Next-Generation Sequencing Reveals Significant Bacterial Diversity of Botrytized Wine

While wine fermentation has long been known to involve complex microbial communities, the composition and role of bacteria other than a select set of lactic acid bacteria (LAB) has often been assumed either negligible or detrimental. This study served as a pilot study for using barcoded amplicon next-generation sequencing to profile bacterial community structure in wines and grape musts, comparing the taxonomic depth achieved by sequencing two different domains of prokaryotic 16S rDNA (V4 and V5). This study was designed to serve two goals: 1) to empirically determine the most taxonomically informative 16S rDNA target region for barcoded amplicon sequencing of wine, comparing V4 and V5 domains of bacterial 16S rDNA to terminal restriction fragment length polymorphism (TRFLP) of LAB communities; and 2) to explore the bacterial communities of wine fermentation to better understand the biodiversity of wine at a depth previously unattainable using other techniques. Analysis of amplicons from the V4 and V5 provided similar views of the bacterial communities of botrytized wine fermentations, revealing a broad diversity of low-abundance taxa not traditionally associated with wine, as well as atypical LAB communities initially detected by TRFLP. The V4 domain was determined as the more suitable read for wine ecology studies, as it provided greater taxonomic depth for profiling LAB communities. In addition, targeted enrichment was used to isolate two species of Alphaproteobacteria from a finished fermentation. Significant differences in diversity between inoculated and uninoculated samples suggest that Saccharomyces inoculation exerts selective pressure on bacterial diversity in these fermentations, most notably suppressing abundance of acetic acid bacteria. These results determine the bacterial diversity of botrytized wines to be far higher than previously realized, providing further insight into the fermentation dynamics of these wines, and demonstrate the utility of next-generation sequencing for wine ecology studies

Imprint of DES super-structures on the Cosmic Microwave Background

Small temperature anisotropies in the Cosmic Microwave Background can be sourced by density perturbations via the late-time integrated Sachs-Wolfe effect. Large voids and superclusters are excellent environments to make a localized measurement of this tiny imprint. In some cases excess signals have been reported. We probed these claims with an independent data set, using the first year data of the Dark Energy Survey in a different footprint, and using a different super-structure finding strategy. We identified 52 large voids and 102 superclusters at redshifts $0.2 < z < 0.65$. We used the Jubilee simulation to a priori evaluate the optimal ISW measurement configuration for our compensated top-hat filtering technique, and then performed a stacking measurement of the CMB temperature field based on the DES data. For optimal configurations, we detected a cumulative cold imprint of voids with $\Delta T_{f} \approx -5.0\pm3.7~\mu K$ and a hot imprint of superclusters $\Delta T_{f} \approx 5.1\pm3.2~\mu K$ ; this is $\sim1.2\sigma$ higher than the expected $|\Delta T_{f}| \approx 0.6~\mu K$ imprint of such super-structures in $\Lambda$CDM. If we instead use an a posteriori selected filter size ($R/R_{v}=0.6$), we can find a temperature decrement as large as $\Delta T_{f} \approx -9.8\pm4.7~\mu K$ for voids, which is $\sim2\sigma$ above $\Lambda$CDM expectations and is comparable to previous measurements made using SDSS super-structure data

Imprint of DES superstructures on the cosmic microwave background

Small temperature anisotropies in the cosmic microwave background (CMB) can be sourced by density perturbations via the late-time integrated Sachs-Wolfe (ISW) effect. Large voids and superclusters are excellent environments to make a localized measurement of this tiny imprint. In some cases excess signals have been reported. We probed these claims with an independent data set, using the first year data of the Dark Energy Survey (DES) in a different footprint, and using a different superstructure finding strategy. We identified 52 large voids and 102 superclusters at redshifts 0.2 < z < 0.65. We used the Jubilee simulation to a priori evaluate the optimal ISW measurement configuration for our compensated top-hat filtering technique, and then performed a stacking measurement of the CMB temperature field based on the DES data. For optimal configurations, we detected a cumulative cold imprint of voids with DeltaTf ≈ -5.0 ± 3.7 muK and a hot imprint of superclusters DeltaTf ≈ 5.1 ± 3.2 muK; this is ˜1.2sigma higher than the expected |DeltaTf| ≈ 0.6 muK imprint of such superstructures in Lambda cold dark matter (LambdaCDM). If we instead use an a posteriori selected filter size (R/Rv = 0.6), we can find a temperature decrement as large as DeltaTf ≈ -9.8 ± 4.7 muK for voids, which is ˜2sigma above LambdaCDM expectations and is comparable to previous measurements made using Sloan Digital Sky Survey superstructure data

Optical–SZE scaling relations for DES optically selected clusters within the SPT-SZ survey

We study the Sunyaev–Zel'dovich effect (SZE) signature in South Pole Telescope (SPT) data for an ensemble of 719 optically identified galaxy clusters selected from 124.6 deg² of the Dark Energy Survey (DES) science verification data, detecting a clear stacked SZE signal down to richness λ ∼ 20. The SZE signature is measured using matched-filtered maps of the 2500 deg2 SPT-SZ survey at the positions of the DES clusters, and the degeneracy between SZE observable and matched-filter size is broken by adopting as priors SZE and optical mass–observable relations that are either calibrated using SPT-selected clusters or through the Arnaud et al. (A10) X-ray analysis. We measure the SPT signal-to-noise ζ–λ relation and two integrated Compton-yY500–λ relations for the DES-selected clusters and compare these to model expectations that account for the SZE–optical centre offset distribution. For clusters with λ > 80, the two SPT-calibrated scaling relations are consistent with the measurements, while for the A10-calibrated relation the measured SZE signal is smaller by a factor of 0.61 ± 0.12 compared to the prediction. For clusters at 20 < λ < 80, the measured SZE signal is smaller by a factor of ∼0.20–0.80 (between 2.3σ and 10σ significance) compared to the prediction, with the SPT-calibrated scaling relations and larger λ clusters showing generally better agreement. We quantify the required corrections to achieve consistency, showing that there is a richness-dependent bias that can be explained by some combination of (1) contamination of the observables and (2) biases in the estimated halo masses. We also discuss particular physical effects associated with these biases, such as contamination of λ from line-of-sight projections or of the SZE observables from point sources, larger offsets in the SZE-optical centring or larger intrinsic scatter in the λ–mass relation at lower richnesses

