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International audience; Photosynthetic microbial mats are stable, self-supported communities. Due to their coastal localization, these mats are frequently exposed to hydrocarbon contamination and are able to grow on it. To decipher how this contamination disturbs the functioning of microbial mats, we compared two mats: a contaminated mat exposed to chronic petroleum contamination and a reference mat. The taxonomic and metabolic structures of the mats in spring and fall were determined using metagenomic and metatranscriptomic approaches. Extremely high contamination disturbed the seasonal variations of the mat. ABC transporters, two-component systems, and type IV secretion system-related genes were overabundant in the contaminated mats. Xenobiotic degradation metabolism was minor in the metagenomes of both mats, and only the expression of genes involved in polycyclic aromatic hydrocarbon degradation was higher in the contaminated mat. Interestingly, the expression rates of genes involved in hydrocarbon activation decreased during the 1-year study period, concomitant with the decrease in easily degradable hydrocarbons, suggesting a transient effect of hydrocarbon contamination. Alteromonadales and Oceanospirillales hydrocarbonoclastic bacteria appeared to be key in hydrocarbon remediation in the contaminated mat. Overall, the contaminated microbial mat was able to cope with hydrocarbon contamination and displayed an adaptive functioning that modified seasonal behaviour.

The problems of recurrent and anomalous pattern discovery in time series, e.g., motifs and discords, respectively, have received a lot of attention from researchers in the past decade. However, since the pattern search space is usually intractable, most existing detection algorithms require that the patterns have discriminative characteristics and have its length known in advance and provided as input, which is an unreasonable requirement for many real-world problems. In addition, patterns of similar structure, but of different lengths may co-exist in a time series. Addressing these issues, we have developed algorithms for variable-length time series pattern discovery that are based on symbolic discretization and grammar inference—two techniques whose combination enables the structured reduction of the search space and discovery of the candidate patterns in linear time. In this work, we present GrammarViz 3.0—a software package that provides implementations of proposed algorithms and graphical user interface for interactive variable-length time series pattern discovery. The current version of the software provides an alternative grammar inference algorithm that improves the time series motif discovery workflow, and introduces an experimental procedure for automated discretization parameter selection that builds upon the minimum cardinality maximum cover principle and aids the time series recurrent and anomalous pattern discovery.

International audience; Objective - The analysis of surgical motion has received a growing interest with the development of devices allowing their automatic capture. In this context, the use of advanced surgical training systems makes an automated assessment of surgical trainee possible. Automatic and quantitative evaluation of surgical skills is a very important step in improving surgical patient care. Material and method - In this paper, we present an approach for the discovery and ranking of discriminative and interpretable patterns of surgical practice from recordings of surgical motions. A pattern is defined as a series of actions or events in the kinematic data that together are distinctive of a specific gesture or skill level. Our approach is based on the decomposition of continuous kinematic data into a set of overlapping gestures represented by strings (bag of words) for which we compute comparative numerical statistic (tf-idf) enabling the discriminative gesture discovery via its relative occurrence frequency. Results - We carried out experiments on three surgical motion datasets. The results show that the patterns identified by the proposed method can be used to accurately classify individual gestures, skill levels and surgical interfaces. We also present how the patterns provide a detailed feedback on the trainee skill assessment. Conclusions - The proposed approach is an interesting addition to existing learning tools for surgery as it provides a way to obtain a feedback on which parts of an exercise have been used to classify the attempt as correct or incorrect.

Display Omitted We propose a method to discover discriminative patterns of surgical practice.The method relies on the vector space model (VSM) and tf*idf framework.Evaluation is performed on surgery recordings to predict location, experience and ID of surgeons.We also propose a visualization technique that can support surgical training. ObjectiveSurgery is one of the riskiest and most important medical acts that is performed today. Understanding the ways in which surgeries are similar or different from each other is of major interest to understand and analyze surgical behaviors. This article addresses the issue of identifying discriminative patterns of surgical practice from recordings of surgeries. These recordings are sequences of low-level surgical activities representing the actions performed by surgeons during surgeries. Materials and methodTo discover patterns that are specific to a group of surgeries, we use the vector space model (VSM) which is originally an algebraic model for representing text documents. We split long sequences of surgical activities into subsequences of consecutive activities. We then compute the relative frequencies of these subsequences using the tf*idf framework and we use the Cosine similarity to classify the sequences. This process makes it possible to discover which patterns discriminate one set of surgeries recordings from another set. ResultsExperiments were performed on 40 neurosurgeries of anterior cervical discectomy (ACD). The results demonstrate that our method accurately identifies patterns that can discriminate between (1) locations where the surgery took place, (2) levels of expertise of surgeons (i.e., expert vs. intermediate) and even (3) individual surgeons who performed the intervention. We also show how the tf*idf weight vector can be used to both visualize the most interesting patterns and to highlight the parts of a given surgery that are the most interesting. ConclusionsIdentifying patterns that discriminate groups of surgeon is a very important step in improving the understanding of surgical processes. The proposed method finds discriminative and interpretable patterns in sequences of surgical activities. Our approach provides intuitive results, as it identifies automatically the set of patterns explaining the differences between the groups.

