Experimental and evaluation studies of a coaxial plasma gun accelerator Final report

Pulsed coaxial plasma gun accelerators in space thrustor developmen

Social interaction, noise and antibiotic-mediated switches in the intestinal microbiota

The intestinal microbiota plays important roles in digestion and resistance against entero-pathogens. As with other ecosystems, its species composition is resilient against small disturbances but strong perturbations such as antibiotics can affect the consortium dramatically. Antibiotic cessation does not necessarily restore pre-treatment conditions and disturbed microbiota are often susceptible to pathogen invasion. Here we propose a mathematical model to explain how antibiotic-mediated switches in the microbiota composition can result from simple social interactions between antibiotic-tolerant and antibiotic-sensitive bacterial groups. We build a two-species (e.g. two functional-groups) model and identify regions of domination by antibiotic-sensitive or antibiotic-tolerant bacteria, as well as a region of multistability where domination by either group is possible. Using a new framework that we derived from statistical physics, we calculate the duration of each microbiota composition state. This is shown to depend on the balance between random fluctuations in the bacterial densities and the strength of microbial interactions. The singular value decomposition of recent metagenomic data confirms our assumption of grouping microbes as antibiotic-tolerant or antibiotic-sensitive in response to a single antibiotic. Our methodology can be extended to multiple bacterial groups and thus it provides an ecological formalism to help interpret the present surge in microbiome data.Comment: 20 pages, 5 figures accepted for publication in Plos Comp Bio. Supplementary video and information availabl

Investigating the association between baseline characteristics (HbA1c and Body Mass Index) and clinical outcomes of fast-acting insulin aspart in people with diabetes: a post hoc analysis

Introduction: The aim of this study was to investigate the association between baseline characteristics [HbA1c and body mass index (BMI)] and the effect of mealtime fast-acting insulin aspart (faster aspart) relative to insulin aspart (IAsp) or basal-only insulin therapy on several efficacy and safety outcomes in people with diabetes. Methods: Post hoc analysis of three randomised phase 3a trials in people with type 1 diabetes (T1D; onset 1) and type 2 diabetes (T2D; onset 2 and 3). Participants (N = 1686) were stratified according to baseline BMI ( 58– 7.5–< 8.0%, ≥ 8.0%). Results: In participants with T2D, the estimated treatment difference for change in HbA1c was similar for all BMI and HbA1c subgroups. No major differences between treatments were observed in risk of overall hypoglycaemia or insulin dose across subgroups. In participants with T1D, change in HbA1c was similar across BMI and HbA1c subgroups, and no major differences between treatments were observed for severe or blood glucose-confirmed hypoglycaemia across subgroups. Total daily insulin dose (U/kg) was similar across all baseline HbA1c groups and the BMI 30 kg/m 2 subgroup. Conclusions: In participants with T1D and T2D, treatment differences (for change in HbA1c and overall hypoglycaemia) between mealtime faster aspart and insulin comparators were similar to the corresponding overall analysis across baseline HbA1c and BMI subgroups. The finding of a lower total daily insulin dose in participants with obesity (BMI > 30 kg/m 2 ) and T1D treated with faster aspart, versus those treated with IAsp, may warrant further investigation. Trial Registration: ClinicalTrials.gov NCT01831765 (onset 1); NCT01819129 (onset 2); NCT01850615 (onset 3). Funding: Novo Nordisk A/S, Søborg, Denmark

16S rRNA Gene Pyrosequencing Reveals Bacterial Dysbiosis in the Duodenum of Dogs with Idiopathic Inflammatory Bowel Disease

BACKGROUND: Canine idiopathic inflammatory bowel disease (IBD) is believed to be caused by a complex interaction of genetic, immunologic, and microbial factors. While mucosa-associated bacteria have been implicated in the pathogenesis of canine IBD, detailed studies investigating the enteric microbiota using deep sequencing techniques are lacking. The objective of this study was to evaluate mucosa-adherent microbiota in the duodenum of dogs with spontaneous idiopathic IBD using 16 S rRNA gene pyrosequencing. METHODOLOGY/PRINCIPAL FINDINGS: Biopsy samples of small intestinal mucosa were collected endoscopically from healthy dogs (n = 6) and dogs with moderate IBD (n = 7) or severe IBD (n = 7) as assessed by a clinical disease activity index. Total RNA was extracted from biopsy specimens and 454-pyrosequencing of the 16 S rRNA gene was performed on aliquots of cDNA from each dog. Intestinal inflammation was associated with significant differences in the composition of the intestinal microbiota when compared to healthy dogs. PCoA plots based on the unweighted UniFrac distance metric indicated clustering of samples between healthy dogs and dogs with IBD (ANOSIM, p<0.001). Proportions of Fusobacteria (p = 0.010), Bacteroidaceae (p = 0.015), Prevotellaceae (p = 0.022), and Clostridiales (p = 0.019) were significantly more abundant in healthy dogs. In contrast, specific bacterial genera within Proteobacteria, including Diaphorobacter (p = 0.044) and Acinetobacter (p = 0.040), were either more abundant or more frequently identified in IBD dogs. CONCLUSIONS/SIGNIFICANCE: In conclusion, dogs with spontaneous IBD exhibit alterations in microbial groups, which bear resemblance to dysbiosis reported in humans with chronic intestinal inflammation. These bacterial groups may serve as useful targets for monitoring intestinal inflammation