Bioinformatics for the human microbiome project

Microbes inhabit virtually all sites of the human body, yet we know very little about the role they play in our health. In recent years, there has been increasing interest in studying human-associated microbial communities, particularly since microbial dysbioses have now been implicated in a number of human diseases [1]–[3]. Dysbiosis, the disruption of the normal microbial community structure, however, is impossible to define without first establishing what “normal microbial community structure” means within the healthy human microbiome. Recent advances in sequencing technologies have made it feasible to perform large-scale studies of microbial communities, providing the tools necessary to begin to address this question [4], [5]. This led to the implementation of the Human Microbiome Project (HMP) in 2007, an initiative funded by the National Institutes of Health Roadmap for Biomedical Research and constructed as a large, genome-scale community research project [6]. Any such project must plan for data analysis, computational methods development, and the public availability of tools and data; here, we provide an overview of the corresponding bioinformatics organization, history, and results from the HMP (Figure 1).National Institutes of Health (U.S.) (NIH U54HG004969)National Institutes of Health (U.S.) (grant R01HG004885)National Institutes of Health (U.S.) (grant R01HG005975)National Institutes of Health (U.S.) (grant R01HG005969

Diversity of Bifidobacteria within the Infant Gut Microbiota

Background The human gastrointestinal tract (GIT) represents one of the most densely populated microbial ecosystems studied to date. Although this microbial consortium has been recognized to have a crucial impact on human health, its precise composition is still subject to intense investigation. Among the GIT microbiota, bifidobacteria represent an important commensal group, being among the first microbial colonizers of the gut. However, the prevalence and diversity of members of the genus Bifidobacterium in the infant intestinal microbiota has not yet been fully characterized, while some inconsistencies exist in literature regarding the abundance of this genus. Methods/Principal Findings In the current report, we assessed the complexity of the infant intestinal bifidobacterial population by analysis of pyrosequencing data of PCR amplicons derived from two hypervariable regions of the 16 S rRNA gene. Eleven faecal samples were collected from healthy infants of different geographical origins (Italy, Spain or Ireland), feeding type (breast milk or formula) and mode of delivery (vaginal or caesarean delivery), while in four cases, faecal samples of corresponding mothers were also analyzed. Conclusions In contrast to several previously published culture-independent studies, our analysis revealed a predominance of bifidobacteria in the infant gut as well as a profile of co-occurrence of bifidobacterial species in the infant’s intestine

Dark energy survey year 1 results: weak lensing shape catalogues

We present two galaxy shape catalogues from the Dark Energy Survey Year 1 data set, covering 1500 deg2 with a median redshift of 0.59. The catalogues cover two main fields: Stripe 82, and an area overlapping the South Pole Telescope survey region. We describe our data analysis process and in particular our shape measurement using two independent shear measurement pipelines, METACALIBRATION and IM3SHAPE. The METACALIBRATION catalogue uses a Gaussian model with an innovative internal calibration scheme, and was applied to riz bands, yielding 34.8M objects. The IM3SHAPE catalogue uses amaximum-likelihood bulge/disc model calibrated using simulations, and was applied to r-band data, yielding 21.9M objects. Both catalogues pass a suite of null tests that demonstrate their fitness for use in weak lensing science. We estimate the 1σ uncertainties in multiplicative shear calibration to be 0.013 and 0.025 for the METACALIBRATION and IM3SHAPE catalogues, respectively

CMB lensing tomography with the DES Science Verification galaxies

We measure the cross-correlation between the galaxy density in the Dark Energy Survey (DES) Science Verification data and the lensing of the cosmic microwave background (CMB) as reconstructed with the Planck satellite and the South Pole Telescope (SPT). When using the DES main galaxy sample over the full redshift range 0.2 2sigma) detections in all bins. Comparing to the fiducial Planck cosmology, we find the redshift evolution of the signal matches expectations, although the amplitude is consistently lower than predicted across redshift bins. We test for possible systematics that could affect our result and find no evidence for significant contamination. Finally, we demonstrate how these measurements can be used to constrain the growth of structure across cosmic time. We find the data are fit by a model in which the amplitude of structure in the z< 1.2 universe is 0.73 ± 0.16 times as large as predicted in the Lambda cold dark matter Planck cosmology, a 1.7sigma deviation

CMB lensing tomography with the DES science verification galaxies

We measure the cross-correlation between the galaxy density in the Dark Energy Survey (DES) Science Verification data and the lensing of the cosmic microwave background (CMB) as reconstructed with the Planck satellite and the South Pole Telescope (SPT). When using the DES main galaxy sample over the full redshift range 0.2 2σ) detections in all bins. Comparing to the fiducial Planck cosmology, we find the redshift evolution of the signal matches expectations, although the amplitude is consistently lower than predicted across redshift bins. We test for possible systematics that could affect our result and find no evidence for significant contamination. Finally, we demonstrate how these measurements can be used to constrain the growth of structure across cosmic time. We find the data are fit by a model in which the amplitude of structure in the z < 1.2 universe is 0.73 ± 0.16 times as large as predicted in the Λ cold dark matter Planck cosmology, a 1.7σ deviation