The increase in available spatial trajectory data has led to a massive amount of geo-positioned data that can be exploited to improve understanding of human behavior. However, the noisy nature and massive size of the data make it difficult to extract meaningful trajectory features. In this work, a context-free grammar representation of spatial trajectories is employed to discover frequent segments or motifs within trajectories. Additionally, a set of basis motifs is developed that defines all movement characteristics among a set of trajectories, which can be used to evaluate patterns within a trajectory (intra-trajectory) and between multiple trajectories (inter-trajectory). The approach is realized and demonstrable through the Symbolic Trajectory Analysis and VIsualization System (STAVIS) 2.0, which performs grammar inference on spatial trajectories, mines motifs, and discovers various pattern sets through motif-based analysis.

International audience; Photosynthetic microbial mats are metabolically structured systems driven by solar light. They are ubiquitous and can grow in hydrocarbon-polluted sites. Our aim is to determine the impact of chronic hydrocarbon contamination on the structure, activity, and functioning of a microbial mat. We compared it to an uncontaminated mat harboring similar geochemical characteristics. The mats were sampled in spring and fall for 2 years. Seasonal variations were observed for the reference mat: sulfur cycle-related bacteria dominated spring samples, while Cyanobacteria dominated in autumn. The contaminated mat showed minor seasonal variation; a progressive increase of Cyanobacteria was noticed, indicating a perturbation of the classical seasonal behavior. Hydrocarbon content was the main factor explaining the differences in the microbial community structure; however, hydrocarbonoclastic bacteria were among rare or transient Operational Taxonomic Units (OTUs) in the contaminated mat. We suggest that in long-term contaminated systems, hydrocarbonoclastic bacteria cannot be considered a sentinel of contamination

Papaya, a fruit crop cultivated in tropical and subtropical regions, is known for its nutritional benefits and medicinal applications. Here we report a 3× draft genome sequence of ‘SunUp’ papaya, the first commercial virus-resistant transgenic fruit tree1 to be sequenced. The papaya genome is three times the size of the Arabidopsis genome, but contains fewer genes, including significantly fewer disease-resistance gene analogues. Comparison of the five sequenced genomes suggests a minimal angiosperm gene set of 13,311. A lack of recent genome duplication, atypical of other angiosperm genomes sequenced so far2–5, may account for the smaller papaya gene number in most functional groups. Nonetheless, striking amplifications in gene number within particular functional groups suggest roles in the evolution of tree-like habit, deposition and remobilization of starch reserves, attraction of seed dispersal agents, and adaptation to tropical daylengths. Transgenesis at three locations is closely associated with chloroplast insertions into the nuclear genome, and with topoisomerase I recognition sites. Papaya offers numerous advantages as a system for fruit-tree functional genomics, and this draft genome sequence provides the foundation for revealing the basis of Carica's distinguishing morpho-physiological, medicinal and nutritional properties.

Mastitis is an infectious disease mainly caused by bacteria invading the mammary gland. Genetic control of susceptibility to mastitis has been widely evidenced in dairy ruminants, but the genetic basis and underlying mechanisms are still largely unknown. We describe the discovery, fine mapping and functional characterization of a genetic variant associated with elevated milk leukocytes count, or SCC, as a proxy for mastitis. After implementing genome-wide association studies, we identified a major QTL associated with SCC on ovine chromosome 3. Fine mapping of the region, using full sequencing with 12X coverage in three animals, provided one strong candidate SNP that mapped to the coding sequence of a highly conserved gene, suppressor of cytokine signalling 2 (Socs2). The frequency of the SNP associated with increased SCC was 21.7% and the Socs2 genotype explained 12% of the variance of the trait. The point mutation induces the p.R96C substitution in the SH2 functional domain of SOCS2 i.e. the binding site of the protein to various ligands, as well-established for the growth hormone receptor GHR. Using surface plasmon resonance we showed that the p.R96C point mutation completely abrogates SOCS2 binding affinity for the phosphopeptide of GHR. Additionally, the size, weight and milk production in p.R96C homozygote sheep, were significantly increased by 24%, 18%, and 4.4%, respectively, when compared to wild type sheep, supporting the view that the point mutation causes a loss of SOCS2 functional activity. Altogether these results provide strong evidence for a causal mutation controlling SCC in sheep and highlight the major role of SOCS2 as a tradeoff between the host’s inflammatory response to mammary infections, and body growth and milk production, which are all mediated by the JAK/STAT signaling pathway., Author Summary Mastitis is an inflammation of the mammary gland mainly caused by invading bacteria. Ruminants show natural variability in their predisposition to mastitis, and therefore provide unique models for study of the genetics and physiology of host response to bacterial infection. A genome-wide association study was conducted in a dairy sheep population for milk somatic cell counts as a proxy for mastitis. Fine mapping, using whole genome sequencing, led to the identification of a mutation in the Suppressor of Cytokine Signaling 2 gene (socs2). This mutation was shown to cause a loss of functional activity of the SOCS2 protein, which suggested impairment of feedback control of the JAK/STAT signaling pathways in susceptible animals. Additionally, size, weight and milk production were increased in animals carrying the susceptible variant suggesting a pleiotropic effect of the gene on production versus health traits. Results gave strong evidence of the role of SOCS2 in the host’s inflammation of the udder and provided new insights into the key mechanisms underlying the genetic control of mastitis.