Impact of the Resident Microbiota on the Nutritional Phenotype of Drosophila melanogaster

Background: Animals are chronically infected by benign and beneficial microorganisms that generally promote animal health through their effects on the nutrition, immune function and other physiological systems of the host. Insight into the host-microbial interactions can be obtained by comparing the traits of animals experimentally deprived of their microbiota and untreated animals. Drosophila melanogaster is an experimentally tractable system to study host-microbial interactions. Methodology/Principal Findings: The nutritional significance of the microbiota was investigated in D. melanogaster bearing unmanipulated microbiota, demonstrated by 454 sequencing of 16S rRNA amplicons to be dominated by the a-proteobacterium Acetobacter, and experimentally deprived of the microbiota by egg dechorionation (conventional and axenic flies, respectively). In axenic flies, larval development rate was depressed with no effect on adult size relative to conventional flies, indicating that the microbiota promotes larval growth rates. Female fecundity did not differ significantly between conventional and axenic flies, but axenic flies had significantly reduced metabolic rate and altered carbohydrate allocation, including elevated glucose levels. Conclusions/Significance: We have shown that elimination of the resident microbiota extends larval development and perturbs energy homeostasis and carbohydrate allocation patterns of of D. melanogaster. Our results indicate that th

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

Fecal Microbiota in Premature Infants Prior to Necrotizing Enterocolitis

Intestinal luminal microbiota likely contribute to the etiology of necrotizing enterocolitis (NEC), a common disease in preterm infants. Microbiota development, a cascade of initial colonization events leading to the establishment of a diverse commensal microbiota, can now be studied in preterm infants using powerful molecular tools. Starting with the first stool and continuing until discharge, weekly stool specimens were collected prospectively from infants with gestational ages ≤32 completed weeks or birth weights≤1250 g. High throughput 16S rRNA sequencing was used to compare the diversity of microbiota and the prevalence of specific bacterial signatures in nine NEC infants and in nine matched controls. After removal of short and low quality reads we retained a total of 110,021 sequences. Microbiota composition differed in the matched samples collected 1 week but not <72 hours prior to NEC diagnosis. We detected a bloom (34% increase) of Proteobacteria and a decrease (32%) in Firmicutes in NEC cases between the 1 week and <72 hour samples. No significant change was identified in the controls. At both time points, molecular signatures were identified that were increased in NEC cases. One of the bacterial signatures detected more frequently in NEC cases (p<0.01) matched closest to γ-Proteobacteria. Although this sequence grouped to the well-studied Enterobacteriaceae family, it did not match any sequence in Genbank by more than 97%. Our observations suggest that abnormal patterns of microbiota and potentially a novel pathogen contribute to the etiology of NEC

Boolean analysis reveals systematic interactions among low-abundance species in the human gut microbiome

The analysis of microbiome compositions in the human gut has gained increasing interest due to the broader availability of data and functional databases and substantial progress in data analysis methods, but also due to the high relevance of the microbiome in human health and disease. While most analyses infer interactions among highly abundant species, the large number of low-abundance species has received less attention. Here we present a novel analysis method based on Boolean operations applied to microbial co-occurrence patterns. We calibrate our approach with simulated data based on a dynamical Boolean network model from which we interpret the statistics of attractor states as a theoretical proxy for microbiome composition. We show that for given fractions of synergistic and competitive interactions in the model our Boolean abundance analysis can reliably detect these interactions. Analyzing a novel data set of 822 microbiome compositions of the human gut, we find a large number of highly significant synergistic interactions among these low-abundance species, forming a connected network, and a few isolated competitive interactions