Single-cell enabled comparative genomics of a deep ocean SAR11 bathytype.

This is the author's accepted mansucript.Final version available from Nature via the DOI in this record.Bacterioplankton of the SAR11 clade are the most abundant microorganisms in marine systems, usually representing 25% or more of the total bacterial cells in seawater worldwide. SAR11 is divided into subclades with distinct spatiotemporal distributions (ecotypes), some of which appear to be specific to deep water. Here we examine the genomic basis for deep ocean distribution of one SAR11 bathytype (depth-specific ecotype), subclade Ic. Four single-cell Ic genomes, with estimated completeness of 55%-86%, were isolated from 770 m at station ALOHA and compared with eight SAR11 surface genomes and metagenomic datasets. Subclade Ic genomes dominated metagenomic fragment recruitment below the euphotic zone. They had similar COG distributions, high local synteny and shared a large number (69%) of orthologous clusters with SAR11 surface genomes, yet were distinct at the 16S rRNA gene and amino-acid level, and formed a separate, monophyletic group in phylogenetic trees. Subclade Ic genomes were enriched in genes associated with membrane/cell wall/envelope biosynthesis and showed evidence of unique phage defenses. The majority of subclade Ic-specfic genes were hypothetical, and some were highly abundant in deep ocean metagenomic data, potentially masking mechanisms for niche differentiation. However, the evidence suggests these organisms have a similar metabolism to their surface counterparts, and that subclade Ic adaptations to the deep ocean do not involve large variations in gene content, but rather more subtle differences previously observed deep ocean genomic data, like preferential amino-acid substitutions, larger coding regions among SAR11 clade orthologs, larger intergenic regions and larger estimated average genome size.This work was supported by the Gordon and Betty Moore Foundation (SJG and EFD), the US Department of Energy Joint Genome Institute (JGI) Community Supported Program grant 2011-387 (RS, BKS, EFD, SJG), National Science Foundation (NSF) Science and Technology Center Award EF0424599 (EFD), NSF awards EF-826924 (RS), OCE-821374 (RS) and OCE-1232982 (RS and BKS), and is based on work supported by the NSF under Award no. DBI-1003269 (JCT). Sequencing was conducted by JGI and supported by the Office of Science of the US Department of Energy under Contract No. DE-AC02-05CH11231

Socio-Economic Instability and the Scaling of Energy Use with Population Size

The size of the human population is relevant to the development of a sustainable world, yet the forces setting growth or declines in the human population are poorly understood. Generally, population growth rates depend on whether new individuals compete for the same energy (leading to Malthusian or density-dependent growth) or help to generate new energy (leading to exponential and super-exponential growth). It has been hypothesized that exponential and super-exponential growth in humans has resulted from carrying capacity, which is in part determined by energy availability, keeping pace with or exceeding the rate of population growth. We evaluated the relationship between energy use and population size for countries with long records of both and the world as a whole to assess whether energy yields are consistent with the idea of an increasing carrying capacity. We find that on average energy use has indeed kept pace with population size over long time periods. We also show, however, that the energy-population scaling exponent plummets during, and its temporal variability increases preceding, periods of social, political, technological, and environmental change. We suggest that efforts to increase the reliability of future energy yields may be essential for stabilizing both population growth and the global socio-economic system

Body Size Measurements as Predictors of Type 2 Diabetes in Aboriginal People

OBJECTIVE: To investigate waist circumference, waist-to-hip ratio, body mass index (BMI), weight and hip circumference as risk factors for type 2 diabetes in Aboriginal Australians. DESIGN: Community-based cross-sectional study. SUBJECTS: In total, 915 Australian Aboriginal adults (age: 18-74 y) from a remote Aboriginal community in the Northern Territory of Australia. MEASUREMENTS: Body size measurements included waist circumference, waist-to-hip ratio, BMI, weight and hip circumference. Diabetes status was determined according to medical history and fasting and 2-h postload plasma glucose values. Logistic regression was used to calculate odds ratio for diabetes associated with 1 standard deviation (s.d.) increase in a body size measurement. The areas under the ROC curves of five body size measurements were calculated and compared. RESULTS: Risk of diabetes increased with increasing levels of body size. ORs (95% CI) for diabetes with adjustment for age and sex were 2.16 (1.75, 2.66), 1.80 (1.49, 2.17), 1.41 (1.17, 1.71), 1.81 (1.51, 2.19) and 1.84 (1.50, 2.24) associated with 1 s.d. increase in waist circumference, BMI, weight, waist-to-hip ratio, and hip circumference, respectively. The area under the ROC curve for waist circumference was significantly higher than those for other measurements. CONCLUSION: Waist circumference is the best body size measurement in predicting diabetes in Aboriginal people