cited By 3; International audience; The contamination of polluted environments is often due to a complex mixture of pollutants sometimes at trace levels which nevertheless may have significant effects on the diversity and functioning of organisms. The aim of this study was to assess the functional responses of a microbial mat exposed to a natural complex mixture of PAHs and metals as a function of the maturation stage of the biofilm. Microbial mats sampled in a slightly polluted environment were exposed to contaminated water of a retention basin of an oil refinery. The responses of the microbial mats differed according to season. In spring 2012, strong inhibition of both oxygen production and respiration was observed relative to the control, with rates representing less than 5% of the control after 72. h of incubation. A decrease of microbial activities was followed by a decrease of the coupling between autotrophs and heterotrophs. In contrast, in autumn 2012, no significant changes for oxygen production and respiration were observed and the coupling between autotrophs and heterotrophs was not altered. The differences observed between the spring and autumn mats might be explained by the maturity of the microbial mat with dominance of heterotrophic bacteria in spring, and diatoms and cyanobacteria in autumn, as well as by the differences in the chemical composition of the complex mixture of PAHs and metals.

International audience; In recent years, RNA-Seq technologies became a powerful tool for transcriptome studies. However, computational methods dedicated to the analysis of high-throughput sequencing data are yet to be standardized. In particular, it is known that the choice of a normalization procedure leads to a great variability in results of differential gene expression analysis. The present study compares the most widespread normalization procedures and proposes a novel one aiming at removing an inherent bias of studied transcriptomes related to their relative size. Comparisons of the normalization procedures are performed on real and simulated data sets. Real RNA-Seq data sets analyses, performed with all the different normalization methods, show that only 50% of significantly differentially expressed genes are common. This result highlights the influence of the normalization step on the differential expression analysis. Real and simulated data sets analyses give similar results showing 3 different groups of procedures having the same behavior. The group including the novel method named "Median Ratio Normalization" (MR N) gives the lower number of false discoveries. Within this group the MR N method is less sensitive to the modification of parameters related to the relative size of transcriptomes such as the number of down- and upregulated genes and the gene expression levels. The newly proposed MR N method efficiently deals with intrinsic bias resulting from relative size of studied transcriptomes. Validation with real and simulated data sets confirmed that MR N is more consistent and robust than existing methods.

Background The phylum Verrucomicrobia is a widespread but poorly characterized bacterial clade. Although cultivation-independent approaches detect representatives of this phylum in a wide range of environments, including soils, seawater, hot springs and human gastrointestinal tract, only few have been isolated in pure culture. We have recently reported cultivation and initial characterization of an extremely acidophilic methanotrophic member of the Verrucomicrobia, strain V4, isolated from the Hell's Gate geothermal area in New Zealand. Similar organisms were independently isolated from geothermal systems in Italy and Russia. Results We report the complete genome sequence of strain V4, the first one from a representative of the Verrucomicrobia. Isolate V4, initially named "Methylokorus infernorum" (and recently renamed Methylacidiphilum infernorum) is an autotrophic bacterium with a streamlined genome of ~2.3 Mbp that encodes simple signal transduction pathways and has a limited potential for regulation of gene expression. Central metabolism of M. infernorum was reconstructed almost completely and revealed highly interconnected pathways of autotrophic central metabolism and modifications of C1-utilization pathways compared to other known methylotrophs. The M. infernorum genome does not encode tubulin, which was previously discovered in bacteria of the genus Prosthecobacter, or close homologs of any other signature eukaryotic proteins. Phylogenetic analysis of ribosomal proteins and RNA polymerase subunits unequivocally supports grouping Planctomycetes, Verrucomicrobia and Chlamydiae into a single clade, the PVC superphylum, despite dramatically different gene content in members of these three groups. Comparative-genomic analysis suggests that evolution of the M. infernorum lineage involved extensive horizontal gene exchange with a variety of bacteria. The genome of M. infernorum shows apparent adaptations for existence under extremely acidic conditions including a major upward shift in the isoelectric points of proteins. Conclusion The results of genome analysis of M. infernorum support the monophyly of the PVC superphylum. M. infernorum possesses a streamlined genome but seems to have acquired numerous genes including those for enzymes of methylotrophic pathways via horizontal gene transfer, in particular, from Proteobacteria. Reviewers This article was reviewed by John A. Fuerst, Ludmila Chistoserdova, and Radhey S. Gupta.

