two such methods, pyrosequencing and a phylogenetic array, and evaluate classifications based on two variable 16S rRNA gene regions. Methods and Findings Over 1.75 million amplicon sequences were generated from the V4 and V6 regions of 16S rRNA genes in bacterial DNA extracted from four fecal samples of elderly individuals. The phylotype richness, for individual samples, was 1,400–1,800 for V4 reads and 12,500 for V6 reads, and 5,200 unique phylotypes when combining V4 reads from all samples. The RDP-classifier was more efficient for the V4 than for the far less conserved and shorter V6 region, but differences in community structure also affected efficiency. Even when analyzing only 20% of the reads, the majority of the microbial diversity was captured in two samples tested. DNA from the four samples was hybridized against the Human Intestinal Tract (HIT) Chip, a phylogenetic microarray for community profiling. Comparison of clustering of genus counts from pyrosequencing and HITChip data revealed highly similar profiles. Furthermore, correlations of sequence abundance and hybridization signal intensities were very high for lower-order ranks, but lower at family-level, which was probably due to ambiguous taxonomic groupings. Conclusions The RDP-classifier consistently assigned most V4 sequences from human intestinal samples down to genus-level with good accuracy and speed. This is the deepest sequencing of single gastrointestinal samples reported to date, but microbial richness levels have still not leveled out. A majority of these diversities can also be captured with five times lower sampling-depth. HITChip hybridizations and resulting community profiles correlate well with pyrosequencing-based compositions, especially for lower-order ranks, indicating high robustness of both approaches. However, incompatible grouping schemes make exact comparison difficult