Do you see what I see? Identification of child protection concerns by hospital staff and general dental practitioners

Aim An exploration of the threshold that dentists, doctors and nurses recognise for dental and child protection (CP) actions in sample clinical cases, and any differences between these professional groups. Method We present a cross-sectional survey of dentists, doctors and nurses (50 each), who regularly examine children, utilised five fictitious vignettes, combining an oral examination image and clinical history reflecting dental and CP issues. Demographics were collected, and each participant gave their likely action for the cases presented. Results Dentists were significantly better at answering the dental element than the doctors and nurses, (P <0.0001) with no significant difference between these two; only 8% of the latter had undergone any training in assessment of dental health. Although 90.6% of all professionals had undergone CP training, dentists were significantly less accurate at identifying the CP component than doctors and nurses, (P <0.0001) between whom there were no significant differences. Those with higher levels of CP training were most accurate at identifying correct CP actions. Conclusions CP training is effective at improving recognition of child maltreatment, although there remains a worrying lack of knowledge about thresholds for action among dentists. Doctors and nurses have minimal training in, or knowledge of, dental health in children, thus precluding appropriate onward referrals

Cerebral activations related to ballistic, stepwise interrupted and gradually modulated movements in parkinson patients

Patients with Parkinson's disease (PD) experience impaired initiation and inhibition of movements such as difficulty to start/stop walking. At single-joint level this is accompanied by reduced inhibition of antagonist muscle activity. While normal basal ganglia (BG) contributions to motor control include selecting appropriate muscles by inhibiting others, it is unclear how PD-related changes in BG function cause impaired movement initiation and inhibition at single-joint level. To further elucidate these changes we studied 4 right-hand movement tasks with fMRI, by dissociating activations related to abrupt movement initiation, inhibition and gradual movement modulation. Initiation and inhibition were inferred from ballistic and stepwise interrupted movement, respectively, while smooth wrist circumduction enabled the assessment of gradually modulated movement. Task-related activations were compared between PD patients (N = 12) and healthy subjects (N = 18). In healthy subjects, movement initiation was characterized by antero-ventral striatum, substantia nigra (SN) and premotor activations while inhibition was dominated by subthalamic nucleus (STN) and pallidal activations, in line with the known role of these areas in simple movement. Gradual movement mainly involved antero-dorsal putamen and pallidum. Compared to healthy subjects, patients showed reduced striatal/SN and increased pallidal activation for initiation, whereas for inhibition STN activation was reduced and striatal-thalamo-cortical activation increased. For gradual movement patients showed reduced pallidal and increased thalamo-cortical activation. We conclude that PD-related changes during movement initiation fit the (rather static) model of alterations in direct and indirect BG pathways. Reduced STN activation and regional cortical increased activation in PD during inhibition and gradual movement modulation are better explained by a dynamic model that also takes into account enhanced responsiveness to external stimuli in this disease and the effects of hyper-fluctuating cortical inputs to the striatum and STN in particular

Millimeter-scale genetic gradients and community-level molecular convergence in a hypersaline microbial mat

To investigate the extent of genetic stratification in structured microbial communities, we compared the metagenomes of 10 successive layers of a phylogenetically complex hypersaline mat from Guerrero Negro, Mexico. We found pronounced millimeter-scale genetic gradients that were consistent with the physicochemical profile of the mat. Despite these gradients, all layers displayed near-identical and acid-shifted isoelectric point profiles due to a molecular convergence of amino-acid usage, indicating that hypersalinity enforces an overriding selective pressure on the mat community

A statistical toolbox for metagenomics: assessing functional diversity in microbial communities

<p>Abstract</p> <p>Background</p> <p>The 99% of bacteria in the environment that are recalcitrant to culturing have spurred the development of metagenomics, a culture-independent approach to sample and characterize microbial genomes. Massive datasets of metagenomic sequences have been accumulated, but analysis of these sequences has focused primarily on the descriptive comparison of the relative abundance of proteins that belong to specific functional categories. More robust statistical methods are needed to make inferences from metagenomic data. In this study, we developed and applied a suite of tools to describe and compare the richness, membership, and structure of microbial communities using peptide fragment sequences extracted from metagenomic sequence data.</p> <p>Results</p> <p>Application of these tools to acid mine drainage, soil, and whale fall metagenomic sequence collections revealed groups of peptide fragments with a relatively high abundance and no known function. When combined with analysis of 16S rRNA gene fragments from the same communities these tools enabled us to demonstrate that although there was no overlap in the types of 16S rRNA gene sequence observed, there was a core collection of operational protein families that was shared among the three environments.</p> <p>Conclusion</p> <p>The results of comparisons between the three habitats were surprising considering the relatively low overlap of membership and the distinctively different characteristics of the three habitats. These tools will facilitate the use of metagenomics to pursue statistically sound genome-based ecological analyses.</p