Bacteria that consume the greenhouse gas methane are potentially important players in the atmospheric budget, with the potential to sop up methane from the Earth's crust that would otherwise contribute to the atmospheric budget. Two new methane-utilizing bacteria have been isolated independently and both break new ground: unlike previous methanotrophic isolates, which are proteobacteria, they belong to the widely distributed Verrucomicrobia phylum. And both isolates display optimum growth and methane oxidation in remarkably acidic conditions, at pHs as low as 0.8 to 2.5. Acidimethylosilex fumarolicum SolV was isolated from a fuming vent on the Solfatara volcano near Naples, Italy, and Methylokorus infernorum from hot soil in the Hell's Gate (Tikitere) geothermal area of New Zealand. In spite of the fact that acidic environments support methane cycles, extreme acidophilic methanotrophs have so far resisted isolation. The isolation and initial genomic and physiological characterization of a bacterium belonging to the Verromicrobia displaying a growth and methane oxidation optimum of pH2—2.5 is described. Aerobic methanotrophic bacteria consume methane as it diffuses away from methanogenic zones of soil and sediment1. They act as a biofilter to reduce methane emissions to the atmosphere, and they are therefore targets in strategies to combat global climate change. No cultured methanotroph grows optimally below pH 5, but some environments with active methane cycles are very acidic2,3. Here we describe an extremely acidophilic methanotroph that grows optimally at pH 2.0–2.5. Unlike the known methanotrophs, it does not belong to the phylum Proteobacteria but rather to the Verrucomicrobia, a widespread and diverse bacterial phylum that primarily comprises uncultivated species with unknown genotypes. Analysis of its draft genome detected genes encoding particulate methane monooxygenase that were homologous to genes found in methanotrophic proteobacteria. However, known genetic modules for methanol and formaldehyde oxidation were incomplete or missing, suggesting that the bacterium uses some novel methylotrophic pathways. Phylogenetic analysis of its three pmoA genes (encoding a subunit of particulate methane monooxygenase) placed them into a distinct cluster from proteobacterial homologues. This indicates an ancient divergence of Verrucomicrobia and Proteobacteria methanotrophs rather than a recent horizontal gene transfer of methanotrophic ability. The findings show that methanotrophy in the Bacteria is more taxonomically, ecologically and genetically diverse than previously thought, and that previous studies have failed to assess the full diversity of methanotrophs in acidic environments.

The difficulty associated with the cultivation of most microorganisms and the complexity of natural microbial assemblages, such as marine plankton or human microbiome, hinder genome reconstruction of representative taxa using cultivation or metagenomic approaches. Here we used an alternative, single cell sequencing approach to obtain high-quality genome assemblies of two uncultured, numerically significant marine microorganisms. We employed fluorescence-activated cell sorting and multiple displacement amplification to obtain hundreds of micrograms of genomic DNA from individual, uncultured cells of two marine flavobacteria from the Gulf of Maine that were phylogenetically distant from existing cultured strains. Shotgun sequencing and genome finishing yielded 1.9 Mbp in 17 contigs and 1.5 Mbp in 21 contigs for the two flavobacteria, with estimated genome recoveries of about 91% and 78%, respectively. Only 0.24% of the assembling sequences were contaminants and were removed from further analysis using rigorous quality control. In contrast to all cultured strains of marine flavobacteria, the two single cell genomes were excellent Global Ocean Sampling (GOS) metagenome fragment recruiters, demonstrating their numerical significance in the ocean. The geographic distribution of GOS recruits along the Northwest Atlantic coast coincided with ocean surface currents. Metabolic reconstruction indicated diverse potential energy sources, including biopolymer degradation, proteorhodopsin photometabolism, and hydrogen oxidation. Compared to cultured relatives, the two uncultured flavobacteria have small genome sizes, few non-coding nucleotides, and few paralogous genes, suggesting adaptations to narrow ecological niches. These features may have contributed to the abundance of the two taxa in specific regions of the ocean, and may have hindered their cultivation. We demonstrate the power of single cell DNA sequencing to generate reference genomes of uncultured taxa from a complex microbial community of marine bacterioplankton. A combination of single cell genomics and metagenomics enabled us to analyze the genome content, metabolic adaptations, and biogeography of these taxa.