Gene identification and protein classification in microbial metagenomic sequence data via incremental clustering

<p>Abstract</p> <p>Background</p> <p>The identification and study of proteins from metagenomic datasets can shed light on the roles and interactions of the source organisms in their communities. However, metagenomic datasets are characterized by the presence of organisms with varying GC composition, codon usage biases etc., and consequently gene identification is challenging. The vast amount of sequence data also requires faster protein family classification tools.</p> <p>Results</p> <p>We present a computational improvement to a sequence clustering approach that we developed previously to identify and classify protein coding genes in large microbial metagenomic datasets. The clustering approach can be used to identify protein coding genes in prokaryotes, viruses, and intron-less eukaryotes. The computational improvement is based on an incremental clustering method that does not require the expensive all-against-all compute that was required by the original approach, while still preserving the remote homology detection capabilities. We present evaluations of the clustering approach in protein-coding gene identification and classification, and also present the results of updating the protein clusters from our previous work with recent genomic and metagenomic sequences. The clustering results are available via CAMERA, (http://camera.calit2.net).</p> <p>Conclusion</p> <p>The clustering paradigm is shown to be a very useful tool in the analysis of microbial metagenomic data. The incremental clustering method is shown to be much faster than the original approach in identifying genes, grouping sequences into existing protein families, and also identifying novel families that have multiple members in a metagenomic dataset. These clusters provide a basis for further studies of protein families.</p

ACVIM consensus statement on the diagnosis of immune-mediated hemolytic anemia in dogs and cats

Immune-mediated hemolytic anemia (IMHA) is an important cause of morbidity and mortality in dogs. IMHA also occurs in cats, although less commonly. IMHA is considered secondary when it can be attributed to an underlying disease, and as primary (idiopathic) if no cause is found. Eliminating diseases that cause IMHA may attenuate or stop immune-mediated erythrocyte destruction, and adverse consequences of long-term immunosuppressive treatment can be avoided. Infections, cancer, drugs, vaccines, and inflammatory processes may be underlying causes of IMHA. Evidence for these comorbidities has not been systematically evaluated, rendering evidence-based decisions difficult. We identified and extracted data from studies published in the veterinary literature and developed a novel tool for evaluation of evidence quality, using it to assess study design, diagnostic criteria for IMHA, comorbidities, and causality. Succinct evidence summary statements were written, along with screening recommendations. Statements were refined by conducting 3 iterations of Delphi review with panel and task force members. Commentary was solicited from several professional bodies to maximize clinical applicability before the recommendations were submitted. The resulting document is intended to provide clinical guidelines for diagnosis of, and underlying disease screening for, IMHA in dogs and cats. These should be implemented with consideration of animal, owner, and geographical factors

Analysis and comparison of very large metagenomes with fast clustering and functional annotation

<p>Abstract</p> <p>Background</p> <p>The remarkable advance of metagenomics presents significant new challenges in data analysis. Metagenomic datasets (metagenomes) are large collections of sequencing reads from anonymous species within particular environments. Computational analyses for very large metagenomes are extremely time-consuming, and there are often many novel sequences in these metagenomes that are not fully utilized. The number of available metagenomes is rapidly increasing, so fast and efficient metagenome comparison methods are in great demand.</p> <p>Results</p> <p>The new metagenomic data analysis method Rapid Analysis of Multiple Metagenomes with a Clustering and Annotation Pipeline (<b>RAMMCAP</b>) was developed using an ultra-fast sequence clustering algorithm, fast protein family annotation tools, and a novel statistical metagenome comparison method that employs a unique graphic interface. RAMMCAP processes extremely large datasets with only moderate computational effort. It identifies raw read clusters and protein clusters that may include novel gene families, and compares metagenomes using clusters or functional annotations calculated by RAMMCAP. In this study, RAMMCAP was applied to the two largest available metagenomic collections, the "Global Ocean Sampling" and the "Metagenomic Profiling of Nine Biomes".</p> <p>Conclusion</p> <p>RAMMCAP is a very fast method that can cluster and annotate one million metagenomic reads in only hundreds of CPU hours. It is available from <url>http://tools.camera.calit2.net/camera/rammcap/</url>.</p